Search Results (819)

This work develops and analyzes a novel fractional-order chemostat system (FOCS) with a Caputo fractional derivative (CFD) featuring a sliding memory window and periodic boundary conditions (PBCs), designed to model microbial pollutant degradation in sustainable water treatment. By incorporating the Caputo fractional derivative with sliding memory (CFDS), the model captures time-dependent behaviors and memory effects in biological systems more realistically than classical integer-order formulations. We reduce the two-dimensional fractional differential equations (FDEs) governing substrate and biomass concentrations to a one-dimensional FDE by utilizing the PBCs. The existence and uniqueness of non-trivial, periodic solutions are established using the Carathéodory framework and fixed-point theorems, ensuring the system’s well-posedness. We prove the positivity and boundedness of solutions, demonstrating that substrate concentrations remain within physically meaningful bounds and biomass concentrations stay strictly positive, with solution trajectories confined to a biologically feasible invariant set. Additionally, we analyze non-trivial equilibria under constant dilution rates and derive their stability properties. The rigorous mathematical results confirm the viability of FOCS models for representing memory-driven, periodic bioprocesses, offering a foundation for advanced water treatment strategies that align with Sustainable Development Goal 6 (Clean Water and Sanitation). This work establishes a comprehensive mathematical framework that bridges fractional calculus with sustainable water treatment applications, providing both theoretical foundations and practical implications for optimizing bioreactor performance in environmental biotechnology. Full article

This paper presents Greenopoli, an innovative framework for sustainability and waste management education that has engaged over 600 schools and 90,000 students since 2014. Greenopoli is founded on the idea that children and youth can grasp environmental issues as well as adults and act as agents of change within their families and communities. The Greenopoli approach combines scientific accuracy with playful, creative pedagogy to simplify complex topics and stimulate peer-to-peer learning. It includes storytelling, games, field visits, and “green raps” (original environmental songs co-created with students). The framework is adaptive, with content and activities tailored to education stages from kindergarten through university. Educators adopt the role of moderators or facilitators, encouraging students to discuss and discover concepts collaboratively. Greenopoli’s participatory method has been implemented across all age groups, yielding enthusiastic engagement and tangible outcomes in waste sorting and recycling behaviors. The program’s reach has extended beyond schools through collaborations with national recycling consortia, NGOs, municipalities, and media (TV programs, social media, TEDx talks). Numerous awards and recognitions (2017–2025) have highlighted its impact. A comparative analysis shows that Greenopoli’s use of peer-led learning, gamification, and creative communication aligns with global best practices while offering a unique blend of tools. Greenopoli is a novel best-practice model in environmental education, bridging theory and practice and contributing to the goals of Education for Sustainable Development and a circular economy. It demonstrates the effectiveness of engaging youth as change-makers through interactive and creative learning, and it can inspire similar initiatives globally. Full article

Background: Acute coronary syndromes (ACSs) remain a leading cause of death and disability. Since the publication of the 2023 ESC ACS guidelines, multiple studies and an ESC/EAS dyslipidaemia update have refined how clinicians diagnose, revascularize, and treat ACS across the care continuum. Content: This state-of-the-art review synthesizes advances from 2023 to 2025 across five domains. Diagnosis: High-sensitivity troponin-based accelerated pathways remain foundational; GRACE 3.0 improves calibration for early vs. delayed angiography, while selective use of CCTA and routine use of intracoronary imaging/physiology help define the mechanism and optimize PCI. Revascularization: complete revascularization continues to underpin care in multivessel disease, with recent data favouring culprit-only PCI acutely and staged non-culprit treatment during the index stay in most STEMI presentations, particularly with heart-failure physiology. Antithrombotic therapy: Aspirin remains critical early after ACS-PCI; emerging evidence supports shorter DAPT and aspirin withdrawal after 1 month in carefully selected, low-ischaemic-risk patients, whereas day-0 aspirin-free strategies in unselected ACS are not non-inferior. Secondary prevention: A “strike early and strong” approach to LDL-cholesterol—often with combination therapy in hospital—is emphasized, alongside nuanced roles for SGLT2 inhibitors and GLP-1 receptor agonists. Special populations and implementation: Sex- and age-aware tailoring (including MINOCA/SCAD evaluation), pragmatic bleeding-risk mitigation, digitally enabled cardiac rehabilitation, and registry-driven quality improvement translate evidence into practice. Summary: Contemporary ACS care is moving from uniform protocols toward risk-stratified, mechanism-informed pathways. We offer practical algorithms and checklists to align interventional timing, antithrombotic intensity/duration, and secondary prevention with individual patient risk—bridging new evidence to bedside decisions. Full article

