Search Results (970)

The identification of the chemical species responsible for the anomalous near-ultraviolet (UV) opacity in the Venusian cloud for “unknown absorber” remains a paramount challenge in planetary science. This study presents a comprehensive quantum chemical investigation into a broad suite of candidate molecules, including isomers of thiosulfeno (S₂O₂), the hydroxysulfonyl radical (HSO₃), disulfur monoxide (S₂O), disulfur dichloride (S₂Cl₂), iron(III) chloride (FeCl₃), phosphine (PH₃), and structural isomers of polysulfur oxides (S₃O). Utilizing Time-Dependent Density Functional Theory (TD-DFT) at the CAM-B3LYP/def2-TZVPP level of theory, we systematically mapped electronic transitions across three distinct environmental phases: gas-phase (without solvent), supercritical CO₂, and concentrated H₂SO₄ aerosols. To establish confidence in the predicted results, our TD-DFT approach was rigorously benchmarked against high-level theoretical methods (CCSD(T), EOM-CCSD, and MRCI+Q) from recent literature. All these electronic transitions were modeled via the Solvation Model based on Density (SMD). Our results demonstrate a profound topological and environmental dependence on spectral signatures. Among the candidates, trans-OSSO (t-OSSO) emerged as the most viable near-UV absorber candidate, exhibiting a highly allowed π → π* transition at 379.37 nm (f = 0.1140) in H₂SO₄, providing a near-perfect alignment with the observed 365 nm planetary albedo drop. Conversely, the polysulfur oxide cis-S₃O was acknowledged as a primary visible-light chromophore, with an intense absorption at 436.31 nm (f = 0.1280) responsible for the characteristic yellow tint of the planet. Additionally, the photochemically maintained SSCl₂ isomer was identified as a critical broadband near-UV absorber. Species such as S₂O and planar S₃O were found to function as critical mid-UV shields (270–300 nm). This work establishes a multi-chromophore model of the Venusian atmosphere, where a chemically stratified network of sulfur-oxygen chains and chlorine-sulfur reservoirs, tuned by the acidic aerosol matrix, collectively governs radiative balance and atmospheric super-rotation of the planet. Furthermore, to account for massive continuum tailing into the visible region (>400 nm), we employed a semi-classical Reflection Principle approach to model 1D vibronic broadening. This analysis revealed that while standard solvent effects induce minor solvatochromic shifts, ground-state structural fluxionality in the OSSO isomers drives intense, symmetry-allowed transitions deep into the visible spectrum, an effect absent in structurally constrained or rigid control species. Full article

This study investigates the seismic behaviour of the Hüsrev Pasha Minaret, a historical masonry structure located in Diyarbakır, Türkiye, characterized by two distinct transition segments and material variation along its height. The dynamic features of the minaret were identified through ambient vibration tests, while material properties were estimated using non-destructive testing methods. A three-dimensional numerical model was then generated and calibrated based on the experimentally identified natural frequencies, achieving an average frequency difference of 2.04%. Nonlinear dynamic analyses were conducted using six earthquake time series scaled to three seismic hazard levels (DD-1, DD-2, and DD-3) defined in the Turkish Building Earthquake Code (TBEC-2018). The results indicate that the second transition segment is the most critical region in terms of damage concentration. Ground motions corresponding to the DD-1 level led to exceedance of the Collapse Prevention (CP) displacement limits, while DD-2 and DD-3 levels resulted in limited or near-limit responses. In addition, compression-only support conditions were found to influence the base shear response, whereas material transitions between basalt and limestone did not significantly affect the overall seismic behaviour of the minaret. Full article

