Search Results (632)

This mixed-methods study examines the associations among fintech advancement, green finance, and financial inclusion in Jordan, an emerging economy. It draws on a distinctive three-part dataset: survey data from 21 commercial banks (N = 21), a national household survey, and semi-structured interviews with stakeholders. The quantitative results indicate that the positive association between fintech adoption and the provision of green finance is statistically consistent with full mediation by banks’ absorptive capacity, particularly their digital maturity and data analytics capabilities. Proactive regulatory support significantly moderates this mediated relationship. Market demand, by contrast, has no statistically significant moderating effect. At the household level, the combined use of digital and green financial products is associated with higher formal account ownership and with the use of a greater number of financial products. The interviews support these results, pointing to institutional capacity and regulatory clarity as essential enabling factors. Given the cross-sectional bank-level data (N = 21) and the exploratory scope of the mediation analysis, causal interpretations should be avoided. Future longitudinal research is needed to examine temporal dynamics. Even so, these findings offer policymakers an initial empirical framework: channeling fintech toward sustainable development will likely require targeted interventions to build institutional digital capacity and establish clear regulatory frameworks, rather than depending solely on market forces. Full article

Infrastructure project reports contain rich narrative evidence on delay causes, yet transforming such unstructured text into reliable causal knowledge remains challenging because reports mix confirmed events with hypothetical, conditional, or localized statements. This study proposes an eight-stage computational pipeline that converts infrastructure project evaluation reports into a Bayesian-network model for exploratory structure learning and probabilistic dependency modeling. The central methodological contribution is a modality-aware extraction layer that distinguishes confirmed, project-wide delay evidence from conditional, hypothetical, or component-level statements before causal analysis. The pipeline was evaluated on 55 road infrastructure project reports financed by the Asian Development Bank, the African Development Bank, and JICA, from which delay events across 15 cause categories were extracted and stratified by epistemic modality and scope. Ablation analysis shows that the principal dependency structure recovered by the Bayesian network is not recoverable without modality-aware filtering, indicating that evidence-quality stratification materially shapes downstream causal-structure exploration. Among the recovered dependencies, a financial-to-project-management pathway was the most consistent signal: its undirected skeleton edge was the only relationship recovered by all four causal-discovery algorithms tested (with the orientation determined only by the score-based search), its association was nominally positive—though weak and not uniformly discernible—across nine extraction models spanning three commercial vendors and open-weight families, and it is consistent with prior delay-factor literature. Its model-based scenario contrast ($\Delta P=+0.638$, 95% CI $[0.470,0.764]$) is reported as hypothesis-generating rather than as a validated policy effect: under structure-learning uncertainty, the interval extends to zero, and the effect magnitude and the specific learned edge depend on the extraction model and the small effective sample. These findings suggest that incorporating modality awareness into narrative-evidence extraction improves the reliability of exploratory causal-structure analysis from infrastructure project reports. Full article

Railway safety is an important component of public security. Ensuring the safety of high-speed rail systems and passengers is also crucial for railway transportation enterprises. The performance of X-ray security inspection systems is one of the key factors in improving intelligent railway security. Previous studies have shown that, due to the complexity of X-ray images, both traditional vision methods and deep learning approaches struggle to meet the requirements of real-world railway security inspection. With the development of vision-language models, this paper proposes XrayCLIP, a CLIP-based method designed to improve the accuracy and robustness of computerized X-ray security inspection. XrayCLIP adapts CLIP to the semantic and imaging characteristics of prohibited-item inspection through security-oriented prompts, texture-aware visual representations, and global–local supervision. The key component of the model is a set of learnable prompt templates, which guide the model to learn generic features of prohibited objects in complex environments. Multi-level global–local (glocal) features enable the model to focus on both global context and local details, while text space optimization, texture enhancement, text–image fusion, and inference enhancement further improve model performance. XrayCLIP is evaluated on the PIDray and derived HiXray(seg) benchmarks against eight conventional and recent single-view baselines under a common evaluation protocol. A railway-station study is additionally conducted using operational X-ray data, including a same-set missed-detection comparison with a commercial system for knives and power banks. The results show that XrayCLIP achieves the best performance among the evaluated methods on PIDray and HiXray(seg), while reducing the missed-detection rate relative to the commercial system on the two categories examined. Full article

