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Ms., Manjusha R., and Sunny Kuriakose A. Dr. "AN APPLICATION OF Gd -METRIC SPACES AND METRIC DIMENSION OF GRAPHS." International Journal on Applications of Graph Theory in Wireless Ad hoc Networks and Sensor Networks(GRAPH-HOC) 7, no. 1 (2019): 1–10. https://doi.org/10.5281/zenodo.3374371.
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The idea of metric dimension in graph theory was introduced by P J Slater in [2]. It has been found applications in optimization, navigation, network theory, image processing, pattern recognition etc. Several other authors have studied metric dimension of various standard graphs. In this paper we introduce a real valued function called generalized metric → + Gd : X × X × X R where X = r(v /W) = {(d(v,v1 ),d(v,v2 ),...,d(v,vk /) v∈V (G))}, denoted Gd and is used to study metric dimension of graphs. It has been proved that metric dimension of any connected finite simple graph remains constant if Gd numbers of pendant edges are added to the non-basis vertices.
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R, Manjusha. "An Application of Gd-Metric Spaces and Metric Dimension of Graphs." International Journal on Applications of Graph Theory In wireless Ad Hoc Networks And sensor Networks 7, no. 1 (2015): 01–10. http://dx.doi.org/10.5121/jgraphoc.2015.7101.
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Yin, Lei, and Qi Gao. "Research on Multi-Objective Flexible Job Shop Scheduling Optimization Based on Improved Salp Swarm Algorithm in Rolling Production Mode." Applied Sciences 15, no. 11 (2025): 5947. https://doi.org/10.3390/app15115947.
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To address the multi-objective flexible job shop scheduling problem in rolling production mode (FJSP-RPM), this study proposes a Multi Objective Improved of Salp Swarm Algorithm (MISSA) that simultaneously optimizes equipment utilization and total tardiness. The MISSA generates initial population through various heuristic strategies to improve the initial population quality. The exploitation capability of the algorithm is enhanced through the global crossover strategy and variety of local search strategies. In terms of improvement strategies, the MISSA (using all three strategies) outperforms other incomplete variant algorithms (using only two strategies) in three metrics: Generational Distance (GD), Inverted Generational Distance (IGD), and diversity metric, achieving superior results in 9 test cases, 8 test cases, and 4 test cases respectively. When compared with NSGA2, NSGA3, and SPEA2 algorithms, the MISSA demonstrates advantages in 8 test cases for GD, 8 test cases for IGD, and 7 test cases for the diversity metric. Additionally, the distribution of the obtained solution sets is significantly better than that of the comparative algorithms, which validats the effectiveness of the MISSA in solving FJSP-RPM.
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Al-Mazrooei, Abdullah, Abdullah Shoaib та Jamshaid Ahmad. "Unique Fixed-Point Results for β-Admissible Mapping under (β-ψˇ)-Contraction in Complete Dislocated Gd-Metric Space". Mathematics 8, № 9 (2020): 1584. http://dx.doi.org/10.3390/math8091584.
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This paper is designed to display some results which generalize the recent results that cannot be established from the corresponding results in other spaces and do not satisfy the remarks of Jleli et al. (Fixed Point Theor Appl. 210, 2012) and Samet et al. (Int. J. Anal. Article ID 917158, 2013). We obtain unique fixed-point for mapping satisfying β-ψˇ contraction only on a closed Gd ball in complete dislocated Gd-metric space. An example is also discussed to shed light on the main result.
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Tyurin, Alexander. "From Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 20938–46. https://doi.org/10.1609/aaai.v39i20.35389.
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We focus on the classification problem with a separable dataset, one of the most important and classical problems from machine learning. The standard approach to this task is logistic regression with gradient descent (LR+GD). Recent studies have observed that LR+GD can find a solution with arbitrarily large step sizes, defying conventional optimization theory. Our work investigates this phenomenon and makes three interconnected key observations about LR+GD with large step sizes. First, we find a remarkably simple explanation of why LR+GD with large step sizes solves the classification problem: LR+GD reduces to a batch version of the celebrated perceptron algorithm when the step size tends to infinity. Second, we observe that larger step sizes lead LR+GD to higher logistic losses when it tends to the perceptron algorithm, but larger step sizes also lead to faster convergence to a solution for the classification problem, meaning that logistic loss is an unreliable metric of the proximity to a solution. Surprisingly, high loss values can actually indicate faster convergence. Third, since the convergence rate in terms of loss function values of LR+GD is unreliable, we examine the iteration complexity required by LR+GD with large step sizes to solve the classification problem and prove that this complexity is suboptimal. To address this, we propose a new method, Normalized LR+GD – based on the connection between LR+GD and the perceptron algorithm – with much better theoretical guarantees.
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Wu,, Yanyan, Jami J. Shah, and Joseph K. Davidson. "Computer Modeling of Geometric Variations in Mechanical Parts and Assemblies." Journal of Computing and Information Science in Engineering 3, no. 1 (2003): 54–63. http://dx.doi.org/10.1115/1.1572177.
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This paper reports on part of a project related to the development of a computer model for GD&T (Geometric Dimensioning and Tolerancing) to support tolerance specification, validation and tolerance analysis. The paper examines the basic elements involved in geometric variation and their interrelations. Logical tolerance classes are defined in terms of a target, a datum reference frame, and metric relations. ASME Y14.5 tolerance classes are mapped to these logical classes. The development of a data model for GD&T and its application in supporting design specification, validation, and tolerance analysis are discussed.
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Hawi, Nazir, and Maya Samaha. "Relationships of gaming disorder, ADHD, and academic performance in university students: A mediation analysis." PLOS ONE 19, no. 4 (2024): e0300680. http://dx.doi.org/10.1371/journal.pone.0300680.
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This study investigates the intersection of Gaming Disorder (GD) and Attention-Deficit/Hyperactivity Disorder (ADHD), and Grade Point Average (GPA), among university students, a critical demographic often overlooked in research on these disorders. A sample of 348 university students was analyzed using the IGD-20 Test for risk of GD, the Adult ADHD Self-Report Scale (ASRS-v1.1) for ADHD symptoms, and GPA as a metric of academic performance. The findings indicate that 4.3% of the surveyed sample scored within the range for GD. The prevalence was higher in males, with 5.3% of the male cohort affected, compared to 1.2% of the female cohort. Significantly, the prevalence of ADHD was substantially higher in the GD group (35.7%) than in the non-GD group (24.2%). Further, ADHD symptoms were found to be a stronger predictor of GD in females than in males. Incorporating the mediating role of Gaming Disorder, this study also probes into how GD may serve as an intermediary in the impact of ADHD on academic performance. By examining the intricate relationship between these disorders, our findings suggest that GD exacerbates the negative effects of ADHD on academic performance, thereby underscoring the potential for Gaming Disorder to act as a bridge in this dynamic. This mediation analysis clarifies how ADHD may indirectly impact academic performance through GD. The study reveals a positive correlation between ADHD symptoms and GD severity, which in turn correlates negatively with academic achievement. In addition, the findings underscore the need for gender-sensitive interventions and highlight the importance of considering the comorbidity of ADHD and GD in academic settings, advocating for systematic screening for GD among students with ADHD, and vice versa. The dual challenges posed by ADHD and GD should be addressed to prevent their escalation into pervasive academic and psychosocial adversities.
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Sonar, A. N., and N. S. Pawar. "Spectrophotometric and pH-Metric Studies of Ce(III), Dy(III), Gd(III),Yb(III) and Pr(III) Metal Complexes with Rifampicin." E-Journal of Chemistry 8, no. 2 (2011): 517–22. http://dx.doi.org/10.1155/2011/848906.
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The metal-ligand and proton-ligand stability constant of Ce(III), Dy(III), Gd(III),Yb(III) and Pr(III) metals with substituted heterocyclic drug (Rifampicin) were determined at various ionic strength by pH metric titration. NaClO4was used to maintain ionic strength of solution. The results obtained were extrapolated to the zero ionic strength using an equation with one individual parameter. The thermodynamic stability constant of the complexes were also calculated. The formation of complexes has been studied by Job’s method. The results obtained were of stability constants by pH metric method is confirmed by Job’s method.
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Zhang, Qinglei, Jing Hu, Zhen Liu, and Jianguo Duan. "Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing." PLOS ONE 19, no. 1 (2024): e0297139. http://dx.doi.org/10.1371/journal.pone.0297139.
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In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified.
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Wishart, Joseph, Ramon Barajas, Andrei Pugachev, et al. "NIMG-33. [18F]-FLUOROMISONIDAZOLE RELATIVE PET INFLUX RATE IMPROVES DIAGNOSTIC ACCURACY FOR GLIOBLASTOMA PROGRESSION FROM THERAPY-INDUCED NEUROINFLAMMATORY PSEUDOPROGRESSION." Neuro-Oncology 25, Supplement_5 (2023): v192. http://dx.doi.org/10.1093/neuonc/noad179.0729.
