Search Results (6,137)

Computer-Aided Design (CAD) software has long served as a foundation for planning and modeling in Architecture, Engineering, and Construction (AEC). In recent years, the introduction of Augmented Reality (AR) and Virtual Reality (VR) has significantly reshaped the CAD landscape, offering novel interaction paradigms that bridge the gap between digital prototypes and real-world spatial understanding. These technologies have enabled users to engage with 3D architectural content in more immersive and intuitive ways, facilitating improved decision making and communication throughout design workflows. As digital design services grow more complex and span multiple media platforms—from desktop-based modeling to immersive AR/VR environments—evaluating usability and User Experience (UX) becomes increasingly challenging. This paper presents a longitudinal usability and UX study of a multiplatform house design pipeline (i.e., structured workflow for creating, adapting, and delivering house designs so they can be used seamlessly across multiple platforms) comprising a web-based application for initial house creation, a mobile AR tool for contextual exterior visualization, and VR applications that allow full-scale interior exploration and configuration. Together, these components form a unified yet heterogeneous service experience across different devices and modalities. We describe the iterative design and development of this system over three distinct phases (lasting two years), each followed by user studies which evaluated UX and usability and targeted different participant profiles and design maturity levels. The paper outlines our approach to cross-platform UX evaluation, including methods such as the Think-Aloud Protocol (TAP), standardized usability metrics, and structured interviews. The results from the studies provide insight into user preferences, interaction patterns, and system coherence across platforms. From both participant and evaluator perspectives, the iterative methodology contributed to improvements in system usability and a clearer mental model of the design process. The main research question we address is how iterative design and development affects the UX of the heterogeneous service. Our findings highlight important considerations for future research and practice in the design of integrated, multiplatform XR services for AEC, with potential relevance to other domains. Full article

Amid growing urban climate uncertainty and complex water demand, conventional standalone rainwater harvesting (RWH) systems often fail to ensure supply reliability and overflow control. Most existing studies focus on single-function building clusters, leaving a gap in understanding how functionally diverse groups with complementary demand patterns can be coordinated. This study addresses this gap by applying an hourly water balance model to compare decentralized and coordinated modes for an integrated RWH system serving a campus and adjacent student dormitories in Kitakyushu, Japan. Five performance metrics were evaluated: potable water supplementation, reliability, non-potable replacement rate, overflow volume, and overflow days. The results show that coordinated operation reduced annual potable supplementation by 14.1%, improved overall reliability to 81.7% (a 9.6% gain over decentralized operation), and increased the replacement rate to 87.9%. Overflow volume decreased by 295 m³ and overflow days by five, with pronounced benefits during summer rainfall peaks. Differential heatmaps further revealed distinct spatiotemporal advantages, though temporary disruptions occurred under extreme events. Overall, the study demonstrates that cross-functional coordination can enhance system resilience and operational stability, while highlighting the need for adaptive scheduling and real-time information systems for broader urban applications. Full article

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods. Full article

Conventional plant disease management primarily depends on chemical pesticides. However, with the rising concerns related to human health, environmental sustainability, and the emergence of resistant pathogens, biocontrol agents (BCAs) have gained more attention as eco-friendly alternatives. Among the potential biocontrol agents, yeasts stand out due to their safety, adaptability, and diverse antagonistic mechanisms, ranging from competition and enzyme secretion to volatile compound production and immunity induction. Despite their potential, yeast-based BCAs face limitations in field efficacy, regulation, and an incomplete understanding of their molecular interactions. Most current studies focus on simple, pairwise interactions, overlooking the complexity of agroecosystems, where plants, pathogens, and BCAs interact within broader microbial communities. This review addresses the importance of understanding tripartite interactions among plants, pathogens, and yeasts, supported by integrated transcriptomic and comparative genomic approaches, as well as meticulous observations of phenotypic expressions to uncover strain-specific defense mechanisms and mode of action. By referring to well-studied models like Blumeria graminis f.sp. hordei–Hordeum vulgare–Pseudozyma flocculosa and Trichoderma tripartite systems, we highlight the underexplored potential of yeasts to modulate plant immunity and influence pathogen behavior through complex molecular crosstalk. Bridging these knowledge gaps through integrating proteomic, metabolomic, and transcriptomic analyses, we can better harness yeasts in sustainable and targeted biocontrol strategies. Full article

