Relevant bibliographies by topics / Indicator-based optimization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Indicator-based optimization'

Author: Grafiati
Published: 7 June 2025
Last updated: 26 June 2025

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Journal articles on the topic "Indicator-based optimization"

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Brockhoff, Dimo, Tobias Wagner, and Heike Trautmann. "2 Indicator-Based Multiobjective Search." Evolutionary Computation 23, no. 3 (2015): 369–95. http://dx.doi.org/10.1162/evco_a_00135.

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In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The [Formula: see text] and the hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the [Formula: see text] indicator exist. In this extended version of our previous conference paper, we thus perform a comprehensive investigation of the properties of the [Formula: see text] indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of [Formula: see text] solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the [Formula: see text] and HV indicator are presented. Furthermore, the [Formula: see text] indicator is integrated into an indicator-based steady-state evolutionary multiobjective optimization algorithm (EMOA). It is shown that the so-called [Formula: see text]-EMOA can accurately approximate the optimal distribution of [Formula: see text] solutions regarding [Formula: see text].
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Li, Fei, Jianchang Liu, Peiqiu Huang, and Huaitao Shi. "An R2 Indicator and Decomposition Based Steady-State Evolutionary Algorithm for Many-Objective Optimization." Mathematical Problems in Engineering 2018 (2018): 1–18. http://dx.doi.org/10.1155/2018/1435463.

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An R2 indicator based selection method is a major ingredient in the formulation of indicator based evolutionary multiobjective optimization algorithms. The existing classical indicator based selection methodologies have demonstrated an excellent performance to solve low-dimensional optimization problems. However, the R2 indicator based evolutionary multiobjective optimization algorithms encounter enormous challenges in high-dimensional objective space. Our main purpose is to explore how to extend the R2 indicator to handle many-objective optimization problems. After analyzing the R2 indicator, the objective space partition strategy, and the decomposition method, we propose a steady-state evolutionary algorithm based on the R2 indicator and the decomposition method, named, R2-MOEA/D, to obtain well-converged and well-distributed Pareto front. The main contribution of this paper contains two aspects. (1) The convergence and diversity for the R2 indicator based selection are analyzed. Two improper selection situations will be properly solved via applying the decomposition method. (2) According to the position of a new individual in the steady-state evolutionary algorithm, two different objective space partition strategies and the corresponding selection methods are proposed. Extensive experiments are conducted on a variety of benchmark test problems, and the experimental results demonstrate that the proposed algorithm has competitive performance in comparison with several tailored algorithms for many-objective optimization.
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Hui, Jiyuan, Zhiyuan Dai, and Jinjin Chen. "Mining Area Production Safety Optimization Based on Multi-objective Particle Swarm Optimization Model." Academic Journal of Science and Technology 13, no. 2 (2024): 308–12. https://doi.org/10.54097/93cmbe94.

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Safe production in metal mines is an important task to ensure the safety of workers and the integrity of production equipment. From the perspective of optimization, this paper establishes an early warning model and a multi-objective particle swarm optimization algorithm model by analyzing the relevant production data affecting the indicator system of mine safety production, and then solves the model through the multi-objective particle swarm optimization algorithm to give an optimization scheme for maximizing the safety production of the relevant mines. For the safety production problems in metal mines, four indicator systems are proposed, namely, production ecological environment safety, production personnel safety standards, production equipment safety and production information security, and then the relevant production data of the four indicator systems are analyzed and the early warning model is established. Based on the relevant production data of the four indicator systems, the mathematical relationship between the production data affecting each indicator system is constructed. Then, a multi-objective particle swarm optimization algorithm is established to construct the relationship between the maximum safe production of the mine and each indicator system, and the index of the maximum safe production production data of the mine is obtained. The feasibility of the mine safety production optimization scheme is given.
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Brockhoff, Dimo, Johannes Bader, Lothar Thiele, and Eckart Zitzler. "Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator." Journal of Multi-Criteria Decision Analysis 20, no. 5-6 (2013): 291–317. http://dx.doi.org/10.1002/mcda.1502.

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Sun, Yanan, Gary G. Yen, and Zhang Yi. "IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems." IEEE Transactions on Evolutionary Computation 23, no. 2 (2019): 173–87. http://dx.doi.org/10.1109/tevc.2018.2791283.