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Understanding the variations in the bidirectional reflectance distribution function (BRDF) and albedo over snow surface under various conditions is important for interpreting the surface–atmosphere processes of the cryosphere, and the kernel-driven model is among the most popular methods to obtain this information for a comprehensive analysis. Recently, the RossThick-LiSparseReciprocal-Snow (RTLSRS) model was developed to better characterize the anisotropic reflectance of snow and shows strong potential for integration into operational remote sensing algorithms for snow BRDF/albedo retrieval. To comprehensively test the ability of the RTLSRS model to reproduce snow reflectance, the fitting accuracy to different multi-angular data derived from ground, tower, aircraft, and satellite platforms across the full optical wavelength range were demonstrated in this study. Special attention in this study was directed to analyzing the model performance under extreme illumination observation geometries, particularly with respect to the retrieval accuracy and stability under large Solar Zenith Angles (SZAs) and different Relative Azimuth Angles (RAAs). The model performance for silt-polluted snow surface with different concentrations is also assessed to provide necessary supplementation, relative to “pure” snow surface in the previous study. The main findings of this study are summarized as follows: (1) The RTLSRS model exhibits strong robustness under various SZAs; even when the SZA exceeds 80°, the model maintains high accuracy in BRDF reconstruction, with root mean square error (RMSE) values below 0.05. (2) The model also demonstrates satisfactory inversion capability when observations deviate from the principal plane (PP); the model can achieve fitting accuracy with R² approaching 0.5 and RMSE below 0.05 for MODIS data. (3) In the spectral range below 1300 nm, the RTLSRS model effectively reconstructs the scattering characteristics of snow surfaces with light impurity levels (<20 g/0.5 m²). (4) The spectral shape of snow reflectance remains consistent across different view zenith angles (VZAs) in general. However, the variations caused by different SZAs can be as high as 38.49% and such SZA-induced difference can result in WSA estimation discrepancy of up to 63.43%. This comprehensive assessment further affirms and demonstrates the applicability of the RTLSRS model for the first time in fitting observations across different platforms with various optical wavelengths and geometries, and provides an improved understanding to analyze BRDF variations for the user community. Full article

Radon (${}^{222}$Rn) and thoron (${}^{220}$Rn) are widely used to investigate diffuse degassing, fault-zone permeability, hydrothermal circulation, and subsurface unrest, but their signals are not direct proxies for a single process. This manuscript is a critical synthesis and methodological article that develops a reduced inference framework for interpreting radon–thoron observations in volcanic, tectonic, and hydrothermal settings. The framework separates accessible support of the immediate radium parents ${}^{226}$Ra and ${}^{224}$Ra, recoil-scale release into the mobile phase, multiphase transport, geological carrier-gas throughput, and observational closure. It also distinguishes total activity flux from activity concentration and chamber throughput from natural carrier-gas dilution. Synthetic illustrative experiments test the internal behavior of the reduced operator; a concise re-reading of the public Upper Rhine Graben dataset illustrates the limits of concentration-only inference; and published volcanic and hydrothermal examples are used as literature-grounded vignettes. The purpose is not to validate a universal inversion model but to define what can be inferred from different observation packages. The paper, therefore, emphasizes three operational levels: anomaly reporting, mechanism discrimination, and local inversion. Full article

In Latin America and the Caribbean, the circular economy approach is embedded in productive structures characterized by a dependence on natural resources and the persistence of informal economies. The general objective of this article is to analyze the circular economy as an approach to production and consumption in Latin America and the Caribbean through a bibliometric and qualitative analysis of scientific literature. This study adopted a mixed, descriptive, and analytical research design. International and regional databases (Scopus, Web of Science, SciELO, and Redalyc) were used to identify articles published between 2015 and 2025. The selection process followed the PRISMA protocol, resulting in a final qualitative analysis of 47 articles. The results reveal an accelerated and sustained growth in scientific production in the region, with a maximum increase of 250% in 2017, indicating a progressive consolidation of the field. The documentary corpus consists mainly of original articles (65%), with a clear preeminence of environmental sciences, engineering, and energy. Qualitatively, the literature shows a conceptual heterogeneity that adapts the circular economy to sustainable development and industrial ecology, uniquely incorporating grassroots recyclers and cooperatives into a “just transition.” However, there is evidence of an implementation gap: while large industries are making progress in eco-design and remanufacturing, adoption in SMEs and responsible consumption—especially in repair and reuse—remains at incipient levels due to structural and cultural limitations. Ultimately, the results suggest a growing concentration of circular economy research within selected Latin American institutions, indicating the emergence of regionally grounded research agendas that may differ in emphasis from dominant Global North framings. Full article

