Search Results (11,712)

New psychoactive substances (NPSs) have emerged as a significant global public health challenge due to their ability to mimic traditional drugs. Among these, mephedrone has gained attention because of its widespread use and associated toxicities. This review provides a comprehensive analysis of the structure, pharmacokinetic properties, and metabolic pathways of mephedrone, highlighting its phase I and phase II metabolites as potential biomarkers for detection and forensic applications. A comprehensive literature search was performed without date restrictions. The search employed key terms such as “mephedrone metabolites”, “pharmacokinetics of mephedrone”, “phase I metabolites of mephedrone”, and “phase II metabolites of mephedrone”. Additionally, the reference lists of selected studies were screened to ensure a thorough review of the literature. Mephedrone is a chiral compound existing in two enantiomeric forms, exhibiting different affinities for monoamine transporters and distinct pharmacological profiles. In vivo animal studies indicate rapid absorption, significant tissue distribution, and the formation of multiple phase I metabolites (e.g., normephedrone, dihydromephedrone, 4-carboxymephedrone) that influence its neurochemical effects. Phase II metabolism involves conjugation reactions leading to metabolites such as N-succinyl-normephedrone and N-glutaryl-normephedrone, further complicating its metabolic profile. These findings underscore the importance of elucidating mephedrone’s metabolic pathways to improve detection methods, enhance our understanding of its toxicological risks, and inform future therapeutic strategies. Full article

Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing speed, particularly in the context of small traffic signs within complex environments. To address these challenges, this study presents Ghost-YOLO-GBH, an innovative lightweight model that incorporates three key enhancements: (1) the integration of a GhostNet backbone, which substitutes the conventional YOLO11s architecture and utilizes Ghost modules to exploit feature redundancy, resulting in a 40.6% reduction in computational load while ensuring effective feature extraction for small targets; (2) the development of a HybridFocus module that combines large separable kernel attention with multi-scale pooling, effectively minimizing background interference and improving contextual feature aggregation by 4.3% in isolated tests; and (3) the implementation of a Bidirectional Dynamic Multi-Scale Feature Pyramid Network (BiDMS-FPN) that allows for bidirectional cross-stage feature fusion, significantly enhancing the accuracy of small target detection. Experimental results on the TT100K dataset indicate that Ghost-YOLO-GBH achieves an impressive 81.10% mean Average Precision (mAP) at a threshold of 0.5, along with an 11.7% increase in processing speed (45 FPS) and an 18.2% reduction in model parameters (7.74 M) compared to the baseline YOLO11s. Overall, Ghost-YOLO-GBH effectively balances accuracy, efficiency, and lightweight deployment, demonstrating superior performance in real-world applications characterized by small signs and cluttered backgrounds. This research provides a novel framework for resource-constrained TSR applications, contributing to the evolution of intelligent transportation systems. Full article

Tau protein is essential for the structural stability of neurons, particularly through its role in microtubule assembly and axonal transport. However, when abnormally hyperphosphorylated or cleaved, Tau can aggregate into insoluble forms that disrupt neuronal function, contributing to the pathogenesis of neurodegenerative diseases such as Alzheimer’s disease (AD). Emerging evidence suggests that similar Tau-related alterations may occur in individuals with chronic exposure to psychoactive substances. This review compiles experimental, clinical, and postmortem findings that collectively indicate a substance-specific influence on Tau dynamics. Alcohol and opioids, for instance, promote Tau hyperphosphorylation and fragmentation through the activation of kinases such as GSK-3β and CDK5, as well as proteases like caspase-3, leading to neuroinflammation and microglial activation. Stimulants and dissociatives disrupt insulin signaling, increase oxidative stress, and impair endosomal trafficking, all of which can exacerbate Tau pathology. In contrast, cannabinoids and psychedelics may exert protective effects by modulating kinase activity, reducing inflammation, or enhancing neuroplasticity. Psychedelic compounds such as psilocybin and harmine have been demonstrated to decrease Tau phosphorylation and facilitate cognitive restoration in animal models. Although the molecular mechanisms differ across substances, Tau consistently emerges as a convergent target altered in substance-related cognitive disorders. Understanding these pathways may provide not only mechanistic insights into drug-induced neurotoxicity but also identify Tau as a valuable biomarker and potential therapeutic target for the prevention or treatment of cognitive decline associated with substance use. Full article

