Search Results (846)

Continuous missing data is a prevalent challenge in long-term air quality monitoring, undermining the reliability of public health protection and sustainable urban development. In this paper, we propose ConFill, a novel contrastive learning-based framework for reconstructing continuous missing data in air quality time series. By leveraging temporal continuity as a supervisory signal, our method constructs positive sample pairs from adjacent subsequences and negative pairs from distant and shuffled segments. Through contrastive learning, the model learns robust representations that preserve intrinsic temporal dynamics, and enable accurate imputation of continuous missing segments. A novel data augmentation strategy is proposed, to integrate noise injection, subsequence masking, and time warping to enhance the diversity and representativeness of training samples. Extensive experiments are conducted on a large scale real-world dataset comprising multi-pollutant observations from 209 monitoring stations across China over a three-year period. Results show that ConFill outperforms baseline imputation methods under various missing scenarios, especially in reconstructing long consecutive gaps. Ablation studies confirm the effectiveness of both the contrastive learning module and the proposed augmentation technique. Full article

The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI)—accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads can be served by hybrid energy storage systems (HESSs) that combine multiple storage technologies operating across different timescales. This review presents an overview of energy storage technologies, their classifications, and recent performance data, with a focus on their applicability to AI-driven computing. Technical requirements of storage systems, such as fast response, long cycle life, low degradation under frequent micro-cycling, and high ramping capability—which are critical for sustainable and reliable data center operations—are discussed. Based on these requirements, this review identifies lithium titanate oxide (LTO) and lithium iron phosphate (LFP) batteries paired with supercapacitors, flywheels, or superconducting magnetic energy storage (SMES) as the most suitable HESS configurations for AI data centers. This review also proposes AI-specific evaluation criteria, defines key performance metrics, and provides semi-quantitative guidance on power–energy partitioning for HESSs in AI data centers. This review concludes by identifying key challenges, AI-specific research gaps, and future directions for integrating HESSs with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers. Full article

A 19 nt fragment that spans the SARS-CoV-2 furin cleavage site (FCS) is identical to the reverse complement of a proprietary human DNA repair gene sequence. Rather than interpreting this overlap as evidence of a laboratory event, this article uses it as a theoretical springboard to explore underappreciated biorisk concerns, specifically in the context of cancer research. Although they are RNA viruses, coronaviruses are capable of hijacking host DNA damage response (DDR) pathways, exploiting nuclear functions to enhance replication and evade innate immunity. Under selective pressures (antivirals, DDR antagonists, or large-scale siRNA libraries designed to silence critical host genes), escape mutants may arise with fitness advantages. Parallel observations involving in vivo RNA interference via chimeric viruses lend plausibility to some of the key aspects underlying unappreciated biorisks. The mechanistic insights that incorporate DNA repair mechanisms, CoVs in the nucleus, specifics of viruses in cancer research, anticancer drugs, automated gene silencing experiments, and gene sequence overlaps identify gaps in biorisk policies, even those unaccounted for by the potent “Sequences of Concern” paradigm. Key concerning attributes, including genome multifunctionality, such as NLS/FCS in SARS-CoV-2, antisense sequences, and their combination, are further described in more general terms. The article concludes with recommendations pairing modern technical safeguards with enduring ethical principles. Full article

Modern power transmission systems are required to meet increasingly stringent demands, including a wide range of transmission ratios, compact dimensions, high precision, energy efficiency, reliability, and thermal stability under dynamic operating conditions. Among the solutions that satisfy these requirements, cycloidal reducers are particularly prominent, with their application continuously expanding in industrial robotics, computer numerical control (CNC) machines, and military and transportation systems, as well as in the satellite industry. However, as with all mechanical power transmissions, friction in the contact zones of load-carrying elements in cycloidal reducers leads to power losses and an increase in operating temperature, which in turn results in a range of adverse effects. These undesirable phenomena strongly depend on lubrication conditions, namely on the type and properties of the applied lubricant. Although manufacturers’ catalogs provide general recommendations for lubricant selection, they do not address the fundamental tribological mechanisms in the most heavily loaded contact pairs. At the same time, the available scientific literature reveals a significant lack of systematic and experimentally validated studies examining the influence of lubricant type on the energetic and thermal performance of cycloidal reducers. To address this identified research gap, this study presents an analytical and experimental investigation of the effects of different lubricant types—primarily greases and mineral oils—on the thermal stability and efficiency of cycloidal reducers. The results demonstrate that grease lubrication provides lower total power losses and a more stable thermal operating regime compared to oil lubrication, while oil film thickness analyses indicate that the most unfavorable lubrication conditions occur in the contact between the eccentric bearing rollers and the outer raceway. These findings provide valuable guidelines for engineers involved in cycloidal reducer design and lubricant selection under specific operating conditions, as well as deeper insight into the lubricant behavior mechanisms within critical contact zones. Full article

