Search Results (10,149)

Associative learning is a fundamental neural mechanism in human memory and cognition. It has attracted considerable attention in neuromorphic system design owing to its multimodal integration, fault tolerance, and energy efficiency. However, prior studies mostly focused on single inputs, with limited attention to multibit pairs or recall under non-orthogonal input patterns. To address these issues, this study proposes a bidirectional associative learning system using paired multibit inputs. It employs a synapse–neuron structure based on spiking neural networks (SNNs) that emulate biological learning, with simple circuits supporting synaptic operations and pattern evaluation. Importantly, the update and read functions were designed by drawing inspiration from the operational characteristics of emerging synaptic devices, thereby ensuring future compatibility with device-level implementations. The proposed system was verified through Cadence-based simulations using CMOS neurons and Verilog-A synapses. The results show that all patterns are reliably recalled under intact synaptic conditions, and most patterns are still robustly recalled under biologically plausible conditions such as partial synapse loss or noisy initial synaptic weight states. Moreover, by avoiding massive data converters and relying only on basic digital gates, the proposed design achieves associative learning with a simple structure. This provides an advantage for future extension to large-scale arrays. Full article

Background: Esophageal adenocarcinoma (EAC) and squamous cell carcinoma (ESCC) follow divergent incidence trajectories in the United States. Rising use of electronic nicotine delivery systems (ENDS) and evolving demographic risk profiles may be reshaping these trends. We aimed to characterize national incidence patterns of EAC and ESCC from 2000 through 2022—stratified by age, sex, and race/ethnicity—and to place these in the context of changing behavioral exposures. Methods: We performed a retrospective cohort study using Surveillance, Epidemiology, and End Results SEER 21 registry data (covering 48% of the U.S. population). We included first-primary, histologically confirmed EAC (ICD-O-3 codes 8140–8576) and ESCC (8050–8084) in individuals aged ≥ 15 years diagnosed between 2000 and 2022. Age-adjusted incidence rates (per 100,000 person-years; 2000 U.S. standard) and annual percent changes (APCs) were estimated via Joinpoint regression models. Results: A total of 90,290 EAC and 47,916 ESCC cases were identified. EAC incidence increased from 2.3 to 2.8 per 100,000 (APC +0.90%; 95% CI, 0.45–1.35), with the largest relative rises in ages 15–39 years (APC +1.50%) and among women (APC +2.65%). Non-Hispanic Black and American Indian/Alaska Native populations experienced the most pronounced EAC increases. Overall ESCC incidence declined (APC −0.78%; 95% CI, −1.10 to −0.46), though Asian/Pacific Islander (+3.59%) and American Indian/Alaska Native (+1.58%) groups saw rising rates. Conclusions: EAC incidence continues to climb—especially in younger adults, women, and select racial/ethnic minorities—while ESCC declines are uneven. These histology-specific patterns highlight the urgency of tailored prevention, targeted early-detection efforts, and mechanistic studies on emerging exposures such as vaping. Full article

Under the framework of the “dual carbon” goals, promoting the coordinated development of carbon emission efficiency, carbon sink capacity, and high-quality growth has become a critical issue for regional sustainability. Using panel data from 2006 to 2021, this study systematically investigates the three-dimensional coupling coordination among carbon emission efficiency, carbon sink capacity, and high-quality development in the Greater Chang-Zhu-Tan urban agglomeration. The spatiotemporal evolution, spatial correlation characteristics, and influencing factors of the coupling coordination were also explored. The results indicate that the coupling coordination system exhibits an evolutionary trend of overall stability with localized differentiation. The overall coupling degree remains in the “running-in” stage, while the coordination level is still in a marginally coordinated state. Spatially, the pattern has shifted from “northern leadership” to “multi-polar support,” with Yueyang achieving intermediate coordination, four cities including Changde reaching primary coordination, and three cities including Loudi remaining imbalanced. Spatial correlation has weakened from significant to insignificant, with Xiangtan showing a “low–low” cluster and Hengyang displaying a “high–low” cluster. The evolution of hot and cold spots has moved from marked differentiation to a more balanced distribution, as reflected by the disappearance of cold spots. The empirical analysis confirms a three-dimensional coupling mechanism: ecologically rich regions attain high coordination through carbon sink synergies; economically advanced areas achieve decoupling through innovation-driven development; while traditional industrial cities, despite facing the “green paradox,” demonstrate potential for leapfrog progress through transformation. Among the influencing factors, industrial structure upgrading emerged as the primary driver of spatial differentiation, though with a negative impact. Government support also exhibited a negative effect, whereas the interaction between environmental regulation and both government support and economic development was found to be significant. Full article

