Spatiotemporal regulation of alginate sub-structures at multiple scales revealed by monoclonal antibodies by Catherine T. Jones, Cassie Bakshani, Ieva Lelenaite, Jozef Mravec, Stjepan Krešimir Kračun, Jeff Pearson, Mathew D. Wilcox, Kevin Hardouin, Sonia Kridi, Cécile Hervé, William G.T. Willats

“…Alginates are comprised of D-mannuronic acid (M) and L-guluronic acid (G) and the ratios and distribution patterns of M and G profoundly impact their physiological and rheological properties. …”
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Protocol for ‘Re: CBT Dialysis’: a realist evaluation—why, for whom and in what circumstances does cognitive behaviour therapy work for people with depressive symptoms receiving di... by Joanne Greenhalgh, Kara Schick-Makaroff, Scott Klarenbach, Megan Kennedy, Richard Sawatzky, Katrin Micklitz, Daniel Cukor, Hubert Wong, Charlotte Berendonk, Loretta Lee, Lori Suet Hang Lo

“…Realist methodology is a theory-driven approach that seeks to explain how generative mechanisms are shaped by contextual features, giving rise to outcome patterns. We will begin by developing an initial programme theory (IPT) from the literature and interviews with CBT therapists to understand how CBT is intended to work and for whom. …”
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An algorithm for annotation and classification of T. cruzi MASP sequences: towards a better understanding of the parasite genetic variability by Aldana Alexandra Cepeda Dean, Luisa Berná, Carlos Robello, Carlos Andrés Buscaglia, Virginia Balouz

“…On the contrary, structural features of canonical MASPs, MASP-related sequences, and MASP-chimeras were largely conserved across parasite genomes. …”
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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. …”
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Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network by Zhuofu Liu, Gaohan Li, Chuanyi Wang, Vincenzo Cascioli, Peter W. McCarthy

“…Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. …”
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Exploring the potential of deep learning models integrating transformer and LSTM in predicting blood glucose levels for T1D patients by Xin Xiong, XinLiang Yang, Yunying Cai, Yuxin Xue, JianFeng He, Heng Su

“…The Transformer Encoder captures long-range dependencies, while the LSTM models short-term patterns. To improve feature extraction, we integrate Bidirectional LSTM and Transformer Encoder layers at multiple stages. …”
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Fine-Tuning Retrieval-Augmented Generation with an Auto-Regressive Language Model for Sentiment Analysis in Financial Reviews by Miehleketo Mathebula, Abiodun Modupe, Vukosi Marivate

“…Additionally, the pre-training of a larger language model (PTLM) struggles to capture bidirectional contextual knowledge learnt through word dependency because the sentence-level representation fails to take broad features into account. We develop a novel structure called language feature extraction and adaptation for reviews (LFEAR), an advanced natural language model that amalgamates retrieval-augmented generation (RAG) with a conversation format for an auto-regressive fine-tuning model (ARFT). …”
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Event-based landslide inventory through very high-resolution optical images and field surveys by P. Confuorto, R. Franceschini, L. Scarpitta, N. Casagli, S. Morelli, F. Raspini, V. Tofani, S. Moretti

“…Objectives This study aims to develop a detailed post-event landslide inventory of the Misa basin and to analyze the spatial distribution, the main morphological features, and the environmental context of the triggered landslides. …”
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Efficient Anomaly Detection for Edge Clouds: Mitigating Data and Resource Constraints by Javad Forough, Hamed Haddadi, Monowar Bhuyan, Erik Elmroth

“…This enables the model to benefit from the learned features and patterns from related tasks such as network intrusion detection, resulting in improved detection accuracy. …”
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Hyper IgE Syndromes: Understanding, Management, and Future Perspectives: A Narrative Review by Mohammad Salehi, Zeinab Neshati, Hamid Ahanchian, Rana Tafrishi, Alireza Pasdar, Mojtaba Safi, Ehsan Ghayoor Karimiani

