Search Results (6,765)

Whether mobile money complements or threatens the banking sector in Sub-Saharan Africa has been a contentious issue. This study aims to empirically investigate the impact of mobile money adoption on bank credit in Sub-Saharan Africa by addressing the question: Does mobile money adoption affect bank credit to the private sector in Sub-Saharan Africa? A quantitative research design was used to answer the research question. The panel Autoregressive Distributed Lag-Pooled Mean Group (ARDL-PMG) model was applied to annual data collected from 2012 to 2024. The study found that mobile money has a positive long-term influence on bank credit growth in SSA. A set of control variables—gross domestic product, the inflation rate, trade openness, and political stability—is also a significant determinant of growth in bank credit to the private sector in SSA. Policy frameworks should facilitate interoperability between mobile money and banks and enhance soft and hard infrastructure that builds trust and confidence in digital finance. Further research can adopt other econometric frameworks and compare the findings with our study. Full article

Background/Objectives: Large language models (LLMs) are increasingly used by the public to seek health information, yet their accuracy in addressing common vaccine myths remains unclear. Sycophantic LLM behavior, where models align with rather than correct user-stated beliefs, poses specific risks in health contexts. Methods: We conducted an exploratory multi-vendor evaluation of three LLMs (GPT-5, Gemini 2.5 Flash, Claude Sonnet 4) using officially curated vaccination myths from Germany’s public health institution and two realistic user framings (curious skeptic, convinced believer). All model responses were independently evaluated by two blinded medical experts for misconception addressal (binary criterion applied to the response text), scientific accuracy, and communication clarity (5-point Likert scales). Additionally, blinded marketing experts ranked models for lay communication clarity. Flesch Reading Ease scores were computed for all outputs. Results: Across all myths, framings, and models (66 response items), both medical raters judged that all responses refuted the targeted misconception; no response affirmed or ignored a myth, including under the adversarial convinced believer framing. Scientific accuracy and clarity ratings were high and tightly clustered (median 4.0–4.5), with no combined score below 3 and substantial inter-rater agreement. Marketing experts independently ranked Gemini 2.5 Flash and GPT-5 highest for lay clarity. Readability analysis revealed generally low accessibility, particularly for the convinced believer framing and for Claude Sonnet 4 outputs. Conclusions: Our findings suggest that general-purpose LLMs can produce scientifically accurate, on-topic rebuttals to widely documented vaccine myths under realistic default conditions, although linguistic complexity and framing-sensitive style may limit accessibility. Whether such outputs change beliefs or behavior in hesitant individuals was not tested. With readability optimization, these outputs could serve as building blocks for myth-debunking tools, given prospective evaluation with behavioral endpoints. Full article

The proliferation of large-scale interaction datasets, from scientific collaboration networks and legislative records to online communication platforms, has made the analysis of group-based, time-varying systems one of the central challenges of modern data analytics. Hypergraphs provide a natural formalism for such systems, where interactions involve arbitrary groups of agents rather than isolated pairs, and temporal hypergraphs extend this to sequential data by capturing how group interactions evolve over time. Yet quantifying how complex, predictable, or volatile this evolution is remains an open problem: existing entropy-based measures either operate on pairwise projections and thus discard multi-way dependencies or are not naturally defined for varying hyperedge sizes. In this paper, we propose an information–theoretic framework for characterizing how the diversity of interaction orders in a temporal hypergraph evolves over time. We introduce the hyperedge-size distribution entropy of a snapshot and, building on the theory of entropy rates for stochastic processes, we define the temporal hypergraph entropy rate as a principled, dataset-agnostic measure of the average diversity of interaction orders exhibited by the snapshot sequence over time. We further equip the framework with a bias-corrected sliding-window estimator and a lightweight change-point detector, assembling a complete pipeline that runs in time linear in the total number of hyperedges and requires no node alignment across datasets or snapshots. We prove that the measure collapses to zero under clique expansion, demonstrating that it captures interaction-order information that is discarded by the standard size-blind pairwise projection. Experiments on six small and large publicly available benchmark datasets show that the entropy rate spans 1.60 bits across domains, detects unsupervised structural change points, and discriminates between structurally distinct interaction cultures even within the same domain. Our framework is computationally lightweight and applicable to any dataset that can be represented as a temporal sequence of hypergraphs, paving the way for practical, scalable, interaction-order-aware analysis of large-scale higher-order temporal data. Full article

