Terms Of Fitness Research Articles

Association between depression severity and fitness in in-patients with major depressive disorders

Introduction
 It has been shown that people with major depressive disorder exercise less and have lower fitness levels than healthy individuals (Schuch et al., 2017). People with major depressive disorder have a decrease in their cardiovascular health which leads to a poorer autonomic function and heart rate variability (HRV; Kemp et al., 2010). However, not only objective parameters are influenced by major depressive disorder, but also subjective ones, like perceived fitness. As differences between people with major depressive disorder and healthy people have often been investigated, this study aims to investigate fitness, perceived fitness and HRV in individuals with different levels of depression severity.
 Methods
 The study is part of the multicentre randomised controlled PACINPAT trial, in which inpatients receive physical activity counselling. Data from baseline assessments are used for this analysis (Gerber et al., 2019). The classification according to depression severity is based on the Beck Depression Inventory (BDI-II). Fitness was determined using the submaximal Åstrand ergometer cycling test. Perceived fitness is self-reported and resting HRV was measured for 5 minutes. The three groups with different severity of depression were compared using ANCOVA for the three mentioned parameters.
 Results
 We analysed data from 215 patients. The average BDI-II score at baseline was 22.2 (SD 10.3; range 2-50). With regard to depression severity, 41.4% (n = 89) were classified as having mild depression, 34.4% (n = 74) presented with moderate depression, and 24.2% (n = 52) fell into the category of severe depression. Data analysis is not finalized yet. The presentation will focus on differences in terms of fitness, perceived fitness and HRV parameters between the three severity groups.
 Conclusion
 The study will show if there are differences in fitness, perceived fitness and autonomic function as measured by HRV between depressive patients of different severity levels. This complements previous research on differences between patients with major depressive disorder and healthy individuals. It may also help practitioners to tailor interventions more accurately to individuals with different levels of depression severity, as different levels of co-occurring symptoms need to be considered.
 References
 Gerber, M., Beck, J., Brand, S., Cody, R., Donath, L., Eckert, A., Faude, O., Fischer, X., Hatzinger, M., Holsboer-Trachsler, E., Imboden, C., Lang, U., Mans, S., Mikoteit, T., Oswald, A., Pühse, U., Rey, S., Schreiner, A.-K., Schweinfurth, N., . . . Zahner, L. (2019). The impact of lifestyle Physical Activity Counselling in IN-PATients with major depressive disorders on physical activity, cardiorespiratory fitness, depression, and cardiovascular health risk markers: Study protocol for a randomized controlled trial. Trials, 20(1), Article 367. https://doi.org/10.1186/s13063-019-3468-3
 Kemp, A. H., Quintana, D. S., Gray, M. A., Felmingham, K. L., Brown, K., & Gatt, J. M. (2010). Impact of depression and antidepressant treatment on Heart Rate Variability: A review and meta-analysis. Biological Psychiatry, 67(11), 1067-1074. https://doi.org/10.1016/j.biopsych.2009.12.012
 Schuch, F., Vancampfort, D., Firth, J., Rosenbaum, S., Ward, P., Reichert, T., Bagatini, N. C., Bgeginski, R., & Stubbs, B. (2017). Physical activity and sedentary behavior in people with major depressive disorder: A systematic review and meta-analysis. Journal of affective disorders, 210, 139-150. https://doi.org/10.1016/j.jad.2016.10.050

Assessment of the interactions among net primary productivity, leaf area index and stand parameters in pure Anatolian black pine stands: A case study from Türkiye

Aim of study: To examine the relationships between net primary productivity (NPP) and leaf area index (LAI) and modeling these parameters with stand parameters such as stand median diameter (dg), dominant height (htop), number of trees (N), stand basal area (BA) and stand volume (V). Area of study: Pure Anatolian black pine (Pinus nigra J.F. Arnold) stands in semi-arid climatic conditions in the Black Sea backward region of Türkiye. Material and methods: In this study, the Carnegie-Ames-Stanford Approach model was used to calculate NPP; LAI, dg, htop, N, BA and V were calculated in 180 sample plots. The relations between NPP and LAI with stand parameters were modeled using multiple regression analysis, support vector machines (SVM) and deep learning (DL) techniques. Relationships between NPP and LAI were investigated according to stand developmental stages and crown closure classes. Main results: The highest level of relations was obtained in the stands containing the a-b developmental stages (r=0.84). The most successful model in modeling NPP with stand parameters was obtained by DL method (model R2=0.64, test R2=0.51). Although DL method had higher success in modeling LAI with stand parameters, SVM method was found to be more successful in terms of model-test fit, and modeling success in independent data set. Research highlights: Grouping parameters affecting NPP and LAI increased the level of correlation between them. In modeling NPP and LAI in relation to stand parameters, machine learning algorithms performed better than linear approach. The overfitting problem can be eliminated substantially by including arguments such as early stopping, network reduction and regularization in the network structure.

