Search Results (615)

Severe Combined Immunodeficiency (SCID), Spinal Muscular Atrophy (SMA), and X-Linked Agammaglobulinemia (XLA) are rare but life-threatening genetic disorders in infants that can lead to severe infections, progressive neuromuscular degeneration, or severe immune dysfunction associated with significant morbidity and mortality if not diagnosed early. Advances in newborn screening (NBS) technologies have enabled pre-symptomatic detection of these conditions, allowing early initiation of life-saving interventions such as hematopoietic stem cell transplantation, gene therapy, and immunoglobulin replacement therapy. However, the absence of a standardized national clinical pathway linking screening, confirmatory testing, and specialist referral in Malaysia continues to contribute to delayed diagnosis and suboptimal patient outcomes. This review examines and synthesizes current evidence on the clinical pathways for early diagnosis and management of SCID, SMA, and XLA, with particular emphasis on diagnostic workflows, screening technologies, and healthcare system challenges within the Malaysian context. The review examines disease epidemiology, consequences of delayed diagnosis, and the role of expanded NBS under the Screening for Health, Intervention, Nurturing of Every Child (SHINE) program in improving early diagnosis and management. In addition, the paper outlines the current NBS landscape, the use of multiplex real-time polymerase chain reaction (PCR) assays for simultaneous detection of T-cell receptor excision circles (TREC), kappa-deleting recombination excision circles (KREC), and survival motor neuron 1 (SMN1) gene deletion of exon 7 from dried blood spot (DBS) samples. A structured diagnostic framework incorporating screening interpretation, confirmatory testing, and urgency-based referral pathways is also proposed. By addressing current operational barriers and coordinating laboratory referral systems, expanding NBS programs could significantly improve early diagnosis and long-term outcomes for infants affected by SCID, SMA, and XLA in Malaysia. Full article

Multimodal recommender systems are challenged by heterogeneous modality noise and coarse-grained feature fusion. Specifically, existing frequency-domain methods typically apply symmetric filtering across modalities, ignoring their distinct spectral characteristics. Consequently, symmetric filtering cannot simultaneously satisfy the denoising requirements of visual features and the semantic preservation requirements of textual features, leading to suboptimal multimodal representations. Meanwhile, current fusion strategies mainly operate at the instance level with static modality weights, lacking flexibility to dynamically adjust feature channels for user-specific collaborative contexts. To address these issues, this paper proposes MFA-GCN, a multimodal recommendation framework that combines asymmetric spectral filtering, multiview graph enhancement, and behavior-guided channel attention. For visual modalities, a multiscale frequency-domain module integrating 1D convolution and self-attention is adopted to suppress high-frequency disturbances while preserving informative structures. For textual modalities, a lightweight complex-domain scaling strategy is introduced to adjust spectral energy while maintaining semantic consistency. In addition, auxiliary user–user and item–item graphs are constructed to supplement sparse user–item interactions and provide richer collaborative signals. A behavior-guided channel attention mechanism is further used to dynamically refine multimodal representations. Experiments on three public Amazon datasets demonstrate that MFA-GCN consistently outperforms several representative baselines. Full article

This study aims to improve wagon unloading processes in a real industrial context characterized by operational variability, process constraints, and strict performance requirements. Traditional decision-making approaches in such contexts often rely on single performance indicators, which may lead to suboptimal and less sustainable decisions. In line with Industry 5.0 principles, which emphasize human-centricity, resilience, and sustainability, this paper proposes a multi-criteria decision framework to support more balanced and adaptive operational decisions. A real-world case study based on anonymized industrial data is used to evaluate different arrival-track operational configurations. The proposed model considers several indicators, including unloading time, throughput, tonnage, process variability, operational losses, and a proxy of operator exposure. To strengthen the human-centric dimension, an Operational Handling Exposure Proxy (OHEP) was introduced to capture manoeuvre-related operator exposure during wagon handling and batch repositioning. A weighted scoring system was then used to identify the most balanced configuration by considering trade-offs between performance, stability, losses and operator exposure. The results show that the arrival-track operational configuration influences loss structure, process stability and overall decision ranking more than direct throughput alone. Track 2 provides the best overall trade-off under the baseline MCDM weighting scheme, while Track 3 may become preferable when wagon-loss minimization is prioritized. The findings highlight the importance of integrating variability and human-centered indicators into industrial decision-making processes. In future work, the proposed framework could be extended using data-driven methods and machine learning to support predictive and adaptive optimization in Industry 5.0 environments. This study contributes to the literature by integrating real-world industrial analysis, multi-criteria decision-making, and sustainability-oriented optimization into a single decision support framework. Full article

