Search Results (355)

The growing scale and complexity of industrial infrastructure make efficient and reliable inspections a critical challenge. Inspection task demands often vary dynamically, requiring efficient and demand-responsive inspection strategies to ensure stable operation. However, existing UAV inspection approaches typically deploy UAV base stations (UAV-BSs) based on fixed inspection frequencies, which are inadequate for adapting to such dynamic demands and may reduce inspection efficiency. Moreover, these approaches often rely on historical inspection data, whose empirical distributions may deviate from the true distributions, thereby compromising solution robustness. To address these issues, this paper proposes a two-stage distributionally robust optimization (TDRO) framework for joint UAV-BS deployment and inspection routing in dynamic environments. The framework accounts for uncertainties in both inspection frequency and distributional perturbations. Uncertainty sets constructed based on probability metrics are employed to capture deviations between empirical and true distributions, forming the foundation of the two-stage distributionally robust optimization model. The resulting model is solved using column-and-constraint generation (C&CG) integrated with column generation (CG), yielding robust deployment decisions and an effective trade-off between total system cost and inspection efficiency. Simulation results show that the framework effectively addresses inspection frequency uncertainty, reducing the total objective by 5.50% on average, with a further 2.16% reduction when distributional perturbations are considered. Full article

Reliable traffic load characterization remains a critical challenge in many African countries due to the lack of continuous field measurements. This study developed an integrated dynamic traffic monitoring and weigh-in-motion system on representative highways in Kenya to obtain long-term, multi-source traffic data. Traffic operations were quantified across hourly, weekly, and monthly scales, including flow variability, vehicle class composition, axle loads, overload behavior, and speed distributions. Results indicate that the spatiotemporal characteristics of traffic volume show pronounced short-term fluctuations but strong long-term stability. Despite their lower proportion, multi-axle heavy trucks dominate structural loading, with overload ratios exceeding 80% and gross weights approaching 100 t. Over 60% of vehicles operate at medium-to-low speeds (20–60 km/h), extending load duration and increasing pavement damage potential. These combined effects indicate that average indicators alone underestimate true loading demand. The proposed framework provides field-based traffic load spectra and a transferable methodology for traffic monitoring and pavement design optimization across developing regions in Africa. Full article

The electric vehicle revolution has created an urgent need for lithium-ion battery (LIB) recycling, with projections exceeding 11 million tons of end-of-life batteries annually by 2030. However, progress toward a circular economy remains fragmented. This perspective article introduces the concept of a ‘Recycling Trilemma,’ arguing that technological advancements in material separation are systematically undermined by economic volatility and regulatory fragmentation. While current literature focuses on isolated domains—chemistry, business models, or policy—this work provides a systems-level synthesis. By analyzing the friction points between material science (e.g., binder removal, impurity sensitivity), economic realities (e.g., logistics costs, LFP profitability), and regulatory frameworks (e.g., EU vs. US divergence), we propose that true circularity requires synchronized design-for-recycling, market stabilization mechanisms, and harmonized digital product passports. The paper concludes that overcoming the trilemma demands a shift from isolated fixes to integrated, cross-sectoral coordination. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article

Security threats in the integrated circuit (IC) supply chain are intensifying as demand drives fabrication to off-shore, potentially untrusted foundries. To mitigate theft and reverse engineering, recent work has focused on logic locking, encryption, and camouflaging. This paper introduces a probabilistic logic-driven algorithm that selects optimal locations for polymorphic gate replacement to strengthen circuit protection. Our approach leverages emerging polymorphic devices—namely the Giant Spin-Hall Effect (GSHE) switch, the 5-terminal magnetic domain wall motion (DWM) device, and the threshold-voltage-defined (TVD) switch—to diversify functional behavior and obscure true circuit intent. Evaluated on ISCAS-85 and ISCAS-89 benchmarks under state-of-the-art SAT and AppSAT Attacks, the proposed method substantially increases decryption time while achieving a marked improvement in Output Corruption Rate (OCR) relative to prior techniques. In particular, by deploying the GSHE Switch at the highest-probability nodes, we achieve more than 40% OCR along with strong resilience against SAT and AppSAT Attacks, further demonstrating the effectiveness of the proposed approach as a practical and scalable hardware obfuscation strategy. Full article