Gender Differences, Motor Skills and Physical Fitness Heterogeneity in Adults with Down’s Syndrome

Background-Adults with Down's syndrome (DS) present lower physical fitness associated with heightened sedentary behaviors and motor skills impairments. Their etiologies and determinants seem to be heterogeneous. This study aims to evaluate physical fitness in adults with DS and to identify specific physical fitness profiles depending on gender and physical activity levels. Methods-Forty adults with DS (16 women, 24 men, 29.7 ± 7.5 years) performed six tests from the EUROFIT Battery and Motor Assessment Battery for Children (MAB-C). Their maximal aerobic capacity was assessed using an incremental treadmill test to assess (VO2peak). Ecological, physical activity, and sedentary levels were evaluated subjectively (Global Physical Activity Questionnaire) and objectively using an Actigraph GT9X® accelerometer over a seven-day period. Results-VO2peak and isometric strength were significantly lower for women (p < 0.01), whereas men had significantly lower flexibility than women (p < 0.05). Using a principal component analysis and an agglomerative hierarchical analysis, we identified three clusters. Cluster 1 (n = 14; 50% men; Body Mass index = 28.3 ± 4.3) was characterized by significantly poorer physical fitness variables (VO2peak (p < 0.01), strength (p < 0.01) and balance (p < 0.05)) compared to Clusters 2 and 3. Cluster 2 (n = 19; 58% men; Body Mass index = 22.9 ± 2.0) and Cluster 3 (n = 19; 58% men; BMI = 22.9 ± 1.9) were characterized by subjects with comparable physical fitness profiles, except for the balance capacities, which were significantly lower in Cluster 3 (p < 0.05). Conclusions-DS subjects exhibited high heterogeneity in terms of physical fitness, PA, and sedentary levels, with a significant gender effect. The present findings are important to identify subjects at higher risk of sedentary behaviors and impaired motor capacities to develop personalized PA programs.

AROA: Adam Remora Optimization Algorithm and Deep Q network for energy harvesting in Fog-IoV network

Electric Vehicles (EV) has gained immense popularity due to the increasing awareness amongst people regarding low carbon emission. Smart vehicles have become a central part of the Internet of vehicles (IoV) infrastructure. Whenever several vehicles distribute its tasks, then the classical centralized model meet various issues, such as security and delay in communication. This paper devises a technique for energy harvesting in the Fog-IoV network. The Fog-IoV network simulation is done for enhanced processing. The three layers, such as fog layer, cloud layer, and IoV layer are adapted for electricity trading. The power prediction is performed with a deep reinforcement learning technique, namely Deep Q network (DQN). The optimal electricity trading is done with the proposed Adam Remora Optimization Algorithm (AROA). The AROA is obtained by the amalgamation of Remora Optimization Algorithm (ROA) and Adam optimization algorithm. The EVs represents buyer of electricity that demands electricity in such a way that Road side unit (RSU) perform bidding. The fitness function is newly modelled using predicted power, price, and distance. The experimentation of the technique is done in terms of fitness, power, and pricing. The proposed AROA-based DQN offered enhanced performance with the highest power of 11.920 and the smallest pricing of 16.949%.

Physical Fitness Among Adolescents Who are Hispanic With Overweight or Obesity.

