Search Results (23,631)

Safflower (Carthamus tinctorius L.) is an economically important crop, and heat stress has become a major environmental constraint that limits its growth and development under global climate change. However, the molecular mechanisms underlying its response to heat stress remain poorly understood. Here, transcriptome sequencing was performed on safflower leaves exposed to heat stress (42 °C) for 0, 1, 2, 4, 8, and 12 h, with three biological replicates per time point. Compared with the control (0 h), a total of 12,964 differentially expressed genes (DEGs) were identified across the five time points (1, 2, 4, 8, and 12 h) using criteria of |log₂ (fold change)| ≥ 1 and false discovery rate (FDR) < 0.05, of which 1097 were common to all comparisons. KEGG enrichment analysis of these DEGs across all five comparison groups consistently showed significant enrichment in plant hormone signal transduction and the MAPK signaling pathway. Furthermore, a total of 750 transcription factors (TFs) were identified as differentially expressed across the five comparison groups, of which 99 were common to all comparisons, with the bHLH, MYB, WRKY, and HSF families being the most abundant. Weighted Gene Co-expression Network Analysis (WGCNA) identified five modules that were significantly associated with different heat stress time points. Furthermore, 13 hub genes were identified as potential targets for future functional studies on heat tolerance in safflower. The reliability of the RNA-seq data was confirmed by qRT-PCR validation of selected hub genes. Notably, a non-specific serine/threonine protein kinase (CtAH03G0292100) from the MEred module, which is also involved in plant hormone signal transduction, emerged as a promising candidate gene for heat tolerance. Collectively, these findings provide candidate genes for future functional studies aimed at further elucidating the mechanisms of heat tolerance in safflower. Full article

The compressive strength of ultra-high-performance concrete (UHPC) is jointly influenced by multiple factors, including material composition, mixture proportion parameters, and curing regime. Conventional empirical methods are therefore insufficient to accurately characterize the highly nonlinear relationships involved. To improve the prediction accuracy of UHPC compressive strength and to achieve mixture proportion optimization that simultaneously considers mechanical performance, economic efficiency, and environmental impact, this study developed random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models based on 810 publicly available UHPC experimental datasets. Model performance was evaluated using R², RMSE, MAE, and MAPE. To enhance the robustness of model validation, repeated K-fold cross-validation, sensitivity analysis with different random seed splits, and benchmark model comparisons were further introduced. The results indicate that the XGBoost model achieved superior predictive performance on both the test set and robustness validation, with test-set R², RMSE, MAE, and MAPE values of 0.9604, 7.77, 5.58, and 4.80, respectively. The model was further interpreted using SHAP, PDP, and ICE methods, and the results revealed that curing age, fiber content, silica fume content, and water-to-binder ratio were important variables affecting the compressive strength of UHPC. Furthermore, XGBoost was used as a surrogate model and coupled with NSGA-II and TOPSIS methods for multi-objective optimization. Under the constraints of compressive strength, water-to-binder ratio, superplasticizer-to-binder ratio, and absolute volume, a computationally recommended UHPC mixture proportion balancing strength, cost, and carbon emissions was obtained. This study provides a reproducible machine-learning-assisted approach for UHPC compressive strength prediction and low-carbon, cost-effective mixture proportion design. Full article

This study examines whether daily supply-chain stress and geopolitical risk improve the forecasting of strategic commodity and clean energy market returns. Using daily data on aluminum, copper, nickel, and clean energy from 10 February 2015 to 27 February 2026, the analysis compares a baseline forecasting model based on conventional market controls with augmented specifications that incorporate supply-chain stress, geopolitical risk, and their joint effects. The empirical framework combines multiple machine-learning algorithms with SHAP-based explainability to evaluate both forecast performance and the relative importance of predictors. Formal Diebold-Mariano tests are also used to assess whether the forecasting gains from augmented specifications are statistically significant. A Model Confidence Set analysis is further used to identify statistically superior model groups across the full set of algorithm-specification combinations. The results show that disruption-related predictors contain asset-specific forecasting information, while the comparison across algorithms indicates that no single model uniformly dominates across all assets and loss functions. The forecasting gains from disruption-related predictors, however, are strongly asset-specific and statistically uneven. For aluminum returns, augmented specifications that include supply-chain stress and/or geopolitical risk significantly improve forecast accuracy relative to the baseline. For copper returns, the evidence is weaker and mainly associated with geopolitical risk. For nickel returns, the joint inclusion of supply-chain stress and geopolitical risk provides the greatest improvement. By contrast, clean energy returns remain more closely tied to conventional macro-financial conditions, with no statistically significant incremental gains from disruption-related variables. SHAP evidence further indicates that predictor importance is asset-specific rather than dominated by a single market factor across all assets. The findings highlight the importance of combining flexible forecasting methods with economically interpretable tools when evaluating disruption-sensitive commodity and clean energy markets. Full article

