Search Results (17,476)

Gas escape relative to magma ascent is a primary control on degassing, yet its governing parameters are difficult to compare across eruptions and planetary environments. This study develops a reduced-order dimensionless framework for diagnosing local gas–melt separation in connected, pre-fragmentation magma. The formulation combines phase mass conservation, porous gas transport, bubble-matrix permeability, independently constrained connected pathways, and inertial resistance. The resulting regime map separates the competition between magma advection and matrix gas segregation from changes caused by sealing, crystallization, fractures, and tuffisite pathways. It shows that crystallization has no unique effect. Matrix obstruction reduces gas escape, whereas brittle pathway formation can enhance it. Global sensitivity analysis indicates that ascent rate and bubble number density account for 98.4% of the variance in the matrix-controlled transport competition over the investigated ranges. When connected permeability is independently specified over the same logarithmic range as ascent rate, both contribute equally to the calculated variance. A literature-constrained calculation for the climactic Chaitén eruption places the bubble-matrix endmember in the advection-dominated regime without adjusting permeability to reproduce the observed outcome. Atmospheric scaling indicates strong near-surface suppression of bubble expansion on Venus, but its magnitude remains highly sensitive to the assumed mechanical overpressure scale. The framework provides a physically testable basis for comparing local degassing states without imposing an arbitrary eruption classifier. It does not predict eruption onset or fragmentation. These require time-dependent dynamics, mechanical failure criteria, and independent validation data. Full article

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns. Full article

Inter-hub feeder service is a promising application for electric vertical takeoff and landing aircraft (eVTOL), especially for passengers connecting to subsequent flights under tight time constraints. We develop an optimization framework that accounts for heterogeneous passenger behavior. Using stated-preference survey data, we incorporate delay-risk perception under remaining connection time constraints, identify heterogeneous preference classes, and formulate a bilevel optimization model. The upper level selects eVTOL schedules under given resource and fare configurations. The lower level captures the stochastic user equilibrium of heterogeneous passengers choosing among eVTOL and external transport alternatives. To solve the resulting mixed-integer nonlinear bilevel problem, we propose a Neural Bilevel Optimization and generalized Benders decomposition (Neur2BiLO-GBD) hybrid algorithm. Numerical experiments on the Shanghai Hongqiao–Pudong corridor show that the baseline profit-maximizing plan also generates positive social net utility for the feeder system. Fleet size, charging infrastructure, and fare affect operator profit and social net utility differently, so their high-value regions do not fully coincide. When external transport has larger potential delays and remaining connection time is short, eVTOL is more likely to achieve both high operator profit and high social net utility. Full article

Calcined clays are increasingly recognized as strategic supplementary cementitious materials for reducing clinker consumption and the environmental impacts of construction. However, life cycle assessments typically represent metakaolin production using a single carbon footprint value, despite substantial differences in calcination technology, energy supply, and feedstock characteristics. This study develops a parameterized cradle-to-gate carbon inventory for metakaolin production and evaluates how this variability affects the environmental assessment of low-carbon construction materials. The methodology combines process-based theoretical modeling, mass and energy balances, literature-derived industrial data, and life cycle assessment. A full-factorial scenario analysis was performed for three calcination technologies: rotary kiln, fluidized bed, and flash calcination, while systematically varying fuel source, electricity mix, kaolinite purity, feedstock moisture, and transport distance. The resulting inventories were subsequently propagated into representative LC3 cement and metakaolin-based geopolymer formulations to quantify their influence at the construction-material level. Across 648 production scenarios, the carbon footprint of metakaolin ranged from 34 to 578 kg CO₂e t⁻¹, demonstrating that production conditions define the environmental performance. Fuel selection was identified as the principal emission driver, while moisture content and feedstock purity produced secondary effects. The variability propagated to downstream products, resulting in carbon footprints of 363–527 kg CO₂e t⁻¹ for LC3 cement and 167–357 kg CO₂e m⁻³ for metakaolin-based geopolymers despite identical material compositions. For the building sector, the proposed framework enables designers, material producers, and LCA practitioners to select calcined clay production routes consistent with the carbon targets of concrete, mortar, masonry, precast elements, and other cement-based building applications. It therefore provides a practical basis for incorporating LC3 and geopolymer technologies into lower-carbon building projects while avoiding environmental benefits based on non-representative upstream assumptions. Full article

