Search Results (3,790)

Sample preparation is frequently a time-consuming process and can be a major bottleneck in many analytical techniques that involve some form of modification to a sample so that it can be analyzed without interference or to increase its sensitivity. As part of the movement towards “green analytical chemistry”, the reduction in organic solvent usage and toxicity via alternative solvents compared to those traditionally used in analytical chemistry has gained increasing interest. Although ionic liquids were thought to have limitations, deep eutectic solvents (DESs) are being looked at as alternatives to traditional organic solvents in analytical chemistry because of their ability to produce a “tunable” set of physico-chemical properties that enable the selective and efficient extraction of a wide variety of analytes from a very diverse array of matrices. Although deep eutectic solvents have attracted increasing attention in analytical extraction applications, a systematic comparison of their performance across various extraction techniques is still lacking. This review fills this gap by offering a comprehensive and integrated evaluation of DES-based extraction approaches, emphasizing the interdependence between solvent characteristics, extraction efficiency, selectivity, and sustainability. The insights presented herein are intended to support the rational selection of appropriate DES-based extraction strategies for diverse analytical purposes. Moreover, these findings are expected to contribute to the advancement of greener, more efficient sample preparation methodologies within the field of green analytical chemistry. In this review article, we describe several analytical chemistry techniques that utilize DESs, such as dispersive liquid–liquid microextraction, solid-phase extraction, ultrasound-assisted extraction, etc., and explain the basic principles and mechanisms behind each technique. Additionally, comparative evaluations are provided to identify the relative advantages and disadvantages of the techniques mentioned above in terms of extraction efficiency and selectivity, and speculation regarding future trends and challenges in DES-based extraction systems will also be included. By integrating recent advances and comparative performance assessments, this review serves as a reference for researchers and industry practitioners, fostering innovation and promoting the wider adoption of sustainable extraction technologies. Full article

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes a green design methodology that integrates TRIZ concepts and is anchored in TRIZ evolutionary trends. The methodology includes function and attribute analysis, the introduction of green features, the identification of TRIZ trends through a two-stage process, and the use of a developed system to improve calculation efficiency. Detailed design solutions are generated by combining green features, TRIZ trends, and inventive principles. A case study validates the methodology, showcasing its value in promoting sustainable development. By leveraging the evolutionary potential of products and incorporating TRIZ, the methodology offers a promising approach to address sustainability challenges and drive innovation. This research serves as a starting point for a practical and efficient design methodology that utilizes TRIZ concepts and a computer-aided application tool. Future steps involve stress-testing the methodology and exploring its application in different domains. Full article

Using panel data from 282 prefecture-level and above cities in China, this study employs a difference-in-differences (DID) model to examine the impact of the National Sustainable Development Plan for Resource-Based Cities on carbon emission efficiency, as well as its underlying mechanisms and heterogeneous effects. The results indicate that the implementation of the Plan significantly improves carbon emission efficiency. This conclusion remains robust after a series of robustness tests, including the propensity score matching difference-in-differences (PSM-DID) approach. Mechanism analysis further reveals that technological innovation, industrial upgrading, and reductions in energy consumption constitute the primary channels through which the Plan enhances carbon emission efficiency. In addition, economic growth pressure and the degree of marketization exert negative and positive moderating effects, respectively, on the relationship between the Plan and carbon emission efficiency. Heterogeneity analysis shows that the positive effect of the Plan is significant only in resource-based cities located in eastern China, southern China, or southeast of the Hu Huanyong Line. In terms of city size, the positive effect is observed only in medium-sized cities and Type II large cities. Full article

This paper combines the application of AI technology in the field of architectural design to construct a “three-stage model” (imitation, exploration, and creation) centered on cultivating students’ creative thinking and innovative ability, with the goals of AI literacy cultivation, digital twin practice, and interdisciplinary collaboration. By integrating the theoretical model with the latest practical cases, the effectiveness of the new generation of AI-driven innovative teaching modes is verified. Taking library architectural design and old building renovation teaching as examples, the teaching process and evaluation system with real-time feedback, intelligent assessment, and full-process traceability are designed to achieve the dual improvement of teaching efficiency and students’ practical innovation ability. The research shows that the characteristics of artificial intelligence, including multimodal generation, immersive interaction, and full-cycle simulation, are reconstructing the core logic of architectural design education, promoting the in-depth transformation of the teaching mode from “imitation” to “creation”, building a talent cultivation system adapted to the future development of the construction industry, and providing a feasible reference path for the innovation of education modes. Full article

