Search Results (1,148)

The World Bank’s Lighthouse Strategy identifies Morocco as a first mover exporter of green hydrogen and its derivatives to Europe; however, the engineering feasibility of the associated transport and storage network has not been quantitatively demonstrated. This study addresses that gap through an integrated spatial and multiperiod optimization framework that couples a spatially explicit Mixed Integer Linear Programming (MILP) model with a FlexSim/FloWorks digital twin for discrete event and hydraulic simulation. The MILP simultaneously optimizes electrolysis deployment, hydrogen storage technologies, and multimodal transport across a four node Moroccan export corridor (TanTan, Mohammedia, Jorf Lasfar, and Tanger Med) for the 2030, 2040, and 2050 planning horizons under a net present value objective. The optimal configuration combines a dedicated hydrogen backbone pipeline for the high volume production corridor with shortsea cabotage for the distribution branches, achieving a full chain levelized cost of ammonia (LCOA) of 1176 USD/t, consistent with the World Bank benchmark and reducing costs by 57 USD/t compared with an all cabotage configuration. The optimal network remains robust over a wide range of capital cost and financing assumptions, while the digital twin confirms the hydraulic and operational feasibility of the integrated pipeline–shipping system without critical port congestion. These findings demonstrate that combining optimization with digital twin validation provides a robust engineering basis for planning Morocco’s green hydrogen export infrastructure and supports investment decisions aligned with future CBAM compliant hydrogen and ammonia supply chains. Full article

This study examines whether environmental, social, and governance (ESG) scores among financial institutions across Asia, Europe, and the United States are converging toward a common global standard or settling into distinct regional regimes. Using a panel of 843 publicly listed banks and financial firms over fifteen fiscal years (7754 firm-year observations), the analysis applies sigma-convergence, cross-sectional beta-convergence, and a dynamic panel specification with firm and year fixed effects. Within-region ESG dispersion has narrowed significantly in Europe and the United States but widened in Asia, even as all three regions display strong beta-convergence, with laggard firms closing the gap within about a year. A pooled model with region interaction terms and a Chow test, both of which impose the regional grouping in advance, reject the hypothesis of a single global convergence process, a conclusion independently corroborated by a model-free Phillips–Sul log-t test and clustering algorithm, supporting a club convergence interpretation in which European and Asian financial firms gravitate toward a materially higher steady-state ESG level than their American counterparts, whose mean score remains twenty points lower at the most recent fiscal year. Robustness checks across all three regions, including industry subsamples in Europe, developed versus emerging market banks in Asia, and coverage-depth splits in the United States, confirm findings are not artifacts of sample composition. These results imply that benchmarks calibrated to a single global ESG threshold would misclassify firms operating under different regional convergence clubs. Full article

Nigeria’s digital economy has expanded rapidly, underpinned by growth in digital payments. This study investigates the influences of digital payment infrastructure growth on the liquidity reliability of cross-border banking systems within the West Africa region. Existing literature predominantly examines the effects of digital payment on liquidity in Nigerian banks; however, there is a paucity of knowledge regarding the influence of digital payment channels on liquidity resilience within multinational banks operating in varying regulatory environments in the West African region. This study takes advantage of monthly time-series data from 2011 to 2021 and employs an Autoregressive Distributed Lag (ARDL) framework to assess the short-run and long-run dynamics of digital payment infrastructure and the liquidity behavior of Nigerian cross-border banks. The results indicate that digital channels have different effects. In the long run, modern digital payment channels, especially electronic fund transfers, make liquidity more stable. Traditional channels, on the other hand, have weaker or short-term effects. The findings have significant implications for policymakers and regional regulatory harmonization within West African economics. Full article

This study examines the efficiency of Fiji’s banking sector from 2000 to 2025 using a two-stage analytical framework. First, data envelopment analysis (DEA) under the variable returns to scale (VRS) assumption is employed to estimate the efficiency scores of seven financial institutions: five commercial banks—four foreign-owned and one locally owned—and two locally owned non-bank financial institutions. Second, a two-limit Tobit model is used to investigate whether the estimated efficiency scores are associated with credit risk, return on assets (ROA), return on equity (ROE), bank size, foreign ownership, loan-loss provisions relative to net income, and real GDP growth. The balanced panel comprises 182 institution-year observations over 26 years. The DEA results indicate a high mean efficiency score of 0.923, although meaningful variation is observed across institutions and over time. Foreign-owned banks record a marginally higher mean efficiency score than locally owned institutions (0.924 compared with 0.921); however, foreign ownership is not statistically significant in the multivariate Tobit model. ROA has a positive and statistically significant association with efficiency, whereas ROE has a negative and statistically significant association, suggesting that asset profitability and equity profitability capture distinct balance-sheet and capital-structure channels. Bank size is positively associated with efficiency, while credit risk, loan-loss provisioning, and real GDP growth are statistically insignificant. Overall, the findings suggest that Fiji’s financial institutions operate relatively close to the estimated best-practice frontier. Nevertheless, the small number of institutions and the resulting dense DEA frontier warrant cautious interpretation. The study concludes that policies aimed at strengthening institutional efficiency should prioritise cost discipline, productive asset utilisation, appropriate capital management, and technology diffusion rather than ownership status alone. Full article

