Search Results (1,899)

This research presents the design and implementation of a cascade Proportional–Integral (PI) controller tailored for a Variable Air Volume (VAV) system that was specially created and executed particularly for hospital operating rooms. The main goal of this work is to make sure that the temperature and positive pressure stay within the limits set by ASHRAE Standard 170-2017. This is necessary for patient safety, surgical accuracy, and system reliability. The proposed cascade design uses dual-loop PI controllers: one loop controls the temperature based on user-defined setpoints by local control touch screen, and the other loop accurately modulates the differential pressure to keep the pressure of the environment sterile (positive pressure). The system works perfectly with Building Automation System (BAS) parts from Automated Logic Corporation (ALC) brand, like Direct Digital Controllers (DDC) and Web-CTRL software with Variable Frequency Drives (VFDs), advanced sensors, and actuators that give real-time feedback, precise control, and energy efficiency. The system’s exceptional responsiveness, extraordinary stability, and resilient flexibility were proven through empirical validation at the Korean Iraqi Critical Care Hospital in Baghdad under a variety of operating circumstances. Even during rapid load changes and door openings, the control system successfully maintained the temperature between 18 and 22°C and the differential pressure between 3 and 15 Pascals. Four performance scenarios, such as normal (pressure and temperature), high-temperature, high-pressure, and low-pressure cases, were tested. The results showed that the cascade PI control strategy is a reliable solution for critical care settings because it achieves precise environmental control, improves energy efficiency, and ensures compliance with strict healthcare facility standards. Full article

We present a new measure of emotion perception called PAGE (Perceiving AI Generated Emotions). The test includes 20 emotions, expressed by ethnically diverse faces, spanning a wide range of ages. We created stimuli with generative AI, illustrating a method to build customizable assessments of emotional intelligence at relatively low cost. Study 1 describes the validation of the image set and test construction. Study 2 reports the psychometric properties of the test, including convergent validity and relatively strong reliability. Study 3 explores predictive validity using a lab experiment in which we causally identify the contributions managers make to teams. PAGE scores predict managers’ causal contributions to group success, a finding which is robust to controlling for personality and demographic characteristics. We discuss the potential of generative AI to automate development of non-cognitive skill assessments. Full article

With the rapid development of automated wheeled vehicle technology, complex vehicle functions require extensive safety testing for verification. Compared with real-vehicle testing, scenario-based virtual testing, which constructs virtual environments to simulate real scenarios and efficiently evaluates vehicle safety and risk decision-making capabilities, has become a core means for the safety evaluation of automated wheeled vehicles. This paper outlines the research progress of scenario-based virtual testing for automated wheeled vehicles (including highway autonomous vehicles and off-highway autonomous vehicles); classifies three key technologies in highway scenarios, hazard evaluation, hazardous scenario generation and generalization, and acceleration evaluation; and reveals the challenges faced when existing methods are migrated to agricultural vehicles, engineering vehicles, etc., such as low scenario adaptability, multi-dimensional coupling of risk targets, and weak data foundation. This study finds that current technologies have formed a symmetric framework in highway scenarios, but there are significant adaptability problems when migrating to off-highway scenarios due to scenario asymmetry. To this end, this paper proposes ideas for realizing off-highway scenario testing by adopting methods such as dynamic safety distance reconstruction, multi-physics simulation, and digital twin-driven approaches, providing theoretical support for building a unified safety assessment platform for automated wheeled vehicles. Full article

The rapid development of smart home and building technologies requires educational methods that facilitate the integration of theoretical knowledge with practical, system-level design skills. Computer-assisted tools play a crucial role in this process by enabling students to experiment with complex Internet of Things (IoT) and building automation ecosystems in a risk-free, iterative environment. This paper proposes a pedagogical framework that integrates simulation-based prototyping with collaborative and spatial design tools, supported by elements of design thinking and blended learning. The approach was implemented in a master’s-level Smart Building Systems course, to engage students in interdisciplinary projects where virtual modeling, digital collaboration, and contextualized spatial design were combined to develop user-oriented smart space concepts. Analysis of project outcomes and student feedback indicated that the use of simulation and visualization platforms may enhance technical competencies, creativity, and engagement. The proposed framework contributes to engineering education by demonstrating how computer-assisted environments can effectively support practice-oriented, user-centered learning. Its modular and scalable structure makes it applicable across IoT- and automation-focused curricula, aligning academic training with the hybrid workflows of contemporary engineering practice. Concurrently, areas for enhancement and modification were identified to optimize support for group and creative student work. Full article

