Search Results (1,414)

Background/Objectives: In late 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a pandemic called the ‘coronavirus disease 2019’ (COVID-19). After the acute SARS-CoV-2 infection, many individuals (up to 33%) complained of unexplained symptoms involving multiple organ systems and were diagnosed as having Long COVID-19 (LC-19). Currently, LC-19 is inadequately defined, requiring the formation of consistent diagnostic parameters to provide a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy. LC-19 represents a significant burden on multiple levels. The reduced ability of workers to return to work or compromised work efficiency has led to consequences at national, economic, and societal levels by increasing dependence on community services. On a personal scale, the isolation and helplessness caused by the disease and its subsequent impact on the patient’s mental health and quality of life are incalculable. Methods: In this paper, we used Walker and Avants’ eight-step approach to perform a concept analysis of the term “Long COVID-19” and define its impact across these parameters. Results: Using this methodology, we provide an improved definition of LC-19 by connecting the clinical symptomology with previously under-addressed factors, such as mental, psychological, economic, and social effects. This definition of LC-19 features can help improve diagnostic procedures and help plan relevant healthcare services. Conclusions: LC-19 represents a complex and pressing public health challenge with diverse symptomology, an unpredictable timeline, and complex pathophysiology. This concept analysis serves as a tool for improving LC-19 definition, but it remains a dynamic disease with evolving diagnostic and therapeutic approaches, requiring deeper investigation and understanding of its long-term effects. Full article

Paper’s inherent hydrophilicity and porosity cause inadequate barrier properties, failing under high humidity/temperature. This study successfully developed a hydrophobic nanocoating agent (xLNPs-OTS) through silanization modification using D276 (lignin nanoparticles with a diameter of 276 nm) as the substrate and OTS (octadecyltrichlorosilane) as the functionalizing agent. By applying the coating to paper surfaces followed by a hot-pressing process, the paper achieved comprehensive performance enhancements, including superior water, oil, and vapor barrier properties, thermal stability, mechanical strength, frictional resistance, and self-cleaning capabilities. The Cobb 60 value of LOTSC3.5T120t30 (the coating made from the OTS silanized lignin with the coating amount of 3.5 g/m² and a hot-pressing at 120 °C for 30 min) coated paper is as low as 3.75 g/m², and can withstand hot water at 100 °C for 60 min. The Cobb 60 value of the LOTSC20T120t30 (the coating made from the OTS silanized lignin with the coating amount of 20 g/m² and a hot-pressing at 120 °C for 30 min) coated paper is reduced to 0.9 g/m², the Kit grade is 6, and all coated papers are endowed with self-cleaning features. This study advances lignin’s high-value utilization, driving sustainable packaging and supporting eco-friendly paper material development. Full article

This paper studies the effect of reversible hydrogen alloying of the TC4 alloy on the microstructure, phase composition, and mechanical properties before and after equal-channel angular pressing. It is shown that the introduction of 0.3% hydrogen followed by quenching from a temperature of 850 °C leads to the formation of a thin-plate α″-martensite, which made it possible to implement 6 passes (ε ~ 4.2) of pressing at 600 °C. As a result of the deformation of the TC4-H alloy and subsequent thermal vacuum treatment to remove hydrogen, an ultrafine-grained structure with an average size of the α-phase of 0.15 μm was formed, which led to strengthening of the alloy to 1490 MPa with a relative elongation of about 5% at room temperature. The reasons for a more significant refinement of the grain/subgrain structure and an increase in the tensile strength of the hydrogenated alloy after equal-channel angular pressing in comparison with hydrogen-free TC4 alloy are discussed. Full article

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article

In the context of the explosive growth of the digital economy, how inter-organizational connectivity affects corporate ambidextrous innovation has emerged as a pressing issue in the current digital economy. Based on the perspectives of the innovation ecosystem and digital coupling, this paper explores the inner mechanism of this issue through structural modeling by using the data of China’s high-tech enterprise alliance cooperation from 2015 to 2022. It is found in the empirical study that the local efficiency and reach rate of the digital innovation ecosystem have an inverted U-shaped relationship with exploratory innovation, and the local efficiency and reach rate of the digital innovation ecosystem have a negative effect on firm exploitative innovation. In addition, the level of firms’ digital transformation mediates the relationship between the local efficiency, reach rate, and ambidextrous innovation. The level of market development plays a moderating role in the relationship between the local efficiency, reach rate, and ambidextrous innovation. The findings provide a theoretical basis for the digital innovation ecosystem to realize the role of a “resource pool” through structural connections, which in turn provides important guidance for the digital transformation and innovation development of high-tech enterprises. Full article

