Academic literature on the topic 'Gd -metric'

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.

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.

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.

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.

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.

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.

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.

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.

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.

Orientadores: Sergio Gama, Pedro Jorge von Ranke<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Fisica Gleb Wataghin<br>Made available in DSpace on 2018-08-08T01:09:57Z (GMT). No. of bitstreams: 1 Carvalho_AlexandreMagnusGomes_D.pdf: 6808779 bytes, checksum: b779b54728ac489a69ae449f1e33d093 (MD5) Previous issue date: 2006<br>Resumo: Este trabalho apresenta resultados de estudos estruturais, magnéticos e magnetocalóricos de alguns compostos à base de gadolínio, germânio e silício. Os estudos estruturais incluem análises por microscopia ótica e eletrônica, além de difração de raios-X. As análises magnéticas restringem-se a medidas de magnetização em função da temperatura e do campo magnético. Utilizando essas técnicas, são estudados compostos sob diferentes condições de processamento, tais como: amostras como fundidas; tratadas termicamente; pulverizadas e sinterizadas. Os compostos Gd5Ge4 e Gd5Ge2Si2 são também analisados magneticamente sob pressão hidrostática. Adicionamos hidrogênio aos compostos Gd5Si4, Gd5G e2Si2 e Gd5Ge2,1S i1,9 e substituímos Ge e Si por Sn no composto Gd5Ge2Si2, criando novas famílias de materiais, as quais também foram analisadas pelas técnicas supracitadas. Além do trabalho experimental, são apresentados resultados teóricos para o efeito magnetocalórico, utilizando o modelo de Landau-Devonshire. Apresentamos também resultados fenomenológicos para o composto Gd5Ge2Si2 sob pressão hidrostática, utilizando o Modelo de Acoplamento Spin-Rede (MASR)<br>Abstract: This work presents the results from structural, magnetic and magnetocaloric studies about some compounds based on Gd, Ge and Si. Structural studies include optical and electronic microscopy analyses, besides X-ray diffraction. Magnetic analyses are limited to measurements of magnetization as a function of temperature and magnetic field. Using these techniques, the compounds are investigated under different processing conditions, such as: as-cast, heat-treated, powdered and sintered samples. Gd5Ge4 and Gd5Ge2Si2 compounds are also analyzed through magnetic measurements performed under hydrostatic pressure. We have inserted hydrogen atoms into Gd5Si4, Gd5G e2Si2 and Gd5Ge2,1S i1,9 compounds and substituted Sn for Ge and Si in Gd5Ge2Si2 compound. Thus, new families of materials were developed, which are also investigated in this work using the techniques mentioned above. Besides the experimental work, theoretical results are presented for the magnetocaloric effect using the Landau-Devonshire model. We also present phenomenological results for Gd5Ge2Si2 compound under hydrostatic pressure using the Coupling Magnetic-Lattice Model<br>Doutorado<br>Física da Matéria Condensada<br>Doutor em Ciências

