Fault diagnosis of a CNC hobbing cutter through machine learning using three axis vibration data by Nagesh Tambake, Bhagyesh Deshmukh, Sujit Pardeshi, Sachin Salunkhe, Robert Cep, Emad Abouel Nasr

“…Among these, the Ensemble model achieved perfect classification accuracy (100 %) with minimal computational cost, making it optimal for real-time applications. Explainable AI techniques, such as LIME and Shapley values, were employed to interpret model predictions, enhancing the system's transparency and reliability. …”
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A Personalised Optimising Level Adaptation (OLA) Difficulty Algorithm for Scenario Simulations in Professional VR Simulators by Marcin Wolański Robert, Karol Jędrasiak

“…Project and methods: The OLA algorithm divides scenario activities into blocks and adjusts their difficulty based on user performance in comparison to a reference group of AI-controlled agents. The algorithm’s efficacy was tested across three proprietary VR simulators covering diverse professional scenarios: public speaking, hydrogen electrolysis and mechanical technician operations. …”
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Lewinnek zone not “the be-all and end-all” functional planning for acetabular component positioning in total hip arthroplasty by Raffaele Iorio, Edoardo Viglietta, Federico Corsetti, Yuri Gugliotta, Carlo Massafra, Daniele Polverari, Andrea Redler, Nicola Maffulli

“…The patient’s functional acetabular inclination (AI) corresponded to the LSZ in one of the 100 patients, whereas the acetabular anteversion (AV) was outside the LSZ in 8 of the 100 patients. …”
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Enhancing the creep resistance in a RE-free cast Mg-Al-Ca alloy through microalloying of Ti by Zhuang Cui, Yang Liu, Ying Zeng, Manping Liu, Xiaochun Liu, Shiwei Xu, Xuefei Chen, Qudong Wang

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Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures by Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun

“…To address this, the development of interpretable ML models is essential to drive further advancements in AI‐driven materials discovery. In this study, we present an interpretable framework that combines traditional machine learning with symbolic regression, using Janus III–VI vdW heterostructures as a case study. …”
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Response of Sesame to Selected Herbicides Applied Early in the Growing Season by W. James Grichar, Jack J. Rose, Peter A. Dotray, Todd A. Baughman, D. Ray Langham, Kaisa Werner, Muthu Bagavathiannan

“…PRE applications of acetochlor and S-metolachlor at 1.26 and 1.43 kg ai·ha−1 showed little or no sesame injury (0 to 1%) 4 wks after herbicide treatments (WAT). …”
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External Validation of Deep Learning Models for Classifying Etiology of Retinal Hemorrhage Using Diverse Fundus Photography Datasets by Pooya Khosravi, Nolan A. Huck, Kourosh Shahraki, Elina Ghafari, Reza Azimi, So Young Kim, Eric Crouch, Xiaohui Xie, Donny W. Suh

“…This study underscores the importance of external validation in enhancing the reliability and applicability of AI models in ophthalmology, paving the way for improved patient care and outcomes.…”
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阿立哌唑与奥氮平对首发年轻成人精神分裂症患者短期内代谢风险的比较

“…【目的】比较阿立哌唑与奥氮平对首发年轻成人精神分裂症患者短期内的代谢风险【方法】 采用开放对照的临床观察方法,对符合美国精神障碍诊断与统计手册第4版(DSM-Ⅳ)精神分裂症诊断标准的首发住院精神分裂症患者,分别使用阿立哌唑(21例)和奥氮平(42例)治疗,自然观察时间不低于2周,不大于4周,于治疗前后各检测一次体质量腰围空腹血脂血糖及胰岛素C肽【结果】 观察结束时:阿立哌唑组的体质量体质量指数(BMI)腰围腰臀比均有增高(P < 0.05),糖脂改变无统计学差异,男女患者间各项代谢指标的变化无统计学差异(P > 0.05);奥氮平组的体质量体质量指数(BMI)腰围腰臀比甘油三酯(TG)总胆固醇(TC)高密度脂蛋白(HDL)低密度脂蛋白(LDL)载脂蛋白AI和B100及脂蛋白LPa较治疗前增高(P < 0.01),且胰岛素(INS)水平和胰岛素抵抗指数(IR)增高(P < 0.05),多元逐步回归分析显示胰岛素抵抗与甘油三脂的增高有关(R2 = 0.107,P = 0.007);奥氮平组男性患者的空腹胰岛素和C肽胰岛素抵抗指数均增高(P < 0.05),女性患者则没有【结论】阿立哌唑和奥氮平对首发年轻成人精神分裂症患者短期内的代谢风险即有差异,性别差异可能影响着非典型抗精神病药物的代谢风险…”
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Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes by B. S. Dhruva Darshan, Niranjana Sampathila, G. Muralidhar Bairy, Srikanth Prabhu, Sushma Belurkar, Krishnaraj Chadaga, S. Nandish