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Abstract Glioblastoma is the most common and aggressive form of primary brain tumor in adults. Standard of care gadolinium-enhanced MRI (Gd-MRI) fails to capture hypoxia, a disease-defining feature of glioblastoma. Hypoxia potentiates resistance to chemo-radiation therapy (CRT) but is not extensively observed with therapy-induced pseudoprogression (PSP). This neuroinflammatory response to CRT occurs in approximately 40% of patients and is indistinguishable from disease progression by Gd-MRI, potentially causing unnecessary termination of effective therapy in patients with PSP. Additionally, delayed second line-therapy can occur in patients with resistance to CRT. This research evaluated whether [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET), a noninvasive metric of tissue hypoxia, improves diagnostic accuracy in this context. Clinically, PET quantification employs the standardized uptake value (SUV), representing summed counts over an acquisition period. To add accuracy to this assessment, static 40-minute PET data acquired starting 90 minutes after FMISO injection was reconstructed into 20x2-minute frames and a relative Patlak model was applied. This technique forgoes blood sampling and extensive examination times required by traditional dynamic PET studies. The model produces two parameters that separately characterize the FMISO relative influx rate (Ki') and blood volume (VB'). In a cohort of 16 patients (3 IDH-mutated) imaged at a time of presumed disease progression, results showed that Ki' within the Gd-MRI enhancing lesion predicts future diagnosis of true progression (n = 11) or inflammation (n =5 ) with a sensitivity and specificity of 91% and 80% respectively. This outperforms diagnosis made with Gd-MRI alone, achieving ~70% for the same metrics. A t-test assuming unequal variance of cohort-wide mean Ki' tended toward significance (p = 0.07) for differentiating progressive disease (0.0036 ± 0.0016 min-1) from PSP (0.0011 ± 0.0017 min-1). RESULTS: from this study suggest relative Patlak analysis adds specificity to Gd-MRI and provides clinically relevant information regarding disease status.
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Severyn, Chris. "#17 Gut decontamination, the microbiome, and the risk of bloodstream infections during pediatric allogeneic hematopoietic cell transplantation." Journal of the Pediatric Infectious Diseases Society 11, Supplement_1 (2022): S1. http://dx.doi.org/10.1093/jpids/piac041.002.
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Abstract Background Gut decontamination (GD) is the practice of using antibiotics to eradicate intestinal microbes before and during hematopoietic cell transplantation (HCT). Pre-clinical studies suggest that GD may be protective against acute graft-versus-host-disease (aGVHD), although no studies have focused on the potential impact of GD on non-aGVHD treatment related illnesses. Recently, we completed a randomized controlled trial of GD in pediatric patients. We report the results of an exploratory analysis of the impact of gut decontamination on the incidence of bloodstream infections (BSI) in pediatric HCT patients. Method We conducted a randomized phase 2 trial (ClinicalTrials.gov NCT02641236) of GD in pediatric patients undergoing allo-HCT with the primary objective to characterize the gut microbiota at 2-weeks post-HCT. Patients were randomized to receive the GD regimen (Arm A, n=10) (oral vancomycin-polymyxin B from day -5 through neutrophil engraftment) or not receive GD (no-GD; Arm B, n=10). Serial stool samples were collected from patients in both arms and subsequently underwent shotgun metagenomic sequencing to characterize the gut microbiome. In an exploratory, post-hoc analysis, stool metagenomic sequences were compared against the assembled sequence of a BSI isolate for any patient with a BSI in the first 100 days using a population average nucleotide identity (popANI) metric. Assembled BSI contigs and binned contigs from stool metagenomic sequences were profiled for antibiotic resistance genes (ARGs) with the Comprehensive Antibiotic Resistance Database (CARD) and the Resistance Gene Identifier (RGI) using default parameters. Results There was no statistical difference between the two arms in Shannon diversity of the gut microbiota at 2 weeks post-transplant for either genus (p=0.80) or species level (p=0.44). Six patients had a total of nine BSI episodes in the first 100 days post-HCT. We observed fewer BSI (1 vs. 5, Fig. 1A, p=0.048) in the GD vs no-GD arm, respectively. Using strain-specific analysis of BSI-causing bacteria and temporal association with the stool microbiome from patients who developed BSIs, we identified BSI-causing pathogens in 7 of 9 BSI episodes in the gut microbiome in the no-GD arm (Fig. 1B), including the genus Staphylococcus (Fig. 1C) in 2 patients. The 1 BSI in the GD arm was not derived from the gut (Fig. 1B). The number of unique colistin-, polymyxin B-, and vancomycin-ARGs were similar between the two arms (Fig. 1D). Conclusion Shannon diversity was similar in patients with or without GD, possibly due to the prevalent use of systemic antibiotics in the no-GD arm. Staphylococcus was found in the gut suggesting the need for an expanded definition of non-mucosal barrier injury (MBI) pathogens in HCT. All gut-localized BSIs were found in the non-GD arm suggesting that GD may protect against BSI in HCT patients by decreasing the prevalence or abundance of pathogens that can translocate across the mucosal barrier and subsequently cause gut-derived BSIs.
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Barajas, Ramon, Randall Woltjer, Seunggu Han, Leslie Muldoon, and Edward Neuwelt. "TMOD-10. PRE-CLINICAL IMAGING MODEL OF GLIOBLASTOMA PSEUDOPROGRESSION USING DUAL FERUMOXYTOL AND GADOLINIUM CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING." Neuro-Oncology 21, Supplement_6 (2019): vi264. http://dx.doi.org/10.1093/neuonc/noz175.1109.
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Abstract BACKGROUND Temozolomide-based chemoradiotherapy (CRT) mediated neuroinflammation (pseudoprogression; Psp) within glioblastoma (GBM) is manifested by progressive gadolinium enhancement on magnetic resonance imaging (Gd-MRI)1. Unfortunately, Gd-MRI cannot reliably differentiate Psp from growing GBM. Macrophage infiltration is a prominent component of Psp, and can be noninvasively assessed by 24 hour delayed ferumoxytol-enhanced MRI (Fe-MRI)2. We have demonstrated that dual Fe- to Gd-contrast mismatch ratio may be a specific metric for identifying Psp3. The purpose of our study was to develop and characterize a preclinical model that specifically replicates the imaging context of macrophage mediated Psp. METHODS Athymic rats bearing intracerebral patient derived xenografts (GBM-39 and GSC-827) or U-87 GBM cells were randomized to; 1) untreated control (N= 5), 2) CRT only (irradiation [5 Gy] and temozolomide [20 mg/kg, PO] (N=4), or 3) CRT with Amphotericin B (Amp B) (0.2 mg/kg, IP QD x 3–7 days) (N=8)4. Three day post-therapeutic Fe- (ferumoxytol, 25 mg/kg IV) and Gd-MRI was performed as well as 7 day post-therapy Gd-MRI (N=7). Semi-automated enhancement segmentation and volume calculation was performed with Horos software5. Significance was evaluated using Students T-test with unequal variances. RESULTS The ratio of Fe- to Gd-enhancement volume 3 days after therapy was found to be significantly elevated within the CRT with Amp B group (1.7 ± 0.53) when compared to the CRT alone (0.62 ± 0.11) and control groups (0.43 ± 0.11); P< 0.01. Seven days after therapy, Gd-enhancement volume was significantly decreased within the CRT with Amp B group (0.87 ± 0.13) when compared to CRT alone (0.38 ± 0.04; P< 0.01), confirming treatment efficacy. CONCLUSION Preliminary results suggest that CRT combined with Amp B in this rat model replicates the imaging characteristics of human Psp and may result in improved efficacy3. Fe-MRI enhancement may capture the efficacious macrophage modulatory effects of Amp B.
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Khan, Mohammad Nazrul Islam. "Novel theorems for metallic structures on the frame bundle of the second order." Filomat 36, no. 13 (2022): 4471–82. http://dx.doi.org/10.2298/fil2213471k.
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It is well known that ?an almost complex structure? J that is J2 = ?I on the manifold M is called ?an almost Hermitian manifold? (M, J,G) if G(JX, JY) = G(X,Y) and proved that (F2M, JD,GD) is ?an almost Hermitian manifold? on the frame bundle of the second order F2M. The term ?an almost complex structure? refers to the general quadratic structure J2 = pJ + qI, where p = 0, q = ?1. However, this paper aims to study the general quadratic equation J2 = pJ + qI, where p, q are positive integers, it is named as a metallic structure. The diagonal lift of the metallic structure J on the frame bundle of the second order F2Mis studied and shows that it is also a metallic structure. The proposed theorem proves that the diagonal lift GD of a Riemannian metric G is a metallic Riemannian metric on F2M. Also, a new tensor field ?J of type (1,1) is defined on F2M and proves that it is a metallic structure. The 2-form and its derivative dF of a tensor field ?J are determined. Furthermore, the Nijenhuis tensor N?J of a metallic structure ?J and the Nijenhuis tensor NJD of a tensor field JD of type (1,1) on the frame bundle of the second order F2M are calculated.
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Sonone, R. S., and G. H. Murhekar. "Formation constants of lanthanide metal ion chelates with some substituted sulfonic acids." Material Science Research India 7, no. 1 (2010): 235–38. http://dx.doi.org/10.13005/msri/070131.
