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With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.

Embedding into undergraduate medical programs experiential research curricula, based on holistic theories of education which emphasize participation in the social world, remains uncommon. The purpose of this study was to investigate the journey of undergraduate medical students in relation to an innovative compulsory curriculum-based research module, which has a prominent experiential learning component. A convergent mixed methods study design was adapted to develop a systemic understanding of the experience of the undergraduate medical students throughout the respective research module. As such, the students' perception of the experience was qualitatively explored using thematic analysis (n = 15). In parallel, the students' performance data were quantitatively analyzed using multi-repeated ANOVA (n = 158). The findings from both types of analyses (i.e., qualitative and quantitative study components) were then mapped onto each using joint display analysis. The exploration generated four themes that correspond to sequential steps that the students go through to effectively integrate the scientific research method. These themes include: 1- Attend-Acquire, 2- Accumulate-Assimilate, 3- Apply-Appreciate, and 4-Articulate-Affect. Quantitatively, two distinct clusters of mean Grade Point Average were revealed (p<0.01). Joint display analysis enabled integrating the qualitative and quantitative findings, generating the 8A-Model. The evidence-driven 8A-Model, generated by this study, highlights that medical students' understanding of the true value of research seems to increase as they progress in the module. They begin expressing appreciation of the significance of the experience when they start implementing what they are learning as part of their own research studies. It is recommended for such a research module, with a firm experiential learning component, to be integral to undergraduate medical programs. This is expected to improve the future physicians' research competences, and in turn add value in terms of quality of care and patient outcomes.

The reliable operation of photovoltaic (PV) systems is often compromised by surface soiling and structural damage, which reduce energy efficiency and complicate large-scale monitoring. To address this challenge, we propose a two-tiered image-classification framework that combines Vision Transformer (ViT) models, lightweight convolutional neural networks (CNNs), and knowledge distillation (KD). In Tier 1, a DINOv2 ViT-Base model is fine-tuned to provide robust high-level categorization of solar-panel images into three classes: Normal, Soiled, and Damaged. In Tier 2, two enhanced EfficientNetB0 models are introduced: (i) a KD-based student model distilled from a DINOv2 ViT-S/14 teacher, which improves accuracy from 96.7% to 98.67% for damage classification and from 90.7% to 92.38% for soiling classification, and (ii) an EfficientNetB0 augmented with Multi-Head Self-Attention (MHSA), which achieves 98.73% accuracy for damage and 93.33% accuracy for soiling. These results demonstrate that integrating transformer-based representations with compact CNN architectures yields a scalable and efficient solution for automated monitoring of the condition of PV systems, offering high accuracy and real-time applicability in inspections on solar farms.

Knowledge Distillation (KD) is a machine learning technique in which a compact student model learns to replicate the performance of a larger teacher model by mimicking its output predictions. Multi-Teacher Knowledge Distillation extends this paradigm by aggregating knowledge from multiple teacher models to improve generalization and robustness. However, effectively integrating outputs from diverse teachers, especially in the presence of noise or conflicting predictions, remains a key challenge. In this work, we propose a Multi-Round Parallel Multi-Teacher Distillation (MPMTD) that systematically explores and combines multiple aggregation techniques. Specifically, we investigate aggregation at different levels, including loss-based and probability-distribution-based fusion. Our framework applies different strategies across distillation rounds, enabling adaptive and synergistic knowledge transfer. Through extensive experimentation, we analyze the strengths and weaknesses of individual aggregation methods and demonstrate that strategic sequencing across rounds significantly outperforms static approaches. Notably, we introduce the Byzantine-Resilient Probability Distribution aggregation method applied for the first time in a KD context, which achieves state-of-the-art performance, with an accuracy of 99.29% and an F1-score of 99.27%. We further identify optimal configurations in terms of the number of distillation rounds and the ordering of aggregation strategies, balancing accuracy with computational efficiency. Our contributions include (i) the introduction of advanced aggregation strategies into the KD setting, (ii) a systematic evaluation of their performance, and (iii) practical recommendations for real-world deployment. These findings have significant implications for distributed learning, edge computing, and IoT environments, where efficient and resilient model compression is essential.

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate.

Adipose tissue is contemplated as a dynamic organ that plays key roles in the human body. Adipogenesis is the process by which adipocytes develop from adipose-derived stem cells to form the adipose tissue. Adipose-derived stem cells’ differentiation serves well beyond the simple goal of producing new adipocytes. Indeed, with the current immense biotechnological advances, the most critical role of adipose-derived stem cells remains their tremendous potential in the field of regenerative medicine. This review focuses on examining the physiological importance of adipogenesis, the current approaches that are employed to model this tightly controlled phenomenon, and the crucial role of adipogenesis in elucidating the pathophysiology and potential treatment modalities of human diseases. The future of adipogenesis is centered around its crucial role in regenerative and personalized medicine.

