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Cordyceps militaris, a fungus widely used in traditional Chinese medicine and pharmacology, is recognized for its abundant bioactive compounds, including cordycepin and carotenoids. The growth, development, and metabolite production in various fungi are influenced by the complex interactions between regulatory cascades and light-signaling pathways. However, the mechanisms of gene regulation in response to light exposure in C. militaris remain largely unexplored. This study aimed to identify light-responsive genes and potential transcription factors (TFs) in C. militaris through an integrative transcriptome analysis. To achieve this, we reconstructed an expanded gene regulatory network (eGRN) comprising 507 TFs and 8662 regulated genes using both interolog-based and homolog-based methods to build the protein–protein interaction network. Aspergillus nidulans and Neurospora crassa were chosen as templates due to their relevance as fungal models and the extensive study of their light-responsive mechanisms. By utilizing the eGRN as a framework for comparing transcriptomic responses between light-exposure and dark conditions, we identified five key TFs—homeobox TF (CCM_07504), FlbC (CCM_04849), FlbB (CCM_01128), C6 zinc finger TF (CCM_05172), and mcrA (CCM_06477)—along with ten regulated genes within the light-responsive subnetwork. These TFs and regulated genes are likely crucial for the growth, development, and secondary metabolite production in C. militaris. Moreover, molecular docking analysis revealed that two novel TFs, CCM_05727 and CCM_06992, exhibit strong binding affinities and favorable docking scores with the primary light-responsive protein CmWC-1, suggesting their potential roles in light signaling pathways. This information provides an important functional interactive network for future studies on global transcriptional regulation in C. militaris and related fungi. Full article

The economy is significantly impacted by environmental, social, and governance (ESG) risks. The growth of the economy can be sped up by the effective management of ESG risks through sustainable business practices. To promote sustainable development and to secure the long-term welfare of employees, customers, and all other stakeholders in the economy, companies must adapt and reposition their business strategies and organizational cultures. The goal of this paper is to determine how a set of common ESG elements, chosen from the viewpoints of sustainability and well-being, influence economic growth in the Western Balkan countries. For each ESG component, we used different variables. The information pertains to the five Western Balkan countries of North Macedonia, Albania, Montenegro, Bosnia and Herzegovina, and Serbia. Because of a lack of data, Kosovo is excluded from the study. Then, we compared results from the analysis of the Western Balkan countries with a set of countries in Southeast Europe, which are members of the European Union and essentially coincide with the Western Europe countries. We performed multiple regression analysis with applied fixed effects to the data model. According to the study’s findings, each of the independent variables had no significant impact on the GDP’s annual growth of the Western Balkan countries, but two of the variables, life expectancy at birth and labor force participation, have certain impact on the GDP growth of Southeast Europe countries, which are members of the European Union. The green transition has gained significant importance in the Western Balkan countries as a crucial pathway toward sustainable economic growth, though it introduces a range of new social and economic challenges. Economically, these nations are confronted with considerable funding requirements for development. To build sustainable societies, it would be beneficial for these countries to explore more creative financing strategies. It is advised to establish financing frameworks that not only increase the transparency in policymaking but also ensure greater accountability in their execution. Full article

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the “soft strategy” and “hard strategy” in the framework of TDM are determined. In the soft strategy, we should focus on “shared mobility education” (shared mobility education, shared mobility publicity and shared mobility “environment” information) and “community organization” (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on “traffic planning and measures”, “dedicated lanes”, “parking facilities”, and “financial subsidies”. (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations. Full article