This paper presents platforms as a solution to rethink how we build, addressing the pressing paradox between meeting growing housing demands. The construction sector has not fully grasped the advantages of platforms beyond standardisation and efficiency. In contrast, other sectors have begun acknowledging that platforms can capture increased value through interactions among firms within a networked ecosystem. Learning from other sectors, this paper investigates platforms in the construction context, aiming to define, classify, and assess their impact on the construction value chain. The research approach was abductive, involving a cross-sectoral review of 190 platforms across 16 Australian and New Zealand Standard Industrial Classification (ANZSIC) industries and semi-structured interviews with stakeholder groups of the construction value chain in Australia. The findings categorise platforms as physical, digital, or hybrid, highlighting their potential to move value-added activities upstream, facilitate collaboration, and foster innovation through data-driven insights. The paper’s novelty lies in the exhaustive cross-sectoral review, the classification of platforms in the construction context, and the proposition of a platform approach as a versatile framework tailored to diverse needs and circumstances that offers a fresh perspective on sustainable building practices. The practical contribution of this study lies in offering guidelines for industry practitioners aiming to develop or refine a platform-based approach tailored to the construction context. Full article

Model-Based Systems Engineering (MBSE) has gained significant attention from both industry and academia as an effective approach to managing product complexity. Despite its progress, current MBSE concepts, tools, languages, and methodologies face notable challenges in industrial applications, particularly in addressing design variability, ensuring model consistency, and enhancing operational efficiency. Based on the authors’ industry observations and literature analysis, this paper identifies the primary limitations of traditional MBSE, and introduces MBSE 2.0, a next-generation evolution characterized by comprehensive, integrated, and intelligent features. Key enabling technologies, such as model governance, integrated design methods, and AI-enhanced system design, are explored in detail. Additionally, several preliminary explorations were introduced under the guidance of the MBSE 2.0 philosophy. This study introduces the MBSE 2.0 concept to stimulate discussion and guide future efforts in academia and industry, emphasizing key advancements and highlighting several key and pressing perspectives to alleviate current limitations in industrial practice. Full article

This article discusses the digital activism model for advocacy of press freedom in Indonesia. This study examined the model and characteristics of digital activism and inhibiting factors in advocacy of press freedom, carried out by civil society organizations, social activists, and media professionals. Using qualitative methods, this paper provides answers to the question of how is the digital activism model aimed at countering threats to press freedom in a post-authoritarian country with a case study of Indonesia? How does digital activism emerge and form cross-sector collaboration? Given the broad scope of digital activism in Indonesia, the researchers chose two cities that represent the national and regional/provincial spectrum, namely Jakarta as the nation’s capital and Yogyakarta as a prominent student city in the country. The current study found a unique digital activism model in Indonesia that is a spectator collaboration: participants and initiators of activism are involved together in clicktivism, metavoicing, and assertion. Social activists and independent media activists develop systematic collective actions in the digital realm, such as online petitions and press releases, republication, and fundraising for the sustainability of the activism itself. This paper also found a gladiatorial model: media managers as victims and activists merged with more organized social movements, signaling that press freedom has become a collective agenda of pro-democracy advocates in Indonesia. Full article

Lithium–ion (Li–ion) batteries are fundamental for advancing intelligent and sustainable transportation, particularly in electric vehicles, due to their long lifespan, high energy density, and strong power efficiency. Ensuring the safety and reliability of EV batteries remains a critical challenge, as undetected faults can lead to hazardous failures or gradual performance degradation. While numerous studies have addressed battery fault detection, most existing reviews adopt isolated perspectives, often overlooking interdisciplinary and intelligent approaches. This paper presents a comprehensive review of advanced battery fault detection using modern machine learning, deep learning, and hybrid methods. It also discusses the pressing challenges in the field, including limited fault data, real-time processing constraints, model adaptability across battery types, and the need for explainable AI. Furthermore, emerging AI approaches such as transformers, graph neural networks, physics-informed models, edge computing, and large language models present new opportunities for intelligent and scalable battery fault detection. Looking ahead, these frameworks, combined with AI-driven strategies, can enhance diagnostic precision, extend battery life, and strengthen safety while enabling proactive fault prevention and building trust in EV systems. Full article