Farmland transfer is a central institutional arrangement for reallocating land, labor, and capital during agricultural transformation. Yet existing studies often treat farmland transfer mainly as scale expansion, paying less attention to how changes in operator composition and rental conditions shape the economic viability of agricultural land use. Using provincial panel data from China for 2009 to 2022, this study examines how three dimensions of farmland transfer, namely transfer scale, the share of land transferred to new-type agricultural operators, and land transfer rent, affect sustainable agricultural land use, with agricultural total factor productivity (TFP) used as an economic indicator of land-use viability. After developing a theoretical model that links the land–labor–capital dimensions of farmland transfer to TFP, the empirical analysis uses dynamic distributed-lag two-way fixed-effect models, nonlinearity tests, mediation and heterogeneity analyses, and a shift–share instrumental-variable approach, with all estimations conducted in Stata 18.0. The results show that farmland transfer has heterogeneous effects across its land–labor–capital dimensions. A larger transfer scale is associated with lower agricultural TFP, whereas a higher share of land transferred to new-type operators and higher transfer rents are associated with higher TFP. These effects differ across migration contexts and grain functional zones. Mechanism analysis shows that farmland transfer affects agricultural TFP partly by reshaping net-labor out-migration, thereby changing the labor conditions under which agricultural production is organized. These findings suggest that sustainable agricultural land use depends not simply on how much land is transferred, but also on who operates the transferred land and how rental incentives are structured. The study highlights the importance of multidimensional land transfer policies for improving factor allocation, maintaining active farmland use, and supporting the economic viability of agricultural production during structural transformation. Full article

Pumped storage hydropower (PSH), with its advantages of fast response and large-scale energy storage, has become a key means of enhancing power system flexibility. However, the time-varying nature of transmission line capacity may constrain the effective utilization of its regulating capability. Most existing studies treat transmission capacity as a fixed boundary, failing to adequately account for the impact of its dynamic variations on scheduling strategies. To address this gap, this paper constructs three typical transmission capacity scenarios: fixed high (no congestion), fixed low (persistent severe congestion), and time varying (capacity reduced during daytime and restored at night). A power system dispatch optimization model incorporating wind power, solar power, and pumped storage is established, with the objective of minimizing total system operating cost. Under different capacity scenarios, the pumping/generating behavior of PSH, unit output structure, controlled line operation status, and economic indicators are compared and analyzed. Furthermore, sensitivity analyses are conducted from three dimensions—congestion severity, congestion time window, and PSH installed power capacity—to comprehensively evaluate the marginal impacts of key factors on system performance. Results based on a modified IEEE 14-bus system indicate that under the fixed high scenario, PSH can achieve free arbitrage with the best economic performance, but does not account for capacity fluctuation risks. Under the fixed low scenario, persistent congestion leads to substantial wind and solar curtailment, significantly increased operating costs, and prolonged full loading of lines, posing the highest security risk. Under the time-varying scenario, PSH is forced to pump at full power during nighttime and generate continuously during daytime, effectively alleviating line flow pressure. Although the operating cost is slightly higher than that of the fixed high scenario, line overload is avoided, renewable energy accommodation is significantly improved, and substantial security gains are achieved at a moderate economic cost. This paper reveals the forcing mechanism of time-varying transmission capacity on PSH scheduling strategies, verifies the feasibility of the time-varying capacity strategy as an effective trade-off between economics and security, and provides a theoretical basis for optimal PSH operation and transmission capacity management in power systems with high shares of renewable energy. Full article

