Search Results (452)

Integrated electricity–gas–heat systems require coordinated scheduling while limiting data sharing and representing network constraints. This paper develops a day-ahead model incorporating reactive power, voltage magnitudes, network losses, demand response, and CHP/P2G coupling. Piecewise linearization and second-order cone relaxation reformulate the model as a mixed-integer second-order cone program, while a hierarchical ATC–ADMM method coordinates the electricity–heat and natural gas subsystems by exchanging coupling variables. Residual checks verify approximation accuracy and original equation feasibility. In the test system, ATC–ADMM reached consensus within five iterations, with a total-cost deviation of 0.0075% from centralized optimization, whereas ATC did not converge within 500 iterations. Coordinated operation reduced the total cost by 1.13%, and Shapley allocation benefited both subsystems. Increasing demand-side flexibility from 5% to 9% reduced the total cost by 0.88% and wind curtailment from 6.02% to 4.86%; increasing reactive compensation from 40% to 60% reduced the total cost by 0.41% and wind curtailment to 5.70%. The results reveal non-monotonic penalty-update effects and diminishing marginal benefits of flexibility resources, providing guidance for parameter selection and capacity allocation. Full article

Background: Gender dysphoria denotes the psychological distress experienced by individuals due to the incongruence between their gender identity and their sex assigned at birth. Gender-affirming surgery (GAS) is one of the treatment options available for people with gender dysphoria. This study aims to identify and analyze all ethical reasons both supporting and opposing GAS as a treatment option for gender dysphoria in adults. Methodology: A systematic review of reasons was conducted in accordance with the methodology proposed by Strech and Sofaer (2012) and the PRISMA guidelines. The literature search was conducted in seven major online databases—PubMed, Scopus, Embase, Web of Science, Philosopher’s Index, ATLA Religion, and Index Theologicus—and in the Google Scholar search engine. Results: Twenty-nine articles published between 2003 and 2024 met the inclusion criteria. The ethical reasons identified in the included literature were grouped into five thematic categories: (1) the origin and nature of gender dysphoria, (2) reasons related to GAS as a treatment for gender dysphoria, (3) anthropological reasons, (4) reasons related to the effects of GAS, and (5) reasons stemming from traditional ethical principles. Discussion: The wide range of ethical reasons both supporting and opposing GAS as a treatment option for gender dysphoria in adults highlights the complexity of ethical deliberation in this area. This review provides a structured overview of the ethical reasons discussed in the literature, identifies important gaps, and suggests directions for future research. Full article

Honey is a natural food product highly valued for its nutritional and biological properties. Its quality and safety are increasingly affected by environmental contamination and apicultural practices. Among the most relevant contaminants, pesticide residues, veterinary antibiotics, and heavy metals represent major concerns due to their potential impact on human health. This review provides a comprehensive overview of the occurrence, sources, and distribution of these contaminants in honey, with particular emphasis on their relationship with agricultural activities, environmental pollution, and beekeeping treatments. Advanced analytical techniques, including liquid chromatography–tandem mass spectrometry (LC-MS/MS), gas chromatography–mass spectrometry (GC-MS), and inductively coupled plasma–mass spectrometry (ICP-MS), are highlighted for their ability to enable sensitive multi-residue and trace-level detection. The application of chemometric tools for data analysis and sample classification is also addressed, supporting the identification of contamination patterns and origin-related differences. In addition, recent developments in rapid screening methods and environmentally sustainable analytical approaches are discussed. Overall, this review emphasizes the importance of continuous monitoring and the integration of advanced analytical strategies to ensure honey safety, support regulatory compliance, and protect consumers. Full article