Identifying area functions around urban rail transit (URT) stations is crucial for optimizing urban planning and infrastructure allocation. Traditional methods relying on static land-use data fail to capture dynamic human–environment interactions, while emerging mobility datasets suffer from spatial granularity limitations. This study bridges this gap by integrating spatiotemporal patterns of dockless bike sharing (DBS) with Point of Interest (POI) configurations to characterize station functions. Taking Beijing as a case study, we develop a cluster analysis framework that synthesizes DBS density fluctuations, parking distribution shifts between day/night periods, and POI features. Cluster results reveal functionally distinct station groups with statistically significant differences in both DBS usage patterns and POI distributions. Critically, high-density urban cores exhibit concentrated bicycle usage aligned with mixed POI agglomerations, while suburban zones demonstrate commuter-oriented fluctuations with evening residential surges. This alignment between DBS-derived activity signatures and POI-based land-use features provides actionable insights: planners can optimize bicycle parking in residential clusters, calibrate last-mile connections in employment cores, and adapt infrastructure to localized functional transitions—ultimately enhancing URT-integrated sustainable development. Full article

Plastic waste management in Romania represents a critical challenge, situated between ambitious European Union (EU) circular economy commitments and the complex realities of national implementation. The analysis was carried out following the methodological steps and transparent reporting guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Out of 200 studies included in this study from major databases (Scopus, Web of Science, Google Scholar) only 77 were retained. This systematic review critically synthesizes existing scientific evidence regarding this disparity through the lens of Life Cycle Assessment (LCA). The analysis highlights three critical findings. Firstly, regarding the status of LCA research, a significant scarcity of primary data for Romania is revealed, with existing studies predominantly relying on static attributional methods that fail to capture dynamic market shifts. Secondly, concerning the alignment with EU directives, the results indicate a severe ‘compliance gap’. While the implementation of the Deposit-Return System (SGR) has successfully diverted Polyethylene Terephthalate (PET) streams, the infrastructure for other plastic fractions remains stagnant, contradicting the efficiency required by EU targets. Finally, regarding strategic recommendations, it is demonstrated that current policies are hindered by a lack of LCA institutionalization. Consequently, the adoption of dynamic LCA models and harmonized reporting standards is proposed as a necessary mechanism to bridge the disparity between sustainability objectives and local operational realities. Full article

Major Depressive Disorder (MDD) poses a significant global health burden, characterized by a complex and heterogeneous pathophysiology insufficiently targeted by conventional single-treatment approaches. This review presents an integrative framework incorporating network pharmacology, artificial intelligence (AI), and multi-omics technologies to advance a systems-level understanding and management of MDD. Its central contribution lies in moving beyond reductionist methods by embracing a holistic perspective that accounts for dynamic interactions within biological networks. The primary objective is to demonstrate how AI-powered integration of multi-omics data—spanning genomics, proteomics, and metabolomics—can enable the construction of predictive network models. These models are designed to uncover fundamental disease mechanisms, identify clinically relevant biotypes, and reveal novel therapeutic targets tailored to specific pathological contexts. Methodologically, the review examines the microbiota–gut–brain (MGB) axis as an illustrative case study, detailing its pathogenic roles through neuroimmune alterations, metabolic dysfunction, and disrupted neuro-plasticity. Furthermore, we propose a translational roadmap that includes AI-assisted biomarker discovery, computational drug repurposing, and patient-specific “digital twin” models to advance precision psychiatry. Our analysis confirms that this integrated framework offers a coherent route toward mechanism-based personalized therapies and helps bridge the gap between computational biology and clinical practice. Nevertheless, important challenges remain, particularly pertaining to data heterogeneity, model interpretability, and clinical implementation. In conclusion, we stress that future success will require integrating prospective longitudinal multi-omics cohorts, high-resolution digital phenotyping, and ethically aligned, explainable AI (XAI) systems. These concerted efforts are essential to realize the full potential of precision psychiatry for MDD. Full article