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

For the last few decades, the use of High-Speed Trains (HSTs) has been growing rapidly in various parts of the world. Despite rapid global expansion, many HST projects fail due to demand overestimation and cost overruns. This study analyzes factors influencing HST demand in Indonesia, aiming to identify impactful determinants from user perspectives. Employing a quantitative cross-sectional approach, this research utilized questionnaires distributed to users of different modes of transportation in the Jakarta–Bandung area, including trains, buses, travel services, and private cars. Structural Equation Modeling (SEM) via Lisrel software was used to analyze the data. The results indicate that Transit-Oriented Developments (TOD) and new urban areas significantly increase HST demand by facilitating urban growth and development. Additionally, supporting infrastructure and external factors such as road accessibility, parking availability, shuttle services, and environmental integration are pivotal in shaping commuter preferences. Although factors such as safety, comfort, and reliability are important, they alone may not be adequate to persuade consumers to use high-speed trains for their travel. Full article

Accurate prediction of pedestrian movements is vital for autonomous driving, smart transportation, and human–computer interactions. To effectively anticipate pedestrian behavior, it is crucial to consider the potential spatio-temporal interactions among individuals. Traditional modeling approaches often depend on absolute position encoding to discern the positional relationships between pedestrians. Unfortunately, this method overlooks relative spatio-temporal relationships and fails to simulate ongoing interactions adequately. To overcome this challenge, we present a relative spatio-temporal encoding (RSTE) strategy that proficiently captures and analyzes this essential information. Furthermore, we design a multi-spatio-temporal graph (MSTG) modeling technique aimed at modeling and characterizing spatio-temporal interaction data across several individuals over time and space, with the goal of representing the movement patterns of pedestrians accurately. Additionally, an attention-based MSTT model has been developed, which utilizes an end-to-end approach for learning the structure of the MSTG. The findings indicate that an understanding of an individual’s preceding trajectory is crucial for forecasting the subsequent movements of other individuals. Evaluations using two challenging datasets reveal that the MSTT model markedly outperforms traditional trajectory-based modeling methods in predictive performance. Full article

Improving charge transport in the aggregated state of nanocomposites is challenging due to the large number of defects present at grain boundaries. To enhance the charge transfer and photogenerated carrier extraction of MnO_x nanofibers, a MnO_x/GO (graphene oxide) nanocomposite was prepared. The effects of GO content and bias on the optoelectronic properties were studied. Representative light sources at 405, 650, 780, 808, 980, and 1064 nm were used to examine the photoelectric signals. The results indicate that the MnO_x/GO nanocomposites have photocurrent switching behaviours from the visible region to the NIR (near-infrared) when the amount of GO added is optimised. It was also found that even with zero bias and storage of the nanocomposite sample at room temperature for over 8 years, a good photoelectric signal could still be extracted. This demonstrates that the MnO_x/GO nanocomposites present a strong built-in electric field that drives the directional motion of photogenerated carriers, avoids the photogenerated carrier recombination, and reflect a good photophysical stability. The strength of the built-in electric field is strongly affected by the component ratios of the resulting nanocomposite. The formation of the built-in electric field results from interfacial charge transfer in the nanocomposite. Modulating the charge behaviour of nanocomposites can significantly improve the physicochemical properties of materials when excited by light with different wavelengths and can be used in multidisciplinary applications. Since the recombination of photogenerated electron–hole pairs is the key bottleneck in multidisciplinary fields, this study provides a simple, low-cost method of tailoring defects at grain boundaries in the aggregated state of nanocomposites. These results can be used as a reference for multidisciplinary fields with low energy consumption. Full article

