Abstract:

Within Sudden Infant Death Syndrome (SIDS) resides several primary phenomena; these include a state of immune immaturity, susceptibility to infection, and an inflammatory state. Most SIDS risk factors pertain in some way or another to higher risk of infection (prematurity, lack of breastfeeding, low or absent transplacental antibody, ethnicity, genetics, risky gene polymorphisms, poverty, etc.). Most SIDS cases display evidence of an inflammatory state (raised inflammatory markers and inflammatory cytokines). The pattern of inflammation is very similar to that observed following vaccination, which for achieving successful levels of protective immunity requires components that induce high reactogenicity. It is this reactogenicity which, under certain circumstances can cause immune paralysis. Immune paralysis leaves a vulnerable infant open to infection and systemic inflammatory response syndrome leading to shock. Such a mechanism is explored in this rapid review in the context of the aetiopathogenesis of SIDS.

Abstract: Digital habits such as screen time, notifications, and social media engagement have increasingly influenced mental health and overall well-being. This research examines the link between these digital practices and mental health outcomes by utilizing an explainable AI framework from a public dataset containing 500 anonymized entries that combine behavioral metrics with self-reported measures. Building on initial logistic regression analyses, this study employs gradient boosting with XGBoost, enhanced by Shapley Additive Explanations (SHAP), to strengthen both predictive accuracy and interpretability. To evaluate reproducibility, models were trained with five random seeds, and performance was assessed using root mean square error (RMSE) and the coefficient of determination (R²). The outcomes demonstrated consistent predictive performance (RMSE ranged from 5.8 to 6.8; R² ranged from 0.25 to 0.31) and consistently highlighted sleep hours, notification count, and focus score as the most significant predictors. SHAP analysis revealed low variance across seeds, reaffirming the reliability of these features. These findings highlight how behavioral data can inform digital wellness initiatives. This research contributes to a transparent, reproducible analytical framework that bridges the gap between computational modelling and psychological research, supporting the application of explainable AI in mental health research.

Abstract: Urban Air Mobility (UAM) and low-altitude drone operations are emerging as a critical component of next-generation urban transportation and logistics systems. As mission volumes increase, operators face growing challenges in coordinating large-scale low-altitude missions, managing heterogeneous operational states, and closing the loop between mission execution and platform-level resource utilization. Existing UAM platforms primarily focus on flight scheduling and monitoring, while lacking systematic mechanisms to model mission lifecycles and their operational value within an integrated platform architecture. This paper presents SkyNetUAM, a low-altitude UAM operations platform that introduces a structured mission lifecycle model to bridge mission planning, execution, and post-mission settlement within a unified system. We propose a hierarchical operational asset model covering individual missions, service packages, and air-corridor time slots, enabling fine-grained tracking of mission states and operational performance. The platform architecture integrates real-time mission scheduling, operational monitoring, and lifecycle state management, allowing mission-level events to drive platform-wide coordination and resource allocation. As an operational extension, the system incorporates a lightweight on-chain persistence mechanism to record mission states and support automated settlement workflows without altering the core operational logic. A prototype implementation demonstrates the end-to-end workflow from mission creation to completion across simulated low-altitude scenarios, and a reproducible 100k-missions/day experiment quantifies approval rate, delay behavior, and latency distributions under congestion and regulatory constraints.

