Search Results (2,225)

Environmental protection tax is levied based on various types of emitted pollutants and has a significant impact on the green behaviors and ESG (environmental, social, and corporate governance) performance of enterprises. This article explores the green effect and the impact of environmental protection tax on the green behavior of listed companies with in-depth empirical analysis based on the data of industrial enterprises listed on the A-shares from 2018 to 2022 in China. Research has found that the implementation of environmental protection tax has played a significant driving role in improving the overall performance level of corporate ESG, and this tax system has formed a driving force mechanism for enterprises to increase investment in green innovation and effectively improve their comprehensive ESG performance. Green innovation plays a significant intermediary role between environmental protection tax and corporate ESG performance. It is suggested that regions should adjust the applicable amount of environmental protection tax, increase green innovation, and standardize pollution control and emission reduction regulations. Full article

Triple-negative breast cancer (TNBC) remains one of the most aggressive and treatment-resistant forms. TNBC is an aggressive and therapeutically resistant subtype of breast cancer, marked by the absence of estrogen, progesterone, and HER2 receptors. The lack of defined molecular targets significantly limits treatment options and contributes to high recurrence rates. Among the key pathways involved in TNBC progression and resistance, the CXCR4/CXCL12 chemokine axis has emerged as a critical player. CXCR4, a G-protein-coupled receptor, binds specifically to its ligand CXCL12, promoting tumour cell proliferation, metastasis, immune evasion, and stromal remodelling. Its overexpression is frequently associated with poor prognosis, disease progression, and resistance to conventional therapies in TNBC. This review explores how the chemokine receptor type 4 (CXCR4/CXCL12) axis facilitates drug resistance through mechanisms such as epithelial–mesenchymal transition (EMT), cancer stemness, and microenvironmental interactions. Notably, CXCR4 antagonists like plerixafor, balixafortide, and POL5551 have shown encouraging preclinical and clinical results, particularly when combined with chemotherapy or immunotherapy. Additionally, innovative strategies, including radiopharmaceuticals, peptide inhibitors, and nanotechnology-based delivery platforms, offer expanded therapeutic avenues. Despite persistent challenges such as tumour heterogeneity and potential toxicity, growing clinical evidence supports the translational relevance of this axis. This manuscript provides an in-depth analysis of CXCR4/CXCL12-mediated drug resistance in TNBC and evaluates current and emerging therapeutic interventions. Full article

The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production. Full article

This paper explores how primary school teachers integrate digital technologies into their classroom practice, with a particular focus on the extent to which their stated intentions align with what actually takes place during lessons. Drawing on data from the Bulgarian SUMMIT project on digital transformation in education, the study employed a mixed-methods design combining semi-structured interviews, structured lesson observations, and analysis of teaching materials. The sample included 44 teachers from 26 Bulgarian schools, representing a range of educational contexts. The analysis was guided by the Digital Technology Integration Framework (DTIF), which distinguishes between three modes of technology use—Support, Extend, and Transform—based on the depth of pedagogical change. The findings indicated a strong degree of consistency between teachers’ accounts and observed practices in areas such as the use of digital tools for content visualisation, lesson enrichment, and reinforcement of knowledge. At the same time, the study highlights important gaps between teachers’ aspirations and classroom realities. Although many spoke of wanting to promote independent exploration, creativity, collaboration, and digital citizenship, these ambitions were rarely realised in observed lessons. Pupil autonomy and opportunities for creative digital production were limited, with extended and transformative practices appearing only occasionally. No significant subject-specific differences were identified: teachers across disciplines tended to rely on the same set of familiar tools, while more advanced or innovative uses of technology remained rare. Rather than offering a definitive account of progress, the study raises critical questions about teachers’ digital pedagogical competencies, contextual constraints and the depth of technology integration in everyday classroom practice. While digital tools are increasingly present, their use often remains limited to supporting traditional instruction, with extended and transformative applications still aspirational rather than routine. The findings draw attention to context-specific challenges in the Bulgarian primary education system and the importance of aligning digital innovation with pedagogical intent. This highlights the need for sustained professional development focused on learner-centred digital pedagogies, along with stronger institutional support and equitable access to infrastructure. Full article