Autonomous driving in complex real-world environments requires robust perception, reasoning, and physically feasible planning, which remain challenging for current end-to-end approaches. This paper introduces VLA-MP, a unified vision-language-action framework that integrates multimodal Bird’s-Eye View (BEV) perception, vision-language alignment, and a GRU-bicycle dynamics cascade adapter for physics-informed action generation. The system constructs structured environmental representations from RGB images and LiDAR, aligns scene features with natural language instructions through a cross-modal projector and large language model, and converts high-level semantic hidden states outputs into executable and physically consistent trajectories. Experiments on the LMDrive dataset and CARLA simulator demonstrate that VLA-MP achieves high performance across the LangAuto benchmark series, with best driving scores of 44.3, 63.5, and 78.4 on LangAuto, LangAuto-Short, and LangAuto-Tiny, respectively, while maintaining high infraction scores of 0.89–0.95, outperforming recent VLA methods such as LMDrive and AD-H. Visualization and video results further validate the framework’s ability to follow complex language-conditioned instructions, adapt to dynamic environments, and prioritize safety. These findings highlight the potential of combining multimodal perception, language reasoning, and physics-aware adapters for robust and interpretable autonomous driving. Full article

The actomyosin complex—nature’s dynamic engine composed of actin filaments and myosin motors—is emerging as a versatile tool for bio-integrated nanotechnology. This review explores the growing potential of actomyosin-powered systems in biosensing and actuation applications, highlighting their compatibility with physiological conditions, responsiveness to biochemical and physical cues and modular adaptability. We begin with a comparative overview of natural and synthetic nanomachines, positioning actomyosin as a uniquely scalable and biocompatible platform. We then discuss experimental advances in controlling actomyosin activity through ATP, calcium, heat, light and electric fields, as well as their integration into in vitro motility assays, soft robotics and neural interface systems. Emphasis is placed on longstanding efforts to harness actomyosin as a biosensing element—capable of converting chemical or environmental signals into measurable mechanical or electrical outputs that can be used to provide valuable clinical and basic science information such as functional consequences of disease-associated genetic variants in cardiovascular genes. We also highlight engineering challenges such as stability, spatial control and upscaling, and examine speculative future directions, including emotion-responsive nanodevices. By bridging cell biology and bioengineering, actomyosin-based systems offer promising avenues for real-time sensing, diagnostics and therapeutic feedback in next-generation biosensors. Full article

The increasing complexity of modern food production demands advanced solutions for quality control (QC), safety monitoring, and process optimization. This review systematically explores recent advancements in machine learning (ML) for QC across six domains: Food Quality Applications; Defect Detection and Visual Inspection Systems; Ingredient Optimization and Nutritional Assessment; Packaging—Sensors and Predictive QC; Supply Chain—Traceability and Transparency and Food Industry Efficiency; and Industry 4.0 Models. Following a PRISMA-based methodology, a structured search of the Scopus database using thematic Boolean keywords identified 124 peer-reviewed publications (2005–2025), from which 25 studies were selected based on predefined inclusion and exclusion criteria, methodological rigor, and innovation. Neural networks dominated the reviewed approaches, with ensemble learning as a secondary method, and supervised learning prevailing across tasks. Emerging trends include hyperspectral imaging, sensor fusion, explainable AI, and blockchain-enabled traceability. Limitations in current research include domain coverage biases, data scarcity, and underexplored unsupervised and hybrid methods. Real-world implementation challenges involve integration with legacy systems, regulatory compliance, scalability, and cost–benefit trade-offs. The novelty of this review lies in combining a transparent PRISMA approach, a six-domain thematic framework, and Industry 4.0/5.0 integration, providing cross-domain insights and a roadmap for robust, transparent, and adaptive QC systems in the food industry. Full article