This study examines how Generative AI (GenAI) was integrated into creative sustainability education through the STEM Education for Sustainability (STEM4S) framework. Conducted in 2024, the study examined a nine-week intervention comprising a breastfeeding-room track in a second-year interior-design course and a sports-gym track in a third-year commercial-space-design course. Twenty students completed the pre-assignment questionnaire, and 18 completed matched pre- and post-assignment questionnaires after two withdrawals. GenAI was introduced as a scaffolded method-bank tool to support prompt-based ideation, visualization, and critique. A qualitative-dominant mixed-methods case-study design combined matched pre- and post-assignment questionnaires, student reflections, classroom observations, project outcomes, and professional critique records. Paired comparisons showed that students’ self-rated SDG 11 knowledge increased from 2.94 ± 1.16 to 3.56 ± 0.92 (p = 0.012, Cohen’s d_z = 0.67), while SDG 5 knowledge decreased from 4.39 ± 0.70 to 3.72 ± 1.02 (p = 0.014, Cohen’s d_z = −0.65); both findings are interpreted as exploratory due to the small sample. Qualitative findings indicate that GenAI supported visual exploration but also produced prompt difficulty, visual repetition, and spatial inconsistency. The findings suggest that GenAI can extend the STEM4S method bank when embedded within civic learning, critique, and human design judgment, while its transferability requires further testing across contexts. Full article

Dentin derived from extracted human teeth has emerged as a promising autogenous biomaterial for bone regeneration owing to its structural and biological similarity to bone tissue. Growing clinical interest in dentin-derived graft materials has also stimulated the development of tooth-banking concepts, in which extracted teeth are preserved for future regenerative applications. However, microbiological contamination remains one of the principal concerns associated with long-term storage before clinical use. This in vitro study evaluated the microbiological status of dentin biomaterials prepared from human teeth stored under long-term refrigerated conditions (approximately 4 °C) and subsequently processed using three commercially available dentin-processing systems: BonMaker (BM), Tooth Transformer (TT), and Smart Dentin Grinder (SDG). A total of 72 extracted teeth collected between 2018 and 2025 were allocated to experimental groups and processed into 24 pooled dentin samples. Five freshly extracted teeth served as controls. Culture-based microbiological assessment was performed before and after dentin processing using Schaedler Broth K3 culture media under aerobic and anaerobic conditions. Microorganisms isolated from positive cultures were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Baseline microbiological assessment detected bacterial contamination in 2 of 24 pooled dentin samples (8.3%) and in 4 of 5 control teeth (80.0%). The identified microorganisms included Staphylococcus epidermidis, Actinomyces viscosus, Streptococcus anginosus, Streptococcus mutans, and Staphylococcus hominis. No microbiologically detectable bacterial growth by culture-based methods was observed following processing with BM, TT, or SDG, irrespective of storage duration, including teeth preserved under refrigerated conditions for up to seven years. Within the limitations of this culture-based investigation, the evaluated dentin-processing protocols effectively eliminated microbiologically detectable cultivable bacterial contamination from long-term refrigerated dentin samples. These findings provide preliminary culture-based microbiological evidence supporting the feasibility of long-term refrigerated tooth preservation under the investigated storage conditions as one component of future tooth-banking research. Since culture-based microbiological methods do not detect viable but non-culturable microorganisms or other non-cultivable pathogens, the present findings should not be interpreted as evidence of complete sterility, overall biological safety, or the superiority of refrigerated storage over alternative preservation methods. Further studies employing molecular microbiological techniques together with biological and physicochemical analyses are required to comprehensively validate long-term tooth-banking protocols. Full article

