Optimal Dimensional Synthesis of Ackermann Steering Mechanisms for Three-Axle, Six-Wheeled Vehicles by Yaw-Hong Kang, Da-Chen Pang, Yi-Ching Zeng

“…The employed optimization methods include Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO), Differential Evolution with golden ratio (DE-gr), and Linearly Ensemble of Parameters and Mutation Strategies in Differential Evolution (L-EPSDE). …”
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Trip route optimization based on bus transit using genetic algorithm with different crossover techniques: a case study in Konya/Türkiye by Akylai Bolotbekova, Huseyin Hakli, Ayse Beskirli

“…The comparison show that combinations of the PBX method are found to be the most suitable and the use of crossover techniques as ensemble is more effective than crossover techniques used separately. …”
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Combined Input Deep Learning Pipeline for Embryo Selection for In Vitro Fertilization Using Light Microscopic Images and Additional Features by Krittapat Onthuam, Norrawee Charnpinyo, Kornrapee Suthicharoenpanich, Supphaset Engphaiboon, Punnarai Siricharoen, Ronnapee Chaichaowarat, Chanakarn Suebthawinkul

“…This study also showed an advantage in accuracy and a similar AUC when compared with the recent ensemble method.…”
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Une histoire des origines des Nkwanta et de leurs relations politiques et sociales avec les Abron (XVIIE siècle à nos jours) by Kouamé Kossonou Frédéric SECRE

“…Vaincus et chassés par le roi Osséi Toutou en 1680, ensemble, ils s’exilent à Bondoukou au nord-est de la Côte d’Ivoire et créent leur royaume Bron Djaïman. …”
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Method for Evaluating Urban Building Renewal Potential Based on Multimachine Learning Integration: A Case Study of Longgang and Longhua Districts in Shenzhen by Dengkuo Sun, Yuefeng Lu, Yong Qin, Miao Lu, Zhenqi Song, Ziqi Ding

“…A regression analysis based on building characteristics and locational factors was conducted using a stacking ensemble machine learning model. In addition, buildings were categorized into residential, industrial, and commercial types based on their usage, enabling both overall- and category-specific predictions of building renewal. …”
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DyCeModel: a tool for 1D simulation for distribution of plant hormones controlling tissue patterning by D. S. Azarova, N. A. Omelyanchuk, V. V. Mironova, E. V. Zemlyanskaya, V. V. Lavrekha

“…Here, we present DyCeModel, a software tool implemented in MATLAB for one-dimensional simulation of tissue with a dynamic cellular ensemble, where changes in hormone (or other active substance) concentration in the cells are described by ordinary differential equations (ODEs). …”
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In-silico screening and analysis of missense SNPs in human CYP3A4/5 affecting drug-enzyme interactions of FDA-approved COVID-19 antiviral drugs by Amro A. Abdelazim, Mohamad Maged, Ahmed I. Abdelmaksoud, Sameh E. Hassanein

“…We also examined the deleterious impact of 751 missense single nucleotide polymorphisms (SNPs) within the CYP3A4/5 genes. An ensemble of bioinformatics tools, [SIFT, PolyPhen-2, cadd, revel, metaLr, mutation assessor, Panther, SNP&GO, PhD-SNP, SNAP, Meta-SNP, FATHMM, I-Mutant, MuPro, INPS, CONSURF, GPS 5.0, MusiteDeep and NetPhos], identified a total of 94 variants (47 SNPs in CYP3A4, 47 SNPs in CYP3A5) to potentially impact the structural integrity as well as the activity of the CYP3A4/5 enzymes. …”
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Using machine learning-based models for personality recognition by Fatemeh Mohades Deilami, Hossein Sadr, Mozhdeh Nazari

“…Owing to the fact that various filter sizes in CNN may influence its performance, we decided to combine CNN with AdaBoost, a classical ensemble algorithm, to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter size using AdaBoost. …”
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Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning by Rafiqul Islam, Al Imran, Md. Fazle Rabbi

“…The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. …”
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CMIP6-based global estimates of future aridity index and potential evapotranspiration for 2021-2060 [version 2; peer review: 1 approved, 2 approved with reservations] by Jianchu Xu, Robert J. Zomer, Antonio Trabucco, Donatella Spano

