“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China by Quanchuan Fu, Jingyuan Zhu, Xiaodi Zheng, Zhengxiang Li, Maini Chen, Yuyuwei He

“…It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. …”
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A dual layer secure and energy-efficient model for border surveillance using sea lion inspired strategy in wireless sensor networks by Jayachandran J, Vimaladevi K

“…Abstract In recent years, networks of sensors have gained significant attention for security-sensitive applications, such as border monitoring, where network durability, efficiency, and security are of utmost importance. …”
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Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis by Raja Waseem Anwar, Mohammad Abrar, Abdu Salam, Faizan Ullah

“…This study underscores the potential of FL-based systems to address key challenges in IoT security, including data privacy, scalability, and performance, making the proposed framework suitable for real-world IoT applications.…”
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Real-world pharmacotherapy treatment patterns among patients diagnosed with postpartum depression in the United States by D. Claire Miller, Devika Chawla, Katherine Johansen Taber, Jennifer L. Payne, Andria L. Del Tredici

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Using a Multivariate Virtual Experiment for Uncertainty Evaluation with Unknown Variance by Manuel Marschall, Finn Hughes, Gerd Wübbeler, Gertjan Kok, Marcel van Dijk, Clemens Elster

“…We extend and formalize a previously published approach for simple additive models to account for a large class of non-linear virtual experiments and measurement models for multidimensionality of the data and output quantities, and for the case of unknown variance of repeated measurements. …”
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Edge-Assisted Label-Flipping Attack Detection in Federated Learning by Nourah S. AlOtaibi, Muhamad Felemban, Sajjad Mahmood

“…Federated Learning (FL) has transformed machine learning by facilitating decentralized, privacy-focused data processing. Despite its advantages, FL remains vulnerable to data poisoning attacks, particularly Label-Flipping Attacks (LFA). …”
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Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness by Junhui Song, Zhangqi Zheng, Afei Li, Zhixin Xia, Yongshan Liu

“…Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. …”
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Enhancing Reliability in Redundant Homogeneous Sensor Arrays with Self-X and Multidimensional Mapping by Elena Gerken, Andreas König

“…The approach is initially validated on synthetic data from tunnel magnetoresistance (TMR) sensors to facilitate method analysis and comparison. …”
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Deep learning‐based reconstruction on intensity‐inhomogeneous diffusion magnetic resonance imaging by Zaimin Zhu, He Wang, Yong Liu, Fangrong Zong

“…The 3T and 7T dMRI data from the Human Connectome Project are used for model training, testing, and evaluation. …”
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Ethical Foundations of AI-Driven Avatars in the Metaverse for Innovation and User Privacy by Ammar Almomani, Ahmad Al-Qerem, Mohammad Alauthman, Amjad Aldweesh, Samer Aoudi, Said A. Salloum

“…However, these advances also introduce critical ethical and legal dilemmas surrounding privacy, identity theft, algorithmic bias, and data governance. This study employs a comprehensive multimethodological approach combining systematic literature review, regulatory gap analysis, and ethical framework synthesis. …”
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Enhancing Human Knowledge and Capabilities with Artificial Intelligence Tools for Education by Emerson Abraham Jackson, Hudson F. Jackson

“…The study suggests that AI significantly improves data processing capabilities, leading to notable advancements in educational research and personalised learning. …”
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Privacy-Aware Detection for Large Language Models Using a Hybrid BiLSTM-HMM Approach by Maryam Abbasalizadeh, Sashank Narain

“…As BiLSTMs do not provide the probability of private data, we design a HMM that estimates the probability of occurrence for this private data. …”
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Remote Sensing for Urban Biodiversity: A Review and Meta-Analysis by Michele Finizio, Federica Pontieri, Chiara Bottaro, Mirko Di Febbraro, Michele Innangi, Giovanna Sona, Maria Laura Carranza

“…A strong association was observed between the source of RS data (e.g., satellite missions), pixel size, and the purpose of its use (e.g., modeling, detection). …”
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Overcoming digital transformation barriers: Chinese SMMEs in the sharing economy by Ji Xiaosheng, Zhang Hua, Zhang Jin, Khalil AL-Bukhaiti, Khalil AL-Bukhaiti, Anping Wan, Anping Wan

“…The sharing economy, through data-sharing and collaborative platforms, mitigates SMMEs' resource constraints and fosters digital transformation. …”
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Characterizing Crop Distribution and the Impact on Forest Conservation in Central Africa by Mohammed S. Ozigis, Serge Wich, Mahsa Abdolshahnejad, Adrià Descals, Zoltan Szantoi, Douglas Sheil, Erik Meijaard

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From Simulation to Field Validation: A Digital Twin-Driven Sim2real Transfer Approach for Strawberry Fruit Detection and Sizing by Omeed Mirbod, Daeun Choi, John K. Schueller

“…Typically, developing new digital agriculture technologies requires substantial on-site resources and data. However, the crop’s growth cycle provides only limited time windows for experiments and equipment validation. …”
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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…By focusing on both the methodological advancements in machine learning applications and the rigorous, detailed empirical forest data collection, this study provides a clear solution to the problem of obtaining reliable, spatially detailed forest attribute maps.…”
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Acylated glucopyranosides: FTIR, NMR, FMO, MEP, molecular docking, dynamics simulation, ADMET and antimicrobial activity against bacterial and fungal pathogens by Nasrin Akter, Supriyo Saha, Md. Ahad Hossain, Kabir M. Uddin, Ajmal R. Bhat, Sumeer Ahmed, Sarkar M.A. Kawsar

“…The chemical structures of the newly synthesized analogs were ascertained by analyzing their physicochemical, elemental, FTIR, and NMR spectroscopic data. In vitro antimicrobial tests against five bacteria and two fungi indicated the promising antibacterial functionality of these synthesized analogs compared with their antifungal activity. …”
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Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study by Soo Hwan Park, Roshini Pinto-Powell, Thomas Thesen, Alexander Lindqwister, Joshua Levy, Rachael Chacko, Devina Gonzalez, Connor Bridges, Adam Schwendt, Travis Byrum, Justin Fong, Shahin Shahsavari, Saeed Hassanpour

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A Quantitative Particle-Based Approach for the Geometallurgical Assessment of Tailings Deposits by Rosie Blannin, Max Frenzel, Raimon Tolosana-Delgado, Kai Bachmann, Alexandra Gomez Escobar, Lucas Pereira, Jens Gutzmer

“…This study explores a novel combination of methods for incorporating particle data, derived from automated mineralogy, into geometallurgical models of tailings deposits to better understand their reprocessing potential and the economic feasibility of re-mining. …”
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