Machine learning for base transceiver stations power failure prediction: A multivariate approach by Sofia Ahmed, Tsegamlak Terefe, Dereje Hailemariam

“…This work demonstrates deep learning’s effectiveness in preemptive BTS failure prediction, enabling proactive maintenance and improved network resilience.…”
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Identifying Learners’ Confusion in a MOOC Forum Across Domains Using Explainable Deep Transfer Learning by Rahaf Alsuhaimi, Omaima Almatrafi

“…These explanations assess the reliability of features and provide supplementary insights into the confusion detection. …”
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Outcomes and Utilization of a Low Intensity Workplace Weight Loss Program by Kelly M. Carpenter, Jennifer C. Lovejoy, Jane M. Lange, Jenny E. Hapgood, Susan M. Zbikowski

“…When examining specific features of the web site, the weight tracking tool was the most predictive of weight loss. …”
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Grouping Digital Health Apps Based on Their Quality and User Ratings Using K-Medoids Clustering: Cross-Sectional Study by Maciej Marek Zych, Raymond Bond, Maurice Mulvenna, Lu Bai, Jorge Martinez-Carracedo, Simon Leigh

“… Abstract BackgroundDigital health apps allow for proactive rather than reactive health care and have the potential to take the pressure off health care providers. …”
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Predicting sleep quality with digital biomarkers and artificial neural networks by Hyolim Lee, Minsung Cho, Sang Won Lee, Sang Won Lee, Sungkyu Park

“…The superior performance of deep learning methods underscores the value of temporal pattern recognition in sleep quality assessment, paving the way for proactive sleep health management in everyday life.…”
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An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction by Zechen Peng, Shaoxing Mo, Alexander Y. Sun, Jichun Wu, Xiankui Zeng, Miao Lu, Xiaoqing Shi

“…BTimesNet's superior performance stems from its ability to extract both short‐ and long‐term temporal features. This approach represents a valuable advancement for accurate GWL prediction and risk‐informed decision‐making, providing critical lead time for proactive groundwater and ecosystem management and agricultural irrigation planning. …”
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Attention activation network for bearing fault diagnosis under various noise environments by Yu Zhang, Lianlei Lin, Junkai Wang, Wei Zhang, Sheng Gao, Zongwei Zhang

“…Abstract Bearings are critical in mechanical systems, as their health impacts system reliability. Proactive monitoring and diagnosing of bearing faults can prevent significant safety issues. …”
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Leisure Activities in Political Perspective by T. V. Vdovenko, N. V. Gants

“…Leisure activities built on expertise and meeting current community demands could become an important factor of shaping a proactive and creative personality.The paper considers ethnic, cultural, and political specificity as well as environmental features. …”
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Modeling interpersonal perception in dyadic interactions: towards robot-assisted social mediation in the real world by Hifza Javed, Weinan Wang, Weinan Wang, Affan Bin Usman, Nawid Jamali

“…Despite the complexity of human interactions and subjective nature of affect ratings, our model demonstrates early capabilities to enable proactive intervention in negative interactions, enhancing neutral exchanges, and respecting positive dialogues. …”
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Machine learning-based drought prediction using Palmer Drought Severity Index and TerraClimate data in Ethiopia. by Tadele Melese, Gizachew Assefa, Baye Terefe, Tatek Belay, Getachew Bayable, Abebe Senamew

“…Accurate drought prediction is essential for proactive water management and agricultural planning, especially in regions like Ethiopia that are highly susceptible to climate variability. …”
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A Comparative Study of Ensemble Machine Learning and Explainable AI for Predicting Harmful Algal Blooms by Omer Mermer, Eddie Zhang, Ibrahim Demir

“…The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques for environmental monitoring.…”
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Potential Distribution of Tribe Erythroneurini in China Based on the R-Optimized MaxEnt Model, with Implications for Management by Xiaojuan Yuan, Weiwei Ran, Wenming Xu, Yuanqi Zhao, Di Su, Yuehua Song

“…The model was configured with LQP features and a default regularization multiplier value of 1. …”
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An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems by Zeinab Awad, Magdy Zakaria, Rasha Hassan

“…During the training phase, adversarial training, label smoothing, and Gaussian augmentation are employed to enhance the model’s resilience against adversarial perturbations. Additionally, a proactive preprocessing defense strategy is deployed during the testing phase, utilizing a denoising sparse autoencoder to cleanse adversarial input samples before they are fed into the IDS classifier. …”
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PUBLIC-PRIVATE PARTNERSHIP IN NUCLEAR ENERGY: THE USA EXPERIENCE by Yu. V. Chernyakhovskaya, D. L. Korolkov

“…Based on the research findings, three fundamental components of a successful PPP for NPP projects were identified: guaranteed return on investments as a result of the stable and receptive market of energy sales at long-term fixed rates and stable costs of energy generation by nuclear power plants, actually independent of the uranium market situation; a solid national nuclear energy support policy implemented in specific support programs; a proactive approach of Vogl NPP shareholders. In the future, the analysis of the PPP experience of other countries in the nuclear industry (e. g., Finland, France, South Korea) will allow creation of a holistic PPP concept, which will make it possible to develop recommendations tailored to the specific features of individual NPP projects.…”
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Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine by Haiyan Wang, Zihan Wang, Biao Shi

“…To enable proactive prediction of marine diesel engine failure time and root causes, thereby reserving sufficient time for maintenance, this study proposes a data-driven multi-algorithm integration framework for performance assessment and fault early warning in marine diesel engines. …”
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Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes by Yucheng Ding, Yingfeng Zhang, Jianfeng Huang, Shitong Peng

“…The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. …”
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Management in the private clinics: impact of the health care reforming by Maryna Saienko, Olena Hrechanychenko, Lesya Prasol

“…The authors concluded that the proactive private pediatrician status, behaviour and real actions are significant in ensuring quality medical services. …”
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Prediction and prevention of crowd-crush accidents using crowd-density simulation based on unity engine by Chul-Ho Choi, Sung-Wook Park, Jun Park, Jong-Hoon Kim, Jin-Seong Kim, Hyun-Sung Yang, Bok-Eun Lee, Se-Hoon Jung, Chun-Bo Sim

“…By analyzing crowd density, flow patterns, and bottleneck situations, the simulator enables users to identify potential hazards, evaluate evacuation strategies, and develop proactive crowd management measures. The system incorporates advanced features such as real-time crowd visualization, interactive scenario creation, and data analytics. …”
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AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model by Berend J. D. Gort, Godfrey M. Kibalya, Angelos Antonopoulos

“…In this paper, we introduce AERO (Adaptive Edge-cloud Resource Orchestration), a novel lightweight forecasting model designed to address these challenges. AERO features an adaptive period detection mechanism that dynamically identifies dominant periodicities in multivariate workload data, allowing it to adjust to varying patterns and abrupt changes. …”
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COMMUNICATION STRATEGIES AND TACTICS IN CRISIS SITUATIONS by Olena Danylina

“…Methods of analysis and synthesis were applied to systematise crisis classifications by scale, subject matter, causes, and structural features; a diachronic review traced the evolution of media strategies from post-crisis reporting to proactive, real-time networked responses. …”
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