Search Results (2,437)

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments. Full article

In the modern business environment, where uncertainty and complexity make decision-making difficult, the need for robust, transparent and adaptable support tools is highlighted. The proposed method, named Flexible Decision Navigator for Evaluating Trends and Strategies (F-DeNETS), offers a complementary perspective to classic Artificial Intelligence (AI), Big Data and Multi-Criteria Decision-Making (MCDM) tools. Despite their broad use, these methods frequently suffer from critical sensitivities In the weighting of criteria and the handling of uncertainty, leading to compromised reliability and limited practical utility in environments with limited data availability. To bridge this gap, F-DeNETS integrates intuition and uncertainty into a transparent and statistically grounded process. It introduces a balanced approach that combines statistical evidence with human judgment, extending the boundaries of classic AI, Big Data and MCDM methods. Classic MCDM methods, although useful, are sometimes limited by subjectivity, staticity and dependence on large volumes of data. To fill this gap, F-DeNETS, a hybrid framework combining Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Non-Asymptotic Fuzzy Estimators (NAFEs) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), transforms expert judgments into statistically sound fuzzy quantifications, incorporates dynamic adaptation to new data, reduces bias and enhances reliability. A numerical application from the shipping industry demonstrates that F-DeNETS offers a flexible and interpretable methodology for optimal decisions in environments of high uncertainty. Full article

Large language models (LLMs) such as GPT-4 are increasingly integrated into research, industry, and enterprise workflows, yet little is known about how input file formats shape their outputs. While prior work has shown that formats can influence response time, the effects on readability, complexity, and semantic stability remain underexplored. This study systematically evaluates GPT-4’s responses to 100 queries drawn from 50 academic papers, each tested across four formats, TXT, DOCX, PDF, and XML, yielding 400 question–answer pairs. We have assessed two aspects of the responses to the queries: first, efficiency quantified by response time and answer length, and second, linguistic style measured by readability indices, sentence length, word length, and lexical diversity where semantic similarity was considered to control for preservation of semantic context. Results show that readability and semantic content remain stable across formats, with no significant differences in Flesch–Kincaid or Dale–Chall scores, but response time is sensitive to document encoding, with XML consistently outperforming PDF, DOCX, and TXT in the initial experiments conducted in February 2025. Verbosity, rather than input size, emerged as the main driver of latency. However, follow-up replications conducted several months later (October 2025) under the updated Microsoft Copilot Studio (GPT-4) environment showed that these latency differences had largely converged, indicating that backend improvements, particularly in GPT-4o’s document-ingestion and parsing pipelines, have reduced the earlier disparities. These findings suggest that the file format matters and affects how fast the LLMs respond, although its influence may diminish as enterprise-level AI systems continue to evolve. Overall, the content and semantics of the responses are fairly similar and consistent across different file formats, demonstrating that LLMs can handle diverse encodings without compromising response quality. For large-scale applications, adopting structured formats such as XML or semantically tagged HTML can still yield measurable throughput gains in earlier system versions, whereas in more optimized environments, such differences may become minimal. Full article

Energy efficiency is widely recognized as a critical strategy for reducing energy consumption in industrial systems. Improving energy efficiency has become a central point in industrial systems aiming to reduce energy consumption and operational costs. Industrial air compressors are among the most energy-intensive assets and often operate under static control policies that fail to adapt to real-time dynamics. This paper proposes a cognitive digital twin (CDT) framework that integrates reinforcement learning as, especially, a Proximal Policy Optimization (PPO) agent into the virtual replica of the air compressor system. CDT learns continuous from multidimensional telemetry which includes power, outlet pressure, air flow, and intake temperature, enabling autonomous decision-making, fault adaptation, and dynamic energy optimization. Simulation results demonstrate that PPO strategy reduces average SEC by 12.4%, yielding annual energy savings of approximately 70,800 kWh and a projected payback period of one year. These findings highlight the CDT potential to transform industrial asset management by bridging intelligent control. Full article