Increasing knowledge of post-release survival and habitat requirements of translocated animals is critical to improve success of conservation programs. We estimated survival of reintroduced captive-bred Asian houbara bustards (Chlamydotis macqueenii) in reserves of western United Arab Emirates where plantations exist as supplementary feeding sites. We explored factors influencing short- (3 months after release) and long-term (tri-monthly periods after third month of release) survival rates of released birds. We modeled life histories of individually tracked houbara using Program MARK. Mean short-term survival probability (0.76 ± 0.14 SD) was lower than mean long-term survival (0.86 ± 0.03 SD), and observed group size and the age of released birds positively correlated with short-term survival. We hypothesize that higher quality habitat (plantations) affected survival; larger groups occurred in plantations and older birds might be better able to maintain access to plantations. Long-term survival was negatively influenced by subsequent release events. Releasing more individuals increases local houbara density. This may lead to food depletion, increase in density-dependent mechanisms between individuals, or both. Short- and long-term survival rates suggest that food availability at the release sites, together with intraspecific interactions, may influence survival of newly released and established individuals. To improve the management of translocated animals, the impact of managed food resources should be quantified to assess how it might affect population vital rates.

Medical educators strive to improve their curricula to enhance the student learning experience. The use of high-fidelity simulation within basic and clinical medical science subjects has been one of these initiatives. However, there is paucity of evidence on using simulation for teaching pharmacology, especially in the Middle East and North Africa region, and the effectiveness of this teaching modality, relative to more traditional ones, have not been sufficiently investigated. Accordingly, this study compares the effects of high-fidelity simulation, which is designed in alignment with adult and experiential learning theories, and traditional case-based tutorial sessions on the performance and perception of undergraduate Year 2 medical students in pharmacology in Dubai, United Arab Emirates. This study employed a convergent mixed methods approach. Forty-nine medical students were randomly assigned to one of two groups during the 16-week pharmacology course. Each group underwent one session delivered via high-fidelity simulation and another via a case-based tutorial. A short multiple-choice question quiz was administered twice (immediately upon completion of the respective sessions and 5 weeks afterwards) to assess knowledge retention. Furthermore, to explore the students' perceptions regarding the two modes of learning delivery (independently and in relation to each other), an evaluation survey was administered following the delivery of each session. Thereafter, the iterative joint display analysis was used to develop a holistic understanding of the effect of high-fidelity simulation in comparison to traditional case-based tutorial sessions on pharmacology learning in the context of the study. There was no statistically significant difference in students' knowledge retention between high-fidelity simulation and case-based tutorial sessions. Yet, students expressed a greater preference for high-fidelity simulation, describing the corresponding sessions as more varied, better at reinforcing learning, and closer to reality. As such, the meta-inferences led to expansion of the overall understanding around students' satisfaction, to both confirmation and expansion of the systemic viewpoint around students' preferences, and lastly to refinement in relation to the perspective around retained knowledge. High-fidelity simulation was found to be as effective as case-based tutorial sessions in terms of students' retention of knowledge. Nonetheless, students demonstrated a greater preference for high-fidelity simulation. The study advocates caution in adapting high-fidelity simulation, where careful appraisal can lend itself to identifying contexts where it is most effective.

Pancreatic adenocarcinoma (PDAC) is an aggressive tumor with poor survival and limited treatment options. PDAC resistance to immunotherapeutic strategies is multifactorial, but partially owed to an immunosuppressive tumor immune microenvironment (TiME). However, the PDAC TiME is heterogeneous and harbors favorable tumor-infiltrating lymphocyte (TIL) populations. Tertiary lymphoid structures (TLS) are organized aggregates of immune cells that develop within non-lymphoid tissue under chronic inflammation in multiple contexts, including cancers. Our current understanding of their role within the PDAC TiME remains limited; TLS are complex structures with multiple anatomic features such as location, density, and maturity that may impact clinical outcomes such as survival and therapy response in PDAC. Similarly, our understanding of methods to manipulate TLS is an actively developing field of research. TLS may function as anti-tumoral immune niches that can be leveraged as a therapeutic strategy to potentiate both existing chemotherapeutic regimens and potentiate future immune-based therapeutic strategies to improve patient outcomes. This review seeks to cover anatomy, relevant features, immune effects, translational significance, and future directions of understanding TLS within the context of PDAC.

Kawasaki disease (KD) is most common among East Asian children. There is a lack of data from Middle Eastern countries. We conducted a retrospective study of 27 paediatric patients with KD in Dubai, United Arab Emirates, 2012–2020. The majority of the patients were male, Asian, aged 1–5 years and presented with typical (complete) KD. Timely high-dose intravenous immunoglobulins were administered to 18 patients. Twelve patients did not develop any cardiac complications, 12 had a coronary artery aneurysm and 2 patients developed serious cardiac complications . No patient experienced non-cardiac complications or died. Paediatric patients with KD in Dubai were similar to those from other countries.