To describe cardiorespiratory, strength, muscular endurance, and flexibility fitness outcomes in a sample of adolescents who are Hispanic aged 11 to 15 years with overweight or obesity, stratified by age and sex. The sample included 280 adolescents (mean age: 13.0 ± 0.83 years, mean body mass index percentile: 94.6). Anthropometric measures included height, weight, body mass index percentile, and waist and hip circumference. Fitness measures included handgrip strength, sit-ups in 60 seconds, sit-and-reach test, and 6-minute walk test. We report mean scores for each fitness outcome measure and correlation coefficients with anthropometric measures. Mean handgrip was 23.7 ± 6.48 kg, sit-and-reach test was 25.3 ± 8.13 cm, average sit-ups in 60 seconds were 19.4 ± 9.28, and 6-minute walk distance was 1960 ± 271 ft. Males outperformed females in all tests except sit-and-reach test. Compared with published fitness values of healthy weight adolescents, our sample of adolescents who are Hispanic with overweight/obesity living in southern Florida is unconditioned in terms of cardiorespiratory fitness, strength, muscular endurance, and flexibility.

Causal intuition in the indefinite world: Meta-analysis and simulations

Modeling our causal intuition can contribute to understanding our behavior. In this paper, we introduce a causal induction model called proportion of assumed-to-be rare instances (pARIs) and examine its adaptive properties. We employ the two-stage theory of causal induction proposed by Hattori and Oaksford in 2007, which divides causal induction into two stages: first, observed events are sifted and likely candidates are extracted; second, each of them is verified through intervention. Here, we focus on the first stage. We conducted a meta-analysis and computer simulations in a similar way to Hattori and Oaksford (2007) but with some corrections and improvements. We added two experiments and excluded one in our reconstructed meta-analysis and augmented the simulations by correcting two problems. Our meta-analysis results show that pARIs outperforms more than 40 existing models in terms of data fit from human causal induction experiments while being simpler. Additionally, our simulation results show that pARIs outperforms DFH in terms of population covariation detection, especially under small sample sizes and rarity of events. Overall, pARIs qualifies as one of the best models for the first stage of causal induction. These findings may enable a deeper understanding of our cognitive biases. The first stage can now be considered a causal discovery stage where the topology of causal models is to be hypothesized.

A Rank Graduation Accuracy measure to mitigate Artificial Intelligence risks

AbstractA key point to assess the applications of machine learning models in Artificial Intelligence (AI) is the evaluation of their predictive accuracy. This because the “automatic” choice of an action crucially depends on the made prediction. While the best model in terms of fit to the observed data can be chosen using a “universal” - and therefore automatable - criterion, based on the models’ likelihood, such as AIC and BIC, this is not the case for the best model in terms of predictive accuracy. To fill the gap, we propose a Rank Graduation Accuracy (RGA) measure which evaluates the concordance between the ranks of the predicted values and the ranks of the actual values of a series of observations to be predicted. We apply the RGA to a use-case that concerns the measurement of the financial risks that arise from crypto assets. The RGA appears as a “universal” alternative predictive model selection criterion that, differently from standard measures, such as the Root Mean Squared Error, is robust to the presence of outlying observations.

Comparison of Stock price prediticon based on XGBoost and GARCH

For a long time, financial issues have been widely discussed by all sectors of society. There are many studies on the stock market, and the main purpose is mostly to predict stock prices and the overall trend of the stock market more efficiently. In this paper, the XGBoost Model and the GARCH Model are established in terms of the Shanghai Composite Index data. To be specific, the models are fitted and predicted by Python and Eviews, in order to find a better prediction mechanism. The XGBoost Model proposed in this paper is not satisfactory in terms of fitting and prediction effects, and has a certain degree of deviation. The GARCH Model shows a better performance in the short term. This research aims to find mathematical models that can effectively fit and predict the stock market through the combination of qualitative analysis and quantitative analysis. These results shed light on rationalizing the improvement of stock price prediction methods through simulation results.