Globally, beef customers are seeking verified information regarding the carbon footprint (CF) of the products they buy. As a major supplier of premium grass-finished and natural grain beef supplying markets world-wide, JBS Southern Australia developed a certified Farm Assured (FA) program, launched in 2013, to provide quality beef from independently audited suppliers. This study conducted a life cycle assessment (LCA) with ‘cradle to farm gate’ and ‘cradle to processor gate’ boundaries, using two reference flows—(i) one kilogram (kg) of liveweight (LW) at the farm gate, and (ii) one kg of boxed beef at the processor gate—to assess the greenhouse gas (GHG) CF for beef produced in southern Australia. This study is the first to integrate individual animal carcass characteristics with brand level CF analysis at scale. This was achieved by developing a uniquely comprehensive dataset, with primary data supplied by 200 farms and individual animal data provided for 514,922 heads of cattle. The mean farm gate CF was 11.7 (standard deviation 0.4) kg carbon dioxide equivalent (CO₂-e) kg⁻¹ LW, and the mean boxed beef CF was 24.1 kg CO₂-e kg⁻¹ boxed beef. The study’s novel approach to data collection allowed for the CF to be stratified by region, carcass characteristics, farm of origin and product brand. Analysis revealed that the lowest farm-average and individual animal CFs were 29% and 48% lower than the supply chain average, respectively. These findings indicate that the CFs of beef produced from grass and natural grain-finished production systems in southern Australia were comparable or lower than the CFs of beef entering similar markets. Full article

Population-based bio-inspired metaheuristics are the dominant tools for tuning hybrid decomposition–deep-learning forecasters, yet their relative behavior on a common problem is rarely assessed with a leakage-free, physically meaningful protocol. We benchmark eight metaheuristics on the joint nine-dimensional hyperparameter optimization of a discrete-wavelet-transform bidirectional-LSTM (DWT–BiLSTM) architecture for short-term wind speed forecasting, using 409,152 hourly observations from eight meteorological stations. The set comprises six nature-inspired methods (Artificial Bee Colony, ABC; genetic algorithm, GA; Particle Swarm Optimization, PSO; Grey Wolf Optimizer, GWO; Hippopotamus Optimization, HO; and the Raindrop Optimizer) together with two recent metaphor-free or social variants (the Farthest-better Nearest-worse Optimizer, FNO; and the Tuckman Optimization Algorithm, TOA). A multi-stage protocol covers 30 independent runs per algorithm, a joint-versus-sequential comparison, a genuine rolling-origin out-of-sample evaluation, and component ablation. Friedman testing reveals significant differences (χ² = 49.76; p < 10⁻⁸), with the Grey Wolf Optimizer attaining the best mean rank (2.27) and Pareto-dominant run-time; ablation shows the DWT front-end is essential (Cohen’s d = 13.09) and bidirectionality negligible at the one-hour horizon (p = 0.674). Critically, evaluating forecasts in reconstructed physical units reveals that the per-component advantage does not persist: at the one-hour horizon the reconstructed forecast does not exceed a naive persistence baseline (skill ≈ −0.5 in m/s versus +0.44 in normalized component space), a discrepancy independent of decomposition leakage that we report transparently. This work thus contributes a rigorous, leakage-controlled bio-inspired benchmark and a cautionary evaluation methodology. Full article

Accurate electricity price forecasting is essential for market participants seeking to optimise bidding and arbitrage strategies. This paper presents a week-ahead (168 h) hourly electricity price forecasting study for the Spanish day-ahead market. Nine competing models—two naïve baselines (a Seasonal Naïve and a Day-of-Week persistence), a Lasso-estimated auto-regressive (LEAR) statistical benchmark, and six machine- and deep-learning models (CatBoost, Random Forest, LSTM, GRU, CNN, and a hybrid CNN–LSTM)—are benchmarked; the two leading models, CNN–LSTM and CatBoost, are then compared under exogenous-feature configurations. The analysis is complemented by an ex-post Add-One-In and Leave-One-Out feature-importance analysis, a controlled comparison of weather-input scenarios, and a rolling battery-arbitrage backtest that translates forecast quality into economic value. Under an endogenous benchmark of weekly rolling origins across 2024 (with a rotating start weekday) and Diebold–Mariano testing, a recursive CatBoost and the hybrid CNN–LSTM are statistically indistinguishable and both significantly outperform a direct multi-horizon CatBoost; once an operational (forecasted) weather input is added, recursive CatBoost becomes significantly the most accurate while remaining simpler and more stable to train, a ranking confirmed on a fully out-of-sample 2025 year. Operational weather forecasts are found to be the best weather input, recovering about 84% of the perfect-foresight weather improvement over a no-weather baseline, with the advantage concentrated at longer lead times. Natural-gas-fired generation emerged as the dominant explanatory feature, consistent with the marginal-pricing mechanism governing the Spanish market. In a rolling battery-arbitrage backtest on the out-of-sample 2025 year, a deployable forecast-driven 4-h grid-scale unit (200 MW/800 MWh) captured about 89% of perfect-foresight value at a 168 h optimisation horizon and about 87% at 24 h; extending the horizon from 24 h to 168 h added about 2.4% of profit, an optimisation-horizon (look-ahead) effect bounded at +4.5% under perfect foresight. Full article