Macau’s Ching Chau Hill, as a composite entity of modern industrial heritage and natural cultural landscape, faces the dual challenges of conservation and regeneration. This study takes Ching Chau Hill as a case study, integrating structural equation modeling (SEM) with Geographic Information System (GIS) technology and combining the theory of the creative class, to construct an evaluation model of “industrial heritage-creative perception-cultural innovation.” Through questionnaire surveys, data from the creative class were collected, and SEM was employed for path analysis and hypothesis testing, while GIS was used for spatial analysis and visualization. This study systematically explores the creative perception pathways of industrial heritage value from the perspective of the creative class and its driving mechanisms for cultural inheritance and innovation. This study found that the retention rate of industrial structures (73%) and the “sacred-industrial” axis formed by the integrity of the spatial sequence (β = 0.58) together constitute the core of the material attachment path, and there is a significant threshold for the site identity effect: when the material authenticity score exceeds the 3.5 critical point, the identity value jumps by 37.8%, which provides a quantitative basis for the precise protection of “ruin aesthetics”. In the process of transforming cultural inheritance into innovative practice, the participation in creative activities showed a mediating effect of 72.1%, and the driving efficiency of co-creation activities was ten times higher than that of ceremonial guided tours, confirming the core position of “learning by doing” in heritage revitalization. The results show the following: (1) the creative class’s perception of the aesthetic uniqueness and historical memory of Ching Chau Hill’s industrial heritage significantly and positively influences their recognition of its creative value; (2) spatial accessibility and environmental atmosphere are key geographical factors affecting creative perception; (3) recognition of creative value effectively drives the innovative transformation of cultural heritage by stimulating participation willingness and innovative ideas. This study provides a strategy basis with both theoretical depth and practical guidance value for the revitalization and utilization of industrial heritage in post-industrial urban renewal. Full article

Evaluating green data centers is a multi-attribute decision problem. To enhance the rigor and precision of green data center assessment, this study verifies the weighting of the national green data center evaluation index system using the Analytic Hierarchy Process (AHP) with the participation of 19 domain experts from various data center sectors. The aim is to gain an in-depth understanding of the perspectives and priorities of different types of institutions regarding evaluation indicators and to investigate the underlying reasons for these perspectives and priorities. Through an analysis of expert sample distribution, this paper reveals the preferences of financial, internet, research, and design, as well as technical consulting service institutions, regarding indicators such as energy-efficient utilization, computational resource utilization, green low-carbon development, scientific layout, and intensive construction. Specifically, financial institutions tend to place a relatively lower emphasis on energy efficiency due to their focus on transaction speed and security. In contrast, internet companies prioritize efficient utilization of computational resources. Research and design institutions consider scientific layout and intensive construction more crucial, while technical consulting service institutions emphasize green and low-carbon development. Meanwhile, we identified substantial discrepancies among experts in determining the weights of specific indicators, suggesting a lack of consensus within the industry about the correlation between these indicators and green data centers. To propel the sustainable development of green data centers, future assessments should refine evaluation dimensions, consider disparities such as data center types and embrace regional differences, actively adopt novel technologies and innovative practices, and establish mechanisms for long-term monitoring and evaluation. Full article