“…In this review we aim to provide a comprehensive understanding of the condition and also discuss latest updates on pathological features, clinical spectrum and its variability, immunological abnormalities, inheritance patterns, new candidate genes, challenges, management strategies, epidemiology and future directions of HIES. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. …”
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Surgical management and outcomes of corrosive esophageal injuries: a prospective study from Sana’a, Yemen by Mohammed Mohammed Al-Shehari, Yasser Abdurabo Obadiel, Ahmed Hamood Al-Helali, Haitham Mohammed Jowah

“…This study evaluated the demographic patterns, clinical presentations, management strategies, and outcomes of patients with corrosive esophageal injuries at a tertiary hospital in Sana’a, Yemen. …”
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Deciphering transcriptomic signatures in schizophrenia, bipolar disorder, and major depressive disorder by Priyanka, Rajesh Kumar, Vinod Kumar, Ashwani Kumar, Sandeep Singh Rana

“…At the same time, each disorder also demonstrated unique transcriptional patterns, supporting the existence of disorder-specific mechanisms. …”
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Roadblocks to ride: Unraveling barriers to access shared micromobility systems in the United States by Farzana Mehzabin Tuli, Suman Kumar Mitra

“…To elucidate the specific barriers faced by users, we employed the K-Prototype clustering methodology, an unsupervised machine learning technique capable of handling datasets with both numerical and categorical features. This approach enabled us to uncover distinct patterns and groupings among shared micromobility services based on these barriers. …”
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A simulation-based network analysis of intervention targets for comorbid symptoms of depression and anxiety in Chinese healthcare workers in the post-dynamic zero-COVID policy era by Chao Zhang, Ruyong Li, Wei Zhang, Yanqiang Tao, Xiangping Liu, Yichao Lv

“…In this new phase, concerns related to work and family, rather than infection, may have become new sources of psychological issues such as depression and anxiety among healthcare workers, leading to new patterns of comorbidity. However, few studies have addressed these issues. …”
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Latent Class Analysis of Gameplay Metrics from Youth Playing Robot ChampionsTM: Relations of Class Membership to Persistence and Intensity by Lawrence Scheier, William Hansen, Alex Stone, Jennifer Javornik

“…Impact: Findings are discussed in terms of ways game developers can utilize  log file data to learn more about the unique ways players engage in gameplay and what drives their actions during the game. …”
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Relationship Between Heart Rate and Perceived Stress in Intensive Care Unit Residents: Exploratory Analysis Using Fitbit Data by Ruijing Wang, Olya Rezaeian, Onur Asan, Linghan Zhang, Ting Liao

“…The study used Spearman rank correlation, point-biserial correlation analysis, 2-tailed paired t tests, and mixed-effect models to analyze the relationship between heart rate features and stress indicators. ResultsThe findings reveal complex interactions between stress levels and heart rate patterns. …”
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A practical temporal transfer learning model for multi-step water quality index forecasting using A CNN-coupled dual-path LSTM network by Kok Poh Wai, Chai Hoon Koo, Yuk Feng Huang, Woon Chan Chong, Ahmed El-Shafie, Mohsen Sherif, Ali Najah Ahmed

“…The CNN-LSTM model effectively extracts inter-parameter features and learns temporal patterns, achieving strong five-step ahead forecasting performance. …”
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Advanced and interpretable corneal staining assessment through fine grained knowledge distillation by Yuqing Deng, Pujin Cheng, Ruiwen Xu, Lirong Ling, Hongliang Xue, Shiyou Zhou, Yansong Huang, Junyan Lyu, Zhonghua Wang, Kenneth K. Y. Wong, Yimin Zhang, Kang Yu, Tingting Zhang, Xiaoqing Hu, Xiaoyi Li, Xiaoying Tang, Yan Lou, Jin Yuan

“…To address these limitations, we developed the Fine-grained Knowledge Distillation Corneal Staining Score (FKD-CSS) model. …”
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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. …”
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