Effects of parental age on salt stress tolerance in an aquatic plant

Parental age influences components of offspring fitness in many species. The ability to tolerate stress also affects fitness, but less is known regarding changes in offspring stress tolerance with increasing parental age, especially in plants. We examined first and fifth‐born clonal offspring (using birth order as a proxy for parental age), and compared their fitness in several sub‐lethal concentrations of salt (NaCl), to investigate the interactive effects of birth order and salt stress on the offspring of the aquatic plant Lemna minor L. We found that increasing salt concentration reduced reproduction particularly at early ages, which detrimentally affected fitness, as measured by the intrinsic rate of natural increase. Fifth offspring had greater fitness than first offspring, potentially due to the hump‐shaped relationship between offspring fitness and birth order observed in other studies on Lemna, with fifth offspring near the peak of the hump. We found no interactive effect of birth order and salt concentration on offspring fitness; however, there were interactive effects on the time to first reproduction and the size of fronds. Specifically, first offspring exposed to increasing salt concentrations exhibited longer delays to first reproduction and grew to a greater size, while fifth offspring showed little change in either variable with increasing salt concentration. Thus, variation in birth order affected offspring response to salt stress, although not in terms of fitness. These results help illuminate factors impacting the age‐specific strength of natural selection and stress responses, and may be environmentally relevant in the context of environmental salinization.

How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?

Hundreds of variants of Swarm Intelligence or Evolutionary Algorithms are proposed each year and numerous competitions and comparisons between algorithms may suggest rapid improvement in the field. However, such comparisons are often done between a limited number of methods and are based on averaged ranks of algorithms. This way they measure whether one method is on average ranked better than the others, without giving any information on how much improvement is in fact obtained. In this study we show a general comparison between 69 algorithms, starting from methods proposed in the 1960’s up to variants developed in the early 2020’s, on single-objective static numerical problems. Algorithms are compared on searching for a minimum of 30 different 50-dimensional mathematical functions, and on 22 real-world problems. We focus on the relative improvement achieved by various algorithms over a single-solution based method proposed in 1960 by Howard Rosenbrock. We find that the general improvement of Evolutionary Algorithms over Rosenbrock’s algorithm is relatively limited. It is high for the artificial benchmarks, for which many Evolutionary Algorithms find solutions 10 times closer to the global optimum in terms of fitness than Rosenbrock’s algorithm, but much lower for real-world problems. Improvement is also higher when performance averaged over many runs is compared, but lower when the best results from multiple runs are analyzed. In the last case, only the best Evolutionary Algorithms are able to find solutions of a “typical” real-world problem that are 2–3 times better in terms of fitness than those found by Rosenbrock’s algorithm. The relative improvement of recently proposed algorithms is not much better than the improvement achieved by algorithms proposed over a decade ago.

Changes in spinescence across leaf ontogeny support the optimal defence hypothesis in blackberries (Rubus adenotrichos)

Abstract Hypotheses based on allocation theory and herbivore selection offer opposite predictions about how defence levels against herbivores change as the plant tissue grows. The growth differentiation balance hypothesis (GDBH) assumes that defences will be resource-limited in immature tissues and predict that defence levels increase as the plant tissue grows. Conversely, the optimal defence hypothesis (ODH) proposes that plants would have the highest level of defences in the parts that have the highest value in terms of fitness and/or are more frequently attacked by herbivores, such as young tissues. We examine whether spinescence in the shrub Rubus adenotrichos (blackberry) change as the leaf grows, and if this change is consistent with the GDBH or the ODH. We compare the petiole area occupied by prickles, the prickles density and the individual prickle area in mature versus young petioles from Rubus adenotrichos. Our results show that, in R. adenotrichos, young tissues are more protected than mature tissues. Prickles density and the petiole area occupied by prickles were up to 25% higher in young petioles than in mature ones. These results support the ODH, reinforcing the idea that extrinsic factors such as herbivores pressure might drive the change of structural defences level across leaf ontogeny.

Multiple Elite Individual Guided Piecewise Search-Based Differential Evolution

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.

THE TREATMENT OF CRANIOFACIAL GUNSHOT WOUNDS WITH INDIVIDUAL IMPLANTS

Background: Demonstrate the use of advanced three-dimensional (3D) computer technology, virtual surgical planning (VSP), three-dimensional modeling (3DM), and the treatment of complex craniofacial gunshot injuries. Methods: We present a clinical series reports of craniofacial gunshot Injuries using modern 3D computer technology. Based on the CT images of the skull, a CAD model of the skull (biomodel) and a restorative implant were digitally constructed. The biomodel was then physically constructed using 3D printing. Comparison of the dimensions of the implant and its corresponding biomodel revealed geometric correspondences. Manufactured prostheses used for the reconstruction of craniofacial defects formed after gunshot wounds. Results: There were no complications in the perioperative period, infection or exposure of the implant in the postoperative period. The results of the study showed that the imprinted individual cranial implants had a high dimensional accuracy, demonstrating clinically acceptable morphological similarity in terms of fit and contour continuity. Patients are satisfied with the aesthetic (improvement of the skull contour) and functional results of reconstruction. Conclusion: Individual implants for the reconstruction of craniofacial defects have shown effectiveness, which is associated with more accurate adaptation to the implantation site, reduced surgery time and better cosmetic effect.

