On the effects of tool nose radius in alleviating parasitic mechanisms for cutting of aluminum alloy by Sweta Baruah, James B. Mann, Srinivasan Chandrasekar, Balkrishna C. Rao

“…An optimal nose radius was identified that balances improved cutting performance with compromises in wear and surface quality, paving the way for frugal, low-cost solutions to enhance machining efficiency and sustainability. This work not only advances the theoretical framework of cutting mechanics but also offers practical insights for developing sustainable manufacturing processes through informed tool design and selection.…”
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Review on Biosynthesis of β-Nicotinamide Mononucleotide and Its Derivatives by WANG Zhaoying, GUO Xinhao, HUANG Xinhe, CHEN Zhuoyan, LIU Yueqi, LI Juan, CHEN Ning, FAN Xiaoguang

“…In contrast, biosynthesis, with its significant advantages of non-toxicity, mild processing conditions, high substrate specificity, strong product selectivity, and high conversion efficiency, has gradually become an ideal alternative for NMN production. …”
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WORKING ANALYSIS OF SPACE TIME TRELLIS CODE ON WIFI MIMO(2X2) SYSTEM OFDM by Wisnu Broto, Noor Suryaningsih, Adhi Mahendra

“…In addition, the application of Orthogonal Frequency Division Multiplexing (OFDM) on MIMO-STC will more efficiently use bandwidth. The Space-Time Trellis Code (STTC) method is one of the diverse techniques in sending data with complex coding so that coding reinforcement is achieved and applied to the OFDM MIMO system. …”
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CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities by Rashik Shahriar Akash, Radiful Islam, SM Saiful Islam Badhon, KSM Tozammel Hossain

“…This study aims to develop CerviXpert, a multi-structural convolutional neural network designed to classify cervix types and detect cervical cell abnormalities efficiently. Methods We introduced CerviXpert, a computationally efficient convolutional neural network model that classifies cervical cancer using images from the publicly available SiPaKMeD dataset. …”
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Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review by Abel García Abejas, David Geraldes Santos, Fabio Leite Costa, Aida Cordero Botejara, Helder Mota-Filipe, Àngels Salvador Vergés

“… BackgroundArtificial intelligence (AI) is increasingly integrated into palliative medicine, offering opportunities to improve quality, efficiency, and patient-centeredness in end-of-life care. …”
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Coverage path planning considering the cell number and starting position by MA Mingyan, HUANG Sirong, DENG Renhui, WU Lei, HE Li

“…To address the inadequate adaptability of the traditional boustrophedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. …”
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A Fire Segmentation Method with Flame Detail Enhancement U-Net in Multispectral Remote Sensing Images Under Category Imbalance by Rui Zou, Zhihui Xin, Guisheng Liao, Penghui Huang, Rui Wang, Yuhu Qiao

“…Additionally, Haar wavelet downsampling is applied to retain image detail and improve the detection of small-scale flame regions. Experimental results show that the FDE U-Net model exhibits robust performance in fire detection, accurately extracting flame regions even when their proportion is low and the background is complex. …”
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A Dynamic State Cluster-Based Particle Swarm Optimization Algorithm by Zhenya Diao, Fei Yu, Hongrun Wu, Xuewen Xia

“…In particle swarm optimization for complex optimization challenges, parameter selection significantly influences performance across various population states. …”
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Rethinking Technological Investment and Cost-Benefit: A Software Requirements Dependency Extraction Case Study by Gouri Ginde, Guenther Ruhe, Chad Saunders

“…Drawing upon two publicly available data sets, we compare decision-making based on 1) exclusively on accuracy and 2) on ROI analysis to provide decision support for the selection and usage of ML classification methods. As such, our results showed that, 1) chasing model accuracy improvisation through increased annotated data does not generate expected returns in traditional ML methods. 2) For complex ML algorithms, the need for larger annotated dataset investment cost is justified by the higher returns, however, the trade-offs between accuracy and ROI become evident.…”
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The Role of TGF-β Signaling Pathway in Determining Small Ruminant Litter Size by Ying Han, Guiling Cao, Wenting Chen, Changfa Wang, Muhammad Zahoor Khan