Reducing carbon emissions has become a central objective of sustainable development policies as countries seek to address the environmental challenges associated with climate change and resource depletion. In this context, circular economy practices and energy transition policies have emerged as key mechanisms for achieving environmental sustainability. This study examines the determinants of carbon emissions within the framework of the circular economy and energy transition for selected Central and Eastern European countries (Romania, Poland, Hungary, Bulgaria, Slovakia, and Slovenia) over the period 2010–2024. Using panel data analysis, the study incorporates key variables including circular economy, economic growth, recycling, renewable energy consumption, and urbanization. To enhance the reliability of the empirical estimates, panel unit root tests, the Hausman specification test, fixed-effects estimation, and Driscoll–Kraay robust standard errors are employed to address heteroskedasticity, serial correlation, and cross-sectional dependence. The results indicate that improvements in circular economy practices together with greater renewable energy use are associated with lower carbon emissions, providing empirical support for the proposed hypotheses. Recycling activities are found to increase emissions in the short run, indicating energy-intensive processes. In contrast, the effects of economic growth and urbanization are found to be context-dependent, providing only partial support for the related hypotheses. Overall, the results highlight that carbon emission dynamics are shaped by a complex interaction of economic, structural, and environmental factors, and that policy effectiveness depends on country-specific conditions. By focusing on transition economies in Central and Eastern Europe, this study extends the existing literature through an integrated panel data analysis of circular economy and energy transition policies and offers policy-relevant evidence for the region. Full article

A novel centrifuge-less cloud-point extraction (CL-CPE) method based on pyrogallol (PG) was developed for the spectrophotometric determination of total vanadium. The method employs a mixed micelle-mediated extraction system comprising the nonionic surfactant Triton X-114 and the ionic liquid Aliquat 336 (A336), which serves as a source of monovalent cations capable of forming an ion pair with the anionic vanadium–PG chelate. The extracted species, (A336⁺)[V^IV(OH)(PG)₂], exhibits several absorption maxima (309, 362, and 436 nm), providing enhanced selectivity through appropriate wavelength selection according to the sample matrix. The principal absorption maximum occurs at 309 nm. At a sevenfold preconcentration factor, the method provides high sensitivity at this wavelength, with a molar absorptivity of 3.2 × 10⁷ L mol⁻¹ cm⁻¹, a limit of detection of 0.11 ng mL⁻¹, and a Sandell sensitivity of 1.6 × 10⁻³ ng cm⁻². The applicability of the method was demonstrated through the analysis of drinking water samples, spent vanadium catalyst materials, and vanadium-containing dietary supplements. The method was further evaluated using the RGBfast model, a white analytical chemistry assessment tool that integrates analytical performance, environmental sustainability, and practical and economic efficiency. The evaluation indicated a high overall whiteness score. Full article

Astragalus membranaceus is an important medicinal plant with considerable pharmacological and economic value; however, its growth and productivity are frequently threatened by drought stress. Basic leucine zipper (bZIP) transcription factors play crucial roles in plant growth, development, and abiotic stress responses, yet a comprehensive investigation of the bZIP gene family in A. membranaceus remains unavailable. In this study, 74 bZIP genes (AmbZIPs) were identified in the A. membranaceus genome and classified into 12 subfamilies based on phylogenetic relationships with Arabidopsis thaliana. Analyses of gene structure, conserved motifs, chromosomal distribution, and duplication events revealed high conservation within subfamilies and indicated that segmental duplication was the major driver of AmbZIP family expansion. Codon usage analysis showed that AmbZIP genes exhibited relatively weak codon usage bias, with codon preference predominantly shaped by natural selection rather than mutation pressure. A total of 23 optimal codons were identified, of which 91.3% were A/T-ending codons. Codon adaptability analysis further demonstrated that tobacco possessed the highest codon compatibility among five tested hosts, whereas Escherichia coli exhibited the lowest adaptability, suggesting that plant expression systems may be more suitable for functional studies of AmbZIP genes. Promoter analysis identified numerous cis-acting elements associated with phytohormone signaling and abiotic stress responses, particularly those related to abscisic acid, methyl jasmonate, salicylic acid, and drought responsiveness. Transcriptome analysis and quantitative real-time polymerase chain reaction (qRT-PCR) validation revealed that several AmbZIP genes were significantly induced under drought stress. Among them, AmbZIP46 displayed strong drought-responsive expression, transcriptional activation activity, and exclusive nuclear localization. These findings provide the first comprehensive characterization of the bZIP gene family in A. membranaceus and establish a valuable foundation for elucidating drought-tolerance mechanisms and facilitating molecular breeding in this medicinal plant. Full article