Cystic fibrosis (CF), the most common hereditary lung disease in Caucasians, is caused by dysfunction of the cystic fibrosis transmembrane conductance regulator (CFTR). We evaluated CFTR function using a newly developed Ussing chamber system, the Multi Trans Epithelial Current Clamp (MTECC), in an in vitro model of human airway epithelia. Air–liquid interface (ALI) cultures were established from nasal brushings of healthy controls (HC) and CF patients with biallelic CFTR variants. ALI layer thickness was similar between groups (HC: 62 ± 13 µm; CF: 55 ± 9 µm). Immunofluorescence showed apical CFTR expression in HC, but reduced or absent signal in CF cultures. MTECC enabled continuous measurement of transepithelial resistance (Rt), potential difference (PD), and conductance (G_t). G_t was significantly reduced in CF cultures compared to HC (0.825 ± 0.024 vs. −0.054 ± 0.016 mS/cm²), indicating impaired cAMP-inducible ion transport by CFTR. Treatment of CF cultures with elexacaftor, tezacaftor, and ivacaftor (Trikafta^®) increased G_t, reflecting partial restoration of CFTR function. These findings demonstrate the utility of MTECC in detecting functional differences in CFTR activity and support its use as a platform for evaluating CFTR-modulating therapies. Our model may contribute to the development of personalized treatment strategies for CF patients. Full article

In high energy density physics, the demand for precise detection of nanosecond-level fast physical processes is high. Ga:ZnO (GZO), GaN, and other fast scintillators are widely used in pulsed signal detection. However, many of them, especially wide-bandgap materials, still face issues of low luminous intensity and significant self-absorption. Therefore, an enhanced method was proposed to tune the wavelength of materials via coating perovskite quantum dot (QD) films. Three-layer samples based on GZO were primarily investigated and characterized. Radioluminescence (RL) spectra from each face of the samples, as well as their decay times, were obtained. Lower temperatures further enhanced the luminous intensity of the samples. Its overall luminous intensity increased by 2.7 times at 60 K compared to room temperature. The changes in the RL processes caused by perovskite QD and low temperatures were discussed using the light tuning and transporting model. In addition, an experiment under a pico-second electron beam was conducted to verify their pulse response and decay time. Accordingly, the samples were successfully applied in beam state monitoring of nanosecond pulsed proton beams, which indicates that GZO wafer coating with perovskite QD films has broad application prospects in pulsed radiation detection. Full article

With the advancement of low-carbon and sustainable development initiatives, electric scooters, recognized as essential transportation tools and leisure products, have gained significant popularity, particularly among young people. However, the current electric scooter market is plagued by severe product similarity. Once the initial novelty fades for users, the usage frequency declines, resulting in considerable resource wastage. This research collected user needs via surveys and employed the KJ method (affinity diagram) to synthesize fragmented insights into cohesive thematic clusters. Subsequently, a hierarchical needs model for electric scooters was constructed using analytical hierarchy process (AHP) principles, enabling systematic prioritization of user requirements through multi-criteria evaluation. By establishing a house of quality (HoQ), user needs were transformed into technical characteristics of electric scooter products, and the corresponding weights were calculated. After analyzing the positive and negative correlation degrees of the technical characteristic indicators, it was found that there are technical contradictions between functional zoning and compact size, lightweight design and material structure, and smart interaction and usability. Then, based on the theory of inventive problem solving (TRIZ), the contradictions were classified, and corresponding problem-solving principles were identified to achieve a multi-functional innovative design for electric scooters. This research, leveraging a systematic industrial design analysis framework, identified critical pain points among electric scooter users, established hierarchical user needs through priority ranking, and improved product lifecycle sustainability. It offers novel methodologies and perspectives for advancing theoretical research and design practices in the electric scooter domain. Full article

Parabens—specifically methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), and butylparaben (BuP)—are widely used substances in everyday life, particularly as preservatives in pharmaceutical and food products. However, these compounds are not effectively removed by conventional water and wastewater treatment processes, potentially causing disruptions to human homeostasis and the endocrine system. This study conducted a transport and dimensional analysis through simulation of the adsorption process for these parabens, using zinc chloride-activated carbon derived from spent coffee grounds (ACZnCl₂) as the adsorbent, implemented via Aspen Properties^® and Aspen Adsorption^®. Simulations were performed for two inlet concentrations (50 mg/L and 100 mg/L) and two adsorption column heights (3 m and 4 m), considering a volumetric flow rate representative of a medium-sized city with approximately 100,000 inhabitants. The results showed that both density and surface tension of the parabens varied linearly with increasing temperature, and viscosity exhibited a marked reduction above 30 °C. Among the tested conditions, the configuration with 50 mg∙L⁻¹ inlet concentration and a 4 m column height demonstrated the highest adsorption capacity and better performance under adsorption–desorption equilibrium. These findings indicate that the implementation of adsorption beds on an industrial scale in water and wastewater treatment systems is both environmentally and socially viable. Full article

