Search Results (335)

The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. However, manually delineating pore structure and estimating porosity are challenging tasks. This work proposes an automated method for 3D pore segmentation and characterization using convolutional neural networks with attention mechanisms. The method introduces a novel approach that combines attention at both channel and spatial levels, enhancing the segmentation and property estimation, providing valuable insights for a more detailed study of soil conditions. In experiments conducted with a private dataset, the segmentation results achieved mean Dice values of 99.10% ± 0.0004 and mean IoU values of 98.23% ± 0.0008. Additionally, in tests with Phaeozem Albic, the automatic method provided porosity estimates comparable to those obtained by a method based on integral geometry and morphology. Full article

In the context of global carbon emissions and climate change, identifying context-specific low-carbon pathways for urban areas is critical for achieving socio-environmental sustainability. This study applies the technology–organization–environment (TOE) framework to examine the driving mechanisms and the diversity in carbon reduction pathways across 81 cities in China. Utilizing partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), this research assesses the roles of technological, organizational, and environmental drivers in urban carbon reduction. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to uncover distinct carbon reduction pathways and causal asymmetries between cities. The findings reveal that technological, organizational, and environmental factors significantly drive carbon reduction, with technological and organizational factors playing the central roles. Environmental factors exert primarily indirect effects, interacting with technological and organizational drivers. This study categorizes cities into three distinct carbon reduction models: cities with high carbon-neutral potential primarily leverage technological innovation and energy efficiency optimization; cities with moderate potential integrate technology and policy, emphasizing green landscape planning to achieve balanced development; and cities with lower carbon reduction potential are mainly policy-driven, constrained by technological and resource limitations. This study underscores the role of computational modeling in providing valuable insights for the development of context-tailored carbon reduction strategies. It highlights the synergetic interactions among technological, organizational, and environmental factors, offering essential guidance for advancing sustainable development planning and facilitating the low-carbon transition of cities and communities. Full article

Laser-induced breakdown spectroscopy (LIBS) is a rapid, non-contact analytical technique that is widely applied in various fields. However, the high dimensionality and information redundancy of LIBS spectral data present challenges for effective model development. This study aims to assess the effectiveness of the minimum redundancy and maximum relevance (mRMR) method for feature selection in LIBS spectral data and to explore its adaptability across different predictive modeling approaches. Using the ChemCam LIBS dataset, we constructed predictive models with four quantitative methods: random forest (RF), support vector regression (SVR), back propagation neural network (BPNN), and partial least squares regression (PLSR). We compared the performance of mRMR-based feature selection with that of full-spectrum data and three other feature selection methods: competitive adaptive re-weighted sampling (CARS), Regressional ReliefF (RReliefF), and neighborhood component analysis (NCA). Our results demonstrate that the mRMR method significantly reduces the number of selected features while improving model performance. This study validates the effectiveness of the mRMR algorithm for LIBS feature extraction and highlights the potential of feature selection techniques to enhance predictive accuracy. The findings provide a valuable strategy for feature selection in LIBS data analysis and offer significant implications for the practical application of LIBS in predicting elemental content in geological samples. Full article

In recent years, policymakers have increasingly focused on environmental quality and economic growth. While various factors influence green growth, two important factors that have been overlooked in research are the global innovation index and the fragile states index. This study employs novel methods, such as necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA), to analyze green growth across 90 countries in 2019, surpassing traditional regression techniques. The NCA model identifies essential variables for green growth, revealing that global innovation, institutional quality, human development, and globalization are crucial conditions. Conversely, the fsQCA model offers intricate solutions by combining key variables for green growth. It presents five solutions for achieving high green growth, each tailored to specific groups of countries. For instance, Solution 1, with a consistency of 0.96%, suggests that increased consumption of renewable energy, greater trade openness, and reduced fragility in states lead to higher green growth in countries like Denmark and Austria. Thus, policymakers can foster both economic growth and environmental improvement by promoting renewable energy adoption, enhancing global trade management, and strengthening institutional quality and political stability. Full article

Electric vehicles can substantially lower the overall carbon footprint of the transportation sector, and their batteries become key enablers of widespread electrification. Although high capacity and efficiency are essential for providing sufficient range and performance in electric vehicles, they can be compromised by the need to lower costs and environmental impacts and retain valuable materials. In the present work, multi-criteria decision analysis was adopted to assess the sustainability of different lithium-ion batteries. Life cycle carbon emissions and toxicity, material criticality, life cycle costs, specific energy, safety, and durability were considered in the analysis as key parameters of the transition to electric mobility. A subjective approach was chosen for the weight attribution of the criteria. Although certain alternatives, like lithium nickel cobalt manganese oxide (NCM) and lithium nickel cobalt aluminum oxide (NCA), outweigh others in specific energy, they lack in terms of safety, material preservation, and environmental impact. Addressing cost-related challenges is also important for making certain solutions competitive and largely accessible. Overall, while technical parameters are crucial for the development of lithium-ion batteries, it is equally important to consider the environmental burden, resource availability, and economic factors in the design process, alongside social aspects such as the ethical sourcing of materials to ensure their sustainability. Full article

