Search Results (283)

Background: Blockchain and distributed ledger technologies are increasingly proposed to strengthen traceability, governance, visibility, and coordination in mining and mineral supply chains, but mining-specific evidence remains fragmented. Methods: We conducted a systematic mapping review of peer-reviewed articles indexed in Scopus and Web of Science to examine application contexts, functional roles, technical architectures, evidence types, and adoption constraints of blockchain-enabled systems in these settings. Results: The review shows that blockchain is used across five functional domains: traceability and provenance; governance and secure data control; operational monitoring and inspection; energy and market coordination; and sustainability and environmental surveillance. Permissioned and consortium-based architectures predominated and were commonly combined with sensors, external storage, identity mechanisms, and smart contracts. Evidence was strongest for technical feasibility under simulated, experimental, comparative, or bounded pilot conditions, whereas durable economic, social, and governance outcomes remained less substantiated. Conclusions: Blockchain is most credible in mining contexts when it supports controlled coordination, auditable recordkeeping, and process integrity. Its practical value depends on reliable physical-to-digital data capture, workable governance arrangements, interoperability, and validation under real institutional and operational conditions. Full article

Business intelligence (BI) and business process management (BPM) have traditionally addressed related managerial problems from partly separate perspectives, while big data analytics, process mining, generative AI, and decision support systems are increasing the pressure toward integration. This review examines how these domains relate within a shared business-processing and decision-making context. Methodologically, the paper adopts a narrative review approach based on peer-reviewed literature published from 2015 onward, drawing on Google Scholar, Scopus, and Web of Science, and synthesizes the literature thematically across conceptual foundations, data and computational infrastructures, process intelligence, generative AI, application domains, and implementation tensions. The review finds that the literature does not support the claim that these areas have already converged into a stable, unified field. Instead, it shows a gradual movement toward a layered architecture in which BI and business analytics support organizational insight, BPM and process mining provide process intelligence, big data analytics supplies the evidentiary and computational base, generative AI functions as an interaction and augmentation layer, and decision support systems translate these elements into managerial action. The paper concludes that this emerging integration is meaningful but still uneven, with its practical value depending on interoperability, evaluation realism, governance, and the preservation of human oversight in AI-supported business processes. Full article

Artificial intelligence (AI) is increasingly being applied in breast cancer care, yet its use across the post-diagnosis phase remains poorly mapped. This scoping review aimed to identify and categorise AI applications in post-diagnosis breast cancer care, encompassing treatment planning, treatment delivery, follow-up and surveillance, survivorship, and palliative care. Following JBI methodology and PRISMA-ScR reporting guidelines, four databases (MEDLINE, EMBASE, CINAHL, and Web of Science) were searched, identifying 3784 records. After screening and full-text assessment, 54 studies published between 2016 and 2024 were included. Machine learning was the predominant technology (81%), followed by generative AI (7%), conversational agents (6%), traditional natural language processing (4%), and data mining (2%). Follow-up and surveillance were the most represented care stage (48%), driven primarily by recurrence prediction models. Most applications were provider-focused (83%), while patient-facing tools accounted for 17% of studies and relied on either conversational agents or generative AI. No studies addressed palliative care. The evidence base was predominantly retrospective (70%) and concentrated in high-income countries (74%). Future research should prioritise prospective evaluation in clinical workflows, address unsupervised patient use of generative AI, and ensure equitable development across diverse populations and care settings. Full article

Based on data from the literature in the Web of Science (WOS) and Scopus databases, this study collected 325 articles published between 2020 and 2025. Using Citespace software (version 6.4) to analyze publication volume, countries, institutions, disciplinary categories, and keywords, we examined research characteristics, hotspots, and bottlenecks in the field of ecological remediation for heavy metal pollution in mining area soils. Results indicate: (1) Publication volume in this field showed an upward trend from 2020 to 2024, accounting for 70.2% of this dataset being from the environmental sciences. Chinese scholars demonstrated significant dominance and high engagement, though interdisciplinary depth remained insufficient; (2) from 2020 to 2025, the research focus shifted from risk identification to precise remediation, forming a complete logical chain of ‘identification–remediation–optimization’. Green technologies (biological/combined remediation) emerged as mainstream approaches in integrated remediation. (3) A significant gap exists between research and practice. Many innovative technologies are costly and difficult for enterprises to bear, while low-cost techniques like ‘waste-to-waste treatment’ lack sufficient research and application, hindering large-scale implementation. This study reveals the current situation of ‘intense research but difficult application’ in the ecological remediation of heavy metal-contaminated soils in mining areas. The findings provide a scientific basis for technological innovation, practical implementation, and policy making. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