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization. Full article

Background: Polypharmacy is highly prevalent among older adults with type 2 diabetes mellitus (T2DM) and is traditionally considered a marker of geriatric vulnerability. However, it remains unclear whether polypharmacy is more closely associated with multidimensional frailty or metabolic burden in this population. Methods: In this retrospective cross-sectional study, 278 adults aged ≥65 years with T2DM underwent comprehensive geriatric assessment (CGA), including evaluation of functional status, cognition, nutrition, depressive symptoms, frailty, and physical performance. Frailty was assessed using the Fried phenotype. Polypharmacy was defined as the concurrent use of ≥5 medications. Multivariable logistic regression and interaction analyses were performed to identify independent predictors of polypharmacy. Receiver operating characteristic (ROC) analyses were conducted to evaluate the discriminative performance of metabolic parameters. Results: Polypharmacy was present in 54.7% of participants. Patients with polypharmacy had significantly higher HbA1c and fasting glucose levels compared with those without polypharmacy (both p < 0.001). In multivariable analysis, higher HbA1c levels remained independently associated with polypharmacy (OR = 4.99, 95% CI: 3.18–7.84, p < 0.001), whereas frailty status was not significantly associated with polypharmacy (OR = 0.58, 95% CI: 0.15–2.21, p = 0.427). No significant interaction was observed between HbA1c and frailty status (p for interaction > 0.05). Among CGA domains, only functional status and gait speed differed in unadjusted analyses, while cognition, nutritional status, and depressive symptoms were not significantly associated with polypharmacy after adjustment. HbA1c demonstrated strong discriminative performance for polypharmacy (AUC = 0.898, 95% CI: 0.863–0.931), with an optimal cut-off of 6.81%. Conclusions: In older adults with T2DM, polypharmacy appeared to be more closely associated with markers of poor glycemic control, particularly HbA1c levels, than with frailty status itself. These findings suggest that medication burden in older adults with T2DM may reflect treatment intensification and suboptimal glycemic control in addition to geriatric vulnerability. Full article

Background: Sport-related injuries are common, and often recurrent, among female college soccer players. This exploratory cohort study investigated whether perceptual decision efficiency and sleep quality could discriminate between injured and uninjured players. Methods: Twenty-seven NCAA Division I women’s soccer players (19.5 ± 1.3 years) completed a perceptual response training program, administered through an immersive virtual reality system, across a 13-week season. Players completed 11 training sessions progressing through four levels of task difficulty, with conjugate eye movements, neck rotation, and whole-body lunge-reach responses measured for each trial. Four metrics, elapsed time, rate correct per second, across-trials variability, and an efficiency index, were calculated for each of three defined time segments: perceptual decision, action initiation, and perceptual–motor response. The Pittsburgh Sleep Quality Index (PSQI) and Global Well-Being Index (GWBI) were administered prior to the first practice session, and all subsequent time-loss injuries were documented. Receiver operating characteristic analyses, Kaplan–Meier survival analysis, and classification tree modeling were used to evaluate injury discrimination. Results: Twelve time-loss injuries, including five concussions and seven lower extremity musculoskeletal injuries, were sustained by 10 of the 27 players. Optimal discrimination between injured and uninjured players was derived from the perceptual decision efficiency (PDE) metric for the most difficult perceptual response training task (AUC = 0.682–0.794), with a binary cut point of ≤6.02 yielding an odds ratio of 5.60 (95% CI: 1.02, 30.90; Mantel–Cox log rank p = 0.025). All five concussions occurred in players classified as high-risk by a suboptimal PDE value. Pre-participation PSQI demonstrated an AUC of 0.735. Notably, no player with both an optimal PDE value and a favorable sleep quality score (PSQI < 4) sustained a time-loss injury. Moderate-to-large training-related improvements in perceptual decision metrics were observed for the least challenging task from early- to late-season sessions. Conclusions: Optimal values for PDE and sleep quality together characterized female college soccer players who avoided injury. Both factors appear to be modifiable, suggesting that perceptual response training combined with interventions to enhance sleep quality may enhance injury resistance. Independent validation in larger, diverse athlete cohorts is warranted. Full article

