Search Results (15,576)

This study proposes a sustainable, integrated biorefinery approach to valorize mussel shell waste into high-value products, including chitin, chitosan, and calcium formate. Formic acid was employed as an effective demineralizing agent, enabling not only efficient mineral removal but also the direct conversion of the demineralization effluent into value-added calcium formate. The sequential extraction processes, demineralization, deproteinization, and decolorization, successfully yielded purified chitin (PCH), which was subsequently deacetylated to produce chitosan (CTS) with a degree of deacetylation of 85% and a molecular weight of 75 kDa. The physicochemical properties of all products were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). FTIR and XRD analyses confirmed the successful extraction of chitin and chitosan, demonstrating the feasibility of mussel shells as an alternative biopolymer source. In parallel, calcium formate (CCF) was obtained from the demineralization effluent with a yield of 94.19%, and its formation was verified by FTIR and XRD. Elemental analysis by XRF exhibited 98.3% CaO with minimal non-toxic impurities. The TGA/DTG profiles of CCF exhibited a well-defined two-step thermal decomposition, confirming its anhydrous form. Overall, this environmentally benign process enables the simultaneous production of multiple value-added products while significantly improving resource utilization and reducing waste generation. The proposed integrated biorefinery model offers a promising, economically viable pathway for marine biomass valorization, aligned with the Bio-Circular-Green (BCG) economy concept. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

Understanding the impact of rural digitalization on the resilience of the swine industry is crucial to promoting its transformation toward efficient and low-carbon production. However, existing research has not yet clarified how rural digitalization influences the resilience of the swine industry, and there is a particular lack of discussion regarding potential nonlinear relationships. Based on panel data from 30 Chinese provinces for the period 2011–2023, we employed the entropy method to measure the level of rural digitalization and the resilience of the swine industry. Two-way fixed-effects, mediation, and threshold models were adopted to empirically examine the relationship and underlying mechanisms. The findings indicated that rural digitalization significantly enhances the resilience of the swine industry, and this finding remained robust after multiple robustness checks and endogeneity treatments. This effect is primarily mediated by two pathways: industrial-scale expansion and industrial agglomeration. Additionally, well-designed environmental policies and higher rural household incomes can strengthen the beneficial effect of rural digitalization on industrial resilience. Heterogeneity analysis further reveals that the positive influence is stronger in regions with poor transportation infrastructure and in central and western China, where digitalization effectively strengthens the industry’s shock resistance and adaptive capacity. This study offers meaningful implications for policymakers seeking to accelerate rural digitalization and promote high-quality development of the swine industry in the digital age. Full article

In this paper, an efficient and accurate framework for nonlinear spacetime fractional diffusion equations is proposed. The methods are based on the spectral deferred correction technique, which employs a compact difference scheme as the preconditioner via the Picard integral collocation formulation. The nonlinear term is incorporated into the preconditioner in a way similar to linear systems without using Newtonian methods. The preconditioner is proven to be a stable operator, and the resulting spectral deferred correction method maintains an arbitrary order of accuracy and excellent stability. Due to the dense property of the central finite difference approximation of the fractional Laplacian ${(-\Delta )}^s$, a dual accelerated algorithm for the exact computation of the matrix–vector product is presented by introducing the discrete sine transform. The numerical results demonstrate that the proposed new methods are highly efficient and precise. Full article

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions. Full article

This research focuses on the synthesis of a novel baking powder enriched with bioactive molecules recovered from olive pomace via ultrasound-assisted extraction using a hydro-ethanolic mixture. The functional ingredient was engineered by anchoring the extracted phytocompounds onto a starch backbone through a sustainable grafting technique. Biscuits formulated with the innovative ingredient showed an increased concentration of phenolic compounds (2.162 mg GAE/g), encompassing both phenolic acids (0.372 mg GAE/g) and flavonoids (0.360 mg CTE/g). Enhanced antioxidant efficacy was recorded, mostly in aqueous media (IC₅₀ = 0.554 mg mL⁻¹ against ABTS radical) compared to organic environments (IC₅₀ = 0.132 mg mL⁻¹ against DPPH radical). Furthermore, Oxitest and oxidation stability reactor analyses revealed exceptional antioxidant capacity (induction period = 37 ± 2 h). By an accelerated shelf-life test, a marked instrumental color difference was observed with the fortified sample showing a darker, redder/brown color (ΔE > 16), as also confirmed by trained panelists. On the contrary, similar scores were achieved for the olfactory, textural and tasting attributes of the two samples, as well as values of the friability index (<1 mm⁻¹) evaluated by instrumental techniques. This approach represents a sustainable strategy, transforming a high-polluting agri-food by-product into a source of bioactive compounds for nutritional and technological improvement of baked foods. Full article