This study estimates daily nitrogen dioxide (NO₂) concentrations at ground level across the Marmara Region of Türkiye at 0.01° resolution. The framework integrates Sentinel-5P (S5P) TROPOspheric Monitoring Instrument (TROPOMI) and GEOS Composition Forecast (GEOS-CF) tropospheric NO₂ vertical column density (VCD) data with meteorological, topographic, land-use, socioeconomic, and temporal features through four tree-based ensemble algorithms trained on 74 ground station observations. Under a temporal split (2019–2022 training, 2023 validation, 2024 testing), S5P-Categorical Boosting (CatBoost) achieved the best performance (Pearson correlation coefficient (R) = 0.706, R² = 0.498, root mean square error (RMSE) = 14.31 µg/m³). Random splitting inflated R by +0.168 due to temporal autocorrelation, while leave-one-station-out and leave-one-province-out cross-validation reduced R to ~0.50 by removing spatial dependence, together revealing the combined effect of temporal and spatial autocorrelation. SHapley Additive exPlanations (SHAP) analysis identified TROPOMI NO₂ VCD, population density, road length, and nighttime light as dominant predictors; population density was the top predictor in the GEOS-CF model, followed by VCD. Concentration maps for 2024 showed that 95.9% of the region’s 26.74 million inhabitants were exposed above the WHO annual air quality guideline of 10 µg/m³, with a population-weighted mean of 21.08 µg/m³. Full article

The shift toward more electric aircraft has intensified thermal management challenges due to increased heat load from electrical actuators, power electronics and energy storage systems concentrated within confined fuselage bays. A Conventional Environmental Control System (ECS) alone is not sufficient to dissipate such high localized heat loads. This creates the need for innovative heat dissipation and heat reuse strategies. This paper presents two thermal management concepts evaluated at the Fraunhofer Flight Test Facility. The first, developed in the ORCHESTRA project, integrates a bilge skin heat exchanger with modified ventilation to dissipate elevated heat loads. The second, under investigation in the TheMa4HERA project, focuses on reusing avionics heat to warm the FWD cargo hold, thereby reducing ECS power demand. Both concepts depend on convective heat exchange, characterized using Fraunhofer’s Convective Heat Transfer Meter (CHM) to determine key heat transfer coefficients. In parallel, an aircraft-level thermal model was developed, validated against experimental data and subsequently used for virtual demonstration of a ground test scenario. Full article

We examined the effect of the coronavirus disease 2019 (COVID-19) pandemic on air quality in the Kanto region of Japan using multiple satellites and ground-based observations. The vertical column density (VCD) of nitrogen dioxide (NO₂) derived from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) showed decreases of 38% and 27%, on average, respectively, in March of 2020, compared with the same month in 2015–2019, for OMI and in 2019 for TROPOMI. Surface NO₂ concentrations measured by the Atmospheric Environmental Regional Observation System (AEROS) also declined by up to 22% relative to the 2015–2019 mean, which is consistent with previously reported reductions. To investigate interactions between ozone (O₃) and NO_x, we calculated the ratio of non-methane hydrocarbon (NMHC) and NO_x and potential ozone (PO) surface concentrations from the AEROS data. The results indicated that the ozone formation regime in the Kanto region remained within the NMHC-limited domain during the COVID-19 period and was unchanged from the previous five years. Nevertheless, the baseline O₃ concentration decreased by 2.5–8.5 ppbv, depending on site (urban vs. suburban) and year (2020 vs. 2021). Diurnal variations in PO concentrations (defined as O₃ + NO₂-0.1NO_x), which is the net O₃ concentration produced by photochemical reactions and/or transport excluding the NO titration effect, showed significant reductions of 6.3 ppbv in 2020 and 3.2 ppbv in 2021, suggesting that lower PO levels were mainly attributed to the reductions in baseline O₃ concentrations in 2020. These findings highlight how pandemic-related emission reductions affected chemical processes and dynamics related to both NO_x and O₃ in a major Japanese metropolitan region. Full article

