Search Results (7,436)

Objectives: To synthesize current evidence regarding advances in periodontal diagnosis and therapy, with emphasis on molecular biomarkers, omics technologies, microbiome profiling, digital imaging, and artificial intelligence-based analytical models that support the transition toward precision periodontology. Methods: This narrative review examines contemporary evidence on emerging molecular, microbiological, and digital technologies applied to periodontal diagnosis, prognostic assessment, and therapeutic planning. The review includes studies addressing salivary and gingival crevicular fluid biomarkers, microbiome characterization, omics approaches, cone-beam computed tomography, three-dimensional imaging, machine-learning algorithms, and personalized periodontal therapies. Relevant literature was identified through searches in major biomedical databases, including PubMed/MEDLINE, Scopus, and Web of Science, focusing on studies published on periodontal diagnostics, biomarkers, digital technologies, artificial intelligence, and precision medicine approaches in periodontology. Results: Peer-reviewed articles addressing innovative diagnostic and therapeutic approaches in periodontology were considered. Priority was given to studies evaluating clinical applicability, diagnostic performance, prognostic utility, and personalized treatment strategies integrating molecular and digital technologies. Conclusions: Emerging molecular and digital technologies are reshaping periodontal diagnosis and therapy by improving disease detection, risk prediction, and individualized treatment planning. Biomarkers, omics technologies, microbiome profiling, and artificial intelligence-assisted imaging may enhance diagnostic precision and clinical decision-making. These developments support the implementation of precision periodontology; however, challenges related to biomarker validation, algorithm standardization, cost, and accessibility remain barriers to routine clinical adoption. Further research is necessary to validate these approaches and facilitate their integration into periodontal practice. The integration of biomarkers, omics technologies, advanced imaging, and artificial intelligence may improve early periodontal diagnosis, prognostic assessment, and personalized treatment planning. These innovations support the transition toward precision periodontology and have the potential to enhance clinical decision-making, treatment outcomes, and long-term periodontal health in routine dental practice. Full article

Greenhouse gas (GHG) emissions have emerged as a critical challenge to sustainable development in Central Asia, where rapid economic transformation, rising energy demand, and increasing globalization have intensified environmental pressures. This study investigates the determinants of GHG emission growth in Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan over the period 2000–2024 within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework. Specifically, the analysis examines the effects of economic growth, energy intensity, renewable energy consumption, technological innovation, urbanization, and globalization on annual GHG emission growth. To ensure robust inference in the presence of cross-sectional dependence and heteroskedasticity, the empirical analysis employs Driscoll–Kraay standard errors (DKSE), Panel-Corrected Standard Errors (PCSE), and Feasible Generalized Least Squares (FGLS). Furthermore, the Method of Moments Quantile Regression (MMQR) is applied to examine distributional heterogeneity by assessing whether the effects of the explanatory variables vary across the lower, median, and upper quantiles of the conditional distribution of GHG emission growth. The empirical findings reveal that energy intensity is the dominant driver of GHG emission growth across all estimation techniques, whereas renewable energy adoption and technological innovation significantly mitigate environmental degradation by reducing emissions growth. The MMQR results further demonstrate that the estimated effects of the explanatory variables vary across the conditional distribution of GHG emission growth. In particular, the mitigating effect of globalization becomes more pronounced toward the upper quantiles of the conditional distribution, indicating that its environmental consequences differ across the distribution of GHG emission growth rather than across predefined groups of countries. By contrast, the effects of economic growth and urbanization exhibit greater heterogeneity across the conditional distribution, while the effects of energy intensity, renewable energy, and technological innovation remain broadly consistent in direction. These findings underscore the importance of improving energy efficiency, accelerating the deployment of renewable energy technologies, strengthening innovation capacity, and promoting environmentally sustainable economic integration to achieve long-term climate objectives in Central Asia. By providing comprehensive evidence based on complementary mean-based estimators and distribution-sensitive quantile analysis, this study contributes to the growing literature on the determinants of GHG emissions in emerging economies and offers important policy implications for balancing economic development with climate change mitigation and environmental sustainability. Full article

