Search Results (59)

This study examines the asymmetric responses of stock market indices in Türkiye and Kazakhstan to oil price shocks during the 2010–2025 period. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model, the study decomposes the nonlinear effects of oil price fluctuations on financial markets. Empirical findings reveal that in Türkiye, a net oil importer, the stock market exhibits a dual-sensitivity: while exchange rate dynamics (2.34) remain the dominant driver, oil price increases (−0.12) exert a direct and statistically significant negative pressure. In contrast, Kazakhstan, a net oil exporter, shows a high vulnerability to oil price decreases (−1.05) at the 1% significance level, confirming a strong asymmetric structure (p = 0.0122). Furthermore, the error correction speed is significantly higher in Türkiye (28%) than in Kazakhstan (4%), indicating divergent market efficiency and recovery mechanisms. These results demonstrate that financial market reactions to external shocks differ fundamentally based on energy trade structures. The findings suggest that oil-importing countries must prioritize exchange rate stability, while oil-exporting nations must develop specific policy buffers against the persistent downside risks of global energy cycles. Full article

Standard Retinex-based algorithms typically rely on gradient constraints to decompose an image, assuming that illumination is spatially smooth while reflectance contains sharp details. However, strictly gradient-based priors frequently produce halo artifacts or over-smoothing because they are unable to differentiate between intrinsic structural edges and high-frequency noise. In this paper, we propose a novel Similarity Image-Guided Retinex (SIMRET) model that fundamentally diverges from traditional derivative-based regularization. We present a color-based pixel-level similarity analysis to build a global guidance matrix rather than merely depending on local gradients. This Similarity Image functions as a reliable weight map during the decomposition process by mathematically encoding the chromatic relationships and spatial coherence between pixels. The model strictly maintains consistency across structural boundaries to avoid halo effects while adaptively enforcing smoothness in homogeneous regions to suppress noise by incorporating this similarity guidance into the optimization objective. We solve the proposed SIMRET model using an alternating optimization framework, where the similarity constraints effectively regularize the ill-posed decomposition problem. Extensive tests on various low-light datasets show that the suggested model successfully overcomes the trade-off between noise reduction and detail preservation, achieving better visual naturalness and signal fidelity than state-of-the-art techniques. Full article

Understanding why cities diverge in motor vehicle ownership trajectories is critical for designing differentiated and sustainable transport policies. This study develops an integrated national–city analytical framework to examine heterogeneous urban motorization processes in China. A national Gompertz curve is first estimated to represent the benchmark income–ownership relationship. City-specific deviations are then decomposed into two interpretable dimensions: a horizontal stage parameter (h), capturing relative advancement or delay in motorization timing, and a vertical scaling parameter (s), reflecting persistent ownership intensity differences conditional on income. Results show substantial and multi-dimensional heterogeneity across cities. Stage timing (h) and ownership intensity (s) are only weakly correlated, indicating that earlier transition into higher motorization stages does not necessarily imply above-benchmark ownership intensity. Random forest models with time-forward validation demonstrate strong explanatory power (R² ≈ 0.88 for h and 0.80 for s). SHAP-based interpretation reveals that stage deviation is primarily associated with transport supply and urban structural characteristics, whereas ownership intensity deviation is more strongly linked to urban spatial scale and economic structure. Regulatory measures, including purchase and driving restrictions, exhibit comparatively smaller and heterogeneous effects. By disentangling timing and intensity dimensions of urban motorization, this study refines conventional income-based diffusion models and provides quantitative evidence that structural urban characteristics play a more fundamental role than regulatory interventions in shaping inter-city motorization differences. Full article