Large Language Models (LLMs) have emerged as powerful tools in cyber security, enabling automation, threat detection, and adaptive learning. Their ability to process unstructured data and generate context-aware outputs supports both operational tasks and educational initiatives. Despite their growing adoption, current research often focuses on isolated applications, lacking a systematic understanding of how LLMs align with domain-specific requirements and pedagogical effectiveness. This highlights a pressing need for comprehensive evaluations that address the challenges of integration, generalization, and ethical deployment in both operational and educational cyber security environments. Therefore, this paper provides a comprehensive and State-of-the-Art review of the significant role of LLMs in cyber security, addressing both operational and educational dimensions. It introduces a holistic framework that categorizes LLM applications into six key cyber security domains, examining each in depth to demonstrate their impact on automation, context-aware reasoning, and adaptability to emerging threats. The paper highlights the potential of LLMs to enhance operational performance and educational effectiveness while also exploring emerging technical, ethical, and security challenges. The paper also uniquely addresses the underexamined area of LLMs in cyber security education by reviewing recent studies and illustrating how these models support personalized learning, hands-on training, and awareness initiatives. The key findings reveal that while LLMs offer significant potential in automating tasks and enabling personalized learning, challenges remain in model generalization, ethical deployment, and production readiness. Finally, the paper discusses open issues and future research directions for the application of LLMs in both operational and educational contexts. This paper serves as a valuable reference for researchers, educators, and practitioners aiming to develop intelligent, adaptive, scalable, and ethically responsible LLM-based cyber security solutions. Full article

This article examines the intersecting forces of gender, class, and education in early twentieth-century Britain through a feminist reading of Pip Williams’ historical novel The Bookbinder of Jericho. Centering on the fictional character Peggy Jones—a working-class young woman employed in the Oxford University Press bindery—the study explores how women’s intellectual ambitions were constrained by economic hardship, institutional gatekeeping, and patriarchal social norms. By integrating close literary analysis with historical research on women bookbinders, educational reform, and the impact of World War I, the paper reveals how the novel functions as both a narrative of personal development and a broader critique of systemic exclusion. Drawing on the genre of the female Bildungsroman, the article argues that Peggy’s journey—from bindery worker to aspiring scholar—mirrors the real struggles of working-class women who sought education and recognition in a male-dominated society. It also highlights the significance of female solidarity, especially among those who served as volunteers, caregivers, and community organizers during wartime. Through the symbolic geography of Oxford and its working-class district of Jericho, the novel foregrounds the spatial and social divides that shaped women’s lives and labor. Ultimately, this study shows how The Bookbinder of Jericho offers not only a fictional portrait of one woman’s aspirations but also a feminist intervention that recovers and reinterprets the overlooked histories of British women workers. The novel becomes a literary space for reclaiming agency, articulating resistance, and criticizing the gendered boundaries of knowledge, work, and belonging. Full article

The production of solid biofuels from torrefied biomass holds significant potential for renewable energy applications. Durable pellet formation from severely torrefied biomass is hindered by the loss of natural binding properties, yet studies on mild torrefaction that preserves sufficient binding capacity for pellet production without external binders or changes to die conditions remain scarce. This paper investigated the production of fuel pellets from torrefied biomass without using external binders or adjusting pelletization parameters. Experiments were conducted using a mild torrefaction temperature (230 °C and 250 °C) and shorter residence time (10, 15, and 30 min). The torrefied materials were then subjected to pelletization using a single-pellet press; and the influence of torrefaction on the mechanical durability, hydrophobicity, and fuel characteristics of the pellets was examined. Results indicated that the mass loss ranging from 10 to 20% among the mild torrefaction treatments was less than the typical extent of mass loss due to severe torrefaction. Pellets made from torrefied biomass (torrefied pellets) had improvement in the hydrophobicity (moisture resistance) when compared to pellets made from untreated biomass (untreated pellets). Improved hydrophobicity is important for storage and transportation of pellets that are exposed to humid environmental conditions, as it reduces the risk of pellet degradation and spoilage. Thermogravimetric analysis of the pyrolysis and combustion behaviour of torrefied pellets indicated the improvement of fuel characteristics in terms of a much higher comprehensive pyrolysis index and greater thermal stability compared to untreated pellets, as evidenced by the prolonged burnout time and reduced combustion characteristics index. Residence time had a more significant impact on pellet durability than temperature, but the durability of the torrefied pellets was lower than that of the untreated pellets. Further research is required to explore the feasibility of producing binder-free durable pellets under mild torrefaction conditions. Overall, the study demonstrated that mild torrefaction could enhance the fuel quality and moisture resistance of biomass pellets, offering promising advantages for energy applications, despite some trade-offs in mechanical durability. Full article

