Search Results (102,280)

Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article

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

This study aimed to develop an optimized film-forming gel for topical anti-acne applications by evaluating the antibacterial efficacy of propolis, honey, and royal jelly, individually and in combination with low-dose salicylic acid. The antibacterial activities were assessed against Staphylococcus aureus, Staphylococcus epidermidis, and Cutibacterium acnes using the inhibition zone assay. Film-forming gels were developed by evaluating the effects of type and concentration of polymers and plasticizers. Each formulation was evaluated for visual appearance, pH, viscosity, and drying time, along with the appearance of the corresponding film. The findings noted that propolis (1% w/w) exhibited the strongest antibacterial activity among individual bee products, producing an inhibition zone of 20.0 ± 1.0 mm against S. aureus. The combination of bee products with low-dose salicylic acid (0.1% w/w) markedly enhanced antibacterial efficacy, particularly against C. acnes (inhibition zone 40.8 ± 0.8 mm). Incorporation of this combination into the optimized film-forming gel, containing polyvinyl alcohol, Carbomer^® 940, polyethylene glycol 400, glycerin, and water, produced a formulation with balanced pH, suitable viscosity, 31 min drying time, and complete inhibition of S. aureus and S. epidermidis. Therefore, bee product-based film-forming gels, combined with low-dose salicylic acid, exhibited favorable physicochemical properties and showed promise as complementary anti-acne therapies. Full article

Cellulose, the most abundant polysaccharide on earth, possesses desirable properties such as biodegradability, low cost, and low toxicity, making it suitable for a wide range of applications. Being a non-conductive material, the structure of the nanocellulose can be modified or incorporated with conductive filler to facilitate charge transport between the polymer matrix and conductive components. Recently, cellulose-based ion exchange membranes (IEMs) have gained strong attention as alternatives to environmentally burdening synthetic polymers in electrochemical energy systems, owing to their renewable nature and versatile chemical structure. This article provides a comprehensive review of the structures, fabrication aspects and properties of various cellulose-based membranes for fuel cells and water electrolyzers, batteries, supercapacitors, and reverse electrodialysis (RED) applications. The scope includes an overview of various cellulose-based membrane fabrication methods, different forms of cellulose, and their applications in energy conversion and energy storage systems. The review also discusses the fundamentals of electrochemical energy systems, the role of IEMs, and recent advancements in the cellulose-based membranes’ research and development. Finally, it highlights current challenges to their performance and sustainability, along with recommendations for future research directions. Full article

The physical demands of overhead tasks can lead to musculoskeletal strain, particularly in scenarios requiring prolonged arm elevation such as in Chemical, Biological, Radiological, and Nuclear (CBRN) operations. To address this, an active shoulder exoskeleton was developed that is compatible with CBRN protective gear. The aim of this laboratory study was to assess the biomechanical and physiological effects of the system during upper limb tasks representative of real-world applications, without the use of protective suits. Twenty-two male participants performed two tasks with and without the exoskeleton: (1) 5 kg lifting task and (2) repetitive spraying tasks with a spray lance. Muscle activity of the m. anterior deltoid was measured using surface electromyography, while energy expenditure was assessed via spiroergometry. The exoskeleton significantly reduced muscular demands in the anterior deltoid, with a decrease of up to 40% during the spraying task and 29% percent during lifting task. Additionally, oxygen consumption per kilogram of body mass decreased by 6.5 to 8.2% across tasks. Participants reported lower fatigue and greater task manageability when using the exoskeleton, particularly for sustained and semi-static overhead postures. The results demonstrate that the exoskeleton effectively reduces workload during upper limb tasks. These findings support its application not only for soldiers in contaminated environments but also in industrial settings involving overhead work. Future research will need to validate these effects under realistic CBRN conditions to confirm operational compatibility. Full article

In recent years, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to tackle constrained multi-objective optimization problems (CMOPs). However, most of them still struggle to achieve a good balance among convergence, diversity, and feasibility. To address this issue, we develop an adaptive constraint-boundary learning-based two-stage dual-population evolutionary algorithm for CMOPs, referred to as CL-TDEA. The evolutionary process of CL-TDEA is divided into two stages. In the first stage, two populations cooperate weakly through environmental selection to enhance the exploration ability of CL-TDEA under constraints. In particular, the auxiliary population employs an adaptive constraint-boundary learning mechanism to learn the constraint boundary, which in turn enables the main population to more effectively explore the constrained search space and cross infeasible regions. In the second stage, the cooperation between the two populations drives the search toward the complete constrained Pareto front (CPF) through mating selection. Here, the auxiliary population provides additional guidance to the main population, helping it escape locally feasible but suboptimal regions by means of the proposed cascaded multi-criteria hierarchical ranking strategy. Extensive experiments on 54 test problems from four benchmark suites and three real-world applications demonstrate that the proposed CL-TDEA exhibits superior performance and stronger competitiveness compared with several state-of-the-art methods. Full article