Abstract: The continuously increasing power density of modern electronic devices poses a major challenge for thermal management, motivating the development of cooling technologies that exceed the limits of conventional heat sinks and heat pipes. Vapor chambers, which utilize highly efficient two-phase heat transfer within a sealed enclosure, have emerged as attractive solutions for high-heat-flux applications in compact systems. In this work, a three-dimensional multiphysics modeling framework is developed to investigate the thermal behavior of a small-scale copper--water vapor chamber under representative operating conditions. The model couples heat transfer in solid and fluid domains with laminar compressible vapor flow and Brinkman flow in a porous wick to capture conjugate heat transport, vapor redistribution, and wick-assisted liquid return. Phase-change effects are incorporated through energy-conserving boundary conditions at the liquid--vapor interface, avoiding explicit interface tracking while retaining the dominant latent-heat transport mechanism. Numerical simulations performed in COMSOL Multiphysics resolve temperature, velocity, and pressure fields within the vapor chamber, revealing strong in-plane heat spreading and reduced peak temperature relative to purely conductive transport. The results demonstrate an effective interfacial thermal conductivity significantly higher than that of the working fluid alone, highlighting the role of two-phase transport in enhancing thermal performance. The proposed modeling framework provides a computationally efficient and extensible tool for analyzing vapor chamber operation and guiding the design and optimization of advanced thermal management solutions for high-power electronic systems.

Abstract:

This work investigates in detail the evaporation-driven dynamics of reactive silver-ink sessile droplets that are relevant to high-precision inkjet printing of conductive tracks and pads. A two-dimensional axisymmetric numerical framework is developed in COMSOL Multiphysics to resolve, in a coupled way, heat transfer, fluid flow, species transport, and free-surface motion during droplet drying. Substrate temperature, solvent composition, and non-uniform evaporation patterns are systematically varied to quantify their influence on internal recirculating flows, compositional gradients, and final silver particle deposition. The results demonstrate that combining moderate substrate heating with a binary water/ethylene-glycol solvent can generate strong thermocapillary circulations that suppress the classical coffee-ring effect and promote more homogeneous particle distributions. The modeling framework therefore provides practical guidance for optimizing ink formulation and thermal processing conditions in printed electronics, and it offers a bridge between commonly used simplified models and more advanced, fully coupled simulations.

Abstract: Conformal prediction (CP) provides distribution-free uncertainty quantification by constructing prediction sets with guaranteed coverage. In human-in-the-loop (HITL) decision systems, these sets naturally define deferral policies: cases with singleton sets proceed automatically, while those with multiple labels require human review. Mondrian CP, which calibrates separately per group, has been proposed to achieve group-conditional coverage validity, ensuring each demographic group meets the target coverage level. However, we demonstrate through extensive experiments (832K evaluations across 14K configurations, 6 datasets, 100 seeds) that improving coverage validity comes at a significant cost: Mondrian CP increases deferral disparity by 143% compared to global CP, despite reducing coverage disparity by 26% on average. This coverage-deferral trade-off is fundamental: it persists across all datasets (p < 0.001), is invariant to HITL parameters, and exhibits monotonic behavior with respect to the shrinkage interpolation parameter γ. We prove an analogous impossibility result for conformal prediction: under specific conditions, coverage parity and deferral parity cannot be simultaneously achieved when base rates differ between groups. We further demonstrate that standard fairness metrics (Equalized Odds, Average Odds Difference) are invariant to CP method choice, identifying deferral gap as a critical operational fairness metric that captures CP’s unique impact on who receives human review, a dimension invisible to standard EO metrics. Our findings provide actionable guidance: use Mondrian for group-conditional coverage validity, global CP for deferral fairness, or shrinkage for balanced trade-offs.

Abstract:

Background: Post-streptococcal glomerulonephritis (PSGN) is a common cause of acute nephritic syndrome in children. Rarely, it may result in life-threatening complications, including acute pulmonary edema and critical hyperkalemia. Case Presentation: We report a 10-year-old Yemeni girl (25 kg) presenting with severe respiratory distress, irritability, and generalized pitting edema. Laboratory tests confirmed PSGN with markedly reduced complement C3 (42.2 mg/dL) and nephritic urine sediment containing numerous red blood cells and casts. The patient developed critical hyperkalemia (7.0 mmol/L) and acute pulmonary edema, requiring urgent intubation and mechanical ventilation using pressure-controlled mandatory ventilation (P-CMV). Management: Aggressive fluid mobilization and electrolyte stabilization were initiated. High-dose intravenous furosemide (4 mg/kg/day), renal-dose dopamine (5 μg/kg/min), and potassium-lowering interventions were applied. Morphine sedation (0.1 mg/kg/dose) was administered every 4 hours during the first 24 hours, then every 8 hours for 12 additional hours, followed by withdrawal prior to extubation. Morphine effectively controlled irritability and optimized patient–ventilator synchronization. The patient produced 1700 mL urine in 17 hours, demonstrating a strong diuretic response. Conclusion: Early recognition of severe extra-renal complications in PSGN is critical. Intensive supportive care—including mechanical ventilation, meticulous fluid and electrolyte management, and appropriate sedation—is essential for survival in cases of acute pulmonary edema and critical hyperkalemia.

