Search Results (49,417)

In the context of China’s “dual carbon” strategy, building materials enterprises (BMEs) are in a critical period of digital and green transformation. Their diverse ownership structure and complex industrial types make them important objects of research. To address gaps in the existing literature, particularly regarding executive cognitive structure segmentation, ecological scenario (ES) influence mechanisms, and enterprise heterogeneity, this study uses Chinese BMEs as samples and incorporates industry characteristics, such as strong policy-driven conditions, a complete industrial chain, and diverse ownership types, to explore the relationship between executive cognition, ability reconstruction, and digital green innovation (DGI) performance (DGIP). Executive cognition is conceptualized through two dimensions: environmental protection cognition and digital intelligence cognition (DIC). A comprehensive test is conducted using fuzzy set qualitative comparative analysis (fsQCA). The results show that (1) both executive cognition and capability reconstruction (CR) significantly promote DGIP, and executive cognition has a positive effect on CR; (2) competency reconfiguration plays a mediating role in the influence of executives’ cognition on innovation performance, with the ES having a positive moderating effect on the relationship between the two types of cognitive role competency reconfiguration; (3) the influence of executive cognition varies depending on the nature of the enterprise and the industry; and (4) three types of performance improvement paths emerge: environmental-cognition-driven, cognitive ability connection, and ES-guided paths. The research’s contributions include (1) dividing executive cognition into two dimensions to enrich its conceptualization; (2) introducing the ES to reveal the dynamic mechanisms of cognition–ability–performance; and (3) conducting a heterogeneity analysis based on the nature of enterprises to deepen insights into paths of differentiated influence. This study provides a theoretical basis and practical inspiration for BMEs to enhance their DGIP. 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

The manufacturing sector is undergoing a paradigm shift driven by the integration of the Internet of Things (IoT), robotics, and advanced technologies such as Digital Twins (DTs), machine learning (ML), and edge computing within the Industry 4.0 framework. This scoping review systematically explores the breadth and depth of research on the disruptive potential of these technologies in manufacturing. Drawing on 14 empirical studies published between 2019 and 2025, we highlight the often-overlooked synergies between IoT and robotics. Following PRISMA guidelines, a comprehensive search of SpringerLink, Science Direct, and Google Scholar was conducted, with data extraction and quality appraisal guided by the Mixed-Methods Appraisal Tool (MMAT). Three thematic areas emerged: IoT-driven optimization, robotics and human–robot collaboration (HRC), and emerging technologies. Findings reveal IoT-enabled cycle time improvements (0.44–1.71%), robotics achieving 9% cycle time reductions with safety metrics (mAP 0.605–0.789), and DTs reporting predictive performance (AUC 0.916). However, challenges persist in data heterogeneity, standardization gaps, and limited real-world validations. This review offers critical insights for manufacturers, researchers, and policymakers to foster scalable and resilient manufacturing ecosystems. Full article

Depression represents a critical global mental health challenge, with social media offering unprecedented opportunities for early detection through computational analysis. We propose a novel BERT–CNN–BiLSTM architecture with attention mechanisms that systematically integrate emoji usage patterns—fundamental components of digital emotional expression overlooked by existing approaches. Evaluated on the SuicidEmoji dataset, our model achieves 97.12% accuracy, 94.56% precision, 93.44% F1-score, 85.67% MCC, and 91.23% AUC-ROC. Analysis reveals distinct emoji patterns: depressed users favour negative emojis (😔 13.9%, 😢 12.8%, 💔 6.7%) while controls prefer positive expressions (😂 16.5%, 😊 11.0%, 😎 10.2%). The attention mechanism identifies key linguistic markers, including emotional indicators, personal pronouns, and emoji features, providing interpretable insights into depression-related language. Our findings suggest that the integration of emojis substantially improves optimal social media-based mental health detection systems. Full article

