Search Results (50,471)

The overreliance on chemical pesticides has caused severe environmental contamination, health risks, and increasing pest and pathogen resistance, creating an urgent need for greener and more efficient alternatives in sustainable agriculture. Microalgae-mediated green nano-synthesis has emerged as a promising strategy because of its environmental compatibility, cost-effectiveness, and multifunctional potential. This review critically summarizes recent advances in microalgae-derived nanomaterials for agricultural applications. First, we discuss the biochemical basis of nanoparticle biosynthesis, highlighting the roles of microalgal polysaccharides, proteins, photosynthetic pigments, extracellular polymeric substances, and secondary metabolites as reducing, capping, and stabilizing agents. We then summarize intracellular and extracellular synthesis pathways, advanced synthesis strategies, and key reaction parameters, including temperature, pH, and metal precursor concentration, which regulate nanoparticle size, morphology, stability, and yield. Subsequently, major microalgae-derived nanomaterials, including gold, silver, selenium, zinc oxide, bimetallic, and other functional nanoparticles, are discussed in relation to their agricultural applications. These nanomaterials show potential in bacterial, fungal, and viral disease control, biofilm disruption, plant growth promotion, yield enhancement, and abiotic stress mitigation. Their agronomic effects are associated with multiple mechanisms, including reactive oxygen species generation, pathogen membrane disruption, inhibition of biofilm formation, enhanced nutrient bioavailability, antioxidant regulation, and activation of plant systemic resistance. In addition, this review evaluates the phytotoxicity, biocompatibility, soil microbial impacts, and environmental safety of microalgae-derived nanomaterials, emphasizing that green synthesis does not automatically guarantee biosafety. Finally, we discuss their integration into circular agriculture through CO₂ capture and wastewater-derived metal recovery, while highlighting remaining challenges in scale-up, quality control, economic feasibility, regulatory classification, and public acceptance. Overall, microalgae-mediated nanotechnology offers a promising platform for developing safer, more efficient, and circular agricultural inputs. Full article

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

Understanding the spatial organization of the new materials industry chain is essential for promoting sustainable industrial development. However, existing research rarely examines it as an integrated intercity network spanning multiple segments and specialized sub-sectors. To address this gap, this study constructs the New Materials City Association Network (NM-CityNet) using firm-level cross-regional equity investment data for 294 Chinese cities from 2010 to 2024. NM-CityNet includes two dimensions: segment networks (upstream, midstream, downstream) and sub-sector networks (advanced basic materials, critical strategic materials, and frontier new materials). A chain-lock model is applied, combined with social network analysis and the quadratic assignment procedure. Location quotients are integrated with weighted degree to capture specialized division-of-labour patterns. Using these methods, this study reveals the regional distribution, network structure, specialization patterns, and formation mechanisms of NM-CityNet. Results show that: (1) upstream core cities cluster in eastern China, midstream activities diffuse toward central and western regions, and downstream activities concentrate along the south-eastern coast; (2) NM-CityNet remains sparse and shows clear community structures, while different segments form differentiated spatial organization mechanisms; (3) sub-sectors exhibit clear specialization, with critical strategic materials showing broader spatial coverage; (4) drivers are heterogeneous: administrative proximity promotes link formation; government S&T financial-support differences are positively associated with link formation, although this association may partly reflect selective investment effects; economic and transport disparities inhibit link formation; innovation differences matter only in the midstream segment; and resource-endowment differences matter upstream and downstream. Full article

Industrial emissions are a central environmental concern, particularly with respect to the spatial distribution of major enterprises and the identification of key determinants. Traditional research has largely focused on characterizing the current status of these enterprises, but this approach exhibits several notable shortcomings. These include a lack of regional statistical analysis, an absence of a comprehensive industrial typology, inadequate cross-evaluation of enterprise scale and pollution emissions, and insufficient exploration of socioeconomic correlations. We introduce a multifaceted evaluation framework for Key Environmental Supervision Units (KESUs), focusing on key industrial classifications and their underlying development drivers. The analysis utilized a comprehensive dataset covering 153,107 individual KESUs across six categories from 2020 to 2024, incorporating distribution patterns across 31 provincial-level regions, 28 industrial classifications of national economic activities, and 18 socioeconomic impact factors. The results showed that KESUs in East China accounted for 41.7% of the total, with the highest concentrations in industrialized cities and economically developed zones. Manufacturing was identified as the dominant industrial classification, with chemical raw materials and products comprising the largest subcategory (13.0% of total KESUs in 2024). Atmosphere KESUs and water KESUs represented the largest proportions, accounting for 29.7% and 25.3% of single-type KESUs, respectively. This study provides a systematic analysis of KESUs, offering a detailed mapping of distribution patterns, emission characteristics, and control challenges for major pollution sources. The findings can provide critical insights to support decision-making aimed at improving regional pollution source management and advancing environmental protection practices. Full article

