Search Results (96)

Heritage Building Information Modeling (HBIM) has emerged as a key tool in advancing heritage conservation and sustainable management. Preceding reviews had typically concentrated on specific technical aspects but did not provide sufficient bibliometric analysis. This study aims to integrate existing HBIM research to identify key research patterns, emerging trends, and forecast future directions. A total of 1516 documents were initially retrieved from the Web of Science Core Collection using targeted search terms. Following a relevance screening, 1175 documents were related to the topic. CiteSpace 6.4.R1, VOSviewer 1.6.20, and Bibliometrix 4.1, three bibliometric tools, were employed to conduct both quantitative and qualitative assessments. The results show three historical phases of HBIM, identify core journals, influential authors, and leading regions, and extract six major keyword clusters: risk assessment, data acquisition, semantic annotation, digital twins, and energy and equipment management. Nine co-citation clusters further outline the foundational literature in the field. The results highlight growing scholarly interest in workflow integration and digital twin applications. Future projections emphasize the transformative potential of artificial intelligence in HBIM, while also recognizing critical implementation barriers, particularly in developing countries and resource-constrained contexts. This study provides a comprehensive and systematic framework for HBIM research, offering valuable insights for scholars, practitioners, and policymakers involved in heritage preservation and digital management. Full article

Background/Objectives: Reading is a core cognitive ability that plays a central role in children’s brain development and academic success. This review aims to examine the neuroplastic relationship between reading acquisition and white matter development from infancy through adolescence, with a focus on implications for literacy interventions and educational planning. Methods: A systematic review of 89 neuroimaging studies published between 1998 and 2024 was conducted. Eligible studies utilized diffusion tensor imaging (DTI) or structural MRI to investigate white matter changes related to reading behavior in children aged 0 to 18. Studies were identified through comprehensive searches in Web of Science and Scopus databases. Results: Children with stronger reading abilities consistently showed increased fractional anisotropy (FA) in key white matter pathways, such as the arcuate fasciculus and superior longitudinal fasciculus, supporting phonological processing and reading fluency. Longitudinal data suggest that early exposure to literacy enhances brain flexibility and white matter organization. In contrast, children with reading disabilities, including dyslexia, often show disorganized white matter structure, but compensatory pathways may emerge through targeted interventions. Conclusions: Reading experience is associated with measurable changes in white matter development across childhood. Early and sustained literacy engagement appears to optimize neural structures for reading. These findings can inform early diagnosis and improve pedagogical strategies for literacy education. Further research is needed on the long-term neurobiological effects of reading interventions. Full article

This study integrates quantitative scientometric analysis with a qualitative systematic review to comprehensively examine the evolution, core research themes, and emerging trends of three-dimensional (3D) visualization technology in architectural heritage conservation from 2005 to 2024. A total of 813 relevant publications were retrieved from the Web of Science Core Collection and analyzed using CiteSpace to construct a detailed knowledge map of the field. The findings highlight that foundational technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have laid a solid technical foundation for accurate heritage documentation and semantic representation. At the same time, the integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive technologies has facilitated a shift from static documentation to dynamic perception, real-time analysis, and interactive engagement. The analysis identifies four major research domains: (1) 3D data acquisition and modeling techniques, (2) digital heritage documentation and information management, (3) virtual reconstruction and interactive visualization, and (4) digital transformation and cultural narrative integration. Based on these insights, this study proposes four key directions for future research: advancing intelligence and automation in 3D modeling workflows; enhancing cross-platform interoperability and semantic standardization; realizing the full lifecycle management of architectural heritage; and enhancing cultural narratives through digital expression. This study provides a systematic and in-depth understanding of the role of 3D visualization in architectural heritage conservation. It offers a solid theoretical foundation and strategic guidance for technological innovation, policy development, and interdisciplinary collaboration in the digital heritage field. Full article

The number of services on the internet has experienced explosive growth, and the rapid and accurate discovery of required services among a vast array of similarly functioning services with differing degrees of quality has become a critical and challenging aspect of service computing. In this paper, we propose a trusted service discovery algorithm based on an ant colony system (TSDA-ACS). The algorithm integrates a credibility-based trust model with the ant colony search algorithm to facilitate the discovery of trusted web services. During the evaluation process, the trust model employs a pseudo-stochastic proportion to select nodes, where nodes with higher reputation have a greater probability of being chosen. The ant colony uses a voting method to calculate the credibility of service nodes, factoring in both credibility and non-credibility from the query node’s perspective. The algorithm employs an information acquisition strategy, a trust information merging strategy, a routing strategy, and a random wave strategy to guide ant search. To evaluate the effectiveness of the TSDA-ACS, this paper introduces the random walk search algorithm (RW), the classic max–min ant colony algorithm (MMAS), and a trustworthy service discovery based on a modified ant colony algorithm (TSDMACS) for comparison with the TSDA-ACS algorithm. The experiments demonstrate that this method can achieve the discovery of trusted web services with high recall and precision rates. Finally, the efficacy of the proposed algorithm is validated through comparison experiments across various network environments. Full article