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems. Full article

Rapid building damage assessments are vital for an effective earthquake response. In Japan, traditional Earthquake Damage Certification (EDC) surveys—followed by the issuance of Disaster Victim Certificates (DVCs)—are often inefficient. With advancements in remote sensing technologies and deep learning algorithms, their combined application has been explored for large-scale automated damage assessment. However, the scarcity of remote sensing data on damaged buildings poses significant challenges to this task. In this study, we propose an Uncertainty-Guided Fusion Module (UGFM) integrated into a standard decoder architecture, with a Pyramid Vision Transformer v2 (PVTv2) employed as the encoder. This module leverages uncertainty outputs at each stage to guide the feature fusion process, enhancing the model’s sensitivity to collapsed buildings and increasing its effectiveness under diverse conditions. A training and in-domain testing dataset was constructed using post-earthquake aerial imagery of the severely affected areas in Noto Prefecture. The model approximately achieved a recall of 79% with a precision of 68% for collapsed building extraction on this dataset. We further evaluated the model on an out-of-domain dataset comprising aerial images of Mashiki Town in Kumamoto Prefecture, where it achieved an approximate recall of 66% and a precision of 77%. In a quantitative analysis combining field survey data from Mashiki, the model attained an accuracy exceeding 87% in identifying major damaged buildings, demonstrating that the proposed method offers a reliable solution for initial assessment of major damage and its potential to accelerate DVC issuance in real-world disaster response scenarios. Full article

Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as the Internet of Things (IoT), blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems. Full article

Deep learning (DL) has revolutionized medical image analysis (MIA), enabling early anomaly detection, precise lesion segmentation, and automated disease classification. However, its clinical integration faces two major challenges: reliance on limited, narrowly annotated datasets that inadequately capture real-world patient diversity, and the inherent “black-box” nature of DL decision-making, which complicates physician scrutiny and accountability. Eye tracking (ET) technology offers a transformative solution by capturing radiologists’ gaze patterns to generate supervisory signals. These signals enhance DL models through two key mechanisms: providing weak supervision to improve feature recognition and diagnostic accuracy, particularly when labeled data are scarce, and enabling direct comparison between machine and human attention to bridge interpretability gaps and build clinician trust. This approach also extends effectively to multimodal learning models (MLMs) and vision–language models (VLMs), supporting the alignment of machine reasoning with clinical expertise by grounding visual observations in diagnostic context, refining attention mechanisms, and validating complex decision pathways. Conducted in accordance with the PRISMA statement and registered in PROSPERO (ID: CRD42024569630), this review synthesizes state-of-the-art strategies for ET-DL integration. We further propose a unified framework in which ET innovatively serves as a data efficiency optimizer, a model interpretability validator, and a multimodal alignment supervisor. This framework paves the way for clinician-centered AI systems that prioritize verifiable reasoning, seamless workflow integration, and intelligible performance, thereby addressing key implementation barriers and outlining a path for future clinical deployment. Full article

Increasing labor costs, labor shortage, high environmental impact, and low productivity levels are the main reasons that have led the construction industry to search for sustainable alternatives to conventional traditional construction techniques, such as Additive Construction. Large-scale concrete 3D printing has emerged as a viable alternative, which can address these major challenges. Through the high material efficiency, design flexibility, and automation levels provided, 3D printing can revolutionize the way buildings are designed and built. The seismic behavior of 3D-printed load bearing elements remains generally underexplored. To that scope, the structural design of a two-story building is investigated. The proposed methodology involves finite element models and stress analysis of critical structural members. The performance of the studied walls is further investigated using 3D solid element models and nonlinear constitutive laws to validate structural adequacy. Different printing patterns and structural details of unreinforced and reinforced 3D-printed concrete walls are analyzed through parametric analyses. The results indicate the acceptable response of 3D-printed load bearing elements, under certain construction configurations, as required by the existing regulatory framework. The proposed methodology could be applied for the design of such structures and for the optimization of printing patterns and reinforcing details. Full article

Interoperability between Building Information Modeling (BIM) and Facility Management (FM) depends on open, vendor-neutral standards. Yet, operational uptake remains constrained by fragmented workflows, incompatible schemas, and non-standardized delivery. This critical review synthesizes OpenBIM pathways—within the buildingSMART ecosystem (Industry Foundation Classes (IFC), Construction–Operations Building information exchange (COBie), Information Delivery Specification (IDS) v1.0, buildingSMART Data Dictionary (bSDD)) and the Level of Information Need (ISO 7817-1:2024)—across technical, managerial, and strategic dimensions. We searched major databases and used guided snowballing to screen a core corpus. Technically, persistent semantic inconsistencies and limited real-time, bidirectional exchange remain; open standards enable machine-checkable deliverables and API-friendly serializations. Managerially, weak Organizational Information Requirements (OIR) → Asset Information Requirements (AIR) → Exchange Information Requirements (EIR) alignment and unclear acceptance criteria undermine FM readiness. Strategically, procurement and risk management should mitigate vendor lock-in. We highlight gaps in FM ontologies and BIM–IoT synchronization and outline an agenda for Digital Twins, automation, and verifiable FM data quality within OpenBIM ecosystems. Full article