Driven by the demand for high-quality housing, fourth-generation residential buildings—known internationally as “Vertical Forest” and in China as “Urban Forest Garden”—have developed rapidly. Initially built in mild southern regions, they have recently expanded to colder northern areas, with over 50 projects underway in provinces such as Shanxi, Hebei, Shaanxi, and Gansu. Several cities have introduced design standards and incentives, and the China Association for Standardization of Engineering Construction has issued the “Design Standards for Urban Forest Garden Housing.” However, in cold regions, where winters are long and cold and summers are short and hot, there is a lack of systematic quantitative research on how balcony design affects building carbon reduction, energy efficiency, and indoor thermal comfort. To address this research gap, this paper poses the following research questions: (1) In fourth-generation residential buildings in cold regions, how do different combinations of balcony orientations affect annual energy consumption and indoor thermal comfort? (2) Which balcony configurations offer the best balance between carbon reduction, energy efficiency, and thermal comfort? Based on statistical analysis of terrace configurations from more than 40 projects, 12 typical configuration models were identified. Using Ladybug and Honeybee tools on the Grasshopper platform, building energy consumption and indoor thermal comfort were simulated. Multi-objective trade-off analysis was performed using the Pareto front method. In this study, indoor thermal comfort was evaluated using the PMV (Predicted Mean Vote) index. PMV is an index proposed by Professor Fanger that comprehensively reflects human thermal sensation, taking into account air temperature, humidity, wind speed, mean radiant temperature, human metabolic rate, and clothing thermal resistance. Its typical range is −3 (cold) to +3 (hot); in this study, the comfort zone was defined as −1 ≤ PMV ≤ 1. Key findings: (1) The southwest + south terrace configuration shows the highest annual energy consumption, exceeding the lowest (northwest + west) by 2.7%, indicating that south-facing terraces are less favorable for carbon reduction. (2) The best thermal comfort is achieved with east, west, and south orientations. Compared to the least comfortable combination (southwest + northwest), the difference in PMV comfort percentage reaches 2.4%. (3) The Pareto front reveals that beyond a certain comfort level, energy consumption increases sharply. The west + south and east + south combinations yield the highest thermal comfort (49.4%) while maintaining relatively low energy consumption (17.98 kWh/m²). Therefore, in cold regions, fourth-generation residential designs should prioritize terrace combinations integrating south-facing and side-facing orientations and avoid pure corner configurations to balance winter solar gain and summer shading. Full article

Young Black women face barriers to exercise and elevated cardiometabolic risk, yet resistance exercise (RE) remains underutilized despite its benefits. We evaluated the feasibility and acceptability of a 24-week culturally adapted RE intervention + text messages in young Black women. Participants were randomized to the motivational exercise group (MEG; n = 14) or the standard exercise group (SEG; n = 13). Both groups received 10 and 11 weeks of supervised (by a Black woman) and unsupervised RE. MEG received additional cultural adaptations and weekly discussions to build competence, autonomy, and self-regulation strategies + mobile support. Feasibility and acceptability were assessed via recruitment, consent, and retention rates, adherence, and thematic analysis of semi-structured interviews. Recruitment and consent rates were 97.2% and 100.0%, respectively. Retention rates were 93.3% (MEG) and 86.7% (SEG) at 12 weeks, and 93.3% and 80.0% at 24 weeks, respectively. Supervised adherence was 93.9% and 88.8% in MEG and SEG, and 14.3% and 15.4%, respectively, during unsupervised RE. Participants desired continued support and a more tailored mobile experience during unsupervised RE. Supervised RE with ethnically matched trainers was feasible and acceptable. Future interventions should incorporate mobile tools with tailored feedback and accountability strategies to sustain long-term RE to improve health outcomes in this population. Full article

