Highly active and stable sensing surfaces are critical for the integration of catalysis-based electrical gas molecular sensors. However, achieving both high sensitivity and durability remains a persistent challenge due to continuous exposure to target molecules often results in surface deactivation and sensing performance degradation. Herein, we demonstrate a robust surface functionalization strategy to simultaneously enhance sensitivity and long-term stability for ammonia (NH3) detection by modifying hexagonal tungsten oxide (h-WO3) nanowires with methylphosphonic acid (MPA). Fourier-transform infrared spectroscopy (FTIR) and density functional theory (DFT) calculations reveal that phosphate groups in MPA selectively bind to the Lewis acid sites (undercoordinated W6+) on h-WO3 nanowires, effectively passivating the surface and mitigating degradation. Concurrently, the electron-rich P=O moiety facilitates strong interaction with NH3 molecules, leading to enhanced chemisorption and signal transduction. As a result, MPA-functionalized h-WO3 nanowire sensors exhibit a nearly tenfold increase in NH3 sensitivity compared to the unmodified h-WO3 sensors and maintain stable performance over 300 days of continuous operation. As a proof of concept for applied scenarios, we integrate the modified sensors into a microelectromechanical system (MEMS)-based smart ventilation system, enabling real-time NH3 monitoring and control in livestock environments. This work presents a viable route for designing high-performance, durable gas sensors through targeted molecular surface engineering.

Abstract. Rising traffic demand around university campuses and sports venues exacerbates parking scarcity and congestion. This study develops a UAV–deep learning workflow for the automatic quantification of parked vehicles and the estimation of occupancy levels across facilities at the Universidad Autónoma de Nuevo León (UANL). UAV surveys of the East and West Estadio UANL lots and the FIME faculty lots were conducted with DJI Mavic 2 and Matrice 350 RTK platforms during high-demand periods, including football matches and student egress peaks. The imagery, processed into centimeter-scale orthomosaics (2.4–2.8 cm ground sampling distance), enabled reliable instance detection using a pretrained Mask R-CNN Car Detection model. A total of 4,591 vehicles were identified across the surveyed areas: 2,336 in the West lot, 1,684 in the East lot, and 571 in the FIME lots. Kernel density estimation and spatial metrics revealed near-saturation of stadium lots during matches, reduced occupancy during off-event periods, and elevated but distributed demand in faculty lots during class dismissal. These geospatial indicators were integrated into a parking management framework using heat maps and bottleneck detection around access and egress roads. The approach demonstrates the potential of UAV–deep learning workflows to support demand-responsive parking control, traffic guidance, and long-term planning in congested university and event-driven environments.

Abstract This study presents the development and validation of compact acoustic lens–based actuators for precise, contactless manipulation of 3D-printed microparticles in water. Two types of passive polydimethylsiloxane acoustic lenses, namely twin-trap and vortex-trap configurations, were designed using spatial phase modulation principles and fabricated through mold casting with 3D-printed molds. Finite element simulations confirmed that the lenses generated structured acoustic pressure fields suitable for 3D microparticle trapping, including dual-lobe pressure distributions for twin-trap lenses and rotationally symmetric fields for vortex-trap lenses. Hydrophone measurements experimentally verified the simulated pressure profiles and demonstrated strong agreement in focal location and acoustic field structure. An 800 µm-diameter 3D-printed microparticle fabricated from VeroWhitePlus RGD835 was used to experimentally validate trapping and manipulation performance. A comprehensive evaluation was performed for four distinct lens–transducer actuator configurations, including both single-element and array-based transducer implementations for each lens type. Stable trapping was achieved at focal distances of 3, 5, 38, and 50 mm above the lens surface, corresponding to the respective actuator designs. The actuators were subsequently translated along predefined 3D trajectories measuring 3 mm × 3 mm for single-element lenses and 10 mm × 10 mm for array-based lenses in the OXZ and OYZ planes, respectively. Accurate trajectory tracking was demonstrated, with positional errors smaller than the microparticle diameter for all configurations. These results demonstrate that the proposed acoustic lens–based actuators enable real-time, non-contact 3D manipulation of solid microparticles with high precision and stability. The passive lens architecture, compact form factor, and ability to generate structured acoustic fields indicate strong potential for applications in mesoscale object handling, biological assay platforms, and microrobotic control in open aqueous environments.

