Search Results (2,667)

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection results in dysregulated inflammatory and coagulation pathways that drive immunothrombosis and contribute to adverse clinical outcomes. While individual cytokines and endothelial biomarkers have been associated with disease severity and mortality, the prognostic relevance of combined inflammatory and endothelial signatures remains incompletely characterised. To identify inflammatory cytokines and markers of endothelial activation associated with mortality in patients with severe COVID-19 requiring supplemental oxygen. This retrospective observational study included 73 consecutive adults admitted to a dedicated supplemental oxygen unit with severe COVID-19. Plasma concentrations of IL-1α, IL-1β, IL-6, IL-8, IL-10, TNF-α, von Willebrand factor (VWF) antigen and propeptide, ADAMTS13 antigen and activity, and ADAMTS13 autoantibodies were measured on admission using ELISA-based assays. Associations with mortality were assessed using non-parametric analyses, age-adjusted logistic regression, multivariable logistic regression, and receiver operating characteristic (ROC) curve analysis. Increasing age was independently associated with mortality. After adjustment for age, higher IL-1α concentrations were associated with increased odds of death, whereas a higher IL-6/IL-10 ratio was independently protective. In multivariable models, including non-ratio variables, ADAMTS13 autoantibody levels remained independently associated with mortality. In ratio-based multivariable analysis, both the ADAMTS13 activity/autoantibody ratio and the IL-6/IL-10 ratio were independently protective, while age was no longer significant. IL-10 and ADAMTS13 autoantibodies demonstrated moderate discriminative performance for mortality prediction (AUC ~0.70). A combined biomarker model incorporating IL-1α, IL-8, IL-10, and ADAMTS13 autoantibodies yielded very high predicted mortality probabilities. Our findings highlight IL-1α and ADAMTS13 autoantibodies as independent predictors of mortality in severe COVID-19, reflecting the interplay between inflammatory and endothelial pathways. Biomarker ratios capturing immune and endothelial balance—particularly the ADAMTS13 activity/autoantibody ratio—may enhance risk stratification and support integrated prognostic models. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

Objectives: This study aimed to investigate the impact of scrotal pain on venous diameter and reflux duration in varicocele, and to assess the predictive value of ultrasonographic findings for varicocele grading. Methods: Fifty-two symptomatic patients with left-sided varicocele, presenting with infertility or scrotal pain, were prospectively evaluated. Grading was based on physical examination. Visual Analog Scale (VAS) scores, venous diameters, and reflux durations were measured using scrotal color Doppler ultrasonography (CDUS) both during active pain and after pain had markedly subsided or resolved. Results: After pain resolution, venous diameters significantly decreased in both resting and Valsalva states (p < 0.001). In grade-specific analysis, this reduction was significant only in grade II varicocele (rest: p = 0.004; Valsalva: p = 0.026). Reflux durations also significantly decreased after pain relief in all varicocele grades, both at rest and during Valsalva (p < 0.001 for all, except G3 Valsalva: p = 0.001). Ultrasonographic parameters during the pain-present state showed better discrimination for detecting grade I varicocele (AUC: 0.88), while the pain-free state provided better diagnostic accuracy for grade III varicocele (AUC: 0.69). Combining measurements from both conditions further improved predictive accuracy, especially for grade III varicocele (AUC: 0.77). Conclusions: Scrotal pain significantly influences scrotal CDUS findings in patients with varicocele, leading to measurable differences in venous diameter and reflux duration between pain-present and pain-free states. Therefore, consideration of symptom status when interpreting scrotal CDUS results may improve diagnostic accuracy and support more informed clinical decision-making. Full article