Objectives: Bovine coronaviruses (BCoV) are endemic worldwide, causing diarrhea, winter dysentery, and bovine respiratory disease in newborn calves. These lead to higher calf mortality, reduced growth of fattening cows, and lower milk production in adult cows, resulting in significant losses to the cattle industry. Since commercial preventive drugs are not available in China, and existing treatments can only reduce the mortality of sick calves without fundamental control, the development of safe and effective vaccines is crucial. Methods: Two mRNA vaccines targeting the BCoV spiny receptor-binding domain (S-RBD) were prepared: XBS01 and XBS02. These two mRNAs, optimized for coding by AI and encapsulated in lipid nanoparticles (LNPs), were injected intramuscularly into mice (10 μg per mouse, twice, 2 weeks apart); a blank control group was not immunized. Serum antibodies, memory B/T cell activation and cytokine secretion were assessed by ELISA, flow cytometry and ELISpot. Results: Both vaccines induced humoral and cellular immunity:anti-S-RBD IgG titers were higher than those of the control group, and there was memory B-cell production and T-cell activation. XBS02 was superior to XBS01 in terms of peak antibody, memory B-cell frequency, T-cell activation rate, and IFN-γ/IL-2 secretion, and showed a stronger Th 1 response. Conclusions: Both BCoV S-RBD mRNA vaccines had good immunogenicity, with XBS02 providing better protection. This study supports the optimization and application of BCoV mRNA vaccines and accumulates data for mRNA technology in veterinary practice. Full article

Soil degradation, declining fertility, and rising greenhouse gas emissions highlight the urgent need for sustainable soil management strategies. Among them, biochar has gained recognition as a multifunctional material capable of enhancing soil fertility, sequestering carbon, and valorizing biomass residues within circular economy frameworks. This review synthesizes evidence from 186 peer-reviewed studies to evaluate how feedstock diversity, pyrolysis temperature, and elemental composition shape the agronomic and environmental performance of biochar. Crop residues dominated the literature (17.6%), while wood, manures, sewage sludge, and industrial by-products provided more targeted functionalities. Pyrolysis temperature emerged as the primary performance driver: 300–400 °C biochars improved pH, cation exchange capacity (CEC), water retention, and crop yield, whereas 450–550 °C biochars favored stability, nutrient concentration, and long-term carbon sequestration. Elemental composition averaged 60.7 wt.% C, 2.1 wt.% N, and 27.5 wt.% O, underscoring trade-offs between nutrient supply and structural persistence. Greenhouse gas (GHG) outcomes were context-dependent, with consistent Nitrous Oxide (N₂O) reductions in loam and clay soils but variable CH₄ responses in paddy systems. An emerging trend, present in 10.6% of studies, is the integration of artificial intelligence (AI) to improve predictive accuracy, adsorption modeling, and life-cycle assessment. Collectively, the evidence confirms that biochar cannot be universally optimized but must be tailored to specific objectives, ranging from soil fertility enhancement to climate mitigation. Full article

Artificial intelligence (AI) is generating a profound and quick transformation in several areas of knowledge, as well as in industry and society on a global scale, and is considered one of the most significant technological advances of the present era. Understanding citizens’ attitudes toward AI is essential forguiding its development and implementation. To achieve this, valid and reliable instruments are needed to assess attitudesin different sociocultural contexts. With this objective, the General Attitudes towards Artificial Intelligence Scale (GAAIS) was adapted to Spanish. The sample comprised 644 participants: 327 men and 316 women, aged between 18 and 78 years (M = 33.06, SD = 14.91). The original two-factor structure (Positive GAAIS and Negative GAAIS) was validated using Confirmatory Factor Analysis (CFA). Both the fit indices and the internal consistency of the scale were adequate. Furthermore, the validity of the measure (i.e., convergent and discriminant) and the invariance of the model were confirmed. The analyses performed support the adequacy of the model and, therefore, the usefulness of the instrument, considering the ambivalence that people often experience regarding AI. The limitations of the study and the implications for the design of public policies and intervention strategies that promote the ethical, equitable, and socially responsible use of AI are discussed in this study. Full article

Surface coatings have proven highly effective in addressing the critical challenges of friction, wear, and corrosion on steel substrates, which are responsible for over 80% of mechanical failures in industrial applications. Recent research highlights that advanced coatings—such as ceramic carbides/nitrides, high-entropy alloys, and metal-matrix composites—significantly enhance hardness, wear resistance, and environmental durability through mechanisms including protective oxide film formation, solid lubrication, and microstructural refinement. Moreover, these coatings exhibit robust performance under combined tribological-corrosive (tribocorrosion) conditions, where synergistic interactions often accelerate material degradation. Key developments include multilayer and composite architectures that balance hardness with toughness, self-lubricating coatings capable of in situ lubricant release, and active or self-healing systems for sustained corrosion inhibition. Despite these advances, challenges remain in predicting coating lifetime under multifield service conditions and optimizing interfacial adhesion to prevent delamination. Future efforts should prioritize multifunctional coating designs, improved tribocorrosion models, and the integration of sustainable materials and AI-driven process optimization. This review consolidates these insights to support the development of next-generation coatings for extending the service life of steel components across demanding sectors such as marine, aerospace, and energy systems. Full article

