Search Results (9,327)

Climate transition risks are increasingly relevant to financial development, particularly in resource-dependent economies. However, the existing literature has largely examined how financial development affects environmental outcomes, while the reverse relationship remains comparatively understudied. This study investigates whether carbon emissions and renewable energy consumption influence financial development in Kazakhstan over 1996–2024, controlling for economic growth, inflation, and foreign direct investment. Financial development is measured by domestic credit to the private sector provided by banks (% of GDP). Using the Autoregressive Distributed Lag (ARDL) bounds testing approach and accounting for structural breaks, the study examines short-run and long-run dynamics. The results confirm a long-run equilibrium relationship among the variables, but none of the individual long-run coefficients is statistically significant. This finding does not imply that climate-transition factors are economically irrelevant; rather, it suggests that their effects have not yet translated into persistent, statistically identifiable changes in aggregate bank-based financial development. In the short run, economic growth has a positive effect, while inflation has a negative effect, with renewable energy consumption and foreign direct investment showing lagged effects. The study contributes new evidence on the climate–finance nexus from a resource-dependent transition economy and highlights the evolving, but still limited, transmission of climate-transition dynamics through Kazakhstan’s banking system. Full article

Research management and administration usually emerge through gradual differentiation within university administrative work. Japan offers a contrasting case: Since 2011, the national government has promoted University Research Administrators (URAs) through personnel subsidies, skill standards, and certification. This article examines how rapid state-led occupational institutionalization interacted with universities’ existing personnel systems. We conduct qualitative document analysis of Japanese policy documents, program guidelines, commissioned reports, and national statistics for 2009–2026, supplemented by stylized reference trajectories from the United States and the United Kingdom. The evidence shows a temporally compressed sequence in which occupational definition and standardization preceded nationwide practitioner organization. Yet national institutionalization was not matched by organizational career embedding. In FY2024, 78% of URAs remained fixed-term although 69.6% were funded from universities’ own budgets; only 28% of URA-hosting institutions reported an established career path. We conceptualize this mismatch as state-led compressed professionalization: the rapid construction of an occupational apparatus alongside lagging organizational career structures. The Japanese case suggests that program-based state intervention can establish posts and credentials, but durable professionalization also depends on integration into employers’ human-resource systems. Full article

The paper examines the volatility dynamics of Bitcoin, Ether, XRP, and Solana as an indicator of market risk. Cryptocurrency price series are variable and non-stationary, which limits the accuracy of risk forecasting. The methodology combines long-memory estimation, multifractal analysis, and neural network modelling. Using daily data, returns, absolute returns, and 30-day rolling volatility were calculated. Bitcoin showed the lowest average volatility, 0.0326, whereas Solana showed the highest, 0.0554; Ether and XRP reached 0.0428 and 0.0458, respectively. The long-memory parameter of price series ranges from 0.4788 to 0.5420, while volatility series exhibit moderate long memory of 0.2326–0.2964 and confirmed stationarity. XRP showed the widest multifractal volatility spectrum, 1.2651. A distinctive feature of the proposed approach is the use of LSTM models with market, lagged, and dynamic fractal features constructed within a rolling window. Forecasting was performed for 1-, 3-, 7-, and 14-day horizons, and the results were aggregated over 12 repeated training runs. Comparison of LSTM models with and without fractal features showed that their forecasting contribution depends on the feature construction method, while volatility-based features were generally more informative. Comparison with GARCH, ARFIMA, and HAR showed that performance depends on the asset, horizon, and evaluation metric. Full article

Previous studies have yielded inconsistent conclusions regarding the relationship between challenge stress and work creativity, suggesting that the effects may depend on contextual and individual conditions. Drawing on the strategy–situation fit hypothesis, this study examines the joint effects of challenge stress and problem-solving pondering on work creativity and further investigates the mediating role of cognitive flexibility. Using a two-wave time-lagged design with a two-month interval, this study ultimately obtained 568 valid responses. The results showed the following: (1) Work creativity was higher when challenge stress and problem-solving pondering were jointly high than when both were low, as indicated by the significant positive slope along the CS = PSP reference line. The curvature along the reference line was nonsignificant, indicating that creativity increased approximately linearly as the two variables increased simultaneously. Along the CS = −PSP reference line, a significant asymmetrical, U-shaped misfit effect emerged. Specifically, creativity was severely hindered when challenge stress exceeded problem-solving pondering, whereas the decline was less pronounced when problem-solving pondering exceeded challenge stress. These findings indicate that work creativity varies across different joint configurations of challenge stress and problem-solving pondering. (2) Cognitive flexibility mediated the relationship between the joint effects of challenge stress and problem-solving pondering and creativity (indirect effect β = 0.17, 95% CI [0.0923, 0.2931]). The findings suggest that higher joint levels of challenge stress and problem-solving pondering are associated with greater cognitive flexibility, which may in turn contribute to greater creativity. Organizations seeking to enhance employees’ creative performance should not focus solely on increasing external challenges but should also cultivate employees’ problem-solving pondering and cognitive flexibility. Full article

