Search Results (5,884)

This study examines the effect of innovation on climate resilience in developing countries, covering annual data from 2008 to 2022, with a focus on how this relationship varies across different levels of vulnerability. The primary purpose is to understand whether innovation contributes uniformly to climate resilience or if its impact differs depending on a country’s resilience status. Addressing this question is crucial for developing evidence-based and context-specific climate policies. To capture these heterogeneous effects, this study employs a panel quantile regression approach using data from developing countries. This method allows the estimation of the influence of innovation proxied by the Global Innovation Index (GII) and the climate resilience Index. The findings show that innovation has a consistently positive and statistically strong impact on climate resilience across all quantiles, with the strongest impact at the median. The results carry important policy implications. Firstly, developing countries should prioritize innovation-driven strategies to strengthen resilience across different climate risk profiles. Secondly, policies supporting renewable energy deployment should target countries with higher emissions to maximize their impact. Thirdly, fiscal tools, such as environmentally aligned tax policies, should be emphasized particularly in more vulnerable contexts. Finally, trade policies, population dynamics and integration of climate finance variables must be integrated into climate strategies to enhance long-term sustainability. Full article

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

To address the challenges associated with laser powder bed fusion (LPBF) of overhanging structures—namely warping deformation, powder adhesion, and inadequate forming accuracy—this study investigates the optimization of the support–part contact interface using Inconel 625 alloy. The objective is to achieve high-quality part formation with minimal support structures. A Taguchi experimental design was employed to systematically evaluate the effects of key block support parameters—tooth height, tooth top length, tooth base length, and tooth base spacing—on the forming performance of overhanging structures, with forming accuracy and support removability as the optimization targets. The results reveal that tooth top length significantly influences both the forming accuracy of overhanging specimens and the ease of support removal. Specifically, an increase in tooth top length leads to a rapid reduction in specimen deformation, but simultaneously increases the difficulty of support removal. When the tooth top length was set to 0.1 mm, all overhanging specimens failed to form successfully. Tooth base length also plays a critical role in support removability, with removal difficulty initially decreasing and then stabilizing as the tooth base length increases. Based on the trade-off between forming quality and support removability, the optimal parameter combination was identified as: tooth height of 0.4 mm, tooth top length of 0.7 mm, tooth base length of 1.0 mm, and tooth base spacing of 0.3 mm. A validation experiment conducted using this optimized configuration demonstrated good forming accuracy in the support contact area, with a deformation value of −0.208 mm, confirming the effectiveness and reliability of the proposed parameters. This study not only provides a theoretical foundation for the optimal design of block supports in LPBF but also offers experimental data and practical guidance for selecting support parameters in the fabrication of overhanging structures. Full article

During the interpretation of Borehole Radar (BHR) B-scan profiles, the accurate determination of the azimuth of geological targets in three-dimensional space is a critical issue for achieving precise anomaly localization and spatial structure inversion. However, existing directional BHR anomaly localization methods exhibit limited intelligence, insufficient adaptability to multi-site data, and weak generalization capability, rendering them inadequate for engineering applications under complex geological conditions. To address these challenges, a robust deep learning model, termed BSS-Pose-BHR, is developed based on YOLOv11n-pose for keypoint detection in directional BHR profiles. The model incorporates three key optimizations: Bi-Level Routing Attention (BRA) replaces Multi-Head Self-Attention (MHSA) in the backbone to improve computational efficiency; Conv_SAMWS enhances keypoint-related feature weighting in the backbone and neck; and Spatial and Channel Reconstruction Convolution (SCConv) is integrated into the detection head to reduce redundancy and strengthen local feature extraction, thereby improving suitability for keypoint detection tasks. In addition, a three-dimensional electromagnetic model of limestone containing a certain density of clay particles is established to construct a simulation dataset. On the simulated test set, compared with current mainstream deep learning approaches and conventional directional borehole radar anomaly localization algorithms, BSS-Pose-BHR achieves superior performance, with an mAP50(B) of 0.9686, an mAP50–95(B) of 0.7712, an mAP50(P) of 0.9951, and an mAP50–95(P) of 0.9952. Ablation experiments demonstrate that each proposed module contributes significantly to performance improvement. Compared with the baseline, BSS-Pose-BHR improves mAP50(B) by 5.39% and mAP50(P) by 0.86%, while increasing model weight by only 1.05 MB, thereby achieving a reasonable trade-off between detection accuracy and complexity. Furthermore, indoor physical model experiments validate the effectiveness of the method on measured data. Robustness experiments under different Peak Signal-to-Noise Ratio (PSNR) conditions and varying missing-trace rates indicate that BSS-Pose-BHR maintains high detection accuracy under moderate noise and data loss, demonstrating strong engineering applicability and practical value. Full article