The assessment of post-earthquake performance of structures is a crucial task in structural health monitoring. To address this challenge, a novel performance-based probabilistic post-earthquake assessment (PBPPA) method is proposed. This method involves a modeling stage and an application stage. First, twenty earthquake waves with fortification intensity and rare intensity are selected from the PEER (Pacific Earthquake Engineering Research Center) database, and the mean and standard deviation are consistent with the target values. Then, OpenSEES is used to establish a finite-element model for the monitored structure. Next, the damage state and performance measures of structural elements are calculated according to the standard for seismic resilience assessment of buildings. Finally, the conditional probability density function of the repair cost relative to the monitoring data is presented, along with a probabilistic rating method of three levels. Using the Old Hall of Nanjing Museum as an example, the proposed method is verified with numerical simulation and real-world monitoring data collected during earthquakes. It is found that the most likely occurring repair cost and deviation of the conditional probability density function are proportional to the intensity of dynamic responses, which is consistent with the actual situation. Therefore, the presented method can be used as the decision-making basis for the determination of the structural post-earthquake condition and provides a useful reference for the future development of post-earthquake assessment methods. Overall, the proposed method is a valuable contribution to the field of structural health monitoring and has the potential to enhance the safety and resilience of structures after earthquakes. Full article

Purpose: Despite growing research on green fintech and sustainable finance individually, no systematic theoretical framework explains how AI-driven green fintech solutions can be adopted in banking for sustainable investment purposes. This paper addresses this demonstrated gap by developing the first bibliometrically grounded, TOE-based conceptual framework for AI-driven green fintech adoption in banking. Design/Methodology/Approach: A two-phase approach is employed. First, a bibliometric analysis of 79 Scopus-indexed documents (2020–2026) using bibliometrix in R provides quantitative evidence of the research gap through keyword co-occurrence networks, thematic mapping, and trend topic analysis. Second, building on this evidence, a conceptual framework integrating the Technology–Organization–Environment (TOE) framework with three mediating constructs, technological readiness, sustainability culture, and regulatory support is developed and five theoretical propositions are derived. Findings: The bibliometric analysis reveals an annual growth rate of 78.4% in the field and confirms that the TOE framework has never occupied the motor themes quadrant of the green fintech literature. The proposed framework theorizes three mediated pathways through which technological, organizational, and environmental conditions translate into improved sustainable investment outcomes including enhanced ESG transparency, increased green investment allocation, and SDG alignment. Practical Implications: The framework provides bank executives with three actionable intervention points: technological infrastructure investment, sustainability culture embedding, and regulatory engagement and offers policymakers evidence-based guidance for designing supportive green fintech adoption frameworks. Originality/Value: This study presents a conceptual framework that is, to the authors’ knowledge, the first to combine TOE theory, AI-driven green fintech, a banking context, an explicit three-mediator architecture (technological readiness, sustainability culture, regulatory support), and sustainable investment outcomes as the dependent variable, grounded in reproducible bibliometric evidence. Existing studies address subsets of these dimensions; none integrates all six simultaneously. Full article