Heatwaves are among the most significant climate extremes affecting Egypt, with direct impacts on human health, energy demand, water resources, and overall thermal comfort. Although several previous studies have examined heatwave characteristics in Egypt, most have relied on station-based or localized analyses, limiting the understanding of national-scale patterns and recurrence behavior. To address this gap, this study provides a comprehensive national-scale assessment of the spatiotemporal characteristics of heatwave occurrences across Egypt from 1990 to 2023 using daily maximum and minimum temperatures derived from the ERA5 reanalysis dataset. Daytime and nighttime heatwaves were defined using the 90th percentile temperature thresholds and a minimum duration of three consecutive days. This made it possible to study their frequency, duration, severity, seasonal distribution, and how often they happened again. The results demonstrate that heatwaves happen more often and with more severity in late July and August. This is especially true for nighttime heatwaves. These findings indicate that daily baseline temperatures in Egypt have been rising steadily since 2010. Nighttime heatwaves show a notable increase in frequency and persistence, indicating a sustained rise in baseline temperatures and reduced nocturnal cooling. By providing the first long-term, spatially consistent national-scale heatwave assessment over Egypt, this study contributes to a more comprehensive understanding of extreme temperature behavior under ongoing climate change. Full article

Polyethylene glycols (PEGs) are emerging as superior and accessible phase change materials and heat transfer fluids, offering improved thermal properties over conventional thermal oils to meet the demand for innovative, sustainable energy solutions. While general research on PEG performance is still scarce, this paper contributes relevant experimental data. As part of a broad investigation into PEG and PEG-based nanocolloids, this experiment helps to clarify the true potential of these new fluids by outlining both their key advantages and their operational limitations. Consequently, PEG 200 and two PEG 200 + PEG 400 mixtures were considered as base fluids for manufacturing MWCNT nanocolloids, resulting in 15 samples that were thoroughly investigated in terms of density, viscosity and isobaric heat capacity variation with both nanoparticle concentration and temperature. Results revealed that nanocolloid density follows the basic rules for nanoparticle-enhanced fluids, with moderate increase with nanoparticle addition and temperature. Viscosity increased with MWCNT concentration and decreased with temperature, while isobaric heat capacity upsurges with nanoparticle addition. These findings are critical, as they can shed some light into the practical benefits, while clearly explaining the potential drawbacks, of employing these novel fluids in heat transfer applications. Full article

Background and Purpose: Neurology requires integration of clinical reasoning, imaging interpretation, and current knowledge, making it an ideal field for evaluating large language models (LLMs). Methods: Using 1715 questions from the Taiwan Neurology Board Examination (2018–2024), we assessed four LLMs—GPT-4o, GPT-o1, DeepSeek-V3, and DeepSeek-R1—across four formats: single-choice, multiple-choice, true–false, and image-based items. Results: GPT-o1 achieved the highest overall accuracy (83.86%) and demonstrated strong performance on cognitively demanding tasks (82.50% on true–false; 77.26% on image-based). DeepSeek-V3 scored lowest (65.62%) and showed the greatest variability. Statistical analyses confirmed significant inter-model differences (p < 0.01). Accuracy declined across all models in 2024, coinciding with shifts in question design. DeepSeek-R1 was further penalized by alignment-based refusals, resulting in up to 3.81% score loss. Conclusions: These results position the Taiwan Neurology Board Exam as a rigorous benchmark for LLM evaluation and underscore GPT-o1’s potential utility in neurology education and decision support. Full article

Background: A significant rise is observed in the incidence of early-onset colorectal cancer (EOCRC) worldwide, demanding discovery of promising non-invasive diagnostic biomarkers. This systematic review and meta-analysis evaluated the diagnostic accuracy of single circulating microRNAs (miRNAs) for EOCRC detection. Methods: The protocol for this systematic review was prospectively registered with PROSPERO (registration number: CRD420251252155). We systematically searched PubMed, Embase, Web of Science and Scopus through September 2025 following PRISMA 2020 guidelines. Studies stating diagnostic accuracy of single circulating miRNAs in blood samples for histologically confirmed CRC were included. The quality assessment of included studies was done by using QUADAS-2 and bivariate random-effect meta-analysis was performed to calculate pooled diagnostic metrics. Results: Sixteen studies comprising 909 CRC cases and 1214 controls evaluating 22 distinct miRNAs were included. In the primary meta-analysis restricted to early-onset colorectal cancer (EOCRC, <50 years), pooled sensitivity was 84.4% and specificity was 85.7%. Analyses including mixed-age or all CRC populations were conducted as secondary analyses and showed comparable diagnostic performance. Subgroup analysis showed EOCRC patients (<50 years, n = 15) demonstrated sensitivity of 84.4% and specificity of 85.7%. Substantial heterogeneity existed (I² > 85%). Quality assessment revealed high risk of bias in patient selection (87.5%) and index test domains (87.5%). Mechanistic analysis identified key pathways including Wnt/β-catenin, PI3K/AKT and EMT regulation. Sensitivity analyses confirmed robust estimates and Deeks’ test (p = 0.99) indicated no publication bias. Conclusion: This study has shown that individual circulating miRNAs provide consistent diagnostic accuracy for early-onset colorectal cancer (EOCRC); however, these findings require prospective validation in true screening settings before clinical implementation. Full article

