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The Fourth Industrial Revolution (Industry 4.0) has profoundly transformed industries worldwide through the integration of advanced digital technologies, including artificial intelligence, digital twins, building information modeling (BIM), and the Internet of Things (IoT). The Architecture, Construction, and Engineering (ACE) sectors are increasingly adopting these innovations to meet the evolving demands of the global market. Within this dynamic context, Saudi Arabia has emerged as a front-runner and significant investor in this sector, as evidenced by the launch of ambitious mega-projects such as NEOM and The Line. These developments prompt valuable discussions about the readiness of graduates to adapt to rapid technological advancements and meet the current demands of the Saudi market. Although numerous studies have explored this issue, the Saudi context presents unique challenges and opportunities due to the accelerated pace of change within the ACE sectors, driven by the goals of Vision 2030. For this reason, this paper aims to address this gap by exploring the readiness of architectural programs in the context of Saudi Arabia to meet the demands of Industry 4.0. To achieve this, a comprehensive literature review was conducted, developing an analytical framework. Subsequently, a multiple-cases approach was employed, with an overall top-level discussion on the undergraduate architecture program subjects available in the five regions in Saudi Arabia. A combination of field observations, domain expertise, and evidence-based coding methods was employed to develop the SWOT analysis. The SWOT framework was utilized to identify key strengths, weaknesses, opportunities, and threats within the current academic programs. The findings were then analyzed in a comprehensive discussion, highlighting necessary transformations in existing programs. The methodology employed in our study involves prolonged engagement and persistent observation to enhance the quality and credibility of the discussion. This paper serves as a roadmap for guiding future educational reforms and aligning architectural education with emerging industry demands and technological advancements in the field. Four key themes are essential for aligning architectural education with Industry 4.0: sustainability in the built environment, innovation and creativity, digital applications in the built environment, and entrepreneurship and leadership in venture engineering. It also strongly emphasized sustainability courses and noted notable deficiencies in preparing students for a digitally driven professional landscape. For example, the average program comprises 162 credit hours and 58 courses, with only six related to Industry 4.0. The top five institutions offering Industry 4.0 courses ranked from highest to lowest are ARCH-U11, ARCH-U8, ARCH-U3, ARCH-U4, and ARCH-U15. ARCH-U11 offers the most Industry 4.0 courses, totaling 15, which account for 26.8% of its courses and 15% of its credit hours, in contrast to ARCH-U20, which offers no courses. The novelty of this research lies in its comprehensive analysis of the readiness of architecture program curricula from 20 Saudi universities to meet the requirements of Industry 4.0. Importantly, these findings support previous studies that established guidelines that mandate the inclusion of sustainability, innovation, and digital skills in architectural education programs. Contribution to the knowledge and findings is valuable for educational institutions, policymakers, and industry leaders, offering insights into evolving architectural education to meet future industry demands and foster technological innovation and sustainable development. Moreover, it provides actionable recommendations for curriculum development in alignment with Vision 2030. Contrary to expectations, findings show that lower-ranked universities offer more Industry 4.0-related courses than higher-ranked ones, emphasizing the need to align university evaluation standards with labor market demands. Full article

The escalating environmental threat of indoor overheating, exacerbated by global climate change, urbanisation, and population growth, poses a severe risk to public health worldwide, specifically to those regions which are exposed to extreme heat events, such as Australia. This study delves into the critical issue of overheating within residential buildings, examining the existing state of knowledge on overheating criteria and reviewing overheating guidelines embedded in (a) international standards and (b) national building codes. Each regulatory document is analysed based on its underlying thermal comfort model, metric, and indices. The advantages and limitations of each document are practically discussed and for each legislative document and standard, and the quantitative measures have been reviewed, analysed, and summarised. The findings illuminate a global reliance on simplistic indices, such as indoor air temperature and operative temperature, in the existing regulatory documents. However, other critical environmental parameters, such as relative humidity, indoor air velocity, and physiological parameters including metabolic heat production and clothing insulation, are often not included. The absence of mandatory regulations for overheating criteria in residential buildings in some countries, such as in Australian homes, prompts the call for a holistic approach based on a thermal index inclusive of relevant environmental and physiological parameters to quantify heat stress exposure based on human thermal regulation. Gaps and limitations within existing guidelines are identified, and recommendations are proposed to strengthen the regulatory framework for overheating risk assessment in residential buildings. The findings hold significance for policymakers, building energy assessors, architects, and public health professionals, providing direction for the improvement of existing, and development of new, guidelines that aim to enhance indoor thermal condition and population health while ensuring energy efficiency and sustainability in the building stock. Full article