The catalytic valorization of carbon dioxide (CO2) has attracted extensive attention as a promising route to mitigate greenhouse gas emissions while producing value-added chemicals. Significant progress has been achieved in the selective reduction of CO2 to C1 and C2 products such as CO, CH4, HCOO-, C2H4, and C2H5OH through precise control of catalysts and reaction environments within single-batch systems. However, the formation of higher-order carbon products (C3+) remains a major challenge because it requires complex multielectron and multiproton transfer steps, typically involving 18-20 electrons and protons for intermediates such as propanol or propylene. These demanding reaction pathways lead to sluggish C-C-C coupling kinetics and limited energy utilization under conventional single-cell configurations. Recent advances have focused on multibatch cascade catalytic systems that integrate thermochemical, photochemical, and electrochemical processes to overcome these intrinsic barriers. By enabling the stepwise conversion of CO2-derived intermediates, such hybrid platforms improve selectivity and efficiency toward C3+ products that are difficult to achieve in single-batch systems. Nevertheless, the integration of distinct reaction environments introduces challenges, including intermediate loss between reactors and reduced overall energy efficiency. This review provides a comprehensive overview of cascade strategies for CO2 conversion, emphasizing mechanistic understanding, reactor design, and operando characterization. The discussion aims to guide the rational design of next-generation catalytic architectures capable of achieving efficient and scalable C3+ production from CO2 through improved control of multistep extended hybrid reaction pathways and interfacial energy management.

Moments of impaired control are common in problematic gaming and pornography use. Previous research has mostly focused on general self-control deficits in laboratory or cross-sectional settings. As a novel approach, we examined craving and inhibitory control in daily life as dual mechanisms of moments of impaired control in the natural environment by combining laboratory tasks with ecological momentary assessment (EMA). In this pre-registered study, 118 participants (M = 26.16 years, SD = 7.72 years, 75 males, 42 females, 1 divers) with nonproblematic, risky or pathological pornography use or gaming (ngaming = 74, npornography = 44) based on a standardized diagnostic interview, completed a cue-reactivity paradigm, craving assessments, Stop-Signal Task, and seven days EMA of craving, behavior-specific inhibitory control, and moments of impaired control (July2023-July 2025). Average frequency of moments of impaired control was predicted by average craving intensity in real life. Intraindividual likelihood of experiencing a moment of impaired control was predicted by reduced behavior-specific inhibitory control in real life. Laboratory craving predicted real-life craving intensity which was linked to real-life behavior specific inhibitory control ratings. Findings generalized across both behavior groups. Craving emerged as an overall (between-person) risk factor, whereas behavior-specific inhibitory control as a situation-specific (within-person) mechanism in moments of impaired control in potentially addictive gaming or pornography use. Prevention, treatment, and future research should address within versus between-person processes and continue combining laboratory tasks with EMA to clarify how lab-indexed mechanisms translate into real-world impaired control.

Sources and distribution of atmospheric microplastics in Northwest China river valleys via land use.

Electronic health records (EHRs) have become a crucial application in cloud computing environments, necessitating advanced privacy‐preserving access control mechanisms. Ciphertext policy attribute‐based encryption (CP‐ABE) is a widely recognized solution for secure access control in outsourced data environments. However, existing CP‐ABE models face challenges related to revocation efficiency, access policy exposure, and computational burden on data owners (DOs). Even though several research works have extensively tackled this issue, most rely on re‐encryption or ciphertext updates and outsourcing strategies to proxies. However, optimization for querying all affected ciphertexts to reduce re‐encryption/ciphertext update costs is often overlooked, and the cost associated with frequent blockchain transactions for ciphertext updates and revocation records has not been addressed. Furthermore, most works do not support both attribute and user revocation efficiently. To address these issues, we propose an enhanced revocable CP‐ABE‐based access control scheme with optimized revocation performance (R‐CP‐ABE‐ORP). This scheme integrates ciphertext aggregation, lazy re‐encryption with revocation tokens, proxy‐assisted lightweight re‐encryption (PRE‐LR), blockchain, and bloom filters for fast queries to significantly improve revocation efficiency. The proposed scheme ensures forward and backward security while maintaining efficient ciphertext update and policy enforcement mechanisms. Experimental evaluations confirm that the proposed scheme outperforms related works in revocation efficiency, computational cost, and query performance.

Living materials for gas therapy.

This paper reviews an easily expandable plan for smart document handling across multiple cloud systems, aiming to make work easier to manage, more resilient to issues, and improve the total cost of ownership. The importance of this task stems from two factors: first, Intelligent Document Processing (IDP) tools are experiencing growth; second, multi-cloud use is expanding more widely. This increases the primary fight between wanting top-notch help for every step and the dangers of being stuck with one provider, having messy operations, and uneven safety rules. The study aims to create and support a complete design that can hide both setup and software links while offering complete control and standard protection in a mixed environment. The innovation is in the coherent four-layer model, which merges a general control plane atop Kubernetes and Crossplane with portable application runtime Dapr, exposing standard APIs for statelessness, messaging, and service invocation, decomposed IDP microservices, and an overlay layer for management and security. The key findings validate that only the combination of Crossplane at the level of the control plane with GitOps and OPA policies together with Dapr at the level of the application-API can provide real portability, elastic scaling, governed security, while maintaining freedom of choice between cloud services. It proves that workflows crossing provider boundaries can be orchestrated, thus reducing vendor lock-in. The article will be helpful to cloud-platform architects, IT executives, data and MLOps engineers, IDP product teams, and researchers in distributed systems and enterprise AI.