Confronted with severe global challenges, including water scarcity, excessive use of chemical fertilizers and pesticides, and heavy metal contamination in soils, conventional agricultural technologies exhibit marked limitations in integrated water–fertilizer management and non-point source pollution control. Leveraging their excellent water retention capacity, intelligent sustained-release properties, and environmental responsiveness, hydrogels offer innovative solutions to advance sustainable agricultural development. This review comprehensively outlines the fundamental types, crosslinking mechanisms, and key functional properties of hydrogels, with a focused discussion on their agricultural deployment as high-efficiency soil conditioners, fertilizer vectors, and pesticide carriers; it deciphers the microscopic water-holding mechanisms under the tristate water model, delineates the divergent water-uptake and retention behaviors between ionic and non-ionic hydrogels, and clarifies the cyclic water-holding and release mechanisms of hydrogels during soil amelioration. Thise paper further synthesizes hydrogel-enabled environmental remediation applications, in which heavy metals and pesticide residues in soils and aquatic systems are removed via functional-group coordination adsorption or photocatalytic degradation; concurrently, hydrogels have been shown to activate plant systemic immunity through calcium-signaling pathways, thereby inducing broad-spectrum antiviral defense responses. Moreover, hydrogels can be integrated into precision agriculture frameworks to enable real-time monitoring of crop physiological status and to support targeted irrigation and fertilization management. This work also evaluates the role of hydrogels in promoting seed germination, root system development, crop metabolic regulation, and stress resilience, while introducing tailored application strategies across distinct plant growth stages. Their documented economic advantages include water conservation, enhanced crop yields, reduced dependence on synthetic fertilizers, and lower labor costs. Nevertheless, the large-scale implementation of hydrogels continues to face multifaceted challenges—particularly poor degradability and latent ecological risks, as conventional polyacrylamide (PAM)-based gels resist soil mineralization and retain potentially neurotoxic monomers, leaving a critical gap in multi-annual field data concerning their non-target interference with native soil aggregate evolution, pore distribution, and rhizospheric carbon–nitrogen footprints. Mechanistically, many hydrogels with tensile strengths below 1 MPa are highly susceptible to three-dimensional network collapse under high-salinity osmotic shock and tillage mechanical stress, exhibiting a precipitous drop in water retention after more than three wet–dry cycles due to deficient long-term structural stability. Compounding these technical gaps are elevated production costs and low farmer adoption, driven by the absence of texture-specific performance thresholds—such as an available water increment ≥ 40% for sandy soils—and the lack of established life-cycle cost models and farmer incentive mechanisms for bio-based hydrogels. Moving forward, hydrogel technology should pivot toward materials innovation and cost-reduction engineering to broaden its applicability, employ ≥3-year, multi-habitat regional trials to delineate ecological benefit–risk boundaries, and ultimately position hydrogels as pivotal enablers of sustainable, green agricultural paradigms. Full article

China has emerged as one of the most influential actors in South–South scientific cooperation, progressively integrating agricultural innovation, technology transfer, and sustainable development into its international engagement strategy, with tropical agriculture positioned as a strategic domain in its relations with Latin America. However, despite China’s growing role as a driver of agricultural research collaboration, the specific mechanisms through which its institutions transfer knowledge and strengthen local scientific capacities in the Latin American region remain insufficiently studied, particularly in the case of Ecuador. This study adopted a qualitative scoping review methodology following PRISMA-ScR guidelines, screening 9199 records across Scopus, Web of Science, and SciELO, of which 61 documents were retained for thematic analysis. Results show that Chinese cooperation has evolved through three distinct phases: an initial phase centered on agricultural investment and resource-oriented cooperation (2000–2010); a second phase (2010–2018) characterized by the initial institutionalization of scientific and technological cooperation through bilateral agreements, joint action plans, researcher training, and institutional exchanges; and a third phase (2018–present), marked by the deliberate integration of science, technology, and innovation as central pillars of China’s engagement with Latin America, including long-term collaborative research, joint laboratories, scientific networks, and initiatives led by institutions such as the Chinese Academy of Tropical Agricultural Sciences (CATAS). In Ecuador, CATAS’s partnership with the National Institute of Agricultural Research (INIAP) illustrates China’s capacity to build joint research platforms, though sustained impact depends on long-term institutionalization, continuous financing, and transparent governance over genetic resources. The findings underscore China’s expanding support in shaping South–South agricultural cooperation and innovation, and the conditions required to translate this support force into durable, mutually beneficial scientific outcomes. From a public policy perspective, China and Ecuador could strengthen their international agricultural cooperation framework through long-term mechanisms supporting strategic scientific partnerships between the two countries, including multi-year joint research programs, dedicated funding instruments, researcher mobility, and institutional mechanisms for transparent governance of genetic resources and jointly generated intellectual property. Full article