Electricity theft remains a pressing challenge in modern smart grid systems, leading to significant economic losses and compromised grid stability. This paper presents a sensor-driven framework for electricity theft detection that leverages data collected from smart meter sensors, key components in smart grid monitoring infrastructure. The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. Smart meter data is compressed using Principal Component Analysis (PCA) and K-means clustering to extract representative consumption patterns, i.e., prototypes, achieving a 92% reduction in dataset size while preserving critical anomaly-relevant features. These prototypes are then used to train base-level one-class classifiers, specifically the One-Class Support Vector Machine (OCSVM) and the Gaussian Mixture Model (GMM). The outputs of these classifiers are normalized and fused in a meta-OCSVM layer, which learns decision boundaries in the transformed score space. Experimental results using the Irish CER Smart Metering Project (SMP) dataset show that the proposed sensor-based detection framework achieves superior performance, with an accuracy of 88.45% and a false alarm rate of just 13.85%, while reducing training time by over 75%. By efficiently processing high-frequency smart meter sensor data, this model contributes to developing real-time and energy-efficient anomaly detection systems in smart grid environments. Full article

Recycling plastic waste has emerged as one of the most pressing environmental challenges of the 21st century. One of the biggest challenges in polyethylene terephthalate (PET) recycling is the requirement to return bottles in their original, undeformed state. This necessitates storing large volumes of waste and takes up substantial space. Therefore, this paper seeks to address this issue and introduces a novel AIoT-based infrastructure that integrates the PET Bottle Identification Algorithm (PBIA), which can accurately recognize bottles regardless of color or condition and distinguish them from other waste. A detailed study of Azure Custom Vision services for PET bottle identification is conducted, evaluating its object recognition capabilities and overall performance within an intelligent waste management framework. A key contribution of this work is the development of the Algorithm for Citizens’ Trust Level by Recycling (ACTLR), which assigns trust levels to individuals based on their recycling behavior. This paper also details the development of a cost-effective prototype of the AIoT system, demonstrating its low-cost feasibility for real-world implementation, using the Asus Tinker Board as the primary hardware. The software application is designed to monitor the collection process across multiple recycling points, offering Microsoft Azure cloud-hosted data and insights. The experimental results demonstrate the feasibility of integrating this prototype on a large scale at minimal cost. Moreover, the algorithm integrates the allocation points for proper recycling and penalizes fraudulent activities. This innovation has the potential to streamline the recycling process, reduce logistical burdens, and significantly improve public participation by making it more convenient to store and return used plastic bottles. Full article

The crystallization of soluble salts is one of the most significant agents of deterioration affecting porous building materials in historical architecture. This process not only compromises the physical integrity of the materials but also results in considerable aesthetic, structural, and economic consequences. Soluble salts involved in these processes may originate from geogenic sources—including soil leachate, marine aerosols, and the natural weathering of parent rocks—or from anthropogenic factors such as air pollution, wastewater infiltration, and the use of incompatible restoration materials. This study examines the role of capillary rise as a primary mechanism responsible for the vertical migration of saline solutions from the soil profile into historic masonry structures, especially those constructed with calcareous stones. It describes how water retained or sustained within the soil matrix ascends via capillarity, carrying dissolved salts that eventually crystallize within the pore network of the stone. This phenomenon leads to a variety of damage types, ranging from superficial staining and efflorescence to more severe forms such as subflorescence, microfracturing, and progressive mass loss. By adopting a multidisciplinary approach that integrates concepts and methods from soil physics, hydrology, petrophysics, and conservation science, this paper examines the mechanisms that govern saline water movement, salt precipitation patterns, and their cumulative effects on stone durability. It highlights the influence of key variables such as soil texture and structure, matric potential, hydraulic conductivity, climatic conditions, and stone porosity on the severity and progression of deterioration. This paper also addresses regional considerations by focusing on the context of Spain, which holds one of the highest concentrations of World Heritage Sites globally and where many monuments are constructed from vulnerable calcareous materials such as fossiliferous calcarenites and marly limestones. Special attention is given to the types of salts most commonly encountered in Spanish soils—particularly chlorides and sulfates—and their thermodynamic behavior under fluctuating environmental conditions. Ultimately, this study underscores the pressing need for integrated, preventive conservation strategies. These include the implementation of drainage systems, capillary barriers, and the use of compatible materials in restoration, as well as the application of non-destructive diagnostic techniques such as electrical resistivity tomography and hyperspectral imaging. Understanding the interplay between soil moisture dynamics, salt crystallization, and material degradation is essential for safeguarding the cultural and structural value of historic buildings in the face of ongoing environmental challenges and climate variability. Full article