Search Results (3,545)

Plants have evolved a complex, multilayered immune system that integrates molecular recognition, signaling pathways, epigenetic regulation, and small RNA-mediated control. Recent studies have shown that DNA-level regulatory mechanisms, such as RNA-directed DNA methylation (RdDM), histone modifications, and chromatin remodeling, are critical for modulating immune gene expression, allowing for rapid and accurate pathogen-defense responses. The epigenetic landscape not only maintains immunological homeostasis but also promotes stress-responsive transcription via stable chromatin modifications. These changes contribute to immunological priming, a process in which earlier exposure to pathogens or abiotic stress causes a heightened state of preparedness for future encounters. Small RNAs, including siRNAs, miRNAs, and phasiRNAs, are essential for gene silencing before and after transcription, fine-tuning immune responses, and inhibiting negative regulators. These RNA molecules interact closely with chromatin features, influencing histone acetylation/methylation (e.g., H3K4me3, H3K27me3) and guiding DNA methylation patterns. Epigenetically encoded immune memory can be stable across multiple generations, resulting in the transgenerational inheritance of stress resilience. Such memory effects have been observed in rice, tomato, maize, and Arabidopsis. This review summarizes new findings on short RNA biology, chromatin-level immunological control, and epigenetic memory in plant defense. Emerging technologies, such as ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing), ChIP-seq (Chromatin Immunoprecipitation followed by Sequencing), bisulfite sequencing, and CRISPR/dCas9-based epigenome editing, are helping researchers comprehend these pathways. These developments hold an opportunity for establishing epigenetic breeding strategies that target the production of non-GMO, stress-resistant crops for sustainable agriculture. Full article

The development of advanced photocatalysts that are efficient, recyclable and sustainable represents a significant challenge in the face of the growing presence of persistent organic contaminants in industrial wastewaters. This paper presents a novel approach based on the design of new heterostructures synthesized from BiOI and magnetic materials, using not only synthetic magnetite, but also magnetic compounds extracted from mine tailings, transforming environmental liabilities in active supporting materials through valorization strategies in line with the circular economy. Through precise control of composition, it was established that a proportion of 6% by mass of the magnetic phase allows the formation of a heterostructure that is highly photocatalytically efficient. These compounds were evaluated using caffeic acid, an organic contaminant of agroindustrial origin, as a target compound. Experiments were carried out under simulated solar radiation for 120 min. Among the materials synthesized, the BiOI/MMA heterostructure, derived from industrial tailing A, displayed an outstanding photodegradation efficiency of over 89.4 ± 0.25%, attributed to an effective separation of photoinduced charges, a broad active surface and a synergic interface interaction between its constituent phases. Furthermore, BiOI/MMA exhibited excellent structural stability and magnetic recovery capacity, which allowed for its reuse through two consecutive cycles without any significant losses to its photocatalytic performance. Thus, this study constitutes a significant contribution to the design of functional photocatalysts derived from industrial tailings, thus promoting clean, technological solutions for the treatment of wastewater and reinforcing the link between environmental remediation and circular economy. Full article

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km²), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km²), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 10⁴ t in 1990 to 169.17 × 10⁴ t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals. Full article

Advancements in material science have allowed us to exploit the potential of new era for aircraft production. High-performance composites and alloys have allowed us to improve the performance and durability of aircraft, but they have become more and more precious with time. These materials can provide significant advantages in use but are costly, energy-intensive to produce, and their recovery and reuse has become a critical step to be addressed. Accordingly, a new approach in which end-of-life aircrafts represent unconventional mines rather than a disposal challenge is becoming increasingly relevant, providing access to high-value strategic raw materials and aligning with circular economy principles including European Green Deal and the United Nations Sustainable Development Goals. The complexity of dismantling and processing hybrid structures composed of metal alloys, ceramics, and advanced composites requires multiple approaches able to integrate chemical, mechanical, and thermal recovery routes. Accordingly, this review critically discusses the state of the art of the routes of end-of-life aircraft treatments, evaluating the connections between technology and regulation, and positions material recycling and reuse as central pillars for advancing sustainability in aerospace. Furthermore, this review provides a comprehensive reference for addressing the technical, economic, and policy challenges of waste management in aviation, contributing to broader goals of resource circularity and environmental preservation set forth by international sustainability agendas. Full article