Abstract: This paper presents a pioneering framework for next-generation e-commerce that integrates federated multimodal artificial intelligence with quantum-safe personalization techniques to optimize user conversions and retention rates. By enabling decentralized training across client devices on diverse data modalities including text, images, videos, and behavioural signals the system generates contextually rich recommendations without centralizing sensitive user data, thereby upholding privacy standards like GDPR. Quantum-resistant cryptographic protocols, such as lattice-based encryption, safeguard model updates against emerging quantum threats, while adaptive algorithms dynamically refine personalization to boost immediate purchase likelihood and long-term engagement. Extensive simulations on large-scale e-commerce datasets demonstrate superior performance, achieving up to 30% gains in conversion metrics and 25% improvements in retention compared to traditional centralized or non-quantum approaches, paving the way for scalable, secure AI deployment in retail.

Abstract:

Background/Objectives: Urinary incontinence (UI) significantly affects women’s quality of life, and minimally invasive surgical options have gained increasing interest. Mini-sling procedures were developed to reduce operative time, minimize tissue dissection, and lower perioperative morbidity compared with traditional mid-urethral slings. Despite increasing use, real-world data on postoperative satisfaction, objective cure, and mesh-related complications remain limited. This study aimed to evaluate the clinical effectiveness and safety profile of the mini-sling procedure in women with UI. Methods: We conducted a retrospective cohort study including 186 women who underwent mini-sling surgery between January 2019 and January 2024. Outcomes included mesh erosion, time to healing, and 6-month postoperative cough stress and pad test results. Although the study design was retrospective, the 6-month evaluations—including objective tests and patient satisfaction scores—were collected prospectively as part of the clinic’s standardized follow-up protocol. Results: More than half of patients (58.1%) had symptoms for over five years, and 83.9% presented with stress UI. All patients had positive preoperative cough stress and pad tests and received postoperative estriol therapy. At six months, 63.4% reported the highest satisfaction score (5). Pad test positivity decreased from 100% preoperatively to 5.4% postoperatively. Reoperation was required in two patients (1.1%). Mesh erosion occurred in 25.8% of cases but resolved completely within three months in all patients. Conclusions: Mini-sling surgery demonstrated high short-term effectiveness and patient satisfaction. Although mesh erosion was relatively common, complete healing within three months supports an acceptable safety profile.

Abstract: The Entropic Framework (EDF) reinterprets the entropy arrow and dimension hierarchy as it identifies in the current paradigm the cause for open issues and singularities yet to be solved by Particle and Cosmological Physics. In EDF asymptotic approach, dimensional mapping find a natural limit point in a pregeometric zero dimensional constraint. The same perspective settles in the 0D the maximum entropy. The Asymptotic Equipartition Property (AEP) for this maximum is S₀ = ln 2. The time arrow tells us entropy should increase toward higher level dimensions, therefore from 4D to 1D, in opposite of the longstanding view. EDF formalizes this through four functorial projections P_n (n = 0, 1, 2, 3) with non-trivial kernels, incorporating supersymmetric Golden algebras, Fibonacci divisors, braid group representations, and writhe saturation conditions. The framework derives three fermions generations from soliton triplication; Planck’s constant ¯h = S_cycle/12, from a twelve-fold angular kernel; color confinement, from braid closure at twelve crossings; Einstein gravity G ∝ 144−1, from writhe saturation and strict ultraviolet finiteness at all loop orders; and a monotonic entropic descent S₀ > S₁ > S₂ > S₃ > S₄ → 0 ruled by thermodynamic. EDF provides four testable predictions: 1) Twelve-fold spectral resonances; 2) Tunable gravitational coupling in analogues; 3) Discrete attosecond temporal bins; 4) Entropy drift in quantum systems.