Porcine group A rotavirus (PoRVA) is a significant cause of viral diarrhea in piglets, necessitating urgent global implementation of effective control strategies. This review assesses advancements in PoRVA in vitro cultivation and amplification, crucial for PoRVA vaccine development. Traditional PoRVA cultivation commonly employs primary porcine kidney cells or finite cell lines like MA-104, posing well-documented challenges in scalability, production cost, and their ability to recapitulate the natural intestinal microenvironment. Consequently, research has increasingly focused on adapting PoRVA to alternative systems, particularly immortalized porcine cell lines or physiologically relevant porcine intestinal organoids. This adaptation process, involving serial passaging, can induce genomic alterations and virulence attenuation in piglets, essential for generating live attenuated vaccine (LAV) candidates. Modern biotechnological tools, such as reverse genetics and synthetic genomics, have expedited the creation of recombinant PoRVA strains with defined antigenic profiles and enhanced in vitro growth characteristics. However, a significant concern regarding LAV candidates derived from cell culture adaptation is the risk of virulence reversion upon pig back-passage, necessitating thorough safety and genetic stability evaluations. Nevertheless, utilizing stable cell lines or organoid platforms presents a feasible and cost-effective approach for large-scale PoRVA vaccine production. Future research should focus on identifying vaccine candidates that provide broad protection and exceptional safety, with an emphasis on cross-protection against divergent epidemic genotypes, while ensuring the economic feasibility of innovative manufacturing approaches. Full article

Selective separation of quartz and feldspar is vital for high-purity silicate raw materials but is challenging due to similar surface chemistries. Conventional flotation typically requires high reagent dosages and hazardous chemicals, raising environmental and economic issues. This study proposes a sustainable flotation strategy using green, bio-derived reagents to improve quartz–feldspar separation by eco-friendly bio-derived reagents. Sodium oleate, a fatty acid collector, was used with low-toxicity modifiers to create synergistic systems. Flotation performance was tested by reagent dosage and pH, with mineral characteristics analyzed via X-ray Fluorescence (XRF) and Particle Size Distribution (PSD). Results showed that the investigated reagent systems improved the differential flotation response between quartz and K-feldspar. Under the optimized flotation conditions (pH 9.24), quartz recovery reached 84.01%, demonstrating that environmentally friendly reagent combinations can achieve favorable flotation performance while reducing chemical consumption. Response Surface Methodology (RSM) was used to optimize flotation variables like pH and reagent dosage, developing a model to predict conditions for favorable flotation response, enabling systematic process improvement. These findings highlight reagent-system optimization as an eco-friendly method for mineral beneficiation, aligning with green chemistry and sustainable practices. Full article

Saudi Arabia’s Vision 2030 aims to make human capital the key driver of economic development and innovation. However, there is a dearth of research on employee well-being in the Gulf Cooperation Council (GCC) states and in Saudi Arabia as well. There is a need to re-examine how the Kingdom’s unique cultural disposition (tribalism, gender segregation, religious customs, expatriate dependence) interacts with the well-being of their employees. With this background, the present study, by using an in-depth qualitative approach and integrating the JD-R model and sociocultural theory, attempts to provide a comprehensive framework for analyzing and understanding employee well-being outcomes among a Saudi public university’s staff members facing the impact of internationalization, digitalization, and policy reforms. The university chosen was one of the largest public universities of Saudi Arabia based in Jeddah. The qualitative approach adopted allowed for a rich, nuanced, and contextualized understanding of the lived experiences of well-being among the university’s teaching and non-teaching employees in their distinct sociocultural setting. The results suggest that while the JD-R model provides a useful starting point, sociocultural theory more adequately explains how cultural tools (religious and tribal identities) and structures (gender segregation, seniority policies) serve as both resources and demands. The reforms introduced under Vision 2030 have created tensions between the government’s new global, meritocratic goals for the sector and traditional Saudi sociocultural norms, with a negative spillover effect disproportionately borne by the expatriate staff, women, and administrative staff members. The study suggests that staff well-being should not be viewed as an outcome but as a precondition for successfully achieving Vision 2030 reform goals. In order to reduce attrition and ensure a more sustainable reform process, policymakers must balance their emphasis on performance with tangible support for human capital development. Full article

