IGZO 2T0C DRAM With V_TH Compensation Technique for Multi-Bit Applications by Kaifei Chen, Wendong Lu, Jiebin Niu, Menggan Liu, Fuxi Liao, Xuanming Zhang, Zihan Li, Naide Mao, Kaiping Zhang, Congyan Lu, Bok-Moon Kang, Jiawei Wang, Di Geng, Nianduan Lu, Guilei Wang, Zhengyong Zhu, Guanhua Yang, Chao Zhao, Arokia Nathan, Ling Li, Ming Liu

“…Unlike traditional 2T0C DRAM, data writing strategy of the novel DG bit-cell is discharging process from storage node (SN) to bit line, achieving in-cell threshold voltage (VTH) compensation without sacrificing bit-cell layout. …”
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EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Toward Large-Scale EEG Model by Chengxuan Qin, Rui Yang, Wenlong You, Zhige Chen, Longsheng Zhu, Mengjie Huang, Zidong Wang

“…To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of "EEG Parser", "Correction", "Batch Processing", and "Large Language Model Boost". Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. …”
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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. …”
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Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification by Tao Liu, Jiahao Liu, Dong Li, Shan Tan

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Patterning the Pore Orientation of Nanoporous Metal via Self‐Organization in Flow Cells by Congcheng Wang, Yang Li, Xiangwei Geng, Jiatao Mao, Qing Chen

“…Abstract Nanoporous metals, a class of free‐standing, high specific‐area materials, evolve from interface‐controlled self‐organization in a selective dissolution (e.g., dealloying). The process creates randomly oriented pores, in which slow mass transport has limited the functional applications of nanoporous metals. …”
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Machine learning for predicting resistance spot weld quality in automotive manufacturing by Nuttapong Chuenmee, Nattachai Phothi, Kontorn Chamniprasart, Sorada Khaengkarn, Jiraphon Srisertpol

“…Resistance Spot Welding (RSW) stands as the primary joining process in the automotive industry, renowned for its suitability for automation and integration into high-production assembly lines. …”
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Using Hybrid Artificial Intelligence Approaches to Predict the Fracture Energy of Concrete Beams by Qinghua Xiao, Congming Li, Shengxiang Lei, Xiangyu Han, Qiaofeng Chen, Zemin Qiu, Biao Sun

“…Firstly, the ridge regression (RR), the classification and regression tree (CART), and the gradient boosting regression tree (GBRT) were selected to construct the predictive models. …”
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Improving Anaerobic Digestion Efficiency of Animal Manure Through Ball Milling Pretreatment by Meicai Xu, Sibel Uludag-Demirer, Yan Liu, Wei Liao

“…Anaerobic digestion (AD) can offer a promising pathway for converting animal waste into biogas. This process improves waste management practices while generating renewable energy. …”
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Mountain flood forecasting in small watershed based on loop multi-step machine learning regression model by Songsong Wang, Bo Peng, Ouguan Xu, Yuntao Zhang, Jun Wang

“….), use a short-term prediction window and loop multi-step input method to improve the Machine Learning (ML) regression models’ accuracy, which can reduce the ML model’s process error. The non-ensemble and ensemble ML regression models is constructed for forecasting by loop multi-step, the non-ensemble models including Linear Regression (LR), Support Vector Machine Regression (SVMR) and k-Nearest Neighbors Regression (k-NNR), and the ensemble ML models include Random Forest Regression (RFR) and Gradient Boosting Regression (GBR). …”
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Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition by Setareh Derakhshandara, Valentina Franzoni, Daniele Mezzetti, Valentina Poggioni

“…After the data was presented, we compared different machine learning techniques (i.e., random forest, gradient boosting machine, support vector machine, multilayer perceptron) that could predict nutritional requirements. …”
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A Study of the Epistemological Principles of Ghazālī and Mullā Sadrā and Their Implications to Rational Training by Mohammad Alaei, Saeed Beheshti, Ahmad Salhshouri

“…Moreover, the differences involve the following subjects: obtaining the essence of objects and the substantiation of imagery in minds, priority of the sensual to rational knowledge, acquaintance with the history and biography of the ancestors, boosting insight, rational perfection by the aid of theology and religious instruction, unity between the knower and known, seeking assistance from the holy religion (the Qur’an, Ahādith, and tradition), acknowledgment of the religious sources as a criterion to assess education, attempts in line with self-awareness in the process of education, … .…”
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DFL-TORO: A One-Shot Demonstration Framework for Learning Time-Optimal Robotic Manufacturing Tasks by Alireza Barekatain, Hamed Habibi, Holger Voos

“…It aims at optimizing the process of Learning from Demonstration (LfD), applied in the manufacturing sector. …”
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The role of circadian rhythm regulator PERs in oxidative stress, immunity, and cancer development by Baimei Zhao, Eugenie Nepovimova, Qinghua Wu

“…At the heart of this interaction lies the PERIOD proteins (PERs), pivotal to the circadian clock, influencing the timing of physiological and behavioral processes and impacting oxidative stress, immune functionality, and tumorigenesis. …”
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Development of Digital Teaching Materials Through Canva and Book Creator for College Students of Elementary Education by Nita Agustina Nurlaila Eka Erfiana, Liatul Rohmah

“…Using a Research and Development (R&D) model, the process unfolds across five key stages: analyze, design, develop, implement, and evaluate. …”
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An Intelligent Deep Learning Model for CO2 Adsorption Prediction by Hanan Ahmed Hosni Mahmoud, Nada Ali Hakami, Alaaeldin M. Hafez

“…The produced weight matrix was able to predict the CO2 adsorption under diverse process settings with high accuracy of 96.4%.…”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…We employed a feature selection process to identify the most relevant gene expressions associated with liver cancer. …”
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The Genus <i>Clonostachys</i> (<i>Bionectria</i>) as a Potential Tool Against Agricultural Pest and Other Biotechnological Applications: A Review by Manuela Reyes-Estebanez, Pedro Mendoza-de Gives

“…They inhabit plant tissues, boosting plant defenses and activating genes for water and nutrient uptake, enhancing plant performance. …”
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A grassroots development in the light of the development theories, a case study in rural India: food for life Vrindavan by László Varga

“…The article is organised into six sections: after a short introduction about the subject, the objectives and the layout of the pa-per, section 2 presents the timeline of the development industry with a particular emphasis on the process of turning from national economy boosting initiatives to the dimensions of human development. …”
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PRODUCTION EFFICIENCY: ROLE OF DECISION MAKING FACTORS, BIG DATA AND PREDICTIVE ANALYTICS by Mohanad Mohammed Sufyan Ghaleb, Sanjar Mirzaliev

“…This study contributes to the literature by providing empirical evidence on the role of decision-making competence and the integration of advanced data analytics in boosting production efficiency. It offers practical implications for managers in the manufacturing industry, emphasizing the importance of leveraging technology and enhancing decision-making capabilities to optimize production processes. …”
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Risk management strategies for fuel shortages in economic dispatch with multiple fuel options using walrus algorithm by Mahmoud Ibrahim Mohamed, Ali M. Yousef, Ahmed A. Hafez

“…This article comprehensively analyzes the ED process for power plants with multiple fuel options, while achieving multiple objectives: optimizing energy management and boosting supply security. …”
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