O presente trabalho incide no desenvolvimento de eletrólitos cerâmicos e compósitos com possível aplicação em células a combustível, para operação a temperaturas na ordem dos 600 °C ou inferiores. Estes materiais, predominantemente à base de céria dopada com gadolínia (GDC) e de misturas eutéticas de carbonatos alcalinos (usados como aditivos de sinterização ou segundas fases), foram produzidos recorrendo a soluções de processamento convencionais (rota cerâmica), mas também por uma rota química inovadora de tipo one step, que permite a síntese de nanopartículas com elevada aptidão para a sinterização. Também são avaliados compósitos de NiO-GDC, precursores de cermets para anodos, e catodos com estrutura tipo perovskita baseados em manganitas e cobaltitas de lantânio. No quadro de uma análise detalhada de resultados da literatura comenta-se de forma crítica a sua (in)suficiência e (in)consistência, justificando deste modo a oportunidade e atenção dada ao conjunto de materiais estudados. Foi usada a difração de raios-X para identificação de estruturas e de solubilidade/reatividade, a microscopia eletrônica de varredura para analisar microestruturas, também a microscopia eletrônica de transmissão num número limitado de situações. A espectroscopia de impedância a diferentes temperaturas e em diferentes atmosferas e a técnica de polarização de HebbWagner permitiram inspecionar as propriedades elétricas (iônicas e eletrônicas, grão e contorno de grão) de muitos destes materiais em grande detalhe. São analisadas de forma original condições ideiais de tratamento da informação obtida por esta última técnica. Foi possível densificar eletrólitos de GDC a temperaturas cerca de 400 °C abaixo dos valores usuais recorrendo a misturas de sais de metais alcalinos como aditivos de sinterização. Estes materiais revelaram propriedades elétricas (iônicas e eletrônicas) altamente competitivas em relação ao material de referência (GDC puro). Em alguns aspectos estes materiais revelaram-se mesmo superiores aos obtidos com Co como aditivo de sinterização, material de referência para este tipo de processamento. Uma configuração original de células (planas) concebidas para avaliar o efeito dos sais fundidos nos óxidos sólidos, permitiu isolar e identificar efeitos específicos ao nível do contorno de grão, tema alvo de grande atenção pela diversidade de efeitos encontrados neste trabalho e referidos na literatura. Para além de contributos originais para o entendimento dos processos de transporte no contorno de grão, esta metodologia abre o caminho para métodos invulgares e promissores de engenharia de contorno de grão. As magnitudes totais e relativas dos arcos dos espectros de impedância foram usadas para obter informações sobre as características microestruturais de diferentes precursores de anodos (compósitos NiO+GDC), originando um contributo original sobre o uso desta técnica em materiais compósitos com uma fase isolante e uma fase condutora.<br>The present work focuses on the development of ceramic and composite electrolytes with application in fuel cells operating at 600 ° C or lower. These materials, predominantly based on gadolinium-doped ceria (GDC) and eutectic mixtures of alkaline carbonates (used as sintering admixtures or second phases), were produced using conventional processing solutions (ceramic route), and an innovative one step chemical route, which enables a nanoparticles synthesis with high sintering aptitude. NiO-GDC composites, anode cermet precursors, and cathodes based on perovskites derived from lanthanum manganite or cobaltite were also evaluated. In the context of a detailed analysis of literature results, its (in)sufficiency and (in)consistency is critically commented, thus justifying the attention given to the set of materials studied. X-ray diffraction was used to identify structures and solubility/reactivity of materials, scanning electron microscopy was used to analyze microstructures and transmission electron microscopy was also used in a limited number of situations. Impedance spectroscopy at different temperatures and atmospheres, and the Hebb-Wagner polarization technique, allowed to inspect the electrical properties (ionic and electronic, grain and grain boundary) of these materials in detail. Handling of raw data obtained with the latter technique is the subject of an original approach. It was possible to densify GDC electrolytes at temperatures around 400 °C below usual values using mixtures of alkali metal salts as sintering admixtures. These materials revealed highly competitive electrical (ionic and electronic) properties over the reference material (pure GDC). In some respects, these materials were superior to those obtained with Co, a reference sintering aid material. An original configuration of (plane) cells designed to evaluate the effect of molten salts on solid oxides allowed to isolate and identify specific effects at the grain boundary level, subject of debate based on disparate effects found in this work and reported in the literature. Besides original information aiming at the understanding of relevant mechanisms involved in grain boundary performance, this methodology paves the way to unusual but promising methods of grain boundary engineering. The total and relative magnitudes of the impedance spectra arcs were used to obtain information on the microstructural characteristics of different tested anode precursors (NiO + GDC composites), giving an original contribution on the use of this technique in composite materials with an insulating and conductive phases.<br>Programa Doutoral em Ciência e Engenharia de Materiais

Abstract Multi-objective optimization is integral to control systems, particularly in engineering, where multiple objectives must be concurrently satisfied. This study pioneers a comprehensive comparison of 77 evolutionary algorithms for optimizing a second-order system under a proportional-integral-derivative (PID) controller. We emphasize the challenges of balancing conflicting metrics like peak time and percentage overshoot and how PID controller tuning is crucial in achieving this balance. To assess the algorithms’ performance, we utilize four different performance metrics, which encompass Generational Distance (GD), Spacing, Spread, and computation time. The optimization results are analyzed using two methods. Firstly, after 1 million iterations, outcomes are visually represented through discrete charts for each algorithm, detailing their performance metrics. The Multiple single objective Pareto sampling II (MSOPSII) algorithm demonstrates the most effective distribution of the solution set and maintains uniformity well. The Indicator-Based Evolutionary Algorithm (IBEA) excels in the Spacing Metric, while the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm exhibits superior convergence. Secondly, algorithms with optimal performance are evaluated by considering multiple metrics simultaneously. The theta-Dominance-based Evolutionary Algorithm (tDEA) leads in combined convergence and spread, Multi-Tasking Constrained Multi-objective Optimization (MTCMO) stands out for convergence and spacing, and MSOPSII and IBEA perform best in spread and spacing. Across all three metrics, MOEA/D with Adaptive Weight Adjustment (MOEADAWA) emerges as the top performer. These outcomes not only advance our understanding of algorithmic efficiency in multi-objective scenarios but also provide valuable insights for the application of these algorithms in practical control system challenges. This research fills a critical gap in existing literature, offering an in-depth evaluation of a wide range of algorithms in the context of control systems.