This systematic review analyzes 4359 archaeologically relevant publications spanning 25 years to examine global disparities in archaeological remote sensing research between Global North and Global South participation. This study reveals deep inequalities among these regions, with 72.1% of research output originating from Global North-only institutions, despite these regions hosting less than half of UNESCO World Heritage Sites. The temporal analysis demonstrates exponential growth, with 47.2% of all research published in the last five years, indicating rapid technological advancement concentrated in well-resourced institutions. Sub-Saharan Africa produces only 0.6% of research output while hosting 9.4% of World Heritage Sites, highlighting a technology gap in heritage protection. The findings suggest an urgent need for coordinated interventions to address structural inequalities and promote technological fairness in global heritage preservation. The research employed bibliometric analysis of Scopus database records from four complementary search strategies, revealing that just three countries—Italy (20.3%), the United States (16.7%), and the United Kingdom (10.0%)—account for nearly half of all archaeological remote sensing research and applications worldwide. This study documents patterns that have profound implications for cultural heritage preservation and sustainable development in an increasingly digital world where advanced Earth observation technologies have become essential for effective heritage protection and archaeological research. Full article

A modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, including the heating rates, was proposed in this study to improve the accuracy of isothermal austenite formation kinetics prediction. Since the ultrafast heating process affects the behavior of ferrite recrystallization and austenite formation before the isothermal process, which in turn influences the subsequent isothermal austenite formation kinetics, the effects of varying austenitization temperatures and heating rates on isothermal austenite formation in cold-rolled quenching and partitioning (Q&P) steel, which remain insufficiently understood, were systematically investigated. Under a constant heating rate, the austenite formation rate initially increases and subsequently decreases as the austenitization temperature rises from formation start temperature A_c1 to finish temperature A_c3, and complete austenitization is achieved more quickly at elevated temperatures. At a given austenitization temperature, an increased heating rate was found to accelerate the isothermal transformation kinetics and significantly reduce the duration required to achieve complete austenitization. The experimental results revealed that both the transformation activation energy ($Q$) and material constant ($k_0$) decreased with increasing heating rates, while the Avrami exponent ($n$) showed a progressive increase, leading to the development of the heating-rate-dependent modified JMAK model. The model accurately characterizes the effect of varying heating rates on isothermal austenite formation kinetics, enabling kinetic curves prediction under multiple heating rates and austenitization temperatures and overcoming the limitation of single heating rate prediction in existing models, with significantly broadened applicability. Full article

The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems. Full article

Circular RNAs are a class of stable non-coding RNAs generated through a back-splicing mechanism. They are now recognized as central players in cell function and are no longer considered byproducts of transcription. CircRNAs regulate gene expression at the transcriptional, post-transcriptional, and translational levels by interacting with various molecules. They act as sponges for miRNAs and proteins, molecular scaffolds, and can also be translated into peptides. Although advances in next-generation sequencing and PCR methods have improved their identification and quantification, technical and bioinformatic challenges remain. Increasing evidence shows their involvement in cardiovascular diseases such as heart failure, hypertrophy, fibrosis, and atherosclerosis, with protective or deleterious effects depending on the context. Given their presence in biological fluids and extracellular vesicles, they can be considered promising biomarkers, but their therapeutic applications are still under investigation. Future studies including a better understanding of their mechanisms of action, the development of standardized validation methods, and potential clinical applications (prevention, early diagnosis, personalized therapies) in diseases are still needed. This review provides an updated overview of the knowledge regarding circRNAs and their translational role in health and disease with a particular focus on cardiovascular diseases. Full article

Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning. Full article

The study provides valuable insights into the sustainable utilization of edible tuber peels from the high mountains of the Argentinian Puna, which constitutes promising reserves of bioactive phenolic compounds with the potential to enhance the biofunctional properties of lactic acid bacteria. Thirty-two extracts derived from peels of different varieties of tubers, such as Oxalis tuberosa Mol., Ullucus tuberosus Caldas, and Solanum tuberosum L. were incorporated into lactobacilli cultures and individually evaluated. These selectively enhance the development of the probiotic strain Lactiplantibacillus plantarum ATCC 10241 and of Lacticaseibacillus paracasei CO1-LVP105 from ovine origin, without promoting the growth of a pathogenic bacteria set (Escherichia coli O157:H12 and ATCC 35218, Salmonella enterica serovar Typhimurium ATCC 14028, and S. corvalis SF2 and S. cerro SF16), in small amounts. To determine the main phenolic group concentrated in the phytoextracts, a bio-guided study was conducted. The most significant results were obtained by O. tuberosa phytochemicals added to the culture medium at 50 µg/mL, yielding promising increases in biofilm formation (78% for Lp. plantarum and 43% for L. paracasei) and biosurfactant activity (112% for CO1-LVP105 strain). These adaptive strategies developed by bacteria possess key biotechnological significance. Furthermore, the bio-detoxification capacity of phenol and o-phenyl phenol, particularly of the novel strain CO1-LVP105, along with its mode of action and genetic identification, is described for the first time to our knowledge. In conclusion, lactobacilli strains have potential as fermentation starters and natural products, recovered from O. tuberosa peels, and added into culture media contribute to multiple bacterial biotechnological applications in both health and the environment. Full article

Fungi play a crucial yet often unnoticed role in our lives and the health of our planet by breaking down organic matter through their diverse enzymes or eliminating environmental contamination, enhancing biomass pretreatment, and facilitating biofuel production. They offer transformative possibilities not only for improving the production of materials they naturally produce, but also for the production of non-native and even new-to-nature materials. However, despite these promising applications, the full potential of fungi remains untapped mainly due to limitations in our ability to control and optimize their complex biological systems. This review focuses on developments that address these challenges, with specific emphasis on fungal-derived rigid and flexible materials. To achieve this goal, the application of synthetic biology tools—such as programmable regulators, CRISPR-based genome editing, and combinatorial pathway optimization—in engineering fungal strains is highlighted, and how external environmental parameters can be tuned to influence material properties is discussed. This review positions filamentous fungi as promising platforms for sustainable bio-based technologies, contributing to a more sustainable future across various sectors. Full article

This study examines the ethical problems of using artificial intelligence (AI) applications in higher education, focusing on activities performed by university educators. Drawing on Slovak legislation that defines educators’ responsibilities, the study classifies their activities into three categories: teaching, scientific research, and other (academic management and self-directed professional development). From standpoint of methodology, a thematic review of 42 open-access, peer-reviewed articles published between 2022 and 2025 was conducted across the Web of Science and Scopus databases. Relevant AI applications and their associated ethical issues were identified and thematically categorized. Results of this study show that AI applications are extensively used across all analysed areas of university educators’ activities. Most notably used are applications that are generative language models, editing and paraphrasing tools, learning and assessment software, management and search tools, visualizing and design tools, and analysis and management systems. Their adoption raises ethical concerns which can be thematically grouped into six categories: privacy and data protection, bias and fairness, transparency and accountability, autonomy and oversight, governance gaps, and integrity and plagiarism. The results provide universities with a structured analytical framework to assess and address ethical risks related to AI use in specific academic activities. Although the study is limited to open-access literature, it offers a conceptual foundation for future empirical research and the development of ethical, institutionally grounded AI policies in higher education. Full article

This study focuses on a novel lightweight technology for manufacturing variable-axial fiber-reinforced polymer components. In the presented approach, channels following the load flow are implemented in an additively manufactured basic structure and impregnated continuous fiber bundles are pulled through these component-integrated cavities. Improved channel cross-section geometries to enhance the mechanical performance are proposed and evaluated. The hypothesis posits that increasing the surface area of the internal channels significantly reduces shear stresses between the polymer basic structure and the integrated continuous fiber composite. A series of experiments, including analytical, numerical, and microscopic analyses, were conducted to evaluate the mechanical properties of the composites formed, focusing on Young’s modulus and tensile strength. In addition, an important insight into the failure mechanism of the novel fiber composite is provided. The results demonstrate a clear correlation between the channel geometry and mechanical performance, indicating that optimized designs can effectively reduce shear stress, thus improving load-bearing capacities. The findings reveal that while fiber volume content influences the impregnation quality, an optimal balance must be achieved to enhance mechanical properties. This research contributes to the advancement of production technologies for lightweight components through additive manufacturing and the development of new types of composite materials applicable in various engineering fields. Full article