Groundwater-dependent vegetation (GDV) is threatened globally by groundwater abstraction. Water resource managers require maps showing its distribution and habitat preferences to make informed decisions on its protection. This study, conducted in the southeast Pilbara region of Western Australia, presents a novel approach based on metrics summarising seasonal phenology (phenometrics) derived from Sentinel-2 imagery. We also determined the preferential habitat using ecological niche modelling based on land systems and topographic derivatives. The phenometrics and preferential habitat models were combined using a framework that allows for the expression of different levels of uncertainty. The large integral (LI) phenometric was capable of discriminating GDV and reduced the search space to 111 ha (<1%), requiring follow-up monitoring. Suitable habitat could be explained by a combination of land systems and negative topographic positions (e.g., valleys). This designated 13% of the study area as requiring protection against the threat of intense bushfires, invasive species, land clearing and other disturbances. High uncertainty represents locations where GDV appears to be absent but the habitat is suitable and requires further field assessment. Uncertainty was lowest at locations where the habitat is highly unsuitable (87%) and requires infrequent revisitation. Our results provide timely geospatial intelligence illustrating what needs to be monitored, protected and revisited by water resource managers. Full article

Unmanned aerial vehicles (UAVs) have seen widespread adoption across diverse sectors, including agriculture, logistics, surveillance, and disaster management, due to their capabilities for real-time data acquisition and autonomous operations. The integration of UAVs with Internet of Things (IoT) systems further amplifies their functionality, enabling sophisticated applications such as smart city management and environmental monitoring. In this context, blockchain technology plays a pivotal role by providing a decentralized, tamper-resistant ledger that facilitates secure data exchange between UAVs and connected devices. Its transparent and immutable characteristics mitigate the risk of a single point of failure, thereby enhancing data integrity and bolstering trust within UAV–IoT communication networks. However, the interconnected nature of these systems introduces significant security challenges, including unauthorized access, data breaches, and a variety of network-based attacks. These issues are further compounded by the limited computational capabilities of IoT devices and the inherent vulnerabilities of wireless communication channels. Recently, a lightweight mutual authentication scheme using blockchain was presented; however, our analysis identified several critical security flaws in these existing protocols, such as drone impersonation and session key disclosure. To address these vulnerabilities, we propose a secure and lightweight authentication scheme for multi-server UAV–IoT environments. The proposed protocol effectively mitigates emerging security threats while maintaining low computational and communication overhead. We validate the security of our scheme using formal methods, including the Real-Or-Random (RoR) model and BAN logic. Comparative performance evaluations demonstrate that our protocol enhances security while also achieving efficiency, making it well-suited for resource-constrained IoT applications. Full article