Objectives: The impact of renal ischemia during partial nephrectomy (PN) on postoperative renal function remains controversial. On-clamp PN provides improved surgical exposure and haemostasis but induces warm ischemia, which may impair renal function. Off-clamp PN avoids ischemia-related injury and may better preserve renal function, although concerns persist regarding blood loss and oncological safety. We systematically compared perioperative and functional outcomes, as well as surgical margin status between on-clamp and off-clamp PN. Methods: We performed a systematic search of PubMed, Embase, Cochrane, Web of Science, and Scopus to identify randomized controlled trials (RCTs) and observational studies comparing on-clamp versus off-clamp PN with no publication time limitations. Outcomes included estimated glomerular filtration rate (eGFR), percentage eGFR change, estimated blood loss (EBL), transfusion rates, positive surgical margins (PSMs), operative time, and complications. Results: Thirty-nine studies (four RCTs) including 10,154 patients were analysed. Off-clamp PN was associated with a smaller decline in eGFR (mean difference [MD] −4 mL/min/1.73 m², 95% CI −5.7 to −2.8) and lower percentage eGFR loss (MD −1.7%, 95% CI −2.8 to −0.7). On-clamp PN was associated with lower EBL (MD −48 mL, 95% CI −72 to −25). Transfusion rates favored on-clamp PN but were not statistically significant (OR 0.7, 95% CI 0.5–1.0). On-clamp PN was associated with a higher risk of PSM (OR 1.3, 95% CI 1.0–1.7) and postoperative complications (OR 1.3, 95% CI 1.1–1.6). Between-study heterogeneity and predominance of observational data were key limitations. Conclusions: Off-clamp PN provides superior renal functional preservation and lower risks of PSMs and complications, at the cost of increased blood loss. These findings support individualized surgical decision-making based on patient and tumor characteristics. What does the study add?: This study provides an extensive and detailed comparison of off-clamp versus on-clamp partial nephrectomy, encompassing more than 10,000 patients from 39 studies. By integrating the available evidence up to late 2024, it delivers comprehensive estimates of the renal functional benefits associated with ischemia-free surgery. Our findings delineate the trade-offs between renal preservation, blood loss, and surgical margin status, thereby informing individualised decision-making in nephron-sparing surgery and refining current understanding of when ischemia avoidance is most clinically advantageous. Patient summary: Our study suggests that performing partial nephrectomy without temporarily clamping the kidney blood vessels may better preserve kidney function and reduce cancer-related surgical risks, but can lead to increased blood loss during surgery. These findings indicate that the choice of surgical technique should be individualised, taking into account tumour features and patient-specific factors. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

The global division of labor system is increasingly refined, and the core components of some manufacturing enterprises are concentrated in a few (or even a single) suppliers, resulting in supply dependence. Excessive concentration of suppliers can lead to a higher risk of supply chain disruption. To this end, taking manufacturing companies listed on the Shanghai and Shenzhen A-share markets in China from 2010 to 2024 as samples and referring to Huazheng ESG rating data, research shows how the ESG performance of manufacturing companies reduces supplier concentration. The research found that (1) the ESG performance of manufacturing enterprises significantly reduces supplier concentration,—this effect is mainly reflected in social responsibility (S dimension)—and firm size has a positive moderating effect; (2) ESG performance has a mediating effect of alleviating financing constraints and enhancing trade credit in the process of reducing supplier concentration; and (3) heterogeneity analysis results show that the inhibitory effect of ESG performance on supplier concentration is more significant in non-state-owned enterprises. Through empirical analysis, the research scope of ESG performance was expanded to the upstream supply chain field, emphasizing the importance of ESG performance in manufacturing enterprises and providing theoretical and empirical evidence for enterprises to achieve high-quality and sustainable development. Full article