Spinal Cord Injury (SCI) rehabilitation is undergoing a transformative shift with the emergence of new treatment strategies. Historically, treatment options were limited, and few offered meaningful recovery. Recent work in human models has shown that neuromodulation specifically with spinal cord epidural stimulation (SCES) paired with task-specific training (TsT) can partially restore motor function such as the ability to stand, step, and perform volitional movements. Despite these advances, the recovery has been shown to plateau even with the combination of therapies. The recovery process typically leads to partial rather than complete restoration of function. This limitation arises because current approaches primarily reactivate existing circuits rather than repair the disrupted pathways. Scar tissue and loss of descending and ascending connections remain major barriers to full recovery, restricting the transmission of neural signals. We argue that the next phase of research should be a synergistic strategy building upon the successes of neuromodulation and TsT while incorporating a regenerative therapy such as stem-cell-based interventions. Whereas neuromodulation and task-specific training increases excitability and reorganizes existing networks, stem cells have the potential to repair structural damage and re-establish communication across injured regions or facilitating the establishment of dormant pathways. The future of SCI recovery relies on multi-modal synergistic interventions that are likely to maximize long-term functional outcomes. In the current perspective, we summarized the basic findings on applications of SCES on restoration of sensory-motor functions. We then projected on current interventions on utilizing stem cell therapy intervention. We highlighted the outcomes of randomized clinical trials, and the major barriers for considering the synergistic approach between SCES and stem cell intervention. We are hopeful that this perspective may lead to roundtable scientific discussion to bridge the gap on how to conduct numerous clinical trials in the field. Full article

Background: Role clarity is a persistent challenge among Patient Care Technicians (PCTs), contributing to inconsistent task performance and safety risks. In Saudi Arabia, little is known about PCTs’ understanding of their responsibilities. This study evaluated the impact of a targeted educational program designed to improve PCTs’ role clarity, safety practices, and communication. Methods: A quasi-experimental pre-post study was conducted in September 2025 with 35 PCTs from the Hail Health Cluster. The one-day intervention included lectures, discussions, role-play, and case scenarios. Outcomes were measured using a validated instrument across four domains: role clarity; core clinical tasks and safety; communication and ethics; and objective knowledge. Pre-post changes were analyzed using paired t-tests (Cohen’s d), and subgroup differences in change scores were examined using one-way ANOVA (η²) in SPSS v29. Results: Baseline scores were lowest in objective knowledge (41.4%) and role clarity (62.8%). Post-training, total composite scores improved significantly (+10.88%, p < 0.001, d = 1.63), with the most significant gain in objective knowledge (+19.8%, p < 0.001, d = 0.99). Role clarity showed only a modest, non-significant increase (+3.98%, p = 0.088, d = 0.30). No demographic differences were found. Conclusions: Targeted training was effective in reducing knowledge gaps; however, improving role clarity may require organizational reinforcement beyond brief training. Full article

AIS, the global ship identification standard, is vulnerable to outages, coverage gaps, and deliberate deactivation, highlighting the need for independent ship identification methods. Optical imaging satellites offer a global, non-compliance-dependent solution. Paired with deep neural networks trained on satellite imagery of ships, it has become possible to determine the identity of specific vessels, based on their unique visual signatures. This enables re-identification, even when cooperative signals like AIS are unavailable or unreliable. Our paper builds on previous work with neural networks for ship identification, and presents an approach based on contrastive self-supervised learning. Self-supervised learning allows for existing, unlabeled, and freely available satellite imagery datasets with ships, to be leveraged for model training. Using these self-supervised models to initialize ship identification training results in almost 32% higher accuracy compared to baseline models. In one case equivalent to doubling the labeled training data. This lowers the threshold for optical ship identification from space by reducing dependence on large labeled datasets. This scalability is crucial for making space-based ship identification viable for global maritime situational awareness. Full article