Large language models (LLMs) and time-series foundation models have advanced traffic forecasting, but almost exclusively on freeway sensors at five-minute, aggregate-flow resolution; their behavior on minute-resolution, movement-level counts at signalized arterials is unknown. This study assembled a real corridor dataset—6449 gap-free one-minute turning-movement records (255,364 counted vehicles) at 20 signalized intersections along 13 miles of Nolensville Pike, Nashville, Tennessee—and ran a controlled benchmark across three model families (classical, deep, and LLM/foundation models). We evaluate in-sample accuracy at 5/10/15 min horizons (RQ1), cross-date leave-intersection-out transfer to morning-only sites (RQ2). On the full-day sites, a per-series ARIMA attains the best short-horizon accuracy (MASE 0.87 at h = 5), while a zero-shot foundation model (Chronos) is the most stable across horizons; under leave-intersection-out transfer the ordering reverses—foundation models transfer best (MASE ≈ 0.61, improving to ≈0.56 with few-shot adaptation) whereas globally trained deep networks fail catastrophically (MASE > 4). All benchmark values reported here are measured on the corridor data; foundation-model families that we could not execute end-to-end in this environment are discussed qualitatively and are not included in the quantitative comparison. The dataset and protocol establish an honest, cost-aware basis for judging whether language and foundation models genuinely forecast an urban corridor at minute resolution. Full article

Historical pax demand estimation is important for understanding long-term demand dynamics and supporting airline planning, strategic decision-making, and transportation policy analysis. However, current decomposition approaches either estimate trends without explicitly modeling seasonality or jointly estimate trend and seasonal components without providing user control over trend smoothness. This paper proposes a Guerrero–Holt–Winters (GHW) hybrid smoothing model to estimate and reconstruct historical route-level airline pax demand. The GHW model combines Guerrero’s user-controlled trend estimation with the multiplicative seasonal structure of the Holt–Winters (HW) model to produce an interpretable decomposition of historical demand into trend, seasonal, and irregular components. The model is applied to quarterly pax demand data for ten representative U.S. domestic air routes covering the period 2015–2023. The results show that the proposed GHW model achieves lower in-sample reconstruction errors than the multiplicative HW model at lower degrees of trend smoothness within the evaluated range. Among the evaluated specifications, λ = 1, corresponding to a Guerrero smoothness index of S(1; 36) = 58.82%, produces the lowest in-sample reconstruction error. The historical reconstruction error, measured by the root mean squared error (RMSE), is reduced by approximately 54.4% relative to the multiplicative HW model. Similar improvements are observed under MAE, MAPE, and sMAPE, indicating that the reduction in reconstruction error at this smoothness level is consistent across the four error measures. An expanded benchmark comparison with HW, ETS state-space exponential smoothing, STL decomposition, and HP1600 further shows that the GHW model produces the lowest in-sample reconstruction errors across all four evaluation error metrics and all ten routes analyzed. A sensitivity analysis further demonstrates that reconstruction accuracy declines as the degree of trend smoothness increases from S(λ; 36) = 58.82% to 88.63%, whereas the estimated seasonal factors remain remarkably stable across the evaluated smoothness range. A COVID-19 robustness analysis further shows that reconstruction errors are substantially lower in the Pre-COVID period than in the Post-COVID period across all ten routes, indicating that reconstruction accuracy is sensitive to the period analyzed. Full article