As modern life accelerates, demand for housekeeping services is rising. However, safety concerns deter potential users, causing a persistent imbalance that poses significant challenges to social sustainability. This research aims to design a home service system that is not only operationally efficient but also socially and economically sustainable. Using a user behavior analysis method, this study investigated the safety and hygiene needs of potential users. From a user-centered design perspective, an innovative housekeeping service system, along with its key service touchpoints: a mobile application and smart products. The system’s design is underpinned by the “Internet+” and Online-to-Offline (O2O) business models, integrating service design and sustainability principles. We present key system architecture and technologies, along with an analysis of implementation challenges. The findings suggest that the proposed system can enhance resource efficiency and economic viability (e.g., reduce operational costs by an estimated 15–25% compared with traditional models); improve user well-being and social equity (e.g., increase user trust by 15–20% through integrated credibility mechanisms and reduce household labor hours by approximately 4–6 h per week through optimized scheduling); and offer a replicable design framework for promoting sustainable service-sector development. Provide a scalable platform for the housekeeping industry’s sustainable transition. This research contributes a design paradigm for service systems that aligns business viability with Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 5 (Gender Equality), and SDG 8 (Decent Work and Economic Growth), by fostering trust, efficiency, and responsible consumption. Full article

The ongoing global energy transition has intensified the need for precise modeling of renewable energy systems, especially photovoltaic–thermal (PV/T) systems that have the ability to produce both electrical and thermal energy. Improving the efficiency and reliability of PV/T systems is a key enabler of the transition toward sustainable energy. Accurate forecasting of their thermal performance is therefore essential to maximize renewable energy use and reduce energy losses. A deep learning-based method is proposed in this study for the prediction of the thermal efficiency of an air-based PV/T system. More specifically, temporal deep learning architectures are investigated to exploit the complex nonlinear relationships and temporal dependencies governing the thermal behavior of the PV/T collector. A comprehensive comparative analysis is conducted using four state-of-the-art architectures, namely Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Furthermore, the influence of sequence length is examined through a sensitivity analysis considering forecasting horizons of 1 h, 6 h, 12 h, and 24 h. The models are evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results demonstrate that forecasting performance is strongly influenced by the selected temporal horizon. Among the investigated configurations, the 24-h horizon provided the most informative temporal context for thermal efficiency prediction. Under this common forecasting horizon, the LSTM model achieved the highest predictive accuracy, reaching an R² of 0.9952, an RMSE of 0.5975, and an MAE of 0.2364, outperforming the TCN, GRU, and Transformer architectures. The residual error and convergence analyses further highlighted the effectiveness of recurrent neural networks in capturing the thermal dynamics of the investigated PV/T system. By enabling accurate and reliable thermal efficiency forecasting, the proposed framework supports improved energy management, higher energy efficiency, and a stronger integration of renewable energy systems, thus contributing to more sustainable operation of hybrid solar energy technologies. Full article

Accurate spray timing is essential for reducing unnecessary pesticide use in irrigated cotton production. This study developed and evaluated a locally calibrated six-stage Spraying Phase (SP) scale for the Karshi/Qashqadaryo production context. The scale was established through a two-round moderated consensus process involving 16 expert panelists representing this production context. A screened dataset of 14,400 non-standardized smartphone images was used to train and evaluate a ResNet-50 convolutional neural network (CNN) for SP-stage classification. Field validation was conducted at the Karshi Engineering and Economics Institute during the 2023 and 2024 seasons using an internally controlled randomized complete block design (RCBD)-style paired comparison of SP-based and BBCH-based spray timing. The CNN achieved 93.0% test accuracy. The mean number of pesticide applications was descriptively lower under SP-guided scheduling than under BBCH-based scheduling (3.75 versus 4.88 applications per season; −23.1%). For the inferentially evaluated outcomes, crop-protection cost decreased by 21.2%, the Environmental Risk Index decreased by 21.6%, and plot-level lint-equivalent yield increased by 4.5%. These findings support SP-guided timing as a promising locally calibrated decision-support approach under the tested Karshi/Qashqadaryo conditions; broader use requires multi-site, multi-cultivar, multi-season, device-stratified, and BBCH-level validation, together with technical deployment testing and implementation-cost assessment. Full article