Research on commuting patterns has mainly focused on urban and metropolitan areas, and such studies are not typically applied to rural and small-town regions, where workers often face longer commutes due to limited job opportunities and inadequate public transportation. By using the Census Transportation Planning Package (CTPP) data, this research fills that gap by analyzing commuting behavior by ethnic group and industry in south-central Minnesota, which is a predominantly rural area of 13 counties in the United States. The results show that both white and minority groups in District 7 experienced an increase in excess commuting from 2006 to 2016, with the minority group in Nobles County showing a significantly higher rise. Analysis by industry reveals that excess commuting in the leisure and hospitality sector (including arts, entertainment, and food services) in Nobles County increased five-fold during this time, indicating a severe spatial mismatch between jobs and affordable housing. In contrast, manufacturing experienced a decline of 50%, possibly indicating better commuting efficiency or a loss of manufacturing jobs. These findings can help city and transportation planners conduct an in-depth analysis of rural-to-urban commuting patterns and develop potential solutions to improve rural transportation infrastructure and accessibility, such as promoting telecommuting and hybrid work options, expanding shuttle routes, and adding more on-demand transit services in rural areas. Full article

Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article

TRPV1 regulates neuronal and vascular function mediated by NO and CGRP. Systemic arterial hypertension (SAH) induces an imbalance in vascular mediators NO and CGRP by altering the transport of Ca²⁺ ions through TRPV1, generating cellular damage. We studied the effect of topical capsaicin (CS) treatment on cardiac mechanical work, oxidative stress (TAC, NO, BH4, and BH2), cellular damage (MDA, MTO, and 8HO2dG), and inflammation (IL-6 and TNFα), generated by SAH, which was induced by L-NAME, in male Wistar rats. CS was added to a moisturizing cream and applied to the abdomen of animals for two weeks. Experimental groups were as follows: (1) Control, (2) Control+Cream, (3) Hypertensive, and (4) Hypertensive+Cream. Hearts were exposed to ischemia-reperfusion (I-R) using the Langendorff technique to study the potential cardioprotection of CS. Expression of SOD1, SOD2, catalase, eNOS, pNOS, TRPV1, and CGRP in cardiac tissue was evaluated. In the Hypertensive group, TRPV1 activation by CS (Hypertensive+Cream) reduced oxidative stress (OS), decreasing cellular damage and inflammation and increasing TAC, modulating biochemical and tissue alterations induced by OS generated by SAH. In parallel, an increase in tissue levels and the expression of CGRP, TRPV1, and eNOS, induced by CS, was observed. These findings indicate that pretreatment with CS attenuates cardiac I-R and SAH injury in rats. The cardioprotective mechanism may be based on TRPV1-mediated CGRP overexpression. Full article

The Shahejie Formation in Nanpu Sag is a crucial region for deep-layer hydrocarbon exploration in the Bohai Bay Basin. To address the impact of faults on sublacustrine fan formation and spatial distribution within the study area, this study integrated well logging, laboratory analysis, and 3D seismic data to systematically analyze sedimentary characteristics of sandbodies from the first member of the Shahejie Formation (Es₁) sublacustrine fans, clarifying their planar and cross-sectional distributions. Further research indicates that Gaoliu Fault activity during Es₁ deposition played a significant role in fan development through two mechanisms: (1) vertical displacement between hanging wall and footwall reshaped local paleogeomorphology; (2) tectonic stresses generated by fault movement affected slope stability, triggering gravitational mass transport processes that remobilized fan delta sediments into the central depression zone as sublacustrine fans through slumping and collapse mechanisms. Core observations reveal soft-sediment deformation features, including slump structures, flame structures, and shale rip-up clasts. Seismic profiles show lens-shaped geometries with thick centers thinning laterally, exhibiting lateral pinch-out terminations. Inverse fault-step architectures formed by underlying faults control sandbody distribution patterns, restricting primary deposition locations for sublacustrine fan development. The study demonstrates that sublacustrine fans in the study area are formed by gravity flow processes. A new model was established, illustrating the combined control of the Gaoliu Fault and reverse stepover faults on fan development. These findings provide valuable insights for gravity flow exploration and reservoir prediction in the Nanpu Sag, offering important implications for hydrocarbon exploration in similar lacustrine rift basins. Full article