Circular economy is today a key driver of every transformation process aimed at reducing and optimizing the use of energy and materials. The production of solid biofuel from waste is a typical route to lower the potential impact of greenhouse-gas emissions. In this context, olive pomace is a relevant feedstock, as 4 million tonnes are generated worldwide each year alongside olive oil production. However, only a small fraction of olive pomace is currently valorized. Fresh olive pomace must first be quickly dried to a low, controlled moisture. This step is performed poorly and at a high energy cost. This paper presents an automation-based approach to enhance biomass production from olive pomace, thereby advancing circular-economy practices in olive oil production. The work is focused on four aspects. In the first part, a review of the state of automation in agricultural engineering with a focus on biomass and olive pomace is proposed. Then, the design and construction of an innovative drying system that integrates an infrared solution directly into the transporting screw conveyor is described, integrating real-time online microwave moisture sensing and PLC control. After that, a cloud-based service is presented for remote monitoring, data analysis, and optimization. The innovative and automated drying system was validated during a preliminary field campaign at an olive mill. After about sixteen hours of continuous, cloud-monitored operation, the resulting olive pomace moisture fell below the 5% threshold across a wide range of inlet-moisture conditions, with a stable electrical power demand of approximately 1.85 kW. Finally, an environmental analysis is provided to evaluate the environmental aspects related to the proposed system. The preliminary analysis confirms a significant avoided-carbon potential if the resulting olive pomace is reused as biomass for energy production. The impact associated with 1 kWh_-eq produced from olive pomace is in the range of 0.006–0.033 kg CO_2-eq. Full article

This work focuses on studying the magnetohydrodynamic flow and heat transfer mechanism of a Casson–Maxwell nanofluid due to a stretching surface through a porous medium, using a physics-informed neural network (PINNs) approach as the main tool for the solution of the physical problem. The mathematical model describes the phenomena of viscosity variation with temperature, viscous dissipation, thermal slip, Brownian motion, thermophoresis, and drag force due to a porous medium, which give a realistic physical scenario of the coupled transport phenomena of momentum, heat, and nanoparticles. First, the nonlinear partial differential equations are converted into a dimensionless boundary layer model using similarity transformations. Then, the yielded system is solved via the PINNs approach, which integrates physical law within the optimization procedure. The proposed technique does not require a significant number of labeled datasets and provides accurate and stable predictions of the strongly nonlinear flow. A comprehensive parametric analysis was performed to explore the impact of the dimensionless controlling factors on the velocity, temperature, and nanoparticle concentration distributions. It is found that the interaction of magnetic field effects, porous media resistivity, thermal and concentration slip, viscosity variation, and viscous heating significantly modifies the transport features for the studied model of the Casson–Maxwell nanofluid, which can be used effectively to control the rate of heat and mass transfer. This study proves the efficiency of the PINN technique in solving this type of model, and it also provides useful insights for designing thermal systems, energy conversion devices, and electrically conducting viscoelastic nanofluid transport problems. The close concordance between the present findings and established data from the literature validates the precision and dependability of the developed PINN-based framework. Full article

Environmental footprint assessment is increasingly used to characterize the secondary impacts of contaminated site remediation. However, many established green and sustainable remediation tools rely on U.S.-based emission inventories and background data, raising questions about how their estimates change when applied under other regional conditions. This study proposes a three-way evaluation framework that examines Sitewise™ and SEFA outputs alongside a Sitewise-informed China-localized recalculation using domestic emission factors. The framework was applied to an excavation-and-transport brownfield remediation project in northern China using five indicators: greenhouse gas emissions (GHG), energy use, NO_x, SO_x, and particulate matter. In this case, Sitewise™ and SEFA produced numerically similar GHG estimates and moderately different energy estimates, with pairwise differences of 7% and 27%, respectively. Under the assumptions of this case, their GHG estimates were 26% and 20% lower, respectively, than the localized recalculation. A screening counterfactual using a common U.S. reference grid factor indicated that grid factor substitution accounted for 19.2% of the Sitewise-to-localized numerical difference and 24.5% of the SEFA-to-localized numerical difference. Air pollutant results showed stronger pathway dependence: NO_x estimates differed by up to approximately eightfold, and the localized result fell between the two tool estimates under the stated assumptions. Within the tested epistemic ranges, the localized GHG result remained above both fixed tool outputs, the NO_x result remained between them, and the SO_x result remained below both in every draw; the baseline PM relationship was retained in 92.7% of draws, while localized energy equaled the Sitewise™ result by construction. Across the native tool runs and a transparent localized transport linkage screen, a 25% reduction in haul distance was associated with a 12.5–22% reduction in total GHG estimates. The comparison harmonized the remediation program and comparison unit, but not the complete background life cycle boundary; the results therefore represent defined-boundary pathway estimates rather than fully boundary-harmonized LCA results. Because the localized pathway retains the Sitewise-derived fuel–energy balance and activity category shares, it is interpreted as a case-specific factor localization recalculation rather than an independently documented project inventory or external comparison standard. The observed directional differences characterize the present case and tested assumptions and do not support a general judgment about the relative performance of the tools. The framework may be adaptable to other jurisdictions, but its transferability should be evaluated using additional sites, remediation technologies, and independently documented activity inventories. Full article