The choice of fitting algorithm in CMM metrology has often been based on mathematical convenience rather than the fundamental GD&amp;T principles dictated by the ASME Y14.5 standard. Algorithms based on the least squares technique are mostly used for GD&amp;T inspection and this wrong choice of fitting algorithm results in errors that are often overlooked and leads to deficiency in the inspection process. The efforts by organizations such as NIST and NPL and many other researchers to evaluate commercial CMM software were concerned with the mathematical correctness of the algorithms and developing efficient and intelligent methods to overcome the inherent difficulties associated with the mathematics of these algorithms. None of these works evaluate the ramifications of the choice of a particular fitting algorithm for a particular tolerance type. To illustrate the errors that can arise out of a wrong choice of fitting algorithm, a case study was done on a simple prismatic part with intentional variations and the algorithms that were employed in the software were reverse engineered. Based on the results of the experiments, a standardization of fitting algorithms is proposed in light of the definition provided in the standard and an interpretation of manual inspection methods. The standardized fitting algorithms developed for substitute feature fitting are then used to develop Inspection maps (i-Maps) for size, orientation and form tolerances that apply to planar feature types. A methodology for Statistical Process Control (SPC) using these i-Maps is developed by fitting the i-Maps for a batch of parts into the parent Tolerance Maps (T-Maps). Different methods of computing the i-Maps for a batch are explored such as the mean, standard deviations, computing the convex hull and doing a principal component analysis of the distribution of the individual parts. The control limits for the process and the SPC and process capability metrics are computed from inspection samples and the resulting i-Maps. Thus, a framework for statistical control of the manufacturing process is developed.

It is widely observed that today’s engineering products demand increasingly strict tolerances. The shape of a machined surface plays a critical role to the desired functionality of a product. Even a small error can be the difference between a successful product launch and a major delay. Thus, it is important to develop measurement tools to ensure the quality and accuracy of products’ machined surfaces. The key to assessing the quality is robust measurement and inspection techniques combined with advanced analysis. However, conventional Geometrical Dimensioning and Tolerancing (GD&amp;T) such as flatness falls short of characterizing the surface shape. With the advancements in metrology methodology utilizing digital holographic interferometry, large amount of surface data can be captured at high resolution and accuracy without changing platform or technique. This captured High Definition Data (HDD) enables the mining of more valuable information from machined surfaces that most current industry practice cannot achieve in a timely manner. Such new metrology system opens the torrent of observable events at plant floor and increases the transparency of machining processes. This presents great opportunities to characterize machined surface into a new level of details, which can be applied in production quality evaluation and process condition monitoring and control. This research work proposes a framework of a multi-scale surface characterization for surface quality evaluation and process monitoring. Case studies are presented to show how proposed metrics could be applied in surface quality evaluation and process monitoring.

Abstract Well flowing bottom hole pressure is an important input parameter for well and reservoir performance evaluation. However, there are limitations in accurate downhole measurements due to a faulty gauge and, sometimes, the inability to obtain complete well test data to implement theoretical models. The goal of this work is to create a hybrid modeling approach for estimating bottom hole pressure in tight sand and shale wells using smart production data, engineering features and machine learning (ML) techniques. The robust feature selection process integrated the critical attributes in the traditional PTA methods. Four out of eight adopted variables were split into training, cross validation and test sets in 60:20:20 proportion and preprocessed by z-score normalization scaling. For the first time, the combination of gradient descent optimization (GDO) algorithm and Cauchy cost function was used in the estimate of bottom hole pressure from production data in this study. A smart data approach for developing ML models was used in this study. Starting with 100 data points, the training and validation input size were incremented and subject to the learning process using both Cauchy and traditional MSE cost function-based GDO algorithm to determine the optimum batch size required to train the BHP model with lowest cost. The results showed that the Cauchy based GDO algorithm provides slightly better performance in training, cross validation and testing data with a global minimum error of 0.82%, 0.64% and 0.41% respectively compared to the traditional MSE-based GD that reaches a global minimum with higher cost. However, the improved metrics scores with Cauchy loss optimization required additional expense of one to ten seconds execution time. The Cauchy cost function presents an alternative technique to obtain better optimized prediction models from production data and can be utilized in sensitivity studies for reservoir characterization and asset management purposes.