Search Results (6,796)

Gingivitis is a common and reversible inflammatory condition caused by dental plaque accumulation, which, if left untreated, can progress to periodontitis. Conventional oral care products like chlorhexidine (CHX) and fluoride are effective in plaque control but are often associated with adverse effects such as dental staining and mucosal irritation. This systematic review aimed to compare the efficacy and safety of natural versus conventional toothpastes and mouthwashes in managing plaque-induced gingivitis. The review followed PRISMA guidelines and was registered in PROSPERO (No. 1008296). A systematic search was conducted across PubMed, Web of Science, and Scopus for English-language clinical studies published between 2015 and 2025. Eligible studies included randomized controlled trials and clinical trials on human subjects with plaque-induced gingivitis. Exclusion criteria were studies on animals, in vitro experiments, review articles, and studies lacking control groups. Data extracted included intervention type, sample characteristics, clinical indices (PI, GI, SBI), inflammatory biomarkers, adverse events, and patient adherence. A narrative synthesis was conducted due to study heterogeneity. Fifteen studies were included. Natural products such as neem, green tea, aloe vera, and propolis demonstrated comparable effectiveness to CHX and fluoride in reducing gingival inflammation and plaque indices, with a lower incidence of side effects. In particular, natural formulations showed superior tolerability and better patient compliance, especially in long-term use. However, variability in concentration and the formulation of natural products limits their clinical standardization. In conclusion, natural oral care products appear to be effective and better-tolerated alternatives to conventional agents in managing gingivitis. Nonetheless, further long-term, standardized clinical trials are needed to confirm their efficacy and define optimal formulations. Full article

The unsuitable performance of or deficit in basic sanitation infrastructure, especially in sparsely populated rural communities, remains critical, particularly in many developing regions, and demands sustainable, cost-effective, and easily operated solutions. Thus, the objective of this Review is to analyze design parameters for evapotranspiration tanks (EvapTs), adopted as nature-based solutions for zero-discharge domestic sewage treatment. The literature search was conducted using the Scopus and Web of Science databases, complemented by backward citation tracking. From 4434 records, 29 studies were selected based on specific criteria, such as the availability of design data and their application in urban or rural contexts. The main findings indicated required areas per inhabitant ranging from 0.5 to 7.7 m², primarily influenced by climate conditions and the type of plant used. Statistical analysis showed a negative correlation between the area of the evaporation tanks and the mean annual temperature, with a Pearson correlation coefficient (r of −0.74). For mean annual temperatures between 19 and 27 degrees Celsius, linear regression showed a variation between 4.7 and 0.6 m²/inhabitant with a reduction coefficient of −0.51 per degree Celsius, suggesting that warmer climates require smaller system areas per capita. Most studies were conducted at full scale, with Brazil accounting for the highest number of publications. EvapT is identified as a promising ecological technology that is particularly suitable for rural settings. However, it still requires technical standardization, cost–benefit analysis, and research on social acceptance. The adoption of clear design criteria may enhance system replicability, support public policy development, and contribute to SDG 6—Clean Water and Sanitation for All. Full article

Green infrastructure (GI) and nature-based solutions (NBSs) in land-use planning and landscape architecture represent a holistic, interdisciplinary response to environmental and societal challenges. Although integrated into Slovak legislation since 2019, their formal implementation has progressed rather slowly, creating a gap that has been increasingly addressed by civic initiatives (CIs) of diverse types and legal forms. This study examines approaches and methods of CIs in Slovakia implementing GI and NBSs, while focusing on their legal forms, activities, spatial delimitations, and their impact on green space development and governance. Analysis of seventeen case studies shows that many CIs operate at multiple levels—local, national, and international—often delivering professional, conceptually grounded solutions. The most frequent NBS activities involve creating or enhancing parks, green public spaces, and community gardens, as well as restoring natural and semi-natural areas through nature-based management. Knowledge creation and awareness-raising are central strategies, including environmental education centres, citizen science, public campaigns, and informal learning platforms. The transformation of derelict areas into multifunctional public spaces emerges as a notable practice, combining ecological regeneration with cultural and social uses. The findings highlight the growing role of civic actors in advancing inclusive, participatory, and knowledge-based environmental management and call for stronger institutional support to integrate their contributions into formal administrative frameworks. Full article