This study explores the readiness of Micro and Small Enterprises (MSEs) in Mexico, specifically grocery stores, to implement the Virtual Value Chain (VVC) through Information and Communication Technologies for Development (ICT4D). A mixed-methods approach was used, combining diagnostic tools, structured surveys, and interviews. Quantitative data were analyzed using descriptive statistics, correlation analysis, and machine learning to identify digital adoption patterns. The results indicate that limited technology adoption remains the main obstacle to VVC integration. Significant associations were found between digital engagement and the age and educational level of store managers. Key digital gaps persist in inventory control, supplier coordination, and demand forecasting. Although machine learning models did not significantly outperform baseline predictions on willingness to adopt technology, the findings emphasize the potential of targeted training and accessible mobile solutions. The study proposes a new diagnostic and predictive framework to assess VVC readiness in low-resource contexts. It shows that ICT, when strategically aligned with business operations and paired with adequate training, can enhance sustainability and livelihoods. Although the study is limited to one geographic area and one business sector, it offers a foundation for scaling similar initiatives. The findings support context-sensitive strategies and capacity-building efforts tailored to the realities of MSEs in emerging economies. Full article

This research delves into the central position of digital technology as a stimulator of sustainable economic advancement. This study examines the complementary convergence of policy structures, law, and environmental economics that unleashes this advancement. This research finds and explores converging strategies in tapping digital innovation towards sustainability goals through benchmarking China, the EU, and the USA. This study bridges a gap in the literature since it undertakes a systematic, cross-disciplinary analysis and offers policymakers valuable inputs to craft effective strategies leveraging technology to a prosperous, sustainable world future. The results prove that new technologies enhance environmental law efficacy, transparency, and enforcement. It provides a prospective framework integrating digital solutions into policy and legal design, connecting economic growth with environmental degradation. Imperial methods in the form of qualitative research grounded on literature review, secondary data, green growth sustainable development, circular economy project studies, and smart conurbations to evaluate the efficacy of digital policy machinery indicate that digitally empowered policies significantly enhance resource productivity, maximise environmental taxation and cap-and-trade instruments, and enhance green innovation. This paper concludes with a policy road map for integrating digital to achieve synergistic economic and environmental objectives, calling for a new paradigm in environmental economic policy and law. Full article

The dynamic development of agent systems and large language models opens up new possibilities for automating spatial and investment analyses. The study evaluated a reactive AI agent with an NLP interface, integrating Apache Spark for large-scale data processing with PostGIS as a reference point. The analyses were carried out for two areas: Nowy Sącz (36,000 plots, 7 layers) and Ostrołęka (220,000 plots). For medium-sized datasets, both technologies produced similar results, but with large datasets, PostGIS exceeded time limits and was prone to failures. Spark maintained stable performance, analyzing 220,000 plots in approximately 240 s, confirming its suitability for interactive applications. In addition, clustering and spatial search algorithms were compared. The basic DFS required 530 s, while the improved one reduced the time almost tenfold to 54–62 s. The improved K-Means improved the spatial compactness of clusters (0.61–0.76 vs. <0.50 in most base cases) with a time of 56–64 s. Agglomerative clustering, although accurate, was too slow (3000–6000 s). The results show that the combination of Spark, improved algorithms, and agent systems with NLP significantly speeds up the selection of plots for renewable energy sources, supporting sustainable investment decisions. Full article

The intensive use of chemical pesticides has significantly impacted the environment and human health, encouraging the search for more sustainable and environmentally safe pest management strategies. In this context, botanical pesticides emerge as a promising solution, distinguished by their natural origin and lower toxicity. Although botanical insecticides have demonstrated their potential, these solutions are still little explored in scientific and technological terms, representing an expanding field to develop safer agricultural biological pesticides. The Meliaceae family, especially the Trichilia genus, has been recognized for its richness in bioactive compounds with insecticidal potential. However, Trichilia claussenii remains little studied, despite its occurrence in Brazilian ecosystems. Therefore, this article aims to analyze the information on the botanical and phytochemical characteristics and bioinsecticidal activity of T. claussenii, aiming to highlight its potential as a natural resource for biological pest control. The leaf and fruit extracts of T. claussenii revealed the presence of bioactive metabolites. The group of terpenes is highlighted, notable for their role in the chemical defense of plants and for their recognized insecticidal activity. In the leaf extract, terpenes were the most abundant class, representing 46.3% of the total identified. In the fruit extract, terpenes were also prominent, although to a lesser extent (34.5%). Additionally, a phytochemical screening of the aqueous leaf extract indicated the presence of coumarins, flavonoids, and alkaloids, compounds commonly associated with insecticidal activity. By consolidating this knowledge, we aim to encourage new research and the development of a botanical bioinsecticide based on this species. Full article

Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance. Full article

The accurate measurement of herbage mass is essential for feed budgeting and the management of sustainable and profitable grazing systems. There are many techniques available to estimate herbage mass in pastoral systems, and these vary in accuracy, cost, and time taken to implement. In situ and remote sensing techniques are both associated with moderate to high error, as herbage mass is affected by a number of dependent and independent factors, including sward composition, soil structure, chemical characteristics and moisture levels, climatic conditions, and grazing management, which must be considered in the development of an accurate local calibration model for precise estimation of herbage mass. This review provides an overview of commonly used herbage mass assessment techniques and describes their limitations, synergies, and trade-offs, and also covers the integration of new technologies which have the potential to monitor pastures at scale. This review highlights the need for further research and to integrate new technologies for accurate and precise measurement of herbage mass, noting the lack of calibration with in situ methods, the need for development of new protocols for assessment, variance in equipment and software compatibility, and the need to evaluate the effectiveness of methods/techniques on a variety of livestock operations for extended periods. Full article

Artificial intelligence (AI), as a strategic technology leading the current technological revolution and industrial transformation, functions as a pivotal catalyst for enhancing high-quality supply chain development and as the primary engine driving supply chains towards environmentally sustainable, low-carbon models. This study seeks to clarify how AI bolsters supply chain resilience through enhanced information transparency and dynamic capabilities, while examining the moderating influence of digital government in this context. Based on this, this study selected A-share listed companies from 2012 to 2023 as research samples. An entropy-based approach was utilized to develop a supply chain resilience indicator system. A two-way fixed-effects model was employed to analyze the mechanism by which business AI impacts supply chain resilience. Studies demonstrate that company artificial intelligence can markedly improve supply chain resilience. In this process, information transparency, innovative capacity, and absorptive capacity partially mediate the effect, while digital governance exerts a positive moderating influence. Heterogeneity studies indicate that artificial intelligence has a significantly greater favorable effect on supply chain resilience for high-tech corporations, manufacturing firms, growth-stage companies, mature-stage businesses, and chain master enterprises. The research findings not only reveal the impact and underlying mechanisms of enterprise artificial intelligence on supply chain resilience, offering a new perspective for systematically understanding the relationship between enterprise AI and supply chain resilience, but also provide key pathways and empirical evidence for leveraging digital technologies to build sustainable supply chains. Full article

Socialized services for rural characteristic industries are becoming a key support for promoting rural industries’ transformation and upgrading. They are permeating the development process of modern agricultural service technologies, achieving significant progress in specialized fields such as mechanized operations and plant-protection services. However, challenges remain, including low efficiency in matching service resources and limited spatiotemporal coordination capabilities. With the deep integration of spatiotemporal information technology and knowledge graph technology, the enormous potential of semantic-level feature spatial representation in intelligent scheduling of service resources has been fully demonstrated, providing a new technical pathway to solve the above problem. This paper systematically analyzes the technological evolution trends of socialized services for rural characteristic industries and proposes a collaborative scheduling framework based on semantic feature space and spatiotemporal maps for characteristic industry service resources. At the technical architecture level, the paper aims to construct a spatiotemporal graph model integrating geographic knowledge graphs and temporal tree technology to achieve semantic-level feature matching between service demand and supply. Regarding implementation pathways, the model significantly improves the spatiotemporal allocation efficiency of service resources through cloud service platforms that integrate spatial semantic matching algorithms and dynamic optimization technologies. This paper conducts in-depth discussions and analyses on technical details such as agricultural semantic feature extraction, dynamic updates of rural service resources, and the collaboration of semantic matching and spatio-temporal matching of supply and demand relationships. It also presents relevant implementation methods to enhance technical integrity and logic, which is conducive to the engineering implementation of the proposed methods. The effectiveness of the proposed collaborative scheduling framework for service resources is proved by the synthesis of principal analysis, logical deduction and case comparison. We have proposed a practical “three-step” implementation path conducive to realizing the proposed method. Regarding application paradigms, this technical system will promote the transformation of rural industry services from traditional mechanical operations to an intelligent service model of “demand perception–intelligent matching–precise scheduling”. In the field of socialized services for rural characteristic industries, it is suggested that relevant institutions promote this technical framework and pay attention to the development trends of new technologies such as knowledge services, spatio-temporal services, the Internet of Things, and unmanned farms so as to promote the sustainable development of rural characteristic industries. Full article