Recent advances in computer science and geomatics have enabled the digitalization of complex two-dimensional and three-dimensional spatial environments and the sharing of geospatial data on the web. Simultaneously, the widespread adoption of Internet of Things (IoT) technology has facilitated the rapid deployment of low-cost sensor networks in various scientific applications. The integration of real-time IoT data acquisition in 3D urban environments lays the foundation for the development of Urban Digital Twins. This work proposes a possible low-cost solution as a sample of a structure for 3D digital twinning on the web, presenting a case study related to weather monitoring analysis. Specifically, an indoor-outdoor environmental conditions monitoring system integrated with 3D geospatial data on a 3D WebGIS platform was developed. This solution can be considered as a first step for monitoring human and environmental wellness within a geospatial analysis system that integrates several open-source modules that provide different kinds of information (geospatial data, 3D models, and IoT acquisition). The structure of this system can be valuable for municipalities and private stakeholders seeking to conduct environmental geospatial analysis using cost-effective solutions. Full article

Road surface information, encompassing aspects like road surface damages and facility distributions, is vital for maintaining and updating roads in smart cities. The proposed mobile sensing framework uses multi-modal sensors, including a GPS, gyroscope, accelerometer, camera, and Wi-Fi, integrated into a Jetson Nano to collect comprehensive road surface information. The collected data are processed, stored, and analyzed on the server side, with results accessible via RESTful APIs. This system enables the detection of road conditions, which are visualized through the web mapping technique. Based on this concept, the Mobile Sensor Framework for Road Surface analysis (MSF4RS) is designed, and its use significantly enhances road surface data acquisition and analysis. Key contributions include (1) the integration of multi-modal IoT sensors to capture comprehensive road surface data; (2) the development of a software environment that facilitates robust data processing; and (3) the execution of experiments using the MSF4RS, which synergistically combines hardware and software components. The framework leverages advanced sensor technologies and server-based computational methods and offers a user-friendly web interface for the dynamic visualization and interactive exploration of road surface conditions. Experiments confirm the framework’s effectiveness in capturing and visualizing road surface data, demonstrating significant potential for smart city applications. Full article

Background/Objectives: Patient safety is a critical component of healthcare quality, yet there remains a significant gap in understanding how patient safety knowledge progresses among nursing students throughout their educational journey. This scoping review aims to map and analyze the existing literature on the development of patient safety knowledge in undergraduate nursing education. Methods: This study will follow the Joanna Briggs Institute methodology for scoping reviews and adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses—Scoping Reviews) guidelines. A comprehensive search strategy will be employed across multiple databases, including CINAHL (Cumulative Index to Nursing and Allied Health Literature), ERIC (Education Resources Information Center), PubMed, Embase, SCOPUS, and Web of Science. The review will include studies published from 2019 to 2024 in English, Portuguese, and Spanish. Two independent reviewers will conduct study selection and data extraction. The data will be synthesized narratively, with quantitative data summarized using descriptive statistics and qualitative data analyzed thematically. Results: The review is expected to identify key patterns in the progression of patient safety knowledge among nursing students, including critical periods for knowledge acquisition, challenges in translating theoretical knowledge into practice, and effective educational strategies. The findings will be presented in both tabular and narrative forms, providing a comprehensive overview of the current state of patient safety education in nursing programs. Conclusions: The anticipated outcomes of this review have significant implications for nursing education, healthcare policy, and clinical practice. For educators, the findings will inform curriculum development and the design of targeted interventions to enhance patient safety competencies. From a policy perspective, the results could support the standardization of patient safety education across nursing programs. In clinical practice, the study may emphasize the importance of continuous professional development in patient safety. This scoping review aims to fill a critical gap in the literature by providing a comprehensive understanding of how patient safety knowledge progresses among nursing students. The findings are expected to contribute significantly to the advancement of nursing education and patient safety, ultimately fostering a culture of safety that benefits both healthcare providers and recipients. Full article