Automated Road defect detection using Unmanned Aerial Vehicles (UAVs) has emerged as an efficient and safe solution for large-scale infrastructure inspection. However, object detection in aerial imagery poses unique challenges, including the prevalence of extremely small targets, complex backgrounds, and significant scale variations. Mainstream deep learning-based detection models often struggle with these issues, exhibiting limitations in detecting small cracks, high computational demands, and insufficient generalization ability for UAV perspectives. To address these challenges, this paper proposes a novel comprehensive network, RoadNet, specifically designed for high-precision road defect detection in UAV-captured imagery. RoadNet innovatively integrates Transformer modules with a convolutional neural network backbone and detection head. This design not only significantly enhances the global feature modeling capability crucial for understanding complex aerial contexts but also maintains the computational efficiency necessary for potential real-time applications. The model was trained and evaluated on a self-collected UAV road defect dataset (UAV-RDD). In comparative experiments, RoadNet achieved an outstanding mAP@0.5 score of 0.9128 while maintaining a fast-processing speed of 210.01 ms per image, outperforming other state-of-the-art models. The experimental results demonstrate that RoadNet possesses superior detection performance for road defects in complex aerial scenarios captured by drones. Full article

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios. Full article

Tree endotherapy has risen to prominence in the field of precision agriculture as an innovative and sustainable method of tree care, being respectful of both environmental protection and consumer health needs. A comprehensive review of the state of the art of research in this field has made it possible to spotlight the main advantages of tree infusion, which has undergone significant progress in step with technological innovation and an increased understanding of tree anatomy and physiology. The major criticalities associated with this technique, as well as the biological and technical–operational obstacles that still hinder its wider use, are also highlighted. What emerges is an innovative and rapidly expanding technique in tree care, in both the cultivation and phytosanitary management of fruit and ornamental trees. Some of the strengths of the endotherapy technique, such as the next-to-no water consumption, the strong reduction in the use of fertilizers and pesticides, the possibility of using biological control agents (BCAs) or other products of natural origin, the precision administration of the product inside the xylem of the tree, and the efficacy (20–90%) and persistence (1–2 years) of treatments, make it one of the cornerstones of sustainable tree protection at present. With a very low consumption of the “active ingredient”, endotherapy has a negligible impact on the external environment, minimizing the drift and dispersal of the active ingredient and thus limiting the exposure of non-target organisms such as beneficial insects, birds, and wildlife. The large-scale application of the technique would therefore also help to achieve an important goal in “climate-smart agriculture”, the saving of water resources, significantly contributing to climate change mitigation, especially in those areas of the planet where water is a precious resource. Full article