Many developing country governments would like to attract investment and create jobs in manufacturing and high-tech industries. Heavy and unpredictable laws and regulations, frequent demands for bribes, high taxes, poor-quality roads, slow and inefficient ports, and unreliable power, however, deter private investors. Moreover, political opposition and fiscal constraints prevent governments from resolving the numerous issues. Rather than attempting to solve everything everywhere, many governments have tried to fix problems in only small regions. These special economic zones (SEZs) often have lower taxes, more liberal regulation, and better infrastructure. This paper asks whether firms located in African and South Asian SEZs report less regulation and corruption than other firms in the same countries. We find, on average, being located in an SEZ is associated with lower burdens due to corruption and regulation. Firms in the zones are less likely to report paying bribes than firms outside the zones and report spending less time dealing with inspections and regulations. However, this is not true in Africa; firms in African zones report that corruption and regulation are as troublesome as for similar firms outside the zones. Full article

As a strategic energy source underpinning industrial security, oil’s resource consumption is often tied to its production site. However, its embedding in goods and trade-related transfer leads to a spatial dislocation between physical supply burden and economic consumption demand, masking the true structure of energy dependency. Focusing on China, a net exporter of embodied oil, this study uses a multi-regional input-output (MRIO) model and structural path analysis (SPA) spanning the period from 2007 to 2017 to trace such flows. Key findings include: (1) China’s embodied oil flows expanded during the study period, with the country remaining a net exporter. (2) On the production side, embodied oil mainly flows out indirectly. Specifically, Liaoning and Shanghai are the core provinces for domestic indirect outflows, while Guangdong and Jiangsu lead in foreign trade exports. In terms of sectors, transportation, warehousing, and postal services play a pivotal role. (3) On the consumption side, flows are also primarily indirect. Guangdong and Jiangsu absorb the largest share of domestic flows, while Liaoning and Shanghai record the highest indirect import volumes. The construction and other service sectors emerge as the core consumers. Methodologically, this study goes beyond aggregate analysis by providing empirical support for optimizing the cross-regional allocation of oil resources and identifying key transmission nodes of energy supply risks. In terms of policy, it designs coordinated conservation strategies to enhance national oil security. Full article

Three-dimensional geological modeling is a fundamental technology for reconstructing subsurface geological structures and plays an important role in resource exploration, disaster prediction, and engineering construction. With increasing energy demand and growing environmental safety challenges, accurate characterization of the morphology and physical properties of subsurface strata has become essential for the efficient development of underground space. Machine learning-based three-dimensional geological modeling methods using borehole data reformulate the modeling process as a stratum classification task, thereby reducing manual intervention and improving the level of automation in geological modeling. In this process, the classification of stratigraphic spatial points is a key step, as its accuracy directly influences the quality of the resulting geological body model. However, traditional algorithms typically rely solely on spatial coordinate features to determine stratum affiliation. Such a single-feature-driven approach has limited capability in representing the true morphology of subsurface strata. To address this limitation, this paper proposes a stratum classification method based on Vertical Alignment–Horizontal Distance Weighting (VA-HDW), which is designed to capture spatial correlations between strata and boreholes. On this basis, a specialized neural network model, termed the Generalized Borehole Autoregressive Neural Network (GBARNN), is designed and trained to improve the classification performance of stratigraphic spatial points, thereby contributing to improved three-dimensional geological body modeling quality. Full article

