Search Results (582)

The global transition to a circular bioeconomy is accelerating the demand for sustainable, high-performance materials. Filamentous fungi represent a promising solution, as they function as living foundries that transform low-value biomass into advanced, self-assembling materials. While mycelium-based composites have proven potential, progress has been predominantly driven by empirical screening of fungal species and substrates. To unlock their full potential, a paradigm shift from empirical screening to rational design is required. This review introduces a conceptual framework centered on the biochemical programming of the fungal cell wall. Viewed through a materials science lens, the cell wall is a dynamic, hierarchical nanocomposite whose properties can be deliberately tuned. We analyze the contributions of its principal components—the chitin–glucan structural scaffold, the glycoprotein functional matrix, and surface-active hydrophobins—to the bulk characteristics of mycelium-derived materials. We then identify biochemical levers for controlling these properties. External factors such as substrate composition and environmental cues (e.g., pH) modulate cell wall architecture through conserved signaling pathways. Complementing these, an internal synthetic biology toolkit enables direct genetic and chemical intervention. Strategies include targeted engineering of biosynthetic and regulatory genes (e.g., CHS, AGS, GCN5), chemical genetics to dynamically adjust synthesis during growth, and modification of surface chemistry for specialized applications like tissue engineering. By integrating fungal cell wall biochemistry, materials science, and synthetic biology, this framework moves the field from incidental discovery toward the intentional creation of smart, functional, and sustainable mycelium-based materials—aligning material innovation with the imperatives of the circular bioeconomy. Full article

Accurate prediction of system degradation and remaining useful life (RUL) is essential for reliable health monitoring of Lithium-ion (Li-ion) batteries, as well as other dynamic systems. While evolving systems can offer adequate adaptability to the nonstationary and nonlinear behavior of battery degradation, existing methods often face challenges such as uncontrolled rule growth, limited adaptability, and reduced accuracy under noisy conditions. To address these limitations, this paper presents a smart evolving fuzzy predictor with customized firefly optimization (SEFP-FO) to provide a better solution for battery RUL prediction. The proposed SEFP-FO technique introduces two main contributions: (1) An activation- and distance-aware penalization strategy is proposed to govern rule evolution by evaluating the structural relevance of incoming data. This mechanism can control rule growth while maintaining model convergence. (2) A customized firefly algorithm is suggested to optimize the antecedent parameters of newly generated fuzzy rules, thereby enhancing prediction accuracy and improving the predictor’s adaptive capability to time-varying system conditions. The effectiveness of the proposed SEFP-FO technique is first validated by simulation using nonlinear benchmark datasets, which is then applied Full article

Metallic nanoclusters (NCs), composed of a few to a hundred atoms, occupy a unique space between molecules and nanoparticles, exhibiting discrete electronic states, strong photoluminescence, and size-dependent catalytic activity. Their ultrasmall cores (<3 nm) and ligand-controlled surfaces confer tunable optical, electronic, and catalytic properties, making them attractive for diverse applications. In recent years, significant progress has been made toward developing faster, more reproducible, and scalable synthesis routes beyond classical wet-chemical reduction. Emerging strategies such as microwave-, photochemical-, sonochemical-, and catalytically assisted syntheses, together with smart, automation-driven platforms, have improved efficiency, structural control, and environmental compatibility. These advances have accelerated the deployment of NCs in imaging, sensing, and catalysis. Near-infrared emitting NCs enable deep-tissue, high-contrast fluorescence imaging, while theranostic platforms combine diagnostic precision with photothermal or photodynamic therapy, gene delivery, and anti-inflammatory treatment. NC-based sensors allow ultrasensitive detection of ions, small molecules, and pathogens, and atomically precise NCs have enabled efficient CO₂ reduction, water splitting, and nitrogen fixation. Therefore, in this review, we highlight studies reported in the past five years on the synthesis and applications of metallic NCs, linking emerging methodologies to their functional potential in nanotechnology. Full article