This study examines the dynamic interactions among gross domestic product (GDP), external public debt, and domestic public debt in Iraq and discusses their implications for fiscal sustainability. The observed annual data cover the period 2005–2024, while the values for 2025 are based on IMF projections. Because continuous official monthly observations were unavailable, the annual series were temporally disaggregated using cubic-spline interpolation to construct an analytical monthly series. These interpolated values do not represent additional independent observations; therefore, the empirical findings are interpreted as exploratory. A recursive Structural Vector Autoregression (SVAR) model was estimated using Cholesky identification with the ordering GDP, external debt, and domestic debt. The variables were transformed into second differences in their natural logarithms, and a three-lag specification was employed. Impulse-response functions and forecast-error variance decomposition were used to examine the transmission and relative importance of the identified innovations. At the 24-month forecast horizon, GDP shocks explained 93.3% of GDP variation, while external debt was predominantly explained by its own shocks (86.3%). Domestic-debt variation was explained by its own shocks (47.3%), GDP shocks (44.1%), and external-debt shocks (8.6%). These results suggest that domestic debt is more closely associated with changes in domestic economic activity, whereas external debt follows a comparatively persistent path. The findings emphasize the importance of debt composition, non-oil revenue diversification, expenditure management, and coordination between domestic and external borrowing. Because oil revenues and government expenditure are not included as separate endogenous variables, the model does not directly identify oil-revenue or government-spending shocks. Future research should employ genuinely observed quarterly or monthly data and incorporate these fiscal variables explicitly. Full article

Lead-free tin-based perovskites, particularly CsSnI₃, offer a promising route toward environmentally benign photovoltaics, yet their device performance still lags behind lead-based counterparts. In this work, we perform a systematic SCAPS-1D simulation study on a planar heterojunction CsSnI₃ solar cell with a TiO₂ electron transport layer and a P3HT hole transport layer. We focus on the interplay between absorber-layer properties, acceptor doping concentration, thickness, defect density, carrier mobility, and parasitic resistances. Our results reveal that an optimal acceptor density around 10¹⁹ cm⁻³ balances built-in potential enhancement against Shockley–Read–Hall recombination, yielding the highest efficiency. Thicker absorbers improve light harvesting but aggravate bulk recombination, while defect densities above 10¹⁶ cm⁻³ cause catastrophic performance collapse. High carrier mobility (>1 cm²/V·s) is essential for efficient collection, and series resistance must be kept below 2 Ω·cm² to avoid fill factor degradation. Under optimized conditions, the device achieves a theoretical efficiency exceeding 27%, demonstrating the critical role of co-optimizing absorber parameters for high-performance, lead-free perovskite solar cells. Full article

This research investigates the impact of bank-specific and macroeconomic variables on return on equity as a measure of bank profitability in Cambodia during the period 2014–2024. Based on panel estimation methods, including dynamic panel modeling, the findings indicate that profitability is driven by both bank-specific features and country-level economic conditions. The dynamic panel estimates imply positive and statistically significant lagged return on equity, which means moderate profit persistence. Nevertheless, the coefficient is still less than one, indicating that profitability gradually reverts rather than being permanent. In terms of bank-specific factors, non-performing loans have a negative and statistically significant effect on profitability in all models, supporting the view that decline in asset quality leads to deterioration in bank performance via higher provisioning costs, reduced income generation and lower shareholder returns. The size of banks is always positive and statistically significant, meaning larger banks benefit from economies of scale, better market dominance positions, broadening customer bases and greater income diversification. The impact of capital-related variables is also mixed, indicating that the effects of capital strength on profitability depend on model specification. It is only when controlling for bank-specific effects and dynamic adjustment that the loan-to-deposit ratio becomes significant in pooled and random-effects models. Macroeconomic conditions also affect profitability. GDP growth has a positive and significant effect; inflation has a negative and significant effect. Full article