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The pKa and logK values of some substituted sulfonic acid in 70% (v/v) dioxane-water mixture have been determined using pH metric measurements. The stability constant of complexes of Gd (III), Tb (III) and Ho (III) with p-aminobenzenesulfonic (L1) and p-sulfophthalic acid (L2), 2-amino-5-chloro benzenesulfonic acid (L3) and trifluoromethanesulfonic acid (L4) have been determined at 0.1M ionic strength at 28±0.1 0C pH metrically. It is observed that both the metal ions formed 1:1 & 1:2 complexes with all the ligands.
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Delić, Milica, Mirjana Ristić, Maja Đolić, Aleksandra Perić-Grujić, and Antonije Onjia. "Dispersive Liquid–Liquid Chelate Microextraction of Rare Earth Elements: Optimization and Greenness Evaluation." Metals 15, no. 1 (2025): 52. https://doi.org/10.3390/met15010052.
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An ultrasound-assisted dispersive liquid–liquid microextraction (DLLME) method was developed to concentrate and quantify rare earth elements (REEs) (Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu) in acidic aqueous solutions. Tetrachloroethylene (PCE) was used as the diluent, di-(2-ethyl hexyl) phosphoric acid (D2EHPA) as the extracting agent, and acetone as the dispersant solvent. The method was optimized at pH = 2.3, T = 25 °C, and VS = 400 µL of a PCE ÷ D2EHPA mixture (10 ÷ 1) using the response surface methodology (RSM) with a Box–Behnken design. Under optimal conditions, the method proved efficient for the DLLME of most REEs (Y, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu), where the achieved recoveries were in the range of 61–109%, while relative standard deviations were in the range 11–28%. The proposed method was applied to recover REEs from real coal ash leachate samples. A greenness evaluation using the Green Analytical Procedure Index (GAPI), Analytical GREEnness (AGREE), and Analytical Eco-Scale (AES) methodologies revealed acceptable metric scores of 74, 0.61, and 26.6–79.8, respectively.
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Du, Zhaosheng, Junqing Li, and Jiake Li. "Hybrid Multi-Objective Artificial Bee Colony for Flexible Assembly Job Shop with Learning Effect." Mathematics 13, no. 3 (2025): 472. https://doi.org/10.3390/math13030472.
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The flexible job shop scheduling problem is a typical and complex combinatorial optimization problem. In recent years, the assembly problem in job shop scheduling problems has been widely studied. However, most of the studies ignore the learning effect of workers, which may lead to higher costs than necessary. This paper considers a flexible assembly job scheduling problem with learning effect (FAJSPLE) and proposes a hybrid multi-objective artificial bee colony (HMABC) algorithm to solve the problem. Firstly, a mixed integer linear programming model is developed where the maximum completion time (makespan), total energy consumption and total cost are optimized simultaneously. Secondly, a critical path-based mutation strategy was designed to dynamically adjust the level of workers according to the characteristics of the critical path. Finally, the local search capability is enhanced by combining the simulated annealing algorithm (SA), and four search operators with different neighborhood structures are designed. By comparative analysis on different scales instances, the proposed algorithm reduces 55.8 and 958.99 on average over the comparison algorithms for the GD and IGD metrics, respectively; for the C-metric, the proposed algorithm improves 0.036 on average over the comparison algorithms.
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Liu, Wenxiu, Qingyan Meng, Mona Allam, Linlin Zhang, Die Hu, and Massimo Menenti. "Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China." Remote Sensing 13, no. 15 (2021): 2858. http://dx.doi.org/10.3390/rs13152858.
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Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations.
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Che, Yueling, Zeyu Zhao, Sheng Luo, Kaishun Wu, Lingjie Duan, and Victor C. M. Leung. "UAV-Aided Wireless Energy Transfer for Sustaining Internet of Everything in 6G." Drones 7, no. 10 (2023): 628. http://dx.doi.org/10.3390/drones7100628.
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Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided.
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Al-Jaberi, Fadil, Melanie Fachet, Christoph Hoeschen, Matthias Moeskes, and Martin Skalej. "Optimization Techniques for Semi-Automated 3D Rigid Registration in Multimodal Image-Guided Deep Brain Stimulation." Current Directions in Biomedical Engineering 9, no. 1 (2023): 355–58. http://dx.doi.org/10.1515/cdbme-2023-1089.
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Abstract Multimodal image registration is vital in Deep Brain Stimulation (DBS) surgery. DBS treats movement disorders by implanting a neurostimulator device in the brain to deliver electrical impulses. Image registration between computed tomography (CT) and cone beam computed tomography (CBCT) involves fusing images with a specific field of view (FOV) to visualize individual electrode contacts. This contains important information about the location of segmented contacts that can reduce the time required for electrode programming. We performed a semi-automated multimodal image registration with different FOV between CT and CBCT images due to the tiny structures of segmented electrode contacts that necessitate high accuracy in the registration. In this work, we present an optimization workflow for multi-modal image registration using a combination of different similarity metrics, interpolators, and optimizers. Optimization-based rigid image registration (RIR) is a common method for registering images. The selection of appropriate interpolators and similarity metrics is crucial for the success of this optimization-based image registration process.We rely on quantitative measures to compare their performance. Registration was performed on CT and CBCT images for DBS datasets with an image registration algorithm written in Python using the Insight Segmentation and Registration Toolkit (ITK). Several combinations of similarity metrics and interpolators were used, including mean square difference (MSD), mutual information (MI), correlation and nearest neighbors (NN), linear (LI), and B-Spline (SPI), respectively. The combination of a correlation as similarity metric, B-Spline interpolation, and GD optimizer performs the best in optimizing the 3D RIR algorithm, enhancing the visualization of segmented electrode contacts. Patients undergoing DBS therapy may ultimately benefit from this.
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Skorska, Malvina N., Lindsey T. Thurston, Jessica M. Biasin, et al. "Cortical Structure Differences in Relation to Age, Sexual Attractions, and Gender Dysphoria in Adolescents: An Examination of Mean Diffusivity and T1 Relaxation Time." Brain Sciences 13, no. 6 (2023): 963. http://dx.doi.org/10.3390/brainsci13060963.
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Recent research found that the combination of masculine gender identity and gynephilia was associated with cortical T1 relaxation time, which is considered to reflect gray matter density. We hypothesized that mean diffusivity (MD), a diffusion tensor imaging metric that reflects the degree to which water movement is free versus constrained, in combination with T1 relaxation time would provide further insight regarding cortical tissue characteristics. MD and T1 relaxation time were measured in 76 cortical regions in 15 adolescents assigned female at birth who experience gender dysphoria (GD AFAB) and were not receiving hormone therapy, 17 cisgender girls, and 14 cisgender boys (ages 12–17 years). Sexual orientation was represented by the degree of androphilia–gynephilia and the strength of sexual attraction. In multivariate analyses, cortical T1 relaxation time showed a weak but statistically significant positive association with MD across the cortex, suggesting that macromolecule-rich cortical tissue also tends to show water movement that is somewhat more constrained. In further multivariate analyses, in several left frontal, parietal, and temporal regions, the combination of shorter T1 relaxation time and faster MD was associated with older age and greater gynephilia in GD AFAB individuals and cisgender boys and with stronger attractions in cisgender boys only. Thus, for these cortical regions in these groups, older age, gynephilia, and stronger attractions (cisgender boys only) were associated with macromolecule-rich tissue in which water movement was freer—a pattern that some prior research suggests is associated with greater cell density and size. Overall, this study indicates that investigating T1 relaxation time and MD together can further inform how cortical gray matter tissue characteristics relate to age and psychosexuality.
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WANG, FU-KWUN, and NORMA F. HUBELE. "QUALITY EVALUATION USING GEOMETRIC DISTANCE APPROACH." International Journal of Reliability, Quality and Safety Engineering 06, no. 02 (1999): 139–53. http://dx.doi.org/10.1142/s0218539399000140.
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Quantitatively assessing quality and using this assessment for competitive benchmarking and diagnostics of manufactured part failure are very important for continuous improvement in modern manufacturing industries. Process capability analysis often entails characterizing or assessing process specification or quality characteristics. When these quality characteristics are related, the analysis should be based on a multivariate statistical technique. A current problem in multivariate quality control is that there is no consensus about a methodology for assessing capability. Thus, the critical first step in instituting a multivariate control scheme is not well defined. While numerous authors have recently proposed alternative definitions of multivariate capability indices, those methods may not be practical in some cases. In this research, a new process control variable, geometric distance (GD), for assessing or evaluating the quality of manufactured product is developed and investigated for reducing dimensionality. The theoretical distribution of the geometric distance is investigated and a suitable performance metric of the multivariate process data is proposed. Finally, some real data are used to demonstrate the capability of the proposed method.
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PRITAM, S. VARMA, CELINE D'SOUZA (Miss), and B. LAXMESHWAR N. "Study of Stability Constants of Complexes of some Divalent Metal Ions and Rare Earth Metal Ions with 5-Benzal-3- phenyl-2-thioxo-4-imidazolidinone." Journal of Indian Chemical Society Vol. 63, Feb 1986 (1986): 201–2. https://doi.org/10.5281/zenodo.6253862.