Background: Moderate-to-severe preterm brain injury (PBI), including intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL), remains an important cause of adverse neurodevelopmental outcomes in preterm infants. Early risk stratification using routinely collected clinical data may help prioritize surveillance in vulnerable infants. Methods: We retrospectively included 318 preterm infants admitted between 2015 and 2024 as the development cohort. Thirty-three candidate predictors derived from perinatal factors, first laboratory tests within 24 h of admission, and selected early hospitalization variables were evaluated. Seven machine-learning algorithms were developed using stratified 10 × 5 nested cross-validation with prespecified preprocessing, class-balancing, and feature-selection procedures. Candidate models were compared primarily using the mean fold-level area under the receiver operating characteristic curve (AUROC). After model selection, the finalized LightGBM model was calibrated using Platt scaling, and its pooled out-of-fold (OOF) performance was summarized. Two prespecified thresholds (Youden and high-sensitivity) were used for risk stratification. A small independent temporal cohort of 35 infants was used for preliminary external validation. Results: PBI occurred in 62/318 infants (19.5%) in the development cohort and 6/35 infants (17.1%) in the temporal external cohort. During candidate-model comparison, LightGBM achieved the highest mean fold-level AUROC (0.768, 95% CI 0.708–0.825). The finalized 14-feature LightGBM model, evaluated using pooled OOF predictions after Platt calibration, yielded an AUROC of 0.747 (95% CI 0.679–0.811), a PR-AUC of 0.392, and a Brier score of 0.136. At the Youden threshold (0.18), sensitivity was approximately 0.70 and specificity approximately 0.85; at the high-sensitivity threshold (0.10), sensitivity was approximately 0.95 and specificity approximately 0.50. Key predictors included ventilation status and early physiologic and laboratory indicators. In the small temporal external cohort (n = 35), the AUROC was 0.897 (95% CI 0.672–1.000); however, this high point estimate should not be overinterpreted because of the limited sample size, wide confidence interval, and suboptimal calibration, and should therefore be considered preliminary. Conclusions: We developed an interpretable LightGBM model using routinely available early postnatal and early hospitalization data to support risk stratification for PBI in preterm infants. The model showed moderate internal discrimination and a positive net benefit across clinically relevant thresholds. Preliminary temporal external validation in a small cohort yielded highly uncertain estimates; larger multicenter studies are needed to confirm generalizability, refine calibration, and determine the most appropriate implementation strategy before routine clinical use. Full article

Effective traffic management relies on the availability of high-quality traffic data to support real-time decision-making for optimizing traffic flow, enhancing safety, and reducing environmental impacts. This study aims to address the lack of integrated and operational approaches for traffic data quality management by proposing a scalable and adaptable framework for the systematic assessment and enhancement of traffic data. The framework consists of four interconnected layers, including data ingestion, data quality assessment, data imputation and correction, and a real-time alerting mechanism. Its applicability is demonstrated through a real-world case study on traffic signal control plan selection, using sensitivity and simulation-based analyses in SUMO. The results indicate that degraded data quality, particularly due to missing or invalid records, can significantly affect system behavior, leading to suboptimal decisions and reduced traffic performance. These findings highlight the importance of continuous and systematic data quality monitoring as a critical component for reliable and efficient traffic management systems. Full article