Crop diseases pose a significant threat to global food security, with both economic and environmental consequences. Early and accurate detection is essential for timely intervention and sustainable farming. This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. The review method includes an in-depth analysis of algorithm performance using key metrics such as accuracy, precision, recall, and F1 score across various datasets. We also highlight the data imbalances in commonly used datasets, particularly PlantVillage, and discuss the challenges posed by these imbalances. The research highlights critical insights regarding ML and DL models in crop disease detection. A primary challenge identified is the imbalance in the PlantVillage dataset, with a high number of healthy images and a strong bias toward certain disease categories like fungi, leaving other categories like mites and molds underrepresented. This imbalance complicates model generalization, indicating a need for preprocessing steps to enhance performance. This study also shows that combining Vision Transformers (ViTs) with Green Chromatic Coordinates and hybridizing these with SVM achieves high classification accuracy, emphasizing the value of advanced feature extraction techniques in improving model efficacy. In terms of comparative performance, DL architectures like ResNet50, VGG16, and convolutional neural network demonstrated robust accuracy (95–99%) across diverse datasets, underscoring their effectiveness in managing complex image data. Additionally, traditional ML models exhibited varied strengths; for instance, SVM performed better on balanced datasets, while RF excelled with imbalanced data. Preprocessing methods like K-means clustering, Fuzzy C-Means, and PCA, along with ensemble approaches, further improved model accuracy. Lastly, the study underscores that high-quality, well-labeled datasets, stakeholder involvement, and comprehensive evaluation metrics such as F1 score and precision are crucial for optimizing ML and DL models, making them more effective for real-world applications in sustainable agriculture. Full article

Argillaceous siltstone is prone to deformation and softening when exposed to water, which poses a great threat to practical engineering. There are significant differences in the degrees of damage to this type of rock caused by solutions with different water temperatures. This study aimed to better understand the effect of temperature on argillaceous siltstone by designing immersion tests at water temperatures of 5, 15, 25, and 35 °C, analyzing the mechanical properties and cation concentration shifts under each condition. A water temperature–force coupled geometric damage model for argillaceous siltstone was developed, incorporating a Weibull distribution function and composite damage factors to derive a statistical damage constitutive model. The findings reveal that, with increasing water temperature, the peak strength and elastic modulus of argillaceous siltstone display a concave trend, initially decreasing and then increasing, while the cation concentration follows a convex trend, first increasing and then decreasing. Between 15 and 25 °C, the stress–strain behavior transitions from a four-phase to a five-phase pattern, with pronounced plasticity. The model’s theoretical curves align closely with experimental data, with the Weibull parameters m and λ effectively capturing the rock’s strength and plastic characteristics. Changes in water temperature notably influence the damage variable D₁₂ in the context of water temperature–peak stress coupling, with D₁₂ initially increasing and then decreasing with higher temperatures. These research results can provide new methods for exploring the paths of soft rock disasters and provide guidance for designing defenses in geotechnical engineering. Full article

Cybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate potential security risks. Machine learning (ML)-based intrusion detection systems (IDSs) are mainly designed to detect malicious network traffic. Unfortunately, ML models have recently been demonstrated to be vulnerable to adversarial perturbation, and therefore enable potential attackers to crash the system during normal operation. Among different attacks, generative adversarial networks (GANs) have been known as one of the most powerful threats to cybersecurity systems. To address these concerns, it is important to explore new defense methods and understand the nature of different types of attacks. In this paper, we investigate four serious attacks, GAN, Zeroth-Order Optimization (ZOO), kernel density estimation (KDE), and DeepFool attacks, on cybersecurity. Deep analysis was conducted on these attacks using three different cybersecurity datasets, ADFA-LD, CSE-CICIDS2018, and CSE-CICIDS2019. Our results have shown that KDE and DeepFool attacks are stronger than GANs in terms of attack success rate and impact on system performance. To demonstrate the effectiveness of our approach, we develop a defensive model using adversarial training where the DeepFool method is used to generate adversarial examples. The model is evaluated against GAN, ZOO, KDE, and DeepFool attacks to assess the level of system protection against adversarial perturbations. The experiment was conducted by leveraging a deep learning model as a classifier with the three aforementioned datasets. The results indicate that the proposed defensive model refines the resilience of the system and mitigates the presented serious attacks. Full article