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Mansour, Imen Ben, and Ines Alaya. "Indicator Based Ant Colony Optimization for Multi-objective Knapsack Problem." Procedia Computer Science 60 (2015): 448–57. http://dx.doi.org/10.1016/j.procs.2015.08.165.

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Zapotecas-Martínez, Saúl, Abel García-Nájera, and Adriana Menchaca-Méndez. "Improved Lebesgue Indicator-Based Evolutionary Algorithm: Reducing Hypervolume Computations." Mathematics 10, no. 1 (2021): 19. http://dx.doi.org/10.3390/math10010019.

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One of the major limitations of evolutionary algorithms based on the Lebesgue measure for multi-objective optimization is the computational cost required to approximate the Pareto front of a problem. Nonetheless, the Pareto compliance property of the Lebesgue measure makes it one of the most investigated indicators in the design of indicator-based evolutionary algorithms (IBEAs). The main deficiency of IBEAs that use the Lebesgue measure is their computational cost which increases with the number of objectives of the problem. On this matter, the investigation presented in this paper introduces an evolutionary algorithm based on the Lebesgue measure to deal with box-constrained continuous multi-objective optimization problems. The proposed algorithm implicitly uses the regularity property of continuous multi-objective optimization problems that has suggested effectiveness when solving continuous problems with rough Pareto sets. On the other hand, the survival selection mechanism considers the local property of the Lebesgue measure, thus reducing the computational time in our algorithmic approach. The emerging indicator-based evolutionary algorithm is examined and compared versus three state-of-the-art multi-objective evolutionary algorithms based on the Lebesgue measure. In addition, we validate its performance on a set of artificial test problems with various characteristics, including multimodality, separability, and various Pareto front forms, incorporating concavity, convexity, and discontinuity. For a more exhaustive study, the proposed algorithm is evaluated in three real-world applications having four, five, and seven objective functions whose properties are unknown. We show the high competitiveness of our proposed approach, which, in many cases, improved the state-of-the-art indicator-based evolutionary algorithms on the multi-objective problems adopted in our investigation.
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Zheng, Maosheng, Haipeng Teng, and Yi Wang. "Application of new robust design by means of probability-based multi-objective optimization to machining process parameters." Vojnotehnicki glasnik 71, no. 1 (2023): 84–99. http://dx.doi.org/10.5937/vojtehg71-39747.

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Introduction/purpose: New robust design by means of probability-based multi-objective optimization takes the arithmetic mean value of the performance indicator and its deviation as twin independent responses of the performance indicator. The aim of this article is to check the applicability of new robust design in optimizing machining process parameters. To conduct the examination in detail, the robust design for optimal cutting parameters to minimize energy consumption during the turning of AISI 1018 steel at a constant material removal rate is applied as well as the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Methods: In the spirit of the probability-based method for multi-objective optimization, the arithmetic mean value of the performance indicator and its deviation are taken as two independent responses of the performance indicator to implement robust design. Each of the above twin responses contributes one part of the partial preferable probabilities to the performance indicator of the alternatives in the treatment. The arithmetic mean value of the performance indicator should be assessed as a representative of the performance indicator according to the function or the preference of the performance indicator, and the deviation is the other index of the performance indicator, which has the characteristic of the 99 smaller-the-better in general. Furthermore, the square root of the product of the above two parts of the partial preferable probability forms the actual preferable probability of the performance indicator. Moreover, the product of partial preferable probabilities gives the total preferable probability of each alternative, which is the overall and unique index of each alternative in the robust optimum. Results: The paper gives the rational optimum cutting parameters for minimizing energy consumption during the turning of AISI 1018 steel at a constant material removal rate and the concurrent optimization of the machining process parameters and the tolerance allocation of a spheroidal graphite cast iron piston. Conclusion: The application study indicates its rationality and convenience of new robust optimization in the optimization of machining process parameters.
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Trautmann, Heike, Tobias Wagner, Dirk Biermann, and Claus Weihs. "Indicator-based Selection in Evolutionary Multiobjective Optimization Algorithms Based On the Desirability Index." Journal of Multi-Criteria Decision Analysis 20, no. 5-6 (2013): 319–37. http://dx.doi.org/10.1002/mcda.1503.