Soybean is a strategic crop for global protein and vegetable oil supply chains; however, genetic improvement remains constrained by genotype-dependent regeneration, variable transformation efficiency, and regulatory concerns regarding stable transgene integration. This review synthesizes emerging DNA-free and genotype-independent genome-editing frameworks for soybean, where genotype independence is defined as the ability to recover fertile, non-chimeric edited plants across elite germplasm. We critically examine the soybean genome-editing toolbox, including CRISPR-Cas9, Cas12a, multiplex editing systems, base editing, and prime editing, and discuss persistent bottlenecks associated with target selection, off-target assessment, editability, and plant recovery. Particular emphasis is placed on artificial intelligence (AI)-assisted approaches that integrate genomic, epigenomic, chromatin-accessibility, and multi-omics datasets to improve target prioritization, guide RNA design, off-target prediction, and locus- and genotype-specific editability assessment. We further evaluate DNA-free genome-editing technologies, including CRISPR-Cas ribonucleoproteins, transient RNA-based systems, and nanocarrier-mediated delivery platforms, highlighting their potential to generate non-integrative edits while reducing prolonged nuclease exposure. In addition, we discuss regeneration reprogramming strategies based on developmental regulators and morphogenic modules, including BBM-WUS, GRF-GIF, de novo meristem induction, and somatic embryogenesis, as enabling technologies for overcoming cultivar-dependent regeneration barriers. Importantly, this review proposes an integrated AI-to-field framework that connects target discovery, editability prediction, DNA-free editing, regeneration reprogramming, phenotypic validation, and breeding deployment into a unified soybean improvement pipeline. We further highlight emerging opportunities in multi-omics-guided target discovery, genotype-aware prediction models, regeneration-aware editing strategies, and closed-loop machine-learning systems that continuously improve editing decisions through experimental feedback. Collectively, these convergent innovations provide a practical foundation for accelerating the development of climate-resilient, nutritionally enhanced, and industry-ready soybean cultivars. Full article

Haze Pollution in China arises from the rapid enlargement of ultrafine particles into light-absorbing fine particulate matter through adsorption processes under atmospheric stagnation conditions. This study focuses on the sources of ultrafine particles (UFPs), the most critical component of haze pollutants during severe pollution periods in China. Utilizing methods including the spatial Durbin model and statistical data for the 28 cities (the “2 + 26” cities) within the Beijing–Tianjin–Hebei air pollution transmission channel—suffering the most severe haze pollution—it investigates the impact of pollution-intensive industries on haze pollution. This study reveals several key findings regarding China’s haze pollution. First, the principal source of ultrafine particles within China’s haze stems from the desulfurization, denitrification, and dust removal processes of pollution-intensive industries (the direct effect of these industries on haze is 0.028 * according to the SDM regression results). Crucially, the specific operational factors driving the abrupt increase in atmospheric UFPs during severe haze periods in China are identified as extensive management practices in desulfurization, the progressive tightening and annual escalation of denitrification emission standards, and the reliance on electrostatic precipitation which is ineffective against ultrafine particles. Second, haze pollution predominantly occurs in regions characterized by concentrations of pollution-intensive industries coupled with weak atmospheric environmental self-purification capacity (this carrying capacity for pollution-intensive industries exerts a significant negative impact on haze, demonstrated by a direct effect of −0.020 **; further analysis reveals that this is caused by regional differences in atmospheric self-purification capacity). Third, regional air transport acts as a contributing source, introducing UFPs from neighboring areas into local haze pollution, reflected by an indirect effect of pollution-intensive industries of 0.151 ** stemming from such spatial spillovers. Based on these conclusions, the study proposes a set of policy recommendations: relocate pollution-intensive industries using a gradient approach based on atmospheric self-purification capacity differences; systematically upgrade wet flue gas desulfurization technologies for industrial emissions; effectively promote technological innovation in denitrification processes; implement scientific controls on ammonia emissions; strengthen R&D in core technologies for UFP removal; innovate dust removal technologies to enhance overall system efficiency; reinforce regional coordinated governance; implement targeted training programs and select qualified management personnel; systematically enhance the environmental management capabilities of staff; and effectively mitigate the spillover effects of haze pollution. Full article