This paper examines the growing role of nature-based solutions (NBS) and the integration of digital technologies in participatory urban planning. It aims to assess the current state of technologies and methods for participatory approaches in NBS projects, the level of participation they can stimulate, and the drivers and barriers to their integration into planning practice. The review uses the PRISMA methodology to examine 275 records from two databases, aiming to minimize bias. Records were selected based on the following criteria: studies were conducted in urban settings; referenced NBS; incorporated participatory methods; and involved digital technologies. Both review articles and case study papers were considered. A bibliometric and content analysis was performed using VOS VIEWER software, an Excel spreadsheet, and comparison tables. The 45 reviewed studies cover citizen science, participatory mapping and co-creation using place-based or non-place-based digital tools. While these tools can improve engagement and efficiency, they also face challenges such as limited data access, demographic bias, institutional resistance, and insufficient resources. The study found that top-down methods often restrict the impact of these tools by treating public input as secondary, thereby highlighting the need for transparent, collaborative planning. Full article

Background and Objectives: Vitiligo is a chronic autoimmune skin disorder characterized by the loss of pigmentation, resulting in depigmented patches due to the destruction of melanocytes. This condition can lead to considerable psychological distress, and research indicates a possible connection with major depressive disorder (MDD). Nonetheless, the depth and nature of this association, particularly within the Saudi population, have not been thoroughly investigated. This case–control study seeks to explore the relationship between vitiligo and depressive symptomatology, evaluate the severity of depressive symptoms in vitiligo patients compared to control subjects, and examine the association between the clinical severity of vitiligo (assessed using the Vitiligo Area Scoring Index, VASI) and depressive symptoms (measured using the PHQ-9, a validated screening instrument based on DSM criteria). Materials and Methods: A total of 700 participants were included, comprising 340 individuals with vitiligo and 360 controls with other skin conditions. Participants completed a web-based questionnaire that collected sociodemographic data and included the PHQ-9 assessment. The severity of vitiligo was determined using the VASI. Statistical analysis involved using the computer program IBM Statistical Program for Social Sciences (SPSS) (version 26.0). Results: The average PHQ-9 score was significantly higher for patients with vitiligo (8.28 ± 7.36) compared to controls (6.30 ± 4.70, p = 0.028). While the overall rates of mild to severe depression were comparable (41.5% in vitiligo patients versus 40.3% in controls, p = 0.748), vitiligo patients exhibited higher occurrences of moderately severe depression (11.8%) and severe depression (10.9%) compared to controls (5.8% and 0.8%, respectively; p < 0.001). A weak, yet significant, positive correlation was found between VASI and PHQ-9 scores (ρ = 0.184, p < 0.001). The vulgaris and segmental types of vitiligo exhibited the highest median depression scores (PHQ-9: 11 and 9, respectively; p < 0.001). Logistic regression analysis indicated that those with genital vitiligo had greater odds of experiencing depression (OR = 12.10, p = 0.039), while those with universalis vitiligo faced even higher odds (OR ≈ 26,837.84, p = 0.001). Interestingly, higher VASI scores were linked to lower odds of depression (OR = 0.927, p = 0.029). Additionally, the risk of depression significantly increased with higher income levels and among individuals aged 50 years and older. Conclusions: Although the overall prevalence of depression was not significantly different between vitiligo patients and controls, the degree of depressive symptoms was notably more severe in those with vitiligo. Specific clinical subtypes, particularly genital and universalis vitiligo, were found to be more closely associated with an increased risk of depression. These results highlight the importance of regular mental health screenings and customized psychosocial support in dermatological care, especially for high-risk groups. Full article