This paper studies the impact of weather shocks on the credit risk of bank portfolios in small and rural municipalities in Colombia, which are highly vulnerable to these climate variations. It addresses the relationship between extreme temperature and precipitation events and the increase in the proportion of loans at risk, using data from 2011 to 2023. Extreme climate shocks negatively affect loan portfolio quality in Colombian rural municipalities, especially in microcredits. Credit risk decreases in municipalities with higher per capita incomes. It varies according to the type of loan and the nature of the weather shock. The results show that low temperature shocks increase risk in commercial and consumer loans. In contrast, low-precipitation shocks increase risk in microcredit. The increasing importance of climatic disturbances stresses the need for banks to develop their own models to manage these risks and to deepen analysis that incorporate borrower-specific information and banking policies to protect both financial institutions and borrowers from the impacts of climate change. The integration of sustainable practices and climate education can be key to improving financial resilience in these vulnerable areas. Full article

This paper examines the threshold impact of media-based attention to transition and physical climate risks on banks’ credit risk among the 230 largest US commercial banks from 2011 to 2022. Using a dynamic panel threshold model, our analysis reveals a non-linear relationship between media-based climate risk and banks’ credit risk. The empirical results indicate the existence of a significant threshold dividing the data into lower and upper regimes for both climate transition risks and physical climate risks. More specifically, the estimated threshold levels are 0.500 for the transition risk index and 0.571 for the physical climate risk index. Below these critical thresholds, banks appear resilient to increased media attention to climate risks; however, once these thresholds are exceeded, growing concern about climate risks significantly increases banks’ vulnerability to credit risk. These findings highlight the critical implications of physical and transition risks for financial stability. Our results are robust to a range of alternative measures and model specifications, providing valuable insights for bank managers, regulators, and policymakers, while emphasizing the need to integrate media-based climate risk considerations into credit risk assessments and policy frameworks to strengthen the banking sector’s resilience. Full article

The determinants of bank credit quality have been studied extensively, yet much of the existing evidence rests on a single regression specification, so a variable’s apparent significance may be conditioned on which controls a researcher chooses to include. We confront this problem directly for the Gulf Cooperation Council (GCC) countries, providing a robustness analysis of non-performing loans (NPL) determinants for the region’s banks. We employ a balanced panel of 45 listed commercial banks drawn from all six GCC countries over the period 2010 to 2024. We examine fifteen bank-specific and four macroeconomic potential determinants of NPLs, utilizing two variants of extreme bounds analysis (EBA), namely, the strict criterion of Leamer and the more lenient criterion of Sala-i-Martin, estimated within a panel fixed-effects framework. The findings show that of the nineteen determinants routinely cited in the literature, seventeen prove fragile once their coefficients are tested across the full range of possible model specifications. None survives Leamer’s strict criterion, whereas Sala-i-Martin’s less restricted test suggests that only two variables are robust, namely, asset quality (loan intensity), which enters positively, and capital adequacy, which enters negatively, while all four macroeconomic variables are fragile on both tests. For regulators, bank-level balance sheet indicators, especially loan intensity and capital adequacy, offer a more robust starting point for NPL early-warning and stress-testing frameworks and complement macroeconomic forecasts. Full article

Agentic artificial intelligence (AI) is a consequential technological frontier in banking because it shifts AI from passive assistance and generative interaction toward goal-directed workflow execution. Responsible and sustainable banking transformation therefore depends not simply on whether banks experiment with agentic AI, but on the readiness conditions under which selected agentic capabilities can move from pilots to governed production and responsible value realization. This study develops a configurational forecasting framework for agentic AI deployment readiness in banking. Because comparable initiative-level evidence remains scarce and commercially sensitive, the paper adopts a transparent, case-informed synthetic configurational simulation. The analysis should therefore be read as a theory-development and foresight exercise, not as empirical evidence of actual bank projects or banking-sector prevalence. Drawing on public banking AI cases, technology-diffusion and foresight literature, AI governance research, and role-based stakeholder archetypes, we construct a synthetic dataset of 90 banking-related agentic AI initiatives and apply fuzzy-set Qualitative Comparative Analysis (fsQCA). Within this bounded simulation, production maturity is internally consistent with the conjunction of data readiness, leadership commitment, governance maturity, workflow redesign capability, human–agent collaboration maturity, and low legacy-system complexity. Supplementary analyses indicate that deployment alone is insufficient for value realization in the simulated design: value requires deployment to be combined with redesigned workflows, governed data use, and human–agent collaboration. The results are not causal estimates; rather, they specify falsifiable readiness expectations that future empirical research can test with real initiative-level data. The study offers a reproducible readiness logic for responsible value realization, customer protection, workforce capability, and financial-system resilience. Full article