Thyroid hormone physiology has traditionally been understood through the hypothalamic–pituitary–thyroid (HPT) axis and the genomic actions of triiodothyronine (T3). However, advances in thyroid hormone biology have expanded this classical paradigm, demonstrating that thyroid hormone signaling is regulated through a coordinated network involving tissue-specific deiodination, specialized membrane transporters, genomic and non-genomic signaling pathways, and mitochondrial regulation of cellular bioenergetics. Among the iodothyronine metabolites generated through thyroid hormone metabolism, 3,5-diiodothyronine (3,5-T₂) has emerged as one of the most extensively investigated because of its reported ability to rapidly influence mitochondrial respiration, oxidative metabolism, and energy expenditure. Experimental studies suggest that 3,5-T₂ enhances mitochondrial respiration, fatty acid oxidation, oxidative phosphorylation, and metabolic efficiency, particularly in metabolically active tissues such as liver and skeletal muscle. These findings have generated considerable interest in the potential role of 3,5-T₂ in metabolic disorders characterized by mitochondrial dysfunction, including metabolic dysfunction-associated steatotic liver disease (MASLD), obesity, and insulin resistance. However, important translational challenges remain. Most available data derive from animal studies using pharmacologic doses, while the endogenous physiology of 3,5-T₂ in humans, its molecular targets, tissue-specific regulation, and long-term endocrine effects remain incompletely understood. In addition, evidence of hypothalamic–pituitary–thyroid axis suppression following exogenous 3,5-T₂ administration and limitations in accurately measuring circulating 3,5-T₂ continue to complicate clinical translation. This review critically evaluates the emerging biology of 3,5-diiodothyronine, integrating current evidence regarding its biosynthesis, mechanisms of thyroid hormone signaling, mitochondrial actions, metabolic effects, translational challenges, and future research priorities. By synthesizing findings from primary experimental studies and emerging translational investigations, this review places 3,5-T₂ within the broader framework of contemporary thyroid hormone biology while highlighting key knowledge gaps that must be addressed before its physiological and therapeutic significance can be fully established. Full article

This study examines the dynamic connectedness and spillover transmission mechanisms among tourism-related exchange-traded funds (ETFs), clean energy markets, oil, geopolitical risks, and tourism transportation using daily percentage returns from 24 March 2017 to 16 March 2026. We employed a battery of econometric methodologies, including the R²-decomposed connectedness approach, a TVP-VAR framework to examine the extent and nature of connectedness and distinguish contemporaneous and lagged spillovers, and the DCC-GARCH specification to assess dynamic conditional correlations. The data exhibit substantial volatility and non-normality, supporting these dynamic econometric approaches. The results reveal that connectedness varies considerably over time and tends to intensify during periods of major economic and geopolitical uncertainty. PEJ emerges as the dominant overall net transmitter of shocks, primarily through lagged spillovers, while Transportation and Clean Energy act as contemporaneous transmitters but become net receivers in the lagged horizon. The decomposition further shows that contemporaneous linkages are relatively weak, while lagged effects are stronger, suggesting that shock transmission occurs gradually rather than instantaneously. The dynamic correlations are time-varying, and ETFs display asymmetric behavior across different market conditions. Overall, this study highlights the time-varying nature of interactions between tourism and clean energy assets, driven by energy market dynamics and geopolitical risks, and provides useful implications for portfolio diversification, risk management, and policy decisions in interconnected tourism and sustainable financial markets. Full article