The increasing integration of renewable energy introduces uncertainty in microgrid operation, making effective energy management more challenging. Rolling-horizon optimization is used to address this challenge by enabling periodic decision updates; however, most existing approaches rely on fixed optimization horizons and predetermined update frequencies. When prediction accuracy decay (PAD) is considered in adaptive rolling-horizon approaches, it is represented using a fixed decay value, not an online indicator that compares forecasted and actual renewable generation during operation. This leads to suboptimal re-optimization timing, unnecessary computational effort, excessive battery switching, or delayed corrective actions. To address these limitations, this paper proposes a PAD-driven adaptive rolling horizon (ARH) approach, in which re-optimization is triggered using an online PAD indicator computed from the percentage deviation between forecasted and realized renewable generation data. Re-optimization is activated when the PAD indicator exceeds a predefined threshold, enabling adaptive scheduling updates according to real-time forecasting degradation. The problem is formulated as a robust mixed-integer linear programming (MILP) model of a high renewable penetration microgrid, including battery degradation and switching penalties. The energy self-sufficiency ratio (SSR) is used as a key sustainability performance indicator to assess the extent to which local renewable generation and storage satisfy microgrid demand. The proposed approach is first compared with a fixed rolling-horizon approach using a fixed re-optimization interval of 1 h, where the results show a profit improvement of 10.5%. A sensitivity analysis tested the proposed approach under bounded renewable forecast uncertainty levels up to ±15 and different battery capacities. The results indicate that performance is strongly influenced by forecast accuracy and battery capacity, with higher economic gains under low uncertainty and more conservative operation under high uncertainty. Full article

Aluminum alloy A356 (Al-7Si-0.3Mg) is widely employed in automotive structural components due to its favorable strength-to-weight ratio, yet its mechanical performance is highly sensitive to T6 heat-treatment processes. Conventional heat-treatment schedules are typically based on uniform, empirically derived parameters and fail to consider variations in component size, geometry, or thermal mass. Consequently, applying a single schedule across all component sizes often leads to inconsistent microstructural development, energy inefficiency, and elevated scrap rates. Smaller components tend to be over-processed, while larger components may be under-processed, both resulting in suboptimal mechanical properties and increased production costs. To overcome these limitations, this study presents a scalable heat-treatment optimization framework that integrates physics-based thermal simulations with machine learning techniques. The framework combines a transient thermal simulator with Long Short-Term Memory (LSTM) networks to predict sample temperature evolution, Random Forest regressors to estimate mechanical properties such as yield strength, hardness, and modulus of toughness, and Bayesian optimization to generate size-dependent, property-compliant heat-treatment schedules. Unlike traditional methods, this approach dynamically adjusts furnace parameters to individual component characteristics, optimizing both processing time and energy consumption while minimizing scrap. Application of the framework to components ranging from 0.5 to 10 kg demonstrates internally consistent simulation-based predictions of temperature profiles, phase-fraction evolution, and mechanical-property trends within the assumed modelling framework. Optimized schedules achieved 15–25% reductions in cycle time while maintaining properties within T6 specifications. These findings underscore the potential of AI-assisted heat-treatment optimization to enhance energy efficiency, reduce material waste, and improve the consistency of mechanical performance in automotive casting operations. Full article