Hierarchical agent architecture-based large-scale AGV cluster real-time motion collaboration control in dynamic and complex production environments

Dual neural mechanisms of sustained response inhibition: Right-lateralized core control and left-lateralized adaptive support.

Electrochemical oxidation degradation of polystyrene nanoplastics by Sm-Mn intermediate layer Ti/Sb-SnO2 anode: Composite metal elements enhance electron transfer and promote the generation of hydroxyl radicals.

The current study was conducted to compare the aggression among students from three distinct educational settings in Sialkot, Pakistan: madrassas, schools and colleges. Cross-sectional research design was used in the study. Sample was selected from schools, madrassas and colleges of Sialkot through stratified sampling technique. Sample size was determined after getting the sampling frame of target population. Sample of 500 students, 100 from madrassa, 200 from school and 200 from colleges participated in the study. Respondents were between the age ranges of 13 to 19 years. Aggression was measured by using the Aggression Scale for Adolescents (Zaqia, & Shehzadi, 2019). Results of t-test analysis showed a significant difference in aggression between males and females (r =.007, p < 0.01). Male (M=27.48) reported higher on aggression than females (M=24.91). The ANOVA revealed that aggression was found significantly lower among madrassa students (M=21.77), as compared to school (M=26.29) and college student (M=28.68). Teachers, school counselors, and legislators can use the study's practical implications to create intervention programs that address emotional control, violence prevention, and mental health assistance in Pakistan's diverse educational environments.

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research.

Abstract Tunnel fires pose severe risks due to confined geometry, limited evacuation options, and rapid smoke propagation. This literature review compiles insights from recent studies on smoke movement, ventilation control, fire suppression, and evacuation behaviour in tunnel environments. It also highlights the growing use of simulation tools like PyroSim and Pathfinder to analyse fire dynamics and human response. Key findings across studies include the significance of critical ventilation velocity, the risks of smoke back- layering, and the throttling effect in high-heat-release fires. While systems like water mist and deluge sprinklers improve tenability, their effectiveness depends on placement, droplet behaviour, and airflow interaction. Evacuation outcomes are shown to rely heavily on visibility, exit spacing, and guidance systems. The review underscores that tunnel safety requires a coordinated approach, where fire suppression, ventilation, and evacuation systems work in synergy. This paper aims to provide a more consolidated foundation for designing a more resilient tunnel fire safety system by thoroughly examining the tested strategies and simulation based evaluations. Keywords: Tunnel Fire, Fire Dynamics, Ventilation, Fire Suppression, Evacuation, PyroSim

Impulsive noise poses a significant challenge to broadband feedforward active noise control (ANC) systems, particularly in sensitive environments such as infant incubators. This paper presents an adaptive impulsive noise cancellation approach based on the Kalman filter, designed to improve noise attenuation performance under nonstationary and impulsive interference. The proposed framework integrates impulsive noise detection with a Kalman filter-based suppression scheme. Simulation studies are conducted to evaluate the performance of the combined system in comparison to traditional ANC methods, such as Filtered-x Least Mean Square (FxLMS) and Filtered-x Normalized LMS (FxNLMS). Results demonstrate that the Kalman filter can effectively reduce the influence of impulsive disturbances without degrading overall broadband noise cancellation. A case study involving an infant incubator illustrates the practical effectiveness and robustness of the proposed technique in a real-world healthcare application. The findings support the integration of Kalman filter-based adaptive control in future ANC designs targeting impulsive noise environments.

This article explores the feasibility of utilizing vibrations as energy sources to develop energy-independent wireless sensing platforms within the Industrial Internet of Things (IIoT) architecture. It proposes deploying piezoelectric sensors on vibrating machinery to enable self-powered sensing in remote or hard-to-access environments. To analyze the energy-harvesting potential of commercial piezoelectric sensors, both preliminary measurements and controlled laboratory experiments are conducted. A vibration-powered LoRaWAN-based sensor node design is introduced, followed by experiments aimed at determining the balance between available energy and optimal sensor sampling rates to ensure continuous operation while maintaining stable energy storage patterns. Ultra-Low-Power Energy-Harvesting Integrated Circuits (EH-ICs) play a key role in efficiently managing harvested energy and regulating device performance. Since the vibration-based energy source is intermittent, system operation—continuous or discontinuous—depends on the balance between harvested and consumed power. Proper transducer selection and configuration of the power generation module are essential for maximizing energy efficiency. To enhance performance evaluation, this study further introduces two hybrid machine learning architectures: one employing Lasso Regression for classification and another using the Self K-Means algorithm for clustering, enabling adaptive energy management and intelligent sensor control in IIoT environments.