In the era of rapidly evolving Internet technologies, influencer marketing has emerged as a transformative force in digital tourism, reshaping how travelers discover, evaluate, and choose destinations and accommodations. This study investigates the relationships between key dimensions of influencer marketing (credibility, authenticity, content format, perceived effectiveness, campaign frequency, and geographic proximity) and consumer behavior in tourism, with particular emphasis on trust formation and decision-making regarding accommodations and destinations among Slovak Generation Z (born 1997–2012) travelers. A structured electronic questionnaire was administered in December 2024 to assess respondents’ perceptions of influencer marketing in the context of travel-related choices. The instrument comprised 12 items measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), capturing various aspects of influencers’ impact on accommodations and destinations. The final sample included 337 Generation Z participants (65% women and 35% men) aged 18–27 years. Data were analyzed using Spearman’s rank correlation to test six hypotheses concerning the influence of influencer marketing on tourism decision-making. The results supported all six hypotheses, revealing significant positive relationships between the examined dimensions of influencer marketing and consumer behavioral outcomes. These findings emphasize the expanding role of influencer marketing as a central mechanism in digital tourism strategies and highlight its importance in understanding how Internet technologies shape the purchasing behavior of Slovak Generation Z travelers. Full article

Objective: This study systematically evaluates the association between breast arterial calcifications (BACs) on mammography and systemic vascular calcifications detected by computed tomography (CT) in female lung transplant recipients, representing the first investigation of this relationship in this population. Methods: This retrospective single-center study included 78 female LTX recipients who underwent both digital mammography and thoracoabdominal CT. BACs were graded on a four-point scale. Calcifications in seven arterial territories were visually assessed on CT with a binary scale (“present”, “not present”) and graded on a severity scale of systemic calcifications ranging from no involvement (0) and minor vascular calcification (1–4 vessels) to major vascular calcification (5–10 vessels). Associations between BACs and systemic vascular calcifications were analyzed using correlation, logistic regression, and ROC analysis. Results: BACs were present in 31 of 78 patients (40%). Vascular calcifications were frequent, most often affecting the thoracic aorta (82%), abdominal aorta (76%), and iliac arteries (72–73%). Coronary calcifications were more common in BAC-positive than BAC-negative patients (77% vs. 40%, p = 0.002). BAC grade correlated with the cumulative vascular calcification (ρ = 0.35, p = 0.0017). In ROC analysis, BACs discriminated patients with vascular calcification with fair accuracy (AUC 0.73, 95% CI 0.67–0.79). In multivariable logistic regression adjusted for age, diabetes, chronic kidney disease, and hypertension, BACs remained independently associated with systemic calcification, with an adjusted odds ratio of approximately 2.0 for ≥1 affected vascular territory. Interreader agreement for the 4-level BAC score was excellent with a quadratic-weighted κ of 0.91 and a linear-weighted κ of 0.88. Conclusions: BACs are common in female LTX recipients and independently predict systemic vascular calcifications. Their detection in routine mammography may provide a simple, opportunistic marker to support cardiovascular risk stratification in this high-risk population. Full article

The rapid digitalization of higher education has transformed how students learn, collaborate, and engage with emerging technologies such as artificial intelligence (AI). While earlier research emphasized generational or academic-level differences in digital behavior, recent evidence suggests convergence in technology use. This study explores whether undergraduate and postgraduate students at the Faculty of Economics and Business, University of Maribor, display distinct technology engagement patterns across five constructs: excessive technology use, online collaboration, the use of digital learning tools (E-boards), the use of AI in education, and perceived academic success. A survey among 285 students was analyzed using the non-parametric Mann–Whitney U test due to a non-normal data distribution. The findings showed that postgraduate students did not report higher levels of E-Board use, online collaboration, or perceived academic success. Undergraduate students scored higher on one item related to excessive technology use, but not across the full construct. However, significant differences emerged in AI use, where postgraduate students showed greater confidence and willingness to integrate AI tools. The findings suggest that digital competence and the quality of technology integration, rather than study level, shape students’ learning experiences. Higher education institutions should promote balanced and ethical technology use, strengthen AI literacy, and foster self-regulated learning skills. Full article

