Search Results (290)

Achieving a win-win situation for both economy and ecology is crucial for promoting sustainable social development and shaping new advantages in high-quality developments. This article constructs an ecological economic resilience (EER) analysis framework by integrating both ecological and economic dimensions from a resilience perspective. Based on panel data from 290 cities in China, it explores the dynamic evolution characteristics, regional differences, and convergence trends of EER. The findings indicate that the EER has weakened nationwide and in the four major economic regions, overall tending towards stability. Significant disparities exist in EER, particularly pronounced in the northeast. There is $\sigma$ convergence in the nation as well as in the northeast and east regions. Additionally, both absolute and conditional $\beta$ convergence is evident nationwide and in all regions, with conditional convergence occurring at a faster pace. The research findings in this paper provide solid theoretical support for promoting regional coordinated development and constructing a new development paradigm. Full article

Accurate assessment of green productivity is essential for advancing sustainable agriculture in ecologically fragile regions. This study examined the evolution of agricultural green total factor productivity (AGTFP) in Tibet over the period 2002–2021 by applying a super-efficiency SBM-GML model that accounts for undesirable outputs. We decompose AGTFP into technical change and efficiency change, conduct redundancy analysis to identify sources of inefficiency and explore its spatiotemporal dynamics through kernel density estimation and convergence analysis. Results show that (1) AGTFP in Tibet grew at an average annual rate of 0.78%, slower than the national average of 1.6%; (2) labor input, livestock scale, and agricultural carbon emissions are major sources of redundancy, especially in pastoral regions; (3) technological progress is the main driver of AGTFP growth, while efficiency gains have a limited impact, reflecting a technology-led growth pattern; (4) AGTFP follows a “convergence-divergence-reconvergence” trend, with signs of conditional β convergence after controlling for regional heterogeneity. These findings highlight the need for region-specific green agricultural policies. Priority should be given to improving green technology diffusion and input allocation in high-altitude pastoral areas, alongside strengthening ecological compensation and interregional coordination to enhance green efficiency and promote high-quality development across Tibet. Full article

This study aims to determine the model parameters of proton exchange membrane fuel cells (PEMFC) by employing the Sea Horse Optimization (SHO) algorithm, a novel metaheuristic approach inspired by natural behaviors. Although conventional algorithms in the literature have achieved considerable success in parametric modeling accuracy, many of them suffer from inherent drawbacks, such as premature convergence and entrapment in local minima. The SHO algorithm, with its adaptive and dynamic nature, is designed to overcome these limitations. To further evaluate its performance, a detailed parametric sensitivity analysis is conducted on SHO-specific control parameters. In this work, experimental polarization data from a Ballard Mark V PEMFC is used as a reference to estimate the equivalent circuit model parameters ${ϵ}_1$, ${ϵ}_2$, ${ϵ}_3$, ${ϵ}_4$, $\beta$, $\lambda$, $R_c$. The SHO algorithm achieved a mean absolute error (MAE) of 0.001079 and a coefficient of determination (R²) of 0.999791, with a model-to-experiment fit ratio of 99.92%. Compared to similar studies reported in the literature, the results indicate that the SHO algorithm offers competitive performance. Moreover, the average convergence time is recorded as 1.74 s for 5000 iteration, highlighting the algorithm’s rapid convergence and low computational cost. Overall, the SHO algorithm is demonstrated to be an efficient, robust, and promising alternative to conventional methods for parameter identification in PEMFC modeling. Full article

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing lung disease characterized by chronic inflammation, vascular remodeling, and immune dysregulation. COVID-19, caused by SARS-CoV-2, shares several systemic immunohematologic disturbances with IPF, including cytokine storms, endothelial injury, and prothrombotic states. Unlike general comparisons of viral infections and chronic lung disease, this review offers a focused analysis of the shared hematologic and immunologic mechanisms between COVID-19 and IPF. Our aim is to better understand how SARS-CoV-2 infection may worsen disease progression in IPF and identify converging pathophysiological pathways that may inform clinical management. We conducted a narrative synthesis of the peer-reviewed literature from PubMed, Scopus, and Web of Science, focusing on clinical, experimental, and pathological studies addressing immune and coagulation abnormalities in both COVID-19 and IPF. Both diseases exhibit significant overlap in inflammatory and fibrotic signaling, particularly via the TGF-β, IL-6, and TNF-α pathways. COVID-19 amplifies coagulation disturbances and endothelial dysfunction already present in IPF, promoting microvascular thrombosis and acute exacerbations. Myeloid cell overactivation, impaired lymphocyte responses, and fibroblast proliferation are central to this shared pathophysiology. These synergistic mechanisms may accelerate fibrosis and increase mortality risk in IPF patients infected with SARS-CoV-2. This review proposes an integrative framework for understanding the hematologic and immunologic convergence of COVID-19 and IPF. Such insights are essential for refining therapeutic targets, improving prognostic stratification, and guiding early interventions in this high-risk population. Full article