Background and Objectives: Early assessment interventions are recommended for older adults with sarcopenia. Gait speed in older adults considerably decreases activities of daily living (ADL). However, the association between ADL and gait speed in older adults with sarcopenia has not yet been fully elucidated. This study aimed to clarify the association between walking speed and ADL in older adults with sarcopenia. Materials and Methods: A total of 72 older adults with sarcopenia who required support or care under Japan’s long-term care insurance system were included. Correlation and multivariate analyses were performed to examine the association between walking speed and ADL performance. A receiver operating characteristic analysis was used to evaluate the discrimination power of gait speed for ADL independence. Results: Gait speed was significantly and positively correlated with the Barthel Index scores for the men and women. ADL were independently and significantly associated with walking speed in the multivariate analysis. The threshold for gait speed that distinguished ADL independence in older adults with sarcopenia was 0.76 m/s (area under the curve = 0.75, sensitivity 72.7%, specificity 74.0%). Conclusions: Decreased gait speed in older adults with sarcopenia was associated with decreased ADL. Gait speed had high discriminatory power in identifying ADL independence. This indicates that an assessment intervention for gait speed in older adults with sarcopenia may have high clinical utility. Full article

Tea is produced from the fresh leaves of the tea plant (Camellia sinensis), and the quality of tea is directly dictated by its raw material. Although factors such as tea cultivar, fertilization, and cultivation practices are known to affect fresh leaf quality, the specific influence of altitude remains poorly understood. In this present study, ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to investigate the non-volatile metabolites in fresh tea leaves grown at two different altitudes (350 m and 600 m). A total of 2323 metabolites were identified, with flavonoids and phenolic acids representing the dominant classes. Orthogonal partial least squares-discriminant analysis (OPLS-DA) further revealed 116 differential metabolites between groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that several key pathways were differentially activated, including those related to the biosynthesis of kaempferol, luteolin, and flavones, as well as nucleotides and jasmonic acid metabolism. In addition, marked differences were observed in the accumulation patterns of lipids, phenolic acids, and flavonoids between leaves grown at the two altitudes. These findings provide valuable insights into the role of altitude in shaping the metabolic composition and flavor formation of tea. Full article

Background/Objectives: Subsequent musculoskeletal injuries are frequent among physically active young adults, yet the roles of body composition, training weekly load (TWL), and physical activity intensity in subsequent injury occurrence remain unclear. This study examined the associations of body composition indices and training-related variables with subsequent injuries in university students and explored whether combining key markers from body composition and training exposure improves discrimination compared with single markers. Methods: The analysis included 418 students from two cohorts merged after confirming negligible between-cohort differences. Participants completed questionnaires on injury history and physical activity and underwent standardized anthropometric and body composition assessments. Intrinsic factors included fat mass index (FMI) and skeletal muscle mass index (SMI), while extrinsic factors comprised training weekly load (TWL), total physical activity (TPA), and vigorous activity percentage (VPA%). Subsequent injury (yes/no) served as the primary outcome. Injuries were assessed retrospectively over the preceding 12 months; subsequent injury was defined as ≥1 injury occurring after a previous (index) injury within this recall period. Analyses used univariate and multivariable logistic regression and exploratory Receiver Operating Characteristic (ROC) analyses for individual markers and combined models. Results: SMI was associated with subsequent injury (OR = 1.09, 95% CI: 1.03–1.15). TWL showed a weak, non-significant association (OR = 1.03, p = 0.307). Models combining SMI and TWL, including their interaction, did not meaningfully improve discrimination compared with SMI alone. ROC analyses indicated limited discriminatory ability across models (AUCs < 0.65), suggesting poor accuracy for identifying individuals with subsequent injury based on these markers. Conclusions: The examined body composition, training weekly load (TWL), and physical activity measures alone or combined showed limited discriminatory utility for subsequent injury status in this cross-sectional sample. These findings support the multifactorial nature of injury susceptibility and indicate that simple anthropometric or TWL-based measures are not suitable as standalone screening tools for subsequent injury in active university populations. Full article