Sudden Infant Death Syndrome (SIDS) is one of the leading causes of postnatal mortality, with the prone sleeping position identified as a critical risk factor. This article presents the design, implementation, and validation of a low-cost embedded system for unobtrusive, real-time monitoring of infant posture. The system acquires data from a pressure mat on which the infant rests, converting the pressure matrix into an image representing the postural imprint. A Convolutional Neural Network (CNN) has been trained to classify these images and distinguish between prone and supine positions with high accuracy. The trained model was optimized and deployed in a data acquisition and processing system (DAQ) based on the Raspberry Pi platform, enabling local and autonomous inference. To prevent false positives, the system activates a visual and audible alarm upon detection of a sustained risk position, alongside remote notifications via the MQTT protocol. The results demonstrate that the prototype is capable of reliably and continuously identifying the infant’s posture when used by people who are not technology experts. We conclude that it is feasible to develop an autonomous, accessible, and effective monitoring system that can serve as a support tool for caregivers and as a technological basis for new strategies in SIDS prevention. Full article

Purpose: The purpose of this paper is to explore how perceptions of digital health transformation play a role in Saudi Arabia’s customer engagement in healthcare, according to Vision 2030. Saudi Vision 2030, a national reform agenda, has prioritized healthcare digitalization to enhance efficiency, access, and patient-centered care. In particular, the research attempts to explore the attitude of the patient and whether cultural values and infrastructure issues play a mediator role in the perception–engagement relationship. Design/methodology/approach: The study used a mixed-method approach, with qualitative interviews from providers and consumers, along with survey responses from 402 users of digital health. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine hypothesized relationships and moderation effects. Findings: Findings establish that digital health perceptions are a crucial driver in enhancing engagement (β = 0.386; p < 0.001). Perceived ease of use (β = 0.368) and usefulness (β = 0.530) exhibited strong positive influences. Moderation analysis revealed that cultural values (β = 0.343) and infrastructure (β = 0.253) further enhance engagement. The findings highlight usability, usefulness, and context as foundational enablers of long-term patient engagement. Originality/value: By combining Technology Acceptance Model (TAM) variables and applying cultural and infrastructural moderators, this research provides new empirical evidence of Saudi Arabian digital health adoption. It provides policy and practical advice in the creation of accessible, culturally appropriate, and adequately supported digital health solutions toward Vision 2030. It also supports United Nations Sustainable Development Goals (SDGs). The study aligns with SDG 3 (Good Health and Well-Being), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) by further promoting sustainable healthcare transformation in a global development agenda. Full article

Sustainability-driven products rely heavily on market success and customer awareness to achieve their intended impact. While knowledge-based enterprises (KBEs) are committed to enhancing the sustainability of their products and services, they often face challenges in building brand equity and raising awareness of what makes their products sustainable, particularly within social media engagement. AI agents transfer social media and enable KBEs to promote sustainable products by enhancing brand equity. However, this requires an effective strategy to enhance brand equity among the new social media generation. This study highlights critical factors identified through a rigorous literature review that influence the enhancement of brand equity in KBEs, with a focus on sustainable development and brand innovation. This research employs a mixed-method approach using VOSviewer and NVivo software. A scientometric review of articles published in the Scopus database and a critical thematic analysis were conducted. Furthermore, Google Trends data were utilized to complement the analysis and propose a set of key factors and future directions. Out of 1552 articles extracted from scientific databases from 1994 until 2024, 33 articles were selected and thoroughly analyzed after a rigorous screening and evaluation process. The review demonstrates that social media enhances brand equity for KBEs by increasing brand awareness, improving their efforts in sustainable development, strengthening brand image, strengthening customer loyalty, and facilitating effective interactions. Moreover, leveraging brand innovation and focusing on sustainable development through social media amplifies these positive impacts, further influencing the brand’s perceived quality. This research uniquely identifies the value of sustainability in the brand equity model and the moderating roles of technological complexity and customer environmental sensitivity in this process. The research offers significant insights for managers of science and technology parks, guiding them to adopt effective social media strategies that create sustainable competitive differentiation and strengthen brand positioning in competitive markets. This study also establishes a strong foundation for future studies focusing on AI-powered tools for brand equity within the digital environment by proposing a set of factors that can be used by researchers to develop an initial model. Full article