With the iterative evolution of SLAM (Simultaneous Localization and Mapping) technology in the robotics domain, the SLAM paradigm based on three-dimensional Gaussian distribution models has emerged as the current state-of-the-art technical approach. This research proposes a novel MSGS-SLAM system (Monocular Semantic Gaussian Splatting SLAM), which innovatively integrates monocular vision with three-dimensional Gaussian distribution models within a semantic SLAM framework. Our approach exploits the inherent spherical symmetries of isotropic Gaussian distributions, enabling symmetric optimization processes that maintain computational efficiency while preserving geometric consistency. Current mainstream three-dimensional Gaussian semantic SLAM systems typically rely on depth sensors for map reconstruction and semantic segmentation, which not only significantly increases hardware costs but also limits the deployment potential of systems in diverse scenarios. To overcome this limitation, this research introduces a depth estimation proxy framework based on Metric3D-V2, which effectively addresses the inherent deficiency of monocular vision systems in depth information acquisition. Additionally, our method leverages architectural symmetries in indoor environments to enhance semantic understanding through symmetric feature matching. Through this approach, the system achieves robust and efficient semantic feature integration and optimization without relying on dedicated depth sensors, thereby substantially reducing the dependency of three-dimensional Gaussian semantic SLAM systems on depth sensors and expanding their application scope. Furthermore, this research proposes a keyframe selection algorithm based on semantic guidance and proxy depth collaborative mechanisms, which effectively suppresses pose drift errors accumulated during long-term system operation, thereby achieving robust global loop closure correction. Through systematic evaluation on multiple standard datasets, MSGS-SLAM achieves comparable technical performance to existing three-dimensional Gaussian model-based semantic SLAM systems across multiple key performance metrics including ATE RMSE, PSNR, and mIoU. Full article

With the growing application of unmanned aerial vehicles (UAVs) in complex, stochastic environments, autonomous navigation and obstacle avoidance represent critical technical challenges requiring urgent solutions. This study proposes an innovative deep reinforcement learning (DRL) framework that leverages multimodal perception through the fusion of LiDAR and depth camera data. A sophisticated multi-sensor data preprocessing mechanism is designed to extract multimodal features, significantly enhancing the UAV’s situational awareness and adaptability in intricate, stochastic environments. In the high-level decision-maker of the framework, to overcome the intrinsic limitation of low sample efficiency in DRL algorithms, this study introduces an advanced decision-making algorithm, Soft Actor-Critic with Prioritization (SAC-P), which markedly accelerates model convergence and enhances training stability through optimized sample selection and utilization strategies. Validated within a high-fidelity Robot Operating System (ROS) and Gazebo simulation environment, the proposed framework achieved a task success rate of 81.23% in comparative evaluations, surpassing all baseline methods. Notably, in generalization tests conducted in previously unseen and highly complex environments, it maintained a success rate of 72.08%, confirming its robust and efficient navigation and obstacle avoidance capabilities in complex, densely cluttered environments with stochastic obstacle distributions. Full article

In the contemporary international context, the preventive conservation of cultural relics has become a widespread consensus. “Risk management” has emerged as a pivotal research focus at the present stage. However, the preventive protection of cultural relics is confronted with deficiencies in risk assessment and prediction. There is an urgent requirement for research to present a comprehensive and in-depth overview of the frontier technologies applicable to the preventive protection of cultural relics, with a particular emphasis on risk prevention and control. Additionally, it is essential to delineate the prospects for future investigations and developments in this domain. Consequently, this study employs bibliometric methods, applying CiteSpace (6.3.R1) and Biblioshiny (4.3.0) to perform comprehensive visual and analytical examinations of 392 publications sourced from the Web of Science (WoS) database covering the period 2010 to 2024. The results obtained from the research are summarized as follows: First, it is evident that scholars originating from China, Italy, and Spain have exhibited preponderant publication frequencies, contributing the largest quantity of articles. Second, augmented reality, digital technology, and risk-based analysis have been identified as the cardinal research frontiers. These areas have attracted significant scholarly attention and are at the forefront of innovation and exploration within the discipline. Third, the “Journal of Culture Heritage” and “Heritage Science” have been empirically determined to be the most frequently cited periodical within this particular field of study. Moreover, over the past decade, under the impetus and influence of the concept of Intangible Cultural Heritage, virtual reality, digital protection, and 3D models have progressively evolved into the central and crucial topics that have pervaded and shaped the research agenda. Finally, with respect to future research trajectories, there will be a pronounced focus on interdisciplinary design. This will be accompanied by an escalation in the requisites and standards for preventive conservation. Specifically, the spotlight will be cast upon aspects such as the air quality within the preservation environment of cultural relics held in collections, the implementation and efficacy of environmental real-time monitoring systems, the utilization and interpretation of big data analysis and early warning mechanisms, as well as the comprehensive and in-depth risk analysis of cultural relics. These multifaceted investigations will be essential for advancing understanding and safeguarding of cultural heritage. These findings deepen our grasp of how risk management in cultural heritage conservation has progressed and transformed between 2010 and 2024. Furthermore, the study provides novel insights and directions for subsequent investigations into risk assessment methodologies for heritage collections. Full article