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Physical Chemistry Laboratory, Institute of Science, Bombay-400 032 <em>Manuscript received 26 December 1984, revised 7 October 1985, accepted 2 December 1985</em> The stepwise stability constants of complexes of 5-benzal-3-phenyl-2-thioxo-4-imida­x)lidinone with Cu<em><sup>Il</sup></em>, Zn<em><sup>II</sup></em>, N<em>I<sup>II </sup></em>and Cd<em><sup>II</sup></em> as transition metal ions and <em>Sm<sup>II</sup>. Gd<sup>III</sup>, </em>and <em>Dy<sup>III</sup>, </em>as rare earth metal ions are determined by Calvin-Bjerrum potentio­metric titration method. The study was conducted in 3 : 1 (v/v) dioxane-water medium at ionic strength 0.1 <em>µ</em><em> </em>and temperature 27± 1°. The order of the stability constants is found to be in agreement with the order reported by earlier workers. In case of rare earth metal ions, the stability constants observed are slightly lower as compared to the divalent metal ions studied, thereby indicating lesser ionic character.
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Zhou, Yulin, and Zhenxia Mu. "Impact of Different Reanalysis Data and Parameterization Schemes on WRF Dynamic Downscaling in the Ili Region." Water 10, no. 12 (2018): 1729. http://dx.doi.org/10.3390/w10121729.
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Different reanalysis data and physical parameterization schemes for the Weather Research and Forecasting (WRF) model are considered in this paper to evaluate their performance in meteorological simulations in the Ili Region. A 72-hour experiment was performed with two domains at the resolution of 27 km with one-way nesting of 9 km. (1) Final Analysis (FNL) and Global Forecast System (GFS) reanalysis data (hereafter, WRF-FNL experiment and WRF-GFS experiment, respectively) were used in the WRF model. For the simulation of accumulated precipitation, both the WRF-FNL (mean bias of 0.79 mm) and WRF-GFS (mean bias of 0.31 mm) simulations can display the main features of the general temporal pattern and geographical distribution of the observed precipitation. For the simulation of the 2-m temperature, the simulation of the WRF-GFS experiment (mean warm bias of 1.81 °C and correlation coefficient of 0.83) was generally better than that of the WRF-FNL experiment (mean cold bias of 1.79 °C and correlation coefficient of 0.27). (2) Thirty-six physical combination schemes were proposed, each with a unique set of physical parameters. Member 33 (with the smallest mean-metric of 0.53) performed best for the precipitation simulation, and member 29 (with the smallest mean-metric of 0.64) performed best for the 2-m temperature simulation. However, member 29 and 33 cannot be distinguished from the other members according to their parameterizations. For this domain, ensemble members that contain the Mellor–Yamada–Janjic (MYJ) boundary layer (PBL) scheme and the Grell–Devenyi (GD) cumulus (CU) scheme are recommended for the precipitation simulation. The Geophysical Fluid Dynamics Laboratory (GFDL) radiation (RA) scheme and the MYJ PBL scheme are recommended for the 2-m temperature simulation.
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Wang, Xiaofang, Shi Yin, Lianyong Luo, and Xin Qiao. "Research on Multi-UAV Task Assignment Based on a Multi-Objective, Improved Brainstorming Optimization Algorithm." Applied Sciences 14, no. 6 (2024): 2365. http://dx.doi.org/10.3390/app14062365.
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In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.
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Chew, Peter K. H., Kuhanesan N. C. Naidu, Jing Shi, and Melvyn W. B. Zhang. "Validation of the Internet Gaming Disorder Scale–Short-Form and the Gaming Disorder Test in Singapore." Asian Journal of Social Health and Behavior 8, no. 3 (2025): 125–32. https://doi.org/10.4103/shb.shb_327_24.
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Introduction: Given that 90% of younger Singaporeans play video games, there is an urgent need for psychometrically valid instruments as a screening tool for problematic gaming. The Internet Gaming Disorder Scale–Short-Form (IGDS9-SF) and the Gaming Disorder Test (GDT) have not been validated for use among Singaporeans. The current study aimed to examine the psychometric properties of both instruments among Singaporeans. Methods: Participants were a representative sample of 1001 adults (aged 18–40 years) in Singapore recruited using stratified sampling. The study used a correlational design. Participants completed instruments that assess Internet gaming disorder (IGD), gaming disorder (GD), and negative emotional states. Data collection was conducted and completed in August 2023. The data were analyzed using (multigroup) confirmatory factor analyses, reliability analyses, Pearson’s correlation coefficient, and t-tests. Results: The results provided support for the one-factor model of the IGDS9-SF (Comparative Fit Index [CFI] = 0.972) and GDT (CFI = 0.996). Both instruments also had configural, metric, scalar, and strict invariances across gender (∆CFI was <0.010). The instruments were also reliable, with a Cronbach’s alpha of 0.93 and 0.90, respectively. Finally, the instruments were significantly correlated with each other (r = 0.83) and with depression, anxiety, and stress (r ranged from 0.65 to 0.71). Conclusion: Overall, both instruments are reliable and valid among Singaporeans. Future research could recruit samples with a wider age range to include children and adolescents and those over the age of 40 years and to compare scores of the instruments against clinician-administered assessments.
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Pati, Sarthak, Vaibhav Sharma, Heena Aslam, et al. "TMOD-09. GLIOBLASTOMA BIOPHYSICAL GROWTH ESTIMATION USING DEEP LEARNING-BASED REGRESSION." Neuro-Oncology 22, Supplement_2 (2020): ii229. http://dx.doi.org/10.1093/neuonc/noaa215.960.
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Abstract BACKGROUND Glioblastomas are arguably the most aggressive, infiltrative, and heterogeneous adult brain tumor. Biophysical modeling of glioblastoma growth has shown its predictive value towards clinical endpoints, enabling more informed decision-making. However, the mathematically rigorous formulations of biophysical modeling come with a large computational footprint, hindering their application to clinical studies. METHODS We present a deep learning (DL)-based logistical regression model, to estimate in seconds glioblastoma biophysical growth, defined through three tumor-specific parameters: 1) diffusion coefficient of white matter (Dw), which describes how easily the tumor can infiltrate through the white matter, 2) mass-effect parameter (Mp), which defines the average tumor expansion, and 3) estimated time (T) in number of days that the tumor has been growing. Pre-operative multi-parametric MRI (mpMRI) structural scans (T1, T1-Gd, T1, T2-FLAIR) from 135 subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal T2-FLAIR signal envelope, for training three DL models for the three tumor-specific parameters. Each of our DL models consist of two sets of convolution layers followed by a single max-pooling layer, with a normalized root mean squared error as the minimization metric and evaluated using 10-fold cross validation. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. RESULTS Pearson correlation coefficients between our DL-based estimations and the biophysical parameters were equal to 0.85 for Dw, 0.90 for Mp, and 0.94 for T. CONCLUSION This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation, paving the way towards their utilization in more clinical studies, while opening the door for leveraging advanced radiomic descriptors in future studies, as well as allowing for significantly faster parameter reconstruction compared to biophysical growth modeling approaches. *denotes equal senior authorship.
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Bareja, Rohan, Marwa Ismail, Douglas Martin, et al. "NIMG-88. A TRANSFER LEARNING APPROACH FOR AUTOMATIC SEGMENTATION OF TUMOR SUB-COMPARTMENTS IN PEDIATRIC MEDULLOBLASTOMA USING MULTIPARAMETRIC MRI: PRELIMINARY FINDINGS." Neuro-Oncology 24, Supplement_7 (2022): vii185—vii186. http://dx.doi.org/10.1093/neuonc/noac209.706.
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Abstract PURPOSE Superior outcomes for medulloblastoma (MB) requires precise surgical resection which can be guided by tumor segmentation. We present the first attempt at automatic segmentation of MB tumors via a hierarchical transfer-learning model that (1) segments the entire tumor habitat (enhancing tumor (ET), necrosis/non-enhancing tumor (NET), edema), followed by (2) training separate models for each of the sub-compartments. Transfer learning from adult brain tumors is used to optimize segmentation of tumor sub-compartments for pediatric MB. METHODS We evaluated 300 adult glioma studies (BRATS) and 49 pediatric MB studies (2-18 years old), both consisting of Gd-T1w, T2w, FLAIR sequences. The MB cohort was collected from Children's Hospital of Los Angeles (Nf19) and Cincinnati Children’s Hospital Medical Center (Nf30). Scans were registered to age-specific pediatric atlases, followed by bias correction and skull-stripping. Ground truth for the tumor sub-compartments was generated via consensus across two experts. We employed a 3D nn-Unet segmentation model on BRATS dataset using initial learning rate of 0.01, stochastic gradient descent as optimizer, and an average of dice loss and cross-entropy loss as the loss function. A hierarchical transfer learning model with Models Genesis was then applied, which allowed for fine tuning every layer on the pediatric MB dataset, across 5-fold cross validation. Dice score was used as performance metric, such that a perfect overlap between ground truth and prediction would yield a Dice score of 1. RESULTS Our 3D hierarchical segmentation model yielded mean dice scores of 0.85±0.03 for the entire tumor habitat; 0.77±0.048 for ET, 0.73±0.09 for edema, and 0.56±0.09 for NET + necrosis segmentation, across cross-validation runs. Overall, tumor outline and segmentation matched well with the ground truth, especially for the entire tumor, ET and enhancing tumor sub-compartments. CONCLUSIONS Our segmentation approach holds promise for accurate automated delineation of the tumor sub-compartments in pediatric Medulloblastoma.