Digitalization and sustainability are fundamentally transforming the way tourism destinations are planned, managed, and experienced. Among the digital tools supporting this transition, simulation emerges as a key enabler for developing smarter, more adaptive, and sustainable tourism systems. By reproducing complex scenarios, simulation allows for the anticipation of environmental, social, and economic impacts and the testing of strategies aligned with sustainable development goals (SDGs), without exposing destinations to high costs or irreversible consequences. Despite its potential, studies on simulation applied to sustainable tourism remain scattered and lack a consolidated vision. This article therefore aims to systematically review and analyze the existing literature on the use of simulation and hybrid simulation in tourism, identifying the main approaches, application contexts, and scientific contributions developed between 1999 and 2025. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review covered publications indexed in databases such as Scopus, Web of Science, IEEE Xplore Digital Library (Institute of Electrical and Electronics Engineers), ScienceDirect, and Multidisciplinary Digital Publishing Institute (MDPI). A total of 34 articles were analyzed, focusing on the role of simulation models in enhancing sustainable destination management and the digital tourist experience. The results show that, although the potential of simulation is widely recognized, challenges persist regarding data quality, methodological fragmentation, and real-world validation. Nevertheless, the review highlights simulation as a catalyst for Tourism 5.0, enabling smarter decision-making and contributing to the sustainability and resilience of tourism destinations in the digital era. Full article

Background: Operating rooms are high-risk environments where ensuring patient safety is essential. Although research on patient safety has increased in recent years, comprehensive analyses of research trends and the core topics specific to operating room safety remain limited. This study aimed to analyze the core keywords and network structures in operating room patient safety, using a scope-based approach to provide suggestions for future research and practice. Methods: We conducted a scope-based keyword network analysis of studies on operating room patient safety published between 2020 and 2024. Data were collected from major academic databases, including CINAHL, MEDLINE, and PubMed. Keyword frequency and network centrality measures (degree, closeness, and betweenness) were used to identify major keywords and their interrelationships. Results: The analysis revealed ‘patient safety’, ‘operating room’, and ‘nurse’ as the most frequent and central keywords, highlighting their critical role in surgical safety research. Other highly connected terms—‘safety culture’, ‘infection control’, and ‘checklist’ emphasized systematic and organizational safety management. Emerging themes such as ‘leadership’, ‘teamwork’, ‘competency’, and ‘education’ reflected increasing attention to collaboration and professional capability, while ‘artificial intelligence’ and ‘telemedicine’ indicated growing interest in digital innovation. Conclusions: Research on patient safety in operating rooms demonstrates a multidimensional structure encompassing patients, healthcare professionals, systems, culture, and education. These findings underscore the need for integrated and multidisciplinary approaches to enhance safety in surgical environments and suggest directions for technology-driven and patient-centered safety models. Full article

Artificial intelligence (AI) is transforming pharmaceutical science by shifting drug delivery research from empirical experimentation toward predictive, data-driven innovation. This review critically examines the integration of AI across formulation design, smart drug delivery systems (DDSs), and sustainable pharmaceutics, emphasizing its role in accelerating development, enhancing personalization, and promoting environmental responsibility. AI techniques—including machine learning, deep learning, Bayesian optimization, reinforcement learning, and digital twins—enable precise prediction of critical quality attributes, generative discovery of excipients, and closed-loop optimization with minimal experimental input. These tools have demonstrated particular value in polymeric and nano-based systems through their ability to model complex behaviors and to design stimuli-responsive DDS capable of real-time therapeutic adaptation. Furthermore, AI facilitates the transition toward green pharmaceutics by supporting biodegradable material selection, energy-efficient process design, and life-cycle optimization, thereby aligning drug delivery strategies with global sustainability goals. However, challenges persist, including limited data availability, lack of model interpretability, regulatory uncertainty, and the high computational cost of AI systems. Addressing these limitations requires the implementation of FAIR data principles, physics-informed modeling, and ethically grounded regulatory frameworks. Overall, AI serves not as a replacement for human expertise but as a transformative enabler, redefining DDS as intelligent, adaptive, and sustainable platforms for future pharmaceutical development. Compared with previous reviews that have considered AI-based formulation design, smart DDS, and green pharmaceutics separately, this article integrates these strands and proposes a dual-framework roadmap that situates current AI-enabled DDS within a structured life-cycle perspective and highlights key translational gaps. Full article