Hydrogen is a clean energy carrier essential for carbon neutrality, but its invisible and odorless nature poses significant safety risks, particularly during low-concentration leaks. Although metal oxide semiconductor (MOS) sensors offer fast response and high sensitivity, their ability to detect ppb-level hydrogen remains limited. In this work, we present a high-performance hydrogen gas sensor based on nickel-doped tin dioxide (Ni-SnO₂) nanorods, directly grown on planar electrodes via aerosol-assisted chemical vapor deposition (AACVD). By optimizing the Ni doping ratio and nanorod morphology, the 3 wt% Ni-SnO₂ sensor achieves a low detection limit of 100 ppb for H₂, demonstrating promising potential for low-concentration hydrogen detection. Moreover, the sensor exhibits outstanding selectivity, with a response to 100 ppm H₂ nearly six times higher than that to the next most responsive interfering gas (NH₃). Comprehensive XPS and Raman analyses reveal that Ni doping introduces abundant oxygen vacancies and lattice defects, which are the key origins of the enhanced sensing performance. Notably, the 3 wt% Ni-SnO₂ sensor strikes an optimal balance between lattice defects and structural stability, delivering both high sensitivity and good moisture resistance with minimal baseline drift over weeks of operation. This work establishes a facile and scalable AACVD strategy for engineering defect-rich SnO₂ nanostructures, enabling sub-ppm hydrogen detection with high selectivity and long-term stability—addressing a critical gap in practical hydrogen safety monitoring. Full article

The onset of quantum behavior in gases is traditionally established through a criterion that is external to classical statistical mechanics. One introduces the thermal de Broglie wavelength and compares it with the mean intermolecular separation, concluding that quantum effects become relevant when $n{\lambda }_T^3\sim 1$. This condition originates in quantum statistical mechanics and is absent from the classical ideal-gas or van der Waals partition functions. In this work, we show that a grand-canonical treatment of the van der Waals fluid naturally generates an interaction-corrected crossover temperature $T_3(a,b,m,n)$ determined by the particle mass, density, and van der Waals interaction parameters. While the thermal de Broglie wavelength provides the standard quantum crossover scale, the interaction-induced correction leading to $T_3$ is obtained without invoking the explicit form of the Bose–Einstein or Fermi–Dirac distributions. Instead, $T_3$ follows from a self-consistent condition within the grand-canonical van der Waals description. We demonstrate that, below this temperature, the statistical assumptions underlying the classical theory become self-inconsistent, indicating the breakdown of the classical description and the onset of the quantum-degenerate regime. The resulting temperature scale therefore provides an interaction-corrected boundary of validity of the classical van der Waals description. These findings provide a new perspective on how intermolecular interactions modify the crossover to the quantum-degenerate regime and clarify the limits of applicability of the classical van der Waals theory. Full article

