Search Results (4,538)

Human innovation has continually expanded the boundaries of knowledge, from mastering atomic science to reaching the Moon and now into the era of Industry 4.0, where artificial intelligence, the Internet, and advanced additive manufacturing turn imagination into reality. Among these achievements, hypersonic vehicles represent a pinnacle of technological advancement. Modern vehicles reach speeds exceeding Mach 27 (approximately 9300 m/s), where the air at the leading edges transforms into a chemically reactive, thermally ionized plasma. At such velocities, stagnation temperatures climb to 9000–12,000 K (8726.85–11,726.85 °C), creating one of the most extreme environments encountered by any human-made system—conditions under which conventional materials cannot survive without advanced cooling strategies. To address this challenge, researchers worldwide have developed and experimentally validated a range of thermal protection and leading-edge cooling techniques. This review presents the historical evolution of hypersonic vehicles, highlights recent advancements, examines the key challenges posed by sustained hypersonic flight, and surveys state-of-the-art cooling strategies. The discussion emphasizes methods that combine passive, active, adaptive, and hybrid approaches to protect vehicle integrity under extreme thermal loads, providing insight into the current and future capabilities of hypersonic thermal manageme nt. Full article

Background: Inborn errors of metabolism (IEMs) represent a diverse group of genetic disorders affecting biochemical pathways. Despite advances in diagnostic technologies, comprehensive understanding of their historical evolution, classification systems, and diagnostic approaches remains fragmented. Objectives: This systematic review and meta-analysis aimed to synthesize evidence on the historical development, classification frameworks, and diagnostic modalities for IEMs, diagnostic accuracy, and prevalence estimates, providing a comprehensive resource for clinicians and researchers. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of seven electronic databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, SciSpace and ArXiv) from January 2000 to March 2026. Studies addressing historical perspectives, classification systems, or diagnostic approaches for IEMs were included. Two independent reviewers performed screening, data extraction, and quality assessment. Meta-analyses were conducted using random-effects models for diagnostic accuracy and prevalence estimates. Results: From 1342 identified records, 54 studies met the inclusion criteria, encompassing 8,234,567 individuals across 35 countries. Historical analysis revealed 16 major milestones from Garrod’s 1902 “chemical individuality” concept to the current AI-powered diagnostics. Four major classification systems were identified: pathophysiological (intoxication, energy deficiency, complex molecule disorders), biochemical pathway (amino acid, organic acid, urea cycle, carbohydrate, fatty acid oxidation, mitochondrial, peroxisomal, lysosomal disorders), organelle-based, and the integrated Society for the Study of Inborn Errors of Metabolism (SSIEM) nosology. Meta-analysis demonstrated high diagnostic performance of tandem mass spectrometry (MS/MS) with a pooled sensitivity of 99.1% (95% CI: 98.6–99.5) and specificity of 99.8% (95% CI: 99.7–99.9%). The pooled global prevalence of IEMs was 50.9 per 100,000 live births (95% CI 45.2–56.8). Next-generation sequencing achieved a diagnostic yield of 42.8% (95% CI: 38.2–47.5%) in suspected cases. Emerging AI-powered diagnostic tools demonstrated high discrimination performance with area under the curve (AUC) values exceeding 0.95 for specific IEM, though external validation remains limited. Newborn screening expanded from single-disease to comprehensive panels detecting over 50 disorders. Conclusions: This comprehensive review demonstrates that IEMs have evolved from rare curiosities to systematically diagnosable conditions through technological advances. Integration of metabolomics, genomics, proteomics and artificial intelligence promises further diagnostic improvements. Standardized classification systems and evidence-based diagnostic algorithms are essential for optimal patient care. Future directions include artificial intelligence-enhanced diagnostics, expanded screening, and personalized medicine approaches. Full article