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Wang, Hai Feng, Hong E. Ren, Kun Zhang, and Hong Xu Wang. "Mining Methods Based on Vague Optimization Evaluation." Advanced Materials Research 659 (January 2013): 128–33. http://dx.doi.org/10.4028/www.scientific.net/amr.659.128.

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Method based on vague optimization evaluation is vague pattern recognition. There are six detailed steps of application. The first, Set up Techno-economic indicator system. Secondly set up preparative optimization scheme sets. Thirdly set up optimal scheme in theory. It is made up of each Techno-economic indicator optimal data. Fourthly transform techno-economic input data into vague data. The fifth, Calculating similarly measures. Similarity measures will be evaluated between preparative optimization scheme vague sets and optimal scheme in theory. The last is vague optimization evaluation. The weight of each preparative optimization scheme is given. The data of weighted similarity measures by the weight factors are obtained. And applying them we obtain the good and bad sort of vague optimization scheme. The new similarity measures formula between vague sets is given. The formula is indispensable in the method of vague optimization evaluation. Application examples show that the Vague optimization evaluation method to the conclusion is reliable.
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Dissertations / Theses on the topic "Indicator-based optimization"

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Nyman, Jacob. "Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298084.

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Assessment of machine health and prediction of future failures are critical for maintenance decisions. Many of the existing methods use unsupervised techniques to construct health indicators by measuring the disparity between the current state and either the healthy or the faulty states of the system. This approach can work well, but if the resulting health indicators are insufficient there is no easy way to steer the algorithm towards better ones. In this thesis a new method for health indicator construction is investigated that aims to solve this issue. It is based on measuring distance after transforming the sensor data into a new space using a feed-forward neural network. The feed-forward neural network is trained using a multi-objective optimization algorithm, NSGA-II, to optimize criteria that are desired in a health indicator. Thereafter the constructed health indicator is passed into a gated recurrent unit for remaining useful life prediction. The approach is compared to benchmarks on the NASA Turbofan Engine Degradation Simulation dataset and in regard to the size of the neural networks, the model performs relatively well, but does not outperform the results reported by a few of the more recent methods. The method is also investigated on a simulated dataset based on elevator weights with two independent failures. The method is able to construct a single health indicator with a desirable shape for both failures, although the latter estimates of time until failure are overestimated for the more rare failure type. On both datasets the health indicator construction method is compared with a baseline without transformation function and does in both cases outperform it in terms of the resulting remaining useful life prediction error using the gated recurrent unit. Overall, the method is shown to be flexible in generating health indicators with different characteristics and because of its properties it is adaptive to different remaining useful life prediction methods.<br>Estimering av maskinhälsa och prognos av framtida fel är kritiska steg för underhållsbeslut. Många av de befintliga metoderna använder icke-väglett (unsupervised) lärande för att konstruera hälsoindikatorer som beskriver maskinens tillstånd över tid. Detta sker genom att mäta olikheter mellan det nuvarande tillståndet och antingen de friska eller fallerande tillstånden i systemet. Det här tillvägagångssättet kan fungera väl, men om de resulterande hälsoindikatorerna är otillräckliga så finns det inget enkelt sätt att styra algoritmen mot bättre. I det här examensarbetet undersöks en ny metod för konstruktion av hälsoindikatorer som försöker lösa det här problemet. Den är baserad på avståndsmätning efter att ha transformerat indatat till ett nytt vektorrum genom ett feed-forward neuralt nätverk. Nätverket är tränat genom en multi-objektiv optimeringsalgoritm, NSGA-II, för att optimera kriterier som är önskvärda hos en hälsoindikator. Därefter används den konstruerade hälsoindikatorn som indata till en gated recurrent unit (ett neuralt nätverk som hanterar sekventiell data) för att förutspå återstående livslängd hos systemet i fråga. Metoden jämförs med andra metoder på ett dataset från NASA som simulerar degradering hos turbofan-motorer. Med avseende på storleken på de använda neurala nätverken så är resultatet relativt bra, men överträffar inte resultaten rapporterade från några av de senaste metoderna. Metoden testas även på ett simulerat dataset baserat på elevatorer som fraktar säd med två oberoende fel. Metoden lyckas skapa en hälsoindikator som har en önskvärd form för båda felen. Dock så överskattar den senare modellen, som använde hälsoindikatorn, återstående livslängd vid estimering av det mer ovanliga felet. På båda dataseten jämförs metoden för hälsoindikatorkonstruktion med en basmetod utan transformering, d.v.s. avståndet mäts direkt från grund-datat. I båda fallen överträffar den föreslagna metoden basmetoden i termer av förutsägelsefel av återstående livslängd genom gated recurrent unit- nätverket. På det stora hela så visar sig metoden vara flexibel i skapandet av hälsoindikatorer med olika attribut och p.g.a. metodens egenskaper är den adaptiv för olika typer av metoder som förutspår återstående livslängd.
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Books on the topic "Indicator-based optimization"