Sea buckthorn leaves are a relatively underutilised component of sea buckthorn processed products; however, various studies have indicated that they possess hypoglycaemic potential. Under alkaline solubilisation and acid-precipitation conditions, the extraction yield of sea buckthorn leaf protein (SLP) reached 19.33%. Trypsin was selected as the hydrolysing enzyme to extract SLPPs-T, with half-maximal inhibitory concentrations (IC₅₀) against α-glucosidase and DPP-IV of 0.1361 ± 0.017 mg/mL and 0.1286 ± 0.012 mg/mL, respectively. UV spectroscopy, Fourier transform infrared spectroscopy, circular dichroism spectroscopy and particle size analysis indicated that the secondary and microstructures of SLP underwent changes following its hydrolysis to SLPPs-T; following separation, purification, sequence identification and computer screening, two novel peptides, PM-8 and VG-11, were obtained; molecular docking, solid-phase synthesis and in vitro experiments confirmed that VG-11 exhibited superior inhibitory activity, with half-maximal inhibitory concentrations (IC₅₀) against α-glucosidase and DPP-IV of 0.3885 ± 0.015 mM and 0.2611 ± 0.021 mM, respectively. In summary, this study explored the potential of sea buckthorn leaf protein as a natural hypoglycaemic product through a combination of computational modelling and experimental methods, thereby significantly enhancing the value of sea buckthorn resources. Full article

Alternative proteins and novel processing technologies are crucial to transforming contemporary food systems into ones with lower environmental impact while meeting the rising global demand for protein. Alternative protein sources from plants, microbes, insects, and cultivated cells offer diverse nutritional and techno-functional attributes that can partially or fully replace conventional animal proteins in meat analogs and related products. This review synthesizes the current knowledge on major categories of alternative protein sources, including plant-based ingredients, microbial- and fermentation-derived proteins, insect and other emerging sources, and cultivated (cell-based) meat, with a specific focus on their suitability for structured meat analog applications. Modern structuring and processing technologies are discussed, including the traditional wet and dry extrusion to modern technologies like high-moisture extrusion, high-pressure processing, shear-cell technology, 3D printing, fermentation-based structuring, and enzymatic protein modification. Furthermore, this review critically evaluates product design and quality attributes of meat analogs, including physicochemical properties, sensory performance, nutritional aspects, and safety considerations. This review highlights technological and scale-up challenges, as well as the necessity of multi-criteria optimization in sensory quality, nutrition, sustainability, and affordability, and presents research priorities focused on combining multiple protein sources and advanced processing pathways for next-generation meat analog. This review provides an integrated framework linking protein sources, processing technologies, antioxidant functionality, and sustainability considerations to support the development of next-generation meat analogs. In addition, this review highlights the intrinsic antioxidant potential of alternative proteins, emphasizing the role of bioactive peptides, polyphenols, and structure–function relationships in enhancing oxidative stability and product quality. Full article

Microbial communities inhabiting natural and anthropogenically impacted environments are exposed to diverse abiotic stressors that can influence the distribution of functional traits. However, distinguishing the processes underlying phenotypic patterns remains challenging in microbial systems, where ecological and evolutionary dynamics often overlap. In this study, we experimentally assessed the distribution of biofilm formation and plastic degradation capacity in bacterial isolates across environments characterized by different stress regimes, to evaluate whether these traits are primarily associated with environmental context rather than phylogenetic relatedness, and may therefore reflect environment-dependent phenotypic modulation on a lineage-specific functional background. Taxonomic affiliation was assessed using 16S rRNA gene sequencing, while expressed biochemical profiles were characterized by Fourier-transform infrared (FTIR) spectroscopy. Multivariate ordination and Partial Least Squares analyses were used to explore relationships among taxonomy, biochemical profiles, functional phenotypes, and environment of isolation. Phylogenetic signal analysis confirmed that neither trait was strongly constrained by vertical inheritance, with Blomberg’s K ≈ 0 and Fritz & Purvis’ D = 0.51, consistent with environment-driven rather than phylogenetically conserved trait distributions. Both biofilm production and plastic degradation capacity showed significant environment-dependent differences in their relative frequencies (Fisher’s exact test, biofilm: p = 5.5 × 10⁻⁵; PCL degradation: p = 2.5 × 10⁻⁴) and were not directly associated with each other (Wilcoxon rank-sum test, p = 0.45; linear model, p = 0.68). Overall, these results indicate that microbial functional traits are unevenly distributed across environments and weakly constrained by taxonomy, consistent with the contribution of multiple, non-mutually exclusive processes that remain difficult to disentangle empirically. Full article

