Search Results (912)

Limited by the material response characteristics and structural design, the development of dynamically tunable terahertz absorbers with multi-functional properties remains a major challenge. In this study, a dual-tunable terahertz absorber based on the synergistic integration of strontium titanate (STO) and Dirac semimetal (BDS) is proposed. By utilizing the temperature-sensitive dielectric constant of STO and the electrically tunable conductivity of BDS, the device can realize on-demand switching between a broadband absorption mode (absorptivity >90% in the 1.347~2.1271 THz band) and a dual-narrowband absorption mode under external field excitation. Notably, the centrosymmetric cross-patterned structure on the top layer ensures the polarization insensitivity of the device, and this single structure can also serve as a high-sensitivity temperature sensor. Simulation results verify that the device exhibits stable performance under different incident angles and environmental variations. This study constructs a compact multi-functional device platform integrating dynamic absorption regulation and in situ sensing, which provides a new technical route for the development of intelligent terahertz systems in the fields of terahertz imaging, communication, detection and other related areas. Full article

In recent years, dietary changes towards reducing animal-based proteins was recognized as a nitrogen pollution-mitigating strategy. This is because producing animal protein generates higher nitrogen emissions compared to its plant-based alternatives. In Japan, there is a switch towards an animal-based diet, potentially leading to degraded water quality. While national-scale studies are common, watershed-level scale dietary changes are not researched, even though nitrogen pollution is often localized. This study aims to evaluate whether dietary and feed self-sufficiency changes can reduce nitrogen load and improve water quality in the Kasumigaura watershed. Firstly, nitrogen load was quantified and spatially distributed. Then, the estimated nitrogen concentration was compared with observed data. Finally, the impact of dietary and feed self-sufficiency changes on nitrogen load and water quality was assessed. Results estimated that nitrogen loading for year 2020 was 4403 tons/N/year, correlating with previous research. Results further showed that switch from livestock to legume protein would significantly improve water quality, up to 0.27 mg N/L. On the other hand, increasing feed self-sufficiency would negatively affect the water quality, up to 0.32 mg N/L. The results emphasize the importance of dietary patterns in mitigating nitrogen pollution. This method can be generalized on other watersheds. Full article

Exploring the lunar complex and extreme terrain presents formidable challenges for conventional lunar rovers. To address these limitations, this study proposes a novel hexapod jumping hybrid robot that incorporates a “figure-of-eight” (butterfly-shaped) six-branched wheel-legged mechanism and a jumping system that stores elastic energy via deformation of its elastic body. Inspired by the multimodal locomotion of grasshoppers, the robot dynamically switches between two operational modes: high-efficiency wheeled locomotion on relatively flat surfaces and agile jumping to traverse steep slopes and surmount large obstacles. A bio-inspired gait, inspired by the crawling patterns of a hexapod insect, is implemented using a Central Pattern Generator (CPG)-based controller to produce coordinated, rhythmic limb movements. Dynamic simulations of the jumping mechanism were conducted to optimize the critical parameters of the elastic structure and its associated control strategy. Experiments on a physical prototype were conducted to validate the robot’s wheeled mobility and jumping performance. The results demonstrate that the robot exhibits excellent adaptability to rugged terrains and obstacle-dense environments. The integration of multimodal locomotion and adaptive gait control significantly enhances the robot’s operational robustness and survivability in the harsh lunar environment, opening new possibilities for future lunar exploration missions. Full article