Online learning resources recommendation model based on improved NSGA-Ⅱ algorithm

&lt;abstract&gt; &lt;p&gt;Due to the characteristics of online learning resource recommendation such as large scale, uneven quality and diversity of preferences, how to accurately obtain various personalized learning resource lists has become an urgent problem to be solved in the field of online learning resource recommendation. This paper proposes an online learning resource recommendation model based on the improved NSGA-Ⅱ algorithm, which integrates the Tabu search algorithm to improve the local search ability of NSGA-Ⅱ algorithm. It takes background fitness, cognitive fitness and diversity as the objective functions for optimization. The dynamic updating of crowding degree is used to avoid the risk that the individuals with low crowding degree in the same area are deleted at the same time. Meanwhile, an adaptive genetic algorithm is applied to assign the optimal crossover rate and the mutation rate according to individual adaptability level, which ensures the convergence of genetic algorithm and the diversity of population. The experimental results show that the proposed model is superior to the traditional recommendation algorithm in terms of accuracy index, mean fitness, recall rate, F1 mean, HV, GD and IGD, etc., thus verifying the feasibility and effectiveness of the algorithm.&lt;/p&gt; &lt;/abstract&gt;

Current and future perspectives on CAR-T cell therapy to adult malignant lymphoma

Recent advances with chimeric antigen receptor T-cell (CAR-T) therapy are changing the current landscape of poor-prognosis relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). Pivotal trials leading to the FDA approval of three CD19 CAR-T cells, namely, Yescarta®, Kymriah®, and Breyanzi®, demonstrated complete response rates of 40-60%, with a significant subset of patients achieving long-term disease remission, and real-world studies confirm these data. In Japan, CAR-T therapy was approved for R/R DLBCL in 2019 and for R/R follicular lymphoma in 2022. However, guidelines are not clear on which CAR-T agents should be indicated for which patients and at which timing, and currently, institutions decide and operate according to their criteria. To optimize CAR-T therapy under the best conditions, the treatment strategy must be decided with the referring institution in terms of T-cell fitness and tumor volume. Therefore, institutional collaboration to monitor long-term adverse events after CAR-T therapy is important.

Physical Activity as an Element of Health Life Style among High School Children’s: an Analytical Approach

Physical inactivity is one of the most alarming health indicators in the world. Physical activity and fitness levels decline worldwide among children and adolescents. The use of electronic entertainment in Pakistan affects the activity levels of students. Objectives: To compare physical activity levels of urban and rural high school children using analytical study. Methods: A cross-section study was conducted on 200 government high school children (ages 12-16 years) in Rawalpindi city, Pakistan. Data on physical fitness were collected through different physical fitness tests and BMI by measuring height and weight. Physical activity was assessed through physical activity questionnaire (PAQ-A) and a youth physical activity questionnaire (Y-PAQ). Results: Analysis of study in term of different fitness shows that the mean score of pushups was 14.9050±8.91704, and the mean for standing vertical jump was 28.3700±5.35035. The mean of the 30-meter sprint was 4.3667±.34039, and the mean of sit and reach test was 27.7700±4.09401. The mean of the handstand was 3.8600±9.02038. Conclusions: Based on the findings, the researcher concluded that physical activity and physical fitness levels are low in rural and urban high school children. Still, there is no significant difference in physical activity and physical fitness levels between rural and urban high school children. The rural students were better in height and weight, but the BMI rate was higher in urban students.