Top-heavy industrial towers, which carry large, concentrated masses of equipment at upper levels and feature open lower stories, are vertically irregular by design and tend to amplify seismic displacement and acceleration demands near the tower top. Although tuned mass dampers (TMDs) have been studied extensively for buildings, bridges and chimneys, their application to this particular class of slender industrial towers—where production-equipment vibration tolerance, retrofit accessibility and limited downtime drive the design—has received little dedicated attention. This paper reports a focused numerical investigation of seismic response mitigation for a 101.2 m molten-asphalt granulation tower retrofitted with a single pendulum-type TMD. A three-dimensional coupled finite element (FE) model was constructed in ABAQUS using C3D8R solid elements for the reinforced-concrete shaft and T3D2 truss elements for the embedded reinforcement; modal analysis returned a fundamental frequency of 0.912 Hz and a torsional-to-translational period ratio of 0.65, indicating a translational-mode-dominated response. Elastic time-history analyses under the El Centro and Taft records together with a code-spectrum-compatible synthetic accelerogram show that a pendulum TMD with mass ratio μ = 2.5%, tuning frequency offset Δf = 5% and damping ratio ξ = 10%—installed at the uppermost equipment level guided by the modal-displacement criterion—reduces the peak top displacement, peak top acceleration and peak base shear by roughly 23%, 23% and 22%, respectively, in both principal directions. The controlled top acceleration falls comfortably below the 2.94 m/s² operational tolerance of the on-tower melting equipment. To address the rationality of the chosen TMD parameters, a single-variable parametric sensitivity study spanning μ ∈ [1%, 5%], ξ ∈ [5%, 15%] and Δf ∈ [0%, 10%] is performed on an equivalent reduced model that captures the qualitative parameter-response trends; the chosen baseline values lie inside a stable performance plateau and are shown to be a balanced compromise among the three response measures. The principal contribution of the work is, therefore, (i) a complete TMD retrofit framework—modal-based placement, parameter design, coupled FE assembly and multi-record verification—adapted to top-heavy industrial towers, and (ii) qualitative evidence, supported by a sensitivity scan, with a robust proposed parameter set for small-to-moderate detuning. The study is restricted to elastic time-history analyses under frequent-earthquake-level excitation, three ground-motion records and a fixed-base assumption; nonlinear response, larger record sets and soil–structure interaction effects are explicitly identified as scope limitations and are left for follow-up work. Full article

Accurate prediction of fishing grounds plays a crucial role in supporting the efficient operation of ocean-going fishing vessels. Based on catch data of Chub Mackerel (Scomber japonicus) and multiple concomitant oceanographic variables from 2014 to 2022 in the Northwest Pacific Ocean, we employed four machine learning methods, including Random Forest (RF; scikit-learn v1.7.2), Extreme Gradient Boosting (XGBoost; xgboost v3.1.3), Light Gradient Boosting Machine (LightGBM; lightgbm v4.6.0) and Categorical Boosting (CatBoost; catboost v1.2.8), to construct a prediction model for high-abundance fishing grounds of Chub Mackerel. After selecting the optimal model through evaluation metrics, we applied the SHapley Additive exPlanations (SHAP; shap v0.44.1) method to visualize and interpret the optimal model, quantifying the importance of environmental factors on high-abundance fishing grounds, thus enhancing the interpretability and credibility of the machine learning model. The results indicated that the catch exhibited significant fluctuations at both interannual and intramonthly scales (p < 0.05). The annual catch showed a phased increasing trend, peaking in 2017 and 2018. Monthly catches were highest in September and October. Evaluated against established performance metrics, the RF model demonstrated the highest predictive performance with the highest values of accuracy and F1-score, 76.33% and 77.73%, Precision 72.81%, Recall 83.36%, ROC-AUC 0.8393, respectively, and was therefore selected as the most suitable for predicting Chub Mackerel fishing grounds. SHAP analysis identified the temporal variables year and month as the most influential predictors, followed by chlorophyll-a concentration (Chl-a), sea surface salinity (SSS), and sea surface temperature (SST). SHAP analysis can comprehensively reveal the degree and direction of influence of each variable at both global and local levels. These findings indicate that integrating machine learning with explainability techniques can enhance the scientific robustness and transparency of fishing ground forecasts, providing data-driven support for ecosystem-based fishery management. Full article

In urban environments, large buildings influence air quality in their surroundings by altering natural wind patterns, obstructing airflow or creating high-velocity wind tunnels, often resulting in stagnant zones that trap pollutants. Furthermore, the extensive shadows cast by these structures reduce ground-level temperatures. For urban planners, accounting for these aerodynamic, thermal and air quality effects is important to fostering healthier, more livable cities. In this work, measurements assessing how shadow and micrometeorological conditions—driven by the proximity of large buildings—influence PM_2.5 levels were conducted in an urban commune of Santiago, Chile, during the winter and spring seasons. This commune is characterized by a mixture of one-story houses and high-rise buildings. PM_2.5 and meteorological parameters were measured outside three pairs of houses in winter of 2021, one of which received shadow from a nearby building and the other was under the sun. In one pair of houses, PM_2.5 concentrations were elevated in the shaded site exclusively during the winter months. This was attributed to shadow-induced temperature reductions, which likely increased local atmospheric stability and inhibited pollutant dispersion. However, this effect was limited to periods of low wind speed; during the spring, the transition to a higher wind speed regime facilitated sufficient mechanical mixing to neutralize the thermal influence of the shadow, resulting in no detectable difference between the sites. In another pair of houses, the result was attributed to the difference in wind speed in one of the houses, because the building acts as a windbreak, no shading effect were observed. Regarding the third pair of houses, no significant impact on PM_2.5 concentrations was observed in the whole period. This lack of variation is likely attributable to the absence of substantial micrometeorological differences between the two sites. Full article