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth. Full article

Background and Objectives: Given that COVID-19 vaccination is a relatively recent development, particularly when compared to immunisation against other diseases, it is crucial to assess its efficacy in vaccinated populations. This literature review analysed studies that monitored antibody titres against SARS-CoV-2 in healthcare workers who received COVID-19 vaccines. Methods: Using the PICO (Population, Intervention, Comparators, Outcomes) model recommended in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines we included 43 publications which analyse antibody dynamics following primary vaccination, the effects of booster doses, and the influence of factors such as COVID-19C infection, age, and sex on antibody kinetics. Results: All the studies demonstrated a strong immunogenic response to the vaccines. Re-gardless of the vaccine used, over 95% of the pre-vaccination seronegative population be-came seropositive in all studies. Depending on the sampling intervals provided by the re-searchers, antibody levels were quantitatively highest during the first three months after vaccination, but levels inevitably declined over time. The monthly decline in antibodies observed in all these studies highlighted the necessity for booster doses. Studies analysing the impact of revaccination on antibody dynamics have confirmed that revaccination is an effective tool to boost humoral immunity against SARS-CoV-2. An-tibodies appear to persist for a longer period of time after revaccination, although they are subject to similar factors influencing antibody dynamics, such as age, comorbidities, and exposure to COVID-19. In addition, heterogeneous revaccination strategies have been shown to be more effective than homogeneous revaccination. Conclusions: Our review demonstrated that antibody levels against SARS-CoV-2 inevitably decline after vaccination, leaving the question of ongoing booster strategies open. The studies reviewed provided evidence of the effectiveness of booster vaccination, despite differences in age, sex, and prior COVID-19 infection. This suggests that repeated vaccination remains a highly effective method for mitigating the continued threat posed by COVID-19. Full article

The presence of pharmaceuticals in nature systems poses a threat to the environment, plants, animals, and, last but not least, human health. Their transport in soils, waters, and sediments plays important roles in the toxicity and bioavailability of pharmaceuticals. The mobility of pharmaceuticals can be affected by their interactions with organic matter and other soil and water constituents. In this study, a model agarose hydrogel enriched by humic acid as a representative of organic matter is used as a transport medium for pharmaceuticals. Sulphapyridine (as a representative of sulphonamide antibiotics) and diclofenac (as a representative of widely used non-steroidal anti-inflammatory drugs) were chosen for experiments in diffusion cells. Pharmaceuticals were passed through the hydrogel from the donor solution to the acceptor compartment and could interact with humic acids incorporated in the hydrogel. The lag time was prolonged if the hydrogel was enriched by humic acids from 134 to 390 s for sulphapyridine and from 323 to 606 s for diclofenac. Similarly, the incorporation of humic acids in the hydrogel resulted in a decrease in the determined diffusion coefficients. The decrease was stronger in the first stage of the experiment when diffusing particles could interact with vacant binding sites. Full article

Disaster-prone mountainous regions face complex human–environment conflicts resulting from the combined influences of natural disaster threats, ecosystem conservation, and resource development. This study takes Mentougou District as the research area, leveraging landscape character identification methods to develop a multidimensional evaluation framework integrating safety, ecology, and landscape aspects, providing a foundation for zoning and management decisions. Four characteristic elements—elevation, geomorphology, vegetation type, and land cover type—were extracted during the landscape character identification phase. Two-step clustering and eCognition multi-scale segmentation were used to identify 12 landscape character types (LCTs) and delineate Landscape Character Areas (LCAs). The MaxEnt model was applied during the evaluation phase to assess debris flow susceptibility. At the same time, AHP and ArcGIS spatial overlay methods were used to evaluate ecological resilience and landscape resource quality. The three-dimensional evaluation results for the 12 LCAs were clustered and manually interpreted, resulting in four levels of protection and development areas. Management strategies were proposed from three perspectives: debris flow disaster prevention, ecosystem conservation, and landscape resource development. This method provides a pathway to balance human–environment conflicts in disaster-prone mountainous regions, promoting scientific zoning management and sustainable development in vast mountainous areas. Full article