In our day-to-day lives, we are regularly exposed to a wide spectrum of dangerous gases. Their origins vary, ranging from industrial activities to objects found within our very homes. Naturally, there is an interest in developing cost-efficient and durable devices that can successfully track these gases within our environment. One such candidate is represented by chemoresistive gas sensors based on metal oxides. This is due to their simple architecture and the possibility of scaling down their size, making them valid contenders for future advancements in portable gas sensors. This review focuses on chemoresistive gas sensors that have been obtained through different Physical Vapor Deposition (PVD) methods, which are easily scalable for potential technological transfer towards commercialization or are already exploited at the industrial level, and how varying different deposition parameters impacts the structure of the active material, thus modifying the gas sensing properties of the device. In this review, we report results obtained for different metal oxides: WO₃, ZnO, CeO₂, TiO₂, NiO, and SnO₂. The main findings of these studies revealed that the sensor’s response was highly impacted by oxygen deficiencies within the deposited material, the specific surface area, and the thickness of the film. Moreover, this study also delves into different strategies of functionalization that result in improved gas sensing properties. Thus, we herein report how tailoring functional properties modifies the gas sensing performance of different metal oxides. Full article

Biogenic or microbial methane (CH₄) emissions, believed to be the main driver of the recent surge in global atmospheric CH₄ emissions, have altered monitoring, measurement, and mitigation of fossil-fuel emissions. As of 1981, over 20% of the world’s natural gas reserves were biogenic in origin. Additional biogenic CH₄ reserves from coal have been discovered since 1981 mixed (40–80%) with thermogenic CH₄. Biogenic CH₄ accumulates up to 100% in coal reservoirs in the Powder River Basin (PRB), USA. Biogenic CH₄ is generated by microbial breakdown of fossil organic matter as an early-stage (primary) type during burial over geologic time and is rarely preserved. Also, biogenic CH₄ is generated as a late-stage (secondary) type from recent geologic to present times and is commonly preserved. Late-stage biogenic CH₄ is sustained by nutrients and microbes in meteoric/surface waters discharged into coal aquifers. Groundwater is pumped from wells in coal aquifers to desorb and produce CH₄ and dewater coal mines. The co-produced water with dissolved CH₄ is discharged into diverse surface aquatic systems. The emission factors (EFs) of co-produced water are 2.0522 × 10⁻⁹ Gg CH₄/gal of water in the PRB and 2.0694 × 10⁻³ Gg CH₄/well in the Black Warrior Basin, U.S. Accurate data on biogenic CH₄ emissions from coal sources is a major gap in the accounting of current global groundwater-driven CH₄ whose average flux is estimated to be 3.9 ± 6.2 mmol/m²/day or accounting for up to 70% of CH₄ emissions from surface aquatic systems. Biogenic CH₄ emissions from coal mining and coalbed gas extractions and related infrastructures are overlooked because the focus has been on coalmine methane (CMM) emissions. CMM data from ground-based measurements is highly variable and used by the Intergovernmental Panel on Climate Change three-tier system to estimate EFs for national inventories. However, 90% of CMM emissions are attributable to a small group of the most coal-consuming-and-producing countries but fails to capture other coal sources worldwide. This created gaps and “blind spots” in “unstructured” low-concentration, diffused biogenic CH₄ emission data. These key “blind spots” include sources from flooded, abandoned coal mines; coalbed methane (CBM) co-produced water with dissolved CH₄ and infrastructures/facilities; and groundwater drawdown from water withdrawals during coal mining and CBM extraction. Also, a critical “blind spot” is the mixing of biogenic CH₄ emissions from subsurface coals with biogenic CH₄ generated at the surface from wetlands, agriculture, and landfills/wastes, which grew 85% from 2008 to 2020. Limited understanding of the mixing of biogenic CH₄ from diverse sources and their contributions to global methane requires accurate attribution of overlapping isotopic signatures (δ¹³C_CH4 and δD). This paper addresses knowledge gaps in coalbed biogenic CH₄ emissions by a systematic review of the literature and specific study cases, which provided insights on key “blind spots” in their mitigation. Full article