This study presents a high-resolution geomorphological and habitat map of the Sinuessa coastal sector (Tyrrhenian Sea), revealing the presence of an extensive and exceptionally shallow coralligenous bank developed between 5 and 16 m water depth. Multibeam bathymetry, side-scan sonar backscatter, sediment analyses, and Remotely Operated Vehicle (ROV) observations were integrated within a Geographic Information System (GIS) framework to characterize seabed morphology, acoustic facies, and associated benthic habitats. ROV surveys document a diverse macro- and epimegabenthic community, including both sciaphilous and photophilous taxa, as well as several protected and structuring species. Water depth alone does not discriminate among the mapped substrate classes (Kruskal–Wallis, p = 0.578), whereas acoustic backscatter, slope, and terrain ruggedness all do (p < 0.01), indicating that depth-independent controls govern the distribution of the bioconstruction. We hypothesize that persistently elevated turbidity and terrigenous input from the Volturno and Garigliano river systems reduce light penetration and generate, at 5–16 m, optical conditions comparable to those normally found at greater depths. This hypothesis is consistent with the geomorphological, sedimentological, and biological evidence presented here and with published oceanographic observations in the Gulf of Gaeta, but it has not been verified by in situ optical measurement, which we identify as the priority for future work. Relative backscatter intensity correlates significantly with mean grain size (Spearman ρ = −0.710, p < 0.001) and with gravel and mud content, and the four mapped classes differ significantly in backscatter, slope, and terrain ruggedness. These findings provide new insights into the environmental controls on coralligenous development and highlight the ecological relevance of shallow, turbidity-driven coralligenous systems within highly impacted Mediterranean coastal areas, with direct implications for habitat conservation and spatial management. Full article

Feature Mode Decomposition (FMD) adaptively decomposes signals into band-limited modes through an adaptive finite impulse response (FIR) filter bank optimized via correlated kurtosis (CK) maximization, yet its denoising performance is highly sensitive to four hyperparameters—the number of decomposition modes $n_m$, the filter length L, the CK shift order M, and the characteristic-period scaling $T_{\mathrm{scale}}$—whose manual tuning is impractical for multi-channel floating offshore wind turbine monitoring deployments. We propose GLO-FMD, an adaptive denoising framework coupling the Gray Langurs Optimizer (GLO) with FMD. GLO autonomously optimizes the FMD parameters, thereby aligning the CK objective with structural modal periods rather than impulsive fault periods. Although the search space spans $(n_m,L,M,T_{\mathrm{scale}})$, the CK shift order M is fixed at 2 and $T_{\mathrm{scale}}$ is estimated automatically from the dominant autocorrelation peak; consequently, only $(n_m,L)$ are actively optimized. The optimized FMD decomposes multi-axis tower-base signals into band-limited modes through iterative CK-maximizing FIR filter optimization; each mode identifies a dominant periodic component, and the original signal is zero-phase band-pass filtered around the identified frequencies to preserve physical phase during reconstruction. Validation employs (i) semi-synthetic signals reproducing the measured tower-base structure (a smooth $0.15$ Hz structural mode plus an impulse-excited $3.77$ Hz resonance) with exactly known ground truth—a best-case benchmark by construction that isolates denoising capability from reference uncertainty—and (ii) real strapdown inertial sensor data acquired at 8 Hz from the tower-base interface of a floating offshore wind turbine at an operational site in Chinese coastal waters, over a six-day measurement campaign (18–23 April 2023). Six kinematic channels spanning triaxial acceleration (north, up, east) and triaxial velocity (north, up, east) are analyzed, with 200-s (1600-sample) continuous windows extracted for algorithmic evaluation. On the semi-synthetic data, GLO-FMD achieves a $9.6$–$10.2$ dB SNR improvement over default wavelet thresholding against the known ground truth, and the GLO optimization is essential for reliability—the default FMD configuration is unstable across noise realizations, whereas the optimized parameters recover the clean components consistently. GLO-FMD also achieves pseudo-reference-relative SNR gains of 5.3–7.8 dB over default wavelet thresholding across all six real-data channels. Bootstrap resampling over 12 independent segments confirms statistical significance ($p<0.001$, Cohen’s $d>8$), and a no-reference smoothness index provides complementary evaluation independent of the pseudo-reference assumption. Multi-day consistency analysis yields coefficients of variation below 5%, demonstrating short-term consistency across the environmental conditions represented in the six-day dataset. The online denoising stage requires approximately 1.5 s per channel, supporting potential deployment on edge-computing hardware at the turbine controller level. Full article