This article analyses the transformation of the competitiveness of the European Union in the context of geopolitical fragmentation, geo-economic rivalry and the growing importance of economic security in contemporary economic governance. The article argues that the traditional efficiency-oriented understanding of competitiveness, associated primarily with productivity growth, market efficiency and trade openness, is increasingly complemented by a resilience-oriented governance framework that emphasizes strategic autonomy, technological capabilities, economic security and long-term adaptive resilience. Using a conceptually oriented qualitative research design based on interpretive analysis and analytically focused comparison, we integrate knowledge from studies of competitiveness, political economy, geo-economics and European economic governance. The research draws on academic literature primarily available in the Web of Science and Scopus databases, EU strategic documents and legislation, and selected aggregated indicators related to productivity, innovation, trade openness, technological development and sustainability. The findings show that geopolitical fragmentation, supply chain vulnerabilities, technological competition and strategic dependencies are increasingly changing the structural foundations of European competitiveness. Innovation and technological capabilities remain key determinants of long-term competitiveness, but their strategic importance is increasingly linked to resilience, technological sovereignty and economic security. The results also show that the European Union is gradually moving towards a hybrid governance model that combines market openness with strategic coordination, industrial policy and resilience-oriented adaptation. At the same time, significant asymmetries between Member States continue to limit the EU’s ability to adapt in a coordinated manner. The article contributes to current debates on competitiveness by establishing an integrated analytical framework linking competitiveness, resilience, economic security, strategic autonomy and sustainable development in the context of geo-economic transformation. Full article

Background and Purpose: Contemporary digital government initiatives in China face a well-documented real-world paradox: massive fiscal outlays on digital governance coexist with marked disequilibrium in regional innovation returns. Two structural mismatches define this paradox. First, local governments overwhelmingly prioritize high-visibility hardware investments such as data centers and large AI models, while neglecting deep-seated institutional reforms including cross-departmental business process reengineering and factor market liberalization. The pervasive phenomenon of “aggregated but non-interoperable data, and interoperable data left unused” reflects a severe asynchrony between rapid technological deployment and lagging institutional restructuring. Second, comparable digital investments yield vastly divergent innovation dividends across eastern, central, and western regions, with regional divergence entrenching into a rigid “higher in the east lower in the west, higher in the south lower in the north” pattern. Extant literature, largely confined to the lens of “instrumental rationality,” reduces digital government to an exogenous technological variable, leaving it unable to explain this core practical puzzle of “homogeneous inputs generating heterogeneous returns.” Moving beyond the narrow “technology-enabled governance” narrative, this study draws on the Digital-Era Governance (DEG) paradigm to investigate the actual impact of institutional restructuring on regional scientific and technological innovation capacity, aiming to provide empirical evidence to unlock the inefficiency lock-in prevalent in digital governance practices. Research Methods: This study uses 280 prefecture-level cities and above in China from 2018 to 2023 as the research sample and constructs a two-way fixed-effects model for benchmark regression analysis. To address endogeneity, the average level of digital government development in other cities within the same province is used as an instrumental variable, and the 2SLS method is employed to identify the causal effect. On this basis, a series of robustness checks are conducted, including excluding the special impact of the COVID-19 pandemic, substituting core variable measures, and decomposing the dimensions of the core explanatory variables, to ensure the reliability of the research conclusions. For mechanism identification, the Bootstrap sampling method is used to test the dual mediating effects of “digital industry agglomeration” and “resource misallocation alleviation”; furthermore, moderating effects and heterogeneity analysis models are introduced to reveal the boundary constraints of regional economic development levels and city types on the empowerment effect. Main Findings: Empirical results show that: (1) Digital government construction significantly improves regional scientific and technological innovation capacity, and this conclusion remains valid after endogeneity treatment and robustness checks. (2) Mechanism analysis demonstrates that digital government drives innovation through the dual paths of “promoting digital industry agglomeration” and “alleviating resource misallocation,” with the marginal contribution of alleviating resource misallocation being significantly higher than that of industrial agglomeration. This suggests that, in transitional economies, eliminating institutional frictions in factor mobility brings greater innovation dividends than simply building physical spatial clusters. (3) Moderating effects indicate that the higher the level of regional economic development, the stronger the innovation empowerment effect of digital government. (4) Heterogeneity analysis further reveals that the innovation dividends of digital government are significant only in non-resource-based cities, non-central cities, and large and medium-sized cities, while in resource-based cities, central cities, and small cities, the effects are systematically absorbed and not significant. Research Conclusions and Contributions: This study breaks through the ontological limitations of existing research that views digital government as a technological tool, grounding it within the DEG theoretical framework and confirming that digital government is an institutional force in the reconstruction of regional innovation ecosystems. The findings suggest to policymakers that digital government construction should promote a shift from a “technology-oriented” to an “institution-oriented” approach. The policy focus should shift from mere infrastructure expansion to the elimination of deep-seated institutional frictions, the improvement of factor allocation efficiency, and the advancement of gradients and the implementation of classified governance, all guided by regional economic foundations and heterogeneity characteristics. 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