Antimicrobial resistance (AMR) is a growing global health threat, and agricultural pesticide exposure has been proposed as an environmental driver of resistance alongside antibiotic consumption. However, long-term, multi-country evidence linking pesticide use to AMR in Escherichia coli remains limited. Panel data analysis was applied to data from 28 European countries between 2013 and 2023 to examine lagged associations between agricultural pesticide use and E. coli resistance to fluoroquinolones, third-generation cephalosporins, aminoglycosides, and aminopenicillins, alongside spatial analysis of resistance distribution. Country-level panel data on E. coli resistance, antibiotic consumption, and pesticide use per cultivated area were compiled for 28 European countries, except for the aminopenicillin resistance model, for which Sweden was excluded owing to insufficient longitudinal data, yielding a 27-country panel for that indicator. Four random-effects generalized least squares (GLS) panel regression models were constructed for each resistance indicator, incorporating same-year and one-, two-, and three-year lagged pesticide use, adjusted for the corresponding antibiotic consumption. Global Moran’s I and Local Indicators of Spatial Association (LISA) analyses assessed spatial autocorrelation and clustering of resistance across countries. Pesticide use was significantly and positively associated only with fluoroquinolone resistance at a three-year lag; no significant associations were found for third-generation cephalosporin, aminoglycoside, or aminopenicillin resistance at any lag. Antibiotic consumption was consistently and positively associated with resistance across all models. All four resistance indicators showed statistically significant positive spatial autocorrelation, with persistent high-resistance clusters in Southeast Europe and low-resistance clusters in Northern Europe. Antibiotic consumption remains the dominant determinant of E. coli resistance, whereas pesticide use shows only a delayed, class-specific association restricted to fluoroquinolone resistance. Full article

Aneurysmal bone cysts (ABCs) are benign but locally aggressive osteolytic lesions that are rarely reported in dogs and may progress or recur following surgical treatment. This case report describes the diagnosis, surgical management, postoperative progression, and long-term outcome of an ABC affecting the left ulna of a four-month-old Labrador Retriever. Radiography and computed tomography revealed a rapidly progressive, expansile osteolytic lesion with marked cortical thinning. Histopathological examination supported the diagnosis of ABC, with no evidence of malignancy. Initial treatment consisted of corticotomy, intralesional curettage, autologous bone grafting, and stabilization of the cortical segment with lag screws. Despite complete clinical resolution of lameness, surveillance radiographs six weeks postoperatively demonstrated progression of the lesion proximal to the original surgical site. Revision surgery involving a larger cortical window and more extensive curettage was subsequently performed, followed by bone grafting. Histopathology again confirmed ABC without malignant transformation. The dog regained normal limb function, and serial radiographic and computed tomographic examinations demonstrated progressive osseous remodeling with no evidence of further progression or recurrence through 35 months of follow-up. This case highlights the importance of adequate surgical excision and scheduled postoperative imaging, as ABC progression may occur despite apparent clinical recovery. Full article

This paper examines the dynamic relationship between innovation, total factor productivity (TFP), and economic growth in Ghana using annual data for the period 1965–2021. Although Ghana has recorded relatively strong economic growth, concerns remain regarding the sustainability of this performance in the absence of consistent productivity improvements. The study combines growth accounting techniques with time-series econometric methods, including the autoregressive distributed lag–unrestricted error correction model (ARDL–UECM), vector error correction modelling (VECM), Granger causality tests, and two-stage least squares estimation. The results provide robust evidence of a stable long-run equilibrium relationship among innovation, productivity, and output. Innovation exerts a positive and statistically significant effect on economic growth, primarily through productivity-enhancing channels, while TFP emerges as the dominant long-run driver of growth. Short-run dynamics reveal feedback effects between innovation, productivity, and economic growth. However, growth accounting results indicate substantial volatility in TFP growth, suggesting that Ghana’s expansion has been driven largely by factor accumulation rather than sustained efficiency gains. The findings offer policy-relevant insights for productivity-centred growth strategies in Sub-Saharan Africa. Full article

China’s dual-carbon goals require firms to pursue low-carbon transformation, while digital infrastructure offers new opportunities for corporate emission reduction. Using data from Chinese A-share listed firms from 2010 to 2024, this study treats the establishment of National Big Data Comprehensive Pilot Zones (NBDCPZ) as a quasi-natural experiment and applies a multi-period difference-in-differences model. The results indicate a statistically significant negative relationship between policy and estimated corporate carbon emissions. This result remains robust to parallel-trend tests, propensity-score-matching difference-in-differences (PSM-DID) estimation, and lagging control variables by one period. Mechanism tests suggest that the policy is negatively associated with estimated corporate carbon emissions through pathways consistent with green innovation, digital transformation, and easing financing constraints. Heterogeneity analysis indicates stronger effects among firms in highly competitive industries, non-heavy-polluting sectors, and southern China. Moreover, the pilot policy enhances corporate Environmental, Social, and Governance (ESG) performance. Overall, this study provides evidence that big-data-related policies can facilitate corporate decarbonization and offers policy implications for carbon reduction through data sharing, openness, and governance. Full article