This paper presents a novel master–slave stochastic optimization framework for the optimal siting and sizing of fixed-step capacitor banks in medium-voltage distribution networks, explicitly addressing the inherent variability of load demand that is typically neglected in conventional deterministic approaches. The proposed methodology integrates a scenario-based stochastic optimization model with a Chu and Beasley genetic algorithm (CBGA) as the master stage, which handles discrete placement decisions, and a successive-approximation power flow method (SAPF) as the slave stage, which evaluates the technical and economic performance of each candidate solution under multiple load scenarios. To capture demand uncertainties, 365 daily load realizations are generated using independent Gaussian noise with a relative standard deviation of 10% applied to each load point. These are subsequently reduced to ten representative scenarios via k-means clustering, reducing the number of power-flow evaluations per candidate solution from 365 to 10 (a 36.5-fold reduction); the reduced scenarios exhibit a low mean absolute error (MAE: <2%) with respect to the original mean, indicating faithful representation of the average load behavior, while the silhouette score is modest (approximately 0.25), consistent with the unimodal nature of the generated data and implying that the clusters are not well separated. Extensive simulations on a 33-bus test feeder considering three energy-cost-escalation scenarios (0%, 10%, and 20%) over a 20-year planning horizon demonstrate that both the deterministic and stochastic approaches reduce the total net present cost by 16.52% to 17.34% compared to the uncompensated network; the stochastic approach consistently delivers solutions that are either superior or comparable to deterministic planning (yielding up to approximately 0.16% additional cost reduction) while offering enhanced robustness against load variability. The stochastic framework offers distinct advantages, including robust solutions across a wide range of operating conditions, an inherent ability to adjust investment levels in response to probabilistic load distributions, and the ability to quantify uncertainty in decision making, with the most significant benefits observed when energy costs are low and load variability is high. The convergence of both approaches at a 20% escalation level further validates the reliability of high-resolution deterministic modeling when economic factors strongly dominate the optimization objective. This study underscores the importance of probabilistic modeling for modern distribution network planning, providing a practical and computationally efficient decision-support tool for utility planners to enhance grid resilience and operational efficiency in the context of increasing demand variability and renewable energy integration. Full article

This study investigates the influence of top management gender diversity on earnings management and examines the moderating role of controlling shareholders in this relationship. The sample comprised 213 rural banks in West Java, Indonesia, yielding 2116 unbalanced panel observations over the period 2016–2025. The primary estimates were obtained using the two-step system generalized method of moments. Robustness checks were conducted using a random effects model and a fixed effects model with robust standard errors. The results reveal that the proportion of female top managers has no significant effect on earnings management, measured through discretionary loan loss provisions. In contrast, the presence of a female top management chair significantly reduces discretionary loan loss provisions, highlighting the importance of structural authority in enabling female top managers to constrain opportunistic financial reporting. Controlling shareholders positively moderate the relationship between a female chair and discretionary loan loss provisions. This finding suggests that high ownership concentration weakens the chair’s ability to constrain earnings manipulation by compromising managerial independence. This study is limited to conventional rural banks and uses only discretionary loan loss provisions as a proxy for earnings management, which may not capture operational manipulation. Future research should incorporate measures of real earnings management and conduct comparative analyses across a broader range of financial institutions to enhance the generalizability of the findings. Full article

Power equipment defect detection is essential for ensuring reliable power grid operation. Existing power equipment defect detection methods typically formulate the task as multi-class object detection, but their performance is limited by long-tailed defect distributions and the neglect of normal operating conditions. To address these challenges, we propose a two-stage framework that decouples component localization from operating-state recognition. Specifically, a conventional detector first localizes equipment components, followed by a hierarchical contrastive learning framework for state recognition. A dual-memory bank is introduced to capture coarse-grained component semantics and fine-grained state prototypes, enabling discriminative feature learning through coarse-to-fine contrastive optimization. We further construct a real-world power equipment inspection dataset with hierarchical component–state annotations. Extensive experiments demonstrate that the proposed framework consistently outperforms conventional multi-class detection methods, especially on rare defect categories, while effectively reducing false alarms in practical inspection scenarios. Full article