Background/Objectives: Oncologic surgery to the pelvis and post-surgery adjuvant therapy are dangerous to the reproductive potential of childbearing-aged women. Clinical practices in fertility preservation (FP) have become an essential aspect of multidisciplinary cancer care; however, uniform practices remain inconsistent among the different varieties of cancer and/or areas. To systematically compare the fertility preservation procedures employed in women who have undergone pelvic oncologic surgery and to measure their reproductive and oncologic stages. This review focuses primarily on gynecologic pelvic malignancies and addresses fertility preservation strategies within the context of multimodal oncologic care, including surgery, chemotherapy, radiotherapy, and multidisciplinary decision-making. Methods: A systematic review was performed using PRISMA 2020 to investigate publications from to 2013–2025 in PubMed, Embase, Scopus, Cochrane Library, and Web of Science. The inclusion criteria were women of childbearing age with pelvic malignancies who underwent either fertility-sparing or cryopreservation procedures. PICO-based data mining was performed, and AMSTAR 2, NOS, and AGREE II methodological quality evaluation instruments were used. Mixed inductive–deductive thematic analysis was used to synthesize the findings of the study. Results: A range of articles, including systematic reviews, cohort studies, and clinical guidelines, were included. Fertility-sparing surgery and cryopreservation were found to be as safe and oncologically effective as traditional therapy, with a five-year survival rate of more than 90. Cryopreservation maintained the functioning of the ovary in over 60 percent of the patients and recorded live delivery rates of up to 40 percent. Thematic analysis revealed five main spheres: oncologic safety, creation of FP approaches, psychosocial benefits, limiting access, and the necessity of standardized procedures. Conclusions: Fertility preservation can securely supplement oncologic treatment courses, favoring tumor traits and individual preferences. Unified reporting, extended follow-up, and equitable access are pertinent in maximizing results and reproductive self-corrective action among female cancer endocrine survivors. Fertility preservation should be considered as an integral component of multidisciplinary oncologic management in women with gynecologic pelvic cancers, extending beyond surgical approaches to include coordinated medical, reproductive, and supportive care. Full article

Against the backdrop of the global pursuit of carbon neutrality, research on coal pore structure has shifted from a single focus on coal mine safety to a dual orientation of hazard prevention and carbon sequestration, forming two distinct research directions worldwide. To clarify the evolutionary trajectory, research heterogeneity and integration paths of this field, this study systematically analyzes 722 core publications on coal pore structure from the CNKI and Web of Science core databases during 2015–2025, combining knowledge visualization analysis and systematic literature sorting (using CiteSpace as an auxiliary analysis tool). The results show that global research on coal pore structure has experienced three developmental stages (embryonic, developmental, and explosive growth) and entered an exponential growth phase after 2020, driven by the dual carbon goals. A clear research divergence has formed between regional engineering practices and international theoretical models: Chinese research is highly oriented to on-site coal mine engineering needs, focusing on the characterization of coal pore structure and its engineering application in gas extraction and outburst prevention of structural coal; international research prioritizes the theoretical exploration of carbon sequestration and CO₂-ECBM, with core research on gas adsorption kinetics, multiphysics coupling mechanisms of coal pore structure, and numerical simulation of reservoir modification. This research disconnect between engineering practice and theoretical modeling has become a key bottleneck restricting the safe application of coal pore structure theory in carbon capture, utilization, and storage (CCUS) projects. To address this issue, a Safety–Sustainability Nexus framework is proposed, which integrates field-based mine safety protocols with theoretical carbon storage models, and realizes cross-scale validation from micro-scale pore characterization to field-scale engineering application. Further, this study points out that the cross-scale data fusion of artificial intelligence and machine learning is the core direction to bridge the gap between engineering practice and theoretical models. In future CO₂-ECBM pilot projects, traditional gas outburst prevention indicators must be taken as mandatory safety thresholds to realize the dynamic matching of carbon injection parameters and coal reservoir stress sensitivity. This study sorts out the global research context and hotspots of coal pore structure, and provides a theoretical and practical reference for the synergy and integration of coal mine gas control engineering and carbon sequestration theoretical research under the dual carbon goals. CBM, coalbed methane; CNKI, China National Knowledge Infrastructure; WOS, Web of Science; CCUS, carbon capture, utilization, and storage; ECBM, Enhanced Coalbed Methane; CO₂-ECBM, CO₂-Enhanced Coalbed Methane. Full article