(1) Background: The integration of digital technologies such as electronic health records (EHRs), telepsychiatry, and communication platforms has transformed the mental health sector a lot compared to in previous years. While these tools enhance service delivery, they also introduce unique stressors. Despite growing concerns, there is no validated instrument specifically designed to measure the digital stress experienced by mental health professionals. (2) Methods: This study involved the development and psychometric validation of the Digital Stress Scale (DSC). The process included item generation through a literature review and qualitative interviews, expert panel validation, and a two-phase statistical evaluation. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted on responses from 423 licensed mental health professionals using EHRs and digital communication tools. The scale’s reliability and convergent validity were assessed via internal consistency and correlations with established mental health measures. (3) Results: The final DSC included four subscales: digital fatigue, technostress, digital disengagement, and work–life digital boundaries. CFA supported the factor structure (CFI = 0.965, RMSEA = 0.038), and the overall reliability was acceptable (Cronbach’s Alpha = 0.87). Descriptive analysis showed moderate-to-high levels of digital stress (M = 11.94, SD = 2.72). Digital fatigue was the strongest predictor of total stress (β = 1.00, p < 0.001), followed by technostress and work–life boundary violations. All subscales were significantly correlated with burnout (r = 0.72), job dissatisfaction (r = −0.61), and perceived stress (r = 0.68), all with a p < 0.001. (4) Conclusions: The DSC is a valid and reliable tool for assessing digital stress among mental health professionals. Findings point out the urgent need for policy-level interventions to mitigate digital overload, promote healthy work–life boundaries, and enhance digital competency in mental health settings. Full article

The European Union (EU)’s climate policy and energy strategy objectives focus on increasing the share of renewable energy sources to reduce greenhouse gas emissions, strengthen energy independence, and achieve sustainable economic transformation. This study empirically examines to what extent and in what direction the GDP per capita, investment rate, and energy intensity influenced the development of the share of renewable energy sources in the 27 Member States of the European Union from 2015 to 2023. This research used multiple linear regression, β-convergence analysis, and a fixed-effects panel model to process panel data from official Eurostat databases. The results show that the effect of GDP per capita is structurally positive but not significant in terms of change within a particular country over time. In contrast, the investment rate is positively and significantly related to the share of renewable energy in all models. The results of the fixed-effects model highlight that in years when the investment rate in a given Member State increased, the share of renewable energy sources in gross final energy consumption also typically increased. In the case of energy intensity, no significant relationship was found. However, the literature suggests that improving energy efficiency continues to play a key role in achieving the EU’s sustainability goals. This study concludes that stimulating investment activity and developing country-specific energy strategies in the EU Member States are essential to accelerating the energy transition. Full article

Evolution equations with fractional-time derivatives and singular memory kernels are used for modeling phenomena exhibiting hereditary properties, as they effectively incorporate memory effects into their formulation. Time-fractional partial integro-differential equations (FPIDEs) represent a significant class of such evolution equations and are widely used in diverse scientific and engineering fields. In this study, we use the $\mathrm{sinc}$-collocation and iterative Laplace transform methods to solve a specific FPIDE with a weakly singular kernel. Specifically, the $\mathrm{sinc}$-collocation method is applied to discretize the spatial domain, while a combination of numerical techniques is utilized for temporal discretization. Then, we prove the convergence analytically. To compare the two methods, we provide two examples. We notice that both the $\mathrm{sinc}$-collocation and iterative Laplace transform methods provide good approximations. Moreover, we find that the accuracy of the methods is influenced by fractional order $\alpha \in (0,1)$ and the memory-kernel parameter $\beta \in (0,1)$. We observe that the error decreases as $\beta$ increases, where the kernel becomes milder, which extends the single-value study of $\beta =1/2$ in the literature. Full article

Emerging evidence highlights the profound and lasting impact of severe illnesses such as COVID-19, particularly among individuals with underlying comorbidities. Patients with pre-existing conditions like diabetes mellitus (DM) are disproportionately affected, facing heightened risks of both disease exacerbation and the onset of new complications. Notably, the convergence of advanced age and DM has been consistently associated with poor COVID-19 outcomes. However, the long-term metabolic consequences of SARS-CoV-2 infection, especially its role in disrupting glucose homeostasis and potentially triggering or worsening DM, remain incompletely understood. This review synthesizes current clinical and experimental findings to clarify the bidirectional relationship between COVID-19 and diabetes. We critically examine literature reporting deterioration of glycemic control, onset of hyperglycemia in previously non-diabetic individuals, and worsening of metabolic parameters in diabetic patients after infection. Furthermore, we explore proposed mechanistic pathways, including pancreatic β-cell dysfunction, systemic inflammation, and immune-mediated damage, that may underpin the development or progression of DM in the post-COVID setting. Collectively, this work underscores the urgent need for continued research and clinical vigilance in managing metabolic health in COVID-19 survivors. Full article