Background/Objectives: Silver sulfadiazine (AgSD) is widely used in the topical treatment of burns and infected wounds, but its conventional formulations present drawbacks such as poor water solubility, the need for multiple daily applications, and patient discomfort. To overcome these limitations, this study aimed to develop and evaluate Laponite^® (LAP)-based hydrogels loaded with AgSD for controlled release and enhanced antimicrobial and antibiofilm efficacy, offering a promising alternative for the treatment of contaminated or biofilm-forming wounds. Methods: Laponite^®-based hydrogels containing 1% and 1.2% AgSD (LAP@AgSD) were prepared using a one-pot method. The formulations were characterized rheologically, thermally, and structurally. In vitro drug release was assessed using Franz diffusion cells, and mathematical modeling was applied to determine release kinetics. Antibacterial and antibiofilm activities were evaluated against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa using standardized microbiological methods. Results: LAP@AgSD hydrogels exhibited pseudoplastic behavior, high structural integrity, and enhanced thermal stability. In vitro release assays revealed a sustained release profile, best fitted by the Weibull model, indicating diffusion-controlled mechanisms. Antibacterial assays demonstrated concentration-dependent activity, with LAP@AgSD 1.2% showing superior efficacy over LAP@AgSD 1% and comparable performance to the commercial silver sulfadiazine cream (CC-AgSD). Biofilm inhibition was significant for all formulations, with CC-AgSD 1% exhibiting the highest immediate activity, while LAP@AgSD 1.2% provided sustained antibiofilm potential. Conclusions: LAP-based hydrogels are promising smart delivery systems for AgSD, combining mechanical robustness, controlled drug release, and effective antibacterial and antibiofilm activities. These findings support their potential use in topical therapies for infected and chronic wounds, particularly where biofilm formation is a challenge. Full article

Marine-derived polysaccharides (MPs) are a class of polysaccharides isolated and purified from marine organisms, which engage in various biological activities such as immunomodulation, anti-tumor, antibacterial, antioxidant, and anticoagulant activities. Excellent biocompatibility, biodegradability, and low toxicity make them ideal biomaterials for the preparation of hydrogels. In recent years, MP-based hydrogels have been successfully fabricated into various novel and smart hydrogels, triggering new transformations in the fields of biomedicine, cosmetics, and food. This review introduces the structural features, bioactive mechanisms, and safety evaluation of MPs. This review focuses on the latest application progress of MP-based hydrogels in the food field, including fruits and vegetables, meat products, aquatic products, bakery products, and health products, aiming to provide fundamental support for further research and development in the food industry. Full article

Conservation agriculture (CA) is widely recognized as the cornerstone of sustainable agriculture. It prioritizes minimizing soil disturbance, maintaining permanent soil cover, and diversifying crop species to restore soil health and ecosystem resilience. This review synthesizes the effects of CA on the soil’s physical–chemical and biological properties. It demonstrates its effectiveness in improving soil structure, enhancing organic carbon sequestration, promoting microbial activity, increasing water-use efficiency, and reducing erosion and nutrient losses. The paper then highlights the broad environmental, economic, and social benefits of CA. These include biodiversity conservation, reduced greenhouse gas emissions, improved yields, and increased food system resilience. The review explores the synergistic role of technological innovations such as precision agriculture, remote sensing, and digital tools in scaling CA for higher productivity and sustainability. The review then examines how socioeconomic conditions, institutional frameworks, and policy interventions shape CA adoption and impact. Despite its growing adoption, CA’s successful implementation will require strategies adapted for local needs, capacity-building, and supportive, inclusive policies. Finally, the review identifies key CA research gaps and future directions. This provides a comprehensive foundation to advance CA as a climate-smart, resilient, and sustainable pathway to ensure global food security and environmental stewardship. Full article

Rapid urbanization and climate change pressure cities to integrate sustainability, digitalization, and circular economy principles. Yet most existing approaches treat these agendas separately, leaving gaps in how urban infrastructures, governance, and data systems can jointly support circular transformations. This paper addresses this challenge by proposing and validating a Sustainable, Smart, and Circular City (SSCC) architecture that operationalizes the waste–energy–information nexus. The architecture is structured into seven interconnected layers—Physical, Digital, Analytical, Participatory Governance, Data Strategy, Innovation Management, and Assessment—and is tested through two integrated experiments in the Fenicia Urban Living Lab, Bogotá: (i) an AI- and drone-based system for waste detection and community reporting and (ii) a solar-powered IoT urban garden for environmental monitoring. These experiments demonstrate how digital twins, participatory governance, and multi-actor collaboration can activate circular strategies while enabling evaluation against international standards (ISO 37106, U4SSC, LEED). The results confirm that the SSCC model can transform siloed services into integrated, circular functions that enhance quality of life, productivity, and ICT-based sustainability. The originality of this study lies in validating an SSCC architecture that incorporates the waste–energy–information nexus across seven layers and demonstrates, through Urban Living Lab experimentation, how such an architecture can guide the transition from Smart Sustainable Cities to Circular Cities. Full article