Heritage buildings are increasingly vulnerable to environmental challenges like air pollution and climate change. Traditional preservation methods primarily rely on periodic inspections and manual interventions and struggle to address these evolving and dynamic threats. This systematic review analyzes how integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies can transform cultural heritage preservation. Using the PRISMA guidelines, 92 articles from SCOPUS were reviewed, highlighting key risk management and environmental monitoring methodologies. The study found that while IoT enables real-time air quality and structural health monitoring, AI enhances data analysis, providing predictive insights. The combination of IoT and AI facilitates proactive risk management, ensuring more resilient conservation strategies. Despite the growing use of these technologies, adoption remains uneven, particularly in regions most impacted by climate change. The study identifies significant research gaps and proposes an innovative framework that leverages Heritage Building Information Modeling (H-BIM) and Digital Twin (DT) for continuous monitoring and predictive maintenance through a multi-step process, beginning with the digitalization of heritage assets using H-BIM, followed by the creation of real-time digital replicas via DT. By integrating advanced technologies, the framework offers a more adaptive and sustainable approach to preserving cultural heritage, addressing both immediate threats and long-term vulnerabilities. This research underscores the need for a global, technology-driven response to safeguard heritage buildings for future generations. Full article

Tomato leaf diseases pose a significant threat to plant growth and productivity, necessitating the accurate identification and timely management of these issues. Existing models for tomato leaf disease recognition can primarily be categorized into Convolutional Neural Networks (CNNs) and Visual Transformers (VTs). While CNNs excel in local feature extraction, they struggle with global feature recognition; conversely, VTs are advantageous for global feature extraction but are less effective at capturing local features. This discrepancy hampers the performance improvement of both model types in the task of tomato leaf disease identification. Currently, effective fusion models that combine CNNs and VTs are still relatively scarce. We developed an efficient CNNs and VTs fusion network named ECVNet for tomato leaf disease recognition. Specifically, we first designed a Channel Attention Residual module (CAR module) to focus on channel features and enhance the model’s sensitivity to the importance of feature channels. Next, we created a Convolutional Attention Fusion module (CAF module) to effectively extract and integrate both local and global features, thereby improving the model’s spatial feature extraction capabilities. We conducted extensive experiments using the Plant Village dataset and the AI Challenger 2018 dataset, with ECVNet achieving state-of-the-art recognition performance in both cases. Under the condition of 100 epochs, ECVNet achieved an accuracy of 98.88% on the Plant Village dataset and 86.04% on the AI Challenger 2018 dataset. The introduction of ECVNet provides an effective solution for the identification of plant leaf diseases. Full article

In the realm of global food security, plants serve as the primary source of sustenance. However, plant diseases pose a significant threat to this security. The process for diagnosing these diseases forms the bedrock of disease control efforts. The precision and expediency of these diagnoses wield substantial influence over disease management and the consequent reduction of economic losses. This research endeavors to diagnose the prevalent crops in Jordan, as identified by the Jordanian Department of Statistics for the year 2019. These crops encompass four key agricultural varieties: cucumbers, tomatoes, lettuce, and cabbage. To facilitate this, a novel dataset known as “Jordan22” was meticulously curated. Jordan22 was compiled by collecting images of diseased and healthy plants captured on Jordanian farms. These images underwent meticulous classification by a panel of three agricultural specialists well-versed in plant disease identification and prevention. The Jordan22 dataset comprises a substantial size, amounting to 3210 images. The results yielded by the CNN were remarkable, with a test accuracy rate reaching an impressive 0.9712. Optimal performance was observed when images were resized to 256 × 256 dimensions, and max pooling was used instead of average pooling. Furthermore, the initial convolutional layer was set at a size of 32, with subsequent convolutional layers standardized at 128 in size. In conclusion, this research represents a pivotal step towards enhancing plant disease diagnosis and, by extension, global food security. Through the creation of the Jordan22 dataset and the meticulous training of a CNN model, we have achieved substantial accuracy in disease detection, paving the way for more effective disease management strategies in agriculture. Full article

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions. Full article