Search Results (1,183)

Photochromic molecules, capable of reversible isomerization under specific light irradiation, are pivotal for developing advanced photo-responsive materials. Azobenzene derivatives, in particular, are renowned for their significant conformational change, excellent reversibility, and high photostability. This study presents a novel cyclic diazo compound (CDTA) comprising two azobenzene units connected via flexible glycol chains. The photo-responsive behavior of CDTA doped into the liquid crystal 4-cyano-4′-octylbiphenyl (8CB) was systematically investigated. The composite exhibits a pronounced photo-induced phase transition from a liquid crystalline to an isotropic state under 365 nm UV irradiation, accompanied by a reversible change in light transmittance. The response kinetics were found to be highly dependent on temperature and dopant concentration. At 35 °C, the UV response time was accelerated to 6.8 s, attributed to the transition of the host 8CB from a smectic to a nematic phase. Furthermore, the composite demonstrated dual responsiveness: optical switching under UV light and electrical switching under an applied field in its nematic state. This work elucidates the interaction between molecular structure and photo-response in a liquid crystalline matrix, offering insights for designing next-generation smart windows and adaptive optical devices. Full article

Greenhouse cultivation enables year-round vegetable production and high yields through precise environmental regulation. Yet, the same stable microclimate that promotes crop growth also favors the proliferation of pests and diseases. This review synthesizes current knowledge on how greenhouse climate variables govern pest and disease epidemiology in tomato, cucumber, and sweet pepper. Only greenhouse-based studies were included to ensure direct relevance to protected horticulture. Microclimatic stability determines infection probability, vector behavior, and host susceptibility. Warm, humid conditions promote fungal and bacterial pathogens, whereas dry, high vapor pressure deficit (VPD) environments favor mites and thrips and enhance virus transmission. Species-specific traits further modulate vulnerability. Tomato is dominated by virus–bacterium complexes and foliar/stem fungal diseases, cucumber by phytopathogenic fungi favored by high relative humidity (RH) and soilborne pathogens, and sweet pepper by virus–vector systems and long-cycle fungal infections. Temperature exerts the strongest influence, while RH and VPD jointly regulate surface moisture and vector activity. Light intensity and spectral composition also affect pest orientation and fungal sporulation. Integrating environmental sensing, biological control, and adaptive climate regulation offers a pathway toward preventive, climate-smart Integrated Pest Management (IPM). The review highlights the emerging role of climate-informed decision-support systems (DSSs) and the need for greenhouse-specific datasets to improve pest and disease forecasting. Full article

Background/Objectives: The Arabidopsis thaliana GDS1 (Growth, Development and Splicing 1) gene has recently been identified as a key regulator linking nitrate signaling to leaf senescence. However, a systematic analysis of the GDS1 gene family in maize (Zea mays L.) is lacking. This study aims to identify and characterize the ZmGDS1 gene family in maize, providing a foundation for functional studies on their roles in growth, development, and low-nitrogen-induced leaf senescence. Methods: Putative ZmGDS1 family members were identified by searching the maize B73 reference genome using BLASTP (version 2.11.0+) and HMMER (version 3.4), with the Arabidopsis GDS1 protein sequence as the query. Candidate sequences were verified for the presence of the conserved zf-CCCH domain using NCBI CD-Search and SMART. Phylogenetic relationships, gene structures, conserved motifs, chromosomal distribution, collinearity, and promoter cis elements were comprehensively analyzed using MEGA 11, TBtools (version 1.098), MEME (version 5.5.9), and PlantCARE. Phylogenetic trees were constructed using the maximum likelihood (ML) method with the LG+G+I model and 1000 bootstrap replicates. Results: Thirteen ZmGDS1 genes were identified, distributed unevenly across eight maize chromosomes. Phylogenetic analysis classified the ZmGDS1 proteins into four distinct groups (A–D), revealing a lineage-specific expansion in group D. While all members contained the conserved zf-CCCH domain, their motif compositions varied considerably; ZmGDS1.1 exhibited the most complex structure, whereas ZmGDS1.12 had the most simplified. Subcellular localization predictions indicated that most ZmGDS1 proteins are targeted to the nucleus, consistent with a potential role as transcription factors. Promoter analysis revealed an abundance of cis elements associated with light response, hormone signaling (methyl jasmonate, abscisic acid, auxin), and stress responses. Notably, phylogenetically related genes tended to share similar cis-element profiles. Conclusions: This genome-wide analysis provides the first characterization of the ZmGDS1 gene family in maize. The observed structural conservation and diversity, together with regulatory elements linked to senescence-associated signals, support the hypothesis that ZmGDS1 genes may contribute to leaf senescence, particularly under low-nitrogen conditions. These findings provide a basis for future functional validation studies. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article