Avian influenza A virus (AIV) and Newcastle disease virus (NDV) are among the most damaging pathogens of poultry, and AIV also carries zoonotic potential that places it within the One Health agenda. The two infections frequently produce overlapping clinical signs, so a method that detects both in a single reaction has clear practical value for surveillance. We developed a duplex one-step real-time reverse-transcription PCR (RT-qPCR) that detects AIV in the FAM channel and NDV in the ROX channel. Primers and hydrolysis probes were designed against conserved regions of the AIV nucleoprotein and matrix genes and the NDV matrix, phosphoprotein and nucleoprotein genes, after alignment of 500 sequences per virus retrieved from GenBank from different geographic regions. Annealing temperature, primer and probe concentrations were optimised, and recombinant plasmids carrying the target fragments served both as positive controls and as quantitative standards. The assay was specific for AIV and NDV, with no cross-reactivity with infectious bronchitis virus and no signal crossover between channels; its analytical sensitivity reached approximately 10–20 RNA copies per reaction. Qualitative results on reference isolates were fully concordant with two commercial veterinary kits (Cohen’s κ = 1.00), and the assay passed inter-laboratory commission testing. This study establishes the design and core analytical performance of a laboratory-developed duplex assay; expanded in silico inclusivity on contemporary sequences, a broader specificity panel, inhibitor-containing matrices, an internal amplification control, and prospective clinical validation are required before routine surveillance deployment. Full article

Ecuador ranks among the world’s leading banana exporters; however, postharvest enzymatic browning, driven by polyphenol oxidases (PPOs), remains an important determinant of fruit quality loss and reduced market value. The genes encoding PPOs in Musa paradisiaca L., a triploid hybrid (AAB) widely cultivated in Ecuador, have not previously been described at the genomic or transcriptional level. The objective of this study was to generate a preliminary de novo draft assembly of Ecuadorian M. paradisiaca, identify PPO-coding loci recovered in this assembly, and compare their relative expression across three field-defined green-fruit developmental stages: early, intermediate, and physiologically mature-green. High-molecular-weight genomic DNA sequencing was performed using the Oxford Nanopore MinION Mk1B platform, and 13,239 contigs were obtained by de novo assembly with Flye v2.9.6. Two intronless PPO-coding loci, designated MpPPO1 and MpPPO2, were identified and deposited in GenBank under accessions PX565008 and PX565009. qRT-PCR showed significantly higher MpPPO1 expression at the physiologically mature-green stage, whereas MpPPO2 showed only a non-significant trend toward higher expression at the intermediate stage. These profiles indicate transcriptional divergence and identify MpPPO1 as a candidate associated with late green-fruit development or physiological maturity. However, because stage classification relied on a qualitative field harvest index and neither controlled postharvest ripening nor quantitative maturity, PPO activity, or browning measurements were performed, the results do not demonstrate ripening-induced activation, functional subfunctionalization, or causality in browning. These findings provide a preliminary molecular basis for future functional and postharvest studies in commercial banana cultivars. Full article