Background: Inhaler device changes at hospital discharge should address patient capacity yet often reflect routine. We evaluated the appropriateness of these decisions and their impact on clinical outcomes. Methods: In this prospective observational study (N = 480), we assessed patient technical capacity using a composite of critical errors, inspiratory flow, adherence, and knowledge. We stratified patients into ‘Need-Positive’ and ‘Need-Negative’ cohorts to quantify patterns of clinical inertia and over-adjustment. Multivariable models identified predictors of decision-making and associations with 30-day outcomes. Results: Device changes were primarily determined by the pre-admission device class (spacers: aOR 0.52; 95% CI 0.28–0.96; p = 0.037) and by the patient’s treatment pathway rather than by clinical need. This disconnect generated two types of errors: 38.3% of Need-Positive patients (n = 214) experienced clinical inertia (no corrective action), while 36.8% of Need-Negative patients (n = 266) underwent over-adjustment (unnecessary switching). Inertia perpetuated errors in patients with need, whereas over-adjustment was associated with the emergence of new errors in patients without need. Successful mismatch resolution was associated with a significantly lower 30-day readmission rate (12.1% vs. 32.5%; OR 0.48; 95% CI 0.26–0.88; p = 0.017). Conclusions: Discharge prescribing is driven more by habit than by objective assessment, leading to widespread missed opportunities for correction. Implementing evidence-based protocols to identify and resolve patient–device mismatches may represent a high-impact strategy to reduce readmissions and associated healthcare use. Full article

In white-collar workplaces, individuals with autism spectrum disorder (ASD) traits may experience psychological strain and reduced productivity. This study examined structural relationships among ASD traits, social camouflaging, psychological distress, and work functioning impairment, with a focus on gender differences using a secondary analysis of data from an online survey of 543 Japanese white-collar workers (284 men, 259 women). Validated instruments were used to assess ASD traits, camouflaging, psychological distress, and work functioning impairment. Multi-group structural equation modeling by gender was applied using a NIOSH-inspired model. Men scored higher on the Imagination subscale of ASD traits, whereas women scored higher on Attention Switching and Assimilation. ASD traits were indirectly associated with work impairment through psychological distress, while the direct path between ASD traits and work impairment became negative when distress was controlled, indicating a statistical suppression pattern that was more pronounced among women. Assimilation was significantly associated with psychological distress in women but not in men, although the gender difference was at the trend level. The findings indicate a cross-sectional, context-dependent association between ASD traits and work functioning and highlight the importance of considering both gender and workplace context in non-clinical working populations. Full article

Genetics strongly impacts the course of metabolic dysfunction-associated steatotic liver disease (MASLD), with the I148M Patatin like phospholipase domain containing 3 (PNPLA3) variant representing the main modifier. Fat accumulation in the hepatic lobule, strongly enhanced by this SNP, may be influenced by the liver’s zonation. Therefore, we applied spatial transcriptomics to investigate the metabolic processes across portal (PZ)-central (CZ) zones in I148M PNPLA3 carriers. Visium CytAssist technology was applied to liver biopsies from MASLD patients sharing similar disease severity, who were wild-type (WT) or homozygous for the I148M variant (Discovery cohort, n = 4). The distribution of steatosis, inflammation, and fibrosis was assessed in the liver biopsies of MASLD patients, stratified according to the I148M variant (validation cohort, n = 100). At the Visium-LOUPE browser, we spatially mapped PZ and CZ hepatocytes (HEPs), revealing higher lipid turnover, glucose signaling, and lower mitochondrial activity in I148M-PZ-HEPs compared to 148M-CZ-HEPs. Thus, the I148M variant could unbalance the physiological hepatic zonation boosting steatosis development in PZ, consequently inducing mitochondrial dysfunction. The unsupervised analysis confirmed the altered metabolic pattern among CZ and PZ in patients carrying the variant. Interestingly, PNPLA3 expression was higher in I148M-PZ, which also showed an enrichment of non-parenchymal cells, thus possibly explaining the more severe injury in this area. Finally, in the validation cohort, we observed a pronounced PZ distribution of steatosis, inflammation, and fibrosis in I148M PNPLA3 subjects compared to WT, confirming the spatial data. The I148M variant contributes to the metabolic switching across different hepatic zones and represents a new clinical perspective by defining a specific histological pattern of MASLD. Full article