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LAND.TECHNIK 2022. VDI Verlag, 2022. http://dx.doi.org/10.51202/9783181023952.

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INHALT Electrical Agricultural Machines Structuring of electrified agricultural machine systems – Diversity of solutions and analysis methods .....1 GridCON2 – Development of a Cable Drum Vehicle Concept to Power 1MW Fully Electric Agricultural Swarms ..... 11 GridCON Swarm – Development of a Grid Connected Fully Autonomous Agricultural Production System ..... 17 Fully electric Tractor with 1000 kWh battery capacity ..... 23 Soil and Modelling The Integration of a Scientific Soil Compaction Risk Indicator (TERRANIMO) into a Holistic Tractor and Implement Optimization System (CEMOS) .....29 Identification of draft force characteristics for a tillage tine with variable geometry ..... 37 Calibration of soil models within the Discrete Element Method (DEM) ..... 45 Automation and Optimization of Working Speed and Depth in Agricultural Soil Tillage with a Model Predictive Control based on Machine Learning ..... 55 Synchronising machine adjustments of combine harvesters for higher fleet performance ..... 65 A generic approach to bridge the gap between route optimization and motion planning for specific guidance points o...
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Book chapters on the topic "Indicator-based optimization"

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Richter, Detlev. "System Optimization Based on Performance Indicator Models." In Flash Memories. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6082-0_7.

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Liao, Futao, Shaowei Zhang, Dong Xiao, Hui Wang, and Hai Zhang. "An Indicator-Based Firefly Algorithm for Many-Objective Optimization." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5581-3_19.

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Wu, Furong, Yuan Zhang, Shaoyun Huang, Jinli Li, Rongrong Zhang, and Qingping Yi. "Optimization of Preparation Process of Paper-Based Colorimetric Indicator." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1673-1_41.

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Pereverdieva, Ksenia, André Deutz, Tessa Ezendam, Thomas Bäck, Hèrm Hofmeyer, and Michael Emmerich. "Comparative Analysis of Indicators for Multi-objective Diversity Optimization." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3538-2_5.

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Abstract Indicator-based (multi-objective) diversity optimization aims at finding a set of near (Pareto)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions. Whereas, in the first diversity-oriented evolutionary multi-objective optimization algorithm, the NOAH algorithm by Ulrich and Thiele, the Solow Polasky Diversity (SP Diversity, also known as Magnitude [1]) served as a metric, other diversity indicators could be considered. We examine the parameter-free Max-Min Diversity and the Riesz $$s$$ s -Energy, which features uniformly distributed solution sets. Focusing on multi-objective diversity optimization, we discuss different diversity indicators from the perspective of indicator-based evolutionary algorithms with multiple objectives. We examine theoretical, computational, and practical properties of these indicators, such as monotonicity in species, twinning, monotonicity in distance, strict monotonicity in distance, uniformity of maximizing point sets, computational effort for a set of size $$n$$ n , single-point contributions, subset selection, and submodularity. We present new theorems—including a proof of the NP-hardness of the Riesz $$s$$ s -Energy Subset Selection Problem—and consolidate existing results from the literature. In the experiments, we apply these indicators in the NOAH algorithm to analyze search dynamics via an example. We study how optimizing one indicator impacts others and propose NOAH-specific modifications for the Max-Min indicator.
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Camacho, Auraham, Gregorio Toscano, Ricardo Landa, and Hisao Ishibuchi. "Indicator-Based Weight Adaptation for Solving Many-Objective Optimization Problems." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12598-1_18.

