Synthetic Biology Innovations in Designing Cellulose-Based Smart Drug Delivery Systems by Samy Selim, Mohammed S. Almuhayawi, Soad K. Al Jaouni, Mohammad Harun-Ur-Rashid

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LISE: A Logic-Based Interactive Similarity Explainer for Clusters of RDF Data by Simona Colucci, Francesco Maria Donini, Verdiana Schena, Floriano Scioscia, Eugenio Di Sciascio

“…LISE combines four core components: (i) a machine learning module leveraging vector embeddings and k-means clustering; (ii) a logic-based reasoning component that computes the common semantic features of clustered items via an optimized Least Common Subsumer (LCS); (iii) a Natural Language Generation (NLG) module that verbalizes these features into structured and human-readable explanations; and (iv) an interactive user feedback loop that captures user perception of explanation relevance to iteratively enhance embedding quality and cluster interpretability. …”
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Adaptive high frequency data streaming for Soft Real-Time Industrial AI: A scalable microservices based architecture with dynamic downsampling by Telmo Fernández De Barrena, Alcides Fernandes, Juan Luis Ferrando, Ander García, Hugo Landaluce, Ignacio Angulo

“…A Proportional-Integral-Derivative (PID) controller dynamically adjusts the downsampling rate based on network conditions, optimizing bandwidth while maintaining data integrity and keeping latency below pre-defined threshold. …”
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Visual Automatic Localization Method Based on Multi-level Video Transformer by Qiping ZOU, Botao LI, Saian CHEN, Xi GUO, Taohong ZHANG

“…This innovative model is developed to identify the clearest frame within a video sequence, a pivotal step for enhancing automated machining precision. …”
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Predicting strength in polypropylene fiber reinforced rubberized concrete using symbolic regression AI techniques by Ahmed A. Alawi Al-Naghi, Kinza Aamir, Muhammad Nasir Amin, Bawar Iftikhar, Kashif Mehmood, Muhammad Tahir Qadir

“…An experimental dataset containing nine key input variables was used to train and validate the models. Among the two, the MEP model demonstrated stronger predictive capability, achieving a coefficient of determination of 0.90 and a mean absolute error of 3.83 MPa. …”
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VTGAN based proactive VM consolidation in cloud data centers using value and trend approaches by Aya I. Maiyza, Hanan A. Hassan, Walaa M. Sheta, Karim Banawan, Noha O. Korany

“…Moreover, traditional time series and machine learning models often struggle to accurately predict the dynamic nature of cloud workloads. …”
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The Impact of Various Filling Patterns and Building Orientations on the Mechanical Characteristics and Building Time of PLA Using FDM by M. Hamoud, Sachin Salunkhe, Lenka Cepova, H. M. A. Hussien

“…In addition, the part can be built with high strength, hardness, and minimum building time, which is useful information for the best utilization of the 3DP machine. Also, the chosen parameters optimize the building process with little human intervention.…”
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A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing. by Md Abid Hasan, Frédéric Li, Philip Gouverneur, Artur Piet, Marcin Grzegorzek

“…This proliferation has resulted in a notable increase in the availability of Time Series (TS) data characterizing behavioral or physiological information from the patient, leading to initiatives toward leveraging machine learning and data analysis techniques. Nonetheless, the complexity and time required for collecting data remain significant hurdles, limiting dataset sizes and hindering the effectiveness of machine learning. …”
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Unraveling C-to-U RNA editing events from direct RNA sequencing by Adriano Fonzino, Caterina Manzari, Paola Spadavecchia, Uday Munagala, Serena Torrini, Silvestro Conticello, Graziano Pesole, Ernesto Picardi

“…Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. …”
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Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods by Weinan Zhou, Diyang Fu, Zhiyu Duan, Jiping Wang, Linfu Zhou, Liquan Guo

“…The R2 between the doctors’ score and the model’s score is 0.9838. The Brunnstrom stage prediction models can predict high-quality stages, achieving a Spearman correlation coefficient of 0.9709. …”
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Mapping individual tree crowns to extract morphological attributes in urban areas using unmanned aerial vehicle-based LiDAR and RGB data by Geonung Park, Bonggeun Song, Kyunghun Park