Integrating Software-Defined Networking (SDN) to enhance mobility management in Vehicular Ad Hoc Networks (VANETs) comes with an additional critical risk. Because centralized controllers are single points of failure, they create the risk that the network will be subject to denial-of-service (DoS) attacks during handovers. Most Intrusion Detection and Prevention systems (IDPSs) do not adequately address these risks because they are topology-blind and have excessive processing layers. This article presents a novel Location-Aware SDN-IDPS Framework that employs a hierarchical defense approach to protect vehicular networks against volumetric attacks. This two-plane system operates with the first tier, which uses dynamic host-location mappings to drop spoofed traffic at the switch level (data plane). In contrast, the second tier analyzes confirmed traffic through a Suricata-based engine to identify and respond to complex flood attack patterns. The experimental results from the Mininet-WiFi testbed show that the system provides a significant improvement over the unprotected state, with controller CPU utilization reduced by up to 18 times (from 9.0% to below 0.5%). In addition, the system provides a 2.3 s guaranteed recovery time, service continuity, successful microsecond-level mitigation time, and a packet delivery ratio (PDR) of 99.73% for legitimate safety messages. In control-plane stress testing, the proposed location-aware logic improved throughput stability by approximately 76.26% compared to the baseline. These findings confirm that offloading anti-spoofing logic to the network edge significantly enhances resilience without compromising performance in safety-critical vehicular environments. Full article

This paper proposes a frequency-reconfigurable and active beam-switching antenna based on an X-shaped slot array integrated with a diode-based switching network. The proposed antenna features four slots arranged at 90° intervals around the feed point. Each slot is integrated with two PIN diodes and one varactor diode. By selectively activating a specific slot through the PIN diodes, the radiation pattern can be switched in four directions at 90° intervals. Dual-band operation is achieved using varactor diodes, and by controlling their equivalent capacitance, the antenna covers two operating bands: a low-frequency band with a 29.51% bandwidth (2.6–3.5 GHz) and a high-frequency band with a 24.52% bandwidth (3.65–4.67 GHz). These frequency ranges include the 5G sub-6 GHz bands, specifically n77 and n78. Experimental results confirm stable beam-switching performance across the entire operating frequency range. Full article

Railway passenger flow forecasting plays a critical role in operational efficiency and resource allocation for transportation systems. However, existing deep learning approaches suffer significant performance degradation when facing rare but high-impact events, primarily due to sample scarcity and their inability to distinguish between routine patterns and disruption regimes. To address these challenges, this study introduces EA-ARIMA-Informer, an adaptive forecasting framework that integrates entropy-augmented ARIMA with Informer through an entropy-guided regime-switching mechanism. The passenger flow series is characterized through a multi-dimensional entropy space comprising four complementary measures: Sample Entropy quantifies local regularity and predictability, Permutation Entropy captures the complexity of ordinal dynamics, Transfer Entropy measures causal information flow from external events (holidays, weather) to passenger demand, and the Conditional Entropy Growth Factor (CEGF)—a novel metric introduced herein—detects regime transitions by tracking the rate of uncertainty change between consecutive time windows. These entropy indicators serve dual roles as feature inputs for representation learning and as state identifiers for segmenting the time series into stable and fluctuating regimes with distinct predictability properties. An adaptive dual-path architecture is then designed accordingly: EA-ARIMA handles low-entropy stable regimes where linear seasonality dominates, while EA-Informer processes high-entropy fluctuating regimes requiring nonlinear residual modeling, with CEGF-guided gating dynamically controlling component weights. Unlike conventional black-box gating mechanisms, this entropy-based switching provides physically interpretable signals that explain when and why different model components dominate the forecast. The framework is validated on a large-scale dataset covering nearly 300 Chinese cities over three years (2017–2019), encompassing normal operations, holiday peaks, and extreme weather disruptions. Experimental results demonstrate that EA-ARIMA-Informer achieves a MAPE of 4.39% for large-scale cities and 7.82% for data-scarce small cities (Tier-3), substantially outperforming standalone ARIMA, XGBoost, and Informer, which yield 15.95%, 13.75%, and 12.87%, respectively, for Tier-3 cities. Ablation studies confirm that both entropy-based feature augmentation and CEGF-guided regime switching contribute significantly to these performance gains, establishing a new paradigm for interpretable and adaptive forecasting in complex transportation systems. Full article