This study aims to analyze various methods, including AI, that can be used to optimize error analysis in digital twins and highlight the advantages and disadvantages of these analysis methods. Furthermore, the study aims to present an automated solution for error analysis of MATLAB/Simulink-based digital twins. This solution can make the error analysis more efficient without the use of AI, meaning that it can be used even if the digital twin is not appropriately known, which can be a considerable advantage in the current automotive industry, where complex digital twins are commonly used for the development and optimization of E/E systems during different types of in-the-Loop simulations. Full article

For a considerable period, China’s eastern and western regions have grappled with imbalances in industrial development, with industrial leapfrogging emerging as a pivotal solution. This study examines the impact of artificial intelligence technology spillovers and sustainable innovation on industrial leapfrogging between eastern and western regions. Empirical analysis is conducted using panel data from 22 provinces and municipalities across eastern and western China spanning 2014–2024, employing both a spatial difference-in-differences model and a dual machine learning model. Findings reveal that both AI technology spillovers and sustainable innovation significantly enhance the efficiency of industrial leapfrogging across regions. Their synergistic effects are pronounced, generating positive spatial spillovers. Institutional environments exert a significant influence on leapfrog industrial development. By regulating AI technology environments and sustainable innovation environments, institutional frameworks enhance leapfrogging efficiency, though this mediation exhibits a dual-threshold effect: most western provinces have yet to cross the first threshold. Industrial and economic heterogeneity weaken the efficiency of AI technology spillovers and sustainable innovation in facilitating industrial leapfrogging between eastern and western regions. This research provides robust empirical support for addressing industrial development imbalances and enhancing industrial resilience between eastern and western regions. Full article

The demand for safe, high-quality, and minimally processed food has intensified interest in non-destructive analytical techniques capable of assessing freshness and safety in real time. Among these, near-infrared (NIR) spectroscopy and biosensors have emerged as leading technologies due to their rapid, reagent-free, and sample-preserving nature. NIR spectroscopy offers a holistic assessment of internal compositional changes, while biosensors provide specific and sensitive detection of biological and chemical contaminants. Recent advances in miniaturization, chemometrics, and deep learning have further enhanced their potential for inline and point-of-need applications across diverse food matrices, including meat, seafood, eggs, fruits, and vegetables. This review critically evaluates the operational principles, instrumentation, and current applications of NIR spectroscopy and biosensors in food freshness and safety monitoring. It also explores their integration, highlights practical challenges such as calibration transfer and regulatory hurdles, and outlines emerging innovations including hybrid sensing, Artificial Intelligence (AI) integration, and smart packaging. The scope of this review is to provide a comprehensive understanding of these technologies, and its objective is to inform future research and industrial deployment strategies that support sustainable, real-time food quality control. These techniques enable near real-time monitoring under laboratory and pilot-scale conditions, showing strong potential for industrial adaptation. The nature of these targets often determines the choice of transduction method. Full article

This review examines the role of industrial data in enabling artificial intelligence (AI) technologies within the framework of Industry 4.0. Key aspects of industrial data management, including collection, preprocessing, integration, and utilization for training AI models, are analyzed and systematically categorized. Criteria for assessing data quality are defined, covering accuracy, completeness, consistency, and confidentiality, and practical recommendations are proposed for preparing data for effective machine learning and deep learning applications. In addition, current approaches to data management are compared, and methods for evaluating and improving data quality are outlined. Particular attention is given to challenges and limitations in industrial contexts, as well as the prospects for leveraging high-quality data to enhance AI-driven smart manufacturing. Full article

