Search Results (1,617)

Protaetia brevitarsis seulensis (Kolbe, 1886) larvae have traditionally been used in East Asian medicine and have recently attracted attention as functional food ingredients because of their pharmacological potential. However, chemical investigations remain limited, and no marker compounds have been established for quality control. This study aimed to isolate and identify a primary constituent from the 70% ethanol extract of P. brevitarsis (PBE) and to develop an analytical method for its quantification. Among the solvent-partitioned fractions, the n-butanol fraction (PBE-B) exhibited a major peak in HPLC analysis. The compound was purified through a combination of vacuum liquid chromatography (VLC), medium-pressure liquid chromatography (MPLC), and recycling preparative HPLC. Its structure was identified as L-tryptophan based on HR-ESI-MS and NMR spectroscopy. Quantitative analysis was conducted using HPLC-DAD under optimized analytical conditions, employing a Thermo Scientific™ Acclaim™ Polar Advantage II column and an acidified mobile phase (0.1% formic acid in water and methanol) to improve resolution. The method demonstrated excellent linearity (r² > 0.9999), and the L-tryptophan content in PBE was determined to be 1.93 ± 0.05 μg/mg. The analyte was well separated with minimal interference, supporting the reproducibility of the method. These results indicate that L-tryptophan is a promising candidate Q-marker for the quality control of P. brevitarsis extracts. Full article

To promote the sustainable development of urban smart transportation systems, this study constructs a structural equation model (SEM) based on the Technology Acceptance Model (TAM), incorporating extended variables including social influence, environmental awareness, privacy concerns, and service similarity to investigate users’ behavioral intentions toward Mobility as a Service (MaaS). The research systematically examines key factors influencing user adoption behavior and their underlying mechanisms, providing theoretical foundations and practical guidance for optimizing MaaS system design and policy making. Using SEM as the core analytical framework, this study employs mediation analysis, moderation analysis, and multigroup comparison to empirically examine the direct and indirect effects among variables, as well as group heterogeneity. Data were collected through an online questionnaire survey, with Analysis of Variance (ANOVA) applied to identify the differential impacts of demographic and travel behavior characteristics on users’ intentions and related psychological constructs, thereby supporting precise user segmentation and evidence-based policy interventions. Key findings include the following: (1) Social influence, ease of use, and environmental awareness boost MaaS adoption, while privacy concerns hinder it. (2) Freelancers/self-employed weaken the positive effects of usefulness, ease of use, and social influence on adoption. (3) Service similarity and ease of use effects vary significantly between single-mode and multimodal commuters. The findings extend the theoretical boundaries of TAM and provide both theoretical and practical support for the development of sustainable urban transportation systems. Full article

This article presents a theoretical kinematic analysis of a mechanism for lifting and emptying household waste containers, critical components of garbage truck operations. The study focuses on optimizing waste handling mechanisms and highlights the impact of design parameters on performance. Using both classical analytical methods and modern simulation tools, including Mathcad 15 and Linkage v.3.16.14 software, the analysis identifies key influences of structural parameters on motion behavior. Unlike previous studies (which, for the mechanism under study, would use five structural groups), this work models the mechanism with fewer structural groups (three structural groups are used), simplifying analysis without sacrificing accuracy. Simulations confirm the validity of the calculations, showing no discrepancies in component movements and a maximum of 2.81% variation in linear velocities at all critical points. Detailed motion graphs illustrate the trajectories of mobile joints, with particular attention to angular variations and linear speeds, underscoring the importance of parameter optimization for enhanced performance. Full article