Background: Elongated styloid processes and ossifications of the stylohyoid chain can be observed on dental imaging modalities. In this study, we assessed the performance of artificial intelligence (AI) in identifying elongated styloid processes and ossifications of the stylohyoid chain. Methods: We performed a systematic review of relevant studies published between April 2020 and April 2026 on PubMed, Scopus, and Web of Science. Relevant data were extracted using predefined criteria. We assessed the risk of bias using categories derived from QUADAS-2, CLAIM and STARD-AI. Results: Four original studies met the inclusion criteria. Of these, only two specifically addressed elongated styloid processes on panoramic images (OPGs). For one study that utilized ML algorithms, both logistic regression and neural networks achieved 100% performance, while naive Bayes demonstrated substantially lower performance than either model. Another study using deep learning algorithms observed accuracy rates of 97.49% and 84.11%, and area under the curve values of 0.9825 and 0.8943 for EfficientNetB5 and InceptionV3 models. A broader study using OPG anomaly detection reported target-level data for stylohyoid ligament ossification. The fourth study used cone-beam computed tomography images, including stylohyoid ligament ossification as part of a multi-class soft tissue calcification/ossification detection task. Due to significant variability in target definitions, imaging modalities, validation methods, and performance metrics across studies, a meta-analysis was not feasible. Conclusions: The use of AI-based systems for detecting elongated styloid processes and stylohyoid chain ossification shows potential for future clinical utility; however, current evidence is insufficient to support independent clinical practice. Future research should incorporate larger-scale prospective multicenter validations as well as external validation on a patient-by-patient basis when possible. Additional research into the clinical implications associated with both false-positive and false-negative results is warranted. Full article

This study developed and evaluated a concept-oriented electricity learning system integrating augmented reality (AR) and non-immersive virtual reality (VR) technologies to support different conceptual learning requirements in the “Basic Electrostatic Phenomena and Electrical Circuits” unit. In the proposed framework, AR supported hands-on circuit construction and visualization of invisible electrical phenomena, whereas non-immersive VR was used for voltage measurement and Ohm’s law experimentation through repeated and controllable exploration. A quasi-experimental design was conducted with 87 ninth-grade students from a public junior high school in Taiwan. Two classes were assigned to the experimental group and two to the control group. The intervention lasted five instructional sessions (225 min). Data were collected using an Electricity Achievement Test and a Science Learning Motivation Questionnaire and analyzed using ANCOVA. The results indicated that the experimental group achieved significantly higher science achievement and learning motivation than the control group. Significant improvements were observed in overall science achievement and across all electricity topics, including basic circuit concepts, voltage and current measurement, and resistance and Ohm’s law concepts. The findings suggest that these learning benefits may be associated with the alignment between technological affordances and conceptual learning requirements. Consistent with the Cognitive Theory of Multimedia Learning, Cognitive Load Theory, and Conceptual Change Theory, the framework may have supported learning through visualization, interaction, experimentation, and conceptual change. This study contributes to educational technology and science education research in two ways. First, it proposes a concept-oriented AR/VR framework that systematically aligns technological affordances with conceptual learning tasks and processing demands in electricity education. Second, it provides empirical evidence for the value of concept-oriented technology integration in supporting science achievement and learning motivation. The findings highlight the importance of aligning technological affordances with conceptual learning requirements when designing technology-enhanced science learning environments. Full article

The present study investigates the semantic structure, dominant themes, and temporal evolution of research on giftedness in early childhood through a comparative topic modeling approach. A final analytic sample (n = 518) of peer-reviewed journal articles indexed in the Scopus and Web of Science databases was analyzed. Three topic modeling methods, Latent Dirichlet Allocation (LDA), Structural Topic Modeling (STM), and BERTopic, were systematically compared using multiple evaluation metrics. BERTopic demonstrated the strongest overall performance, producing approximately 11% higher coherence than STM and approximately 34% higher coherence than LDA. In terms of diversity, it achieved 14% to 17% greater thematic variety and, according to the Gini coefficient, revealed a 58% to 60% more balanced thematic distribution. BERTopic-based analyses identified five major thematic axes: Socio-Linguistic Development and Family Context, Psychometric Intelligence, Identification, and Cognitive Differences, Program Access, Identification, and Educational Equity, Early Academic Skills and Cognitive Development, and Creativity, Higher-Order Thinking, and Enrichment Programs. Thematic mapping and topic similarity analysis were used to examine the semantic structure of the field, while linear regression-based trend analysis over the 1918–2026 publication period showed that family context, socio-linguistic development, and equity-related themes have gained increasing importance over time, whereas psychometric identification largely maintained its central position within the field. These findings indicate that the field is moving toward a more inclusive, semantically grounded, and equity-oriented perspective. However, they should be interpreted in light of the study’s reliance on article abstracts, the sensitivity of BERTopic clustering parameters, and the use of linear trend modeling. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article