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Tuchinov, Bair N., Andrey Yu Letyagin, Evgeniya V. Amelina, Mihail E. Amelin, Evgeniy N. Pavlovskiy, and Sergey K. Golushko. "Software for brain tumor diagnosis on magnetic resonance imaging." Digital Diagnostics 4, no. 1S (2023): 138–40. http://dx.doi.org/10.17816/dd430372.
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BACKGROUND: The main reason for the development and implementation of artificial intelligence (AI) technologies in neuro-oncology is the high prevalence of brain tumors reaching up to 200 cases per 100,000 population. The incidence of a primary focus in the brain is 5%10%; however, 60%70% of those who die from malignant neoplasms have metastases in the brain. Magnetic resonance imaging (MRI) is the most common method for primary non-invasive diagnosis of brain tumors and monitoring disease progression. One of the challenges is the classification of tumor types and determination of clinical parameters (size and volume) for the conduct, diagnosis, and treatment procedures, including surgery.
 AIM: To develope a software module for the differential diagnosis of brain neoplasms on MRI images.
 METHODS: The software module is based on the developed Siberian Brain Tumor Dataset (SBT), which contains information on over 1000 neurosurgical patients with fully verified (histologically and immunohistochemically) postoperative diagnoses. The data for research and development was presented by the Federal Neurosurgical Center (Novosibirsk). The module uses two- and three-dimensional computer vision models with pre-processed MRI sequence data included in the following packages: pre-contrast T1-weighted image (WI), post-contrast T1-WI, T2-WI, and T2-WI with fluid-attenuated inversion-recovery technique. The models allow to detect and recognize with high accuracy 4 types of neoplasms, such as meningioma, neurinoma, glioblastoma, and astrocytoma, and segment and distinguish components and sizes: ET (tumor core absorbing Gd-containing contrast), TC (tumor core) = ET + Necr (necrosis) + NenTu, and WT (whole tumor) = TC + Ed (peritumoral edema).
 RESULTS: The developed software module shows high segmentation results on SBT by Dice metric for ET 0.846, TC 0.867, WT 0.9174, Sens 0.881, and Spec 1.000 areas. The testing and validation were done at the international BraTS Challenge 2021 competition. The test dataset yielded DiceET 0.86588, DiceTC 0.86932, and DiceWT 0.921 values, placing the developed software module in the top ten. According to the classification, the results demonstrate high accuracy rates of up to 92% in patient analysis (up to 89% in slice analysis), a very high potential, and a perspective for future research in this area.
 CONCLUSIONS: The developed software module may be used for training specialists and in clinical diagnostics.
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Kurban, Rifat. "Gaussian of Differences: A Simple and Efficient General Image Fusion Method." Entropy 25, no. 8 (2023): 1215. http://dx.doi.org/10.3390/e25081215.
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The separate analysis of images obtained from a single source using different camera settings or spectral bands, whether from one or more than one sensor, is quite difficult. To solve this problem, a single image containing all of the distinctive pieces of information in each source image is generally created by combining the images, a process called image fusion. In this paper, a simple and efficient, pixel-based image fusion method is proposed that relies on weighting the edge information associated with each pixel of all of the source images proportional to the distance from their neighbors by employing a Gaussian filter. The proposed method, Gaussian of differences (GD), was evaluated using multi-modal medical images, multi-sensor visible and infrared images, multi-focus images, and multi-exposure images, and was compared to existing state-of-the-art fusion methods by utilizing objective fusion quality metrics. The parameters of the GD method are further enhanced by employing the pattern search (PS) algorithm, resulting in an adaptive optimization strategy. Extensive experiments illustrated that the proposed GD fusion method ranked better on average than others in terms of objective quality metrics and CPU time consumption.
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Behera, Sudersan, and P M Suresh. "Forecasting Bitcoin Price Utilizing Evolutionary Radial Bias Function Networks." International Journal of Innovation in Multidisciplinary Scientific Research 02, no. 01 (2024): 46–55. http://dx.doi.org/10.61239/ijimsr.2024.2116.
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A semi-parametric evolutionary method called the Fireworks method (FWA) and a radial bias function neural network (RBFN) are combined in this study to make a new type of network called RBFN-FWA. Effectively adjusting the RBFN's biases and weights is the goal of integrating FWA. We next used this proposed methodology to predict how Bitcoin's price will go in the future. The solo RBFN was trained using various optimization techniques such as GA, PSO, and GD for comparison purposes. This resulted in three new models: RBFN-GA, RBFN-PSO, and RBFN-GD. We also use all the other models that can do the same thing. Utilizing error metrics like Mean Absolute Percentage Error (MAPE) and Normalized Mean Squared Error (NMSE), we evaluated the models' performance. In terms of NMSE and MAPE, the experimental findings show that RBFNN-FWA is the best comparison model, demonstrating its higher predictive potential.A semi-parametric evolutionary method called the Fireworks method (FWA) and a radial bias function neural network (RBFN) are combined in this study to make a new type of network called RBFN-FWA. Effectively adjusting the RBFN's biases and weights is the goal of integrating FWA. We next used this proposed methodology to predict how Bitcoin's price will go in the future. The solo RBFN was trained using various optimization techniques such as GA, PSO, and GD for comparison purposes. This resulted in three new models: RBFN-GA, RBFN-PSO, and RBFN-GD. We also use all the other models that can do the same thing. Utilizing error metrics like Mean Absolute Percentage Error (MAPE) and Normalized Mean Squared Error (NMSE), we evaluated the models' performance. In terms of NMSE and MAPE, the experimental findings show that RBFNN-FWA is the best comparison model, demonstrating its higher predictive potential.
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Aimone, Ashley M., Diego G. Bassani, Huma Qamar, et al. "Complementary and alternative metrics for tracking population-level trends in child linear growth." PLOS Global Public Health 3, no. 4 (2023): e0001766. http://dx.doi.org/10.1371/journal.pgph.0001766.
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Stunting prevalence is commonly used to track population-level child nutritional status. However, other metrics derived from anthropometric datasets may be used as alternatives to stunting or provide complementary perspectives on the status of linear growth faltering in low- and middle-income countries (LMICs). Data from 156 Demographic and Health Surveys in 63 LMICs (years 2000 to 2020) were used to generate 2 types of linear growth metrics: (i) measures of location of height distributions (including stunting) for under-5 years (<5y) and 2 to 5 years (2-5y); (ii) model-derived metrics including predicted mean height-for-age z-score (HAZ) at 0, 2, and 5 years; interval slopes of HAZ, height-for-age difference (HAD), and growth delay (GD) from 1 month to 2 years (1mo-2y) and 2-5y; and the SITAR intensity parameter (SITAR-IP) for <5y. Using Spearman’s rank correlation coefficient (r), metrics were considered alternatives to stunting if very strongly correlated with stunting (|r|≥0.95) and at least as strongly correlated as stunting with selected population indicators (under 5y mortality, gross domestic product, maternal education). Metrics were considered complementary if less strongly correlated with stunting (|r|<0.95) yet correlated with population indicators. We identified 6 of 15 candidate metrics (stunting 2-5y, mean HAZ <5y and 2-5y, p25 HAZ <5y and 2-5y, predicted HAZ at 2y) as potential alternatives to stunting and 6 as complementary metrics (SITAR-IP, predicted HAZ at 5y, HAZ slope 1m-2y, HAD slope 1m-2y, GD slopes 1m-2y and 2-5y). Three metrics (HAZ slope 2-5y, HAD slope 2-5y years and predicted HAZ at birth) had weak correlations with population indicators (|r| ≤ 0.43). In conclusion, several linear growth metrics could serve as alternatives to stunting prevalence and others may be complementary to stunting in tracking global progress in child health and nutrition. Further research is needed to explore the real-world utility of these alternative and complementary metrics.
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Kida, Adriana de Souza Batista, Clara Regina Brandão de Ávila, and Simone Aparecida Capellini. "Marcadores sintáticos no reconto oral de escolares disléxicos." CoDAS 27, no. 6 (2015): 557–64. http://dx.doi.org/10.1590/2317-1782/20152015106.