As bike-sharing systems become increasingly integral to sustainable urban mobility, understanding their economic viability requires moving beyond conventional linear models to capture complex operational dynamics. This study develops an interpretable analytical framework to uncover non-linear relationships governing bike-sharing economic performance in Xi’an, China, utilizing one-month operational data across 202 Transportation Analysis Zones (TAZs). Combining spatial analysis with explainable machine learning (XGBoost–SHAP), we systematically examine how operational factors and built environment characteristics interact to influence economic outcomes, achieving superior predictive performance (R² = 0.847) compared to baseline linear regression models (R² = 0.652). The SHAP-based interpretation reveals three key findings: (1) bike-sharing performance exhibits pronounced spatial heterogeneity that correlates strongly with urban functional patterns), with commercial districts and transit-adjacent areas demonstrating consistently higher economic returns. (2) Gradual positive relationships emerge across multiple factors—including bike supply density (maximum SHAP contribution +1.0), commercial POI distribution, and transit accessibility—with performance showing consistent but moderate improvements rather than dramatic threshold effects. (3) Significant interaction effects are quantified between key factors, with bike supply density and commercial POI density exhibiting strong synergistic relationships (interaction values 1.5–2.0), particularly in areas combining high commercial activity with good transit connectivity. The findings challenge simplistic linear assumptions in bike-sharing management while providing quantitative evidence for spatially differentiated strategies that account for moderate threshold behaviors and factor synergies. Cross-validation results (5-fold, R² = 0.89 ± 0.018) confirm model robustness, while comprehensive performance metrics demonstrate substantial improvements over traditional approaches (35.1% RMSE reduction, 36.6% MAE improvement). The proposed framework offers urban planners a data-driven tool for evidence-based decision-making in sustainable mobility systems, with broader methodological applicability for similar urban contexts. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

The significant development of technology has greatly influenced crucial sectors of society, including health, economy, public health, and business. Technological tools have become essential in daily life, impacting the educational process across all age groups. Previous research has demonstrated the pervasive integration of technology into everyday activities, emphasizing the compelling attraction that screens and mobile devices provide, particularly among younger generations. However, earlier studies have often overlooked the detailed impact and practical applications of these technologies within the educational sector, particularly through computer games. This study employs a comprehensive analysis of scientific articles available on the internet, examining global research on the use of computer games in education. The research methods include a systematic review of publications, focusing on primary education while also considering other educational levels to provide a holistic view. The analytical approach highlights the practices employed during the implementation of educational computer games and their effects on the learning process. The major findings reveal that educational computer games have become a highly popular pedagogical method, effectively capturing the interest of both students and educators. The study underscores the growing demand for these educational tools and the promise of continuous improvements and additions to this type of teaching. The results suggest that integrating computer games into education not only enhances engagement but also signifies a progressive shift in teaching methodologies, paving the way for innovative educational practices. Full article

A homogenisation procedure for energy-buffering structural layers with integrated electrical energy storage systems (capacitors) is described with the aim of calculating their shielding effectiveness to the electromagnetic waves when they are installed inside building walls. In fact, these storage systems may attenuate electromagnetic fields in the frequency ranges employed by mobile telephony, radio broadcasting, and wireless data transmission, thus impairing the operation of Internet of Things infrastructures. The capacitors inside the individual energy-buffering modules have a multilayered structure, in which the layers have very small thicknesses, making an analytical solution of the electromagnetic field for this kind of object practically impossible. Similarly, numerical solutions may not be practical due to the very small thickness of the layers compared to the overall object size. Therefore, this paper presents a simple and effective analytical method to model multilayered structures consisting of homogenising the whole capacitor, which can then be treated as a unique block of material with fictitious (but effective) electric and magnetic parameters. The method is based on multi-section transmission lines, and a quick and reliable analytical methodology is proposed to evaluate the shielding capabilities using the homogenised capacitor’s effective parameters. Moreover, experimental measurements on a real prototype have also been carried out to validate the methodology. Results show that the trend of the simulated and measured SE is the same, proving that the method can be employed to obtain a conservative estimation of the SE from numerical simulations. Full article