Industry 5.0 introduces a shift toward human-centric, sustainable, and resilient industrial ecosystems, emphasizing intelligent automation, collaboration, and adaptive operations. Predictive Maintenance (PdM) plays a critical role in this transition, addressing the limitations of traditional maintenance approaches in increasingly complex and data-driven environments. The convergence of Artificial Intelligence and the Industrial Internet of Things, referred to as the Artificial Intelligence of Things (AIoT), enables real-time sensing, learning, and decision-making for advanced fault detection, Remaining Useful Life estimation, and prescriptive maintenance actions. This study provides a systematic and structured review of AIoT-enabled PdM aligned with Industry 5.0 objectives. It presents a unified taxonomy integrating AI models, Industrial Internet of Things (IIoT) infrastructures, and AIoT architectures; reviews AI-driven techniques, sector-specific implementations in manufacturing, transportation, and energy; and analyzes emerging paradigms such as Edge–Cloud collaboration, federated learning, self-supervised learning, and digital twins for autonomous and privacy-preserving maintenance. Furthermore, this paper synthesizes strengths, limitations, and cross-industry challenges, and outlines future research directions centered on explainability, data quality and heterogeneity, resource-constrained intelligence, cybersecurity, and human–AI collaboration. By bridging technological advancements with Industry 5.0 principles, this review contributes a comprehensive foundation for the development of scalable, trustworthy, and next-generation AIoT-based predictive maintenance systems. Full article

Yeast-derived biomolecules are redefining the boundaries of green nanotechnology. Biosurfactants, exopolysaccharides, enzymes, pigments, proteins, and organic acids—when sourced from carbohydrate-rich lignocellulosic hydrolysates—offer a molecular toolbox capable of directing, stabilizing, and functionalizing nanoparticles (NPs) with unprecedented precision. Beyond their structural diversity and intrinsic biocompatibility, these biomolecules anchor a paradigm shift: the convergence of biorefineries with nanotechnology to deliver multifunctional materials for the circular bioeconomy. This review explores: (i) the expanding portfolio of metallic and metal oxide NPs synthesized through yeast biomolecules; (ii) molecular-level mechanisms of reduction, capping, and surface tailoring that dictate NP morphology, stability, and reactivity; (iii) synergistic roles in intensifying lignocellulosic processes—from enhanced hydrolysis to catalytic upgrading; and (iv) frontier applications spanning antimicrobial coatings, regenerative packaging, precision agriculture, and environmental remediation. We highlight structure–function relationships, where amphiphilicity, charge distribution, and redox activity govern resilience under saline, acidic, and thermally harsh industrial matrices. Yet, critical bottlenecks remain: inconsistent yields, limited comparative studies, downstream recovery hurdles, and the absence of comprehensive life-cycle and toxicological evaluations. To bridge this gap, we propose a translational roadmap coupling standardized characterization with real hydrolysate testing, molecular libraries linking biomolecule chemistry to NP performance, and integrated techno-economic and environmental assessments. By aligning yeast biotechnology with nanoscience, we argue that yeast-biomolecule-driven nanoplatforms are not merely sustainable alternatives but transformative solutions for next-generation lignocellulosic biorefineries. Full article

This study proposes an artificial intelligence (AI)-powered multimodal system designed to enhance the appreciation of traditional poetry, using Japanese haiku as the primary application domain. At the core of the system is an intelligent data analysis pipeline that extracts key emotional features from poetic texts. A fine-tuned Japanese BERT model is employed to compute three affective indices—valence, energy, and dynamism—which form a quantitative emotional representation of each haiku. These features guide a generative AI workflow: ChatGPT constructs structured image prompts based on the extracted affective cues and contextual information, and these prompts are used by DALL·E to synthesize stylistically consistent watercolor illustrations. Simultaneously, background music is automatically selected from an open-source collection by matching each poem’s affective vector with that of instrumental tracks, producing a coherent multimodal (text, image, sound) experience. A series of validation experiments demonstrated the reliability and stability of the extracted emotional features, as well as their effectiveness in supporting consistent cross-modal alignment. These results indicate that poetic emotion can be represented within a low-dimensional affective space and used as a bridge across linguistic and artistic modalities. The proposed framework illustrates a novel integration of affective computing and natural language processing (NLP) within cultural computing. Because the underlying emotional representation is linguistically agnostic, the system holds strong potential for cross-cultural extensions, including applications to Chinese classical poetry and other forms of traditional literature. Full article