Single-sensor gas classification remains challenging because metal oxide semiconductor sensors exhibit limited selectivity and different gases can produce similar response patterns. This paper proposes a multi-scale temporal-dependency-learning framework that combines reservoir-based multi-scale dynamic learning (MsDL) with lagged-correlation features. To reduce information leakage caused by window construction, each continuous gas-response sequence is divided chronologically into training, validation, and test blocks before window extraction, and 2000 raw samples are excluded between adjacent subsets. The primary evaluation uses non-overlapping windows and fits all data-dependent preprocessing using the training subset only. On a dataset containing seven volatile organic compounds measured using a single sensor, the proposed MsDL+Corr representation with a random-forest classifier achieves a test accuracy of 92.14% and a macro-F1 score of 92.08%. Across ten random-forest seeds, the accuracy is 92.43% ± 0.37%. The corresponding micro-average one-vs-rest AUC is 0.9948 (99.48%); this threshold-independent ranking metric is distinct from classification accuracy. Feature ablation shows that combining MsDL and correlation features improves the macro-F1 score over either branch alone. These results demonstrate that temporal-dependency representations provide useful discriminative information under a conservative temporally separated evaluation protocol. Full article

Multi-view clustering aims to discover consistent cluster structures from heterogeneous features without supervision. Anchor-based methods improve scalability by representing samples through a compact set of anchors, but fixed anchors may be misaligned with the evolving cluster geometry. This mismatch is the main problem addressed here: the shared sample–anchor graph and the global anchor geometry need to be refined together across heterogeneous views, rather than in two disconnected stages. This paper proposes Learnable Anchor Graph Network (LAG-Net), a deep unfolding framework that jointly learns a shared anchor graph, view-specific anchor indicators, and global anchor alignment within a unified model. The global anchor alignment provides geometric guidance for shared anchor graph learning and promotes anchor consistency across heterogeneous views. By unfolding the derived optimization procedure into a trainable network, LAG-Net enables the anchor representations and sample–anchor relationships to be progressively refined. Experiments on six benchmark datasets demonstrate the effectiveness and scalability of the proposed method compared with representative multi-view clustering approaches. Full article

Accurate interwell connectivity characterization is critical for fine-grained waterflood optimization and reservoir management, often requiring integrated analysis across multiple disciplines and methods. Among these, injection–production response analysis stands as the most cost-effective and widely adopted approach. However, it remains highly subjective, heavily reliant on senior engineers’ decades of accumulated experience, and prohibitively labor-intensive for large-scale oilfields with hundreds of wells. With the exponential growth of production data in modern oilfields, manual analysis has become the bottleneck restricting the timeliness of reservoir management decisions. While signal processing techniques offer a promising path to automation, general-purpose algorithms fail to incorporate fundamental reservoir fluid flow laws, resulting in insufficient accuracy for practical engineering applications. To address this gap, we propose a novel connectivity identification method that mimics expert analysis logic by focusing on large-amplitude fluctuation segments rather than full-curve matching. Using curve slope as the core metric, cosine similarity quantifies trend consistency, while Root Mean Square Error (RMSE) measures amplitude proximity. Three targeted strategies enhance accuracy: key region screening with segmented matching, outlier removal accounting for time-varying lags, and multi-index weighted fusion. Validated on synthetic and mature carbonate waterflood field cases, the method improves the identification performance over benchmark Normalized Cross-Correlation (NCC) and Capacitance-Resistance Model (CRM) methods by more than 12% in both cases. It retains the reliability of traditional response analysis while achieving full automation, and can help estimate the timing of preferential flow path formation, requiring only routine production data to provide valuable reference for timely field development decision-making. Full article

Understanding how photovoltaic generation is distributed between local use, surplus export, and grid import requires analysis of the temporal structure of individual energy flows rather than only aggregate PV–load matching. This study proposes a flow-oriented Dynamic Time Warping (DTW) framework based on three physically interpretable relationships: self-consumption versus PV production (SC–PV), surplus export versus PV production (EXP–PV), and grid import versus total consumption (IMP–LOAD). The method was applied to 30 complete 24-h profiles recorded at 15-min resolution in a university building equipped with a 49.7 kWp rooftop PV system. SC–PV exhibited the lowest median normalized DTW distance (DTW* = 0.0186), EXP–PV the highest (0.4468), and IMP–LOAD an intermediate value (0.2323) with the greatest day-to-day variability. The differences among the three components were highly significant (Friedman $X^2\left(2\right)=54.60, p<0.001$, Kendall’s W = 0.91). Direct comparison with conventional PV–LOAD DTW* showed markedly different day-by-day associations for SC–PV (ρ = −0.523), EXP–PV (ρ = −0.006), and IMP–LOAD (ρ = −0.760), demonstrating that a single PV–LOAD distance does not retain the flow-specific information captured by the proposed representation. Cross-correlation provided complementary evidence, with a median optimal lag of zero for all three flow pairs. The proposed framework therefore provides a compact diagnostic representation of local PV utilization, surplus formation, and residual grid dependence without implying causal relationships. Full article