Sport Development Index (Sdi) In Wonogiri District

Purpose of this Researchto: (1) identify the level of participation of the people of Wonogiri Regency in exercising in 2022; (2) identify the level of physical fitness of the Wonogiri Regency community in 2022; (3) identify the level of sports development in Wonogiri Regency in terms of the participation index and the community physical fitness index in 2022. The research is a quantitative research with survey research methods and a descriptive evaluation approach. The research sample is the people of Wonogiri Regency who live in Wonogiri District, Ngadirojo District, and Paranggupito District and are divided into age and gender groups, with a total of 108 people. The sampling technique used followed the guidelines for the method of determining the Sport Development Index sample. Techniques for collecting data were questionnaires (questionnaires) and test techniques. Validity of the instrument.iukuriyesngiused isistandar who haveisetiin the Sport Development Index (SDI). Results research is as follows. (1) Community participation index is 0.477. The Wonogiri Regency Community Participation Index shows that the level of community participation in sports is included in the low category, (2) The Wonogiri Regency Community's physical fitness index is 0.440. The physical fitness index of the Wonogiri Regency community shows that the community's fitness is included in the low category, (3) The level of sports development in Wonogiri Regency in terms of participation and physical fitness of the community is included in the low category with an index of 0.468. In conclusion, the Participation Index, Physical Fitness and Sports Development of the Wonogiri Regency community are in the low category.

Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China

For buyers, investors and urban policy, understanding drivers of community-level house prices across space and across time, are important for urban management and economic planning. In this study, we interrogated two housing market datasets, one from 2015, the other from 2019, for Wuhan, China, in order to uncover diversities and similarities in the spatial relationships between house price and contextual data; and in the context of increasingly volatile markets. A non-stationary approach was adopted with basic geographically weighted regression (GWR) and multiscale GWR (MGWR), where only the latter enables relationships to vary at their own spatial scale. In terms of model fit, both MGWR (adj. R2: 0.94 and 0.97, for 2015 and 2019, respectively) and GWR (adj. R2: 0.87 and 0.81) represented an improvement over the usual linear regression (adj. R2: 0.11 and 0.09) and the spatial lag mode (adj. R2: 0.21 and 0.27). Similarly marked improvements for GWR and for MGWR were found using corrected Akaike Information Criterion (AICc) based fit diagnostics. However, of more importance and via MGWR, the spatially varying drivers of house price were found to operate at a range of spatial scales, that in turn changed in strength and significance between the two study years. Such insights allow for spatially- and temporally-aware decision- and policy-making for housing price control and urban planning, given China’s housing markets can be increasing prone to strong growth coupled with severe depressions.

A novel approach to estimating urban land surface temperature by the combination of geographically weighted regression and deep neural network models

Growing concerns on excessive urban heat call for better approaches to modeling urban thermal environment and developing effective mitigation strategies. A hybrid model integrating the geographically weighted regression (GWR) and deep neural network (DNN) was developed to estimate land surface temperature (LST). This model was compared with three other data-driven approaches to predicting LST, including the ordinary least squares (OLS) regression, GWR, and DNN. Sixteen satellite image datasets (a total of 155,728 data points) during a four-year period in Hong Kong were used for model development, validation, and comparison. The datasets cover two distinguishable geographical regions and consist of sixteen explanatory variables from five groups, including (1) land use and land cover (LULC) composition and surface characteristics, (2) LULC configuration, (3) urban form, (4) anthropogenic activities, and (5) location and local climate. The results indicate that the hybrid model performs the best in terms of model fitness and prediction accuracy, with R2 equal to 0.85 and 0.73 and the mean squared error (MSE) equal to 0.52 and 0.70 in the two regions, respectively. Compared to the OLS, DNN, and GWR models, the overall R2 for all the datasets of the hybrid model increases by 97.3%, 16.6%, and 6.9%, respectively. The promising result of the hybrid model is due to its ability to capture both spatial heterogeneity and address possible correlations between explanatory variables. Sensitivity of LST to various explanatory variables is also discussed and strategies to mitigate excessive heat are recommended. This study is anticipated to contribute to model development in urban LST estimation and quantitative evaluation of those factors driving LST variations.

Optimal Stator and Rotor Slots Design of Induction Motors for Electric Vehicles Using Opposition-Based Jellyfish Search Optimization

This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and computational burden. Therefore, this study proposes a new hybrid optimization technique called opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and rotor slots based on the formulated multi-objective. The performance is compared with conventional optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore, the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average can be achieved compared with JSO. In conclusion, the proposed technique can be used to help engineers in the automotive industry design a high-performance IM for EVs as an alternative to the existing motor.