Tailless underactuated hypersonic glide vehicles rely on elevons alone to control both roll and yaw. The dual-loop cascade architecture, in which differential elevon deflection regulates sideslip and the resulting sideslip response drives roll, exploits the high roll-to-yaw ratio to address lateral–directional underactuation. However, because the inner loop relies on sideslip angle feedback, its performance is sensitive to composite measurement bias. This study first analyzes lateral–directional open-loop divergence, the high roll-to-yaw ratio, and adverse yaw induced by differential elevons using a six-degree-of-freedom model and linearizations at multiple trim points, and then develops a baseline cascade controller employing stability-axis yaw-rate feedback. A first-order Gauss–Markov process with a constant offset is subsequently used to represent sideslip angle measurement bias. A linear extended state observer is introduced only in the velocity–bank angle outer loop to jointly estimate and compensate for the equivalent effect propagated by the bias, aerodynamic perturbations, center-of-mass offsets, and residual inner-loop dynamics. Observer parameters are selected through input direction identification, hierarchical two-dimensional parameter sweeps, and local grid verification. Local stability and boundedness near the design operating point are analyzed under bounded-input assumptions at the levels of the observer error, velocity–bank angle tracking error, and complete attitude control closed loop. Strictly paired Monte Carlo simulations and ablation experiments show that the proposed method effectively attenuates the propagation of sideslip angle measurement bias into the roll channel, improves velocity–bank angle tracking accuracy and response consistency under random uncertainties, and does not appreciably increase the required elevon position command envelope. Full article

Large language model (LLM) agents that retrieve external content and use tools are vulnerable to indirect prompt injection, in which untrusted content contains instructions intended to influence agent behavior. We evaluated four defenses and an undefended control across GPT-5.4, GPT-5.4-mini, and Claude Sonnet 4.6 on the AgentDojo banking benchmark (Tool Filter was evaluated only for the OpenAI models), reporting attack success rate (ASR), benign utility, utility under attack, operational measures, and two independent benchmark replications. Raw undefended ASR was 0/288 for GPT-5.4, 11/288 for GPT-5.4-mini, and 1/288 for Claude Sonnet 4.6; these cross-model differences require cautious interpretation because benchmark goals were not equally reachable across models. For GPT-5.4-mini, the Prompt Injection Detector and Tool Filter were associated with lower observed ASRs but also lower benign utility, and Tool Filter restricted available actions. None of the four paired GPT-5.4-mini comparisons reached significance after Holm correction; only Tool Filter had an unadjusted p-value below 0.05. Benign utility was more stable across runs than individual low-frequency attack outcomes. The findings show that defense evaluation should report attack outcomes, goal feasibility, legitimate-task utility, action availability, operational measures, and run-to-run variation. Results are limited to the evaluated benchmark, models, defenses, and conditions. Full article

This study investigates how prudential regulatory instruments introduced by the National Bank of Ethiopia (NBE) influence the profitability and liquidity of private commercial banks in Ethiopia. Using balanced panel data from seven private commercial banks covering the period 2014–2023, the study employs fixed-effects and random-effects regression models to examine the relationship between regulatory measures—including legal reserve requirements, capital adequacy, capital requirements, equity investment limitations, and NBE bill purchase requirements—and bank performance. The findings indicate that prudential regulations affect different dimensions of bank performance in varying ways. Specifically, the legal reserve requirement has a positive and statistically significant effect on bank liquidity, suggesting that higher reserve holdings improve banks’ ability to meet short-term obligations. However, it is associated with a weak negative effect on profitability, highlighting the potential trade-off between maintaining liquidity and generating income. The results also show that higher capital adequacy significantly reduces return on equity, indicating that stronger capital buffers may limit shareholders’ returns. Among bank-specific factors, managerial efficiency is found to be an important driver of profitability, whereas greater dependence on deposit funding is associated with lower profitability. In contrast, capital requirements, equity investment limitations, and NBE bill purchase requirements do not exhibit statistically significant short-run effects on bank profitability or liquidity. Overall, the findings suggest that Ethiopia’s prudential regulatory framework has contributed more strongly to strengthening liquidity and financial stability than to improving bank profitability. The study underscores the need for regulators to maintain an appropriate balance between financial stability objectives and banks’ operational efficiency and profitability in emerging banking systems. Full article