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

In response to the growing importance of vocational education for youth employability, this study examines students’ perceptions of dual vocational education and training (dVET) in Bulgaria, focusing on the following determinants of student loyalty (SL) and word-of-mouth communication (WOM) in the secondary education context: brand associations, brand relevance, brand image, image of dVET, service quality, and student satisfaction, based on previously validated scales adapted to the Bulgarian context. A structured questionnaire was administered to a target population of 608 students across nine vocational secondary schools in the Plovdiv region. A total of 507 usable surveys were collected from students in 11th and 12th grades who were actively participating in work-based learning. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS 4 software. The findings indicate that brand image is the strongest direct predictor of the image of dVET. Furthermore, student satisfaction stands out as the most influential antecedent of WOM. The indirect pathways from service quality to both SL and WOM, mediated by student satisfaction, underscore the pivotal role of satisfaction as a transmission mechanism. The study contributes to the limited empirical research on branding in dVET and offers insights for policymakers, school administrators, and employers seeking to improve the attractiveness of these pathways. Full article

The integration of emulsion gels in 3D food printing has emerged as a promising strategy to enhance both the structural fidelity and functional performance of printed foods. Emulsion gels, composed of proteins, polysaccharides, lipids, and their complexes, can provide tunable rheological and mechanical properties suitable for extrusion and shape retention. This review explores the formulation strategies, including phase behavior (O/W, W/O, and double emulsions); stabilization methods; and post-printing treatments, such as enzymatic, ionic, and thermal crosslinking. Advanced techniques, including ultrasound and high-pressure homogenization, are highlighted for improving gel network formation and retention of active compounds. Functional applications are addressed, with a focus on meat analogs, bioactive delivery systems, and personalized nutrition. Furthermore, the role of the oil content, interfacial engineering, and protein–polysaccharide interactions in improving print precision and post-processing performance is emphasized. Despite notable advances, challenges remain in scalability, regulatory compliance, and optimization of print parameters. The integration of artificial intelligence can also provide promising advances for smart design, predictive modeling, and automation of the 3D food printing workflow. Full article

This study explores the factors influencing the willingness of Cashew Feni producers to adopt GI certifications, delving deeper into the behavioural factors. This study is guided by the extended Theory of Planned Behaviour. This study was conducted in Goa, India, from June 2024 to January 2025 using a quantitative approach. Face-to-face interviews using structured questionnaires were conducted with Cashew Feni producers actively producing, processing, and distributing Feni in the key production regions. A total of 200 producers were approached, and after validation, 148 responses were considered valid for analysis. The respondents were chosen using a stratified random sampling method. This study employed Partial Least Squares-based Structural Equation Modelling (PLS-SEM) in the SmartPLS 4 software to analyse the data. This study found that attitude is a strong predictor significantly driving adoption. Perceived economic benefits also impact attitudes and directly affect the willingness to adopt GIs, emphasising the role of economic factors. Additionally, awareness influences attitudes and subjective norms, indicating that informed producers are likelier to have a positive attitude towards GI adoption. This study also found a significant impact of subjective norms on attitudes and perceived behavioural control. These insights can assist policy formulation and boost sustainable growth and cultural preservation. Full article

Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their functionality and safety must be ensured. Therefore, simulations of their strength and kinematics must meet set criteria. This paper aims to present a methodology for simulating the dynamics of rehabilitation exoskeletons during activities of daily living and determining the reactions in the construction’s joints, as well as the required driving torques. The methodology is applied to the SmartEx-Home exoskeleton. Two versions of a multibody model were developed in the Matlab/Simulink environment—a rigid-only version and one with deformable components. The kinematic chain of construction was reflected with the driven rotational joints and modeled passive sliding open bearings. The simulation outputs include the driving torques and joint reaction forces and the torques for various input trajectories registered using IMU sensors on human participants. The results obtained in the investigation show that in general, to mobilize shoulder flexion/extension or abduction/adduction, around 30 Nm of torque is required in such a lightweight exoskeleton. For elbow flexion/extension, around 10 Nm of torque is needed. All of the reactions are presented in tables for all of the characteristic points on the passive and active joints, as well as the attachments of the extremities. This methodology provides realistic load estimations and can be universally used for similar structures. The presented numerical results can be used as the basis for a strength analysis and motor or force sensor selection. They will be directly implemented for the process of mass minimization of the SmartEx-Home exoskeleton based on computational optimization. Full article