Ground-penetrating radar surveys on structures have a wide range of applications, and they are very useful in solving engineering problems: from detecting reinforcement, studying concrete characteristics, unfilled joints, analyzing brick elements, detecting water content in building bodies, and evaluating structural deformation. They generally pursued small investigation areas with measurements made in direct contact with target structures and for small depths. Detecting deep piles presents specific challenges, and surveys conducted from the ground level may be unsuccessful. To reach great depths, medium-low frequencies must be used, but this choice results in lower resolution. Furthermore, the pile signals may be masked when they are located beneath massive reinforced foundations, which act as an electromagnetic shield. Finally, GPR equipment looks for differences in the dielectric of the material, and the signals recorded by the GPR will be very weak when the differences in the physical properties of the investigated media are modest. From these weak signals, it is difficult to identify information on the differences in the subsurface media. In this paper, we are illustrating an exploration on plinth foundations, supported by drilled piles, submerged in soil, extensive, deep and uninformed. Pulse GPR prospecting was performed in common-offset and single-fold, bistatic configuration, exploiting the exposed faces of an excavation around the foundation. In addition, three velocity tests were conducted, including two in common mid-point and one in zero-offset transillumination, in order to explore the range of variation in relative dielectric permittivity in the investigated media. Thanks to the innovative survey on the excavation faces, it is possible to perform profiles perpendicular to the strike direction of the interface. The electromagnetic backscattering analysis approach allowed us to extract the weighted average frequency attribute section. In it, anomalies emerge in the presence of drilled piles with four piles with an estimated diameter of 80 cm. Full article

This qualitative study investigates how academic faculty in a UK university conceptualise and implement alternative assessment practices aimed at fostering critical 21st-century skills—such as problem-solving, collaboration, and creativity—in an increasingly diverse higher education context. Drawing on in-depth interviews with six academic faculty members, the study explores the extent to which inclusive and alternative assessment practices are embedded in teaching and examines the institutional and cultural barriers that shape these practices. Thematic analysis reveals that while staff broadly value critical skills, there is considerable variation in how these skills are understood and operationalised in assessment. Many staff face structural constraints, including rigid assessment policies and market-driven accountability frameworks, that limit their ability to innovate. Furthermore, the findings highlight a disjunction between staff awareness of inclusive pedagogies and their capacity to enact them systematically in assessment design. The study contributes to the literature by foregrounding the complex interplay between institutional logics, assessment practices, and inclusive pedagogical aims. It argues that advancing genuinely inclusive and skills-oriented assessment requires systemic change at both institutional and policy levels. Full article

Exploration of coalbed methane (CBM) has long been plagued by critical technical challenges, including a low signal-to-noise (S/N) ratio in seismic data, difficulty identifying thin coal seams, and inadequate accuracy in interpreting complex structures. This study presents an innovative methodological framework that integrates artificial intelligence (AI) with advanced seismic processing and interpretation techniques. Its effectiveness is verified through a case study in the North Bowen Basin, Australia. A multi-scale seismic data enhancement approach combining dynamic balancing and blue filtering significantly improved data quality, increasing the S/N ratio by 53%. Using deep learning-driven, multi-attribute fusion analysis, we achieved a prediction error of less than ±1 m for the thickness of thin coal seams (4–7 m thick). Integrating 3D coherence and ant-tracking techniques improved the accuracy of fault identification, increasing the fault recognition rate by 30% and reducing the spatial localization error to below 3%. Additionally, a finely tuned, spatially variable velocity model limited the depth conversion error to 0.5%. Validation using horizontal well trajectories revealed that the rate of reservoir encounters exceeded 95%, with initial gas production in the predicted sweet spots zone being 25–30% higher than with traditional methods. Notably, this study established a quantitative model linking structural curvature to fracture intensity, providing a robust scientific basis for accurately predicting CBM sweet spots. Full article