Carbon dioxide (CO₂), the primary anthropogenic greenhouse gas, drives significant and potentially irreversible impacts on ecosystems, biodiversity, and human health. Achieving the Paris Agreement target of limiting global warming to well below 2 °C, ideally 1.5 °C, requires rapid and substantial global emission reductions. While recent decades have seen advances in clean energy technologies, carbon capture, utilization, and storage (CCUS) remain essential for deep decarbonization. Despite proven technical readiness, large-scale carbon capture and storage (CCS) deployment has lagged initial targets. This review evaluates CCS technologies and their contributions to net-zero objectives, with emphasis on sector-specific applications. We found that, in the iron and steel industry, post-combustion CCS and oxy-combustion demonstrate potential to achieve the highest CO₂ capture efficiencies, whereas cement decarbonization is best supported by oxy-fuel combustion, calcium looping, and emerging direct capture methods. For petrochemical and refining operations, oxy-combustion, post-combustion, and chemical looping offer effective process integration and energy efficiency gains. Direct air capture (DAC) stands out for its siting flexibility, low land-use conflict, and ability to remove atmospheric CO₂, but it’s hindered by high costs (~$100–1000/t CO₂). Conversely, post-combustion capture is more cost-effective (~$47–76/t CO₂) and compatible with existing infrastructure. CCUS could deliver ~8% of required emission reductions for net-zero by 2050, equivalent to ~6 Gt CO₂ annually. Scaling deployment will require overcoming challenges through material innovations aided by artificial intelligence (AI) and machine learning, improving capture efficiency, integrating CCS with renewable hybrid systems, and establishing strong, coordinated policy frameworks. Full article

Small-object detection (SOD) remains an important and growing challenge in computer vision and is the backbone of many applications, including autonomous vehicles, aerial surveillance, medical imaging, and industrial quality control. Small objects, in pixels, lose discriminative features during deep neural network processing, making them difficult to disentangle from background noise and other artifacts. This survey presents a comprehensive and systematic review of the SOD advancements between 2023 and 2025, a period marked by the maturation of transformer-based architectures and a return to efficient, realistic deployment. We applied the PRISMA methodology for this work, yielding 112 seminal works in the field to ensure the robustness of our foundation for this study. We present a critical taxonomy of the developments since 2023, arranged in five categories: (1) multiscale feature learning; (2) transformer-based architectures; (3) context-aware methods; (4) data augmentation enhancements; and (5) advancements to mainstream detectors (e.g., YOLO). Third, we describe and analyze the evolving SOD-centered datasets and benchmarks and establish the importance of evaluating models fairly. Fourth, we contribute a comparative assessment of state-of-the-art models, evaluating not only accuracy (e.g., the average precision for small objects (AP_S)) but also important efficiency (FPS, latency, parameters, GFLOPS) metrics across standardized hardware platforms, including edge devices. We further use data-driven case studies in the remote sensing, manufacturing, and healthcare domains to create a bridge between academic benchmarks and real-world performance. Finally, we summarize practical guidance for practitioners, the model selection decision matrix, scenario-based playbooks, and the deployment checklist. The goal of this work is to help synthesize the recent progress, identify the primary limitations in SOD, and open research directions, including the potential future role of generative AI and foundational models, to address the long-standing data and feature representation challenges that have limited SOD. Full article

Machine vision (MV) is reshaping numerous industries by giving machines the ability to understand what they “see” and respond without human intervention. This review brings together the latest developments in deep learning (DL), image processing, and computer vision (CV). It focuses on how these technologies are being applied in real operational environments. We examine core methodologies such as feature extraction, object detection, image segmentation, and pattern recognition. These techniques are accelerating innovation in key sectors, including healthcare, manufacturing, autonomous systems, and security. A major emphasis is placed on the deepening integration of artificial intelligence (AI) and machine learning (ML) into MV. We particularly consider the impact of convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer architectures on the evolution of visual recognition capabilities. Beyond surveying advances, this review also takes a hard look at the field’s persistent roadblocks, above all the scarcity of high-quality labeled data, the heavy computational load of modern models, and the unforgiving time limits imposed by real-time vision applications. In response to these challenges, we examine a range of emerging fixes: leaner algorithms, purpose-built hardware (like vision processing units and neuromorphic chips), and smarter ways to label or synthesize data that sidestep the need for massive manual operations. What distinguishes this paper, however, is its emphasis on where MV is headed next. We spotlight nascent directions, including edge-based processing that moves intelligence closer to the sensor, early explorations of quantum methods for visual tasks, and hybrid AI systems that fuse symbolic reasoning with DL, not as speculative futures but as tangible pathways already taking shape. Ultimately, the goal is to connect cutting-edge research with actual deployment scenarios, offering a grounded, actionable guide for those working at the front lines of MV today. Full article