In recent years, applying building information modeling (BIM) digital technologies to cultural heritage management, monitoring, restoration, with the objective of advancing the sustainable development of both cultural heritage protection and tourism in China, has become a prominent research focus. However, there are a few studies that comprehensively investigate the relationship between BIM, Chinese cultural heritage, and sustainable tourism development. In order to explore the application of BIM in the protection and inheritance of Chinese cultural heritage, as well as its potential in promoting the sustainable development of cultural heritage tourism, this paper adopts the quantitative research method of bibliometrics to explore the research hotspots, development background, and evolution trends of BIM-driven sustainable development in Chinese cultural heritage tourism. By using data obtained from the China Knowledge Network database, multi-level bibliometrics analysis has been conducted through visualized knowledge graphs. The results suggest that the popular research keywords for driving sustainable cultural heritage tourism in China through BIM since year 2000 (23 years) include heritage tourism, heritage protection, building heritage, digital technology, and tourism development. Three research hotspots have been identified, which are cultural heritage protection, cultural heritage tourism development, and cultural heritage tourism management. In terms of tourism development and management, building virtual interactive scenes of cultural heritage facilitated by BIM to enhance tourism experience of tourists, using BIM to assist in efficient management, intelligent decision-making, and personalized services of cultural heritage tourism, assist in better promoting the sustainable development of cultural heritage tourism. In terms of coordinating and managing stakeholders in cultural heritage tourism, BIM technology provides technical support to the government, industry managers, and community residents in information communication, and industry management by constructing a digital model of cultural heritage to better balance the rights and interests of stakeholders. Full article

This study presents the first application of the pressure-state-response (PSR) model in the comprehensive assessment of construction and demolition waste (CDW) recycling benefits. Unlike traditional methods, the PSR model provides a multi-dimensional analysis that integrates economic, environmental, and social factors, offering a more holistic approach to evaluating the impact of CDW recycling strategies. This model enables stakeholders to better understand the pressures, states, and responses involved in CDW management, providing actionable insights to optimize recycling efforts and support sustainable urban development. Using the pressure-state-response (PSR) logical framework of sustainable economics, this paper systematically analyzed the comprehensive benefit mechanism of the recycling of construction and demolition waste (CDW), and designed a comprehensive benefit evaluation model for CDW recycling. At the same time, taking Chongqing as an example, the management status of construction and demolition waste, the supply and demand matching of sustainable recycling products, and the impact of the input and output of CDW management were analyzed. The results were as follows: (1) The recovery rate of urban manure fluctuated between 0.13 and 0.17, mainly in temporary landfill. (2) Based on the latest market demand data of CDW recycled products, the supply–demand ratio of recycled products fluctuated between 0.11 and 0.21. This change in the supply–demand ratio reflects improvements in recycling technologies, such as the introduction of C2CA technology, which has greatly increased the supply of high-quality recycled materials. In addition, government policies encouraging the use of recycled products in public projects have contributed to this shift, further aligning supply with market demand. (3) The benefit–cost ratio of CDW management reflects new recycling technologies and the improved efficiency of CDW management. The benefit–cost ratio, which currently fluctuates between 0.32 and 0.39, more accurately reflects the current state of CDW management, which is increasingly adopting advanced technologies, resulting in increased efficiency and reduced costs. Based on this, this paper discusses the supply–demand relationship and benefit–cost ratio in CDW management from supply-side and demand-side perspectives, and puts forward corresponding countermeasures and suggestions. The research results provide a clear reference for improving the efficiency of building demolition waste resource utilization, especially in optimizing the balance of market supply and demand, and improving the economic benefits of recycled products. By analyzing the balance between the supply and demand ratio and the benefit–cost ratio, this study helps inform policy makers, businesses, and investors, to promote the sustainable development of CDW recycling projects to maximize resource efficiency, while reducing environmental pressures. These results not only provide practical guidelines for the implementation of CDW recycling projects, but also lay a foundation for future policy formulation and the setting of industry standards. Full article

Traditional teaching methods in construction management education often face challenges in providing students with practical, real-world experiences crucial for skill development. To address these limitations, this study explores the potential of integrating building information modeling (BIM) and virtual reality (VR) as educational tools for construction management students. Our aim is to assess the effectiveness of a 4D-BIM-based VR simulation in enhancing student’s learning experiences and performance in construction project management. This research employs a mixed-method approach, combining quantitative data and qualitative insights from a comparative experiment involving undergraduate students. Quantitative data were collected through objective error detection measures in construction sequences and processes, while qualitative insights were gathered from participant feedback. The findings highlight that students using VR-based simulations detected more errors in construction sequences and processes than in traditional 2D drawings, showcasing the utility of BIM and VR-enabled approaches in teaching construction management. This study contributes to the ongoing discourse on integrating advanced technologies into educational practices, particularly in construction management, where practical hands-on experiences are crucial for skill development and real-world application. Full article