Promoting the coordinated development of digitalization and greening has become an important pathway for firms to achieve high-quality growth. Using panel data for A-share listed firms in China’s Yangtze River Basin from 2010 to 2022, this study examines the effect of supply chain innovation policy on firms’ digital–green development. We measure the synergy between digitalization and greening using a composite system synergy approach and identify the policy effect through a quasi-natural experiment based on the supply chain innovation policy, combined with a synthetic difference-in-differences model. The results show that the policy significantly improves the coordinated development of firm digitalization and greening, and the findings remain robust across a series of tests. Mechanism analysis indicates that this effect operates through three channels: easing financing constraints, increasing supply chain diversification, and promoting industrial chain modernization. Moderating effect tests further show that supply chain efficiency, supply chain resilience, and entrepreneurship strengthen the policy’s positive effect on digital–green development. Heterogeneity analysis suggests that the policy effect varies systematically with firm size, market competitiveness, and information asymmetry. This study provides micro-level evidence on how supply chain innovation policy can promote firms’ digital–green transformation and offers useful implications for policies aimed at improving firm competitiveness and supporting sustainable development. Full article

This study examines how the strategic integration of digital transformation and national innovation capacity contributes to accelerating sustainable development in Saudi Arabia by focusing on six Sustainable Development Goals (SDGs): SDG 4—Quality Education, SDG 7—Affordable and Clean Energy, SDG 8—Decent Work and Economic Growth, SDG 9—Industry, Innovation and Infrastructure, SDG 12—Responsible Consumption and Production, and SDG 13—Climate Action. Using annual data on ICT use, ICT access, R&D expenditure, and patent applications, the analysis evaluates both the direct and joint relationships between these indicators and SDG performance. Digital transformation is captured through ICT use and ICT access, while national innovation capacity is represented by R&D expenditure and patent applications, reflecting the input and output dimensions of formal innovation activity. The findings indicate that the direct long-run effects of digital transformation and national innovation capacity on the six SDGs are not statistically significant, suggesting that these domains have not yet become standalone drivers of educational advancement, clean-energy adoption, economic performance, industrial upgrading, sustainable resource management, or emissions reduction. In contrast, their interaction produces substantial positive effects on SDG 4, SDG 7, SDG 8, and SDG 9, highlighting improvements in educational quality, renewable energy transition, productivity, and industrial innovation. The interaction also has significant negative effects on SDG 12 and SDG 13, as reflected by reductions in CO₂ intensity and environmental pressures. These results indicate that meaningful progress toward the SDGs emerges when digital capabilities and national innovation capacity evolve jointly, rather than through isolated improvements in ICT infrastructure or innovation inputs. Robustness checks using a composite SDG index confirm the stability of these complementary effects. These findings suggest that Saudi Arabia can accelerate progress toward the SDGs by adopting integrated policies that link ICT expansion with stronger R&D systems, patent commercialization, technological innovation, and sustainability-oriented industrial transformation across education, energy, industry, resource efficiency, and climate action. Full article