Flexible and biocompatible sensors are vital for a wide range of biomedical applications, including real-time health monitoring, intracranial pressure monitoring, knee replacement surgeries, wearables, and smart prosthetics. While various highly sensitive and stable pressure sensors have been demonstrated, they often lack the conformability and biocompatibility crucial for their wider application in various bio-integrated electronic systems. Herein, a piezoelectric pressure sensor is proposed using a hybrid polymer composite by leveraging the unique properties of Chitosan and Parylene C. Various material characterisations, such as XRD and FTIR, were performed to reveal structural and chemical characteristics of the novel composite material. Next, electromechanical characterisations of the pressure sensor were performed to reveal its dynamic sensing properties. The pressure sensor exhibits excellent sensitivity for both pressure and frequency, as well as cyclic stability (10³ cycles), wide pressure range (20–70 kPa), and biocompatibility. Full article

Illegal artisanal and small-scale mining (galamsey) and climate stress jointly degrade ecosystems and livelihoods in Ghana. This paper demonstrates how community-driven governance can realign incentives toward environmental stewardship and inclusive livelihoods. Using an explanatory sequential mixed-methods design—quantitative difference-in-differences followed by qualitative case analysis and Participatory Action Research—we evaluate a structured program combining vocational training, financial literacy, environmental stewardship, and governance alignment. We operationalize Environmental, Social, and Governance (ESG) outcomes via transparent composite indices and triangulate survey, administrative, and focus group evidence. The study identifies conditions under which alternative livelihoods reduce participation in illegal mining, strengthen women’s economic agency, and improve adoption of climate-smart practices. Implications include practical guidance for program design (community delivery, matched incentives, oversight), policy (local climate finance and accountability mechanisms), and research (scalable indicators and rigorous impact evaluation in resource-dependent communities). Full article

Cities are increasingly expected to address digital transformation and sustainability challenges at the same time. However, existing urban indices generally approach smart city and sustainable city perspectives separately, which limits their ability to capture the integrated nature of contemporary urban development. In addition, many index-based studies rely on similar methodological choices. This study develops a composite Sustainable Smart City (SSC) index supported by a systematic scoring framework that brings smartness and sustainability together. The proposed framework follows a step-by-step procedure covering data preparation, normalization, weighting, aggregation, and final scoring. To address information overlap among indicators, a Redundancy-Penalized Entropy Weighting (RPEW) approach is applied. Then, overall SSC scores are calculated using a soft non-compensatory aggregation to emphasize balanced performance across dimensions. The framework is empirically illustrated through a cross-country case study including 38 OECD (Organization for Economic Co-Operation and Development) countries. A machine-learning-based polynomial forecasting approach is used for a limited number of indicators to deal with data gaps allowing the assessment to reflect more up-to-date conditions. The results highlight clear differences in SSC performance and show that strong outcomes in a single dimension are not sufficient to achieve high overall SSC scores. Instead, balanced progress across economic, digital, environmental, governance, mobility, and social dimensions plays an important role. In addition, the proposed framework provides a practical basis for comparative analysis, benchmarking, and policy-oriented evaluation of smart and sustainable urban development. Full article