The integration of Artificial Intelligence (AI) in higher education is reshaping institutional decision-making, governance, policy development, and educational innovation. Effective leadership in AI-enabled environments requires more than technical competence, extending to strategic awareness, ethical judgement, and the ability to critically evaluate the broader implications of AI technologies. Drawing on survey and interview data from 52 academic leaders across UK higher education institutions, this study examines current levels of AI literacy and explores how AI capability relates to institutional readiness and leadership practice. The findings reveal variation in participants’ self-reported AI literacy and engagement, identify technical, strategic, ethical, and organisational capability gaps, and highlight structural barriers including limited time, fragmented professional development, and insufficient institutional support for institution-wide AI adoption. In response, the paper presents the AI Literacy Leadership Framework (AILLF), which conceptualises AI literacy as a multidimensional leadership capability comprising technical, strategic, ethical, and applied dimensions that support four interconnected leadership domains: innovation, decision-making, ethical governance, and policy development. Informed by leadership theory, international AI governance frameworks, and the study’s exploratory empirical findings, the AILLF is accompanied by a proposed capability progression model and role-differentiated leadership competency guide to provide implementation guidance for higher education institutions. The study contributes an empirically informed conceptual framework that integrates AI literacy, leadership theory, and AI governance, providing a foundation for future research and institutional approaches to AI leadership within higher education. Full article

Background: Artificial intelligence (AI) is one of the most influential technological innovations in contemporary mental healthcare. Advances in machine learning, natural language processing, conversational agents, and large language models have accelerated the integration of AI into clinical practice, professional education, and healthcare. Despite its increasing adoption, important questions remain regarding its clinical effectiveness, implementation, safety, and ethical governance. Objective: This systematic review aimed to synthesize the current evidence on the application of artificial intelligence in mental health, with particular emphasis on clinical practice, educational transformation, and ethical governance. Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, Web of Science, PsycINFO, and Google Scholar were systematically searched. The electronic database search was last conducted on 31 December 2025, and studies published between January 2019 and December 2025 were considered eligible. Eligible studies examined the application of artificial intelligence in mental health across clinical practice, educational contexts, and ethical governance. Study selection, data extraction, and methodological quality assessment were carried out independently by two reviewers using predefined eligibility criteria and standardized extraction forms. Owing to substantial methodological heterogeneity across the included studies, the findings were synthesized narratively. Results: A total of 88 studies met the eligibility criteria and were included in the final qualitative synthesis. The findings showed that AI demonstrated potential to improve diagnostic support, risk prediction, treatment planning, symptom monitoring, and access to psychological support. AI also supported educational innovation and workforce development while highlighting the importance of ethical governance for responsible implementation in mental healthcare. Conclusions: Future progress will depend on interdisciplinary collaboration to ensure that AI complements rather than replaces human expertise. Although AI demonstrates substantial potential, many systems remain experimental, with limited external validation. Prospective multicenter evaluation, transparent algorithm development, and robust ethical governance are therefore essential before widespread clinical implementation. Full article

Electric vehicles are rapidly reshaping global transportation, emerging as a central pillar of efforts to cut greenhouse gas emissions and end dependence on fossil fuels. This review provides a critical and integrative synthesis of recent advances in electric vehicle technologies, focusing on three interconnected domains: battery innovations, charging strategies, and grid integration. Progress in high-energy-density lithium-ion chemistries, emerging solid-state and sodium-ion batteries, and advanced battery management systems is examined with respect to their implications for driving range, safety, and lifecycle sustainability. Charging infrastructure developments, including fast and ultra-fast charging, wireless charging, and battery-swapping networks, are evaluated in terms of technical feasibility, grid impact, and user adoption. The evolving role of EVs in enhancing energy system flexibility is further analyzed through vehicle-to-grid (V2G) and smart grid interactions, with emphasis on control algorithms, grid stability, and renewable energy integration. By critically analyzing recent literature, this review identifies key technological, infrastructural, and system-level challenges, as well as emerging research directions that require coordinated optimization across domains. The insights presented aim to guide future research, technology development, and policy design toward the realization of a resilient, efficient, and scalable electric mobility ecosystem. Full article