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

Humans are being exposed to a variety of potentially toxic metal compounds through the diet and/or the intravenous administration of metal-containing medicinal drugs. The organomercurial thimerosal (THI) is a bactericidal that is present in vaccines, but its potential degradation by biomolecules in vivo is incompletely understood. To probe its interaction with low-molecular-weight thiols that are highly abundant within cells, we have employed an LC-based analytical approach in conjunction with a mercury-specific detector. The injection of THI into a C₁₈-HPLC column equilibrated with mobile phases that contained increasing concentrations of up to 15 mM of glutathione (GSH) and 30% acetonitrile revealed the elution of a GS-EtHg adduct in conjunction with THI, as evidenced by electrospray ionization mass spectrometry. These results were confirmed by ¹⁹⁹Hg-NMR spectroscopy. While these results imply a rapid degradation of THI by GSH at physiological pH, it is important to point out that our results were obtained in aqueous solutions containing 30% (v:v) acetonitrile. Further studies need to confirm if the GS-EtHg adduct is also formed in biological fluids. Our results nevertheless demonstrate that GSH and L-cysteine (Cys) are potential targets of THI at physiological pH, which is relevant to better understand its side effects, including previously reported effects on Ca²⁺ channels. Full article

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

This study applies a multicriteria decision analysis to explore how experts from different backgrounds assess traditional and emerging criteria for urban cycling. A hierarchical model with 7 main criteria and 31 subcriteria was evaluated by 30 specialists from academic, technical, and user-focused groups. Using pairwise comparisons and aggregated judgments, this study reveals points of agreement and divergence among expert priorities. Safety and infrastructure were rated as the most important factors. In contrast, contextual and technological aspects, such as Multimodality, Environmental Quality, Shared Systems, and Digital Solutions, received moderate to lower weights, with differences linked to expert profiles. These results highlight how different disciplinary perspectives influence the understanding of bikeability-related factors. Conceptually, the findings support a broader view of cycling conditions that incorporates both established and emerging criteria. Methodologically, this study demonstrates the value of the Analytic Hierarchy Process (AHP) as a participatory and transparent tool to integrate diverse stakeholder opinions into a structured evaluation model. This approach can support cycling mobility planning and policymaking. Future applications may include case studies in specific cities, combining expert-based priorities with local spatial data, as well as longitudinal research to track changes in cycling conditions over time. Full article

A simple and reliable method was developed using LC-MS/MS to quantify alprazolam, bromazepam, clonazepam, diazepam, and flunitrazepam in clinical samples. This method was validated for the simultaneous determination of alprazolam, bromazepam, clonazepam, diazepam, and flunitrazepam. It was applied to human urine samples collected from people suspected of drug abuse in the Kuwaiti region. Formic acid in water and acetonitrile was used in mobile phase with a gradient mode of elution using C18 reverse-phase column. The instrument was operated in a positive mode with an electrospray ionization source using multiple reaction monitoring. For sample extraction, the liquid-liquid extraction technique was used. The method was validated for limit of detection, limit of quantitation, selectivity, linearity, accuracy, and precision. The concentration for limit of quantitation was 6.0 ng/mL, the linearity ranged from 2.0 to 300 ng/mL for each of the analytes, and the r² values were ≥0.99. The accuracy was found to be within a range of 80–120% and precision had a %RSD of ≤15% for each of the analytes. The method was applied to 48 urine samples collected from those suspected of drug abuse by the Toxicology Department of the General Department of Criminal Evidence, Kuwait, and alprazolam, bromazepam, clonazepam, diazepam and flunitrazepam were identified commonly in the samples. The overall drug positivity rate obtained considering 48 samples was 93.75%. Based on these results and successful determination of alprazolam, bromazepam, clonazepam, diazepam and flunitrazepam in human urine samples from those suspected of drug abuse, this method is deemed to be suitable for its routine analysis. Full article