This paper proposes a robust convolutional neural network (CNN) architecture for human activity recognition (HAR) using smartphone accelerometer data, evaluated on the WISDM dataset. We introduce two novel pooling mechanisms—Pooling A (Extrema Contrast Pooling (ECP)) and Pooling B (Center Minus Variation (CMV))—that enhance feature discrimination and noise robustness. ECP emphasizes sharp signal transitions through a nonlinear penalty based on the squared range between extrema, while CMV Pooling penalizes local variability by subtracting the standard deviation, improving resilience to noise. Input data are normalized to the [0, 1] range to ensure bounded and interpretable pooled outputs. The proposed framework is evaluated in two separate configurations: (1) a 1D CNN applied to raw tri-axial sensor streams with the proposed pooling layers, and (2) a histogram-based image encoding pipeline that transforms segment-level sensor redundancy into RGB representations for a 2D CNN with fully connected layers. Ablation studies show that histogram encoding provides the largest improvement, while the combination of ECP and CMV further enhances classification performance. Across six activity classes, the 2D CNN system achieves up to 96.84% weighted classification accuracy, outperforming baseline models and traditional average pooling. Under Gaussian, salt-and-pepper, and mixed noise conditions, the proposed pooling layers consistently reduce performance degradation, demonstrating improved stability in real-world sensing environments. These results highlight the benefits of redundancy-aware pooling and histogram-based representations for accurate and robust mobile HAR systems. Full article

Electroencephalography (EEG) has attracted significant attention as an effective modality for interaction between the physical and virtual worlds, with EEG-based person identification serving as a key gateway to such applications. Despite substantial progress in EEG-based person identification, several challenges remain: (1) how to design an end-to-end EEG-based identification pipeline; (2) how to perform automatic electrode selection for each user to reduce redundancy and improve discriminative capacity; (3) how to enhance the backbone network’s feature extraction capability by suppressing irrelevant information and better leveraging informative patterns; and (4) how to leverage higher-level information in EEG signals to achieve intent recognition (i.e., EEG-based task/activity recognition under controlled paradigms) on top of person identification. To address these issues, this article proposes, for the first time, a unified deep learning framework that integrates automatic electrode selection, person identification, and intent recognition. We introduce a novel backbone network, AES-MBE, which integrates automatic electrode selection (AES) and intent recognition. The network combines a channel-attention mechanism with a multi-scale bidirectional encoder (MBE), enabling adaptive capture of fine-grained local features while modeling global temporal dependencies in both forward and backward directions. We validate our approach using the PhysioNet EEG Motor Movement/Imagery Dataset (EEGMMIDB), which contains EEG recordings from 109 subjects performing 4 tasks. Compared with state-of-the-art methods, our framework achieves superior performance. Specifically, our method attains a person identification accuracy of 98.82% using only 4 electrodes and an average intent recognition accuracy of 91.58%. In addition, our approach demonstrates strong stability and robustness as the number of users varies, offering insights for future research and practical applications. Full article

Landslides are a common type of geological hazard, characterized by sudden onset, high destructiveness, and frequent occurrence, and are widely distributed in mountainous areas with complex terrain. In recent years, due to extreme weather and intensified human activities, both the frequency and intensity of landslide disasters in China have increased significantly, posing serious threats to human life, property, and socio-economic development. Although various methods for landslide susceptibility assessment have been proposed, the accuracy of existing models still needs improvement. In this context, this study takes the landslide-prone Dongchuan District of Kunming City, Yunnan Province, as a case study and proposes a coupled model that integrates an autoencoder and a feedforward neural network (AuFNN). The model uses the autoencoder to extract low-dimensional and highly discriminative feature representations, which are then used as input to the feedforward neural network to perform landslide susceptibility assessment. To evaluate the effectiveness of the proposed model, it is compared with four commonly used models, Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Feedforward Neural Network (FNN), based on performance metrics such as the ROC curve, recall, and F1 score. The results indicate that the AuFNN model provides an alternative representation learning framework and achieves performance comparable to that of established machine learning models in landslide susceptibility assessment, as reflected by similar AUC, accuracy, and F1 score values. Full article