Intelligent detection of tomato seedling transplant quality represents a core technology for advancing agricultural automation. However, in practical applications, existing algorithms still face numerous technical challenges, particularly with prominent issues of false detections and missed detections during recognition. To address these challenges, we developed the ESG-YOLO object detection model and successfully deployed it on edge devices, enabling real-time assessment of tomato seedling transplanting quality. Our methodology integrates three key innovations: First, an EMA (Efficient Multi-scale Attention) module is embedded within the YOLOv8 neck network to suppress interference from redundant information and enhance morphological focus on seedlings. Second, the feature fusion network is reconstructed using a GSConv-based Slim-neck architecture, achieving a lightweight neck structure compatible with edge deployment. Finally, optimization employs the GIoU (Generalized Intersection over Union) loss function to precisely localize seedling position and morphology, thereby reducing false detection and missed detection. The experimental results demonstrate that our ESG-YOLO model achieves a mean average precision mAP of 97.4%, surpassing lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n in precision, with improvements of 9.3, 7.2, 5.7, and 2.2%, respectively. Notably, for detecting key yield-impacting categories such as “exposed seedlings” and “missed hills”, the average precision (AP) values reach 98.8 and 94.0%, respectively. To validate the model’s effectiveness on edge devices, the ESG-YOLO model was deployed on an NVIDIA Jetson TX2 NX platform, achieving a frame rate of 18.0 FPS for efficient detection of tomato seedling transplanting quality. This model provides technical support for transplanting performance assessment, enabling quality control and enhanced vegetable yield, thus actively contributing to smart agriculture initiatives. Full article

Gel-type electrofluorochromic (EFC) devices, which reversibly modulate photoluminescence under electrical stimuli, have emerged as versatile platforms for advanced optoelectronic applications. By integrating redox-active luminophores with soft, ion-conductive gel matrices, these systems combine the structural robustness of solids with the ionic mobility of liquids, enabling a high-contrast, flexible, and multifunctional operation. This review provides a comprehensive overview of gel-based EFC technologies, outlining fundamental working principles, device architectures, and key performance metrics such as contrast ratio, switching time, and cycling stability. Gel matrices are categorized into ionogels, organogels, and hydrogels, and their physicochemical properties are discussed in relation to EFC device performance. Recent advances are highlighted across applications ranging from flexible displays and rewritable electronic paper to strain and biosensors, data encryption, smart windows, and hybrid energy-interactive systems. Finally, current challenges and emerging strategies are analyzed to guide the design of next-generation adaptive, intelligent, and energy-efficient optoelectronic platforms. Full article

Anthocyanins, the most ubiquitous water-soluble phytopigments in terrestrial flora, have garnered substantial attention in sustainable food packaging research owing to their exceptional chromatic properties, pH-responsive characteristics, and putative health-promoting effects. Nevertheless, their inherent chemical lability manifests as rapid chromatic fading, structural degradation, and compromised bioactivity/bioavailability, ultimately restricting industrial implementation and incurring significant economic penalties. Recent advances in stabilization technologies through molecular encapsulation within polymeric matrices or nanoscale encapsulation systems have demonstrated remarkable potential for preserving anthocyanin integrity while augmenting multifunctionality. The integration of anthocyanins into advanced functional materials has emerged as a promising strategy for enhancing food safety and extending shelf life through smart packaging solutions. Despite their exceptional chromatic and bioactive properties, anthocyanins face challenges such as chemical instability under environmental stressors, limiting their industrial application. Recent advancements in stabilization technologies, including molecular encapsulation within polymeric matrices and nanoscale systems, have demonstrated significant potential in preserving anthocyanin integrity while enhancing multifunctionality. This review systematically explores the integration of anthocyanins with natural polymers, nanomaterials, and hybrid architectures, focusing on their roles as smart optical sensors, bioactive regulators, and functional components in active and smart packaging systems. Furthermore, the molecular interactions and interfacial phenomena governing anthocyanin stabilization are elucidated. The review also addresses current technological constraints and proposes future directions for scalable, sustainable, and optimized implementations in food preservation. Full article