Daunomycin (daunorubicin) is one of the most clinically significant anthracyclines used in chemotherapy, and its efficient production remains a major objective for biotechnological researchers. Industrial manufacturing relies on the fermentation of Streptomyces peucetius and Streptomyces coeruleorubidus, which produce daunomycin as a secondary metabolite under controlled conditions. This review will focus on the methods to enhance the total efficiency of biotechnological production, from upstream biosynthesis to downstream processing. Given the complexity of the daunomycin biosynthetic pathway in Streptomyces spp., substantial progress has been made in strain improvement to increase yield, metabolic robustness, and process stability. Advances in classical mutagenesis, pathway engineering, regulatory network modulation, and precursor supply optimization, along with rational medium design and advanced process control, have led to substantial increases in product titers and productivity. At the same time, innovations in downstream processes, such as extraction, purification and process integration, have increased recovery efficiency, product quality, and economic feasibility. With improvements in the production process, novel drug delivery modalities have been developed (e.g., drug carriers based on erythrocytes, drug nanocarriers based on hyaluronic acid) with increased efficiency and lower systemic toxicity. These developments indicate an evolution from pathway-level engineering to industrial-scale manufacturing and clinical application, underlining the evolution of daunomycin research and biotechnological production. Full article

Automatic storage and retrieval systems (AS/RSs) are essential components of modern high-tech production facilities. This study presents the dynamic model and control of an innovative multi-platform automated storage crane system. A novel autonomous AS/RS crane design with six vertically aligned and independently controlled load platforms is proposed, enabling the simultaneous transport of six load units within a single travel cycle. The simulation results indicate up to a 58% reduction under the assumed operating conditions, indicating a significant improvement in storage efficiency. In addition, the vertical position control problem of a load-carrying platform in the proposed crane structure is investigated. The performance of classical PID and model reference adaptive PID (MRAC-PID) was evaluated comparatively. The multi-platform crane design, for which a Decision on Grant of Patent was issued by the Turkish Patent and Trademark Office on 21 April 2026 (TR Patent Application No. 2023/018753, under Industrial Property Law No. 6769), offers an engineering solution that can be applied to increase production efficiency in automated storage and retrieval systems. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

Gas–solid bubbling fluidized beds involving Geldart Group B particles are fundamental to numerous industrial thermochemical processes, where bubble dynamics dictate the efficiency of heat and mass transfer. However, accurately predicting these complex hydrodynamic behaviors remains a challenge due to the non-linear coupling of phase interactions. This study presents a comprehensive validation of 2D and 3D Eulerian–Eulerian Two-Fluid Models (TFM) against an extensive experimental dataset. A ‘core-flow’ consistency principle is adopted, demonstrating that the 3D cylindrical simulation provides a physically equivalent representation of the central bubbling dynamics in the rectangular experimental bed. A key innovation of this work is a novel post-processing framework that bridges raw CFD datasets and quantitative bubbling metrics. Unlike traditional threshold-based segmentation or localized probe measurements, which are often limited by spatial resolution and noise sensitivity, the integrated use of Autodesk 3DS Max for volumetric reconstruction and customized MATLAB (R2024a) algorithms allows for the seamless processing of heterogeneous 2D and 3D data. This methodology significantly enhances the capability to track complex bubble coalescence and breakup events while improving batch-processing efficiency, providing a high-fidelity alternative for analyzing gas–-solid flow patterns in complex geometries. The results show that both experimental data and 2D simulations align with Werther’s correlation, yielding Mean Relative Errors (MRE) of 8.2% and 10.5%, respectively. In contrast, the 3D simulation tracks Darton’s prediction closely with a lower MRE of 7.4%, demonstrating superior concordance in volumetric bubble growth. The core innovation lies in the definition of a clear dimensional choice framework: 2D simulations are computationally sufficient and accurate for predicting macro-scale bubble heights and frequencies under pseudo-2D or narrow-bed constraints. However, 3D simulations are strictly necessary when evaluating unconstrained radial expansion, core-flow dynamics, and precise volumetric bubble diameters (d_v) where full multi-directional degrees of freedom dictate hydrodynamics. Full article