“…By considering the unique qualities of each patient, medical professionals who must rely on AI-assisted diagnosis and treatment suggestions, XAI offers arguments to strengthen their faith in the model outcomes.…”
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Kazakh khans of the 15th–17th centuries in the scientific heritage of Shigabutdin Marjani by Atygayev N.A.

“…It is established that the main sources for his work were the studies of such historians of the previous period as A.I. Levshin, V.V. Velyaminov-Zernov, as well as famous historical works by Muslim authors of the Middle Ages, written in Persian and Turkic languages. …”
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Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring by Ruiqian Qin, Yiming Wang, Xiaoyan Liu, Xiaoyan Liu, Helong Yu

“…By improving model efficiency and robustness, our approach not only advances agricultural disease monitoring but also contributes to the broader adoption of AI-driven solutions for sustainable crop management in diverse climates.…”
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An Efficient CNN Model for COVID-19 Disease Detection Based on X-Ray Image Classification by Aijaz Ahmad Reshi, Furqan Rustam, Arif Mehmood, Abdulaziz Alhossan, Ziyad Alrabiah, Ajaz Ahmad, Hessa Alsuwailem, Gyu Sang Choi

“…Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. …”
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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems by Sankaramoorthy Muthubalaji, Naresh Kumar Muniyaraj, Sarvade Pedda Venkata Subba Rao, Kavitha Thandapani, Pasupuleti Rama Mohan, Thangam Somasundaram, Yousef Farhaoui

“…The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. …”
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Study of fertility and cytogenetic variability in androgenic plants (R0 and R1) of alloplasmic introgression lines of common wheat by T. S. Osadchaya, N. V. Trubacheeva, L. A. Kravtsova, I. A. Belan, L. P. Rosseeva, L. A. Pershina

“…Lines 311/134, 311/FL, 311/IR with the cytoplasm from H. vulgare were studied. 311/134 carries the wheat-rye 1RS.1BL and wheatwheatgrass 7DL-7Ai translocations; 311/FL has the 1RS.1BL translocation and probably introgressions from A. glaucum; and 311/IR has the wheat-rye 1RS.1BL and wheat-Ae. speltoides T2B/2S#2 translocations. …”
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Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

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Biofilm as a supracellular organization of pathogenic bacteria by B.P. Kuzminov, K.D. Mazhak

“…This signaling system is called Quorum sensing (QS) and it depends on the density of the bacterial population and is mediated by signaling molecules called pheromones or autoinducers (AI). Bacteria use QS to regulate activities and behavior including competence, conjugation, symbiosis, virulence, motility, sporulation, antibiotic production and biofilm formation. …”
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The large-scale whole-genome sequencing era expedited medical discovery and clinical translation by Qingxin Yang, Shuhan Duan, Yuguo Huang, Chao Liu, Mengge Wang, Guanglin He

“…Coupled with the widespread adoption of electronic health records, wearable devices, and AI, the translation of these findings into clinical practice has been established for disease prediction, surveillance, and treatment across the lifespan. …”
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Alteration of Mastication Force via Intraoral Closed-Loop Electrical Stimulation by Junxiang Zheng, Bing Jiang, Saurabh Biswas, Su Young Lee, Erin Ealba Bumann, Teresa E. Lever, Jeonghee Kim, Hangue Park

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Balanced operation strategies of district heating systems based on dynamic hydraulic-thermal modeling by Xiaojie Lin, Ning Zhang, Zheng Luo, Encheng Feng, Wei Zhong

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Strategies for Healthcare Disaster Management in the Context of Technology Innovation: the Case of Bulgaria by R. Vazov, R. Kanazireva, T.V. Grynko, O.P. Krupskyi

“…The purpose of the study is to assess the impact of modern technological innovations on the effectiveness of disaster management in health care in Bulgaria with a focus on Health Information Systems (HIS), Telemedicine, Telehealth, e-Health, Electronic Health Records, Artificial Intelligence (AI), Public Communication Platforms, and Data Security and Privacy. …”
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