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article

Rotary steerable drilling technology is fundamentally aimed at achieving precise wellbore trajectory control. As a representative directional tool, a dynamic push-the-bit RSS generates steering force during rotary drilling through the interaction between its extendable steering pads and the borehole wall, and it is distinguished from static push-the-bit RSS by the rotational friction that develops at the pad–wall interface. To further clarify the influence of friction on the resultant steering force and the build-up rate, this study develops a steering-force optimization model that explicitly incorporates tangential friction, validates the model, and then conducts numerical simulations to examine how PDC bit design parameters and formation properties affect the build-up rate. The results indicate that the friction-aware optimization model can achieve a higher build-up rate. Quantitatively, relative to the friction-free allocation model that is commonly used as the baseline in push-the-bit BUR prediction, the friction-aware formulation increases the final lateral displacement from approximately 28.4 to 30.6 mm in the analytical comparison (+7.7%) and from approximately 24.3 to 26.9 mm in the full-scale finite-element comparison (+10.7%) over the same steering-force action time. In soft formations with a low internal friction angle, a bit design combining a moderate gauge-protection dimension, an appropriate inner cone angle, and a large crown radius can effectively enhance lateral cutting and steering-force transmission, thereby improving build capability and trajectory stability. These findings provide a theoretical basis for improving build-rate efficiency in push-the-bit rotary steerable drilling systems. Full article

Purpose/Background: Small and medium-sized enterprises (SMEs) that deploy Enterprise Resource Planning (ERP) systems face a persistent paradox: although ERP centralises organisational data, frontline users frequently lack the technical expertise to navigate complex menu structures, preventing efficient information retrieval for decision-making. This study presents a preliminary Research and Development (R&D) effort to design, build, and qualitatively evaluate a prototype intelligent question-answering system that connects to Odoo ERP through application programming interfaces (APIs) via Retrieval-Augmented Generation (RAG), enabling natural-language access to real SME business data. Methods: A single-organisation R&D case study was conducted at an SME in Khon Kaen Province, Thailand. The development cycle comprised problem analysis and requirement specification, system architecture design, prototype construction, integration and deployment, iterative testing and refinement, and multidimensional evaluation. The prototype was implemented with Chainlit (conversational interface), FastAPI (orchestration and tool-calling layer), and Odoo XML-RPC/JSON-RPC APIs (structured data retrieval). A fixed set of 20 test questions spanning four complexity levels (easy, moderate, complex, out-of-scope) was evaluated by three automated tools (OpenAI Evals, DeepEval, Ragas), by real-task verification against live ERP data, by five domain experts using 5-point Likert-scale questionnaires, and by three end users from the case-study organisation, who additionally completed the System Usability Scale (SUS). Given the very small expert and user samples, all human evaluation results are reported descriptively as preliminary, exploratory indicators rather than as statistically generalisable measures. Results: Automated evaluation achieved indicative pass rates of 95.00% (OpenAI Evals, 19/20), 90.00% (Ragas, 18/20), and 85.00% (DeepEval, 17/20). Descriptive expert feedback (n = 5) yielded an overall mean of 3.82 (high level), and descriptive end-user feedback (n = 3) yielded an overall satisfaction mean of 4.33 (highest level). The SUS score was 66.67/100, sitting at the boundary between ‘OK’ and ‘Good’ and revealing a divergence between high stated satisfaction and lower confidence in independent system use (item 9, raw mean = 2.33) and a stronger perceived need for expert assistance (item 4, raw mean = 2.67). These results are interpreted as preliminary diagnostic signals for further development rather than as confirmatory evidence. Conclusions: This preliminary R&D study suggests that a RAG-based ERP chatbot can meaningfully simplify ERP data access for SME users, while exposing persistent gaps in multi-step reasoning, user confidence, and data privacy boundaries that must be addressed in subsequent development cycles. The SUS pattern, in particular, suggests a ‘novelty effect’ in which users are enthusiastic about the natural-language interface yet remain anxious about correctness and stability during real tasks. Originality/Value: This work contributes a transparent, replicable preliminary R&D blueprint that combines (i) live API-mediated RAG over structured ERP data, (ii) a complementary multi-tool automated evaluation set (OpenAI Evals + DeepEval + Ragas), (iii) descriptive expert and end-user feedback, and (iv) SUS-based usability assessment, all documented for a single Thai SME using Odoo 18. The study explicitly positions itself as an early step toward larger, multi-site, and on-premise deployments of trustworthy ERP-integrated conversational agents. Full article