To mitigate the degradation of positioning accuracy in sixth-generation mobile communication systems under dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, this paper proposes an improved adaptive Kalman filter positioning method based on Orthogonal Time Frequency Space (OTFS)-modulated signals. Firstly, the distance can be measured by using the OTFS-modulated signals transmitted between base stations and nodes. Secondly, the distance information is converted into the distance difference information to establish the time difference of arrival (TDOA) positioning equation, which is preliminarily solved using the Chan algorithm. Thirdly, residuals are calculated based on the preliminary positioning results, dividing the complex environment into distinct regions and adaptively determining corresponding genetic factors for each region. Finally, the selected genetic parameters are substituted into the Sage–Husa adaptive Kalman filter equations to estimate positioning results. The simulation analysis demonstrates that in complex environments featuring both line-of-sight and non-line-of-sight conditions, the vehicle motion trajectories estimated using this method more closely approximate actual trajectories. Additionally, both the accuracy and stability of positioning results show significant improvement compared to traditional methods. Full article

Antibiotic resistance remains one of the most formidable challenges to modern medicine, threatening to outpace therapeutic innovation and undermine decades of clinical progress. While resistance was once viewed narrowly as a clinical phenomenon, it is now understood as the outcome of complex ecological and molecular interactions that span soil, water, agriculture, animals, and humans. Environmental reservoirs act as silent incubators of resistance genes, with horizontal gene transfer and stress-induced mutagenesis fueling their evolution and dissemination. At the molecular level, advances in genomics, structural biology, and systems microbiology have revealed intricate networks involving plasmid-mediated resistance, efflux pump regulation, integron dynamics, and CRISPR-Cas interactions, providing new insights into the adaptability of pathogens. Simultaneously, the environmental dimensions of resistance, from wastewater treatment plants and aquaculture to airborne dissemination, highlight the urgency of adopting a One Health framework. Yet, alongside this growing threat, novel therapeutic avenues are emerging. Innovative β-lactamase inhibitors, bacteriophage-based therapies, engineered lysins, antimicrobial peptides, and CRISPR-driven antimicrobials are redefining what constitutes an “antibiotic” in the twenty-first century. Furthermore, artificial intelligence and machine learning now accelerate drug discovery and resistance prediction, raising the possibility of precision-guided antimicrobial stewardship. This review synthesizes molecular insights, environmental drivers, and therapeutic innovations to present a comprehensive landscape of antibiotic resistance. By bridging ecological microbiology, molecular biology, and translational medicine, it outlines a roadmap for surveillance, prevention, and drug development while emphasizing the need for integrative policies to safeguard global health. Full article

Traffic flow forecasting plays a significant role in intelligent transportation systems (ITSs) and is instructive for traffic planning, management and control.Increasingly complex traffic conditions pose further challenges to the traffic flow forecasting. While improving the accuracy of model forecasting, the parameter effectiveness of the model is also an issue that cannot be ignored. In addition, existing traffic prediction models have failed to organically integrate data with well-designed model architectures. Therefore, to address the above two issues, we propose the STAE-BiSSSM model as a solution. STAE-BiSSSM consists of Spatio-Temporal Adaptive Embedding (STAE) and Bidirectional Selective State Space Model (BiSSSM), where STAE aims to process features to obtain richer spatio-temporal feature representations. BiSSSM is a novel structural design serving as an alternative to Transformer, capable of extracting patterns of traffic flow changes from both the forward and backward directions of time series with much fewer parameters. Comparative tests between baseline models and STAE-BiSSSM on five real-world datasets illustrates the advance performance of STAE-BiSSSM. This is especially so on METRLA and PeMSBAY datasets, compared with the SOTA model STAEformer. In the short-term forecasting task (horizon: 15min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 1.89%/13.74%, 3.72%/16.19% and 1.46%/17.39%, respectively. In the long-term forecasting task (horizon: 60min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 3.59%/13.83%, 7.26%/16.36% and 2.16%/15.65%, respectively. Full article