“…Additionally, deep learning (DL) models, which excel in image analysis, are constrained by the labor-intensive label generation process. …”
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Precision Adverse Drug Reactions Prediction with Heterogeneous Graph Neural Network by Yang Gao, Xiang Zhang, Zhongquan Sun, Payal Chandak, Jiajun Bu, Haishuai Wang

“…Abstract Accurate prediction of Adverse Drug Reactions (ADRs) at the patient level is essential for ensuring patient safety and optimizing healthcare outcomes. Traditional machine learning‐based methods primarily focus on predicting potential ADRs for drugs, but they often fall short of capturing the complexity of individual demographics and the variations in ADRs experienced by different people. …”
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Differential impacts of physical activity volume and intensity on blood lead levels in children and adolescents: a cross-sectional study by Shengrong Ouyang, Yan Yin, Yuanyuan Li, Jianxin Wu, Zhuo Liu

“…Weighted multivariable linear regression, general additive models, mediation analysis, and Bayesian kernel machine regression (BKMR) were employed to explore associations. …”
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Research on the Maximum Regenerative Energy Commutation Control Strategy of a Dual-Mode Synergistic Energy Recovery Pump-Controlled Grinder by Bo Yu, Gexin Chen, Keyi Liu, Guishan Yan, Yaou Zhang, Yinping Liu

“…Machine learning models were compared for predicting the peak time of total recovered energy, with a neural network (NN) demonstrating superior accuracy (R² ≈ 0.99997). …”
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Synergy pathway innovation of carbon emission trading, tradable green certificate and green power trading policies: achieving China’s dual carbon goals by Lili Liu, Tiantian Feng, Cheng Zhong, Mingli Cui, Haoran Wang

“…A dual machine learning model is innovatively introduced to test the robustness of the results. …”
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Heterogeneity Assessment of Breast Cancer Tumor Microenvironment: Multiparametric Quantitative Analysis with DCE-MRI and Discovery of Radiomics Biomarkers by Ma W, Yang L, Zhang Y, Gao Y, Jie H, Huang C

“…Concurrently, radiomics technology, leveraging high-throughput feature extraction and machine learning modeling, identifies potential biomarkers associated with TME biological properties. …”
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Landscape dynamics and its related factors in the Citarum River Basin: a comparison of three algorithms with multivariate analysis by Moh. Dede, Sunardi Sunardi, Kuok-Choy Lam, Susanti Withaningsih, Hendarmawan Hendarmawan, Teguh Husodo

“…LULC changes was more pronounced in the lower areas near Bandung City. LR model highlighted X1, X3 and X6 as the significant driving forces for built-up areas expansion (r-square 0.44 with p-value < 0.01 and 95% confidence level). …”
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Streamlining medical software development with CARE lifecycle and CARE agent: an AI-driven technology readiness level assessment tool by Steven N. Hart, Patrick L. Day, Christopher A. Garcia

“…This approach, combined with a thorough review of existing methodologies, informed the creation of a lifecycle model that guides technology maturation from initial concept to full deployment. …”
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A Study on the Spatial Perception and Inclusive Characteristics of Outdoor Activity Spaces in Residential Areas for Diverse Populations from the Perspective of All-Age Friendly Des... by Biao Yin, Lijun Wang, Yuan Xu, Kiang Chye Heng

“…By integrating multi-group perception data, standardizing experimental environments, and applying intelligent data mining, this study achieves multi-modal data fusion and in-depth analysis, providing theoretical and methodological support for precisely optimizing outdoor activity spaces in residential areas and advancing the development of all-age friendly communities.…”
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SuperBand: an Electronic-band and Fermi surface structure database of superconductors by Tengdong Zhang, Chenyu Suo, Yanling Wu, Xiaodan Xu, Yong Liu, Dao-Xin Yao, Jun Li

“…As an example, we have curated a dataset containing information on 1,362 superconductors along with their experimentally determined superconducting transition temperatures (T c ) as well as 1,112 experimentally verified non-superconducting materials, which is well-suited for machine learning applications. This dataset is constructed with a focus on data quality, accessibility, and usability for machine learning models aimed at predicting superconducting properties.…”
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