Compact XOR/XNOR-Based Adders and BNNs Utilizing Drain-Erase Scheme in Ferroelectric FETs by Musaib Rafiq, Yogesh Singh Chauhan, Shubham Sahay

“…The recent advancements in the field of emerging non-volatile memories (e-NVMs), such as FeFETs, RRAMs, MRAMs, etc., have propelled the development of the PIM technique where the logic operations are performed in situ (where the operands are stored) to reduce the energy draining data movement. Considering the promising potential of the doped-hafnium oxide (HfO2) based FeFETs, such as CMOS compatibility, high scalability, high integration density, and fielddriven programming capability, in this work, for the first time, we propose a novel input-to-voltage mapping scheme and exploit drain-erase phenomenon to realize compact and energy-efficient majority logic gate using a single Fe-FDSOI FET, XOR and XNOR logic gates using two Fe-FDSOI FETs. …”
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Demonstration of Integrated Quasi-Vertical DMOS Compatible with the Bipolar-CMOS-DMOS Process Achieving Ultralow R_ON,sp by Feng Lin, Tuanzhuang Wu, Weidong Wang, Zhengxuan Wang, Yi Zhang, Sheng Li, Ran Ye, Long Zhang, Jiaxing Wei, Siyang Liu, Weifeng Sun

“…The measured data of the latest manufactured device is presented. …”
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Process optimization as a tool for analyzing performance indicators of additional taxi-out and taxi-in time of the Brazilian airspace control system by Alessandro Soares de Freitas, Edilson Marques Magalhaes

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An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging by N. A. S. Vinoth, J. Kalaivani, R. Madonna Arieth, S. Sivasakthiselvan, Gi-Cheon Park, Gyanendra Prasad Joshi, Woong Cho

“…Initially, the image pre-processing stage applies the median filter (MF) model to eliminate the unwanted noise from the input image data. …”
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Fusing Transformer-XL with bi-directional recurrent networks for cyberbullying detection by Md. Mithun Hossain, Md. Shakil Hossain, Md. Shakhawat Hossain, M. Firoz Mridha, Mejdl Safran, Sultan Alfarhood, Dunren Che

“…Extensive data preparation was performed, including data cleaning, data analysis, and label encoding. …”
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Design of a Multi-Node Data Acquisition System for Logging-While-Drilling Acoustic Logging Instruments Based on FPGA by Zhenyu Qin, Junqiang Lu, Baiyong Men, Shijie Wei, Jiakang Pan

“…The acquisition system, a core component of the LWD acoustic logging suite, is tasked with capturing, transmitting, and processing acoustic signals from the formation, which directly affects the accuracy and timeliness of the logging data. …”
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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. …”
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A Deep Learning Algorithm for Multi-Source Data Fusion to Predict Effluent Quality of Wastewater Treatment Plant by Shitao Zhang, Jiafei Cao, Yang Gao, Fangfang Sun, Yong Yang

“…In this research, we introduce a deep learning method that fuses multi-source data. This method utilises various indicators to comprehensively analyse and predict the quality of effluent water: water quantity data, process data, energy consumption data, and water quality data. …”
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Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma, Yue Zhou

“…Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. …”
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Empirical classification of fatigue-induced physiological tremor in robot-assisted manipulation tasks using BiLSTM-GRU network by Poongavanam Palani, Siddhant Panigrahi, Gunarajulu Renganathan, Yuichi Kurita, Asokan Thondiyath

“…The pattern-tracing task (PTT) was carried out over five repetitions, with fatigue-inducing exercise occurring between task epochs, thus accumulating fatigue throughout the data collection process. The extracted features from human movement aid the classification of the stages of tremor using BiLSTM-GRU, showing the significance of a cross-sectional area informed model.ResultsThe stages of progression of tremor are classified into five levels in this study, and classified using BiLSTM GRU with four different input feature sets. …”
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Secure Cooperative Dual-RIS-Aided V2V Communication: An Evolutionary Transformer–GRU Framework for Secrecy Rate Maximization in Vehicular Networks by Elnaz Bashir, Francisco Hernando-Gallego, Diego Martín, Farzaneh Shoushtari

“…In this paper, we investigate the problem of secrecy rate maximization in a cooperative dual-RIS-aided V2V communication network, where two cascaded RISs are deployed to collaboratively assist with secure data transmission between mobile vehicular nodes in the presence of eavesdroppers. …”
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Greenhouse gas emissions from the US liquefied natural gas operations and shipping through process model based life cycle assessment by Mainak Mukherjee, James Littlefield, Harshvardhan Khutal, Krista M. Kirchner-Ortiz, Kaitlyn G. Davis, Liang Jing, Farah Ramadan, Hassan El-Houjeiri, Mohammad S. Masnadi, Adam R. Brandt

“…We utilize detailed process-level data for liquefaction terminals and ships to generate granular estimates. …”
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Ai-powered digital twin in the industrial IoT by Željko Bolbotinović, Saša D. Milić, Žarko Janda, Dragan Vukmirović

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Optimised autoencoder-based ensemble deep learning approaches for cyber-physical event classification utilizing synchrophasor PMU data by Dhinu Lal M, Ramesh Varadarajan

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Indexing is not merely a badge; it is a bridge connecting local insights to global solutions: experience from six years of editorial process by Kapil Amgain

“…This article reflects on our indexing journey from Google Scholar to Hinari, shedding light on the challenges, strategies, and outcomes of this transformative process. …”
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A Study on the Lightweight and Fast Response GRU Techniques for Indoor Continuous Motion Recognition Based on Wi-Fi CSI by Kyongseok Jang, Chao Sun, Junhao Zhou, Yongbin Seo, Youngok Kim, Seyeong Choi

“…The experiment results show that the proposed LFR-GRU model achieved an F1-score accuracy of 94.16% when trained with one person’s data and 96% accuracy when trained with two people. …”
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Student employment forecasting model based on random forest and multi-features fusion by Zhenguo Xing, Xiao Wu, Jiangjiang Li

“…Therefore, this paper proposes a novel student employment forecasting model based on random forest and multi-features fusion. Firstly, the student data is preprocessed to remove irrelevant attributes to achieve data consistency. …”
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Short-term forecast of wind power based on the division of wind speed fluctuation characteristics by QIAO Titang, XIE Lirong, YE Jiahao, GAO Yang, DAI Bing

“…The dynamic time warping algorithm is used to mine the fluctuating wind similar data in the historical data, and a training sample data set is constructed combining with the corresponding historical wind power; a hunger game search algorithm is used to optimize the hyperparameters of the gated recurrent unit neural network, and a combined prediction model for three fluctuation stages is established. …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…This study reveals that incorporating lag times of snow depth data significantly enhances predictive accuracy. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

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