This study analyzes the long-run relationships between digitalization, investment, innovation, and economic growth in connection with the energy transition in the SPRING-F group (Spain, Poland, Romania, Italy, the Netherlands, Germany, and France) using annual data for the period of 2000–2024. The analysis starts from the premise that digitalization affects economic performance not only directly, but also through structural transmission mechanisms linked to investment and the energy transition. To capture these dynamics, this study employs three complementary panel ARDL models. The first model explains economic growth (GDP per capita) as a function of digitalization, capital accumulation, R&D expenditure, renewable energy consumption, trade openness, and foreign direct investment. The second model estimates gross capital formation (GCF) in order to assess the investment transmission channel. The third model explains renewable energy consumption (RNEC) in order to capture the sustainability dimension. The results show that trade openness and capital accumulation are the strongest long-run drivers of economic growth in the SPRING-F group. Internet use, R&D expenditure, and FDI also display positive long-run associations with GDP per capita, whereas fixed broadband subscriptions and renewable energy consumption enter the growth equation with negative coefficients, suggesting that digital infrastructure and the green transition do not automatically generate immediate growth gains. The GCF model confirms that investment acts as an important transmission mechanism, especially through the robust GDP–GCF linkage. The RNEC model indicates that the energy transition is positively associated with investment, innovation, and trade openness, while GDP and digital infrastructure remain negatively associated with the renewable energy share. Overall, the findings point to a conditional and nonlinear relationship between growth, digitalization, investment, and sustainability, with the sustainability channel remaining more specification-sensitive than the growth and investment equations. The long-run results for the GDP equation should also be interpreted with additional caution, given the comparatively weaker cointegration evidence for Model 1. Full article

The rapidly growing data demands of autonomous driving and onboard multimedia services pose significant challenges to traditional roadside unit (RSU) based content delivery, particularly under limited cache capacity and coverage gaps. In the content-centric Internet of Vehicles (CIoV), vehicle-to-vehicle (V2V) precaching has emerged as an effective solution to mitigate these limitations. However, existing schemes rely on static precaching roles, leading to inefficiencies when a precaching vehicle initiates its own content request. To address this issue, we propose a dynamic relay assignment scheme (DRAS) that enables seamless role transitions without discarding cached data. Upon detecting such a role-transition event, the RSU assigns two new precaching vehicles to independently serve the original requester vehicle and the newly transitioned requester vehicle, ensuring continuous service. Furthermore, we extend this to an energy-efficient DRAS (EE-DRAS) that incorporates vehicle-to-infrastructure (V2I) and V2V transmission energy costs into the selection process, achieving a balanced trade-off between energy consumption and delivery efficiency. Extensive NS-3 simulations show that DRAS reduces average delay by up to 53% and improves throughput by 8% over existing baselines. EE-DRAS further reduces energy consumption by up to 66% while maintaining service fairness. Full article