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Anwar, Adeem Ali, Irfan Younas, Guanfeng Liu, and Xuyun Zhang. "A Preference-Based Indicator Selection Hyper-Heuristic for Optimization Problems." In Advanced Data Mining and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46661-8_30.

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Rosenthal, Susanne, and Markus Borschbach. "Indicator-Based Versus Aspect-Based Selection in Multi- and Many-Objective Biochemical Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91641-5_22.

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Falcón-Cardona, Jesús Guillermo, Arnaud Liefooghe, and Carlos A. Coello Coello. "An Ensemble Indicator-Based Density Estimator for Evolutionary Multi-objective Optimization." In Parallel Problem Solving from Nature – PPSN XVI. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58115-2_14.

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Basseur, Matthieu, and Eckart Zitzler. "A Preliminary Study on Handling Uncertainty in Indicator-Based Multiobjective Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11732242_71.

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Song, Zhenshou, Handing Wang, and Hongbin Xu. "Pareto-Based Bi-indicator Infill Sampling Criterion for Expensive Multiobjective Optimization." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72062-9_42.

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Conference papers on the topic "Indicator-based optimization"

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Li, Xu, Lin Shi, Jian-Yu Li, Jing Xu, and Zhi-Hui Zhan. "Pheromone Matrix Eigenvalue-based Convergence Assessment Indicator for Ant Colony Optimization." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751702.

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Liang, MaoMao, Babooshka Shavazipour, Bhupinder Saini, Michael Emmerich, and Kaisa Miettinen. "A Modified Preference-Based Hypervolume Indicator for Interactive Evolutionary Multiobjective Optimization Methods." In 16th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012934600003837.

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Liu, Mingyu, Dongchen Li, and Changwei Zhao. "Assessment and Optimization of Laboratory Air Quality Based on Multi-Indicator Fusion Algorithm." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11020234.

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Tian, Hao, Fei Li, Baiyu Zhou, Yujie Yang, and Xun Huang. "An IGD+ Indicator-Based Metric for Interactive Evolutionary Multi-Objective Optimization Algorithms." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865357.

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Vally, D., and Vinay Bhardwaj. "An Effective Path Planning Optimization in Cellular Networking Based on Key Performance Indicator: A Review." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725247.

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Xu, Dong. "Towards intelligent cracking analysis and design for concrete bridges based on three-layer stress indicator system." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0106.

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&lt;p&gt;A three-layer stress indicator system is proposed for analysis and design of concrete bridges with box section. The system includes in-plane and out-of-plane representative stresses of each slab of box section covering global and local effects. Although some of the indicators were already specified in the codes for decades, they were not complete, leading to deficiencies in the cracking analysis of concrete bridge structures. The indicator system can establish an intelligent road map for cracking analysis: to match cracks with the system to obtain relevant stress indicators, to determine the load cases that contribute the most to those stress indicators from refined analysis model. The load cases include deadload, live load, concrete creep and shrinkage, prestressing, thermal effect etc. The intelligent workflow will help engineers to know which load cases are the most unfavourable and to judge the most possible causes of cracks.&lt;/p&gt;&lt;p&gt;The proposed stress indicator system can be applied for obtaining optimal design for prestressed concrete bridges. Traditional design processes are often empirical and iterative, leading to diversified results which are usually not optimal, even though they meet the requirements in the specifications. Current research on optimization predominantly focuses on economic objectives, lacking comprehensive consideration of structural performance. An intelligent design method that integrates the stress indicators with machine learning is proposed. The objective function of this method combines stress distribution and cost, thereby achieving designs with both economic efficiency and favourable stress distribution. By employing a machine learning model to replace multiple rounds of computationally expensive FEA, the method enhances the optimal process and design efficiency.&lt;/p&gt;
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Hisao Ishibuchi, Noritaka Tsukamoto, and Yusuke Nojima. "Iterative approach to indicator-based multiobjective optimization." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424988.

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Zhang, Lun, and Niaoqing Hu. "Roc analysis based condition indicator threshold optimization method." In 2017 Prognostics and System Health Management Conference (PHM-Harbin). IEEE, 2017. http://dx.doi.org/10.1109/phm.2017.8079234.