Background: Borderline Personality Disorder (BPD) often emerges during adolescence and young adulthood, a period marked by heightened vulnerability to impulsivity and affective dysregulation. While impulsivity is a core feature of BPD, its multidimensional expression in this age group remains insufficiently documented. This study examined impulsivity traits in young adults with BPD, their associations with depressive and anxiety symptoms, and their links to risk behaviors. Methods: A total of 160 participants aged 16–25 were recruited in Belgium between 2021 and 2023: 44 with BPD from inpatient and outpatient psychiatric services and 116 healthy controls from schools and universities. Assessments included the short UPPS-P, Beck Depression Inventory-II (BDI-II), State-Trait Anxiety Inventory (STAI-T), and the Diagnostic Interview for Borderlines–Revised (DIB-R). Logistic regressions with robust errors and Kendall’s tau-b correlations were used. Results: Compared with controls, individuals with BPD scored higher on all UPPS-p subscales except Sensation Seeking (e.g., Negative Urgency: 14 vs. 10, p < 0.001). Logistic regression identified Negative Urgency (OR = 5.31, 95% CI: 2.07–13.62, p = 0.001) and Positive Urgency (OR = 3.26, 95% CI: 1.37–7.75, p = 0.007) as independent predictors of BPD. Within the BPD group, depressive and anxiety symptoms correlated with several UPPS-P dimensions, notably Negative Urgency and Lack of Perseverance. Suicide attempts were associated with the DIB-R total score, BDI-II, and STAI-T, while substance use was linked to the DIB-R impulsivity subscale and STAI-T. Conclusions: Emotional impulsivity—particularly Negative Urgency—emerges as a central feature of BPD in emerging adulthood. Its interplay with depressive and anxiety symptoms, and its associations with suicidal and addictive behaviors, support a dual-level conceptualization of impulsivity as both a dispositional trait and a state-dependent clinical risk factor. Full article

The utilization of carbon dioxide (CO₂) offers an effective approach for alleviating the carbon-reduction pressures associated with fossil energy consumption. However, studies on the use of CO₂ as an auxiliary agent in water treatment to enhance the removal of emerging contaminants are limited. In this study, the photodegradation of ciprofloxacin (CIP) was investigated using ultraviolet (UV) irradiation combined with CO₂ dosing (UV/CO₂). The results demonstrated that the UV/CO₂ system effectively degraded CIP, with CO₂ concentration and solution pH exerting a critical influence. Inorganic anions and metal cations had negligible effects on CIP degradation efficiency, whereas natural organic matter (NOM) had a pronounced inhibitory effect. Mechanistic analysis revealed that superoxide radicals ($·{\mathrm{O}}_2^{-}$) and carbonate radicals (${\mathrm{CO}}_3^{•-}$) were the primary oxidizing species, whereas the excited triplet state of CIP (³CIP*) and singlet oxygen played crucial roles in initiating radical generation. LC–MS analysis and density functional theory calculations indicated that the main degradation routes involved defluorination, decarboxylation, and epoxidation of the piperazine ring. Toxicity assessment indicated that the transformation products generated by UV/CO₂ were less toxic than the parent compound. Furthermore, the UV/CO₂ process demonstrated high energy efficiency, with a low electrical energy per order (EEO) value of 0.4193 kWh·m⁻³·order⁻¹. These findings suggest that the UV/CO₂ system is a promising alternative for the treatment of photosensitive organic pollutants and provides a beneficial pathway for CO₂ utilization. Full article

This study examines the descriptive trajectories through which service innovation is associated with customer exit dynamics after service failures, drawing on a three-wave panel of 532 online travel agency users and employing partial least squares structural equation modeling with predictive assessment. We analyze how innovation is associated with switching intentions via brand hate and brand distrust over time. Results reveal distinct temporal patterns: service innovation is linked to consistent reductions in both hate and distrust, yet only hate emerges as a salient mediator whose marginal association with switching intensifies over time. In contrast, distrust, although mitigated by innovation, remains relatively stable and behaviorally inert. Rather than asserting a causal explanation, we document temporal associations—labelled here as a “dilution effect”—to indicate that innovation coincides with weakening negative emotions but only partial attenuation of their behavioral correlates. By distinguishing between the fading but influential role of hate and the persistent yet inert nature of distrust, this study clarifies differentiated pathways through which negative states coincide with customer exit. For managers, the results highlight the need for staged innovation strategies to dissipate hate, complemented by long-term trust-repair initiatives to address enduring distrust and reduce customer churn. Full article