Non-small cell lung cancer (NSCLC) represents over 80% of all lung cancer cases and still has a huge mortality worldwide. Targeting epidermal growth-factor receptor (EGFR) alterations with overall response rates of more than 80% has provided a paradigm shift in the treatment of NSCLC; however, NSCLC patients harbouring uncommon mutations and exon 20 insertions still have a dismal prognosis underscoring the urgent need to develop novel EGFR tyrosine kinase inhibitors (TKIs) with proven activity against these EGFR alterations. Zipalertinib is a newly developed oral, irreversible compound which is characterized by its unique pyrrolopyrimidine structure which discriminates this novel TKI from others. It is active against the classical mutations (i.e., del19, L858R) and some of the uncommon mutations (e.g., T790M, G719X, S768I, L861Q, but not C797S) and is predominantly active in NSCLC cells harbouring exon20ins. Zipalertinib is currently being extensively evaluated in several clinical NSCLC trials (REZILIENT 1–4) and has shown significant clinical activity in NSCLC patients with uncommon mutations, exon20ins, and in brain metastases (REZILIENT 3 trial). Moreover, zipalertinib in combination with platinum-based chemotherapy followed by zipalertinib monotherapy as first-line therapy is currently being evaluated in the pivotal, ongoing REZILIENT 3 randomized trial. In addition, the efficacy of zipalertinib is also studied in the adjuvant setting (REZILIENT 4 trial, stage IB-IIIA NSCLCs with exon20ins and uncommon mutations). The role and the integration of therapies targeting exon20ins or uncommon mutations into the first- and second-line treatment armamentarium for NSCLC patients is not yet fully established, and the therapeutic impact of monotherapies (e.g., sunvozertinib, firmonertinib) versus combinations with standard platinum-based chemotherapy (e.g., zipalertinib, amivantamab) currently still lacks robust evidence to further change the therapeutic landscape for these patients. Therefore, results from the ongoing trials are eagerly awaited and are expected to shed some light on these open questions. Full article

Green cities can contribute to greater mental and physical well-being. In addition, many people enjoy being active outdoors (green exercise). As yet, no questionnaire jointly emphasises mental health, willingness to pay for sustainability, and the incentive of a green environment for physical exercise in cities. Therefore, I developed the new Green Cities Questionnaire (GCQ), comprising 18 items, and used it to survey the perceptions of 249 participants (130 female, 119 male, 0 diverse; aged 18 to 84). Then, I applied exploratory factor analyses where the three factors of mental health (MH; nine items), willingness-to-pay (WTP; five items), and green exercise (GE; four items) were extracted. Additional statistical analyses revealed that women reported higher values on the MH and GE factors than men. In particular, women and men reported a beneficial effect of green cities on mental health (higher ratings on MH than on GE and on WTP). However, there was no gender effect on WTP. From an urban-planning perspective, the two strongest implications are as follows: First, the GCQ facilitates measurement of the three key latent factors: MH, WTP, and GE. However, future validation studies with larger sample sizes and applications of the GCQ alongside additional similar and different recognised scales are necessary to establish convergent and discriminant validity. Second, mental health is reported to be much more important than WTP and GE. Hence, green initiatives, educational programs, and green city workshops should not only focus on expanding urban green spaces but also on providing appropriate relaxation areas to promote and foster psychological well-being and quality of life in green cities. Full article