Achieving high-order accuracy in finite difference/spectral methods for space-time fractional differential equations often relies on very restrictive and usually unrealistic smoothness assumptions in the spatial and/or temporal domains. For spatial discretization, spectral methods using smooth basis functions are commonly employed. However, spatial–fractional derivatives pose challenges, as they often lack guaranteed spatial smoothness, requiring non-smooth basis functions. In the temporal domain, finite difference schemes on uniformly graded meshes are commonly employed; however, achieving accuracy remains challenging for non-smooth solutions. In this paper, an efficient algorithm is adopted to improve the accuracy of finite difference/Pertrov–Galerkin spectral schemes for a time-space fractional reaction–diffusion equation, with a hyper-singular integral fractional Laplacian and non-smooth solutions in both time and space domains. The Pertrov–Galerkin spectral method is adapted using non-smooth generalized basis functions to discretize the spatial variable, and the L1 scheme on a non-uniform graded mesh is used to approximate the Caputo fractional derivative. The unconditional stability and convergence are established. The rate of convergence is $O\left(N^{\mu -\gamma }+K^{-min\{\rho \beta ,2-\beta \}}\right),$ achieved without requiring additional regularity assumptions on the solution. Finally, numerical results are provided to validate our theoretical findings. Full article

Irrigation water can serve as a reservoir and transmission route for pathogenic Escherichia coli, posing a threat to food safety and public health. This study builds upon a previous survey conducted in Hermosillo, Sonora (Mexico), where 445 samples were collected from a local Honeydew melon farm and associated packing facilities. Among the 32 E. coli strains recovered, two strains, A34 and A51, were isolated from irrigation water and selected for further molecular characterization by PCR, due to their high pathogenic potential. Both strains were identified as hybrid aEPEC/STEC pathotypes carrying bfpA and stx1 virulence genes. Adhesion assays in HeLa cells revealed aggregative and diffuse patterns, suggesting enhanced colonization capacity. Phylogenetic analysis classified A34 within group B2 as associated with extraintestinal pathogenicity and antimicrobial resistance, while A51 was unassigned to any known phylogroup. Serotyping revealed somatic antigens shared with Shigella boydii 16, suggesting possible horizontal gene transfer or antigenic convergence. Antibiotic susceptibility testing showed resistance to multiple β-lactam antibiotics, including cephalosporins, linked to the presence of bla_CTX-M-151 and bla_CTX-M-9. Although no plasmid-mediated quinolone resistance genes were detected, resistance may involve efflux pumps or mutations in gyrA and parC. These findings are consistent with previous reports of E. coli adaptability in agricultural environments, suggesting potential genetic adaptability. While our data support the presence of virulence and resistance markers, further studies would be required to demonstrate mechanisms such as horizontal gene transfer or adaptive evolution. Full article

The scientific evaluation of ecological total factor productivity of cultivated land use (ETFPCLU) is fundamental for advancing sustainable utilization of cultivated land resources and safeguarding national food security and ecological stability. Using the epsilon-based measure and the global Malmquist–Luenberger (EBM–GML) index, this study quantifies and decomposes ETFPCLU across China. Spatial–temporal variations and convergence patterns are systematically investigated via an analytical toolkit comprising the spatial mismatch index, Dagum’s Gini coefficient decomposition, and convergence models. The results indicate that Chinese ETFPCLU increased by an average of 2.1% per year from 2001 to 2022, primarily attributed to technical change (TC), with limited contributions from efficiency change (EC). The spatial mismatch between ETFPCLU and TC, as well as EC, is predominantly characterized by low to medium mismatch types, exhibiting a high degree of spatial distribution similarity; inter-regional differences are the main contributors to regional disparities. Furthermore, except for the central region, significant σ-convergence exists in ETFPCLU across the country and in other regions, alongside absolute β-convergence and conditional β-convergence in the four major regions. The analysis concludes that to enhance ETFPCLU, it is essential to strengthen technological innovation, synergistically improve technological efficiency, formulate ecological protection policies tailored to local conditions, and foster collaboration among regions for cultivated land protection. Full article