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

This study analyses the influence of yarn composition and knitted macrostructure on the structural and functional performance of passive smart knitted fabrics with optical functionalities. Twelve knitted macrostructures were produced using folded composite yarns combining cotton, reflective, and photoluminescent components and different stitch patterns. Thickness, air permeability, and reflectance under UV and visible illumination were experimentally evaluated. The results indicate that knitted macrostructure primarily controls thickness and air permeability, whereas optical response is governed by yarn composition. Variations in stitch pattern enable regulation of air permeability independent of optical behaviour, while UV-responsive yarn components dominate reflectance performance. The findings support independent optimisation of structural and optical properties through combined yarn and macrostructural design. Full article

This article evaluates how artificial intelligence (AI) and educational technology (EdTech) support inclusive learning in Russia and Kazakhstan, two Eurasian countries that share post-Soviet educational legacies but differ in their levels of digital infrastructure and teacher preparedness. Using an asymmetric mixed-methods design, the study draws on a primary survey of N = 2570 educators and staff in four Russian cities (Moscow, Saint Petersburg, Yekaterinburg, Novosibirsk; October–December 2024; response rate 59.8%) and secondary policy/indicator analysis for Kazakhstan. Russia exhibits higher broadband access, AI/EdTech platform adoption, and teacher digital skill levels compared with Kazakhstan. Structural equation modeling (SEM; SmartPLS 4.1) tested four latent constructs—learning environment (LE), general digital competencies (HCg), specialized AI skills (HCs), and inclusion (I)—with satisfactory validity (AVE > 0.5; HTMT ≤ 0.85). A three-stage Measurement Invariance of Composite Models (MICOM) procedure confirmed configural, compositional, and full mean/variance invariance across Russian city subgroups, enabling pooled path analysis. Kazakhstan indicators from secondary sources are discussed as a descriptive benchmark. Semi-structured interviews with 24 stakeholders (12 Russia, 12 Kazakhstan; March 2025; analyzed with NVivo 14) revealed four themes: policy coherence, teacher readiness, infrastructure access, and ethical AI governance. Key SEM paths were LE → HCg (β = 0.278), HCg → HCs (β = 0.652), and HCs → I (β = 0.188), all p < 0.001. A formal mediation analysis confirmed a significant indirect effect across the full LE → HCg → HCs → I chain. The findings indicate that infrastructure is necessary but insufficient: the key to inclusion lies in sustained development of both basic and specialized digital skills, supported by coherent policies and continuous professional development. China and India are discussed as secondary international benchmarks drawn from published reports, not as sites of primary data collection. Full article

This review presents a comprehensive analysis of modern thermosensitive polymer systems for in situ systems (ISSs) which are used for targeted drug delivery in situ. The main classes of polymers used to create “smart” hydrogels that undergo a “sol–gel” phase transition in response to a temperature stimulus in the physiological range are considered. Key representatives of thermosensitive matrices are described in detail: synthetic block copolymers (poloxamers, block copolymers of polylactic-co-polyglycolic acid with polyethyleneglycol, etc.) and natural, modified natural, and semi-synthetic polymers (chitosan, including in combination with β-glycerophosphate, xyloglucan, etc.). This paper systematizes the advantages and disadvantages of various thermosensitive systems and highlights the key risks in their pharmaceutical development, including the influence of the nature and concentration of the active pharmaceutical ingredients and excipients on the rheological properties and phase transition temperature. Particular attention is paid to the difference between thermoreversible and irreversible gel-forming systems. Modern in vitro, ex vivo, and in vivo methods for evaluating critical quality parameters of thermosensitive systems, such as gelation temperature and time, gel strength, mucoadhesive properties, and release kinetics, are discussed. The need to develop standardized and biologically relevant methods to improve the reproducibility and success of preclinical studies is emphasized. The review is intended to help researchers to make informed choices about polymer matrices and optimize compositions for successful pharmaceutical development. Full article