Background and Objectives: Pneumonia is a leading cause of intensive care unit (ICU) admission and is associated with high mortality, particularly among patients with multiple comorbidities. Accurate early risk stratification is essential for guiding clinical decision-making in critically ill patients. However, the prognostic benefit of combining clinical scoring systems with nutritional and endothelial stress indices in ICU patients with pneumonia remains unclear. Materials and Methods: This retrospective, single-center cohort study included adult patients admitted to the ICU with a diagnosis of pneumonia between 1 January 2023 and 1 July 2025. Demographic characteristics, comorbidities, clinical variables, laboratory parameters, and prognostic scores were obtained from electronic medical records. The National Early Warning Score (NEWS), Prognostic Nutritional Index (PNI), and Endothelial Activation and Stress Index (EASIX) were calculated at ICU admission. The primary outcome was in-hospital mortality. Univariate and multivariate logistic regression analyses were performed to examine variables associated with in-hospital mortality. The discriminative performance of individual and combined prognostic models was evaluated using receiver operating characteristic (ROC) curve analysis. Results: A total of 221 patients were included; 79 (35.7%) survived and 142 (64.3%) died during hospitalization. Non-survivors had significantly higher NEWS and EASIX values and lower PNI values compared with survivors (all p < 0.05). In multivariate analysis, endotracheal intubation (OR: 12.46; p < 0.001), inotropic use (OR: 5.14; p = 0.001), and serum lactate levels (OR: 1.75; p = 0.003) were identified as being independently associated with in-hospital mortality. Models combining NEWS with PNI or EASIX demonstrated improved discriminatory performance. Conclusions: In critically ill patients with pneumonia, integrating NEWS with nutritional and endothelial stress indices provides numerically improved discrimination compared with NEWS alone, although the incremental gain did not reach statistical significance. Full article

Lung cancer (LC) remains the leading cause of cancer-related death worldwide, largely due to late-stage diagnosis and the limited performance of current screening strategies. In this preliminary study, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) was used to comprehensively characterize the urinary volatilome of LC patients and healthy controls (HCs), with the dual aim of defining an LC-associated volatilomic signature and identifying volatile organic metabolites (VOMs) with discriminatory potential. A total of 56 VOMs spanning multiple chemical classes were identified, revealing a distinct metabolic footprint between groups. LC patients exhibited markedly increased levels of terpenoids and aldehydes, consistent with heightened oxidative stress, including lipid peroxidation, and perturbed metabolic pathways, whereas HCs showed a predominance of sulphur-containing compounds and volatile phenols, likely reflecting active sulphur amino acid metabolism and/or microbial-derived processes. Multivariate modelling using partial least squares-discriminant analysis (PLS-DA, R² = 0.961; Q² = 0.941; p < 0.001), supported by hierarchical clustering, demonstrated robust and clearly separated group stratification. Among the detected VOMs, octanal, dehydro-p-cymene, 2,6-dimethyl-7-octen-2-ol and 3,7-dimethyl-3-octanol displayed the highest discriminative power, emerging as promising candidate urinary biomarkers of LC. These findings provide proof-of-concept that HS-SPME/GC-MS-based urinary volatilomic profiling can capture disease-specific molecular signatures and may serve as a non-invasive approach to support the early detection of LC, warranting validation in independent cohorts and integration within future multi-omics diagnostic frameworks. Full article

Medium-milled rice is increasingly valued for its health benefits and distinctive aroma, which differs from that of white rice because differences in milling degree modify the content of lipids and other aroma precursors. However, its aroma profile remains underexplored. This study aimed to systematically analyze aroma differences among four Simiao rice cultivars after medium milling (8% degree of milling) and to elucidate the chemical basis underlying consumer preference. Odor sensory evaluation identified Xiangzhuxiang as the cultivar with the highest aroma acceptance. Subsequently, gas chromatography–olfactometry–mass spectrometry and odor activity value analysis characterized the volatile profile, identifying 45 volatile compounds across the four cultivars, including 17 key odor-active components. Multivariate statistical analysis pinpointed the discriminating key odor-active compounds responsible for the superior aroma quality of Xiangzhuxiang. The results showed that (E,E)-2,4-decadienal and indole (VIP > 1.0, FDR-adjusted q < 0.05, FC > 1.2, OAV > 1.0, confirmed by GC-O) significantly increased the aroma scores of Xiangzhuxiang; imparted nutty, fatty, and sweet notes; and thus played a decisive role in shaping its characteristic aroma. Moreover, the moderate levels of hexanal and octanal in Xiangzhuxiang facilitated its characteristic aroma expression. These findings provide a basis for developing premium fragrant Simiao rice cultivars optimized for medium milling. Full article