The intestinal microbiome plays a fundamental role in canine health and well-being, regulating functions, including digestion, immunity, metabolism, and behavior. Dysbiosis refers to the disruption of the balanced composition of resident commensal communities, and gut bacteria can influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Growing evidence supports the existence of a bidirectional communication between the gut and the central nervous system, known as the gut–brain axis, through which intestinal microorganisms may influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Despite the expanding interest in this field, the contribution of intestinal dysbiosis to the development and severity of behavioral and neurological disorders in companion dogs remains poorly understood. This review aims to critically analyze the literature from 2011 to 18 September 2025 concerning the association between dysbiosis, the gut–brain axis, and both gastrointestinal and non-gastrointestinal illnesses in dogs. To our knowledge, this review represents the first application of Text Mining (TM) in this domain: TM facilitates the identification and analysis of valuable information from extensive datasets, converting unstructured content into structured data, thereby enabling quantitative analysis. We used the following search terms on three bibliographic databases (PubMed, Scopus, and Web of Science): “dysbiosis” AND “canine” OR “dog” AND “gut–brain axis” AND “behavior”. Of the 1176 records retrieved, 35 studies were checked following the PRISMA guidelines, and they met the predefined inclusion criteria in the final analysis. Full article

Addressing the challenges in 3D reconstruction of large-scale open-pit mines, such as dramatic terrain undulations, complex texture features, and the difficulty of balancing geometric accuracy with real-time rendering efficiency using traditional methods, this paper proposes a high-fidelity reconstruction framework integrating UAV multi-modal data with the state-of-the-art 3D Gaussian Splatting (3DGS) architecture. First, an integrated air-ground multi-modal data acquisition system is established. Using a UAV equipped with LiDAR and a high-resolution camera, high-quality geometric and textural data of the mining area are acquired through terrain-adaptive flight planning. Second, to tackle the VRAM bottlenecks and loose geometric structures inherent in original 3DGS for large scenes, we adopt the advanced CityGaussianV2 architecture as our core reconstruction engine. By leveraging its divide-and-conquer parallel training strategy, 2DGS planar geometric constraints, and Decomposed Gradient Densification (DGD) mechanism, this framework effectively overcomes memory limitations and significantly enhances the geometric sharpness of slope crests and toes. Finally, engineering validation was conducted at Kambove Mining. Experimental results demonstrate that the proposed method achieves centimeter-level geometric accuracy, a real-time web rendering frame rate exceeding 60 FPS, and a model storage compression rate of over 90%. The digital twin control platform built upon this model successfully achieves deep fusion and visual scheduling of multi-source heterogeneous data, providing a novel technical path for constructing high-precision reality-based foundations for smart mines. Full article