Abstract: This work studies the ability of a single-sided vibro-impact nonlinear energy sink (SSVI NES) and a tuned mass damper (TMD) to maintain their vibration reduction performance when the natural frequency of the primary structure (PS), which is determined by its stiffness, changes. Both types of dampers demonstrate high efficiency in mitigating the PS vibrations under periodic excitation if their parameters are optimized at a specific PS natural frequency. Their ability to reduce PS vibrations changes similarly for both types of dampers when this structural parameter is changed.

Abstract: A structure based analysis of the pion’s decay path reveals that neutrinos show up in three flavours, each built up by three identical mass eigenstates. It requires a proper understanding of the nature of charged leptons, such as why the loss of binding energy stops the lepton generation at the tauon level. The analysis reveals fundamental interrelationships between mesons, charged leptons and neutrinos. It is shown that the results of the theoretical model for neutrinos developed in the article are in agreement with the results of the phenomenological PMNS model. The article ends with a discussion on the pros and cons of a structure based theory developed from first principles and phenomenological modelling.

Abstract: Background: Electrocardiography (ECG) represents an important noninvasive screening tool for heart disease in preparticipation screening of competitive athletes. However, interpretation of pediatric ECG based on age-specific reference values remains challenging, due to considerable variation among studies, influenced by population characteristics and documentation methodology. The variability of normal values in key pediatric ECG features regarding left ventricular hypertrophy (LVH), QTc prolongation and pre-excitation detection seem to have a significant impact in the efficacy of pediatric ECG as a preparticipation screening tool. Aims and scope of the study: This review aims to compare contemporary pediatric ECG reference ranges for key ECG features relevant to LVH, QTc, PR and QRS duration and highlight physiological and methodological sources of observed variability. Methods: A review of current literature was conducted using common biomedical databases for studies reporting certain quantitative ECG reference values in healthy children from infancy through adolescence regarding the above selected key features. Reported values were summarized descriptively, with emphasis on developmental trends and methodological differences among studies af-fecting ECG values. Results: Across 16 pediatric studies, ECG parameters demonstrated consistent age-dependent developmental patterns, despite variability in absolute values. R-wave amplitudes in left precordial leads increased from infancy through early childhood and remained stable in older children, whereas S-wave amplitudes in right precordial leads showed greater variation between studies. PR intervals and QRS du-ration increased progressively with age across all datasets, while QTc values remained relatively stable throughout childhood and adolescence, with minimal sex-related dif-ferences. Variability in reported reference ranges was most pronounced for ampli-tude-based- compared to interval duration parameters, and was influenced by differ-ences in population characteristics, ECG acquisition techniques, and measurement methodology. Conclusions: This review summarizes contemporary ECG reference data in healthy children for the early detection of LVH, pre-excitation and QT prolongation, which are the main objectives of ECG screening in young athletes.

Abstract: Cancer remains a leading cause of death worldwide, with breast, lung, colorectal, prostate, and cervical cancers contributing significantly to global cancer-related morbidity and mortality. While individual lethality varies among these cancers, their combined impact on public health is substantial due to high incidence and, in some cases, limited access to early detection and effective treatment. These malignancies arise from a complex interplay of genetic, hormonal, lifestyle, and infectious factors, with molecular mechanisms that inform targeted therapies and precision medicine approaches. Advances in screening, immunotherapy, AI-assisted diagnostics, and minimally invasive surgical techniques have improved outcomes; however, challenges such as late diagnosis, treatment resistance, and healthcare disparities persist, particularly in low- and middle-income countries. This review provides a comprehensive synthesis of the epidemiology, risk factors, molecular pathogenesis, clinical features, current treatment strategies, emerging technologies, public health implications, and future research directions for the five deadliest cancers. Emphasis is placed on preventive measures, early detection, and equitable access to care, highlighting strategies to reduce the global cancer burden and improve survival outcomes.