Cooperative afforestation, reforestation, and forest management initiatives between state-owned forest farms and village collectives serve as pivotal strategies for restoring degraded ecosystems, establishing new forested areas, and revitalizing collective forestland resources. These collaborations offer a practical pathway to enhance forest resource utilization while contributing to rural revitalization in forest-dominated regions. Despite their significance, achieving the sustainability of Forest–Village Cooperation through efficient resource allocation remains a critical challenge. This study investigates Forest–Village Cooperation cases in Fujian Province, employing resource orchestration theory to develop an analytical framework for sustainable resource allocation in these partnerships. By integrating Data Envelopment Analysis (DEA), Necessary Condition Analysis (NCA), and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), the research examines how policy resources, human resources, natural resources, economic resources, grassroots connectivity capability, and technological innovation capability collectively influence sustainability. The findings reveal that no single resource factor is necessary for Forest–Village Cooperation Sustainability (FVCS). However, economic resources, human resources, and technological innovation capability emerge as key drivers of high sustainability. State-owned forest farms with weaker grassroots connectivity capability can offset this limitation through natural resource advantages, while those with stronger connectivity achieve cooperation upgrades via efficient economic resource allocation. Furthermore, this study identifies three pathways for FVCS: “Resource Integration-Driven”, “Technology Innovation-Enabled”, and “Capability–Resource Synergy”, each tailored to specific resource endowment contexts. This research not only extends the application of resource orchestration theory in the forestry cooperation domain but also provides actionable policy recommendations for optimizing collaborations between state-owned forest farms and village collectives. Full article

This study addresses the challenges of tomato maturity recognition in natural environments, such as occlusion caused by branches and leaves, and the difficulty in detecting stacked fruits. To overcome these issues, we propose a novel YOLOv8n-CA method for tomato maturity recognition, which defines four maturity stages: unripe, turning color, turning ripe, and fully ripe. The model is based on the YOLOv8n architecture, incorporating the coordinate attention (CA) mechanism into the backbone network to enhance the model’s ability to capture and express features of the tomato fruits. Additionally, the C2f-FN structure was utilized in both the backbone and neck networks to strengthen the model’s capacity to extract maturity-related features. The CARAFE up-sampling operator was integrated to expand the receptive field for improved feature fusion. Finally, the SIoU loss function was used to solve the problem of insufficient CIoU of the original loss function. Experimental results showed that the YOLOv8n-CA model had a parameter count of only 2.45 × 10⁶, computational complexity of 6.9 GFLOPs, and a weight file size of just 4.90 MB. The model achieved a mean average precision (mAP) of 97.3%. Compared to the YOLOv8n model, it reduced the model size slightly while improving accuracy by 1.3 percentage points. When compared to seven other models—Faster R-CNN, YOLOv3s, YOLOv5s, YOLOv5m, YOLOv7, YOLOv8n, YOLOv10s, and YOLOv11n—the YOLOv8n-CA model was the smallest in size and demonstrated superior detection performance. Full article

AI-generated content (AIGC) is uniquely positioned to drive the digital transformation of professional education in the animation, comic, and game (ACG) industries. However, its collaborative application also faces initial novelty effects and user discontinuance. Existing studies often employ single-variable analytical methods, which struggle to capture the complex mechanisms influencing technology adoption. This study innovatively combines necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) and applies them to the field of ACG education. Using this mixed-method approach, it systematically explores the necessary conditions and configurational effects influencing educational users’ continuance intention to adopt AIGC tools for collaborative design learning, aiming to address existing research gaps. A survey of 312 Chinese ACG educational users revealed that no single factor constitutes a necessary condition for their continuance intention to adopt AIGC tools. Additionally, five pathways leading to high adoption intention and three pathways leading to low adoption intention were identified. Notably, the absence or insufficiency of task–technology fit, and perceived quality do not hinder ACG educational users’ willingness to actively adopt AIGC tools. This reflects the creativity-driven learning characteristics, and the flexible and diverse tool demands of the ACG discipline. The findings provide theoretical and empirical insights to enhance the effective synergy and sustainable development between ACG education and AIGC tools. Full article