The high commercial value and the increasing demand for caviar make it susceptible to illegal trade and fraudulent activities, while mislabelling can occur both deliberately and unintentionally. Therefore, reliable methods are urgently needed to distinguish even closely related sturgeon species for the authentication of declared species in caviar products. In this study, we explore the effectiveness of ONT sequencing combined with nanopore adaptive sampling (NAS) to recover mitochondrial genomes from genomic DNA extracted from two caviar samples. NAS yielded similar percentages of mitochondrial reads as sequencing experiments without using the NAS option. However, NAS substantially increased the percentage of bases that could be mapped against a mitochondrial reference genome, demonstrating its effectiveness in enriching ONT sequencing data for mitochondrial sequences. The percentage of mapped mitochondrial reads varied between two samples in our sequencing experiments, ranging between 2.37% and 10.46%, which is exceptionally high compared to previous studies focusing on the recovery of mitochondrial genomes from genomic DNA. This increase may be attributed to the extraction of genomic DNA from individual fish eggs. To determine the taxonomic identities of the two caviar samples, a phylogenetic analysis was performed that included all available acipenseriform mitochondrial genomes from GenBank, as well as the newly recovered mitochondrial genomes. During this process, four problematic mitochondrial genomes obtained from GenBank were identified, which were characterized by either suggestively low sequence quality, chimeric sequence information, or potential misidentification. This underscores the need for reviewing sequencing data before database submission to avoid negative impacts on research and DNA-based species authentication. Full article

Increasing demand in radial distribution systems intensifies active power losses, voltage drops, and power factor deterioration, leading to reduced network efficiency and increased annual operating costs. Shunt capacitor banks can mitigate these effects by supplying reactive power locally; however, determining their locations and discrete ratings constitutes a nonlinear optimization problem governed by the radial network structure, nonlinear load flow equations, reactive power compensation limits, power factor requirements, and commercially available capacitor sizes. This paper presents a hybrid methodology that combines Loss Sensitivity Factors (LSFs) for candidate-bus screening with the Flower Pollination Algorithm (FPA) for discrete capacitor sizing. The LSF stage ranks buses according to the local sensitivity of active power losses to reactive power variations and applies a voltage-based screening criterion to reduce the number of decision variables. The FPA subsequently explores the reduced search space through global and local pollination, while a projection-and-repair procedure maps every continuous trial vector onto admissible capacitor ratings in 50 kVAr increments. The objective function minimizes the annual cost of active power losses, capacitor bank installation, and installed reactive power capacity. The method is evaluated on the IEEE 33-bus, 69-bus, and 141-bus radial distribution systems. For the IEEE 33-bus system, active power losses decrease from 202.7 to 133.5 kW and the power factor increases from 0.8502 to 0.9827. For the IEEE 69-bus system, losses decrease from 225.0 to 145.9 kW and the power factor increases from 0.8159 to 0.9705. For the IEEE 141-bus system, losses decrease from 632.7 to 453.7 kW and the power factor increases from 0.8500 to 0.9876. The corresponding cost savings in terms of annual loss are USD 36,371.52, USD 41,574.96, and USD 94,082.40. The compensation configurations improve the voltage profiles in all three systems; nevertheless, the resulting minimum voltages of 0.939, 0.932, and 0.948 p.u. remain below the 0.95 p.u. lower reference because voltage deviation is evaluated as a performance indicator rather than enforced through a hard constraint or penalty term. The results demonstrate that the proposed LSF–FPA framework provides effective loss reduction, power factor correction, and voltage profile improvement, while the economic comparison shows that the solution with the greatest loss reduction benefit does not necessarily produce the lowest total annual cost. Full article

This study examines the optimal coordination of internal and external rescue for troubled banks under systemic contagion. Using annual data for 210 Chinese commercial banks from 2013 to 2024, it constructs a region-constrained minimum-density interbank network and embeds it in an EN-GLT dual-channel contagion framework that captures both direct default losses and asset fire-sale losses. Each bank is sequentially treated as the initially shocked institution, and pure internal rescue, pure external rescue, and mixed rescue strategies are compared under risk-tolerance, rescue-capacity, cost, and moral-hazard constraints. The results show that capital-loss contagion and fire-sale amplification are economically meaningful under the no-rescue scenario and become stronger as the fire-sale markdown rate rises. Mixed rescue outperforms pure internal or pure external rescue in most years, with the optimal internal rescue share mainly concentrated between 30% and 55%. The findings indicate that problem-bank resolution should combine internal loss absorption with external stabilization and should be differentiated according to contagion channels, bank type, and the nature of the crisis. Full article