Agricultural economic resilience (AER) is not only a key pathway for promoting rural revitalization and ensuring food security, but also an important guarantee for sustainable agricultural development. Based on panel data for 1410 counties in China from 2014 to 2023, this study employs the entropy weight method, a double machine learning model (DML), an instrumental variable model, and a panel threshold model to systematically analyze the impact of traditional rural finance (TRF) on AER and its underlying mechanisms. It also examines the threshold effect of digital finance (DF) in the process through which TRF influences AER, and further explores the roles of DF and TRF in narrowing agricultural development disparities, with the aim of providing scientific evidence for rural revitalization and food security in China and other developing countries, and contributing to the sustainable development of agriculture. The results show that (1) TRF can significantly improve AER, with agricultural technological innovation (ATI) and agricultural socialized services (ASS) playing mediating roles; (2) DF and its dimensions, including coverage breadth, usage depth, and degree of digitalization, exhibit threshold effects in the impact of TRF on AER, and as the levels of DF and its dimensions increase, the positive effect of TRF shows a diminishing marginal trend, indicating a competitive crowding-out effect between the two; (3) the promoting effect of TRF on AER exhibits significant heterogeneity, being stronger in agricultural counties and in the eastern, central, and western regions, following a “Central > Eastern > Western” pattern, while it is not significant in the northeastern region; (4) TRF significantly reduces agricultural development disparities, whereas DF overall significantly exacerbates such disparities, although its different dimensions exhibit clear heterogeneity in their effects, with coverage breadth consistently and significantly widening regional agricultural development gaps. Full article

Porcine rotavirus (PoRV) has emerged as a primary pathogen causing viral diarrhea in pigs, resulting in significant economic losses. This study was conducted to systematically characterize the epidemiology and genotypic characteristics of PoRV in Guangxi, China. A total of 870 diarrheic pig samples were collected from Guangxi during 2020–2025. The qRT-PCR results indicated an overall PoRV-positive rate of 41.38% (360/870), and the annual positivity rate showed an overall upward trend. The genetic evolutionary analysis of the VP4, VP6, and VP7 genes indicated that PoRV predominantly belonged to the A group and the predominant P genotype observed was P[13] (76.83%), while the G genotypes were G5 (36.56%) and G9 (33.33%). The most prevalent genotype combinations were G9P[13]I5 and G5P[13]I5. CH-GXGL-PoRV-3151-2021, a PoRV strain isolated from positive samples, was identified via RT-PCR, qRT-PCR, whole-genome sequencing, and IFA. This strain was assigned the 11-segment genotype constellation G9-P[23]-I5-R1-C1-M1-A8-N1-T1-E1-H1 based on whole-genome sequencing. NSP1 and NSP2 showed high similarity to human rotavirus strains, whereas VP1–VP4, VP6, VP7, and NSP3–NSP5 showed high similarity to porcine rotavirus strains. This study indicates the widespread circulation of PoRV in Guangxi, with multiple G genotypes, including G9, G5, G4, G3, G2, and G26, being detected. The isolated G9P[23]I5 strain exhibits the same genotype as the strains that have become increasingly prevalent in recent years. This strain may represent a possible reassortant between porcine and human rotaviruses. This study offers significant insights into the epidemiology of PoRV and the prevalent genotypes in Guangxi, thereby supporting the development of targeted prevention strategies and novel vaccines. Full article

In warm and humid tropical regions, balancing thermal comfort and energy efficiency presents a significant challenge due to high cooling demands. Strategies to reduce energy use and integrate renewable energy into buildings have increasingly focused on achieving self-sufficiency. Aligning with the United Nations Sustainable Development Goals 7 and 13, which call for access to sustainable energy and climate change mitigation, this study assessed the potential of facade-mounted solar photovoltaic (PV) systems to offset the cooling energy demand of buildings in the urban area of Quezon City, Philippines. A geospatial-computational workflow was developed utilizing QGIS 3.28 and Python 3.9 for LiDAR-derived 3D building model generation and hourly solar ray tracing. This workflow was used to estimate direct PV electricity generation and passive cooling effects from facade shading based on the ASHRAE radiant time series method. Results showed that east and west facades achieved the highest annual yields of up to 86 kWh/m² and cooling load reduction by up to 7.3% due to the shading effect. Techno-economic analysis found several setups commercially viable, particularly installations on east–west walls with minimal self-shading and limited obstruction, focusing capital on the most productive surfaces. These findings support vertical solar PV as a complementary solution in dense tropical environments. Full article