Microbial components are part of the composition of all waste, including lignocellulosic biomass (e.g., agricultural, domestic, industrial, and municipal wastes) generated via human activities. If little attention is given to these wastes or if they are not adequately managed, they tend to end up in the environment (soil, water, and farmland), decomposing naturally through microbial activities, producing greenhouse gases, causing eutrophication, preventing sunlight penetration, and depleting oxygen in the water. Several treatment methods are applicable to these wastes. However, anaerobic digestion is presented as the best option to properly treat the waste. It is regarded as the best technique to achieve sustainable energy development in both developing and developed countries. During anaerobic digestion, the organic matter in the waste is converted via the concerted activities of microbes belonging to different trophic levels, in the absence of oxygen, to yield biogas (renewable energy), bio-fertiliser, and sanitisation of the waste, rendering it better and safer for human handling. Varying levels of loss of bacterial viability and their antibiotic-resistance genes are observed with this process, as bacteria differ in susceptibility to temperature, pH, nutrient scarcity, and the presence of antimicrobials. Anaerobic digestion of agricultural residues and the immediate processing (post-treatment) of the digestate help to stabilise the digestate, making it safe for land applications, tackling waste management, and protecting food chains from contamination, in addition to the environment. This review focuses on the anaerobic digestion of lignocellulosic biomass, yielding biogas as energy, alongside sanitising the wastes by inactivating microbial components found therein, therefore reducing the contamination potential of the effluent or digestate discharged from the biodigester following the process. Several findings registered by different researchers through different studies performed in different countries under different scenarios while employing varying methods have been assembled in a chronological fashion to emphasise similarities and divergences or variations that deepen knowledge pertaining to the significance of the anaerobic digestion process in terms of the microbial interactions responsible for producing energy, addressing sanitisation and hygiene crisis, and the post-treatment of the digestate to ensure its use as biofertiliser. In other words, it is a comprehensive review that synthesises knowledge from multiple fields covering comparative aspects of anaerobic digestion in terms of sanitation, hygiene, and energy production and consolidates it in a single document to present and address the problem of waste management through anaerobic digestion technology. Full article

Plastic mulch is widely used in organic strawberry production but raises sustainability concerns due to its persistence, disposal challenges, and contribution to microplastic pollution. This study evaluated the potential of high-residue cover crops and living mulches as alternatives to plastic mulch in coastal California. Over two seasons (2022–2024), we compared five mulching treatments: black polyethylene mulch (Plastic); a white clover (Trifolium repens) living mulch (Clover); two roller-crimped sorghum–sudangrass and field pea mixtures (Sorghum 1, Sorghum 2); and a roller-crimped buckwheat–pea mixture (Buckwheat). The objectives were to evaluate the effectiveness of these treatments on (i) soil properties and biological indicators, (ii) weed suppression, and (iii) strawberry yield in organic systems. A schematic timeline was developed to depict cover-crop growth, termination, and strawberry production across both years. Compost (10 t·ha⁻¹) and fish emulsion (5–1–1 NPK, 4 L·ha⁻¹ biweekly) were applied to all treatments during fruiting. Sorghum residues produced the highest biomass (up to 23 t·ha⁻¹) and supported yields comparable to plastic mulch in 2023. Under lower-yield conditions in 2024, sorghum-based treatments outperformed plastic. Soil responses were modest and time-point specific: Sorghum 1 showed higher organic C and organic N pre-harvest in 2023, and both sorghum treatments increased soil organic matter pre-harvest in 2024. Biological indicators such as CO₂–C and microbially active carbon declined seasonally across all treatments, indicating strong temporal control. Weed outcomes diverged by system—Clover suppressed weeds effectively but reduced yield by >50% due to competition, while Buckwheat decomposed rapidly and provided limited late-season suppression. These results demonstrate that rolled high-residue cover crops, particularly sorghum-based systems, can reduce dependence on plastic mulch while maintaining yields and enhancing soil cover. Living mulches and short-lived covers may complement residue systems when managed to minimize competition and extend ground cover. Full article

Orbital angular momentum (OAM) beams exhibit divergence during transmission, which constrains the capacity of communication system channels. To address these challenges, intelligent reflecting surfaces (IRSs), which can independently manipulate incident electromagnetic waves by adjustment of their amplitude and phase, are employed to construct IRS-assisted OAM communication systems. By introducing additional information pathways, IRSs enhance diversity gain. We studied the simulations of two placement methods for an IRS: arbitrary placement and standard placement. In the case of arbitrary placement, the beam reflected by the IRS can be decomposed into different OAM modes, producing various reception powers corresponding to each OAM mode component. This improves the signal-to-noise ratio (SNR) at the receiver, thereby enhancing channel capacity. In particular, when the IRS is symmetrically and uniformly positioned at the center of the main transmission axis, its elements can be approximated as a uniform circular array (UCA). This configuration not only achieves optimal reception along the direction of the maximum gain of the orbital angular momentum beam but also reduces the antenna radius required at the receiver to half or even less. Full article