The integration of digital technologies in managing technical and design information is transforming architecture, engineering, and construction (AEC) processes within educational institutions. Despite this, construction education lacks practical, interactive learning tools, and there is insufficient collaboration between academia and the construction industry. To address these challenges, the CAMPUS 2.0 project at Pontificia Universidad Javeriana developed a web-based platform that integrates building information modeling (BIM) and gamification elements. This platform improves project coordination, facilitates interdisciplinary learning, and enhances the management of technical and design information for campus buildings. CAMPUS 2.0 also promotes collaboration and active user engagement, filling a critical gap in the practical tools in construction education. This study assesses the usability of CAMPUS 2.0 among 235 students, teachers, and staff members, demonstrating a positive impact on the university community. The findings provide insights into how digital tools can improve project management, interdisciplinary collaboration, and knowledge sharing within educational settings, offering broader implications for other institutions. Full article

In the context of urban sustainability and the development of resilient cities, the use of 4D geospatial data and the integration and association of building information with geographical information are of considerable interest. Achieving this integration is particularly significant in the scientific field from a technical standpoint but poses significant challenges due to the incompatibility between the two environments. This research proposes various methodologies for the effective integration of BIM/GIS data by analyzing their pros and cons and highlights the innovative aspects of the integration between these systems. Starting with the use of commercial software that has enabled the integration of a building’s 3D model within a GIS environment (this system is particularly useful for its ease of management and the potential for practical applications), this study progresses to an experimental virtual/augmented/mixed reality app developed by the authors that allows for the virtual integration of a building with its territorial context. It concludes with an innovative methodology that, by using the customizable and extensible libraries of the Cesium platform, facilitates the integration of structural data within a 4D geospatial space. This study demonstrates the feasibility of integrating BIM and GIS data despite inherent incompatibilities. The innovative use of Cesium platform libraries further enhances this integration, providing a comprehensive solution for intelligent and sustainable urban planning. By addressing the challenges of incompatibility, the final solution offers critical insights for a deeper understanding of evolving urban landscapes and for monitoring urban expansion and its environmental impacts. Full article

Synthetic Aperture Radar (SAR) imaging is essential for monitoring geomorphic changes, urban transformations, and natural disasters. However, the inherent complexities of SAR, particularly pronounced speckle noise, often lead to numerous false detections. To address these challenges, we propose the Multidirectional Attention Fusion Network (MDAF-Net), an advanced framework that significantly enhances image quality and detection accuracy. Firstly, we introduce the Multidirectional Filter (MF), which employs side-window filtering techniques and eight directional filters. This approach supports multidirectional image processing, effectively suppressing speckle noise and precisely preserving edge details. By utilizing deep neural network components, such as average pooling, the MF dynamically adapts to different noise patterns and textures, thereby enhancing image clarity and contrast. Building on this innovation, MDAF-Net integrates multidirectional feature learning with a multiscale self-attention mechanism. This design utilizes local edge information for robust noise suppression and combines global and local contextual data, enhancing the model’s contextual understanding and adaptability across various scenarios. Rigorous testing on six SAR datasets demonstrated that MDAF-Net achieves superior detection accuracy compared with other methods. On average, the Kappa coefficient improved by approximately 1.14%, substantially reducing errors and enhancing change detection precision. Full article

BIM is being strongly implemented in design companies. General contractors are using it during investment projects, and boards are using it for the maintenance and operation of buildings or infrastructure. Without the so-called BIM mandate (mandatory in public procurement), this is hard to imagine, even though it has already been implemented in many countries. In Poland, work in this direction is still being carried out. Due to the high complexity of investment and construction processes, the multiplicity of stakeholder groups, and conflicting interests, work on BIM adoption at the national level is hampered. The paper conducts an in-depth literature review of BIM implementation in Poland and presents a critical analysis of the current state of work. As a result of the literature research, proposals for changes in the processes of implementing the BIM mandate in Poland were formulated. This paper presents an excerpt from a potential BIM strategy and the necessary steps on the road to making BIM use mandatory. The results of the study indicate strong grassroots activity conducted by NGOs, which, independent of government actions, lead to measurable results. The authors propose that these activities must be coordinated by a single leading entity at the government level. The study could influence decisions made in other countries in the region or with similar levels of BIM adoption. BIM is the basis of the idea of the digital twin, and its implementation is necessary to achieve the goals of the doctrine of sustainable development and circular economy. Full article