The rapid growth of populations and industrial activities has intensified the need to optimize resource management and reduce environmental impacts. A promising pathway toward sustainable development is the gradual replacement of fossil fuel vehicles with electric vehicles (EVs). However, managing EV operations, particularly regarding depot siting and vehicle routing, is a complex challenge that requires balancing economic, environmental, and social objectives. This research proposes a model for designing an intelligent and sustainable transportation system for waste collection using EV fleets. The model simultaneously determines optimal depot locations from a set of candidates and identifies efficient vehicle routes. Its dual objectives are to minimize total costs, including depot set-up, operation, and travel costs, and to minimize maximum travel time, ensuring equitable workload distribution among drivers. Beyond reducing costs and emissions, the model incorporates social equity considerations in balancing driver travel times. EV limitations, such as restricted range, are explicitly addressed. To solve small-scale instances, the $ϵ$-constraint method was applied, while medium- and large-scale instances were tackled with two multi-objective metaheuristics: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The results demonstrate the model’s sensitivity to system parameters such as vehicle capacity and demand rates. Statistical comparative analysis revealed that both algorithms successfully optimized the primary objective functions without significant differences. However, they exhibited distinct performance metric strengths; NSGA-II demonstrated statistically significant advantages in computational efficiency, solution quantity, and uniform distribution, while MOPSO excelled in convergence quality and closeness to the true Pareto front. Furthermore, the practical applicability of the proposed model is validated through a real-world case study of a municipal solid waste management network in Southern Tehran. This research contributes a comprehensive framework for optimizing EV-based waste collection systems, offering a meaningful step toward sustainable and intelligent urban transportation. The findings provide a theoretical framework and strategic insights for transportation managers and policymakers seeking effective strategies for environmentally responsible and socially equitable waste collection. Full article

A catastrophic Ms7.8 earthquake occurred in Tangshan in 1976 at a focal depth of approximately 12 km, resulting in severe casualties and substantial economic losses. Given its unique tectonic setting, the seismogenic structure and dynamic genesis of the Tangshan earthquake have long remained a key research topic in seismotectonic studies. To better characterize the tectonic framework, seismogenic mechanisms, and deep–shallow dynamical coupling within the Tangshan seismic zone, we employ multi-scale wavelet decomposition on high-resolution residual gravity anomalies to isolate crustal structure signals across different depth ranges. Integrating these structural signatures with the spatial distribution of seismicity yields a comprehensive framework for interpreting the regional tectonic evolution. The Tangshan seismic zone is positioned within the intricate structural architecture of the Tangshan rhombic fault block, a system embedded within the broader context of the North China Craton (NCC) destruction. Seismicity displays a distinct preferred orientation, with events concentrated along block-bounding faults and gravity anomaly gradient zones. With increasing wavelet decomposition levels, the gravity anomalies exhibit a systematic transition from spatially dispersed patterns associated with shallow structures to more concentrated features reflecting deeper geological domains. Shallow anomalies from the first to third decomposition orders, which are primarily controlled by Quaternary sedimentary layers, show a fragmented distribution that corresponds well with the development of local flower structures and the occurrence of diffuse shallow seismicity. The fourth- to seventh-order anomalies clearly delineate the rhombic block and its bounding peripheral faults, highlighting the structural intersections that hosted the Tangshan mainshock and its associated aftershock sequence. In contrast, the eighth- to tenth-order deep-seated anomalies corresponding to deeper structural levels exhibit pronounced coalescence, effectively imaging mantle upwelling and large-scale density heterogeneities within the lithospheric mantle. These concentrated gravity highs are closely coupled with mantle thermal activity, whose upward ascent induces thermal weakening of the lower crust and facilitates progressive stress transfer toward shallower crustal levels. Concurrently, frictional locking of shallow high-angle faults promotes intense stress accumulation within the rigid basement. The interplay between deep-seated dynamic concentration and shallow structural confinement ultimately triggers the catastrophic coseismic rupture responsible for the Tangshan earthquake. By delineating the structural transition from deep-seated aggregation centers to shallow dispersed fracture zones, this study establishes a robust framework for assessing seismogenic environments and regional seismic hazard potential across the progressively destroyed NCC. Full article