This paper proposes a novel transport quasi-Monte Carlo framework that combines randomized quasi-Monte Carlo sampling with a neural autoregressive flow architecture for efficient sampling and integration over complex, high-dimensional distributions. The method constructs a sequence of invertible transport maps to approximate the target density by decomposing it into a series of lower-dimensional marginals. Each sub-model leverages normalizing flows parameterized via monotonic beta-averaging transformations and is optimized using forward Kullback–Leibler (KL) divergence. To enhance computational efficiency, a hidden-variable mechanism that transfers optimized parameters between sub-models is adopted. Numerical experiments on a banana-shaped distribution demonstrate that this new approach outperforms standard Monte Carlo-based normalizing flows in both sampling accuracy and integral estimation. Further, the model is applied to A-share stock return data and shows reliable predictive performance in semiannual return forecasts, while accurately capturing covariance structures across assets. The results highlight the potential of transport quasi-Monte Carlo (TQMC) in financial modeling and other high-dimensional inference tasks. Full article

Under the “double security” goal of achieving both food security and ecological protection, this study explores the green and low-carbon utilization efficiency of cultivated land (GLCUECL) in the Huaihe River Ecological Economic Belt (HREEB). This study identifies the spatiotemporal evolution characteristics and trends, promoting the green, low-carbon, and sustainable utilization of arable land resources in the HREEB, thus contributing to regional and national food and ecological security. Using a global super-efficiency EBM framework that accounts for undesirable outputs, as well as the GML index, the researchers measured and decomposed the GLCUECL in 25 prefecture-level cities of the HREEB from 2005 to 2021. The Theil index and kernel density estimation were applied to analyze regional disparities and changing developmental traits. Spatial convergence and divergence were assessed using the coefficient of variation and spatial convergence models. Key findings include the following: (1) Over time, the GLCUECL in the HREEB exhibited an overall upward trend and a non-equilibrium characteristic, namely the “East Sea-river-lake Linkage Area (ESLA) > Midwest Inland Rising Area (MIRA) > Huaihe River Ecological Economic Belt (HREEB) > North Huaihai Economic Zone (NHEZ)”. The increase in the GML index of the GLCUECL is mainly attributable to a technical progress change. (2) The overall difference in the GLCUECL tends to decline, which is mainly attributable to the intra-regional differences. (3) The overall kernel density curves for the HREEB and its three sub-regions exhibited a “rightward shift” trend. Except for the expansion and polarization of the absolute difference in the GLCUECL in the NHEZ, the absolute difference in GLCUECL in other regions, such as the HREEB, ESLA, and MIRA, exhibited a decreasing trend. (4) Spatial convergence analysis revealed that only the NHEZ lacks σ-convergence, whereas all regions exhibited β-convergence. Moreover, factors such as rural economic development level, cultivated land resource endowment, agricultural subsidy policy, crop planting structure, and technological input exerted a heterogeneous effect on the change in the GLCUECL. Based on these findings, this study offers recommendations for improving GLCUECL in the HREEB. Our recommendations include the implementation of the concept of green new development, optimization of the institution supply, establishing a regional cooperation mechanism for green and low-carbon utilization of cultivated land, and formulation of differentiated paths for improving the green and low-carbon utilization efficiency of cultivated land according to local conditions. Full article

