SLR Systematic Literature Review: Metode Penilaian Kinerja Karyawan Menggunakan Human Performance Technology by Roni Habibi, Artha Glory Romey Manurung

“…Therefore, this systematic literature review is carried out to identify the methods used for employee performance appraisal and mentions that optimization algorithms and fuzzy comprehensive methods are used as evaluation methods to assess performance appraisal methods. …”
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Design of Gain-Scheduled Lateral Controllers for Autonomous Vehicles by Ákos Bokor, Ádám Szabó, Szilárd Aradi, László Palkovics

“…The results show that while traditional controllers, such as Pure Pursuit, perform adequately under constant speed conditions, adaptive model-based algorithms significantly enhance the performance, especially in dynamic driving situations involving speed variations.…”
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Intelligent Modeling; Single (Multi-layer perceptron) and Hybrid (Neuro-Fuzzy Network) Method in Forest Degradation (Case Study: Sari County) by somayeh mehrabadi

“…The mean squared error (MSE) was used to evaluate the performance of models, which was 0.0535, 0.0704, and 0.0908 for the perceptron neural network in the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, respectively. Also, the MSE value for the neuro-fuzzy model in the optimization and hybrid algorithms was 0.0190 and 0.0102, respectively. …”
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Modern fixation techniques versus traditional tension band wiring for olecranon fractures: a systematic review and meta-analysis of functional outcomes, healing time, and complicat... by Chengjing Wang, Changqing Li

“…Cable-pin System appears optimal for simple fractures, while Eyelet K-wire System shows advantages for comminuted fractures. …”
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GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

“…Finally, the introduction of the Wise-ShapeIoU loss function further accelerates the model’s convergence and optimization process. Experimental results demonstrate that, compared to the original model, the number of parameters and FLOPs are reduced by 36.7% and 28.4%, respectively, while the Precision is increased by 5.1%. …”
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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

“…The SHAP (Shapley Additive Explanations) analysis revealed that the X-site electron affinity positively influences the bandgap, while the B″-site first and third ionization energies exhibit strong negative effects. …”
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Exploring T-cell metabolism in tuberculosis: development of a diagnostic model using metabolic genes by Shoupeng Ding, Chunxiao Huang, Jinghua Gao, Chun Bi, Yuyang Zhou, Zihan Cai

“…The TB cohort exhibited increased inflammatory T-cell populations, while CD8+ T-cell-mediated MHC-I and BTLA signaling pathways were identified as key regulators of immune clearance and modulation. …”
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ES-UNet: efficient 3D medical image segmentation with enhanced skip connections in 3D UNet by Minyoung Park, Seungtaek Oh, Junyoung Park, Taikyeong Jeong, Sungwook Yu

“…While the framework incorporates established components, its core contributions lie in the optimized skip connection strategy and supporting techniques like RSS and DWD. …”
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Safety of lifitegrast: A real-world pharmacovigilance study based on FAERS. by Chu Zhang, Hui Wang, Xianglei Chen, Yong Liu, Puen Jiang

“…AEs not mentioned in the drug labeling were identified, such as glaucoma and cataracts.AEs associated with Lifitegrast are more commonly observed in male patients and those aged over 65 years.The insights derived from this FAERS database analysis are significant for optimizing the use of Lifitegrast while minimizing potential side effects, thereby enhancing the safety of its clinical application.…”
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A dynamic cobweb resistance network solution based on a structured zeroing neural network and its applications by Xiaoyu Jiang, Dexin Meng, Yanpeng Zheng, Zhaolin Jiang

“…By leveraging the hidden structural attributes of a Laplacian matrix, the study devises optimized algorithms for the neural network models, which markedly improve computational efficiency. …”
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Load balancing for high performance computing using quantum annealing by Omer Rathore, Alastair Basden, Nicholas Chancellor, Halim Kusumaatmaja

“…While the methodology for obtaining real simulation data to partition is application specific, the proposed balancing protocol itself remains completely general. …”
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Research on Credit Risk Prevention in Supply Chain Finance Driven by Blockchain by Lin Kejing

“…Distributed ledger technology is utilized through blockchain to ensure the transparency and traceability of information, while smart contracts optimize the credit execution and supervision process, playing a key role in preventing and controlling credit risks. …”
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Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification by Hai-Long Nguyen, Van Su Pham, Hai-Chau Le

“…Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.…”
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A Summary of the Existing Data on Cleft Surgical Outcomes: What Do We Not Know? by Priyanka Naidu, MBChB, MS, Alexander T. Plonkowski, MBBS, MRes, Caroline A. Yao, MD, MS, William P. Magee, III, MD, DDS

“…These protocols all aim to achieve optimal aesthetic appearance and speech outcomes while limiting significant complications such as velopharyngeal insufficiency and midface hypoplasia. …”
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Two Power Allocation and Beamforming Strategies for Active IRS-aided Wireless Network via Machine Learning by Q. Cheng, J. Bai, X. Wang, B. Shi, W. Gao, F. Shu

“…Simulation results show that the proposed algorithms can effectively improve the rate performance compared to fixed PA strategies, only optimizing PA factor, aided by passive IRS, and without IRS.…”
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Complex Neutrosophic Approach for Uncertainty Management in Employment Competence for Higher Vocational College Students by Shuo Du, Lijuan Zhao

“…The proof-of-concept analysis highlighted the best-ranked alternative while offering intuitions about the sensitivity of the decision process. …”
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A Solution for Predicting the Timespan Needed for Grinding Roller Bearing Rings by Cezarina Chivu, Mitica Afteni, Gabriel Radu Frumusanu, Florin Susac

“…The optimal management of manufacturing processes can be achieved through a set of optimal decisions, which must be made to choose the best method to follow every time the process planner reaches a point when several potential manufacturing paths branch off. …”
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Slip and tractive efficiency of an electric tractor with a 4WID E-axle system by SeungYun Baek, HyeonHo Jeon, CheolGyu Park, YongJoo Kim

“…The highest tractive efficiency was observed when slip was within the 10–20% range, indicating that this slip range corresponds to the optimal operating condition. The primary findings of this study identify the appropriate slip range and provide fundamental data for developing slip control algorithms for the 4WID E-axle system. …”
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A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy by Hua Zhan, Changxu Jiang, Zhen Lin

“…The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues like slow computational speed and difficulties in solving large-scale models, while heuristic algorithms are prone to fall into local optima. …”
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Robust Cross-Validation of Predictive Models Used in Credit Default Risk by Jose Vicente Alonso, Lorenzo Escot

“…Model validation is a challenging Machine Learning task, usually more difficult for consumer credit default models because of the availability of small datasets, the modeling of low-frequency events (imbalanced data), and the bias in the explanatory variables induced by the train/test sets split of the validation techniques (covariate shift). While many methodologies have been developed, cross-validation is perhaps the most widely accepted, often being part of the model development process by optimizing the hyperparameters of predictive algorithms. …”
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