Methane (CH₄) is the second most important greenhouse gas after carbon dioxide (CO₂), and its atmospheric concentration is on the rise. Soil CH₄ consumption (=absorption) capacity is declining due to reduced forests and green spaces, as well as other environmental factors and anaerobic stresses. Environmental and stand structure parameters were cross-referenced with publicly available international ecosystem databases, such as FLUXNET, ICOS, NEON, AmeriFlux, the TRY plant trait database and the Oak Ridge FACE site. Searches were conducted using keywords such as region, water level, and stand density. The data indicate that under high-CO₂ conditions, the increase of forest canopy density leads to increased litter accumulation on the forest floor and reduced sunlight penetration, creating anaerobic conditions. This can cause forests to shift from CH₄ consumption to CH₄ release. Based on these findings, we discussed methods to maintain and enhance the CH₄-absorbing capacity of forest soils. This can be achieved through management practices that improve environmental conditions and increase soil fauna’s activity, such as those associated with thinning operations in overmature forest stands across various regions. This ecological manipulation through thinning practices promotes ground-level temperature increases and the activities of soil fauna, as well as maintaining aerobic conditions near the soil surface. Full article

Hybrid workplace environments are increasingly adopted in public-sector organizations; however, empirical evidence on how these spaces function in practice remains limited. This study evaluates space utilization and workplace experience in the Los Angeles Department of Water and Power (LADWP) WorkHub, a hybrid-ready and technology-enabled workplace, using a convergent mixed-methods post-occupancy evaluation grounded in affordance theory. Phase I consisted of a participatory Think Tank session with 24 employees to identify perceived strengths, barriers, and improvement priorities related to indoor environmental quality (IEQ) and spatial design. Phase II employed a Space-Centered Behavioral and Environmental Mapping (SC-BEM) protocol over four weeks, generating 272 valid zone-level observations capturing occupancy, activity type, seat utilization, functional alignment, and perceived environmental conditions. The results indicate that workplace use was concentrated in reservable, enclosed, and technology-supported spaces, whereas many open seating areas remained underutilized. Observed behaviors were primarily associated with collaborative and communal activities, with comparatively fewer focused individual activities, suggesting that the WorkHub functioned predominantly as a hub for interaction. Although acoustics and thermal comfort emerged as consistent experiential constraints, observer-rated IEQ did not significantly predict occupancy. Interpreted through an affordance-based lens, these findings suggest that space use was shaped more strongly by the clarity and usability of spatial affordances than by environmental quality alone. Full article

The development of tight heavy-oil reservoirs is severely hampered by the high viscosity and poor mobility of crude oil caused by strong intermolecular stacking interactions among asphaltenes, coupled with the substantial adsorption loss and inadequate deep transport capacity of conventional displacement agents. By targeted penetrant delivery, a novel nanoemulsion system with a well-defined “core–shell” architecture was synthesized to address these critical challenges. The physicochemical properties, stability and oil displacement performance were evaluated. The prepared nanoemulsion exhibited an ultrasmall and uniform particle size distribution between 10 nm and 20 nm. It also demonstrated exceptional dispersibility in aqueous media and remarkable thermal and salinity stability under reservoir conditions. Furthermore, an ultralow critical micelle concentration of approximately 0.01% could be achieved and the oil–water interfacial tension was reduced to 7.3 × 10⁻² mN/m, significantly outperforming the conventional surfactant AES. Core flooding tests revealed that the proposed nanoemulsion enhanced oil recovery by 37.1% and attained a displacement efficiency of 68.9% in oil-wet capillary models. Molecular dynamics simulations further elucidated the underlying synergistic mechanism. The hydrophilic shell minimized adsorption on rock surfaces, facilitating deep migration within nanoporous channels. The hydrophobic core, containing terpinene as a penetrant, effectively disrupted the π-π stacking of asphaltenes due to its nonplanar molecular configuration. This disruption transformed the asphaltene aggregates from a tightly packed state to a dispersed state, resulting in substantial viscosity reduction. This work elucidated the mechanism of asphaltene aggregate disruption by nanoemulsions at the molecular level, offering a promising and theoretically grounded strategy for the efficient exploitation of tight heavy-oil reservoirs. Full article