The COVID-19 pandemic triggered unprecedented economic disruptions, raising critical questions about the resilience and adaptability of macroeconomic productivity across countries. This study examines the impact of COVID-19 on macroeconomic efficiency and productivity dynamics in 37 OECD countries using quarterly data from 2018Q1 to 2024Q4. By employing a Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist Productivity Index (MPI), we decompose total factor productivity (TFP) into efficiency change (EC) and technological change (TC) across three periods: pre-pandemic, during-pandemic, and post-pandemic. Our framework incorporates both desirable (GDP) and undesirable outputs (inflation, unemployment, housing price inflation, and interest rate distortions), offering a multidimensional view of macroeconomic efficiency. Results show broad but uneven productivity gains, with technological progress proving more resilient than efficiency during the pandemic. Post-COVID recovery trajectories diverged, reflecting differences in structural adaptability and innovation capacity. Regression analysis reveals that stringent lockdowns in 2020 were associated with lower productivity in 2023–2024, while more adaptive policies in 2021 supported long-term technological gains. These findings highlight the importance of aligning crisis response with forward-looking economic strategies and demonstrate the value of DEA-based methods for evaluating macroeconomic performance beyond GDP. 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

In modern industrial environments, quickly and accurately identifying faults is crucial for ensuring the smooth operation of production processes. Non-negative Matrix Factorization (NMF)-based fault detection technology has garnered attention due to its wide application in industrial process monitoring and machinery fault diagnosis. As an effective dimensionality reduction tool, NMF can decompose complex datasets into non-negative matrices with practical and physical significance, thereby extracting key features of the process. This paper presents a novel approach to fault detection in industrial processes, called Graph-Regularized Orthogonal Non-negative Matrix Factorization with Itakura–Saito Divergence (GONMF-IS). The proposed method addresses the challenges of fault detection in complex, non-Gaussian industrial environments. By using Itakura–Saito divergence, GONMF-IS effectively handles data with probabilistic distribution characteristics, improving the model’s ability to process non-Gaussian data. Additionally, graph regularization leverages the structural relationships among data points to refine the matrix factorization process, enhancing the robustness and adaptability of the algorithm. The incorporation of orthogonality constraints further enhances the independence and interpretability of the resulting factors. Through extensive experiments, the GONMF-IS method demonstrates superior performance in fault detection tasks, providing an effective and reliable tool for industrial applications. The results suggest that GONMF-IS offers significant improvements over traditional methods, offering a more robust and accurate solution for fault diagnosis in complex industrial settings. Full article

Background: Rheumatic heart disease (RHD) remains a major contributor to childhood cardiovascular morbidity and mortality globally, particularly in low-resource settings. This study offers a thorough evaluation of the global, regional, and national burden of RHD among children aged 0–14 years, from 1990 to 2021, utilizing data from the 2021 Global Burden of Disease (GBD) study. Methods: We analyzed age-standardized incidence, prevalence, mortality, and disability-adjusted life years (DALYs) for RHD in 204 countries and territories. Novel methodological approaches included APC analysis to decompose temporal trends into age, period, and cohort effects, and inequality analysis to assess socioeconomic disparities. We calculated age-standardized rates and average annual percentage changes (AAPC) by sex, region, and socio-demographic index (SDI) level. Results: From 1990 to 2021, the global age-standardized death rate due to RHD in children declined by approximately 74%, from 1.24 to 0.32 per 100,000 (AAPC: −4.27%). Similarly, DALY rates dropped from 117.22 to 41.56 per 100,000 (AAPC: −3.30%). Despite this progress, the global age-standardized incidence rate increased modestly from 55.84 to 66.76 per 100,000 (AAPC: 0.58%), and prevalence rates also rose (AAPC: 0.53%). Females consistently experienced higher burden across all metrics. Inequality analysis demonstrated a concerning divergence: while mortality and DALY inequalities narrowed substantially (mortality slope index of inequality (SII) improved from −1.35 to −0.31), incidence and prevalence inequalities widened (incidence SII worsened from −112.60 to −131.90), indicating growing disparities in disease occurrence despite improved survival. Conclusions: While global mortality and DALYs from childhood rheumatic heart disease have declined substantially over the past three decades, a troubling paradox has emerged: rising incidence rates alongside widening socioeconomic inequalities in disease occurrence. This represents a critical public health challenge demanding targeted intervention strategies. The divergent trends in health outcomes, namely, improved survival rates but increased disease burden, reveal that while access to treatment has advanced, upstream prevention efforts remain critically inadequate among socioeconomically disadvantaged populations. Full article