Radon (²²²Rn) is a chemically inert (noble) gas, naturally occurring α-emitter radionuclide, and the direct progeny of ²²⁶Radium; it is produced in the uranium (²³⁸U) decay chain. Short-term measurements of the concentration of radon can be performed to identify locations and objects with potentially increased concentrations. The goal of this study is to present a comparison of models for the seasonal prediction of ²²²Rn active concentration variations from the results of ²²²Rn short-term measurements acquired by active instruments. Several predictive models are compared in this study, with estimation and validation datasets from ²²²Rn concentration measurements from two significant micro-locations: the St. Barbara mine and a ground-floor room of the University of Zagreb Faculty of Mining, Geology and Petroleum engineering (UNIZG-FMGPE) building, in Zagreb, Croatia. MATLAB version R2025b System identification and a Signal multiresolution analyzer were used for the estimation of predictive models and validation for mid-term prediction. This research provides one method for estimating the concentration variations from a smaller number of observations from 8 days of measurements. It shows that the best models for the estimation and prediction of radon concentration time series are the auto-regressive non-linear ARX model (NLarx), with a one-step-ahead prediction fit of up to around 90% for a minimum measurement duration of 8 days, 192 samples, and a 1 h floating mean for the estimation and ARMAX estimation from the reconstructed signal as a simple polynomial approximation of the original measurement signal, with a one-step-ahead prediction fit of almost 100%. The ARMAX model with a one-step-ahead predicted output gives excellent estimation of the MODWT and EMD reconstructed signal, which has approximately the same mean as the original signal and, thus, can be used for indirect prediction of the Rn mean. The NLarx model showed good results in the validation of Rn concentration variations; thus, this model is selected as the preferred model to predict Rn concentration variations from short-term measurements. Full article

The Tajik Basin is located on the eastern edge of the Central Asian segment of the Tethyan tectonic domain. The basin underwent intense tectonic transformation during the Himalayan period, resulting in complex structural styles, unclear original sedimentary characteristics and oil and gas geological conditions, and a complex process of oil and gas accumulation, which restricts the further evaluation of the basin’s exploration potential. Studying the Tajik Basin in the macro background of the Tethys tectonic domain, the tectonic sedimentary evolution of the Tethys tectonic domain has a significant effect on the basin’s tectonic evolution, sedimentary characteristics, and oil and gas accumulation conditions. The Tajik Basin has gone through four stages of tectonic evolution: the Late Permian to Triassic was the stage of back arc foreland basin; the Jurassic period was the stage of back arc extensional faulting depression; the Cretaceous–Paleogene period was the stage of depression basins; and the Neogene is the stage of the regenerated foreland basins. Through field geological surveys and analysis of outcrop samples, it has been determined that the Tajik Basin has developed three sets of source rocks: the Middle and Lower Jurassic, Cretaceous, and Paleogene. Among them, the organic matter abundance of the Middle and Lower Jurassic is relatively high, most of them are in the mature stage, and they are primarily gas-generating source rocks. The Cretaceous and Paleogene source rocks are mainly oil generating and in a low-mature state. There are four sets of reservoirs developed in the Tajik Basin: Middle-Upper Jurassic carbonate rocks, Lower Cretaceous clastic rocks, Upper Cretaceous carbonate rocks and Paleogene carbonate rocks. Comprehensive research shows that the Tajik Basin mainly develops three types of oil and gas reservoirs: Jurassic carbonate gas reservoirs, distributed in the southwestern Gissar Uplift and Surhan Depression in the western part of the basin; Paleogene carbonate reservoirs, distributed in the southern Vakhsh Depression and the eastern Kuliabu Depression; and multi layer–multi lithology oil and gas reservoirs, distributed in the northern Dushanbe Depression. The primary controlling factor for the three types of oil and gas reservoirs is tectonic movement, which forms traps and simultaneously reshapes the reservoirs, ultimately leading to effective accumulation of oil and gas. The distribution of oil and gas in the Tajik Basin is characterized by “west gas and east oil, west more and east less, west pre-salt and east post-salt, and pre-salt gas and post-salt oil”. Affected by the regional tectonic movements of the Tethys rich oil and gas tectonic domain, the basin has high-quality hydrocarbon source rocks, reservoirs, and cap rock conditions. The pre-salt Jurassic has the potential to form large natural gas reservoirs, while the post-salt Cretaceous and Paleogene still have further potential for exploration. Full article