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments. Full article

Orosanga japonica (Hemiptera: Ricaniidae) is an invasive, polyphagous planthopper established in Türkiye since 2007 that threatens economically important perennial and field crops across the Black Sea region. Although its establishment and rapid spread suggest considerable adaptive capacity, the genomic basis of this potential remains unexplored. We combined whole-genome sequencing with RNA-Seq data to generate a functionally annotated genome, recovering 92.5% of conserved single-copy orthologs. Utilizing these resources, we performed comparative genomic analyses across 20 additional hemipteran and thysanopteran species, investigating gene family dynamics and genome-wide positive selection. These analyses revealed 52 gene families with statistically significant, lineage-specific size changes (24 expansions and 28 contractions): expanded families were enriched for C2H2 zinc-finger transcription factors, α/β-hydrolases, major facilitator transporters, serpins, and chemosensory proteins, whereas contracted families included a cytochrome P450 family. Additionally, branch site tests identified 122 candidate genes under lineage-specific positive selection. These loci fall into three functional categories potentially associated with invasion-related traits: host detection and neuronal excitability, stress tolerance (xenobiotic, oxidative, and proteostatic), and barrier remodeling. Together, these genomic signatures highlight candidate molecular mechanisms that may contribute to host detection, xenobiotic tolerance, and ecological plasticity, providing a framework for evolutionary analysis and the targeted management of this emerging agricultural pest. Full article

Precise data regarding the overall mass of the machinery and the gradient of the track are crucial for optimizing the control of monorail cranes and enhancing energy efficiency. Within the context of electric monorail cranes (EMCs), accurately estimating the total mass of the machinery and the track gradient poses a formidable challenge. This challenge arises from the strong coupling between the overall mass of the machine and the track gradient, the robustness of parameter estimation methods under varying operational conditions, and the generalizability of the algorithm to real-world operations and rail scenarios of EMCs. To address these challenges, this paper proposes a novel parameter estimation scheme that comprehensively considers the impact of parameter coupling relationships and multiple influencing factors in the transportation scenarios of EMCs under actual working conditions. First, to overcome measurement difficulties induced by strong coupling between the EMC mass and track gradient, a decoupling estimation method based on cascaded dual observers is proposed to jointly estimate the two states. Secondly, to mitigate track slope estimation errors under complex track types and diverse operating conditions, an enhanced immune optimization algorithm, integrating a Weibull function and Levy flight mechanism, in conjunction with an unscented Kalman filter (UKF), is developed. Furthermore, to achieve high-precision and stable parameter identification results, a Weibull dynamic forgetting factor is incorporated into the RLS algorithm, leading to the design of a WDFF-RLS estimator. Finally, real vehicle experiments were conducted on complex tracks at the test site to validate the accuracy and robustness of the proposed estimation method. Full article

Ammonia nitrogen represents a critical environmental stressor in aquaculture and exerts adverse impacts on the growth, reproduction and survival of shellfish. In the present study, diploid Pacific oysters (Magallana gigas) were exposed to ammonia nitrogen stress for 0, 6, and 48 h. We combined histopathological examination, antioxidant enzyme measurements, transcriptome profiling, and metabolome profiling to characterize time-dependent hepatopancreatic responses. Tissue injury became progressively more severe with prolonged exposure. Superoxide dismutase activity increased throughout the experiment, whereas catalase and glutathione peroxidase activities and malondialdehyde content showed an increase followed by a decrease, with no statistically significant differences in malondialdehyde content among the three time points. Transcriptome data revealed alterations in genes associated with ATP-binding cassette transporters, lysosomal function, endocytosis, and autophagy. Metabolic alterations were mainly associated with nucleotide, purine, pyrimidine, and sphingolipid metabolism. Cross-omics integration linked these molecular shifts to transmembrane transport, glutathione metabolism, sulfur-containing amino acid metabolism, and amino acid biosynthesis. The consistency between transcriptomic and metabolomic signals further highlights the central roles of transport processes, antioxidant defense, and metabolic adjustment under ammonia nitrogen exposure. These findings indicate that diploid Pacific oysters respond to ammonia nitrogen stress through coordinated regulation of oxidative stress responses, transmembrane transport, intracellular degradation and clearance, and metabolic reorganization. These findings provide insights into the adaptive mechanisms of bivalves exposed to ammonia nitrogen stress. Full article