Postharvest losses in climacteric fruits are largely driven by ethylene, and inhibiting its biosynthesis is an active research goal. RNA aptamers are attractive candidates for modulating the two rate-limiting enzymes—ACC oxidase (ACO) and ACC synthase (ACS), without the toxicity concerns of chemical inhibitors. We built a computational pipeline using three independent docking engines: ITD DOCK (Graphics Processing Unit [GPU]-accelerated, Assisted Model Building with Energy Refinement [AMBER] force fields), HDOCK Lite (knowledge-based geometric potentials), and HADDOCK3 (semi-flexible refinement), screened against large aptamer libraries. Applied to ACC oxidase with 9813 aptamers, engine scores were largely complementary, with all pairwise correlations statistically non-significant on the full dataset (Spearman $\left|\rho \right|\le 0.013$, $p>0.20$ for all pairs, $n=9813$). A Random Forest on sequence-only features failed to predict docking scores ($R^2=-0.012$, Root Mean Square Error [RMSE] = 21.79 kcal/mol), while a positive-control model on HADDOCK3 energy components achieved $R^2=0.991$, confirming that predictive information only becomes available after docking. A preliminary pilot screening against ACC synthase ($n=97$ valid complexes) suggested pipeline generalizability to a second structurally distinct target and identified two putative dual-active candidates, pending confirmation against the full library. The complete workflow is automated in AptaDock, a standalone desktop application. Full article

Sustainable rural tourism governance in the Global South faces a persistent challenge: enabling genuine community participation in destination management while protecting environmental assets and cultural heritage. This study examines participatory governance practices at Kampung Senyum Homestay, Cibeusi Village, West Java, Indonesia, a community-based tourism (CBT) initiative that has sustained operations for over eight years, despite a 60% failure rate among comparable initiatives. A qualitative case study design was employed, with data collected over six months (November 2022–May 2023) through participant observation (12 days), in-depth interviews with 14 stakeholders, and document analysis. Data were analyzed using Miles et al.’s interactive model and critical discourse analysis. Findings reveal three interrelated participation layers shaping tourism governance outcomes: interpersonal engagement fostering horizontal host–guest relationships (89% of tourists report kinship-based experiences); deliberative governance through musyawarah desa enabling community-led environmental stewardship, including the collective rejection of a proposal to bring 100 tourists monthly to protect waterfall ecosystems; and digital storytelling by youth extending local heritage narratives globally (150 posts, 7.2% engagement rate). The study proposes a ‘layered participation’ model demonstrating that tourism sustainability depends on participatory governance mechanisms that build social trust, integrate traditional ecological knowledge, and balance economic development with environmental conservation and cultural heritage management. The study also critically examines structural inequalities, including gender asymmetries, unequal benefit distribution, and linguistic barriers, that persist within participatory governance structures, offering a contextually grounded governance framework for rural tourism destinations in the Global South. Full article

Green walls, as part of nature-based solutions, have been implemented in urban environments, enhancing energy efficiency, thermal regulation, biodiversity, environmental quality, and human well-being. Despite these benefits, green walls’ environmental performance across their life cycle is reported inconsistently in the literature, limiting robust comparisons and evidence-based decision-making in the built environment. This review synthesizes current knowledge on the environmental performance of green walls, living wall systems, and active living walls, including systems that improve indoor air quality and enable water reuse. A systematic literature review was conducted following PRISMA guidelines using the databases ScienceDirect, Scopus, and Google Scholar. The results show that methodology gaps in life cycle assessment (LCA) studies of living wall systems restrict their applicability for evidence-based design and specification. Future research should integrate embodied and operational impacts in scenario-based and sensitivity analyses considering plant selection, irrigation strategies, maintenance regimes, replacement rates and service-life assumptions. More focus should be given to tropical cities to understand the impact of climate, water demand, vegetation performance. and maintenance intensity. These improvements would lead to more comparable, context-sensitive, and design-oriented LCA evidence for sustainable building applications. Full article