Building information modeling (BIM) is the hottest topic of the last decade in the construction sector. BIM is interacting with other technologies toward the realization of digital twins. The integration of laser scanning technology and BIM is progressing. Increasingly, solid, mesh models are being semantically enriched for BIM. A point cloud can provide an excellent source of data for developing a BIM model. The BIM model will be refined not only geometrically but can also be saturated with non-graphical data. The problem is the lack of a clear methodology for compiling such models based on TLS and images. The research and development work between universities and companies has put modern digital solutions into practice. Thus, the purpose of this work was to develop a universal methodology for the acquisition and extraction of data from disconnected sources. In this paper, three BIM models were made based on point clouds derived from laser scanning. The case studies presented confirm the validity of the “scan to BIM approach, especially in the context of historic buildings (HBIMs). The paper posits that the integration of laser scanning, digital photogrammetry and BIM provides value in the preservation of heritage buildings. In the process of the practical work and an in-depth literature study, the ever-present limitations of BIM were identified as research challenges. The paper contributes to the discussion on the use of BIM in the design, construction and operation of buildings, including historic buildings. The acronym HBIM (heritage building information modeling) will increasingly resonate in the academic and practical work of the discipline of conservation and maintenance of historic buildings and cultural heritage sites. Full article

This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) share the same optics and, thus, cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors. Full article

The idea of experimental design has been widely used in subsampling algorithms to extract a small portion of big data that carries useful information for statistical modeling. Most existing subsampling algorithms of this kind are model-based and designed to achieve the corresponding optimality criteria for the model. However, data generating models are frequently unknown or complicated. Model-free subsampling algorithms are needed for obtaining samples that are robust under model misspecification and complication. This paper introduces two novel algorithms, called the Projection-Uniform Subsampling algorithm and its extension. Both algorithms aim to extract a subset of samples from big data that are space-filling in low-dimensional projections. We show that subdata obtained from our algorithms perform superiorly under the uniform projection criterion and centered $L_2$-discrepancy. Comparisons among our algorithms, model-based and model-free methods are conducted through two simulation studies and two real-world case studies. We demonstrate the robustness of our proposed algorithms in building statistical models in scenarios involving model misspecification and complication. Full article

Texts in medical fields containing sensitive information pose challenges for AI research usability. However, there is increasing interest in generating synthetic text to make medical text data bigger for text-based medical AI research. Therefore, this paper suggests a text augmentation system for cerebrovascular diseases, using a synthetic text generation model based on DistilGPT2 and a classification model based on BioBERT. The synthetic text generation model generates synthetic text using randomly extracted reports (5000, 10,000, 15,000, and 20,000) from 73,671 reports. The classification model is fine-tuned with the entire report to annotate synthetic text and build a new dataset. Subsequently, we fine-tuned a classification model by incrementally increasing the amount of augmented data added to each original dataset. Experimental results show that fine-tuning by adding augmented data improves model performance by up to 20%. Furthermore, we found that generating a large amount of synthetic text is not necessarily required to achieve better performance, and the appropriate amount of data augmentation depends on the size of the original data. Therefore, our proposed method reduces the time and resources needed for dataset construction, automating the annotation task and generating meaningful synthetic text for medical AI research. Full article

Building fires pose a critical threat to life and property. Therefore, accurate fire risk prediction is essential for effective building fire prevention and mitigation strategies. This study presents a novel approach to predicting fire risk in buildings by leveraging advanced machine learning techniques and integrating diverse datasets. Our proposed model incorporates a comprehensive range of 34 variables, including building attributes, land characteristics, and demographic information, to construct a robust risk assessment framework. We applied 16 distinct machine learning algorithms, integrating them into a stacking ensemble model to address the limitations of individual models and significantly improve the model’s predictive reliability. The ensemble model classifies fire risk into five distinct categories. Notably, although the highest-risk category comprises only 22% of buildings, it accounts for 54% of actual fires, highlighting the model’s practical value. This research advances fire risk prediction methodologies by offering stakeholders a powerful tool for informed decision-making in fire prevention, insurance assessments, and emergency response planning. Full article