“…This improved the understanding of the genetic basis for prolificacy provides valuable insights for marker-assisted selection strategies aimed at enhancing reproductive efficiency in small ruminant breeding programs, with significant implications for improving livestock productivity and economic outcomes.…”
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Optimization of Lattice-Based Cryptographic Key Generation using Genetic Algorithms for Post-Quantum Security by Tara Nawzad Ahmad Al Attar, Rebaz Nawzad Mohammed

“…Through the evolutionary capacity of GAs, the proposed method optimizes lattice-based keys through selection, crossover, and mutation to ensure high entropy and computationally feasible with experimental results indicating that the GA-based method can cut down memory requirements and computational complexity, making it favorable for resource-constrained environments such as the Internet of Things and embedded systems. …”
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A mutual mapping model of cross-sections between the axial and normal planes of ball screws based on the forming of helical ball track by Chaoqun Qian, Yi Ou, Hutian Feng, Jian Wu

“…This high precision and efficiency underscore the algorithm’s value in enhancing design, manufacturing, and inspection processes. …”
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An MCMC Approach to Bayesian Image Analysis in Fourier Space by Konstantinos Bakas, John Kornak, Hernando Ombao

“…Bayesian image analysis methods are commonly applied to solve image analysis problems such as noise reduction, feature enhancement, and object detection. A primary limitation of these methods is their computational cost due to the complex joint interdependencies between pixels, which limits the efficiency of performing posterior sampling through Markov chain Monte Carlo (MCMC). …”
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A Multitask CNN for Near-Infrared Probe: Enhanced Real-Time Breast Cancer Imaging by Maryam Momtahen, Farid Golnaraghi

“…This multitasking approach leverages the robust feature extraction capabilities of CNNs to perform complex tasks simultaneously, thereby improving the model’s efficiency and accuracy. …”
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From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification by Amirali Arbab, Aref Habibi, Hossein Rabbani, Mahnoosh Tajmirriahi

“…Results: While our model achieves an accuracy of 99.80% on the OCT2017 dataset, its standout feature is its parameter efficiency–requiring only 6.9 million parameters, significantly fewer than larger, more complex models such as Xception and OpticNet-71. …”
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A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines by Mathew Habyarimana, Abayomi A. Adebiyi

“…The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. …”
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A Collaborative Auto-Diversified Optimization Scheme  by Besma Hezili, Hichem Talbi

“…For re-diversification, CADOS utilizes both CMA-ES, known for its efficiency in complex fitness landscapes, and the Auto-Enhanced Population Diversity (AEPD) technique. …”
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Fusion of Visual Attention and Scene Descriptions With Deep Reinforcement Learning for AAV Indoor Autonomous Navigation by Hussein Samma, Ahmed Abubaker, Mubarak Badamasi Aremu, Mohammed Abdel-Nasser, Sami El-Ferik

“…This ensures safe and efficient navigation in complex indoor spaces. The approach was tested in a simulated indoor environment with pedestrians, created using Unreal Engine. …”
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Electric-field-induced assists in the preferential extraction of lithium from spent LiFePO4 by Shangpei Yong, Yan Lin

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TAE Predict: An Ensemble Methodology for Multivariate Time Series Forecasting of Climate Variables in the Context of Climate Change by Juan Frausto Solís, Erick Estrada-Patiño, Mirna Ponce Flores, Juan Paulo Sánchez-Hernández, Guadalupe Castilla-Valdez, Javier González-Barbosa

“…The findings highlight the potential of integrating modern techniques to address complex, high-dimensional problems. By combining advanced prediction models and feature selection strategies, this study advances the reliability of climate forecasts and contributes to the development of effective adaptation and mitigation measures in response to climate change challenges.…”
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