The sustainability of buildings and the built environment extends beyond energy and environmental performance to circular resource use, health and safety, digital infrastructure, and stakeholder collaboration. This exploratory integrative literature review examines how these established but insufficiently connected domains reshape business models in the built environment. Since the built environment is a major source of global carbon emissions and waste, a primary research stream concerns the transition toward circular economy principles beyond traditional profit-maximization logics. The literature also highlights the potential of health- and safety-oriented innovations to reduce risks and improve indoor environments. Other studies highlight the potential of digital innovations to improve life-cycle management, resource efficiency, and risk mitigation. However, their widespread adoption faces systemic barriers, including data interoperability and cybersecurity issues, implementation costs, skills shortages, organizational resistance, and regulatory and governance challenges. The literature often emphasizes technical potential while paying less attention to value-capture mechanisms and the allocation of costs, risks, benefits, and responsibilities among the actors involved. Integrating sustainable practices, digital infrastructures, circular-economy principles, and collaborative governance is therefore essential to develop economically viable, organizationally feasible, and ethically responsible business models for the built environment, across the building life cycle and among public and private stakeholders. Full article

Chemistry teaching in youth and adult education has faced challenges related to students’ disengagement, interrupted educational trajectories, and difficulty in connecting scientific concepts with learners’ everyday and professional experiences. In Brazil, Professional and Technological Secondary Education Integrated with Youth and Adult Education (PROEJA) aims to address its challenges by combining basic education with vocational training. This study investigated how a contextualized pedagogical intervention based on the production of aromatic candles contributed to students’ engagement, contextualized conceptual learning and vocational reflection in a technical administration course offered at the Brazilian Federal Institute (IFMS-CG), Campo Grande, Mato Grosso do Sul, Brazil. A qualitative participatory action research design was adopted, and qualitative data were generated through participatory observations, classroom interactions, students’ written reflections, photographic records, and reflective discussions. Data were interpreted by Reflexive Thematic Analysis. Throughout the intervention, students explored Organic Chemistry concepts related to hydrocarbons, n-paraffins, physicochemical properties, volatility, combustion, formulation and laboratory safety while simultaneously discussing entrepreneurship, branding, product development and income generation. The qualitative findings demonstrated students’ active participation, collaborative teacher–student interactions, contextualized conceptual learning and a broader perception of Chemistry as a socially and professionally relevant discipline connected to everyday life, vocational education and entrepreneurial practice. Students’ written reflections further revealed that scientific knowledge was increasingly associated with technological innovation, product quality, professional opportunities and income generation. These findings suggest that contextualized, interdisciplinary and humanized pedagogical practices may promote meaningful scientific learning while strengthening vocational reflection and reinforcing the relevance of Chemistry within youth and adult professional and technological education. Full article

Artificial intelligence is increasingly adopted by tourism organisations, yet its relationships with creativity, innovation, and organisational growth intention remain insufficiently understood across national contexts. This study examined relationships among current organisational AI use, intention to use or expand the use of AI, perceived usefulness of AI for tourism service development, creativity at workplace, organisational innovation, and organisational growth intention in Lithuania, Poland, and Sweden. Survey responses, provided by 436 representatives of tourism organisations, were analysed using structural equation modelling, bias-corrected bootstrapping, and exploratory multi-group comparisons. Current organisational AI use was positively associated with creativity at workplace in the pooled model, but the association was modest and was not robust in country-specific bootstrap analyses. Perceived usefulness of AI for tourism service development showed a more robust positive association with organisational innovation, while creativity at workplace was strongly associated with organisational innovation and organisational innovation was positively associated with organisational growth intention. Intention to use or expand the use of AI was positively associated with organisational growth intention in the hypothesised structural model, but this relationship was specification-dependent and did not remain statistically significant after controlling for main activity group and number of employees. Significant indirect associations were also identified, and country-specific analyses indicated heterogeneity in selected relationships. The findings support distinguishing the three AI-related dimensions and suggest that creativity-supportive and innovation-management practices remain important alongside AI-related practices. Full article