Background and Clinical Significance: Advanced periodontitis with severe vertical bone loss and grade III mobility is usually managed by extraction and implant placement. Digital workflows and modern regenerative techniques have opened the possibility of preserving teeth that would traditionally be considered for extraction. This report describes a digitally guided modified intentional replantation (MIR) protocol applied to a maxillary tooth with severe periodontal involvement and unfavourable prognosis. Case Presentation: A 68-year-old male, non-smoker, with a history of heart transplantation under stable medical control, presented with generalized Stage IV, Grade C periodontitis. Tooth 21 showed >75% vertical bone loss, probing depths ≥ 9 mm, bleeding on probing, and grade III mobility. After non-surgical therapy and periodontal stabilization, a CAD/CAM-assisted MIR procedure was planned. Cone-beam computed tomography (CBCT) and a 3D-printed tooth replica were used to design a surgical guide for a new recipient socket. The tooth was atraumatically extracted, stored in chilled sterile saline, and managed extraorally for approximately 10 min. Apicoectomy and retrograde sealing with Biodentine^® were performed, followed by immediate replantation into the digitally prepared socket, semi-rigid splinting, and guided tissue regeneration using autologous bone chips, xenograft (Bio-Oss^®), enamel matrix derivative (Emdogain^®), and a collagen membrane (Bio-Gide^®). A conventional orthograde root canal treatment was completed within the first month. At 12 months, tooth 21 exhibited grade 0 mobility, probing depths of 3–4 mm without bleeding on probing, and stable soft tissues. Standardized periapical radiographs and CBCT showed radiographic bone fill within the previous defect and a continuous periodontal ligament-like space, with no signs of ankylosis or root resorption. The tooth was fully functional and asymptomatic. Conclusions: In this medically complex patient, digitally guided MIR allowed preservation of a tooth with severe periodontal involvement and poor prognosis, achieving favourable short-term clinical and radiographic outcomes. While long-term data and larger series are needed, MIR may be considered a tooth-preserving option in carefully selected cases as an alternative to immediate extraction and implant placement. Full article

Background/Objectives: The precision and bonding reliability of space maintainers are critical to their clinical success longevity. This study aimed to evaluate and compare the fit accuracy and shear peel bond strength of digitally fabricated space maintainers—cobalt–chromium (Co-Cr) and polyetheretherketone (PEEK)—against conventional space maintainers. Methods: Seventy-eight space maintainer bands were fabricated—milled PEEK, selective laser-melted (SLM; an additive manufacturing technique) Co-Cr, and conventional stainless steel (SS)—and tested. Fit accuracy was evaluated on 39 bands by measuring the root mean square (RMS) deviation from a master model using digital 3D analysis. Shear peel bond strength (SPBS) was tested on another 39 samples using a universal testing machine, and the adhesive remnant index (ARI) was recorded after debonding. Statistical analyses included a Welch ANOVA for fit accuracy and the Kruskal–Wallis test for the SPBS test; the ARI was analyzed using Fisher’s exact test (significance level p < 0.05). Results: Digitally fabricated bands demonstrated significantly higher fit accuracy than the stainless steel bands (mean RMS deviation: Co-Cr = 0.151 mm, PEEK = 0.152 mm, SS = 0.344 mm; p < 0.001). Co-Cr and PEEK demonstrated comparable adaptation. In contrast, bond strength was significantly greater in Co-Cr (1.657 MPa) and SS (1.481 MPa) compared to PEEK (0.393 MPa). ARI distribution varied significantly across the three groups. Conclusions: Both milled PEEK and Co-Cr bands demonstrated excellent adaptation compared with conventional SS bands. However, Co-Cr exhibited reliable bonding performance, yet PEEK may require additional surface treatment or bonding optimization to enhance adhesion. Full article