Background/Objectives: Community health workers (CHWs) play a critical role in advancing health equity by bridging gaps in care for underserved populations. However, limited institutional support, inconsistent training, and lack of integration contribute to high rates of burnout. The Lunch and Learn program was launched in Maryland in fall 2023 as a virtual continuing education and peer-support initiative designed to foster professional development, enhance connections among CHWs, and align with Maryland state CHW certification requirements. This article describes the program’s first year of implementation as a proof-of-concept and model for scalable CHW workforce support. Methods: The program offered twice-monthly, one-hour virtual sessions that included expert-led presentations, Q&A discussions, and dedicated peer-support time. Participant engagement was assessed using attendance metrics, post-session surveys, and annual feedback forms to identify trends in participation, learning outcomes, and evolving professional priorities. Results: Participation increased over time with the program’s listserv expanding from 29 to 118 members and average session attendance more than doubling. CHWs highlighted the program’s value in meeting both educational and emotional support needs. Conclusions: The Lunch and Learn program demonstrates a promising model for addressing burnout through education and community connection. As an adaptable, CHW-informed initiative, it supports both professional growth and well-being. Ongoing development will focus on expanding access, incorporating experiential learning assessments, and advocating for sustainable funding to ensure long-term program impact and CHW workforce stability. Full article

Gold mining has played a central role in shaping Peruvian society from pre-Inca civilizations to the present. However, existing literature offers fragmented perspectives, often focusing on isolated themes such as metallurgy, colonial mercury use, or environmental degradation, without integrating these across time and territory. This review addresses that gap by offering a place-based synthesis that combines archaeological, historical, legal, environmental, and comparative insights. Drawing on both Spanish-language sources and international literature, the paper reconstructs Peru’s gold mining trajectory through five historical phases—pre-Inca, Inca, colonial, republican, and contemporary—highlighting continuities and ruptures in governance, labor systems, and environmental impacts. The analysis reveals persistent challenges in Peru’s gold sector, including informality, mercury pollution, and weak institutional capacity. Compared to other mining economies such as Chile, Ghana, and South Africa, Peru exhibits greater fragmentation and limited integration of mining into national development strategies. The review also explores the role of gold in the global energy transition, emphasizing its relevance in clean technologies and green finance, and identifies policy gaps that hinder Peru’s alignment with sustainability goals. By bridging linguistic and disciplinary divides, this synthesis contributes to a more inclusive historiography of extractive industries and underscores the need for interdisciplinary approaches to mining governance. Ultimately, the paper calls for a reimagining of Peru’s gold sector, one that prioritizes environmental justice, social equity, and long-term resilience. Full article

Three-dimensional printed food has rapidly positioned itself at the intersection of food technology and personalized nutrition, opening up new perspectives for sustainable production, creative customization, and more efficient resource use. Although global interest in this innovation continues to grow, consumer acceptance remains largely underexplored in Central and Eastern Europe. This study analyzes how Romanian consumers approach the adoption of 3D-printed food by applying an extended UTAUT2 framework to a sample of 608 urban respondents. Using structural equation modeling, it examines the influence of expected effort, performance expectancy, social influence, and perceived compatibility on adoption intention, while price perception is introduced as a key mediating variable—a novel and meaningful contribution to the literature on food technology acceptance. Given the non-probabilistic sampling design, the difficulties encountered in measuring Hedonic Motivation and Facilitating Conditions, and the early diffusion stage of 3D food printing in Romania, the present work should be viewed as a robust exploratory investigation based on Structural Equation Modeling (SEM) among urban Romanian consumers, providing first empirical evidence on 3D-printed food acceptance in Eastern Europe rather than definitive conclusions for the entire population. The results highlight that utilitarian and social factors are decisive: expected effort enhances perceived performance, while performance, social influence, and compatibility significantly strengthen perceptions of price fairness. In turn, price perception strongly predicts consumers’ behavioral intention to adopt 3D-printed food. Hedonic motivation and facilitating conditions were not statistically significant and were therefore removed from the final model. These findings show that, in emerging food markets, consumers tend to make adoption decisions based more on rational value assessments than on novelty or convenience. The study contributes to theory by embedding price perception into the UTAUT2 framework and to practice by identifying the key elements that can boost market readiness—transparent pricing and closer alignment with consumer values. By filling an important gap in the empirical literature from Eastern Europe and focusing on price as a cognitive bridge between technological and psychological drivers, this paper offers a timely and relevant contribution to ongoing research on consumer perception and acceptance of food innovations. For Eastern European food innovation research, this study provides one of the first quantitative analyses of 3D-printed food acceptance that explicitly links technology-related beliefs to price perception in a regional, price-sensitive context. Full article