Abstract: This paper presents a novel AI-driven ensembleapproach for discerning sentiment polarity in text documents,specifically consumer reviews. We address the binary classifica-tion problem of identifying positive versus negative sentimentby proposing a uniquely hybrid framework that integratesgenerative, discriminative, and deep embedding-based models.Our key contribution is a carefully designed, optimized weightedvoting mechanism that leverages cross-validation to assignmodel-specific weights, effectively harnessing the complementarystrengths of its diverse constituents. This ensemble strategy isevaluated on a widely recognized movie review dataset, whereit demonstrates robust and superior performance compared tostate-of-the-art standalone models. The findings confirm that ourmulti-paradigm fusion leads to significant gains in accuracy, ad-vancing the capabilities of automated sentiment analysis systemsby mitigating the individual limitations of each model family.

Abstract: The increasing complexity of clinical decision-making demands advanced support, yet traditional Clinical Decision Support Systems (CDSS) lack flexibility, and general Large Language Models (LLMs) struggle with medical specificity, factual accuracy, and resource demands. This paper presents an Enhanced Lightweight Clinical Decision Support System, optimizing the "lightweight LLM + Retrieval-Augmented Generation (RAG)" architecture for superior accuracy, robustness, and resource efficiency. Our method employs a QLoRA fine-tuned base model and features two key innovations: a refined medical domain data fine-tuning strategy using semantic labeling and ontology-based domain balancing to enhance specialized knowledge; and an intelligent context optimization module within the RAG pipeline. This module utilizes secondary relevance re-ranking with a lightweight cross-encoder, redundancy reduction, and key information extraction to provide the LLM with precise and compact context. Experiments on medical benchmarks demonstrate that our system consistently outperforms a standard QLoRA fine-tuned model, achieving notable accuracy improvements in challenging domains such as College Medicine and Medical Genetics. This enhanced performance is achieved while maintaining a lightweight computational footprint, making our system a practical and reliable tool for clinical decision support, especially in resource-constrained settings.

Abstract: Objective: To compare oral health-related quality of life (OHRQoL) between patients undergoing root canal treatment (RCT) and tooth extraction in Malaysia. Methods: A cross-sectional study was conducted among 484 adult patients (243 RCT, 241 extrac-tion) attending public restorative dental clinics in Selangor and Kuala Lumpur. Par-ticipants completed the validated 14-item Malaysian Oral Health Impact Profile (S-OHIP(M)). Independent t-tests and chi-square tests were applied (p < 0.05). Results: The RCT group reported significantly lower mean OHIP-14 scores (12.9 ± 8.4) com-pared to the extraction group (18.5 ± 10.5), indicating better OHRQoL. Across all OHIP-14 domains, RCT patients experienced fewer functional limitations, less pain, reduced psychological discomfort, and lower social handicap. Sociodemographic dif-ferences were observed in age, gender, and education, but not ethnicity. Conclusion: RCT is associated with better OHRQoL outcomes than extraction, supporting its role as a tooth-preserving, cost-effective option. Findings highlight the importance of inte-grating patient-centred outcomes into oral health policy and clinical decision-making.