Liquids like water are not expected to produce a thermal change under shear strain or flow (away from extreme conditions). In this study, we reveal experimental conditions for which the conventional athermal hydrodynamic assumption is no longer valid. We highlight the establishment of non-equilibrium hot and cold thermal states occurring when a mesoscopic confined liquid is set in motion. Two stress situations are considered: low-frequency shear stress at large strain amplitude and microfluidic transport (pressure gradient). Two liquids are tested: water and glycerol at room temperature. In confined conditions (submillimeter scale), these liquids exhibit stress-induced thermal waves. We interpret the emergence of non-equilibrium temperatures as a consequence of the solicitation of the mesoscopic liquid elasticity. In analogy with elastic deformation, the mesoscopic volume decreases or increases slightly, which leads to a change in temperature (thermo-mechanical energy conversion). The energy acquired or released is converted to heat or cold, respectively. To account for these non-equilibrium temperatures, the mesoscopic flow is no longer considered as a complete dissipative process but as a way of propagating shear and thus compressive waves. This conclusion is consistent with recent theoretical developments showing that liquids propagate shear elastic waves at small scales. Full article

Methane pyrolysis produces hydrogen (H₂) with solid carbon black as a co-product, eliminating direct CO₂ emissions and enabling a low-carbon supply when combined with renewable or low-carbon heat sources. In this study, we propose a hybrid geothermal pyrolysis configuration in which an enhanced geothermal system (EGS) provides base-load preheating and isothermal holding, while either electrical or solar–thermal input supplies the final temperature rise to the catalytic set-point. The work addresses four main objectives: (i) integrating field-scale geothermal operating envelopes to define heat-integration targets and duty splits; (ii) assessing scalability through high-pressure reactor design, thermal management, and carbon separation strategies that preserve co-product value; (iii) developing a techno-economic analysis (TEA) framework that lists CAPEX and OPEX, incorporates carbon pricing and credits, and evaluates dual-product economics for hydrogen and carbon black; and (iv) reorganizing state-of-the-art advances chronologically, linking molten media demonstrations, catalyst development, and integration studies. The process synthesis shows that allocating geothermal heat to the largest heat-capacity streams (feed, recycle, and melt/salt hold) reduces electric top-up demand and stabilizes reactor operation, thereby mitigating coking, sintering, and broad particle size distributions. High-pressure operation improves the hydrogen yield and equipment compactness, but it also requires corrosion-resistant materials and careful thermal-stress management. The TEA indicates that the levelized cost of hydrogen is primarily influenced by two factors: (a) electric duty and the carbon intensity of power, and (b) the achievable price and specifications of the carbon co-product. Secondary drivers include the methane price, geothermal capacity factor, and overall conversion and selectivity. Overall, geothermal-assisted methane pyrolysis emerges as a practical pathway to turquoise hydrogen, if the carbon quality is maintained and heat integration is optimized. The study offers design principles and reporting guidelines intended to accelerate pilot-scale deployment. Full article

Cyberbullying or cyberharrassment is a form of harassment or bullying that is carried out through electronic technologies and devices. The article aims to explore the structure of scholarly networks identified through a bibliometric analysis of research on adolescence within the context of postdigital society. The study focuses on academic output linked to school, family and social environments, using data retrieved from the Web of Science database. Seven hundred documents were obtained, and the networks generated, connections between the different nodes, were analyzed to determine in the results the existence of prominent authors and institutions in the field of cyberbullying. The analysis, conducted using VOSviewer software 1.6.20, reveals that cyberbullying constitutes a growing and significant field of study. It highlights numerous opportunities for advancing research focused on intervention strategies and policy development aimed at addressing this issue. Research reveals that Psychology and Education are key areas, with the United States and Spain as leaders, and prominent authors such as Rosario Ortega and Heidi Vandebosch. Three historical phases are identified: emergence, expansion, and urgency. The findings make it possible to detect trends, research gaps, and to guide educators, policymakers, and technology platforms in the field of digital and media literacy. Full article