The integration of renewable energy and power electronics has intensified the occurrence of complex power quality disturbances (PQDs), which increasingly threaten grid stability. To address the challenges of multi-class PQD identification under noisy conditions, this paper proposes a novel framework that combines an enhanced time–frequency analysis method with an optimized AdaBoost decision tree. The main contributions are three-fold: (1) We develop an instantaneous frequency adaptive Fourier synchrosqueezing transform (IFAFSST) equipped with a custom adaptive operator that aligns closely with the frequency modulation patterns in PQD signals, thereby improving time–frequency energy localization. (2) The IFAFSST outputs are decomposed into low-frequency and high-frequency components, from each of which a set of 16 discriminative features is extracted. (3) An improved AdaBoost classifier is introduced, incorporating forward feature selection and Hyperband-based hyperparameter optimization to enhance classification performance. Hyperband accelerates the optimization process by dynamically allocating computing resources and iteratively eliminating suboptimal configurations, thereby enabling efficient determination of the optimal hyperparameters. The method proposed in this paper achieved an accuracy rate of 99.50% on simulated data containing 30 dB white noise and 98.30% on hardware platform data. This framework can effectively handle 23 types of interference, including seven types of single interference, 12 types of double compound interference, three types of triple compound interference, and one type of quadruple compound interference. It performs particularly well in identifying composite interference scenarios. This research has made a significant contribution to power quality analysis, providing a powerful solution with high accuracy and practical applicability, and offering great potential for the implementation of smart grid monitoring systems and the integration of renewable energy. Full article

Background: The smart city concept has become a central framework in contemporary urban governance; however, empirical research predominantly focuses on large metropolitan areas, while small municipalities remain comparatively underexplored. This study examines how citizens of Ivanić-Grad perceive and use smart applications and how these patterns relate to its smart city transition. Methods: A quantitative survey was conducted in 2024 on a convenience sample of 100 residents of Ivanić-Grad. The questionnaire included closed-ended questions on sociodemographic characteristics, awareness and use of smart applications, and attitudes toward further digitalization of local public services. Descriptive statistics, χ² tests of independence, and correlation analysis based on composite indices were applied. Results: The findings reveal statistically significant associations between gender and the use of smart applications (χ² = 5.76; p = 0.016) and between age and the perceived importance of smart applications (χ² = 8.42; p = 0.014). No significant association was identified between education level and application use. Composite index analysis further demonstrated a moderate positive correlation between digital engagement and the perceived importance of smart services (ρ = 0.418; p < 0.001), indicating that experiential interaction with digital services is closely linked to their perceived relevance. Conclusions: The results suggest that smart service adoption in small urban contexts is shaped less by formal educational attainment and more by behavioral engagement and perceived usefulness. The case of Ivanić-Grad highlights the importance of citizen-oriented, accessibility-focused digital leadership strategies in sustaining inclusive smart city development. Full article

NiO electrochromic films have significant potential for applications in smart windows, displays, energy-efficient buildings, and portable electronics, owing to their excellent electrochemical stability, favorable optical modulation performance, and environmental friendliness. However, several challenges remain, such as limited long-term durability, stability under extreme environmental conditions, and the cost-effectiveness of large-scale production. Future research efforts should focus on enhancing the cyclic stability and environmental adaptability of NiO films, developing low-cost fabrication techniques, and advancing multifunctional composite materials for smart devices. This review summarizes recent advances in the preparation and performance optimization of NiO electrochromic films. Several key fabrication methods—including magnetron sputtering, hydrothermal synthesis, electrodeposition, chemical bath deposition, sol–gel processing, and spray pyrolysis—are highlighted, and their effects on film structure, thickness uniformity, and optical properties are analyzed. Furthermore, the critical role of different electrolytes (inorganic, organic, and gel-based) in the electrochromic process is discussed, with a comparative evaluation of their influence on the electrochromic performance of NiO films. This article offers a comprehensive overview of the progress in high-performance NiO electrochromic films and provides theoretical insights and technical support for their broader application in renewable energy and smart home technologies. Full article