With the increasing demand for quality control of spare parts in the context of intelligent manufacturing, minimizing the number of samples while ensuring high accuracy has become a core issue in industrial production. Traditional sampling methods typically rely on large sample sizes, leading to increased inspection costs, extended production cycles, and difficulty in balancing accuracy and efficiency. To address this challenge, this paper proposes a adynamic sampling framework that integrates Bootstrap resampling, hypothesis testing, and Monte Carlo simulation. By generating representative Bootstrap samples, conducting Z-test-based hypothesis testing to estimate defect rates, and optimizing sampling rates through Monte Carlo simulation-driven stochastic programming, the framework achieves minimal sample size while maintaining high detection accuracy. Validation through simulation experiments and empirical analysis using industrial datasets demonstrates that the proposed method reduces quality control costs by 30%-40% compared to traditional methods while preserving detection precision above 95%. This study provides an efficient and practical solution for quality control in resource-constrained manufacturing environments, with significant implications for smart production systems.

In the field of competitive sports, the scientific evaluation of the effect of sprinting training has long faced the core challenges of insufficient integration of multi-dimensional indicators and static weight distribution. Traditional methods rely on a single physiological indicator or linear model, making it difficult to quantify the nonlinear synergy of physical fitness, skills and mental ability, especially with significant limitations on the threshold effect of psychological factors and adaptability to dynamic training cycles. To this end, this study developed a fuzzy comprehensive evaluation system based on dynamic coupling of AHP-entropy weight method. By integrating the subjective experience weighting of Analytic Hierarchy Process (AHP) with the objective data drive of entropy weight method, a three-dimensional evaluation system covering nine secondary indicators including vertical jump height, step frequency coefficient of variation and anxiety control score was constructed. The system innovatively introduces the dynamic adjustment coefficient α to achieve adaptive optimization of subjective and objective weights at different stages of the training cycle, and uses the structural equation model to verify the path contribution rate of physical fitness, skill and psychological dimensions to the comprehensive training effect, where the marginal effect of skill parameters increases non linearly with the training intensity. The independent contribution rate of psychological factors in the pre competition period increased significantly to 31%. Empirical research shows that by using membership function quantification indicators to blur boundaries and combining multi-source data fusion techniques, the model reduced the overall prediction error by 47.7%, increased the model fit index (CFI) to 0.93, and improved the sensitivity of the psychological assessment module by 38.2% compared with traditional methods. The dynamic weighting mechanism successfully captured the cross-system association between the blood lactate threshold and psychological resilience. During the weighting transition from the enhancement period to the pre competition period, the skill weighting was dynamically adjusted from 0.46 to 0.53, and the psychological weighting jumped from 0.44 to 0.58, effectively matching the demand for neuromuscular coordination and stress control in the competition environment. The study confirmed that the millisecond-level optimized response of the fuzzy rule base to the coefficient of variation of step size and the time to touch the ground solved the problem of nonlinear modeling of technical parameters. The application of this system breaks through the empirical evaluation paradigm and provides a full-chain solution for sprinting training from data collection, dynamic decision-making to personalized intervention. Its core innovation lies in achieving three breakthroughs: dynamic weight distribution, psychophysiological collaborative quantification, and cross-cycle adaptive optimization.

With the rapid development of Reinforcement Learning (RL) in the field of intelligent control, vision-based Deep Reinforcement Learning (DRL) methods have shown broad prospects for application in autonomous UAV flight control. This study constructs a three-dimensional virtual environment on the AirSim simulation platform and proposes an improved UAV flight control algorithm, Clipped and Reward-optimized PPO with Channel Attention (CR-PPO), which is a PPO-based variant that optimizes the reward function and integrates a Channel Attention (CA) mechanism. The method first extends the standard Proximal Policy Optimization (PPO) algorithm by incorporating adaptive clipping range adjustment, entropy scheduling, and KL-divergence-based early stopping to form the Clipped PPO (C-PPO) algorithm to enhance training stability and convergence speed. On this basis, a Convolutional Neural Network (CNN) perception module with CA and a finely designed reward function are integrated to form the complete CR-PPO framework. The proposed perception-decision system takes image data as input and achieves end-to-end autonomous control without reliance on physical state information. Experimental results show that CR-PPO significantly outperforms DQN, A2C, standard PPO, and C-PPO in terms of training efficiency, policy stability, and task completion rate, while also demonstrating strong generalization and deployment potential. This work provides an effective technical pathway and a feasible validation basis for intelligent UAV control in complex environments.