Background: Poor adherence and monotony in home-based pelvic floor muscle training (PFMT) often lead to suboptimal rehabilitation outcomes. Serious games using virtual reality (VR) may improve training motivation and precision. This study aimed to explore user demands for a VR pelvic floor rehabilitation training system with game-based features. Methods: A Kano model-based questionnaire was developed and distributed to patients receiving PFMT. The survey assessed 20 demand items spanning five dimensions: system operation, exercise guidance, personalization, device use, and interaction. Traditional Kano categorization and an optimized mixed-method classification were used to identify core demand attributes. Satisfaction and dissatisfaction indices were also calculated. Results: A total of 112 valid questionnaires were analyzed. Using the Kano model, 20 demand items were classified as attractive (n = 7), one-dimensional (n = 5), must-be (n = 6), or indifferent (n = 2). Personalization-related demands were mainly identified as attractive attributes, whereas exercise guidance-related demands were primarily classified as must-be or one-dimensional attributes. Satisfaction Index (SI) values ranged from 0.27 to 0.64, and absolute Dissatisfaction Index (DSI) values ranged from 0.34 to 0.71. Optimized Kano analysis identified nine mixed attributes. The questionnaire demonstrated excellent internal consistency (Cronbach’s α = 0.96). Conclusions: Participants demonstrated positive willingness to adopt a game-based VR system for PFMT, with diverse needs identified across functional and motivational dimensions. These findings suggest that integrating immersive, personalized, and gamified design features may hold promise for enhancing user engagement and anticipated training adherence, though direct evaluation of clinical effectiveness awaits future prototype-based studies. The identified demand priorities provide structured, evidence-informed guidance for the user-centered design of serious game–oriented VR pelvic floor rehabilitation systems. Full article

As an active defense paradigm, cyber deception technology effectively misleads attackers by constructing deceptive network environments, thereby increasing the cost of attack operations and introducing uncertainty into their decision-making, while providing defenders with critical response time. However, existing deception strategies are mostly based on predefined static rules derived from expert knowledge and lack the ability to adapt to dynamic attack scenarios autonomously and intelligently. This limitation results in poor adaptability and suboptimal performance of the strategy. To solve these issues, this paper proposes an Adaptive Cyber Deception Defense System (ACDDS). Different from off-the-shelf MDP/DQN frameworks in existing adaptive defense, the core innovation of ACDDS is a scenario-customized Trigger-based Adaptive Deception Strategy evolution method using Deep Q-Networks (TADS-DQN). We specifically formulate the dynamic deception strategy optimization as a cyber-deception-tailored Markov Decision Process (MDP). In this model, the state of the system is represented as a state matrix, and the attack behavior defines the environment for agent interaction. The TADS-DQN method employs a trigger-based mechanism: when a threat to real services is detected, a Deep Q-Network agent is activated. This agent takes the current system state as input and outputs the optimal reconfiguration action. The simulation results indicate that, compared to the baseline methods, TADS-DQN provides more stable defense performance, as evidenced by a smaller fluctuation range and a lower standard deviation of the attack success rate. At the same time, it achieves a reduction in the hit rate against real services that is competitive with the baseline methods. Full article

Recently, the volume of the generated data has increased significantly, leading to the need for computational techniques capable of handling such data efficiently. As a result, many quantum algorithms have been developed, and the domain of quantum machine learning (QML) has become more extensive. Traditional k-means algorithms for quantum computers usually make use of deep or shallow quantum circuits, resulting in sub-optimal clustering results. In our work, we introduce two types of quantum-inspired k-means algorithm: (1) the quantum subtraction operation and (2) the rotational-difference dissimilarity measure. Our second framework measures the dissimilarity using a quantum rotation-based dissimilarity circuit, which encodes the relative difference of the states. We employ a Hadamard-test-based circuit design, as well as an alternative technique that is not used in other quantum dissimilarities like the swap test. The introduced algorithms are evaluated on six different datasets—Iris, Wine, Breast Cancer Wisconsin, Blobs, Moons, and the noisier Iris dataset. Evaluation involves clustering validity metrics, as well as the classic classification performance metrics. The findings show that the rotational-based dissimilarity metric allows us to obtain clustering results comparable with the results obtained by the classical counterpart, thus showing the feasibility of the introduced distance calculation technique. Full article