We propose a universal framework for understanding system evolution based on structural complexity, offering a directional signature that applies across physical, chemical, and biological domains. Unlike entropy, which is constrained by its definition in closed, equilibrium systems, we introduce Kolmogorov Complexity (KC) and Fractal Dimension (FD) as quantifiable, scalable metrics that capture the emergence of organized complexity in open, non-equilibrium systems. We examine two major classes of systems: (1) living systems, revisiting Schrödinger’s insight that biological growth may locally reduce entropy while increasing structural order, and (2) irreversible natural processes such as oxidation, diffusion, and material aging. We formalize a Universal Law: expressed as a non-decreasing function Ω(t) = α·KC(t) + β·FD(t), which parallels the Second Law of Thermodynamics but tracks the rise in algorithmic and geometric complexity. This framework integrates principles from complexity science, providing a robust, mathematically grounded lens for describing the directional evolution of systems across scales-from crystals to cognition. Full article

Understanding and modeling the flow of information in human societies is essential for capturing phenomena such as polarization, opinion formation, and misinformation diffusion. Traditional agent-based models often rely on simplified behavioral rules that fail to capture the nuanced and context-sensitive nature of human decision-making. In this study, we explore the potential of Large Language Models (LLMs) as data-driven, high-fidelity agents capable of simulating individual opinions under varying informational conditions. Conditioning LLMs on real survey data from the 2020 American National Election Studies (ANES), we investigate their ability to predict individual-level responses across a spectrum of political and social issues in a zero-shot setting, without any training on the survey outcomes. Using Jensen–Shannon distance to quantify divergence in opinion distributions and F1-score to measure predictive accuracy, we compare LLM-generated simulations to those produced by a supervised Random Forest model. While performance at the individual level is comparable, LLMs consistently produce aggregate opinion distributions closer to the empirical ground truth. These findings suggest that LLMs offer a promising new method for simulating complex opinion dynamics and modeling the probabilistic structure of belief systems in computational social science. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

Silver nanoparticles (AgNPs) have garnered significant attention due to their potent antimicrobial properties and broad-spectrum efficacy against pathogens. Recent advances in polymer science have enabled the development of AgNPs-integrated hydrogels and membranes, offering multifunctional platforms for biomedical and food-related applications. This review provides a comprehensive overview of recent strategies for synthesizing and incorporating AgNPs into polymeric matrices, highlighting both natural and synthetic polymers as carriers. The structural and functional properties of these nanocomposite systems, such as biocompatibility, mechanical stability, controlled silver ion release, and antimicrobial activity, are critically examined. The focus is placed on their application in wound healing, drug delivery, food packaging, and preservation technologies. Challenges such as cytotoxicity, long-term stability, and regulatory concerns are discussed alongside emerging trends and safety paradigms. This work underscores the potential of AgNPs–polymer hybrids as next-generation materials and outlines future directions for their sustainable and targeted application in biomedical and food systems. Full article

Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article

This review provides a comprehensive analysis of recent developments in the additive manufacturing of green composites, with a particular focus on their mechanical behavior. A bibliometric analysis of 482 research articles indexed in the Web of Science Core Collection and published between 2015 and 2025 reveals a sharp increase in publications, with dominant contributions from countries such as China, India, and the United States, as well as strong collaboration networks centered on materials science and polymer engineering. Thematic clustering highlights a growing emphasis on natural fiber reinforcement, biodegradable matrices, and performance optimization. Despite these advances, few studies have combined bibliometric analysis with a technical evaluation of mechanical performance, leaving a gap in understanding the relationship between research trends and material or process optimization. Building on these insights, the review synthesizes current knowledge on material composition, print parameters, infill design, and post-processing, identifying their combined effects on tensile strength, stiffness, and durability. The study concludes that multi-variable optimization—encompassing fiber-matrix compatibility, print architecture, and thermal control—is essential to achieving eco-efficient and high-performance green composites in additive manufacturing. Full article