Indian public sector banks (PSBs) operate under Basel III guidelines, which mandate capital adequacy, liquidity standards, and systematic risk management across credit, market, and operational risk dimensions. This study analyses Basel III Pillar 3 disclosure reports and Reserve Bank of India published data for all twelve PSBs in India for the period 2021–2025, examining capital requirements for credit, market, and operational risks, gross and net non-performing assets, capital adequacy ratios, and return on assets. The study period spans the transition from forbearance-supported stability in 2021 to organic recovery evidenced by declining gross nonperforming asset ratios and improved capitalisation by 2025. Coefficient of variation, Kruskal–Wallis non-parametric tests, one-way ANOVA, and Tukey HSD post hoc analysis reveal significant inter-bank variation across all capital requirement variables, nonperforming asset levels, and CRAR. Further, return on assets does not vary significantly across banks. The study provides empirical confirmation of a systemic capital buffer in Indian banking across both the pandemic stress period and the organic recovery period. Sector mean CRAR rose from 14.40% in 2021 to 17.64% in 2025, while sector mean GNPA declined from 7.5% to 2.2% over the same period, with all twelve banks maintaining CRAR above both the RBI-prescribed minimum. Post hoc analysis attributes credit risk capital variation almost entirely to State Bank of India’s scale, with no significant differences among the remaining eleven banks. These findings have direct implications for national financial stability and systemic risk management in an emerging market context. Full article

This study examines the relationship between the financial sustainability of local government units (LGUs) and their investment capacity, focusing on differences between municipalities and cities with county rights in Poland. It investigates how operating surplus, debt-servicing costs, and revenue autonomy are associated with investment capacity and whether these relationships differ across LGU types. The analysis uses data for Polish municipalities and cities with county rights for 2018–2024 obtained from the Local Data Bank of Statistics Poland. Various panel regression specifications were employed, supplemented by descriptive Pearson correlation analysis. The results indicate a consistent positive tendency in the relationship between operating surplus and investment capacity, whereas higher debt-servicing costs were significantly associated with lower investment capacity. Cities with county rights exhibited higher investment capacity than municipalities, although the strength of the evidence varied depending on the measure used. Revenue autonomy, in contrast, was found to be negatively associated with investment capacity. This study provides a comparative assessment of the relationship between financial sustainability and investment capacity, offering implications for local fiscal policy and future research. Full article

Operational risk management (ORM) in financial institutions has traditionally relied on quantitative loss event databases that capture what went wrong, but rarely why from a human factors perspective. This paper proposes a comprehensive Human Factors Framework for ORM that integrates the Human Factors Analysis and Classification System (HFACS) taxonomy with deep learning and large language models (LLMs) to extract, classify, and predict human-factor-driven operational risks from unstructured consumer complaint narratives. Using the U.S. Consumer Financial Protection Bureau (CFPB) Consumer Complaints Database (300,000 banking narratives), we (1) define and validate an eight-factor HFACS-Banking taxonomy through a Delphi study with five domain experts (Cohen’s κ = 0.86), (2) compare three extraction approaches—regex, supervised BERT, and Mistral-7B zero-shot LLM—achieving 0.81 average F1 with the LLM-distilled BERT relative to Mistral-generated reference labels, (3) propose a hybrid HF-BERT-BiLSTM-Attention architecture that fuses contextual text embeddings with structured features and HFACS factor probabilities for predicting a four-class company-response-based complaint severity proxy, and (4) provide explainability through SHAP feature attribution applied to the Random Forest baseline and attention-weight visualization of the proposed neural model. Under a temporal split comprising training data from 2014 to 2022, validation data from 2023H1, and held-out test data from 2023H2 to 2025, the proposed model achieves 91.42% accuracy and 89.78% macro F1 (95% CI from 1000-iteration paired bootstrap: [89.34, 90.21]), outperforming Random Forest (+9.84% F1, p < 0.001 Bonferroni-corrected), BiLSTM (+5.46%, p < 0.001), FinBERT (+3.21%, p = 0.003), and BERT-only (+3.92%, p = 0.002) baselines. Under the secondary random-split ablation analysis, removing the HFACS features and replacing additive attention with mean pooling reduced macro-F1 by 3.79 and 2.35 points, respectively. The findings demonstrate the retrospective feasibility of integrating theory-grounded human-factor representations with neural language models for complaint-outcome analysis; prospective institutional validation is required before operational use. Full article