The development of digital learning technologies has introduced innovative tools to enhance science and chemistry education, including PhET simulations. This study aims to evaluate the effectiveness of PhET simulations on students’ learning outcomes through a systematic literature review following the PRISMA 2020 guidelines. A systematic search of Scopus and Crossref databases was conducted (last search: January 2026) using predefined keywords. Eligible studies were empirical research published between 2020 and 2026 that investigated PhET simulations in science-related education and reported learning outcomes, while non-empirical studies and non-Scopus-indexed articles were excluded. Risk of bias was assessed using an adapted Joanna Briggs Institute critical appraisal tool. Due to heterogeneity in study designs and outcome measures, the results were synthesized using a narrative approach. A total of 14 studies across elementary to higher education levels were included. The findings indicate that PhET simulations consistently improve learning outcomes, particularly academic achievement and conceptual understanding, with effects generally favoring simulation-based instruction over traditional methods. However, higher-order skills and affective outcomes such as motivation and attitude remain less frequently investigated. The evidence is limited by variability in study designs, incomplete reporting of non-cognitive outcomes, and the absence of quantitative synthesis. Overall, PhET simulations demonstrate strong potential as an effective interactive learning medium, although their impact depends on instructional design, teacher facilitation, and technological accessibility. Full article

Following the PRISMA-ScR framework for scoping reviews, we systematically searched five databases (Scopus, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science) using a Boolean query combining real-time processing, 3D reconstruction, and deformation modelling terms. From 86 records identified, 56 peer-reviewed publications (2009–2025) were retained after two-stage screening and organized into a unified taxonomy covering sensing modalities (RGB-D, LiDAR, tactile), reconstruction pipelines (volumetric fusion, NRSfM, neural radiance fields), and deformation models (FEM, PBD, mass-spring, GNN-based surrogates, differentiable simulators). Of the 56 included works, 60% were published between 2022 and 2025, confirming the field’s rapid growth. Neural and implicit representations account for 20% of contributions, FEM-based methods for 16%, and hybrid or application-specific pipelines for 21%. Four systemic gaps emerge: the absence of a unified physics-aware benchmark; unresolved speed–accuracy trade-offs (PBD achieves >30 FPS on desktop GPUs for 10³–10⁴ vertex meshes but lacks mapping to physical material constants (Young’s modulus, Poisson’s ratio), limiting material fidelity; full-order FEM ensures physically consistent stress–strain behavior but runs at only 1–10 FPS without order reduction; reduced-order FEM recovers interactive rates for low-frequency deformation modes); fragile handling of occlusions and multi-object contact; and limited end-to-end integration of sensing and simulation. The findings support the presentation of a research roadmap centered on model order reduction, differentiable physics, multimodal sensing fusion, and standardized evaluation protocols, with implications for robust digital twins of deformable environments. Full article

To clarify the internal mechanism through which the scale of higher education influences regional economic development, this work constructed an operational framework of education, talents, science and technology, and industry. Based on the 2023 data of 31 provincial administrative regions in China, covering 178 national high-tech industrial development zones, an empirical analysis was conducted using descriptive statistics and the Bootstrap mediating-effect test. The findings indicate that the expansion of higher education scale can enhance the level of talent supply, promote the agglomeration of scientific and technological innovation resources, drive the development of industrial scale, and thereby significantly boost economic growth. Among these pathways, the scale of the undergraduate and postgraduate student population exerts a complete mediating effect, while research and development investment and the number of enterprises in high-tech zones demonstrate a partial mediating effect. Notably, a striking contrast emerges between regular undergraduate institutions and double-first-class universities. The former exhibit significant positive mediating effects, whereas the latter’s economic driving effect remains largely unrealized. Furthermore, the uneven distribution of high-quality educational resources, particularly the spatial polarization of double-first-class universities, coupled with a mismatch between talent cultivation and industrial demands, and the “spatial isolation” of achievements, all restricted the radiating effect of higher education on regional economies. Therefore, it is necessary to implement a regionally differentiated layout of higher education, optimize the allocation mechanism of scientific and technological innovation resources, strengthen industry–university–research collaboration, and give full play to the effect of industrial agglomeration. Full article

Background: AI-supported game-based learning (AI-GBL) integrates artificial intelligence mechanisms, including adaptive difficulty adjustment, large language model (LLM) scaffolding, intelligent non-player characters (NPCs), and stealth assessment, into game-based educational environments. Objective: This systematic review synthesizes the empirical evidence on AI-GBL effectiveness, adaptive mechanisms, and intelligent assessment approaches across diverse educational contexts. Method: Following PRISMA 2020 guidelines, 55 peer-reviewed empirical studies (2021–2026) were identified from Web of Science and Scopus databases. Two independent reviewers screened records (κ = 0.89; 100% consensus on disagreements), extracted data using a standardized coding scheme, and assessed methodological quality using a five-criterion rubric. A thematic synthesis approach was adopted due to the heterogeneity of the evidence base. Results: The reviewed studies generally suggest promising positive effects of AI-GBL on knowledge acquisition, intrinsic motivation, and affective engagement under a range of educational conditions. LLM-based scaffolding reduces cognitive load but risks fostering passive dependency; adaptive difficulty adjustment benefits depend critically on the direction and magnitude of adaptation; AI NPCs function as credible instructional partners in both EFL and STEM contexts; stealth assessment achieves AUCs of 0.848–0.913. Challenges include algorithmic bias in assessment models, LLM latency, over-reliance risks, and a near absence of longitudinal evidence. Conclusions: AI-GBL’s effectiveness rests on principled alignment between AI mechanisms and learning theory rather than algorithmic sophistication per se. Equity-by-design approaches and longitudinal evidence constitute the field’s priority research needs. Full article