Multi-Hop Knowledge Graph Question Answering (multi-hop KGQA) aims to obtain answers by analyzing the semantics of natural language questions and performing multi-step reasoning across multiple entities and relations in knowledge graphs. Traditional embedding-based methods map natural language questions and knowledge graphs into vector spaces for answer matching through vector operations. While these approaches have improved model performance, they face two critical challenges: the lack of clear interpretability caused by implicit reasoning mechanisms, and the semantic gap between natural language queries and structured knowledge representations. This study proposes the DRKG (Decomposed Reasoning over Knowledge Graph), a constrained multi-hop reasoning framework based on large language models (LLMs) that introduces explicit reasoning plans as logical boundary controllers. The innovation of the DRKG lies in two key aspects: First, the DRKG generates hop-constrained reasoning plans through semantic parsing based on LLMs, explicitly defining the traversal path length and entity-retrieval logic in knowledge graphs. Second, the DRKG conducts selective retrieval during knowledge graph traversal based on these reasoning plans, ensuring faithfulness to structured knowledge. We evaluate the DRKG on four datasets, and the experimental results demonstrate that the DRKG achieves 1%–5% accuracy improvements over the best baseline models. Additional ablation studies verify the effectiveness of explicit reasoning plans in enhancing interpretability while constraining path divergence. A reliability analysis further examines the impact of different parameters combinations on the DRKG’s performance. Full article

This numerical study addressed the multi-objective optimization of a rocket-type Pulse Detonation Engine nozzle. The Pulse Detonation Engine consisted of a constant length, constant diameter cylindrical section plus a nozzle that could be either convergent, divergent, or convergent–divergent. The space of five design variables contained: equivalence ratio of the H2-Air mixture, convergent contraction ratio, divergent expansion ratio, dimensionless nozzle length, and convergent to divergent length ratio. The unsteady Euler-type numerical solver was quasi-one-dimensional with variable cross-sectional area. Chemistry was simulated by means of a one-step global reaction. The solver was used to generate three coarse five-dimensional data tensors that contained: specific impulse based on fuel, total impulse, and nozzle surface area, for each configuration. The tensors were decomposed using the High Order singular Value Decomposition technique. The eigenvectors of the decompositions were used to generate continuous descriptions of the data tensors. A genetic algorithm plus a Gradient Method optimization algorithm acted on the densified data tensors. Five different objective functions were considered that involved specific impulse based on fuel, total impulse, and nozzle surface area either separately or in doublets/triplets. The results obtained were discussed, both qualitatively and quantitatively, in terms of the different objective functions. Design guidelines were provided that could be of interest in the growing area of Pulse Detonation Engine engineering applications. Full article

The simulation of the transport properties of confined, low-dimensional fluids can be performed efficiently by means of multi-particle collision (MPC) dynamics with suitable thermal-wall boundary conditions. We illustrate the effectiveness of the method by studying the dimensionality effects and size-dependence of thermal conduction, since these properties are of crucial importance for understanding heat transfer at the micro–nanoscale. We provide a sound numerical evidence that the simple MPC fluid displays the features previously predicted from hydrodynamics of lattice systems: (1) in 1D, the thermal conductivity $\kappa$ diverges with the system size L as $\kappa \sim L^{1/3}$ and its total heat current autocorrelation function $C\left(t\right)$ decays with the time t as $C\left(t\right)\sim t^{-2/3}$; (2) in 2D, $\kappa$ diverges with L as $\kappa \sim \ln \left(L\right)$ and its $C\left(t\right)$ decays with t as $C\left(t\right)\sim t^{-1}$; (3) in 3D, its $\kappa$ is independent with L and its $C\left(t\right)$ decays with t as $C\left(t\right)\sim t^{-3/2}$. For weak interaction (the nearly integrable case) in 1D and 2D, there exists an intermediate regime of sizes where kinetic effects dominate and transport is diffusive before crossing over to the expected anomalous regime. The crossover can be studied by decomposing the heat current in two contributions, which allows for a very accurate test of the predictions. In addition, we also show that, upon increasing the aspect ratio of the system, there exists a dimensional crossover from 2D or 3D dimensional behavior to the 1D one. Finally, we show that an applied magnetic field renders the transport normal, indicating that pseudomomentum conservation is not sufficient for the anomalous heat conduction behavior to occur. Full article