Viral RNA structure plays a critical regulatory role in viral replication, serving as a dual-purpose mechanism for encoding genetic information and controlling biological processes. However, these structural elements also serve as pathogen-associated molecular patterns (PAMPs), which are recognized by pattern recognition receptors (PRRs) of the host innate immune system. This review discusses the complex and poorly understood relationship between viral RNA structure and recognition of RNA by PRRs, specifically focusing on Toll-like receptor 3 (TLR3) and Retinoic acid-inducible gene I (RIG-I). While current interaction models rely upon data generated from use of synthetic ligands such as poly(I:C) or perfectly base-paired double-stranded RNA stems, this review highlights significant gaps in our understanding of how PRRs recognize naturally occurring viral RNAs that fold into highly complex three-dimensional structures. Furthermore, we explore how viral evolution and nucleotide variations, such as those observed in influenza viruses, can drastically alter local and distal RNA structure, potentially impacting immune detection. We conclude that moving beyond synthetic models to understand natural RNA structural dynamics is essential for elucidating the mechanisms of viral immune evasion and pathogenesis. Full article

Background and Objectives: Chronic low back pain (CLBP) is a prevalent condition that impairs quality of life, functionality, and work productivity. While most acute episodes of back pain resolve, 4–25% become chronic due to factors such as high pain intensity, psychological distress, and maladaptive behaviors. Nonspecific CLBP is best understood through the biopsychosocial model, encompassing biological, psychological, and social influences, including kinesiophobia. Management relies on physical activity, pain education, and psychological interventions, with therapist knowledge and attitudes affecting outcomes. This study aimed to assess the prevalence of CLBP among healthcare workers, examine their knowledge of pain neurophysiology, evaluate kinesiophobia, and explore how personal experience with CLBP influences their beliefs, attitudes, and interactions with patients. Materials and Methods: A cross-sectional observational study was conducted from January to May 2025 among healthcare professionals. A total of 50 participants completed an online questionnaire, of which 42 were valid and included in the analysis. The questionnaire collected demographic and professional data, determined the presence of CLBP, and included three standardized instruments: the Revised Neurophysiology of Pain Questionnaire (rNPQ) to assess knowledge of pain mechanisms, the Health Care Providers’ Pain and Impairment Relationship Scale (HC-PAIRS) to evaluate beliefs about pain and disability, and the Tampa Scale of Kinesiophobia (TSK-11) to measure fear of movement. Data were analyzed using SPSS and Microsoft Excel. Results: Among the 42 participants, 11 demonstrated low, 28 moderate, and 3 high knowledge of pain neurophysiology (rNPQ), with a mean score of 5.66. On the HC-PAIRS, the majority (30 participants) scored above 60, indicating beliefs that pain leads to disability, while 12 scored below 60, reflecting a biopsychosocial perspective; gender did not significantly affect HC-PAIRS scores (p = 0.213). As for kinesiophobia (TSK-11), 24 participants had low, 17 moderate, and 1 clinically significant fear of movement. Correlation analysis revealed that younger participants had higher rNPQ scores (r = −0.358, p = 0.020) and lower TSK-11 scores (r = −0.389, p = 0.011). TSK-11 scores increased with age (r = 0.432, p = 0.004), while HC-PAIRS scores showed no significant correlations. Conclusions: Healthcare professionals, particularly physiotherapists, show gaps in knowledge of pain neurophysiology and a tendency toward biomedical beliefs regarding chronic low back pain. This cross-sectional study indicates that a greater understanding of pain mechanisms is associated with lower kinesiophobia, emphasizing the importance of education. Integrating the biopsychosocial model into undergraduate and continuing professional training, through interdisciplinary and practical modules, may improve knowledge, reduce maladaptive fear-avoidance behaviors, and enhance patient care. Future studies should include larger, more diverse samples and assess the long-term impact of educational interventions on clinical practice. Full article