Additive manufacturing (AM) requalification and change-control workflows often require evidence that a candidate machine, parameter set, scanner subsystem, facility, or measurement workflow remains comparable to a stable reference process after a change, but fabrication and testing costs limit exhaustive multifeature studies. The aim of this study was to address this engineering design problem by developing a practical multifeature equivalency screening framework for AM settings in which prior engineering evidence already suggests that the candidate process should be comparable to the reference process. Building on prior work focused on the univariate problem, the proposed framework uses reference-defined percentile bins, feature-wise distributional tests, and family-wise error-rate control to screen for evidence of non-equivalency across multiple measured attributes. A direct joint-binning approach was first shown to become sample-intensive as dimensionality increases, after which an independent feature-wise method and an exploratory dependent bivariate extension were developed. Simulation-based power analyses quantified the trade-offs among power, detectable effect size, distributional resolution, feature count, and the combined costs of fabrication and measurement. In a laser-based powder bed fusion validation study with 40 observations per process and three corner-deviation features, the expected-equivalent AconityMIDI+ candidate satisfied all feature-wise equivalency criteria ($\tilde{V}=0.207$–$0.214<C{I}^{+}=0.276$), whereas the expected non-equivalent SLM280 HL candidate failed all three feature-wise tests ($\tilde{V}=0.357$–$1.000>C{I}^{+}=0.276$). These results support multivariate equivalency as a requalification screening tool for AM process comparability and change control, while confirming that it should not be interpreted as proof of physical-process identity or as a replacement for first-time formal qualification. Core procedures are implemented in the open-source R package MultivariateEquivalency. Full article

Background/Objectives: The interplay between oral and systemic diseases is highlighted by the shared inflammatory mechanisms and epidemiological associations between periodontitis and rheumatoid arthritis (RA). Building on previous syntheses of the effect of periodontal therapy on RA disease activity, we sought to refine the evidence base through strict restriction to randomized controlled trials (RCTs), separate analysis of the two non-interchangeable formulations of the Disease Activity Score on 28 joints (DAS28-CRP and DAS28-ESR, based on either C-reactive protein or erythrocyte sedimentation rate, respectively), and inclusion of recent randomized trials. We aimed to determine whether the first two steps of periodontal therapy (steps 1 and 2 of the 2020 EFP S3-level clinical practice guideline), delivered through supragingival professional mechanical plaque removal and subgingival instrumentation, reduce DAS28 in adults with concurrent RA and periodontitis. Methods: The review protocol was registered in PROSPERO (CRD420261400735). Five databases were searched in accordance with PRISMA 2020. Only RCTs were eligible. Risk of bias was assessed with RoB-2. DAS28-CRP and DAS28-ESR were analyzed in separate random-effects forest plots. Sensitivity analyses addressed adjunctive antibiotics and high baseline disease activity. Results: Ten trials (n = 430 randomized patients) were included. At 3 months, DAS28-CRP was significantly reduced (between-group MD = −0.84, 95% CI −1.38 to −0.29; change-from-baseline MD = −0.55, −0.92 to −0.19). On DAS28-ESR at 3 months, the change-from-baseline estimate was significant (MD = −1.27, −2.22 to −0.31) and the follow-up estimate concordant in direction but not significant (MD = −0.89, −1.85 to 0.07), with substantial heterogeneity. Conclusions: Periodontal therapy may be associated with short-term reductions in RA disease activity, particularly DAS28-CRP at 3 months, with directionally concordant but less certain effects on DAS28-ESR. The evidence remains limited by small sample sizes, risk of bias, substantial heterogeneity of the DAS28-ESR estimates, and sparse follow-up beyond 3 months. As no trial reported individual responder categories, these group-level findings support periodontal therapy as a possible adjunctive measure in RA rather than a predictable, clinically meaningful benefit at the individual patient level. Full article