A focused crawler automatically retrieves, organizes, and extracts specific topic-related information from the internet for analysis and application. Currently, most focused crawlers assess the relevance of web pages to a given topic through methods such as keyword matching, semantic analysis, and link structures. However, these existing focused crawlers suffer from issues such as misleading directions and reduced accuracy due to the lack of semantic analysis of topic terms, as well as biased computation of topic relevance caused by the absence of effective weighting factors. To solve the above-mentioned problems, this study proposes a semantic and optimized focused crawler based on Semantic Graph and Genetic Algorithm. The proposed crawler eliminates ambiguous terms by constructing a semantic graph, optimizes the weighting factors of topic relevance with asymmetry by using a genetic algorithm, and combines both above two points to predict the priority of each unvisited hyperlink. The experiment results indicate that the proposed SG-GA Crawler improves the evaluation indicators compared with the other three focused crawlers, including VSM Crawler, SSRM Crawler, and SG Crawler. More specifically, the percentage improvement achieved by the proposed method exceeds 19%, 19%, and 13% in terms of three evaluation indicators, including the number of relevant web pages, acquisition rate, and average relevance, respectively. In conclusion, the proposed focused crawler can grab more quantity and higher quality topic-related web pages from the Internet. Full article

Tropical trees that remain evergreen and exhibit leaf litterfall that is gradual over time coexist with trees that are seasonally deciduous and exhibit pulsed litterfall. The manner in which these trees acquire, store, and contribute nutrients to the biogeochemical cycle may differ. Green and senesced leaves from deciduous Ficus prolixa trees were compared with those from Ficus tinctoria on the island of Guam. The results enabled stoichiometry and resorption calculations. F. prolixa’s young green leaf nitrogen (N) and potassium (K) concentrations were double, and the phosphorus (P) concentration was triple, those of F. tinctoria. Concentrations converged as the leaves aged such that no differences in concentration occurred for senesced leaves, indicating that nutrient resorption proficiency did not differ between the two species. In contrast, the resorption efficiency was greater for F. prolixa than F. tinctoria for all three nutrients. The N:P values of 6–11 and K:P values of 5–7 were greater for young F. tinctoria leaves than young F. prolixa leaves. The N:K values were 1.1–1.6 and did not differ between the two species. No differences in pairwise stoichiometry occurred for senesced leaves for any of the nutrients. These Guam results conformed to global trends indicating that seasonally deciduous plants are more acquisitive and exhibit greater nutrient resorption efficiency. The differences in how these two native trees influence the community food web and nutrient cycling lies mostly in the volume and synchronicity of pulsed F. prolixa litter inputs, and not in differences in litter quality. These novel findings inform strategic foresight about sustaining ecosystem health in Guam’s heavily threatened forests. Full article

This scoping review was conducted under the Joanna Briggs Institute (JBI) framework. It included primary studies published until 30 April 2023, obtained through a systematic search across PubMed, Web of Science, CINAHL, and MEDLINE databases. The review focused on primary studies that used the Delphi technique in nursing competence research, especially those related to defining core competency frameworks and developing instruments to assess professional competence. The goal was to analyze the different methodological approaches used by authors, synthesize them, and propose recommendations to enhance methodological rigor, reliability, and validity in the application of the Delphi technique. For this purpose, the following review question was established: “What is the available evidence on the use of the Delphi technique in the study of professional competence in nursing?”. The extracted textual elements underwent a content analysis, resulting in dimensions established through an inductive approach. Twenty studies were included, yielding insights into diverse methodological options for conducting Delphi studies, organised around a set of dimensions: (1) preparatory procedures; (2) procedures for accessing and selecting experts; (3) acquisition of expert input; (4) data analysis and consensus; and (5) ethical and legal procedures and guarantees. The study’s limitations include the inability to include certain studies due to a lack of response to requests for clarification from corresponding authors. Additionally, the primary studies’ methodological quality was not assessed, which is another relevant aspect. The study’s results offer valuable insights for researchers intending to utilise the Delphi technique within the context of the research referenced in the included studies. This information encompasses important methodological choices, highlighting their potential benefits and associated risks. The review was prospectively registered on the Open Science Framework (Registration No: osf.io/kp2vw). Full article