Enteric methane (CH₄) emissions from ruminants are a significant contributor to agricultural greenhouse gas emissions and represent an increasing concern in African livestock systems. This systematic review evaluates the effectiveness of tannin-rich plants as a dietary strategy for mitigating enteric CH₄ emissions in African ruminant production systems. The review followed PRISMA guidelines and included peer-reviewed original studies published between 2015 and 2025 that investigated tannin-rich plant interventions in cattle, sheep, or goats within African production systems. Eligible studies comprised both in vivo feeding trials and in vitro rumen fermentation experiments. Studies were included if they reported enteric CH₄ or greenhouse gas-related outcomes, while reviews, modeling studies, non-ruminant studies, and studies without CH₄-related outcomes were excluded. A total of eight eligible studies were identified, all conducted in South Africa despite the Africa-wide scope of the review. Overall, tannin-rich plant interventions showed potential to reduce CH₄ emissions, although the magnitude and consistency of responses varied depending on tannin type, source, inclusion level, form of administration, and dietary context. Purified and encapsulated tannin extracts generally produced more consistent CH₄ reductions than crude or whole-plant sources. Responses also differed between controlled total mixed ration systems and forage-based feeding systems. However, the small number of studies and their strong geographic concentration limit broader generalization across the continent. In conclusion, tannin-rich plants show promise as a natural CH₄ mitigation strategy in ruminants, but more regionally distributed and methodologically robust studies are needed across Africa to strengthen the evidence base. Full article

The acceleration and deceleration of high-speed gas flow within a pipeline, induced by the action of flow-restriction devices, frequently result in the emergence of unsteady flow phenomena. Consequently, the generated excitation forces provoke intense vibrations in the pipeline, thereby substantially elevating the operational risks of the pipeline system. To mitigate such risks, the pipeline is typically subjected to fixed constraints to reduce vibration. A pipeline designed to simulate unsteady airflow was developed for the purpose of validating the vibration attenuation effect. Within this context, the effects of binding and friction constraints were compared through fluid–structure interaction simulation, and their respective mechanisms of action were analyzed individually. The results demonstrate that the constraints, in conjunction with the original pipeline, will result in a higher first-order natural frequency, which constitutes one of the primary methods for mitigating resonance effects. Both friction constraints and binding constraints significantly elevate the first-order natural frequency of the pipeline system, with binding constraints demonstrating higher efficiency. This phenomenon is attributable to the arch-like bending deformation observed in such experimental pipelines during first-order resonance, as binding constraints effectively maximize the restriction on pipeline strain. Through a comparative analysis of the time-domain and frequency-domain results of outlet pipe 1 before and after constraint application, it was observed that the axial RMS value of the constrained pipe decreased by 21.8%, while the radial value diminished by 33%. This finding further substantiates that imposing binding constraints at the location of maximum strain can elevate the pipe’s natural frequency by reducing both strain and the effective length of the “beam”, thereby significantly alleviating pipe vibrations induced by unsteady flow. Full article

The assessment of humanity’s ecological footprint has become increasingly critical in contemporary discourse due to growing environmental challenges. This study examines the economic evaluation of the ecological footprint with a particular focus on forest ecosystem services and food productivity. Using harmonized secondary data from FAOSTAT, EUROSTAT, the World Bank, and IPBES, the analysis covers selected developed and emerging economies, including the European Union, the United States, China, Brazil, and other representative countries. This study investigates the macroeconomic implications of natural capital degradation by applying a panel data econometric model to European Union countries over the period 2010–2023. Moving beyond descriptive approaches, the research formulates and tests three hypotheses linking biodiversity, environmental pressure, and green transition variables to economic performance. Using harmonized data from Eurostat and Statista, the study employs a fixed-effects regression framework to estimate the impact of biodiversity indicators, greenhouse gas emissions, renewable energy share, and environmental protection expenditures on GDP per capita. The results demonstrate that biodiversity preservation and resource efficiency are positively associated with economic performance, while environmental degradation—proxied by greenhouse gas emissions—exerts a statistically significant negative effect. Additionally, the findings confirm that investments in renewable energy and environmental protection contribute to long-term economic stability. By providing a transparent data structure, explicit variable operationalization, and reproducible econometric specification, the study offers an original empirical contribution to ecological economics and addresses the limitations of prior literature that relied primarily on descriptive synthesis. Full article