Objectives: Acute obstructive pyelonephritis (AOP) is a urological emergency that combines bacterial infection with upper urinary tract obstruction. This retrospective study focuses on the microbial etiology and causes of obstruction, clinical manifestations, antibacterial therapy, drainage type, and outcomes in patients diagnosed with AOP at a tertiary urology center between 1 January 2020 and 30 December 2024. Methods: One hundred patients with a mean age of 61.30 years were included in this retrospective study, which examines demographic data, comorbidities, clinical features, pathogens involved, antimicrobial regimens, and hospital outcomes. Results: Urolithiasis was the most frequent cause of obstruction (62%), followed by ureteral stenosis (14%) and tumors (11%). AOPs were mainly produced by Escherichia coli (58%), followed by Klebsiella spp. (21%); 18% of all identified bacteria were ESBL-producing Gram-negative bacilli, and 29% were MDR bacteria. The most used IV antibiotics were fluoroquinolones (52%), followed by cephalosporins (19%) and carbapenems (18%). Carbapenems were administered to all patients with AOP caused by ESBL-producing pathogens and to 62% of those with MDR bacteria. The duration of antibiotic therapy was individualized based on clinical response. Switch to oral administration was made after 4.3 ± 1.5 days, and the antibiotic treatment lasted 10.8 ± 3.2 days. Conclusions: The results of the present study support integrating evidence-based guidelines with regional patterns of bacterial susceptibility to optimize therapeutic approaches and reduce severe outcomes in patients with AOP, most of whom have multiple comorbidities. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by irreversible cognitive decline and synaptic dysfunction and represents the most prevalent etiology of dementia, accounting for an estimated 60–70% of all clinically diagnosed cases worldwide. The growing focus on microglia–neuron interactions in AD research highlights their diverse, region-specific responses, which are driven by the functional and pathological heterogeneity across different brain regions. Therefore, investigating the interactions between microglia and neurons is of crucial importance. To explore the regional heterogeneity of microglia–neuron crosstalk in AD, we integrated human single-nucleus RNA sequencing data from the prefrontal cortex (PFC), hippocampus (HPC), and occipital lobe (OL) provided by the ssREAD database. Our study delineated four microglial subtypes and uncovered a pseudotime trajectory activation trajectory leading to the disease-associated microglia (DAM) phenotype. The transition along this trajectory is driven and stabilized by a key molecular switch: the coordinated downregulation of inhibitory factors (e.g., LINGO1) and upregulation of immune-effector and antigen-presentation programs, which collectively establish the pro-inflammatory DAM state. Furthermore, we observed that each brain region displayed unique microglia–neuron communication patterns in response to AD pathology. The PFC and OL engage a THY1-ITGAX/ITGB2 signaling axis; the HPC predominantly utilizes the PTPRM pathway. Notably, THY1 dysregulation strongly correlates with pathology in the PFC, HPC, and OL, suggesting that microglia–neuron crosstalk in AD possesses both heterogeneity and commonality. The main contribution of this study is the systematic characterization of region-specific microglia-neuron interactions and the identification of THY1 as a potential mediator that may be targeted therapeutically to modulate microglial function in affected brain regions. Full article