Geometric image transformations are fundamental to image processing, computer vision and graphics, with critical applications to pattern recognition and facial identification. The splitting-integrating method (SIM) is well suited to the inverse transformation $T^{-1}$ of digital images and patterns, but it encounters difficulties in nonlinear solutions for the forward transformation T. We propose improved techniques that entirely bypass nonlinear solutions for T, simplify numerical algorithms and reduce computational costs. Another significant advantage is the greater flexibility for general and complicated transformations T. In this paper, we apply the improved techniques to the harmonic, Poisson and blending models, which transform the original shapes of images and patterns into arbitrary target shapes. These models are, essentially, the Dirichlet boundary value problems of elliptic equations. In this paper, we choose the simple finite difference method (FDM) to seek their approximate transformations. We focus significantly on analyzing errors of image greyness. Under the improved techniques, we derive the greyness errors of images under T. We obtain the optimal convergence rates $O\left(H^2\right)+O(H/N^2)$ for the piecewise bilinear interpolations ($\mu =1$) and smooth images, where $H(\ll 1)$ denotes the mesh resolution of an optical scanner, and N is the division number of a pixel split into $N^2$ sub-pixels. Beyond smooth images, we address practical challenges posed by discontinuous images. We also derive the error bounds $O\left(H^{\beta }\right)+O(H^{\beta }/N^2)$, $\beta \in (0,1)$ as $\mu =1$. For piecewise continuous images with interior and exterior greyness jumps, we have $O\left(\sqrt{H}\right)+O(\sqrt{H}/N^2)$. Compared with the error analysis in our previous study, where the image greyness is often assumed to be smooth enough, this error analysis is significant for geometric image transformations. Hence, the improved algorithms supported by rigorous error analysis of image greyness may enhance their wide applications in pattern recognition, facial identification and artificial intelligence (AI). Full article

This study examines the relationship between technological innovation and economic development in the Yangtze River economic belt context. Specifically, the study employs the SBM-GML model to assess the efficiency of industrial green technology innovation across 110 prefecture-level cities between 2006 and 2022. The study also employs exploratory spatial data analysis (ESDA) and the Spatio-temporal transition method to analyze the spatial evolution pattern of the GML index of industrial green technology innovation. In addition, the study investigates the convergence mechanism using absolute and conditional β convergence models. The findings reveal that the GML index of industrial green technology innovation in the Yangtze River Economic Belt exhibits an upward trend, and technological progress is a key driver. Moreover, the spatial and temporal transition of the GML index of industrial green technology innovation shows substantial spatial dependence and solid spatial stability. The study also finds regional heterogeneity in the absolute and conditional β convergence characteristics and their influencing factors. Considering regional differences, the results suggest differentiated policy recommendations to promote the coordinated development of industrial green technological innovation efficiency in the Yangtze River Economic Belt. The study contributes to the literature on the relationship between technological innovation and economic development, highlighting the importance of spatial considerations and regional heterogeneity in promoting sustainable economic growth. Full article

This study establishes a novel mathematical framework for stochastic maintenance optimization in production systems by integrating Markov decision processes (MDPs) with convex programming theory. We develop a Z-transformation-based dual-space decomposition method to reconstruct MDPs into a solvable linear programming form, resolving the inherent instability of traditional models caused by uncertain initial conditions and non-stationary state transitions. The proposed approach introduces three mathematical innovations: (i) a spectral clustering mechanism that reduces state-space dimensionality while preserving Markovian properties, (ii) a Lagrangian dual formulation with adaptive penalty functions to handle operational constraints, and (iii) a warm start algorithm accelerating convergence in high-dimensional convex optimization. Theoretical analysis proves that the derived policy achieves stability in probabilistic transitions through martingale convergence arguments, demonstrating structural invariance to initial distributions. Experimental validations on production processes reveal that our model reduces long-term maintenance costs by 36.17% compared to Monte Carlo simulations (1500 vs. 2350 average cost) and improves computational efficiency by 14.29% over Q-learning methods. Sensitivity analyses confirm robustness across Weibull-distributed failure regimes (shape parameter $\beta \in$ [1.2, 4.8]) and varying resource constraints. Full article

This work presents the performance projection of a metal-insulator-graphene diode as the building block of a radiofrequency power detector, highlighting its rectifying figures of merit. The analysis was performed by means of a computer-aided design tool validated with experimental measurements of fabricated devices. Transient simulations were used to accurately determine the detector output voltage, while particular consideration was given to suitable convergence of the non-linear circuit response. The diode was analyzed in both ideal and non-ideal cases, with the latter accounting for its parasitic effects. In the non-ideal case, the diode exhibited a tangential responsivity of 26.9 V/W at 2.45 GHz and 31.9 V/W at 1.225 GHz. However, when parasitic elements were neglected in the ideal case, the responsivity significantly increased to 47.3 V/W at 2.45 GHz and 38.7 V/W at 1.225 GHz. Additionally, the diode demonstrated a non-linearity of 6.64 at 0.7 V and an asymmetry of 806.6 in a bias window of ±1 V, which resulted in a competitive value compared to other state-of-the-art rectifying technologies. Tangential responsivities (${\beta }_{\mathrm{v}}$) of graphene diodes at less-studied frequencies in the gigahertz band are presented, showing a high ${\beta }_{\mathrm{v}}$ value of 63.7 V/W at 1 GHz. Full article