Background: Liuwei Dihuang Pills, a classic traditional Chinese medicine formula, has been widely used in clinical practice for its multiple pharmacological effects. However, the systematic characterization and identification of its chemical constituents, especially the aqueous decoction, remain insufficient, which hinders in-depth research on its pharmacodynamic material basis. Thus, there is an urgent need for a comprehensive analysis of its chemical components using advanced analytical techniques. Methods: After screening chromatographic columns, the ACQUITY UPLC™ HSS T3 column (100 mm × 2.1 mm, 1.8 μm) was selected. The column temperature was set to 40 °C, and the mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). A gradient elution program was adopted, and the separation was completed within 20 min. Ultra-high performance liquid chromatography–Orbitrap mass spectrometry (UPLC-Orbitrap-MS) combined with a self-established information database was used for the analysis. Results: A total of 80 compounds were tentatively identified, including 13 monoterpenoids, 6 phenolic acids, 16 iridoids, 11 flavonoids, 25 triterpenoids, and 9 other types. Triterpenoids are mainly derived from Poria cocos and Alisma orientale; iridoids are mainly from Rehmannia glutinosa; monoterpenoids are mainly from Moutan Cortex; and flavonoids are mainly from Dioscorea opposita. Among them, monoterpenoids, iridoids, and triterpenoids are important pharmacodynamic components. The cleavage pathways of typical compounds (such as pachymic acid, catalpol, oxidized paeoniflorin, and puerarin) are clear, and their mass spectral fragment characteristics are consistent with the literature reports. Conclusions: Through UPLC-Orbitrap-MS technology and systematic optimization of conditions, this study significantly improved the coverage of chemical component identification in Liuwei Dihuang Pills, providing a comprehensive reference for the research on its pharmacodynamic substances. However, challenges remain in the identification of trace components and isomers. In the future, analytical methods will be further improved by combining technologies such as ion mobility mass spectrometry or multi-dimensional liquid chromatography. Full article

The global rise in population, increased life expectancy, and heightened international mobility have escalated disease prevalence and pharmaceutical demand. This growth intensifies energy consumption and chemical waste production within the pharmaceutical industry, challenging environmental sustainability and operational efficiency. Chromatography, a vital analytical technique for ensuring product quality and regulatory compliance, can also contribute to material waste and energy inefficiencies if not properly maintained and optimized. This study applies Failure Mode and Effects Analysis (FMEA) to chromatographic equipment maintenance within Hovione’s Engineering and Maintenance Department, aiming to identify and mitigate failure risks. By integrating environmental metrics derived from Life Cycle Assessment (LCA) into the FMEA framework, a hybrid risk evaluation tool was developed that prioritizes both equipment reliability and sustainability performance. The findings demonstrate how this integrated approach reduces unplanned downtime, lowers solvent waste, and improves energy efficiency. Additionally, the study proposes a conceptual dashboard to support proactive, sustainability-driven asset management in pharmaceutical laboratories. By bridging reliability engineering and environmental sustainability, this research offers a strategic model for optimizing resource use, minimizing chemical waste, and enhancing long-term operational resilience in regulated pharmaceutical environments. Full article

The field of electric vehicles and electric mobility, like other modern engineering practice, not only requires deep analytical skills but increasingly demands the ability to synthesise and integrate knowledge across multiple disciplines (e.g., electrical engineering, mechanical engineering, sustainability engineering, design engineering) to create innovative systems. Education today, however, still has a strong analysis focus: learning, exploring, and understanding theories and concepts is the main drive. Design and synthesis build on those and aim at bringing together theories and concepts into creative and innovative systems. Teaching design and synthesis is notoriously hard. The design of electric vehicles exemplifies the complexity of contemporary engineering problems, requiring the integration of multiple domains to experience the challenges connected to design and synthesis. This paper presents the need for, rationale behind, setup of, and experiences with a 5 European Credit (140 h) Master’s-level (postgraduate) course named “Electric Vehicle System Design” that we developed as a joint effort for the University of Twente and the University of South-Eastern Norway. The course is specifically designed to immerse students in the multidisciplinary design and synthesis processes central to electric mobility. In the paper, the course framework, project-based approach, and lessons learned are discussed. This highlights how engineering students can be equipped for the challenges inherent to designing electric vehicles. Full article