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RESUMO Objetivo: Caracterizar marcadores sintáticos na expressão oral de escolares disléxicos em tarefa de reconto oral da leitura de textos. Métodos: Avaliou-se 32 escolares que compuseram dois grupos: Grupo Dislexia (GD) - 16 com diagnóstico de dislexia do desenvolvimento, Grupo Controle (GC) - 16 sem queixas de leitura, pareados ao GD por idade, gênero e escolaridade. Todos leram um texto narrativo e um expositivo e os recontaram oralmente. Os recontos foram gravados, transcritos e analisados por meio doCoh-Metrix-Port. Analisou-se, estatisticamente, o desempenho por meio dos índices de produtividade, de diversidade lexical, de complexidade e de competências gramaticais. Resultados: GD mostrou menor média de sentenças corretas em recontos narrativos e expositivos, menor número de palavras por sentença e incidência de palavras de conteúdo para o expositivo. Conclusão: Escolares disléxicos apresentaram menor competência gramatical ao recontarem textos narrativos e expositivos. Menos palavras por sentença e menor incidência de palavras de conteúdo também caracterizaram o reconto de textos expositivos na dislexia, possível efeito da maior demanda cognitiva imposta pelo tipo de texto à compreensão leitora.
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Hurst, Aaron, Daniel E. Lucani, and Qi Zhang. "PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression." Proceedings of the VLDB Endowment 17, no. 6 (2024): 1432–45. http://dx.doi.org/10.14778/3648160.3648181.
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Exponential growth in data collection is creating significant challenges for data storage and analytics latency. Approximate Query Processing (AQP) has long been touted as a solution for accelerating analytics on large datasets, however, there is still room for improvement across all key performance criteria. In this paper, we propose a novel histogram-based data synopsis called PairwiseHist that uses recursive hypothesis testing to ensure accurate histograms and can be built on top of data compressed using Generalized Deduplication (GD). We thus show that GD data compression can contribute to AQP. Compared to state-of-the-art AQP approaches, Pairwise-Hist achieves better performance across all key metrics, including 2.6× higher accuracy, 3.5× lower latency, 24× smaller synopses and 1.5--4× faster construction time.
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FERNANDES, PEDRO C., FRÉDÉRIC RISSO, PATRICIA ERN, and JACQUES MAGNAUDET. "Oscillatory motion and wake instability of freely rising axisymmetric bodies." Journal of Fluid Mechanics 573 (February 2007): 479–502. http://dx.doi.org/10.1017/s0022112006003685.
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This paper reports on an experimental study of the motion of freely rising axisym- metric rigid bodies in a low-viscosity fluid. We consider flat cylinders with height h smaller than the diameter d and density ρb close to the density ρf of the fluid. We have investigated the role of the Reynolds number based on the mean rise velocity um in the range 80 ≤ Re = umd/ν ≤ 330 and that of the aspect ratio in the range 1.5 ≤ χ = d/h ≤ 20. Beyond a critical Reynolds number, Rec, which depends on the aspect ratio, both the body velocity and the orientation start to oscillate periodically. The body motion is observed to be essentially two-dimensional. Its description is particularly simple in the coordinate system rotating with the body and having its origin fixed in the laboratory; the axial velocity is then found to be constant whereas the rotation and the lateral velocity are described well by two harmonic functions of time having the same angular frequency, ω. In parallel, direct numerical simulations of the flow around fixed bodies were carried out. They allowed us to determine (i) the threshold, Recf1(χ), of the primary regular bifurcation that causes the breaking of the axial symmetry of the wake as well as (ii) the threshold, Recf2(χ), and frequency, ωf, of the secondary Hopf bifurcation leading to wake oscillations. As χ increases, i.e. the body becomes thinner, the critical Reynolds numbers, Recf1 and Recf2, decrease. Introducing a Reynolds number Re* based on the velocity in the recirculating wake makes it possible to obtain thresholds $\hbox{\it Re}^*_{cf1}$ and $\hbox{\it Re}^*_{cf2}$ that are independent of χ. Comparison with fixed bodies allowed us to clarify the role of the body shape. The oscillations of thick moving bodies (χ < 6) are essentially triggered by the wake instability observed for a fixed body: Rec(χ) is equal to Recf1(χ) and ω is close to ωf. However, in the range 6 ≤ χ ≤ 10 the flow corrections induced by the translation and rotation of freely moving bodies are found to be able to delay the onset of wake oscillations, causing Rec to increase strongly with χ. An analysis of the evolution of the parameters characterizing the motion in the rotating frame reveals that the constant axial velocity scales with the gravitational velocity based on the body thickness, $\sqrt{((\rho_f-\rho_b)/\rho_f)\,gh}$, while the relevant length and velocity scales for the oscillations are the body diameter d and the gravitational velocity based on d, $\sqrt{((\rho_f-\rho_b)/\rho_f)\,gd}$, respectively. Using this scaling, the dimensionless amplitudes and frequency of the body's oscillations are found to depend only on the modified Reynolds number, Re*; they no longer depend on the body shape.
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Gou, Wanglong, Congmei Xiao, Xinxiu Liang, et al. "Physical Activity During Pregnancy and Preterm Birth Among Women With Gestational Diabetes." JAMA Network Open 7, no. 12 (2024): e2451799. https://doi.org/10.1001/jamanetworkopen.2024.51799.
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ImportancePhysical activity, as a modifiable factor, emerges as a primary intervention strategy for the prevention and management of gestational diabetes (GD). Among women with GD, the association of physical activity during pregnancy with preterm birth remains unclear.ObjectiveTo examine the association of accelerometer-derived physical activity metrics and patterns with preterm birth among women with GD.Design, Setting, and ParticipantsThis prospective cohort study recruited pregnant women with GD in Hangzhou, China, from August 2019 to August 2023 as part of the Westlake Precision Birth Cohort study. Statistical analysis was performed between August and November 2023.ExposuresWearable accelerometer–derived physical activity metrics and patterns. Measurements of physical activity via wearable accelerometer were performed at a median (IQR) of 25.4 (24.6-26.6) weeks’ gestation.Main Outcomes and MeasuresPreterm birth was determined through the examination of delivery records. Incident preterm birth was defined as the delivery of infants before completing 37 weeks of gestation.ResultsAmong the 1427 women meeting the inclusion criteria, the mean (SD) age was 31.3 (3.8) years, and there were 80 cases of preterm birth. An increase in moderate-to-vigorous intensity physical activity (MVPA) and the fraction of physical activity energy expenditure derived from MVPA exhibited an inverse association with preterm birth, with an odds ratio per 30 minutes of 0.64 (95% CI, 0.42-0.98) and an odds ratio per SD of 0.69 (95% CI, 0.55-0.88). In the dose-response analysis, there was a progressive decrease in the odds of preterm birth with increasing duration of MVPA per day, reaching a plateau at approximately 74 minutes per day. Furthermore, the findings indicated that active MVPA (MVPA ≥30 minutes per day), whether it was concentrated into a few days or followed a more regular pattern, had similar beneficial association with preterm birth.Conclusions and RelevanceIn this prospective cohort study, MVPA during pregnancy exhibited an inverse association with preterm birth among women with GD. Concentrated physical activity was associated with similar benefits in reducing preterm birth risk as regular physical activity.
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McGown, Kathryn I., Kevin L. O’Hara, and Andrew Youngblood. "Patterns of size variation over time in ponderosa pine stands established at different initial densities." Canadian Journal of Forest Research 46, no. 1 (2016): 101–13. http://dx.doi.org/10.1139/cjfr-2015-0096.
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We used six metrics of size and growth variation (standard deviation (SD), coefficient of variation (CV), skewness coefficient (S), Gini coefficient (G), Lorenz asymmetry coefficient (LAC), and growth dominance coefficient (GD)) to describe changes in two long-term ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) initial spacing trials in Oregon and Washington, USA. Trends were examined over a 35-year time period and across a range of initial stand densities (from 154 to 2470 trees·ha−1) for four measures of tree size: diameter at breast height (dbh, 1.37 m), basal area (BA), height, and volume. Unlike many previous studies of size variation in monospecific stands, our results suggest that variation declined or remained relatively stable for all treatments at both study areas. This suggests that these stands are experiencing size symmetric competition for belowground resources. We found that a combination of metrics is necessary to provide a complete picture of size variability and differentiation in developing stands. We recommend using the CV or G, as there were clear trends with increasing density for all size variables. If the objective of the assessment was to track changes in absolute size within an individual stand, we would recommend using the SD, as there were consistent trends with time for all size variables. S, LAC, and GD may be less suited for comparing differentiation during the early stages of stand development because of a lack of clear trends with stand density and time.
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Jurdana, Vedran, Miroslav Vrankic, Nikola Lopac, and Guruprasad Madhale Jadav. "Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis." Sensors 23, no. 10 (2023): 4680. http://dx.doi.org/10.3390/s23104680.
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Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike and oscillatory characteristics. Unlike previous methods that use IF alone, the proposed method utilizes information obtained from localized Rényi entropies (LREs) to generate a binary map that automatically identifies regions requiring a different estimation strategy. The method combines IF estimation algorithms for multicomponent signals with time and frequency support information to improve signal ridge estimation in the time–frequency distribution (TFD). Our experimental results indicate the superiority of the proposed combined IF and GD estimation approach over the IF estimation alone, without requiring any prior knowledge about the input signal. The LRE-based mean squared error and mean absolute error metrics showed improvements of up to 95.70% and 86.79%, respectively, for synthetic signals and up to 46.45% and 36.61% for real-life EEG seizure signals.