Generative artificial intelligence (AI) chatbots are increasingly prominent in higher education, but temporal evidence on student perceptions remains limited in English-medium transnational STEM education. This repeated cross-sectional study surveyed students at a Sino-British engineering institution in Spring 2023 (Wave 1; N = 165) and Spring 2025 (Wave 2; N = 297), and found that familiarity (37.6% vs. 67.3%; OR = 3.43), willingness (55.8% vs. 77.8%; OR = 2.78), comfort (63.6% vs. 74.4%; OR = 1.66), and perceived overall learning enhancement (46.1% vs. 69.0%; OR = 2.61) were higher in 2025. In a respondent-level model combining Wave 1 with the Spring four-programme subset (analytic N = 426), survey wave remained associated with willingness after adjustment for programme and year (OR = 3.51, 95% CI 2.17–5.69, p < 0.001). In the Wave 2 Spring concern checklist (N = 297), accuracy (51.5%) and deduplicated over-reliance (50.2%) were the leading concerns; because the concern blocks differed in format and options, specific concerns cannot be shown to have increased or matured. The findings indicate higher population-level adoption readiness, not individual progression or routine use. “Concern maturation” remains a hypothesis requiring invariant instruments and panel designs. Full article

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

Agricultural production trusteeship (APT) represents an innovative model of agricultural socialized services in which specialized service organizations provide scaled operations to smallholders without transferring land rights. It can effectively address rural labor shortages while contributing to China’s grain security and agricultural modernization by improving production efficiency and promoting more sustainable resource allocation. Using survey data from farm households across six Shandong cities, this study applies an Ordered Probit model to examine how off-farm employment influences farmers’ adoption of APT, together with its underlying mechanisms and heterogeneous patterns. The results show that a higher share of household labor engaged in off-farm employment is associated with a higher probability of APT adoption. This association is stronger under flexible employment model and weaker as the location of off-farm work becomes more distant. Mechanism analysis shows capital endowment mediates the association between off-farm employment and APT adoption, with physical capital having the strongest effect and economic capital the weakest. Higher risk perception weakens the positive effect of off-farm employment on farmers’ adoption of APT. These findings provide novel evidence and practical policy implications for improving APT implementation and facilitating smallholders’ effective participation in modern agricultural production systems. Full article

Therapeutic glycoproteins, a pivotal class of biopharmaceuticals, have transformed modern medicine through their broad applications in oncology, immunotherapy, and infectious disease management. Their structural complexity and biological specificity make them highly effective in targeting disease pathways; however, challenges related to stability, bioavailability, and delivery efficacy limit their full potential. Recent advancements in delivery technologies have sought to address these challenges through innovative approaches such as nanotechnology-based carriers, controlled-release systems, and molecular engineering. These strategies have demonstrated the ability to enhance glycoprotein stability, optimize pharmacokinetics, and achieve targeted delivery with minimal off-target effects. This review provides a comprehensive overview of state-of-the-art lipid-based delivery systems specifically designed to overcome the unique pharmaceutical challenges associated with therapeutic glycoproteins, highlighting their design principles, formulation strategies, mechanisms of encapsulation and release, and therapeutic advantages in improving glycoprotein stability, bioavailability, targeted delivery, and treatment efficacy. In addition to surveying the current landscape, this review delves into the key challenges impeding the widespread adoption of advanced delivery systems, including immunogenicity, manufacturing scalability, and clinical translation. The review concludes with insights into emerging trends in the development of lipid-based delivery systems, positioning glycoprotein therapeutics at the forefront of innovation in biopharmaceuticals. This overview of advancements and challenges aims to provide a roadmap for future progress in the field of glycoprotein delivery and therapeutic applications. Full article

The rapid adoption of Generative Artificial Intelligence (GenAI) is transforming higher education by reshaping learning experiences, student engagement, and creativity. However, little is known about the mechanisms through which GenAI enhances creativity, particularly within tourism and hospitality education. Unlike prior studies that primarily examine students’ intention to adopt GenAI, this study explains how GenAI adoption translates into creativity through enhanced learning and learning engagement while accounting for ethical risks. Drawing upon contemporary learning and creativity literature, this study examined how students’ adoption of GenAI influences creativity through the mediating roles of enhanced learning and learning engagement, while also investigating the moderating effect of perceived ethical risks associated with GenAI use. Data were collected from 420 students enrolled in tourism and hospitality faculties and institutes across Egypt. The proposed research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that enhanced learning and learning engagement are the principal mechanisms through which GenAI adoption improves students’ creativity, whereas perceived ethical risks significantly weaken these positive relationships. This study contributed to the emerging GenAI literature by moving beyond technology adoption perspectives to explain how GenAI shapes creativity outcomes in higher education. It further extends tourism and hospitality education research by providing one of the first empirical examinations of the mechanisms through which GenAI enhances student creativity while accounting for ethical concerns. The findings provide practical guidance for higher education institutions seeking to integrate GenAI responsibly while fostering creativity, engagement, and innovative learning environments. Full article