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

Lung cancer is considered to be a significant cause of death in the world, and the timely identification of nodules in the lungs in CT scans is very important to enhance the prognosis of patients. Although the state of the art of nodule delineation using deep learning-based segmentation models was achieved, major problems, including high feature diversity, low spatial discrimination, and overfitting of the models, require stronger feature-processing approaches. This research explores an enhanced symmetric encoder–decoder segmentation network known as the Improved Group–Shuffle Module (IGSM) to overcome these shortcomings. The most important feature of the proposed method is the IGSM, which hierarchically divides feature maps into a few groups, then transforms them independently, and then randomly switches channels between groups to increase inter-group interaction of features and diversity. This IGSM method is inspired by human brain functions, which are processed in specialized cortex areas, which are mimicked in this work through small-group feature processing. Channel shuffling is designed based on inter-modular communication in the human brain through coherent information sharing among the small groups of cortices. Through this mechanism, the model is much better at capturing discriminative spatial and contextual patterns, especially on complex and subtle nodule structures. The IGSM configurations have been optimized, specifically, the placement of the modules, grouping size, and shuffle permutation strategies. The proposed model’s performance is then compared with the benchmarked models, like U-Net and DeepLab, with various performance indicators such as mean Intersection over Union (mIoU), Dice Score, Accuracy, Sensitivity, and Specificity. The simulation results proved the superiority of the IGSM-enhanced model with the mIoU of 0.7735, the Dice Score of 0.9665, and the Accuracy of 0.9873. The addition of the group and shuffle module not only enhances the discrimination between the nodules and their background, but it also improves the ability to generalize over a variety of nodules’ morphology, thus producing a reliable tool for automated detection of lung cancer. Full article

Sensory mapping in architecture lacks a guiding theoretical model, leaving practitioners without a clear way to relate spatial design to embodied experience. This study introduces a structured methodology that links phenomenological observation with affordance theory and sensory semiotics, framing sensory data within architectural contexts. Fieldwork in fourteen courtyard houses of Damascus had residents trace their movements on simplified floor plans, switching colors as sight, sound, touch, smell and taste became dominant. The analysis reveals that visitors pass through a narrow entry corridor, enter the courtyard, and converge at the central fountain, which emerges as a focal point for multiple senses. Residents consistently trace tactile interactions along the fountain’s stone rim and at raised benches in the liwan (open space). Gustatory (taste-related, food-linked) markers appear along the route from kitchen thresholds toward the fountain, suggesting how food preparation and communal gathering overlap. Using 28 sensory maps and a three-level analytical process, comparison, synthesis, and spatial interpretation, the study produced a unified sensory map of the Damascene courtyard house. This pattern highlights how sequential spatial arrangements shape sensory engagement and suggests conservation strategies that preserve these experiential pathways. Architects and conservators can reinforce welcome gestures at thresholds and design water features and planting schemes that invite lingering. The proposed methodology fills the theoretical gap and offers clear guidelines for crafting spaces that respond to human perception. Full article

Intelligent Tutoring Systems are increasingly used in higher education to support personalized learning and academic monitoring in large-scale digital environments. However, existing systems are predominantly based on static architecture and rigid rule-based mechanisms, which limit scalability and hinder effective adaptation to heterogeneous learners, evolving learning behaviors, and real-world educational contexts. This paper presents a self-adaptive multi-agent architecture based on Reinforcement Learning for autonomous decision-making in intelligent systems deployed in real environments. The proposal integrates an RL Meta-Agent that dynamically optimizes the selection of specialized agents through an intelligent switching mechanism, considering the user’s state, behavior, and interaction patterns. The architecture was implemented in Moodle using flows orchestrated in n8n, LLMs, databases, APIs developed in Django, and real academic data. For the empirical evaluation, a real and a simulated case study were designed. A questionnaire was administered to university students, considering dimensions of usability, satisfaction and usefulness, and accessibility and interaction, to understand the perception of the system and improvements. The quantitative data were analyzed using descriptive statistics and nonparametric tests (Mann–Whitney U and Kruskal–Wallis), while the qualitative data were examined using thematic categorization. A simulated case study was conducted to analyze the behavior of the system. The results show that the RL Meta-Agent significantly improves system efficiency, response relevance, and adaptive agent selection, demonstrating that self-adaptive RL-based MAS architectures are a viable solution for intelligent systems applied in real-world contexts, providing empirical evidence of their performance and adaptability in complex scenarios such as higher education. Full article