Stillbirth rates, trend and distribution in the Volta region, Ghana: findings from institutional data analysis, 2018–2022 by Chrysantus Kubio, Williams Azumah Abanga, Ignatius Aklikpe, Dzidefo Kofi Agbavor, Victor Zeng, Samuel Adolf Bosoka, Desmond Klu, Senanu Kwesi Djokoto

“…This study determined the stillbirth rate and its distribution in the Volta Region of Ghana. Methods A review of institutional stillbirths in the Volta Region from 2018 to 2022 was done using data extracted from the District Health Information Management System 2 database. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…By 2050, zero-carbon electric power systems will rely heavily on innumerable distributed energy resources (DERs), such as wind and solar. …”
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Default Priors in a Zero-Inflated Poisson Distribution: Intrinsic Versus Integral Priors by Junhyeok Hong, Kipum Kim, Seong W. Kim

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Decentralized Detection and Mitigation of False Data Injection Attacks in DC Microgrids Using Artificial Neural Network by Omid Danaei Koik, Shahram Karimi, Khaled M. Almustafa, Juliano Katrib

“…In this framework, the MLP neural networks are trained offline using local data under various conditions and are subsequently deployed online within the distributed generator units for fault detection and mitigation. …”
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Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks by Hun Kim, Jaewoo So

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Annotation-free Generation of Training Data Using Mixed Domains for Segmentation of 3D LiDAR Point Clouds by Cop Konrad, Sułek Bartosz, Trzciński Tomasz

“…Our approach requires no manual annotation, no detailed knowledge about actual data feature distribution, and no real-life data of objects of interest. …”
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Precise Recognition and Feature Depth Analysis of Tennis Training Actions Based on Multimodal Data Integration and Key Action Classification by Weichao Yang

“…The model optimizes multimodal data integration and complex action classification performance, enabling precise analysis of key action features in tennis training. …”
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DiffGAN: A Fault Diagnosis Data Augmentation Method for Hydropower Units Based on Adversarial Training and Diffusion Model by Hongwei Zhang, Zhao Liu, Hansong Si, Zhongzhi Chen, Huiping Xie

“…To address this issue, this paper proposes a data augmentation method based on adversarial training and diffusion models—DiffGAN. …”
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Optimal Autonomous Control for Distribution Transformer Area With High Photovoltaic Penetration Based on CSBO-LSTM by Yukai Wei, Chun He, Zhuo Chen, Yinyuan Guo, Zongyuan Li

“…Furthermore, leveraging the advantage of LSTM in processing time-series data, a real-time response model is constructed through deep training to achieve rapid perception of grid status and dynamic control decisions. …”
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Estimation of Sediment Grain Size Distribution Using Optical Image-Based Spatial Feature Representation Learning with Data Augmentation by Jongwon Choi, Sulki Kim, Jaejoong Jin, Jinhoon Kim, Sungyeol Chang, Inho Kim

“…Additionally, to improve robustness and reliability, data augmentation techniques, including horizontal and vertical flipping, are used during training. …”
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Privacy attack in federated learning is not easy: an experimental study by Hangyu Zhu, Liyuan Huang, Zhenping Xie

“…Unlike traditional centralized learning approaches, FL enables multiple users to collaboratively train a shared global model without disclosing their own data, thereby significantly reducing the potential risk of privacy leakage. …”
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Improving distributed systems failure prediction via multi-objective feature selection and deep forest by Zhidan Yuan, Yikai Zhang, Yingqi Yu, Taizhi Lv, Tao Huang

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Proprioceptive and Exteroceptive Information Perception in a Fabric Soft Robotic Arm via Physical Reservoir Computing with Minimal Training Data by Jun Wang, Zhi Qiao, Wenlong Zhang, Suyi Li

“…In this study, instead of using specialized sensors, only distributed pressure data inside a pneumatics‐driven soft arm are collected and the physical reservoir computing principle is applied to simultaneously predict its kinematic posture (i.e., bending angle) and payload status (i.e., payload mass). …”
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Is Anonymization Through Discretization Reliable? Modeling Latent Probability Distributions for Ordinal Data as a Solution to the Small Sample Size Problem by Stefan Michael Stroka, Christian Heumann

“…In fact, combining probability distributions with a small training sample can effectively infer true metric values from discrete information, depending on the model and data complexity. …”
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Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers by Fábio Polola Mamede, Roberto Fray da Silva, Irineu de Brito Junior, Hugo Tsugunobu Yoshida Yoshizaki, Celso Mitsuo Hino, Carlos Eduardo Cugnasca

“…Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. …”
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Federated learning with joint server-client momentum by Boyuan Li, Shaohui Zhang, Qiuying Han

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An Interpretable Data-Driven Dynamic Operating Envelope Calculation Method Based on an Improved Deep Learning Model by Yun Li, Tunan Chen, Jianzhao Liu, Zhaohua Hu, Yuchen Qi, Ye Guo

“…This paper proposes an interpretable model-free DOE calculation method that leverages smart meter data to address this issue. We train a CNN-LSTM-Attention neural network for voltage estimation, where we employ the whale optimization algorithm (WOA) to adjust hyperparameters automatically. …”
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Highly Qualified Scientific Personnel in Economic Sciences: Sectoral Distribution by Sergei I. Pakhomov

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Federated Learning Framework Based on Distributed Storage and Diffusion Model for Intrusion Detection on IoT Networks by Ricardo Manzano, Marzia Zaman, Darshana Upadhyay, Nishith Goel, Srinivas Sampalli

“…The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. …”
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On non-approximability of zero loss global L2 minimizers by gradient descent in deep learning by Chen Thomas, Muñoz Ewald Patricia

“…As a consequence, we conclude that the distribution of training inputs must necessarily be non-generic in order to produce zero loss minimizers, both for the method constructed in [2, 3], or for gradient descent [1] (which assume clustering of training data).…”
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