This paper presents a kind of dual-channel coupled radial magnetic field resolver (DCCRMFR). The exciting winding and signal winding of this resolver adopt the structure of orthogonal phase. The number of turns and distribution of the four phase signal winding have been designed. The rotor has a double-wave magnetic conductive material structure. The variable reluctance mechanism between the stator and the rotor is derived by analytical method, and the feasibility of changing the coupling area for variable reluctance is obtained. The inductance of DCCRMFR was theoretically derived through the winding function method and combined with the finite element simulation method to obtain the inductance variation law and verify the correctness of the resolver design. Then simulation analysis was conducted on the output signal of DCCRMFR to extract the total harmonic distortion (THD) of the envelope of the electromotive force (EMF) output from the signal winding. Taking THD as the optimization objective, the optimized DCCRMFR simulation model is obtained by analyzing the air-gap length between the stator and the rotor and the thickness ratio of rotor. Finally, experimental measurements were conducted on a prototype model of a two pole pairs DCCRMFR, and the measurement results were compared and analyzed with simulation results to verify the correctness of the structural design and optimization of this DCCRMFR. Full article

Background: Dietary habits established during young adulthood have long-term implications for health, and food choices among university students are strongly shaped by contextual factors. Institutional eating environments represent a relevant setting for promoting healthier dietary behaviors, yet limited evidence integrates students’ engagement with these settings, their food consumption patterns across contexts, and the individual decision-making processes underlying food choice. Methods: This cross-sectional study analyzed survey data from 1519 students enrolled at a large Italian university. Measures included sociodemographic characteristics, self-identified dietary style, engagement with the university canteen, consumption frequency of selected food categories across institutional and non-institutional contexts, and category-specific food-choice motivations. Data were analyzed using descriptive analyses, Borda count rankings, paired comparisons, and multiple linear regression models. Results: Clear contextual differences in food consumption emerged across all food categories, with consistently lower consumption frequencies within the university canteen compared to outside settings (all p < 0.001). The largest contextual gap was observed for fruit consumption (d = 0.94), with similarly pronounced differences for plant-based foods. Taste was the most salient decision-making factor across food categories (overall M ≈ 4.4), while health-related motives were more prominent for healthier foods and gratification for desserts. Across contexts, self-identified dietary style was the most consistent predictor of food consumption, explaining substantial variance for animal-based protein consumption (R² = 0.293 in the canteen; R² = 0.353 outside), whereas age and gender showed smaller, food-specific associations. Conclusions: The findings highlight institutional eating settings as distinct food environments in which individual dietary preferences are only partially expressed. Effective strategies to promote healthier eating among university students should move beyond generic approaches and integrate interventions targeting service-related engagement, category-specific choice architecture, and students’ dietary identities. Full article

The simultaneous measurement of strain and temperature using Fiber Bragg Grating (FBG) sensors presents a significant challenge due to the intrinsic cross-sensitivity of the Bragg wavelength. While recent studies have increasingly employed “black-box” machine learning algorithms to address this ambiguity, such approaches often overlook the physical limitations of the sensor’s spectral response. This paper challenges the assumption that advanced algorithms alone can compensate for data that is physically ambiguous. We propose a “Sensor-Algorithm Co-Design” methodology, demonstrating that robust discrimination is achievable only when the sensor architecture exhibits a unique, orthogonal physical signature. Using a rigorous Transfer Matrix Method (TMM) and 4 × 4 polarization analysis, we evaluate three distinct architectures. Quantitative analysis reveals that a standard Quadratically Chirped FBG (QC-FBG) functions as an “ill-conditioned baseline” failing to distinguish measurands due to feature space collapse ($K_{cond}>4600$). Conversely, we validate two robust co-designs: (1) An Amplitude-Modulated Superstructure FBG (S-FBG) paired with an Artificial Neural Network (ANN), utilizing thermally induced duty-cycle variations to achieve high accuracy ($~3.4 °\mathrm{C}$ error) under noise; and (2) A Polarization-Diverse Inverse-Gaussian FBG (IG-FBG) paired with a 4 × 4 K-matrix, exploiting strain-induced birefringence ($K_{cond}\approx 64$). Furthermore, we address the data scarcity issue in AI-driven sensing by introducing a Physics-Informed Neural Network (PINN) strategy. By embedding TMM physics directly into the loss function, the PINN improves data efficiency by 2.2× compared to standard models, effectively bridging the gap between physical modeling and data-driven inference, addressing the critical data scarcity bottleneck identified in recent optical sensing roadmaps. Full article