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Background: Precise intraoperative localisation of small colorectal tumours during laparoscopic surgery remains challenging due to absent tactile feedback and subserosal tumour location. Current standard methods, particularly India ink tattooing, demonstrate 15–30% failure rates for lesions less than 10 mm, leading to prolonged operative times, incomplete resections, and re-operations. Multiple emerging technologies promise improved localisation, yet comparative evidence remains fragmented. Objective: To map and characterise the current landscape of intraoperative marking and identification technologies for small colorectal tumour localisation during laparoscopic surgery, with emphasis on radiofrequency-based methods and alternative approaches, and to identify evidence gaps guiding future research. Methods: Following PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus databases from January 2000 through December 2025 for studies evaluating tumour localisation technologies in colorectal cancer surgery, including primary tumour localisation during laparoscopic colectomy and localisation of colorectal liver metastases during hepatic surgery, or transferable anatomical applications with documented translational potential to colorectal surgery. Two independent reviewers screened all records, with discrepancies resolved through discussion and a third senior reviewer consulted for unresolved disagreements; data were extracted on technical performance, safety, feasibility, cost-effectiveness, usability, innovation potential, and evidence quality. Results: We included 89 studies comprising 18 colorectal-specific articles and 71 transferable/GI-adjacent studies. Detection success rates ranged from 71% to 100% across modalities. Near-infrared fluorescence with indocyanine green demonstrated the strongest clinical evidence with 75–100% detection across eight colorectal studies encompassing 2134 procedures and seamless workflow integration. Radiofrequency identification systems achieved 91.9–99% detection in feasibility studies with promising tissue penetration of 15–35 mm but limited colorectal validation. Electromagnetic navigation excelled in rigid organs with 85–98% success but showed degraded performance in mobile bowel at 71–75%. Critical evidence gaps included absent head-to-head comparative trials, non-standardised outcome metrics limiting cross-study comparability, and limited long-term safety data with only 14 studies providing follow-up exceeding six months. Conclusions: ICG fluorescence represents the most clinically mature technology identified, representing a priority candidate for colorectal-specific validation in challenging localisation scenarios. RFID systems demonstrate promising characteristics justifying prioritised research investment through adequately powered comparative trials. Future research must emphasise consortium-based comparative effectiveness studies, standardised outcome metrics, and integration with robotic and AI-assisted surgical platforms to accelerate clinical translation. Full article

Deep-mining operations are increasingly challenged by severe thermal hazards, which have become a critical bottleneck for achieving safe, efficient, and sustainable mineral extraction. While research on deep-mine cooling and heat hazard mitigation has proliferated, the field lacks a systematic, critical review that explicitly examines these advances through the lens of sustainability science. To address this gap, this study conducted a comprehensive bibliometric analysis of 432 publications (1994–2024) retrieved from the Web of Science Core Collection. The methodology employs Bibliometrix, Vosviewer, and CiteSpace to map the intellectual landscape, research hotspots, and evolving frontiers of the field. The results reveal a clear three-stage development trajectory and identify China, the USA, South Africa, and Canada as leading contributors, with national research emphases on ventilation, energy conservation, and refrigeration, respectively. Crucially, keyword clustering and burst detection uncover a notable paradigm shift: the focus has moved from isolated cooling techniques toward integrated, multi-objective strategies—including geothermal energy co-exploitation, phase-change material applications, and system-level energy optimization—signaling a growing alignment with resource efficiency and low-carbon mining principles. However, a critical finding is that the literature remains predominantly techno-centric, overwhelmingly evaluating performance through operational energy savings while largely neglecting life-cycle environmental impacts, holistic sustainability assessment metrics, and the influence of policy drivers. This review thus not only provides a structured overview of the domain, but, more importantly, exposes these critical knowledge gaps. We argue that future research must pivot toward a multi-dimensional sustainability framework that integrates technical, economic, and environmental dimensions, thereby guiding the next generation of research toward truly sustainable deep-mining practices. Full article