“…The “Future_Global_AI_PET Database” provides high-resolution (30 arc-seconds) average annual and monthly global estimates of potential evapotranspiration (PET) and aridity index (AI) for 22 CMIP6 Earth System Models for two future (2021–2041; 2041–2060) and two historical (1960–1990; 1970–2000) time periods, for each of four shared socio-economic pathways (SSP). Three multimodel ensemble averages are also provided (All; Majority Consensus, High Risk) with different level of risks linked to climate model uncertainty. …”
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Prediction of Convective Storms at Convection-Resolving 1 km Resolution over Continental United States with Radar Data Assimilation: An Example Case of 26 May 2008 and Precipitatio... by Ming Xue, Fanyou Kong, Kevin W. Thomas, Jidong Gao, Yunheng Wang, Keith Brewster, Kelvin K. Droegemeier

“…It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours.…”
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ACCIDENTS AND OCCUPATIONAL ILLNESS ASSESSMENTS FOR 400/220/110/20 kV URECHESTI POWER SUBSTATION by Nicolae Daniel FÎȚĂ, Mila Ilieva OBRETENOVA, Florin MURESAN – GRECU, Gheorghe Eugen SAFTA, Adrian Mihai SCHIOPU, Dan Cristian LAZAR

“…The value and strength of the managerial approach to risk lies in the fact that it combines various valuation and consultation techniques, uniting them into an ensemble that gives consistency to the decision-making process. …”
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CMIP6 GCMs Projected Future Koppen‐Geiger Climate Zones on a Global Scale by Young Hoon Song, Eun‐Sung Chung, Brian Odhiambo Ayugi

“…To reduce uncertainties in future projected precipitation and temperature, multimodel projections comprising 25 general circulation models (GCMs) were sourced from the recent Coupled Model Intercomparison Project phase six (CMIP6) and used to create a Multi‐Model Ensemble. The changes in historical climate zones on CMIP6 simulations were divided into six periods considering data availability (1954–1964; 1964–1974; 1974–1984; 1984–1994; 1994–2004; and 2004–2014). …”
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The role of music performance anxiety in musical training: four personal histories by Oscar Casanova, María Elena Riaño, Francisco Javier Zarza-Alzugaray, Santos Orejudo

“…The first years of training were those when our interviewees recalled experiencing the greatest enjoyment in music-making: positive elements included family support, ensemble playing, and initial encounters with non-classical repertoire and improvisation. …”
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Ecological Stress Modeling to Conserve Mangrove Ecosystem Along the Jazan Coast of Saudi Arabia by Asma A. Al-Huqail, Zubairul Islam, Hanan F. Al-Harbi

“…This study employs AI-based classification techniques to classify mangroves using Landsat 8-SR OLI/TIRS sensors (2023) along the Jazan Coast, identifying a total mangrove area of 19.4 km². The ensemble classifier achieved an F1 score of 95%, an overall accuracy of 93%, and a kappa coefficient of 0.86. …”
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Assimilation of temperature and relative humidity observations from personal weather stations in AROME-France by A. Demortier, M. Mandement, V. Pourret, O. Caumont, O. Caumont

“…Separate assimilation of each variable in the atmosphere with the three-dimensional ensemble variational (3DEnVar) DA scheme significantly reduces the root-mean-square deviation between SWS observations and forecasts of the assimilated variable at 2 <span class="inline-formula">m</span> height above ground level up to 3 <span class="inline-formula">h</span> of forecasts. …”
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Explainable machine learning framework for cataracts recognition using visual features by Xiao Wu, Lingxi Hu, Zunjie Xiao, Xiaoqing Zhang, Risa Higashita, Jiang Liu

“…Subsequently, the SHapley Additive exPlanations and Pearson correlation coefficient methods are applied to analyze the feature importance and select significant visual features. Finally, an ensemble multi-class ridge regression method is applied to recognize the cataracts severity levels based on the selected visual features. …”
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Learning-based pattern-data-driven forecast approach for predicting future well responses by Yeongju Kim, Baehyun Min, Alexander Sun, Bo Ren, Hoonyoung Jeong

“…In this study, we propose two simpler alternatives, a learning-based pattern-data-driven forecast approach (LPFA) and an ensemble conditioning step (ECS), to resolve the issues associated with DSI and LDFA. …”
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Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points by Jiaqi Liu, Chengpeng Jiang, Qianbo Yu, Yao Ni, Cunjiang Yu, Wentao Xu

“…Each individual device component emulates essential synaptic functions for neural computing, while the collective ensemble replicates muscle actuation in response to efferent neuromuscular commands. …”
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Enhanced gap-filling for satellite-derived crop monitoring using temperature-driven reconstruction techniques by Flavian Tschurr, Lukas Valentin Graf, Achim Walter, Helge Aasen

“…By employing a probabilistic ensemble Kalman filtering data assimilation scheme, we can integrate high-resolution air temperature data and satellite imagery while quantifying uncertainties. …”
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