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Mohapatra, Sushanta, Kumar Pradhan, and Prasanna Sahu. "Resolving the bias point for wide range of temperature applications in High-k/Metal Gate nanoscale DG-MOSFET." Facta universitatis - series: Electronics and Energetics 27, no. 4 (2014): 613–19. http://dx.doi.org/10.2298/fuee1404613m.
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This article investigates the Zero-Temperature-Coefficient (ZTC) bias point and its associated performance metrics of a High-k Metal Gate (HKMG) DG-MOSFET in nanoscale. The ZTC bias point is defined as the point at which the device parameters are independent of temperature. The discussion includes sub threshold slope (SS), drain induced barrier lowering (DIBL), on-off current ratio (Ion/Ioff), transconductance (gm), output conductance (gd) and intrinsic gain (AV). From the results, it is confirmed that there are two different ZTC bias points, one for IDS (ZTCIDS) and the other for gm (ZTCgm). The points are obtained as: ZTCIDS=0.552 V and ZTCgm =0.410 V, which will open important opportunities in analog circuit design for wide range of temperature applications.
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Ruiz-Vélez, Andrés, José García, Gaioz Partskhaladze, Julián Alcalá, and Víctor Yepes. "Enhanced Structural Design of Prestressed Arched Trusses through Multi-Objective Optimization and Multi-Criteria Decision-Making." Mathematics 12, no. 16 (2024): 2567. http://dx.doi.org/10.3390/math12162567.
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The structural design of prestressed arched trusses presents a complex challenge due to the need to balance multiple conflicting objectives such as structural performance, weight, and constructability. This complexity is further compounded by the interdependent nature of the structural elements, which necessitates a comprehensive optimization approach. Addressing this challenge is crucial for advancing construction practices and improving the efficiency and safety of structural designs. The integration of advanced optimization algorithms and decision-making techniques offers a promising avenue for enhancing the design process of prestressed arched trusses. This study proposes the use of three advanced multi-objective optimization algorithms: NSGA-III, CTAEA, and SMS-EMOA, to optimize the structural design of prestressed arched trusses. The performance of these algorithms was evaluated using generational distance and inverted generational distance metrics. Additionally, the non-dominated optimal designs generated by these algorithms were assessed and ranked using multiple multi-criteria decision-making techniques, including SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR. This approach allowed for a robust comparison of the algorithms and provided insights into their effectiveness in balancing the different design objectives. The results of the study indicated that NSGA-III exhibited superior performance with a GD value of 0.215, reflecting a closer proximity of its solutions to the Pareto front, and an IGD value of 0.329, indicating a well-distributed set of solutions across the Pareto front. In comparison, CTAEA and SMS-EMOA showed higher GD values of 0.326 and 0.436, respectively, suggesting less convergence to the Pareto front. However, SMS-EMOA demonstrated a balanced performance in terms of constructability and structural weight, with an IGD value of 0.434. The statistical significance of these differences was confirmed by the Kruskal–Wallis test, with p-values of 2.50×10−15 for GD and 5.15×10−06 for IGD. These findings underscore the advantages and limitations of each algorithm, providing valuable insights for future applications in structural optimization.
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Bouali, Hamid, Soufiane Abi, Bachir Benhala, and Mohammed Guerbaoui. "Multi-Objective Design Optimization of Planar Spiral Inductors Using Enhanced Metaheuristic Techniques." Statistics, Optimization & Information Computing 13, no. 2 (2024): 857–76. https://doi.org/10.19139/soic-2310-5070-1873.
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The study presented in this paper improves the Multi-Objective Artificial Bee Colony (MOABC) method. It evaluates its performance using Generational Distance (GD), Spread (SP), and Hypervolume (HV) metrics on the Zitzler-Deb-Thiele (ZDT) benchmark functions. Subsequently, the improved MOABC method, along with Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), is applied to optimize the design of a square planar spiral inductor. The objectives are to maximize the quality factor ($Q$) and minimize the inductor area ($A$) simultaneously while maintaining a necessary inductance of $4\, \text{nH}$ at a $2.4\, \text{GHz}$ operating frequency, utilizing $0.13\, \mu \text{m}$ CMOS technology. The optimization findings are verified and confirmed using Advanced Design System (ADS) Momentum, demonstrating the feasibility of multi-objective optimization for integrated inductor design.
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Peng, Jicheng, Qianshuai Wang, Bingyu Jin, Yong Zhang, and Kelin Lu. "Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration." Biomimetics 9, no. 9 (2024): 510. http://dx.doi.org/10.3390/biomimetics9090510.
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This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system’s observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.
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Kinoshita, Manabu, Hideyuki Arita, Yoshiko Okita, et al. "Comparison of diffusion tensor imaging and 11C-methionine positron emission tomography for reliable prediction of tumor cell density in gliomas." Journal of Neurosurgery 125, no. 5 (2016): 1136–42. http://dx.doi.org/10.3171/2015.11.jns151848.
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OBJECTIVE Diffusion MRI is attracting increasing interest for tissue characterization of gliomas, especially after the introduction of antiangiogenic therapy to treat malignant gliomas. The goal of the current study is to elucidate the actual magnitude of the correlation between diffusion MRI and cell density within the tissue. The obtained results were further extended and compared with metabolic imaging with 11C-methionine (MET) PET. METHODS Ninety-eight tissue samples from 37 patients were stereotactically obtained via an intraoperative neuronavigation system. Diffusion tensor imaging (DTI) and MET PET were performed as routine presurgical imaging studies for these patients. DTI was converted into fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps, and MET PET images were registered to Gd-administered T1-weighted images that were used for navigation. Metrics of FA, ADC, and tumor-to-normal tissue ratio of MET PET along with relative values of FA (rFA) and ADC (rADC) compared with normal-appearing white matter were correlated with cell density of the stereotactically obtained tissues. RESULTS rADC was significantly lower in lesions obtained from Gd-enhancing lesions than from nonenhancing lesions. Although rADC showed a moderate but statistically significant negative correlation with cell density (p = 0.010), MET PET showed a superb positive correlation with cell density (p < 0.0001). On the other hand, rFA showed little correlation with cell density. CONCLUSIONS The presented data validated the use of rADC for estimating the treatment response of gliomas but also caution against overestimating its limited accuracy compared with MET PET.
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Bouali, Hamid, Bachir Benhala, and Mohammed Guerbaoui. "An enhanced multi-objective artificial bee colony algorithm with non-dominated sorting strategy." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1736. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1736-1747.
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<p>This paper presents an improved metaheuristic technique inspired by the foundational concepts of the artificial bee colony (ABC) algorithm adapted to deal with multi-objective optimization challenges. Our approach combines the main ideas of ABC with a non-dominated sorting strategy including aspects of Pareto dominance, crowding distance, and greedy selection method. Furthermore, the chosen non-dominated solutions are archived in a repository with a static size. The presented approach, multi-objective artificial bee colony (MOABC), is compared to other state-of-the-art algorithms including the non-dominated sorting genetic algorithm II (NSGA II) and the multi-objective particle swarm optimization (MOPSO). MOABC and selected algorithms from the literature are applied to five zitzler-deb-thiele (ZDT) Multi-objective benchmark functions. Then three key metrics are employed for performance evaluations: generational distance (GD), spread (SP), and hypervolume (HV). The simulation results suggest that the proposed method is competitive and presents an effective choice for tackling multi-objective optimization problems.</p>
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Bouali, Hamid, Bachir Benhala, and Mohammed Guerbaoui. "An enhanced multi-objective artificial bee colony algorithm with non-dominated sorting strategy." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1736–47. https://doi.org/10.11591/ijeecs.v33.i3.pp1736-1747.
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This paper presents an improved metaheuristic technique inspired by the foundational concepts of the artificial bee colony (ABC) algorithm adapted to deal with multi-objective optimization challenges. Our approach combines the main ideas of ABC with a non-dominated sorting strategy including aspects of Pareto dominance, crowding distance, and greedy selection method. Furthermore, the chosen non-dominated solutions are archived in a repository with a static size. The presented approach, multi-objective artificial bee colony (MOABC), is compared to other state-of-the-art algorithms including the non-dominated sorting genetic algorithm II (NSGA II) and the multi-objective particle swarm optimization (MOPSO). MOABC and selected algorithms from the literature are applied to five zitzler-deb-thiele (ZDT) Multi-objective benchmark functions. Then three key metrics are employed for performance evaluations: generational distance (GD), spread (SP), and hypervolume (HV). The simulation results suggest that the proposed method is competitive and presents an effective choice for tackling multi-objective optimization problems.
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Gu, Zhiyong, Jiancheng Lai, Chunyong Wang, Wei Yan, Yunjing Ji, and Zhenhua Li. "Generalized Gaussian decomposition for full waveform LiDAR processing." Measurement Science and Technology 33, no. 6 (2022): 065201. http://dx.doi.org/10.1088/1361-6501/ac4eff.