Achieving urban climate neutrality and interim mitigation targets requires rapid demand-side emission reductions, yet current user-centred interventions remain fragmented, are often concentrated on low-impact actions, and rarely provide a traceable basis for comparing outcomes, validity conditions, and equity implications across contexts. This paper reframes demand-side mitigation as a design problem of “use infrastructures”: integrated configurations of communication, product-technology, services, interaction, and governance that make low-carbon choices practicable within everyday routines. We introduce the Design Evidence Link (DEL) as a traceability device supporting ex ante configuration (selection and orchestration of levers) and ex post verification (monitoring, attribution of outcomes, and trade-off control). Through a design-led comparative analysis of nine international cases in high-impact sectors (household energy, ground mobility, food systems, and circular economy/materials), we derive and consolidate a shared extraction and coding protocol that links determinants (barriers and enablers) to design requirements and decision-grade metrics (carbon impact, adoption, continuity, and equity), explicitly qualifying uncertainty and evidence levels. Cross-case results show that effective interventions rely less on isolated information and more on coordinated action packages that reduce cognitive and economic frictions, enhance data credibility through standards and accountability, and embed follow-up mechanisms that support behavioural continuity. DEL also surfaces recurring validity conditions and failure modes (digital exclusion, trust erosion, rebound, and lock-in), translating them into operational criteria for policy and design. Compared with behaviour-change or theory-of-change framings, DEL focuses on the observable orchestration of integrated conditions of use and on the explicit grading of evidence. It should therefore be read as a structured analytical–operational framework for ex ante and ex post assessment, whose transferability remains conditional on source quality, contextual prerequisites, and the limits of the selected cases. Full article

Resource-constrained IoT systems face a fundamental conflict between cryptographic security and energy efficiency, particularly in critical infrastructure monitoring requiring long-term autonomous operation. This study presents a hybrid framework integrating signal-adaptive compression with hardware-accelerated authenticated encryption to resolve this trade-off. The Dynamic Payload Compression with Selective Encryption framework classifies sensor data into three SNR regimes and applies adaptive compression strategies: 24.15-fold compression for low-SNR backgrounds, 1.77-fold for transitional states, and no compression for high-SNR leak detection events. Experimental validation using 2714 acoustic sensor samples demonstrates 5.91-fold average payload reduction with 100% detection accuracy. The integration with STM32L5 hardware AES acceleration reduces power–data correlation from 0.820 to 0.041, increasing differential power analysis attack complexity from 500 to over 221,000 required traces. Compression-induced timing variance provides additional side-channel masking, burying cryptographic signals beneath a 0.00009 signal-to-noise ratio. Projected on 19,200 mAh lithium thionyl chloride batteries, the system achieves 14-year operational lifetime under realistic duty cycles, exceeding industrial requirements for critical infrastructure protection while maintaining robust security against physical attacks. Full article

This study proposes a machine learning framework tailored to the institutional characteristics of Taiwan’s stock market, aiming to enhance forecasting accuracy for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The model employs the Radius Neighbors Regressor with a dynamic radius-based similarity measure and integrates domain-specific features including technical indicators, volume–price relationships, and Qualified Foreign Institutional Investor (QFII) activity. A custom 60-day input window with a 20-day forecast horizon is applied to capture medium-term market dynamics. The framework was evaluated through extensive backtesting and real-world validation with the TAIEX Futures. The results demonstrate that the model achieves a peak directional accuracy of 85.1% under optimal parameter settings. Moreover, trading simulations confirm its practical viability, yielding a cumulative return on investment (ROI) of approximately 1600% during the short-term evaluation period (2023–2025) and nearly 2000% in the long-term evaluation (2019–2025), even after accounting for transaction costs and stop-loss mechanisms. These findings indicate that combining historical pattern similarity with institutional investor behavior substantially improves predictive power and profitability. Nevertheless, the framework remains constrained by its reliance on Taiwan-specific institutional features and historical trading data, limiting generalizability. Future research should extend applications to other markets while incorporating macroeconomic variables, corporate fundamentals, and news-driven signals to enhance adaptability. Full article