Automatic speech recognition (ASR) has advanced rapidly, evolving from early template-matching systems to modern deep learning frameworks. This review systematically traces ASR’s technological evolution across four phases: the template-based era, statistical modeling approaches, the deep learning revolution, and the emergence of large-scale models under diverse learning paradigms. We analyze core technologies such as hidden Markov models (HMMs), Gaussian mixture models (GMMs), recurrent neural networks (RNNs), and recent architectures including Transformer-based models and Wav2Vec 2.0. Beyond algorithmic development, we examine how ASR integrates into intelligent information systems, analyzing real-world applications in healthcare, education, smart homes, enterprise systems, and automotive domains with attention to deployment considerations and system design. We also address persistent challenges—noise robustness, low-resource adaptation, and deployment efficiency—while exploring emerging solutions such as multimodal fusion, privacy-preserving modeling, and lightweight architectures. Finally, we outline future research directions to guide the development of robust, scalable, and intelligent ASR systems for complex, evolving environments. Full article

This study presents detailed microstructural observations of the mouthparts and sensory organs of adult cicadomorphan species, obtained using scanning electron microscopy (SEM). Despite microstructural variation, the overall morphology of the mouthparts, comprising a three-segmented labium and a bundle of interlocking stylets (maxillae and mandibles), is highly conserved across species, supporting its evolutionary significance in sap feeding from floem, xylem, or epidermis cells. Variations in the number and shape of mandibular stylet barbs likely reflect adaptations to different host plant tissues. The presence of an identical dual interlocking system between the maxillary stylets, which is found consistently across taxa, enhances functional stability during feeding and indicates a conserved mechanism among cicadomorphans. The species studied exhibit two distinct types of salivary canal closure: hooked and T-shaped. The latter potentially represents a state linked to specialised feeding strategies, such as sap xylem feeding. On the labial tip, there are different shapes of the anterior sensory fields. This area hosts a complex array of sensilla of different numbers, including gustatory (sensilla peg, PS1 and PS2, basiconica, BS3, double basiconica, DB), olfactory (finger–like, FLS) and thermo-hygroreceptive (sensillum dome-shaped, DS, and coeloconicum, CS) types, which facilitate host detection and feeding site selection. In the posterior sensory field, sensilla contact-chemosensory (sensilla basiconica, BS1 and BS2, and sensillum trichoideum, TS) are present. Mechanosensilla chaetica (CH1–CH3) are widely distributed on the last labial segment and may contribute to labium positioning. These findings emphasise the presence of both conserved and specialised morphological traits reflecting evolutionary and ecological diversification within Cicadomorpha. Full article

As global demand for clean and renewable energy continues to rise, wind power has become a critical component of the sustainable energy transition. However, the increasingly complex operating conditions and structural configurations of modern wind turbines pose significant challenges for system reliability and control. Specifically, accurate load torque estimation is crucial for supporting the long-term stable operation of the wind power system. This paper presents a novel load torque estimation approach based on a nonlinear extended state observer (NLESO) for wind turbines with permanent magnet synchronous generators. In this method, the load torque is estimated using current measurements and observer-derived acceleration, thereby eliminating the need for torque sensors. This not only reduces hardware complexity but also improves system robustness, particularly in harsh or fault-prone environments. Furthermore, the stability of the observer is rigorously proven through Lyapunov theory using the variable gradient method. Finally, simulation results under different wind speed conditions validate the method’s accuracy, robustness, and adaptability. Full article

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications. Full article

This paper pioneers the integration of hyperspectral imaging and robotics for the automated analysis of cultural heritage, representing a measurable advancement over existing manually operated systems. For the first time in the cultural heritage domain, a compact push-broom hyperspectral camera working in the VNIR range has been successfully mounted on a robotic arm, enabling precise and repeatable acquisition trajectories without the need for manual intervention. Unlike traditional approaches that rely on fixed paths or manual repositioning, the proposed approach allows dynamic and programmable imaging of both planar and volumetric objects, greatly improving adaptability to complex geometries. The integrated system achieves spectral reliability comparable to established manual methods, while offering superior flexibility and scalability. Current limitations, particularly regarding the illumination setup, are discussed alongside planned optimisation strategies. Full article