Introduction: Minimally invasive pancreatoduodenectomy (MIPD), including laparoscopic (LPD) and robotic approaches (RPD), has gained increasing attention as an alternative to open pancreatoduodenectomy (OPD). Despite rapid technological progress, concerns persist regarding safety, reproducibility, and oncological adequacy. The publication of randomized controlled trials (RCTs) provides essential high-level evidence to reassess the true benefits and limitations of MIPD. Methods: This narrative review synthesizes all available RCTs comparing LPD and RPD with OPD. Major domains evaluated include mortality, major morbidity, intraoperative parameters, postoperative recovery, oncological outcomes, conversion, costs, and the influence of surgeon experience and institutional volume. The objective is to contextualize RCT findings rather than perform a quantitative meta-analysis. Discussion: Across studies, LPD demonstrates comparable mortality and complication rates to OPD in high-volume centers, with consistent reductions intraoperative blood loss (IBL) and shorter recovery or length of stay (LOS). RPD shows more heterogeneous results: one large trial reported improved postoperative recovery, whereas the EUROPA trial identified higher rates of pancreatic fistula (POPF) and delayed gastric emptying (DGE) alongside significantly increased costs. Both LPD and RPD achieve oncological outcomes equivalent to OPD, and 3-year survival data confirm the long-term non-inferiority of LPD. However, operative time remains longer for all minimally invasive approaches, and conversion persists as a marker of technical difficulty and incomplete learning curve. Conclusions: Current RCT evidence indicates that MIPD is safe, feasible, and oncologically sound only when performed by surgeons who have surpassed the demanding learning curve within specialized, high-volume centers. The benefits, mainly reduced IBL and faster recovery, must be weighed against longer operative times, conversion risks, and substantially higher costs for RPD. MIPD should therefore be considered an advanced option rather than a universal standard, and its broader implementation requires structured training pathways, appropriate patient selection, and institutional readiness. Full article

Second-generation bioethanol from food industry lignocellulosic residues offers a promising route toward low-carbon, circular bioenergy systems. However, the reported environmental impacts differ markedly across studies, challenging efforts to assess the true sustainability of these waste-derived bioethanol routes. This review synthesizes current knowledge on the production of bioethanol from key agro-industrial wastes including oil palm empty fruit bunches, sugarcane bagasse, brewers’ spent grain, spent coffee grounds, tea waste, citrus residues, and potato peel waste. We outline feedstock characteristics, availability, and prevailing management practices, and map the principal biochemical conversion routes to identify process steps that drive environmental performance. A systematic comparison of life cycle assessments reveals substantial methodological heterogeneity across functional units, system boundaries, allocation procedures, and impact assessment methods. Nonetheless, consistent hotspots emerge, particularly associated with pretreatment severity, enzyme production, thermal energy demand, and co-product handling. The review highlights robust cross-study trends, pinpoints methodological gaps, and proposes recommendations for harmonized LCA practice. By integrating technological and methodological perspectives, this work aims to support the development and policy uptake of sustainable, waste-based bioethanol within circular bioeconomies. Full article

This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. We benchmark three univariate forecasting approaches for load demand, photovoltaic (PV) generation, and wind generation under a consistent 24-to-1 input setup, including GRU, LSTM, and a persistence baseline. We report point-forecast metrics (RMSE, MAE, and $R^2$) and also provide 90% prediction intervals (PI90) using conformal calibration to quantify uncertainty. To assess downstream impact, forecasts are coupled with a dual-branch MATLAB/Simulink microgrid model. One branch uses True profiles and the other uses forecast-driven Pred inputs, while both branches share the same rule-based EMS and BESS constraints. System performance is evaluated using time-series comparisons and monthly key performance indicators (KPIs) covering grid import and export, grid peak power, battery throughput, and state-of-charge (SoC) statistics. We further report an illustrative cost sensitivity under a flat tariff and a throughput-based degradation proxy. Results show that forecasting performance is target dependent. GRU achieves the best overall point accuracy for load and PV, whereas wind is strongly driven by short term persistence at the one hour horizon, and in this measurement only setup without meteorological covariates the persistence baseline can match or outperform the deep learning models. In the microgrid simulations, Pred and True trajectories remain qualitatively consistent, and SoC-related indicators and peak power remain comparatively consistent across months. In contrast, energy-flow indicators, especially grid export and battery throughput, show larger deviations and dominate the observed cost sensitivity. Overall, the findings suggest that compact hour-ahead forecasts can be adequate to preserve operational reliability under a constraint-driven EMS, while forecast improvements mainly translate into economic efficiency gains rather than reliability-critical benefits. Full article