This study investigates whether fluctuations in the real exchange rate give rise to symmetric or asymmetric J-curve effects in manufacturing trade between Türkiye and Germany, thereby positioning the analysis within and contributing to the broader scholarly discourse on exchange rate–trade balance dynamics. Using monthly data for the period 2013M01–2025M07, the paper first estimates a linear Autoregressive Distributed Lag (ARDL) model for the bilateral manufacturing trade balance and subsequently extends the framework to a nonlinear ARDL (NARDL) specification, which explicitly incorporates symmetry and asymmetry by decomposing real exchange rate changes into positive (depreciation) and negative (appreciation) partial sums. The linear ARDL results provide no evidence of a conventional J-curve and suggest that the aggregate impact of the real exchange rate is weak and often statistically insignificant. In contrast, the NARDL estimates uncover pronounced long-run and cumulative short-run asymmetries: real depreciations of the Turkish lira are associated with a persistent improvement in the bilateral manufacturing trade balance, whereas appreciations exert weak and statistically insignificant effects, a finding that remains robust when a real effective exchange rate measure is employed. Overall, the evidence indicates that Türkiye–Germany manufacturing trade does not conform to the standard J-curve pattern. These findings suggest that trade policy should adopt an asymmetric stance toward exchange rate movements: since depreciations yield persistent trade balance improvements while appreciations produce negligible effects, policies designed to support export competitiveness should prioritize the management of depreciation episodes rather than assuming symmetric adjustment dynamics. Full article

The Internet of Things (IoT) plays an increasingly central role in healthcare by enabling continuous patient monitoring, remote diagnosis, and data-driven clinical decision-making through interconnected medical devices and sensing infrastructures. Despite these advances, IoT healthcare systems remain constrained by persistent challenges related to data privacy, computational efficiency, scalability, and regulatory compliance. Federated learning (FL) reduces reliance on centralised data aggregation but remains vulnerable to inference-based privacy risks, while edge-oriented approaches are limited by device heterogeneity and restricted computational and energy resources; the deployment of large language models (LLMs) further exacerbates concerns surrounding privacy exposure, communication overhead, and practical feasibility. This study introduces Trustworthy Intelligence (TI) as a guiding framework for privacy-preserving distributed intelligence in IoT healthcare, explicitly integrating predictive performance, privacy protection, and deployment-oriented system design. Within this framework, split learning (SL) is examined as a core architectural mechanism and extended to support split-aware LLM integration across heterogeneous devices, supported by a structured taxonomy spanning architectural configurations, system adaptation strategies, and evaluation considerations. The study establishes a systematic mapping between SL design choices and representative healthcare scenarios, including wearable monitoring, multi-modal data fusion, clinical text analytics, and cross-institutional collaboration, and analyses key technical challenges such as activation-level privacy leakage, early-round vulnerability, reconstruction risks, and communication–computation trade-offs. An energy- and resource-aware adaptive cut layer selection strategy is outlined to support efficient deployment across devices with varying capabilities. A proof-of-concept experimental evaluation compares the proposed SL–LLM framework with centralised learning (CL), federated learning (FL), and conventional SL in terms of training latency, communication overhead, model accuracy, and privacy exposure under realistic IoT constraints, providing system-level evidence for the applicability of the TI framework in distributed healthcare environments and outlining directions for clinically viable and regulation-aligned IoT healthcare intelligence. Full article

This study examines selected factors influencing white maize price movements in South Africa over the period 1994–2024. Given the importance of white maize for food security, understanding the drivers of producer price dynamics is essential for effective policy formulation and managing price stability. Annual time-series data are analysed using an Autoregressive Distributed Lag (ARDL) modelling framework, complemented by bounds testing, an error-correction model, Toda–Yamamoto causality and structural break tests. The bounds test confirms the existence of a stable long-run cointegrating relationship between maize prices and the selected explanatory variables. In the short run, imports and fuel prices exert significant upward pressure on maize producer prices, while lagged fuel prices and rainfall reduce prices. In the long run, imports and fuel prices remain statistically significant determinants, whereas maize production, exports, the exchange rate, and rainfall are insignificant. Complemented with the structural break tests that identify regime shifts in the early 2000s, 2012, and 2021, causality results indicate that imports, rainfall and fuel prices lead to Granger causality in maize producer prices. Collectively the findings reinforce the conclusion that white maize prices in South Africa are governed by long-run structural relationships, while short-run price movements reflect temporary adjustments rather than permanent shifts in market fundamentals. An integrated, long-horizon analysis that jointly incorporates climatic, trade, and macroeconomic determinants within an ARDL framework is provided by the study. Therefore, the findings have important implications for climate-risk management, transport cost containment, trade and price-stabilisation policies. Full article