Leakage from defective buried pipelines can lead to progressive soil erosion and void formation, ultimately resulting in ground collapse or sinkhole development. To better understand the underlying mechanisms of this process, this research utilizes a coupled computational fluid dynamics (CFD)–discrete element method (DEM) modeling approach to investigate soil erosion processes driven by water leakage from defective underground pipelines. The numerical model captures fluid–particle interactions at both macroscopic and microscopic scales, providing detailed insights into erosion initiation, void zone evolution, and particle transport dynamics under varying hydraulic and geometric conditions. Parametric studies were conducted to evaluate the effects of exfiltration pressure, defect size, and particle diameter on erosion behavior. Results show that erosion intensity and particle migration increase with hydraulic pressure up to a threshold, beyond which compaction and particle bridging reduce sustained transport. The intermediate defect size (12.7 mm) consistently produced the most continuous and stable erosion channels, while smaller and larger defects exhibited localized or asymmetric detachment patterns. Particle size strongly influenced erosion susceptibility, with finer grains mobilized more readily under the same flow conditions. The CFD–DEM simulations successfully reproduce the nonlinear and self-reinforcing nature of internal erosion, revealing how hydraulic gradients and particle rearrangement govern the transition from local detachment to large-scale cavity development. These findings advance the understanding of subsurface instability mechanisms around leaking pipelines and provide a physically consistent CFD–DEM framework that aligns well with published studies. The model effectively reproduces the key stages of erosion observed in the literature, offering a valuable tool for assessing erosion-induced risks and for designing preventive measures to protect buried infrastructure. Full article

Egypt’s heritage villages are living cultural landscapes that remain largely undocumented and insufficiently protected under current national frameworks. This study develops the Enhanced Heritage Value Index (EHVI) a UNESCO-aligned framework designed to quantitatively and qualitatively assess the conservation readiness of heritage villages in Egypt. EHVI was developed through a mixed-method approach integrating conceptual analysis of UNESCO and ICOMOS standards, expert-based weighting of 31 validated heritage indicators, and community perception surveys conducted across seven villages in Luxor Governorate. The EHVI provides a scalable and replicable assessment tool that classifies villages into high, moderate, and low readiness levels, enabling decision-makers to prioritize conservation interventions. As Egypt’s first composite index tailored to rural heritage contexts, this framework bridges national policy needs with international heritage criteria and offers a strategic foundation for future UNESCO nominations and sustainable heritage management. This research fills a critical theoretical and practical gap by operationalizing the Historic Urban Landscape (HUL) paradigm in a rural Egyptian context, thereby advancing heritage assessment methodologies beyond urban-centered models. Full article

The increasing complexity of the built environment—encompassing three-dimensional spatial dynamics, environmental footprints, and socio-cultural dimensions—necessitates innovative educational tools. Serious games have emerged as immersive platforms bridging theoretical knowledge and practical application in this domain. This narrative literature review examines the extent to which serious games effectively integrate and reflect sustainability principles within the context of the built environment, as well as their strategies for engaging learners. A comprehensive search was conducted across multiple databases using keywords such as “serious games,” “built environment,” and “sustainability.” The review identifies that while many games address tangible challenges like retrofitting simulations and resource management, their incorporation of sustainability concepts is often superficial. Critical aspects such as inclusivity, stakeholder engagement, and alignment with SDGs are frequently underrepresented. Furthermore, a lack of a common language among stakeholders and the tendency to focus on isolated aspects of sustainability, rather than adopting a holistic approach, were noted. Despite these limitations, the engaging nature of these games that are based on real scenarios offers potential for impactful learning experiences. However, challenges persist, including technical constraints, pedagogical limitations, and deeper epistemological and ethical tensions in game design. The findings underscore the need for a more integrated and comprehensive approach to embedding sustainability in serious games, along with more effective engagement strategies to ensure they function as impactful tools for education and learning in the built environment domain. Full article