Background/Objectives: Inflammatory bowel disease (IBD), including Crohn’s Disease and Ulcerative Colitis, requires complex treatment and active patient participation. Health literacy (HL), defined as the ability to access, understand, and apply health information, is a key factor influencing adherence, self-management, and quality of life in chronic illness. However, evidence regarding HL in IBD remains limited and fragmented. This systematic review aimed to synthesize existing literature on HL in IBD, exploring its impact on outcomes, as well as its predictors and barriers. Methods: A systematic search of PubMed, Scopus, CINAHL, and Cochrane Library identified studies published between January 2000 and August 2025 in English or Italian. Eligible studies examined HL among adults with IBD and its associations with clinical, behavioral, or psychosocial outcomes. Methodological quality was assessed using the QuADS tool. Due to heterogeneity across studies, a narrative synthesis was conducted. Results: Seventy studies were included, comprising observational, qualitative, mixed-methods, and interventional designs. Higher HL was consistently associated with better treatment adherence, self-management, communication with healthcare providers, and quality of life. Conversely, low HL was linked to poor adherence, greater disease activity, and lower psychological well-being. Predictors of low HL included older age, lower education, minority status, and socioeconomic disadvantage. Barriers included inadequate communication, lack of tailored information, cultural and linguistic challenges, and the digital divide. Interventions such as structured education, telemedicine, and digital tools showed potential to improve HL and patient engagement. Conclusions: HL is a crucial determinant in IBD management. Enhancing HL through integrated clinical, educational, and digital strategies is essential to improve outcomes and reduce health disparities. Full article

This article contends that Sofia Coppola’s Priscilla (2023) uses digital filmmaking to re-animate the commodified image of Priscilla Presley, privileging surface and affect over historical realism. Though Coppola predominantly shoots on film, her decision to film Priscilla digitally—an adaptation of Presley’s memoir—marks a formal shift in her filmography aligned with her ongoing exploration of feminine interiority and aesthetic control. The film traces Priscilla’s life from her first encounter with Elvis Presley to their separation, presenting a visually stylized narrative that immerses viewers in what Walter Benjamin terms a phantasmagoria: a spectacle of commodification divorced from historical consciousness (The Arcades Project). Rather than striving for veracity, Coppola evokes a nostalgic atmosphere that re-members Priscilla through pre-circulated cultural images. This article examines Coppola’s often-criticized “flat” visual style in relation to the Freudian uncanny, i.e., the estrangement of the familiar through temporal and affective distortion. Coppola manipulates digital temporality—looping and flattening time—to produce an oneiric repetition that heightens the artifice of Presley’s image while emotionally distancing viewers. These formal strategies dissipate emotional depth but intensify aesthetic control. Finally, this article considers the political valences of Coppola’s digital aesthetics in a media landscape that both enables visibility and enacts erasure. Full article

We evaluated six global digital elevation DEMs at 1-arc-sec resolution: CopDEM and AW3D30, which are digital surface models (DSMs), and EDTM, GEDTM, FABDEM, and FathomDEM, which are digital terrain models (DTMs). We compared them to reference DTMs created by mean aggregation from 1–2 m lidar-derived DTMs from national mapping agencies, using 1510 approximately 10 × 10 km test tiles from the United States and western Europe. Our criteria used the grids for elevation and derived land surface parameters (LSPs), including characteristics of the difference distributions and the fraction unexplained variance derived from grid correlations. The best DEM depends on the LSP used and the characteristics of the test tile, especially average slope, barrenness, and forest coverage. FathomDEM emerged as the best among the DEMs, with CopDEM the best overall for the DEMs with unrestricted licenses. GEDTM performed poorly. This is especially important for LSPs like curvature measures, which require higher-order partial derivatives for computation, and which should be used very cautiously. Full article