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Abstract Waveform decomposition techniques are commonly used to extract attributes of targets from light detection and ranging (LiDAR) waveforms. Since the shape of a real LiDAR waveform varies for different systems, the conventional models (e.g. the Gaussian function, lognormal function, and generalized normal function) cannot be universally used. In this paper, we present a generalized Gaussian decomposition (GGD) algorithm, which considers the received waveform as the convolution of an arbitrary system waveform with the target response assumed as a Gaussian mixture model. The proposed method was validated using the experimental waveforms sampled from our self-designed LiDAR system with two different system responses. Metrics, including the mean absolute error (MAE) for range retrieval and the root-mean-squared error (RMSE) for waveform fitting, were used to provide a comprehensive quantitative evaluation of the performance. Three classical models for waveform decomposition—the Gaussian, lognormal, and generalized normal functions—were introduced and studied for the comparison. As for the system waveform with a right-skewed profile, the experimental results showed that the GGD algorithm provided the lowest RMSE for waveform fitting, and the most accurate range estimates with an MAE of 0.030 m . The Gaussian decomposition (GD), lognormal decomposition (LND), and generalized normal decomposition (GND) algorithms produced much worse results with MAEs of 0.362, 1.091, and 0.417 m , respectively. As for the system waveform with a negative tail, the GGD algorithm also performed best with an MAE of 0.019 m , while the GD, LND and GND algorithms provided much larger MAEs of 0.457, 0.489, and 0.354 m , respectively. Therefore, the proposed method has the potential to extract more accurate model parameters from a variety of LiDAR waveforms regardless of the shape of the system waveform.
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Xie, Yuanliang, Hongfeng Zhang, Chaoling Jin та ін. "Gd-EOB-DTPA-enhanced T1ρ imaging vs diffusion metrics for assessment liver inflammation and early stage fibrosis of nonalcoholic steatohepatitis in rabbits". Magnetic Resonance Imaging 48 (травень 2018): 34–41. http://dx.doi.org/10.1016/j.mri.2017.12.017.
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Chen, Song, Ming Gong, Hua Sun, Ming Chen, and Binbin Wang. "Estimation of Forest Canopy Height from Spaceborne Full-Waveform LiDAR Data Using a Bisection Approximation Decomposition Method." Forests 16, no. 1 (2025): 145. https://doi.org/10.3390/f16010145.
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Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. However, the impact of terrain undulations on forest parameter estimation remains challenging. To address this issue, this study proposes a bisection approximation decomposition (BAD) method for processing GEDI L1B data and FCH estimation. The BAD method analyzes the energy composition of simplified echo signals and determines the fitting parameters by integrating overall signal energy, the differences in unresolved signals, and the similarity of inter-forest signal characteristics. FCH is subsequently estimated based on waveform peak positions. By dynamically adjusting segmentation points and Gaussian fitting parameters, the BAD method achieved precise separation of mixed canopy and ground signals, substantially enhancing the physical realism and applicability of decomposition results. The effectiveness and robustness of the BAD method for FCH estimation were evaluated using 2049 footprints across varying slope conditions in the Harvard Forest region of Petersham, Massachusetts. The results demonstrated that digital terrain models (DTMs) extracted using the GEDI data and the BAD method exhibited high consistency with the DTMs derived using airborne laser scanning (ALS) data (coefficient of determination R2 > 0.99). Compared with traditional Gaussian decomposition (GD), wavelet decomposition (WD), and deconvolution decomposition (DD) methods, the BAD method showed significant advantages in FCH estimation, achieved the smallest relative root mean square error (rRMSE) of 17.19% and greatest mean estimation accuracy of 84.57%, and reduced the rRMSE by 10.74%, 21.49%, and 28.93% compared to GD, WD, and DD methods, respectively. Moreover, the BAD method exhibited a significantly stronger correlation with ALS-derived canopy height mode data than the relative height metrics from GEDI L2A products (r = 0.84, p < 0.01). The robustness and adaptability of the BAD method to complex terrain conditions provide great potential for forest parameters using GEDI data.
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Geyer, Sebastian, and Christian Hölzl. "Comparison of CAD Software for Designing Cellular Structures for Additive Manufacturing." Applied Sciences 14, no. 8 (2024): 3306. http://dx.doi.org/10.3390/app14083306.
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Additive manufacturing (AM) technologies provide significant design freedom, which is highly desirable in today’s fast-paced product design processes. However, most of the parametric CAD software tools used today do not fully utilize this potential for freedom of form and design. Design mechanisms, such as topology optimization (TO), generative design (GD), and lattice structures, are available on the market to help designers minimize weight and material cost while maximizing the stiffness and flexibility of planned designs. This paper proposes a benchmarking approach for designers and engineers to select a suitable software tool for lattice structure generation for their specific applications. The approach includes preselecting software tools based on a weighted point evaluation of seven significant criteria. The tools are then evaluated based on key metrics such as computing time and file size of exported structures, as well as the following six distinctive attributes: usability, reliability, availability, performance, support, and cost. The evaluation process considers a total of 32 defined features. The investigation produced clear recommendations regarding overall performance, reliability, and user experience. The findings indicate that the option of a comprehensive support offering, as well as the initial and operational costs, are significant drivers in the decision-making process.
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Jiao, Xianqi, Jia Liu, and Zhiping Chen. "Learning Complexity of Gradient Descent and Conjugate Gradient Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17671–79. https://doi.org/10.1609/aaai.v39i17.33943.
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Gradient Descent (GD) and Conjugate Gradient (CG) methods are among the most effective iterative algorithms for solving unconstrained optimization problems, particularly in machine learning and statistical modeling, where they are employed to minimize cost functions. In these algorithms, tunable parameters, such as step sizes or conjugate parameters, play a crucial role in determining key performance metrics, like runtime and solution quality. In this work, we introduce a framework that models algorithm selection as a statistical learning problem, and thus learning complexity can be estimated by the pseudo-dimension of the algorithm group. We first propose a new cost measure for unconstrained optimization algorithms, inspired by the concept of primal-dual integral in mixed-integer linear programming. Based on the new cost measure, we derive an improved upper bound for the pseudo-dimension of gradient descent algorithm group by discretizing the set of step size configurations. Moreover, we generalize our findings from gradient descent algorithm to the conjugate gradient algorithm group for the first time, and prove the existence a learning algorithm capable of probabilistically identifying the optimal algorithm with a sufficiently large sample size.
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Deng, Shengwen, Walter Zhao, Abraham Nirappel, et al. "NIMG-53. DELTA RELAXOMETRY WITH CONTRAST ENHANCED MAGNETIC RESONANCE FINGERPRINTING: INITIAL APPLICATION TO DIFFERENTIATE BRAIN METASTASES AND GLIOBLASTOMA." Neuro-Oncology 24, Supplement_7 (2022): vii175—vii176. http://dx.doi.org/10.1093/neuonc/noac209.671.
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Abstract PURPOSE The utility of post-contrast magnetic resonance fingerprinting (MRF) for brain tumor assessment has not been assessed. The aim of this work is to report differences in MRF-derived delta-relaxometry metrics between brain metastases and glioblastoma, offering a new strategy for tumor differentiation. METHODS Post- and pre-contrast MRF (T1, T2 and proton density maps) were acquired from 29 patients (14 brain metastases (Met), 15 glioblastoma (GB)) along with conventional MRI (T1w, T1w-Gd, T2w, T2w-FLAIR, and ADC). Post-contrast MRF was skull-stripped and non-linearly co-registered with pre-contrast MRF. Delta relaxometry metrics (ΔR1/ΔR2 ratio, ΔR1, ΔR2, and normalized ΔR1/ΔR2) were calculated in the native image space (1.2x1.2x3.0 mm3). Tumor regions were segmented using DeepMedic into necrotic core (NC), enhancing tumor (ET), and peritumoral edema (ED). ROI-averaged means of delta relaxometry metrics were compared using paired t-test for ET and ED regions in Met vs GB. Voxel-wise ΔR1/ΔR2 ratios (log transformed) were compared using unpaired two-tailed t-test with Bonferroni correction to quantify distribution differences in delta relaxometry between Met and GB. RESULTS Across all voxels of all patients in each group, ΔR1/ΔR2 ratios between Met and GB were different (0.272±0.61 vs 0.247±0.66 in ED, p &lt; .001; 0.278±0.69 vs 0.264±0.67 in ET, p &lt; .001). On a per-patient basis, the median, 75th percentile, and 90th percentile of ΔR1/ΔR2 ratios were different between ET and ED regions (p &lt; .001). Within-ROI (NC, ET, and ED) averaged mean ΔR1/R2 ratios were not significantly different between Met and GB. CONCLUSION Voxel-wise distribution of ΔR1/ΔR2 ratios was different between tumor types (Met and GB) in both ET and ED regions. ROI-averaged ΔR1/ΔR2 ratios were different between ET and ED regions, but not specific for tumor type. Delta relaxometry provides a unique tumor-specific contrast and shows potential for solid tumor as well as peritumoral edema differentiation between Met and GB.