Forest fire monitoring in remote sensing imagery has long relied on traditional perception models that primarily focus on detection or segmentation. However, such approaches fall short in understanding complex fire dynamics, including contextual reasoning, fire evolution description, and cross-modal interpretation. With the rise of multi-modal large language models (MLLMs), it becomes possible to move beyond low-level perception toward holistic scene understanding that jointly reasons about semantics, spatial distribution, and descriptive language. To address this gap, we introduce FireMM-IR, a multi-modal large language model tailored for pixel-level scene understanding in remote-sensing forest-fire imagery. FireMM-IR incorporates an infrared-enhanced classification module that fuses infrared and visual modalities, enabling the model to capture fire intensity and hidden ignition areas under dense smoke. Furthermore, we design a mask-generation module guided by language-conditioned segmentation tokens to produce accurate instance masks from natural-language queries. To effectively learn multi-scale fire features, a class-aware memory mechanism is introduced to maintain contextual consistency across diverse fire scenes. We also construct FireMM-Instruct, a unified corpus of 83,000 geometrically aligned RGB–IR pairs with instruction-aligned descriptions, bounding boxes, and pixel-level annotations. Extensive experiments show that FireMM-IR achieves superior performance on pixel-level segmentation and strong results on instruction-driven captioning and reasoning, while maintaining competitive performance on image-level benchmarks. These results indicate that infrared–optical fusion and instruction-aligned learning are key to physically grounded understanding of wildfire scenes. Full article

Background: Glypican-3 (GPC3) is frequently overexpressed in hepatocellular carcinoma (HCC) and serves as a circulating biomarker. Limited evidence exists regarding whether plasma or serum constitutes the optimal matrix for GPC3 measurement. This study aimed to investigate this gap. Methods: Between December 2024 and September 2025, 100 participants were prospectively enrolled, including 33 healthy controls, 29 individuals with chronic liver disease, and 38 patients with HCC. Paired serum and plasma samples were analyzed under fresh conditions and after storage for seven days at 4 °C and −70 °C. GPC3 concentrations were compared across groups. Subsequently, correlation and area under the receiver operating characteristic curve (AUROC) analyses were conducted. Results: In fresh samples of the controls, median plasma GPC3 levels were significantly higher than those in serum (82.36 pg/mL, IQR: 67.56–92.42 vs. 30.89 pg/mL, IQR: 20.36–41.12; p < 0.001). After seven days of storage, plasma GPC3 concentrations declined markedly at both 4 °C (41.73 pg/mL, IQR: 32.49–55.37; p < 0.001) and −70 °C (45.53 pg/mL, IQR: 25.30–55.65; p < 0.001), with no significant difference between the two storage conditions (p = 0.610). In contrast, serum GPC3 levels remained relatively stable across fresh, 4 °C (31.10 pg/mL, IQR: 16.84–38.60), and −70 °C (25.31 pg/mL, IQR: 14.36–40.74) conditions (p = 0.645). Both matrices under −70 °C storage effectively discriminated HCC from non-HCC cases, although serum demonstrated a significantly better diagnostic performance (AUROC: 0.836, 95% CI: 0.749–0.902 vs. 0.772, 95% CI: 0.677–0.850; p = 0.013). Conclusions: Although plasma offers operational convenience and higher baseline GPC3 levels, serum provides both greater stability and superior diagnostic accuracy under frozen conditions, thus supporting its use as the preferred specimen matrix in clinical and research applications. Full article

Tannery wastewater from textile-related industries poses treatment challenges due to its high load of recalcitrant pollutants. Various advanced hybrid treatments, such as electro-oxidation (EO), have been proposed but mainly focus on electrode material development. Several studies on EO using multiple electrode pairs with large electroactive surface areas exist, however, none have reported on mass transfer characterization. This study addresses these gaps by investigating the electro-degradation performance of active (mixed-metal oxide, MMO) and non-active (boron-doped diamond, BDD) anodes paired with carbonaceous (graphite) and non-carbonaceous (stainless steel, SS) cathodes under applied current densities of 2 to 6 mA/cm². A 2 L volume of simulated tannery wastewater containing recalcitrant tannic acid was treated using three electrode pairs with a total surface area of 500 cm². Results showed optimal condition was identified at 4 mA/cm² across all electrode combinations and better degradation using BDD anodes and SS cathodes, with total organic carbon (TOC) removed up to 500 mg/L (98% removal). Adopting the 3-electrode configuration, mass transfer coefficients ranged from 4.15 to 5.18 × 10⁻⁶ m/s. Energy consumption evaluation suggested MMO as a more cost-effective option, while BDD remained preferable for highly recalcitrant waste. Higher currents show diminishing returns due to mass transfer and parasitic reactions. Full article