Prime editing (PE) is a CRISPR-based tool for genome engineering that can be applied to generate human induced pluripotent stem cell (hiPSC)-based disease models. PE technology safely introduces point mutations, small insertions, and deletions (indels) into the genome. It uses a Cas9-nickase (nCas9) fused to a reverse transcriptase (RT) as an editor and a PE guide RNA (pegRNA), which introduces the desired edit with great precision without creating double-strand breaks (DSBs). PE leads to minimal off-targets or indels when introducing single-strand breaks (SSB) in the DNA. Low efficiency can be an obstacle to its use in hiPSCs, especially when the genetic context precludes the screening of multiple pegRNAs, and other strategies must be employed to achieve the desired edit. We developed a PE platform to efficiently generate isogenic models of Mendelian disorders. We introduced the c.25G>A (p.V9M) mutation in the NMNAT1 gene with over 25% efficiency by optimizing the PE workflow. Using our optimized system, we generated other isogenic models of inherited retinal diseases (IRDs), including the c.1481C>T (p.T494M) mutation in PRPF3 and the c.6926A>C (p.H2309P) mutation in PRPF8. We modified several determinants of the hiPSC PE procedure, such as plasmid concentrations, PE component ratios, and delivery method settings, showing that our improved workflow increased the hiPSC editing efficiency. Full article

Coronary heart disease (CHD) is the leading cause of morbidity and mortality worldwide despite significant improvements in diagnostic modalities. Emerging evidence suggests that erythrocytes, or red blood cells (RBCs), are one of the most important contributors to the events implicated in atherosclerosis, although the molecular mechanisms behind it are under investigation. We used NMR-based lipidomic technology to investigate the RBC lipidome in patients with CHD compared to those with normal coronary arteries (NCAs), all angiographically documented, and its correlation with coronary artery stenosis. Targeted and untargeted lipidomic analysis revealed that CHD patients presented significant lipid alterations in the RBC membrane, characterized by higher cholesterol, sphingolipids, saturated and monounsaturated fatty acids, lower phospholipids (glycerophospholipids and ether glycerolipids), and unsaturated and polyunsaturated fatty acids. These aberrations gradually distinguish the three subgroups of patients with mild, moderate, and severe coronary stenosis, potentially indicating their non-negligible involvement in the onset and progression of atherosclerosis. The comprehensive analysis of RBC-membrane-derived lipids with omics approaches could unravel specific lipid abnormalities taking place at the silent subclinical stage of atherosclerosis and could have the potential to identify patients with subtle, but still proatherogenic, abnormalities that may confer a higher risk for the development of CHD. Full article

This study aimed to understand the factors that influence individuals’ intention to continue using advanced driver assistance systems (ADASs) through an integrated approach that extends the technology acceptance model (TAM). First, perceived safety, perceived quality, and satisfaction were incorporated into the traditional TAM framework as additional constructs to address the complexities of ADAS usage. Second, an approach that combines partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) was employed to identify both the sufficient and necessary conditions for the continuous intention to use ADASs. This combined approach was directed toward data collected from 843 drivers hailing from the Greater Bay Area of China and experienced with ADAS usage. The findings revealed that perceived usefulness, perceived quality, perceived safety, and satisfaction significantly influenced continuance intention, while perceived ease of use indirectly affected it through perceived usefulness and satisfaction. This study underscores the paramount importance of safety and quality perceptions in ADAS adoption and offers practical insights that can help product design and marketing professionals enhance the acceptance and sustained use of ADAS technologies. Full article

The increasing environmental impacts caused by the high demand for concrete production have underscored the need for sustainable alternatives in the design of eco-concrete mixtures. Additionally, important industries, such as construction and mining, generate massive amounts of waste/by-products that could be repurposed towards sustainability. Consequently, this study investigates the valorization of copper slag (CS), a by-product of the mining industry as a supplementary cementitious material (SCM), and concrete as recycled coarse aggregate (RCA), derived from construction and demolition waste, as partial substitutes for Ordinary Portland Cement (OPC) and natural coarse aggregate (NCA), respectively. Eco-concrete mixtures were designed with varying replacement levels: 15% for CS, and 0%, 20%, 50%, and 100% for RCA. The mechanical properties (compressive, indirect tensile, and flexural strengths), permeability characteristics (porosity and capillary suction), and environmental impacts (carbon footprint) of these mixtures were evaluated. The results showed that the use of CS and of increasing proportions of RCA led to a monotonic loss in each of the concretes’ mechanical strength properties at 7, 28 and 90 days of curing. However, at extended ages (180 days of curing), the concrete mixtures with CS and only NCA presented an average compressive strength 1.2% higher than that of the reference concrete (mixture with only OPC and natural aggregate). Additionally, the concrete mixture with CS and 20% RCA achieved 3.2% and 5.8% higher average values than the reference concrete in terms of its indirect tensile strength and flexural strength, respectively. Finally, a cradle-to-gate life cycle assessment (LCA) analysis was implemented, whose results showed that the greatest effect on reducing the carbon emission impacts occurred due to the substitution of OPC with CS, which confirmed that the adequate technical performances of some of the concrete mixtures developed in this study are positively complemented with reduced environmental impacts. Moreover, this study presents a viable approach to minimizing resource consumption and waste generation, contributing to the advancement of eco-friendly construction materials, which aligns with the sustainable development goals. Full article