Despite being fundamental to modern infrastructure, the cement and concrete industry is a major contributor to global carbon emissions, necessitating urgent decarbonisation strategies to mitigate climate change and achieve net-zero targets by 2050. This review explores technological pathways and innovations essential for lowering carbon emissions, including low-carbon materials, energy-efficient processes, carbon capture, utilization and storage (CCUS), and advanced production technologies. It also highlights the importance of supportive policy frameworks, financial incentives, and international collaboration in accelerating the transition to a low-carbon industry. While challenges such as high initial costs, resistance to change, and knowledge gaps persist, these can be addressed through innovation, education, and robust financial mechanisms. Furthermore, circular economy principles, sustainable procurement practices, and continued research and development are emphasized as critical enablers of the industry’s transformation. The paper concludes with recommendations for future actions, highlighting the role of cross-sector cooperation, research funding, and knowledge sharing in achieving a sustainable and decarbonised cement and concrete sector that can “go green” for eco-constructions. Full article

Let $U,V\subset \mathbb{H}$ be bounded $C^1$ domains, and let f be quaternion-valued on $U\times V$. We study the mixed Cauchy–Fueter system $D_x^{L}f=0$ and $f{D}_y^R=0$ on product domains by iterating the classical one-variable Borel–Pompeiu formulas in an order consistent with quaternionic multiplication. Under closure regularity on $\overline{U}\times \overline{V}$, we prove an iterated representation formula and show that, in the biregular case, the boundary contribution reduces to the distinguished boundary $\partial U\times \partial V$. This leads to a distinguished boundary transform, ${\mathcal{T}}_{U,V}$, on continuous boundary data. We prove that ${\mathcal{T}}_{U,V}$ maps $C(\partial U\times \partial V;\mathbb{H})$ into $C^{\infty }(U\times V;\mathbb{H})$, establish compact subset estimates for mixed real derivatives, and derive a local approximation theorem within the transform range by finite sums of separated one-variable Cauchy transforms. The analysis is restricted to this representation framework. In particular, the paper does not address a general solvability theory for the mixed inhomogeneous system and does not characterize the full range of ${\mathcal{T}}_{U,V}$. Full article

The paper deals with the concept of how to regulate structure formation in the interfacial transition zone (ITZ) between the Ordinary Portland Cement (OPC) matrix and basalt to ensure the durability of basalt fiber-reinforced concretes. It has been demonstrated that the alkali–silica reaction (ASR) can be transformed from a destructive (negative) process into a constructive one in OPC concrete through activation by sodium water glass combined with the incorporation of an Al₂O₃-containing additive, namely metakaolin. Alkaline activation increased the compressive strength of OPC basalt fiber-reinforced concrete by 1.6–1.9 times. The formation of stable zeolite-like hydration products within the Na₂O-CaO-Al₂O₃-SiO₂-H₂O system promoted self-healing of the ITZ. This resulted in a 5.6-fold increase in ITZ microhardness compared to the cement matrix, as well as transforming expansion into shrinkage of concrete with a final value of 0.01 mm/m after 360 days. The structure-forming processes in the ITZ ensured a 1.14-fold increase in the compressive strength of 180-day alkali-activated OPC basalt fiber-reinforced concrete compared to its 30-day strength, in contrast to a 0.92-fold decrease in the strength of the non-modified OPC analog under conditions accelerating the development of ASR. Full article

This paper argues that the most significant challenge artificial intelligence poses to theological anthropology is not ontological but epistemic. Rather than asking whether machines can think, feel, or bear the image of God, this paper redirects attention to the prior question of what happens to the human when core epistemic capacities, judgment, discernment, interpretive authority, and moral reasoning are progressively delegated to computational systems. Drawing on the concept of epistemic automation, understood as the systematic transfer of knowledge-producing functions from human agents to algorithmic processes, this paper develops a threefold analytical framework. First, it distinguishes epistemic authority from ontological status as the more productive locus for theological anthropological inquiry. Second, it introduces the distinction between fluency and understanding as an anthropological boundary condition that AI renders newly visible. Third, it analyses delegated cognition as a form of agency deformation with theological significance. The paper concludes that theological anthropology must move beyond reactive commentary on AI and instead generate a theory of the human under conditions of epistemic transformation. The argument engages constructively with philosophy of technology, social epistemology, and Christian theological traditions to offer a framework applicable across confessional boundaries. Full article