Background: Periodontal disease is a common chronic inflammatory disease affecting the oral cavity involving the tissues supporting teeth. It is a significant oral health concern worldwide, particularly amongst individuals living with human immunodeficiency virus (HIV). Biological aging is associated with a natural decline in the immune system, which can also affect the severity of periodontitis and other potential risk factors. In people living with HIV (PLWH), the contribution of both the HIV infection and the aging process can lead to increased susceptibility to periodontal disease. Objectives: This paper aims to review the recent literature about the relationships between HIV infection and early aging and their impact on periodontitis, and to inform interested clinicians about the current literature on the intersection between and within these topics. Methods: This review explores the recent literature on the complex relationship between HIV, aging, and periodontitis. The PubMed, ScienceDirect, and Medline databases were used to find clinical research studies within the last 10 years to identify significant correlations between HIV, aging, and periodontitis. Results: These studies identify key pathogens, molecules, or cellular pathways that contribute to a more comprehensive understanding of the pathophysiologic processes that link HIV, aging, and periodontitis. This complex relationship is multifactorial, involving immune dysfunction, microbial dysbiosis, and inflammatory pathways that still need further research. Conclusions: Overall, this exploration through molecular and cellular mechanisms underlying the relationships between aging, HIV, and periodontitis can provide therapeutic implications for dental clinicians to prevent and treat their affected patients. Full article

This study introduces a novel approach to enhance the sustainability of road pavement construction by utilizing palm oil clinker (POC), an industrial waste product, as a replacement for fine aggregates (passing 4.75 mm) in stone mastic asphalt (SMA) mixtures. Departing from conventional practices, this research comprehensively evaluates the feasibility of using POC at varying replacement levels (0% to 100%) across a range of binder contents (5.0% to 7.0%). A significant contribution of this work is the application of Response Surface Methodology (RSM) to optimize the proportions of POC and binder content (BC), achieving target Marshall and volumetric properties for superior pavement performance. The results demonstrate that POC can effectively substitute fine aggregates in SMA mixtures, meeting all requirements for Marshall stability, flow, stiffness, and volumetric properties, even at a 100% replacement rate. Statistical analysis using RSM confirmed the model’s validity, exhibiting a high R-squared value (>0.80), significant p-values, and an adequate precision exceeding 4. Optimization analysis revealed that a 60% POC content with a 6% BC yields the most desirable combination for achieving optimal SMA mixture characteristics. Further validation through experimental testing showed a strong correlation with the theoretical RSM predictions, with an error margin below 5%. This research underscores the potential of POC as a sustainable alternative to traditional aggregates, paving the way for more environmentally friendly and cost-effective road construction practices while simultaneously addressing waste management challenges in the palm oil industry. Full article

This paper addresses the urgent need to redefine education strategies for the Net Generation in the context of rapid technological and societal changes. First, the educational challenge is placed within a broader philosophical and cultural framework, focusing on the fluid and evolving nature of knowledge and human experience. Building on the paradigm shift from Web 2.0 to Web 4.0 and the emergence of Education 5.0, this paper investigates the pedagogical implications of these developments. Through conceptual analysis supported by contemporary educational theory, this paper proposes a model of education that integrates personalized learning, real-time feedback, and collaborative, interdisciplinary environments. A special focus is placed on the role of educators as mentors, rather than mere transmitters of information, and on the ethical, social, and emotional dimensions of digital learning. This article highlights the importance of adjusting educational practices to real-life contexts and future challenges of young learners while ensuring that the humanistic essence of education is not lost. Full article

Contemporary society demands the development of creative critical thinking (CCT) as a fundamental objective in science education. However, there appears to be a dissonance between this recognised need and its actual implementation in educational practices. This study explores the potential of modelling practices to intentionally, explicitly, and reflectively integrate the development of CCT in educational settings. To examine this possibility while laying the foundations for future research, the theoretical frameworks of CCT and modelling are synthesised, and their possible points of convergence are analysed in order to test the proposed hypothesis. Two elements that may strengthen their synergy are identified: first, the resources for modelling, such as analogies, which activate analytical, evaluative, creative, and argumentative skills, thereby fostering critical dispositions and a deeper understanding of the nature of science. Second, argumentation, closely connected with communication and information management, is considered a relevant component, especially when addressing socioscientific issues. While further empirical research is needed, the analysis indicates that modelling practices could contribute to the development of CCT. Full article