Smart manufacturing is an important initiative to promote the transformation and upgrading of industries and the high-quality development of the economy. However, the current situation of digitalized smart transformation in manufacturing enterprises is not optimistic, which is primarily attributed to the ambiguity surrounding the pathways. This study is based on the technology-organization-environment-individual (TOE-I) analytical framework; it selects 20 case studies of advanced manufacturing enterprises; and employs case studies and necessary condition fuzzy set qualitative comparative research methods (NCA and fsQCA) to investigate the pathways through which technology, organization, the environment, and individual microelements synergistically drive smart manufacturing from a configurational perspective. The study reveals that digital technology breakthroughs, digital infrastructure, digital talent, digital sharing, organizational resilience, organizational culture, and the entrepreneurial spirit are the core influencing factors in advancing smart manufacturing for manufacturing enterprises, and four implementation paths driven by smart manufacturing are analyzed. Among them, digital technology breakthroughs and digital infrastructure have a potential substitutive relationship in the “technology + talent” empowerment organizational model. Organizational resilience, organizational culture, and the entrepreneurial spirit are important safeguards for successful advancements in smart manufacturing. In contrast, digital infrastructure plays a more indirect, supporting role. Accordingly, this paper provides theoretical reference and practical guidance. Full article

The impact of digital empowerment on green innovation is increasingly evident, enabling various subjects to improve the integration of innovation elements and enhance innovation efficacy across a broader temporal and spatial scope. A comprehensive examination of the mechanisms that underlie this process is required. This paper constructs the ‘elements-subjects-environments’ research framework of digital innovation ecosystems, collecting data from 30 provinces in China from 2017 to 2021 and using green total factor productivity (GTFP) to evaluate the level of green regional development. In this study, the dynamic qualitative comparative analysis (QCA) method is employed to analyze the intricate causal mechanisms and configurations of green regional development that are driven by digital innovation ecosystems from both temporal and spatial perspectives. The results show that: (1) green regional development requires the interaction of multiple elements, subjects, and the environment, and a single condition does not constitute a necessary condition; (2) there are four pathways with different configurations for high-level green development: data elements-driven enterprise application innovation, data elements-driven enterprise-user co-creation, data elements-driven multi-collaborative innovation, and digital environment-driven university basic innovation; (3) the temporal and spatial dimensions of China’s green regional development pathways are heterogeneous: the significance of data elements in fostering green regional development is increasing; the multi-collaborative innovation configuration is facilitating the green development of the eastern and central regions, whereas the western and northeastern regions are progressing at a relatively slow pace. This study provides theoretical and practical insights to promote the integration of digital innovation and green development. Full article

Digitalization offers an opportunity to narrow the economic gap between urban and rural areas; however, there are fragmented and competing explanations regarding its impact mechanisms. Responding to calls for research on the complex effects of digitalization, this paper, based on a contextual perspective and configurational theory, analyzes the impact of digitalization conditions embedded in contexts on the urban–rural income gap. The study, based on a sample of 274 prefecture-level administrative regions in China from 2014 to 2021, employs a Panel Fuzzy-Set Qualitative Comparative Analysis (Panel fsQCA) and Necessary Condition Analysis (NCA). The combined application of necessity analysis and sufficiency analysis reveals that certain digitalization conditions—such as digital infrastructure, digital industry, and digital finance—have a universal influence on the urban–rural income gap. Importantly, the sufficiency analysis demonstrates that the impact mechanisms of digitalization conditions exhibit configurational effects, varying with changes in contextual and conditional combinations. The models that significantly narrow the urban–rural income gap include (1) the “infrastructure–finance–governance” model, (2) the comprehensive digital transformation model, (3) the “technology–infrastructure–industry” model, and (4) the digital infrastructure transformation model. Among these, the comprehensive digital transformation model is the most universally effective. These configurations reflect the logic of completeness and substitutability and exhibit specific dynamic evolutionary trends and spatial distribution characteristics. These findings provide contextual and adaptable empirical insights for economies, including China, to implement targeted digital transformation strategies that effectively narrow the urban–rural income gap. For instance, economies can focus on developing comprehensive digital transformation in prosperous and open regions to reduce income gap. Full article