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Jia, Shujin, Jun Zhu, Bin Du, and Heng Yue. "Indicator-based particle swarm optimization with local search." In 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022168.

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Antonio, Luis Miguel, and Carlos A. Coello Coello. "Indicator-based cooperative coevolution for multi-objective optimization." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7743897.

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Reports on the topic "Indicator-based optimization"

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Wang, Zhen, Christina Y. Y. Chen, Jane W. Njeru, et al. Evidence Map on Home- and Community-Based Services and Person-Centered Care for Older Adults. Agency for Healthcare Research and Quality, 2024. https://doi.org/10.23970/ahrqepctb49.

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Background. People who receive home- and community-based services (HCBS) have diverse and unique needs that can be met with these services aiming to support their independence. Purpose. To map the existing literature on HCBS in terms of interventions, populations, outcomes, person-centeredness, and relevant quality measures, and identify research gaps for older adults. Methods. A comprehensive literature search of multiple databases including Medline, Embase, and Scopus was conducted up to December 7, 2023, and complemented with grey literature search and feedback from Key Informants. Eligible studies evaluated HCBS interventions in adults aged 60 years or older with a functional limitation requiring assistance with activities of daily living. Findings. We identified 27 primary studies, 25 systematic reviews, and 29 quality measures. The most common types of interventions evaluated in HCBS studies involved optimization of person-centered planning, nonpharmacological approaches for dementia care, physical rehabilitation, collaborative care models, home-based palliative care programs, home healthcare via telehealth, self-directed home care, geriatric resources for practical support at home, interdisciplinary care coordination for high-risk conditions and delivery of specific services. Populations studied in HCBS studies included those with functional disability, cognitive impairment, high-risk or frail conditions, and people with specific conditions, most commonly Parkinson’s disease, Alzheimer’s disease, or end-stage kidney disease. Person-centered planning and self-direction of HCBS services were not explicitly described in most of the primary studies, and very few of these studies focused on addressing health-related social needs, whereas the majority had primary outcomes that can be considered medical or clinical. Numerous quality measures exist for HCBS. Some of them are validated, address multiple person-centered domains, and can apply across various conditions and populations. Key challenges in the literature on HCBS include lack of randomized trials, inadequate descriptions of interventions to determine person-centeredness, and limited information on facilitators and barriers. Because of the variability in how person-centeredness is operationalized in HCBS interventions, Key Informants reinforced the need to evaluate person-centered outcomes as a quality indicator of HCBS interventions. Key Informants also highlighted workforce challenges in recruiting, retaining, and training personnel delivering HCBS. Conclusion. This evidence map summarizes the HCBS literature in terms of interventions, populations, outcomes, and relevant quality measures for older adults and older adults with disabilities.
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Ruangpornvisuti, Vithaya. A Study of conformational equilibrium of semicarbazone derivatives and their complexes with cations : research report. Chulalongkorn University, 2006. https://doi.org/10.58837/chula.res.2006.36.

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The structure optimizations of picolinaldehyde N-oxide thiosemicarbazone (Hpiotsc), 2-benzoylpyridine semicarbazone (H2BzPS), their imino tautomers and their complexes with Ni(II), Cu(II) and Zn(II) were carried out using DFT calculations at the B3LYP/LANL2DZ level of theory. Thermodynamic properties of tautomerizations of Hpiotsc and H2BzPS and complexations of their complexes derived from the frequency calculations at the same level were obtained. The B3LYP/LANL2DZ-optimized geometry parameters for the complexes of [[Ni(Hpiotsc)[subscript 2]][superscript 2+]], [Cu(Hpiotsc).Cl[subscript 2]] and [Zn(Hpiotsc).Cl[subscript 2]] show good agreement with their corresponding X-ray crystallographic data. Aryl semicarbazone derivatives have been studied for the development of new antituberculous agents. The quantitative structure activity relationship (QSAR) analysis for the antituberculous activity of the aryl semicarbazones were carried out in terms of the molecular hydrophobicity and indicator variables using the multiple linear regression method. The new definition for indicator variables based on the substituents of the aryl semicarbazones was proposed and employed in the QSAR analysis.
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