The manuscript adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol to conduct a comprehensive analysis of the methodologies and critical factors influencing the site selection for offshore photovoltaic (PV) projects. The study innovatively proposes a three-stage site selection method model of “exclusion–optimization–correction” and a four-dimensional factor framework, which encompasses “technology, economy, environment, and policy.” The study underscores the pivotal roles of solar radiation, water depth, and grid connection conditions in site selection, while also highlighting the “veto” impact of military exclusion zones and ecological redlines. Additionally, it identifies the challenges associated with data acquisition and standardization in site selection, advocates for the establishment of standardized assessment procedures, and offers theoretical underpinnings and decision-making guidance for the rational site selection of offshore PV projects. Full article

Three-dimensional (3D) reconstruction technology is not only a core and key technology in computer vision and graphics, but also a key force driving the flourishing development of many cutting-edge applications such as virtual reality (VR), augmented reality (AR), autonomous driving, and digital earth. With the rise in novel view synthesis technologies such as Neural Radiation Field (NeRF) and 3D Gaussian Splatting (3DGS), 3D reconstruction is facing unprecedented development opportunities. This article introduces the basic principles of traditional 3D reconstruction methods, including Structure from Motion (SfM) and Multi View Stereo (MVS) techniques, and analyzes the limitations of these methods in dealing with complex scenes and dynamic environments. Focusing on implicit 3D scene reconstruction techniques related to NeRF, this paper explores the advantages and challenges of using deep neural networks to learn and generate high-quality 3D scene rendering from limited perspectives. Based on the principles and characteristics of 3DGS-related technologies that have emerged in recent years, the latest progress and innovations in rendering quality, rendering efficiency, sparse view input support, and dynamic 3D reconstruction are analyzed. Finally, the main challenges and opportunities faced by current 3D reconstruction technology and novel view synthesis technology were discussed in depth, and possible technological breakthroughs and development directions in the future were discussed. This article aims to provide a comprehensive perspective for researchers in 3D reconstruction technology in fields such as digital twins and smart cities, while opening up new ideas and paths for future technological innovation and widespread application. Full article

Urban pluvial waterlogging has become a major challenge for densely populated cities due to increasingly extreme rainfall events and the rapid expansion of impervious surfaces. In response to the growing demand for localized waterlogging risk assessments, an automated evaluation framework is proposed that integrates high-resolution digital elevation models (DEMs), rainfall scenarios, and classified building data within a GIS-based modeling system. The methodology consists of four modules: (i) design of rainfall scenarios and runoff estimation, (ii) waterlogging depth simulation based on volume-matching algorithms, (iii) construction of depth–damage curves for residential and commercial buildings, and (iv) building-level economic loss estimation though differentiated depth–damage functions for residential/commercial assets—a core innovation enabling sector-specific risk precision. A case study was conducted in the Lixia District, Jinan City, China, involving 15,317 buildings under a 50-year return period rainfall event. The total economic losses were shown to reach approximately USD 327.88 million, with residential buildings accounting for 88.6% of the total. The model achieved a mean absolute percentage error within 5% for both residential and commercial cases. The proposed framework supports high-precision, building-level urban waterlogging damage assessment and demonstrates scalability for use in other high-density urban areas. Note: all monetary values were converted from Chinese Yuan (CNY) to U.S. Dollars (USD) using an average exchange rate of 1 USD = 7.28 CNY. Full article