A Hybrid Deep Learning Approach for Integrating Transient Electromagnetic and Magnetic Data to Enhance Subsurface Anomaly Detection by Zhijie Qu, Yuan Gao, Shiyan Li, Xiaojuan Zhang

“…Recent advancements in geophysical exploration have highlighted the importance of integrating electromagnetic (EM) and magnetic data to enhance subsurface target detection. Conventional inversion techniques often struggle with the non-uniqueness of solutions and sensitivity to noise when relying on a single data modality. …”
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Survey data on firm-level surplus food and food waste management from the Italian food processing industry by Giulia Valentini, Niso Randellini, Giovanni Scotti, Paola Garrone

“…The adoption of effective prevention and reduction strategies requires awareness of the quantity of waste generated, yet firm-level data is lacking. Collecting and exploiting more data is essential for addressing societal, climate, and environmental challenges. …”
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JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation by Leqi Xu, Geyu Zhou, Wei Jiang, Haoyu Zhang, Yikai Dong, Leying Guan, Hongyu Zhao

“…Through extensive simulations and real data applications to 22 quantitative and four binary traits in five continental populations evaluated using the UK Biobank (UKBB) and All of Us (AoU), JointPRS consistently outperforms six state-of-the-art methods across three data scenarios: no tuning data, same-cohort tuning and testing, and cross-cohort tuning and testing. …”
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MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data by Meng Fang, Xiangpeng Wan, Fei Lu, Fei Xing, Kai Zou

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UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning by Yaxi Liu, Xulong Li, Boxin He, Meng Gu, Wei Huangfu

“…Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. …”
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Analysis of competing risks model using the generalized progressive hybrid censored data from the generalized Lomax distribution by Amal Hassan, Sudhansu Maiti, Rana Mousa, Najwan Alsadat, Mahmoued Abu-Moussa

“…The performance of different estimators was compared using Monte Carlo simulation, and real-data applications were used to verify the proposed estimates.…”
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Image preference estimation with a data-driven approach: A comparative study between gaze and image features by Yusuke Sugano, Yasunori Ozaki, Hiroshi Kasai, Keisuke Ogaki, Yoichi Sato

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Model-Based Offline Reinforcement Learning for AUV Path-Following Under Unknown Ocean Currents with Limited Data by Xinmao Li, Lingbo Geng, Kaizhou Liu, Yifeng Zhao

“…Simulation results demonstrate that CMQL achieves effective path-following under unknown ocean currents with a limited dataset of only 1000 data points. This method also achieves zero-shot transfer, demonstrating its generalization and potential for real-world applications.…”
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High-accuracy physical property prediction for pure organics via molecular representation learning: bridging data to discovery by Qi Ou, Hongshuai Wang, Minyang Zhuang, Shangqian Chen, Lele Liu, Ning Wang, Zhifeng Gao

“…After fine-tuning with public experimental data, the model can accurately predict various physical properties of pure organics, with test set R 2 values exceeding 0.92. …”
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Soil Organic Carbon Monitoring and Modelling via Machine Learning Methods Using Soil and Remote Sensing Data by Dimitrios Triantakonstantis, Andreas Karakostas

“…Feature selection techniques were applied to indicate the most important predictors, improving model performance and interpretability. (3) Results: The results demonstrate the potential of ML-driven approaches to achieve high accuracy in SOC prediction. (4) Conclusions: This research highlights the advantages of leveraging big data and artificial intelligence in soil monitoring, providing a scalable and cost-effective framework for SOC assessment in agricultural and environmental applications.…”
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Accurate and Effective Data Collection with Minimum Energy Path Selection in Wireless Sensor Networks using Mobile Sinks by Ghaida Muttashar Abdulsahib, Osamah Ibrahim Khalaf

“…The method of acquiring data from the sensor nodes and forwarding this data to the node or base station is Data Collection (DC). …”
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A Data-Driven Method for Predicting and Optimizing Industrial Robot Energy Consumption Under Unknown Load Conditions by Qing Chang, Tiantian Yuan, Haifeng Li, Yuxiang Chen, Xuehao Wang, Sen Gao, Hongsheng Ren, Xiangyun Zhao, Lingyu Wang

“…The growing diversity and number of industrial robots make energy consumption prediction and optimization increasingly essential. Current data-driven approaches, particularly those based on multi-layer perception (MLP), have shown feasibility but typically overlook the variability or unknown nature of load-related parameters in real-world applications. …”
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Data-Driven Insights into Concrete Flow and Strength: Advancing Smart Material Design Using Machine Learning Strategies by Muwaffaq Alqurashi

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Using geographic information systems to link population estimates to wastewater surveillance data in New York State, USA. by Dustin T Hill, David A Larsen

“…The methods we employed to create these data will be useful to inform similar efforts in other jurisdictions and the data have many public health applications as well as being informative for water/environmental research and infrastructure projects.…”
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Research on the Cultivation of Practical English Talents Based on a Big Data-Driven Model and Sentiment Dictionary Analysis by Qiuwei Fang

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Postoperative human motion evaluation in orthopedics based on kinematic constraints and improved IMU-generated data processing algorithm. by Linzhi Xie, Zhengnan Li

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Multi-criteria routing metric for supporting data-differentiated service in hybrid wireless mesh networks in coal mines by Liansheng Lu, Haifeng Jiang, Guangzhi Han, Shanshan Ma, Renke Sun

“…This routing metric divides data into two types: urgent and non-urgent. End-to-end delay is calculated when transmitting urgent data, and hop count and link load are measured when transmitting non-urgent data. …”
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An inter-regional input-output table series of China from 1987–2017 with integrated carbon emission data by Qiong Zhao, Chen Pan, Zengkai Zhang, Xuefan Guo, Kunfu Zhu, Jianwu He, Shantong Li

“…The compiled IRIO tables, integrated with carbon emission data, have significant applications in regional economic analysis, policy evaluation, and environmental studies.…”
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Development of a High-Reliability Hybrid Data Transmission System for Unmanned Surface Vehicles Under Interference Conditions by Sergiy Kurdiuk, Vadym Dremliuk, Oleksiy Melnyk, Oleg Onishchenko, Oleksij Fomin, Václav Píštěk, Pavel Kučera

“…Its potential extends to commercial and dual applications requiring sustained data transmission in challenging maritime environments.…”
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Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework by Kaushik Sathupadi, Sandesh Achar, Shinoy Vengaramkode Bhaskaran, Nuruzzaman Faruqui, M. Abdullah-Al-Wadud, Jia Uddin

“…This framework offers a scalable, efficient solution for real-time predictive maintenance, making it highly applicable to resource-constrained, data-intensive environments.…”
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