Abstract: Zeolite Y has been considered as one of the most versatile materials that are used in catalysis, adsorption, and separation. However, its inherent microporosity often impedes the diffusion of reactants and products, thus constraining overall performance. This review systematically investigates the major post-synthetic modification strategies intended to mitigate these limitations and to refine the structural and physicochemical properties of zeolite Y. Particular focus is placed on the mechanisms and structural consequences of dealumination, desilication, ion exchange, and surface functionalization, each of which uniquely influences acidity, porosity, and framework stability. The synergistic combination of dealumination and desilication is especially highlighted for its capacity to generate hierarchical structures containing mesoporosity with optimal acidity robustness. Recent developments that integrate the use of microwave and ultrasound-enhanced methods are considered sustainable and energy-efficient solutions that offer accurate control over the framework transformation and shorten processing times. These post-synthetic advancements have led to hierarchical, multifunctional zeolite Y materials that show high levels of catalytic activity, enhanced adsorption capacity, and improved selectivity over a wide range of industrially related reactions. This review concludes how such modification techniques expand the functional range of zeolite Y, thereby enabling its use in new areas of application, including CO2 capture, biofuels production, and environmentally friendly catalytic processes. Future perspectives emphasize ongoing refinement of structure-function relationships, scalability of processes, and integration of modification methodologies to reinforce zeolite Y’s pivotal role in sustainable chemical manufacturing.

Abstract: Background: The monocarboxylate transporter 1 (MCT1) plays a central role in myocardial lactate handling and metabolic adaptation. The functional rs1049434 polymorphism (T1470A; Asp490Glu) affects MCT1-mediated lactate transport and substrate utilization, but its clinical relevance in sarcomere-related hypertrophic cardiomyopathy (HCM) remains poorly defined. Methods: We studied 56 carriers of pathogenic or likely pathogenic sarcomeric variants followed in a familial HCM program. All participants underwent standardized clinical phenotyping, including electrocardiography, transthoracic echocardiography and cardiac magnetic resonance imaging. Genotyping of MCT1 rs1049434 was performed on genomic DNA. Analyses focused on sex-stratified genotype distribution, phenotypic expression among the 26 individuals who fulfilled diagnostic criteria for HCM, and the influence of habitual vigorous exercise. Septal wall thickness was the primary structural endpoint. Results: Among the 26 patients with established HCM (10 women, 16 men), a marked sex-specific effect emerged. Female carriers of the T-allele (TT/TA) exhibited significantly greater interventricular septal thickness compared with AA homozygotes (23.2 vs. 14.2 mm; p = 0.037). In men, septal thickness did not differ by genotype. However, male patients engaged in vigorous physical activity showed a consistently milder structural phenotype, including lower septal thickness (18.3 vs. 19.9 mm; p = 0.585) and directionally favorable markers of mechanical severity. Phenotypic distribution was predominantly asymmetric septal hypertrophy in both sexes, without genotype-dependent differences. Conclusions: The phenotypic impact of MCT1 rs1049434 in sarcomere-positive HCM is context-dependent. In women, impaired monocarboxylate handling is associated with greater hypertrophic remodeling, whereas in men, exercise-related metabolic conditioning appears to attenuate disease severity. These findings support a genotype–sex–environment interaction relevant to precision medicine approaches in HCM..

Abstract: Video synthetic aperture radar (SAR) enables observation of moving targets by leveraging temporal information across successive frames. In particular, dynamic shadows in video SAR image sequences provide critical cues for detecting moving objects whose energy is smeared or Doppler-shifted. To achieve high-resolution imaging at a high frame rate for effective dynamic scene monitoring, video SAR systems typically operate at extremely high frequencies or even in the terahertz band, rather than the microwave band. However, terahertz video SAR suffers from significant signal attenuation due to atmospheric absorption. We present a deep learning framework for high-frame-rate and high-resolution imaging with microwave video SAR system. In this framework, the problem of microwave video SAR imaging is formulated as an image super-resolution reconstruction task for low-resolution yet high-frame-rate image sequences from microwave video SAR. We develop a simple yet effective image super-resolution reconstruction network that is completely built upon convolutional neural networks. The designed network takes a low-resolution image sequence and the corresponding high-resolution image with blurred shadows as input, and then produces a high-resolution image sequence where shadows are clearly visible. Furthermore, the network is trained in a self-supervised manner and thus does not require desired high-resolution image sequences as ground truth, which is appealing to practical applications. Processing results of real data from two different video SAR systems have shown good performance of the proposed approach with convincing generalization ability.