Background/Objectives: Children and adolescents with type 1 diabetes mellitus (T1DM) may be particularly vulnerable to vitamin D deficiency; however, its association with glycemic control remains incompletely understood. This study aimed to determine the prevalence of vitamin D deficiency among children and adolescents with T1DM and to evaluate its relationship with glycemic control. Methods: This cross-sectional study enrolled individuals aged 1–18 years diagnosed with T1DM. Serum 25-hydroxyvitamin D concentrations were assessed, with deficiency defined as <20 ng/mL, insufficiency as 20–29 ng/mL, and sufficiency as ≥30 ng/mL. Glycemic control was defined as HbA1c < 9.0% (controlled) versus ≥9.0% (poor control). Multivariable logistic regression was performed to assess the independent association of 25-hydroxyvitamin D with glycemic control. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were conducted to evaluate the diagnostic and potential clinical utility of 25-hydroxyvitamin D levels. Results: A total of 266 participants were included; their median age was 13.0 years (IQR: 10.0–15.0), with a slight male predominance (56.8%). Overall, 64.3% of patients had suboptimal 25-hydroxyvitamin D status, including 30.1% with deficiency and 34.2% with insufficiency. Patients with poor glycemic control had significantly lower 25-hydroxyvitamin D levels compared to those with controlled diabetes (21.0 ng/mL [IQR: 17.0–26.0] vs. 35.0 ng/mL [IQR: 28.0–45.0], p < 0.001). A strong negative correlation was observed between HbA1c and 25-hydroxyvitamin D levels (Spearman’s ρ = −0.651, p < 0.001). Vitamin D insufficiency was significantly associated with poor glycemic control (aOR = 4.74, 95% CI: 2.48–9.32), while vitamin D deficiency was associated with substantially greater odds (aOR = 40.59, 95% CI: 15.55–129.23). The optimal cut-off for predicting poor control was 26.5 ng/mL, achieving a sensitivity of 75.5% and specificity of 82.2%. Decision curve analysis confirmed that the 25-hydroxyvitamin D model provided superior net benefit compared to treat-all and treat-none strategies across a threshold probability range of 32–85%. Conclusions: Vitamin D deficiency and insufficiency are highly prevalent among children and adolescents with T1DM. Lower 25-hydroxyvitamin D levels are independently associated with poorer glycemic control. Serum 25-hydroxyvitamin D demonstrates good diagnostic accuracy and potential clinical utility for risk stratification. Screening for 25-hydroxyvitamin D status and consideration of supplementation may serve as an adjunctive strategy to support metabolic management in this population. Full article

We study the cooperation for the joint unicast and multicast (JUMC) transmission system through a full-duplex (FD) decode-and-forward (DaF) mode relay and propose sub-optimal beamforming schemes for this cooperative JUMC FD relay channel. The beamforming vectors at the access point (AP) and at the full-duplex relay (FDR) are optimized with the metric based on the end-to-end information rate. The cooperation lets the user terminals (UT) outside of the direct coverage of the AP to be served by JUMC from the AP, and hence, the focus of this paper is on the sum rate resulting from the cooperation. As a reference scheme, a zero-forcing-based (ZF) beamforming algorithm is proposed, which suppresses the self-interference (SI) at the FDR perfectly. The SI at the FDR and the minimum operation for the signal-to-interference power ratios (SINR) at involved nodes of the DaF protocol are leveraged in designing and optimizing the second beamforming algorithm, which is the regularized beamforming scheme, since it allows an optimal amount of the SI at the FDR. This algorithm relies on the iterative applications of a quadratically constrained quadratic problem (QCQP) in its central part, while a few one-dimensional searches are running for the optimal SI levels for the individual rates. We consider three different scenarios depending on the existence of an FDR unicast message and the multiple UTs in the coverage of FDR for the application of the proposed algorithms, with some necessary modifications. Corroborating simulation results are presented to show the strengths and weaknesses of the proposed algorithms for the cooperative JUMC system. Full article