Cellular senescence is a heterogeneous and dynamic state characterised by stable proliferation arrest, macromolecular damage and metabolic remodelling. Although markers such as SA-β-galactosidase staining, yH2AX foci and p53 activation are widely used as de facto standards, they are imperfect and differ in terms of sensitivity, specificity and dependence on context. We present a multifactorial imaging platform integrating scanning electron, flow cytometry and high-resolution confocal microscopy. This allows us to identify senescence phenotypes in three in vitro models: replicative ageing via serial passaging; dose-graded genotoxic stress under serum deprivation; and primary fibroblasts from young and elderly donors. We present a multimodal imaging framework to characterise senescence-associated phenotypes by integrating LysoTracker and MitoTracker microscopy and SA-β-gal/FACS, p16INK4a immunostaining provides independent confirmation of proliferative arrest. Combined nutrient deprivation and genotoxic challenge elicited the most pronounced and concordant organelle alterations relative to single stressors, aligning with age-donor differences. Our approach integrates structural and functional readouts across modalities, reducing the impact of phenotypic heterogeneity and providing reproducible multiparametric endpoints. Although the framework focuses on a robustly validated panel of phenotypes, it is extensible by nature and sensitive to distributional shifts. This allows both drug-specific redistribution of established markers and the emergence of atypical or transient phenotypes to be detected. This flexibility renders the platform suitable for comparative studies and the screening of senolytics and geroprotectors, as well as for refining the evolving landscape of senescence-associated states. Full article

Visible Light Positioning (VLP) has emerged as a pivotal technology for industrial Internet of Things (IoT) and smart logistics, offering high accuracy, immunity to electromagnetic interference, and cost-effectiveness. However, fluctuations in signal gain caused by target motion significantly degrade the positioning accuracy of current VLP systems. Conventional approaches face intrinsic limitations: propagation-model-based techniques rely on static assumptions, fingerprint-based approaches are highly sensitive to dynamic parameter variations, and although CNN/LSTM-based models achieve high accuracy under static conditions, their inability to capture long-term temporal dependencies leads to unstable performance in dynamic scenarios. To overcome these challenges, we propose a novel dynamic VLP algorithm that incorporates a Spatio-Temporal Feature Information Network (STFI-Net) for joint localization and orientation estimation of moving targets. The proposed method integrates a two-layer convolutional block for spatial feature extraction and employs modern Temporal Convolutional Networks (TCNs) with dilated convolutions to capture multi-scale temporal dependencies in dynamic environments. Experimental results demonstrate that the STFI-Net-based system enhances positioning accuracy by over 26% compared to state-of-the-art methods while maintaining robustness in the face of complex motion patterns and environmental variations. This work introduces a novel framework for deep learning-enabled dynamic VLP systems, providing more efficient, accurate, and scalable solutions for indoor positioning. Full article

Autoimmune diseases result from dysregulated immune responses that mistakenly attack the body’s own tissues, causing chronic inflammation and progressive damage. Macrophages, with their remarkable plasticity, play key roles in both promoting and resolving inflammation, with pro-inflammatory M1 and anti-inflammatory M2 states shaping disease outcomes. Macrophage-derived exosomes have emerged as important mediators of intercellular communication, reflecting the functional state of their parent cells while influencing recipient cell behavior. Exosomes from M1 macrophages amplify inflammation through cytokines and microRNAs, whereas M2-derived exosomes support tissue repair and immune regulation. Studies in rheumatoid arthritis, lupus, multiple sclerosis, inflammatory bowel disease, type 1 diabetes, and psoriasis highlight their dual roles in pathology and resolution. In addition, macrophage exosomes can be engineered to deliver targeted therapeutic molecules, offering cell-free interventions with advantages in specificity, biocompatibility, and immunomodulation. This review summarizes current insights into macrophage-derived exosomes, their role in autoimmune pathogenesis, and emerging strategies to harness their therapeutic potential, highlighting their promise as precision-guided treatments for autoimmune diseases. Full article