This paper presents a bibliometric analysis and literature review of methodologies for optimal capital structure decision making, focusing on research published between 2000 and 2024. This study reviews current research, identifies gaps, and outlines a plan to support with financial decisions. A mixed-methods approach was employed, combining data from the Web of Science and Scopus databases using the search string “capital structure” AND (“decision making” OR “optimal structure”). The study used Bibliometrix(R), VOSviewer, and NVivo tools, and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart for choosing studies. The findings show that this field is well-developed but still changing. The intellectual structure is organized around two main clusters: one focused on testing classical theories and another oriented toward optimization and managerial applications, revealing a clear theory–practice divide. The mapping also highlights the dominance of Chinese and U.S. scholarship and the central role of practitioner-oriented journals such as Managerial Finance, indicating both a shift toward emerging markets and a strong demand for applicable research. The study provides three key contributions. First, it identifies important countries, authors, outlets, and themes. Second, it uses a method that combines bibliometric and text-mining tools. Third, it introduces a new decision-support framework that is thorough, context-sensitive, and flexible. There are some limitations. These include relying on Scopus and Web of Science, language limits, and the fact that bibliometrics cannot judge the quality of methods. Future research should empirically validate the proposed framework in different contexts, expand studies in emerging markets, test emerging theories such as Brusov–Filatova–Orekhova (BFO) theory, and develop more dynamic and stochastic models to better capture financial uncertainty. Full article

With the prosperity of e-commerce applications, the web data of products are presented by multiple modalities, e.g., vision and language. For mining the product characteristics, multimodal attribute values are crucial, which are extracted from textual descriptions, assisted by helpful image regions. However, most previous works (1) fuse the multimodal information within a newly learned range based on co-occurrence rather than language meanings and (2) predict the outputs within a range of all attributes rather than the product-related ones. These issues yield unsatisfactory results; thus, we propose a novel approach via Dynamic Range Modulation (DRAM): (1) First, we propose an Information Range Calibration (IRC) method to dynamically fuse multimodal features of related meanings as Text-Related Embeddings (TEM) within a language range, which is calibrated from the range to fuse language features by a powerful attention mechanism of a pretrained language model. (2) Moreover, an Attribute Range Minimization (ARM) method is proposed to minimize the output attribute range based on the adaptive selection of product-related attribute prototypes. Experiments on the popular multimodal e-commerce benchmarks show that our DRAM performs well compared with previous methods. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

Underground gas storage (UGS), encompassing hydrogen, natural gas, and compressed air, is a cornerstone of large-scale energy transition strategies, offering seasonal balancing, security of supply, and integration with renewable energy systems. However, the complexity of geological conditions, multiphysics coupling, and operational uncertainties pose significant challenges for UGS design, monitoring, and optimization. Artificial intelligence (AI)—particularly machine learning and deep learning—has emerged as a powerful tool to overcome these challenges. This review systematically examines AI applications in underground storage types such as salt caverns, depleted hydrocarbon reservoirs, abandoned mines, and lined rock caverns using bibliometric and knowledge-graph analysis of 176 publications retrieved from the Web of Science Core Collection. The study revealed a rapid surge in AI-related research on UGS since 2017, with underground hydrogen storage emerging as the most dynamic and rapidly expanding research frontier. The results reveal six dominant research frontiers: (i) AI-assisted geological characterization and property prediction; (ii) physics-informed proxy modeling and multi-physics simulation; (iii) gas–rock–fluid interaction, wettability, and interfacial behavior prediction; (iv) injection-production process optimization; (v) intelligent design and construction of underground storage, especially salt caverns; and (vi) intelligent monitoring, optimization, and risk management. Despite these advances, challenges persist in data scarcity, physical consistency, and generalization. Future efforts should focus on hybrid physics-informed AI, digital twin-enabled operation, and multi-gas comparative frameworks to achieve safe, efficient, and intelligent underground storage systems aligned with global carbon neutrality. Full article

Trajectory data from GPS and sensors are increasingly available, necessitating effective preprocessing techniques for data mining. To systematically review the methods and models for trajectory data preprocessing, we conducted a systematic literature search in IEEE Xplore, Association for Computing Machinery Digital Library (ACM DL), Scopus, Web of Science, and Transport Research International Documentation published over the past several decades, using keywords related to trajectory data preprocessing. The studies were screened and selected based on predefined inclusion and exclusion criteria. We included 138 studies, summarizing techniques in data cleaning, compression, segmentation, and map matching. Key algorithms and their performance are compared. This review synthesizes current preprocessing methods and identifies future research directions, including real-time processing, semantic labeling, and privacy protection. Full article