Global climate change mitigation has prompted the construction sector to pursue decarbonization strategies, with timber structures offering significant carbon reduction potential. Wood serves as a sustainable material that sequesters carbon during growth while reducing emissions across the entire construction supply chain. Robotic construction of timber structures is increasingly promoted as a low-carbon, intelligent alternative for small- and medium-scale projects, yet the energy consumption and environmental impacts of robotic automated assembly using self-tapping screws remain understudied. This study presents a construction-phase life-cycle assessment (LCA) of an innovative vertically mobile robotic construction system for automated timber structure. The system integrates a KUKA KR 6 R900 (KUKA Robotics Corporation, Augsburg, Germany) six-axis robot with an electrically actuated lifting platform and specialized end-effector, enabling fully autonomous assembly of a Layered Interlaced Timber Arch-Shell (LITAS) structure using Hinoki cypress timber and self-tapping screws. This research provides the first comprehensive LCA dataset for robotic screw-fastened timber construction and establishes a replicable framework for sustainable automated building practices, with methodology scalability enabling application to diverse timber construction scenarios and advancing intelligent and decarbonized transformation in the construction industry. Full article

Obstructive sleep apnea–hypopnea syndrome (OSAHS) is a prevalent sleep disorder strongly linked to increased cardiovascular and metabolic risk. While prior studies have explored snore-based analysis for OSAHS, they have largely focused on either detection or classification in isolation. Here, we present a two-stage framework that integrates precise snoring event detection with deep learning-based classification. In the first stage, we develop an Adaptive Multi-Feature Fusion Endpoint Detection algorithm (AMFF-ED), which leverages short-time energy, spectral entropy, zero-crossing rate, and spectral centroid to accurately isolate snore segments following spectral subtraction noise reduction. Through adaptive statistical thresholding, joint decision-making, and post-processing, our method achieves a segmentation accuracy of 96.4%. Building upon this, we construct a balanced dataset comprising 6830 normal and 6814 OSAHS-related snore samples, which are transformed into Mel spectrograms and input into ERBG-Net—a hybrid deep neural network combining ECA-enhanced ResNet18 with bidirectional GRUs. This architecture captures both spectral patterns and temporal dynamics of snoring sounds. The experimental results demonstrate a classification accuracy of 95.84% and an F1 score of 94.82% on the test set, highlighting the model’s robust performance and its potential as a foundation for automated, at-home OSAHS screening. Full article

The optimization of thermal performance in buildings is essential for sustainable urban development, yet the high cost and complexity of traditional thermal conductivity measurement methods limit broader research and educational applications. This study developed and validated a low-cost, replicable prototype that determines the thermal conductivity of roof tiles and composites using the Lee Disc method automated with Arduino-based acquisition. Standardized samples of ceramic, fiber–cement, galvanized steel, and steel coated with a castor oil-based polyurethane composite reinforced with miriti fiber (Mauritia flexuosa) were analyzed. The experimental setup incorporated integrated digital thermocouples and strict thermal insulation procedures to ensure measurement precision and reproducibility. Results showed that applying the biocompatible composite layer to metal tiles reduced thermal conductivity by up to 53%, reaching values as low as 0.2004 W·m⁻¹·K⁻¹—well below those of ceramic (0.4290 W·m⁻¹·K⁻¹) and fiber–cement (0.3095 W·m⁻¹·K⁻¹) tiles. The system demonstrated high accuracy (coefficient of variation < 5%) and operational stability across all replicates. These findings confirm the feasibility of open-source, low-cost instrumentation for advanced thermal characterization of building materials. The approach expands access to experimental research, promotes sustainable insulation technologies, and offers practical applications for both scientific studies and engineering education in resource-limited environments. Full article

The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient requirements management and validation. While digital twins promise transformative real-time decision-making, reliance on static unstructured data formats inhibits progress. This paper presents a novel framework that integrates Building Information Modelling (BIM) and Model-Based Systems Engineering (MBSE), using Linked Data principles to preserve semantic meaning during information exchange between physical abstractions and requirements. The proposed approach automates a step of compliance validation against regulatory standards explored through a case study, utilising requirements from a high-speed railway station fire safety system and a modified duplex apartment digital model. The workflow (i) digitises static documents into machine-readable MBSE formats, (ii) integrates structured data into dynamic digital models, and (iii) creates foundations for data exchange to enable compliance validation. These findings highlight the framework’s ability to enhance traceability, bridge static and dynamic data gaps, and provide decision-making support in digital twin environments. This study advances the application of Linked Data in infrastructure, enabling broader integration of ontologies required for dynamic decision-making trade-offs. Full article

This study investigates the integration of Large Language Models (LLMs) into supply chain workflow automation, with a focus on their technical, operational, financial, and socio-technical implications. Building on Dynamic Capabilities Theory and Socio-Technical Systems Theory, the research explores how LLMs can enhance logistics operations, increase workflow efficiency, and support strategic agility within supply chain systems. Using two developed prototypes, the Q inventory management assistant and the nodeStream© workflow editor, the paper demonstrates the practical potential of GenAI-driven automation in streamlining complex supply chain activities. A detailed analysis of system architecture and data governance highlights critical implementation considerations, including model reliability, data preparation, and infrastructure integration. The financial feasibility of LLM-based solutions is assessed through cost analyses related to training, deployment, and maintenance. Furthermore, the study evaluates the human and organisational impacts of AI integration, identifying key challenges around workforce adaptation and responsible AI use. The paper culminates in a practical roadmap for deploying LLM technologies in logistics settings and offers strategic recommendations for future research and industry adoption. Full article