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA. Full article

Neurodegenerative diseases significantly impact individuals’ nutritional status. Therefore, nutritional education plays a crucial role in enhancing the understanding of food and nutrition, preventing or minimizing malnutrition, promoting well-being, and empowering patients and caregivers. Educommunication is a methodology that utilizes communication as a pedagogical tool, with the potential to positively enhance the teaching–learning process. This study aims to identify and map educommunication strategies designed to educate caregivers and patients with neurodegenerative diseases about food and nutrition. Methods: This scoping review followed the JBI Institute Reviewer’s Manual. The search was conducted between June 2022 and March 2023 in databases including PubMed/MEDLINE, Embase, Scopus, and Web of Science. Results: Out of 189 studies identified, 29 met the eligibility criteria, and only 3 were suitable for inclusion in this review. Conclusion: Studies using educommunication for food and nutrition education are scarce. Despite the limited number of studies included in this review, various educommunication strategies utilizing communication and information technologies were used. Educommunication strategies can facilitate knowledge acquisition in food and nutrition and change behaviors, resulting in health benefits for the participants. More studies on this subject are needed. Full article

Understanding subnivean life is crucial, particularly due to the major role in food webs played by small animals inhabiting this poorly known habitat. However, challenges such as remoteness and prolonged, harsh winters in the Arctic have hampered our understanding of subnivean ecology in this region. To address this problem, we present an improved autonomous, low-power system for monitoring small mammals under the snow in the Arctic. It comprises a compact camera paired with a single-board computer for video acquisition, a low-power-microcontroller-based circuit to regulate video acquisition timing, and motion detection circuits. We also introduce a novel low-power method of gathering complementary information on animal activities using passive infrared sensors. Meticulously designed to withstand extreme cold, prolonged operation periods, and the limited energy provided by batteries, the system’s efficacy is demonstrated through laboratory tests and field trials in the Canadian Arctic. Notably, our system achieves a standby power consumption of approximately 60 µW, representing a seventy-fold reduction compared to previous equipment. The system recorded unique videos of animal life under the snow in the High Arctic. This system equips ecologists with enhanced capabilities to study subnivean life in the Arctic, potentially providing insights to address longstanding questions in ecology. Full article

Recent advancements in digital technologies and automated analysis techniques applied to Historic Built Environment (HBE) demonstrate significant advantages in efficiently collecting and interpreting data for building conservation activities. Integrating digital image processing through Artificial Intelligence approaches further streamlines data analysis for diagnostic assessments. In this context, this paper presents a scoping review based on Scopus and Web of Science databases, following the PRISMA protocol, focusing on applying Deep Learning (DL) architectures for image-based classification of decay phenomena in the HBE, aiming to explore potential implementations in decision support system. From the literature screening process, 29 selected articles were analyzed according to methods for identifying buildings’ surface deterioration, cracks, and post-disaster damage at a district scale, with a particular focus on the innovative DL architectures developed, the accuracy of results obtained, and the classification methods adopted to understand limitations and strengths. The results highlight current research trends and the potential of DL approaches for diagnostic purposes in the built heritage conservation field, evaluating methods and tools for data acquisition and real-time monitoring, and emphasizing the advantages of implementing the adopted techniques in interoperable environments for information sharing among stakeholders. Future challenges involve implementing DL models in mobile apps, using sensors and IoT systems for on-site defect detection and long-term monitoring, integrating multimodal data from non-destructive inspection techniques, and establishing direct connections between data, intervention strategies, timing, and costs, thereby improving heritage diagnosis and management practices. Full article

The advancement of technology and its developments have provided the forensic sciences with many cutting-edge tools, devices, and applications, allowing forensics a better and more accurate understanding of the crime scene, a better and optimal acquisition of data and information, and faster processing, allowing more reliable conclusions to be obtained and substantially improving the scientific investigation of crime. This article describes the technological advances, their impacts, and the challenges faced by forensic specialists in using and implementing these technologies as tools to strengthen their field and laboratory investigations. The systematic review of the scientific literature used the PRISMA^® methodology, analyzing documents from databases such as SCOPUS, Web of Science, Taylor & Francis, PubMed, and ProQuest. Studies were selected using a Cohen Kappa coefficient of 0.463. In total, 63 reference articles were selected. The impact of technology on investigations by forensic science experts presents great benefits, such as a greater possibility of digitizing the crime scene, allowing remote analysis through extended reality technologies, improvements in the accuracy and identification of biometric characteristics, portable equipment for on-site analysis, and Internet of things devices that use artificial intelligence and machine learning techniques. These alternatives improve forensic investigations without diminishing the investigator’s prominence and responsibility in the resolution of cases. Full article