Global urbanization has caused widespread ecological degradation, yet habitat quality in agricultural plains remains understudied. This study addresses this gap by assessing and predicting land use and habitat quality changes in the Northern Anhui Plain from 2000 to 2030 using the PLUS and InVEST models under four scenarios (natural development, farmland protection, economic development, and sustainable development). The optimal parameters-based geographical detector (OPGD) was employed to identify driving factors. Results show that farmland continuously shrank while built-up land expanded, and habitat quality remained low and declined over time, with low-grade areas expanding. All four 2030 scenarios exhibited declines, with the farmland protection scenario yielding the highest habitat quality and the economic development scenario the lowest. The optimal spatial scale was 4 km, and discretization algorithms and break numbers significantly influenced driver analysis. Locational factors had relatively higher explanatory power, though the overall q-statistic was moderately low, indicating limited single-factor explanation. The study reveals the spatiotemporal dynamics and driving mechanisms of habitat quality in this farmland-dominated plain, providing useful insights for spatial planning and policy-making to support sustainable development in agricultural regions. Full article

Long-term energy system models often represent renewable energy technologies using aggregated potentials and average capacity factors, which may insufficiently reflect the spatial and technological heterogeneity of weather-dependent resources. This study develops and implements resource- and performance-based renewable energy supply curves for solar photovoltaics, onshore wind and offshore wind in the TIMES-PL energy system model for Poland. These supply curves are coupled with time-dependent techno-economic assumptions in TIMES-PL, allowing the modelled attractiveness of individual renewable resource classes to change across model years. The proposed approach combines spatial resource assessment, GIS-based data processing and differentiated hourly capacity factor profiles. The supply curves were constructed using data from the JRC ENSPRESO database, the PVGIS interface and the Copernicus Climate Data Store, with QGIS applied to classify renewable resource potential according to regional conditions, wind farm location and photovoltaic panel orientation. Two model scenarios were compared: a base scenario without supply curves and a scenario with implemented supply curves. The results show that incorporating spatial and technological constraints changes the modelled optimal capacity mix, although the overall system-level differences remain moderate. Accordingly, the results should be interpreted primarily in terms of installed capacity expansion rather than as a full comparison of system costs, electricity generation, unit dispatch or balancing effects. The total installed capacity in the supply-curve scenario is 1.91–3.44 GW higher than in the base scenario, corresponding to less than 3% of total system capacity. This increase results from the model being required to use renewable resource classes with lower capacity factors once the most favourable potentials are fully utilised. This study demonstrates that renewable energy supply curves can improve the representation of spatially differentiated renewable deployment options in long-term national energy system modelling. Full article

Over a decade of research on media for cultured meat and seafood production has resulted in multiple highly efficient serum-free and chemically defined formulations for some species, but it has also identified challenges yet to be solved—especially for aquatic cell lines. Depending on the product and cell type, the approach to develop highly efficient, sustainable, and low-priced media can diverge greatly. In this review, we provide an in-depth overview of this complex research area to facilitate strategic decision-making for stakeholders. We evaluate the advantages and limitations of utilizing hydrolysates, growth factor mutants, growth factor alternatives, and stabilizers in serum-free media formulations published for cultured meat production, as well as ongoing research efforts on developing adequate media for cultured seafood. We critically analyze strategies aimed at reducing medium costs and enhancing sustainability of cultured meat and seafood production, including their food-compatibility assessment. We summarize topics that require further exploration, such as identification of species-specific growth factors—particularly for aquatic species; exploration of hydrolysates as a substitute for basal medium; waste medium recycling strategies; and the potential application of artificial intelligence (AI) and machine learning (ML) technologies to enhance these areas. Additionally, we consider possible emerging regulatory issues and their impact on media formulation development. Finally, key performance indicators for media formulations are proposed to guide future strategic and operational improvements regarding an economical and sustainable production process. Full article

Agritourism has emerged as an increasingly important strategy for rural diversification and multifunctional agricultural development, particularly in transition economies characterized by fragmented farm structures, limited rural investment capacity, and growing tourism demand. Despite the expansion of agricultural support schemes aimed at promoting rural diversification, empirical evidence on their effectiveness in supporting agritourism development at the farm level remains limited, especially in Southeast European contexts. Against this background, the study addresses the following research question: To what extent have agricultural support schemes contributed to agritourism development, rural diversification, and rural resilience in Albania, and what factors constrain their long-term effectiveness? To answer this question, the study examines the role of agricultural support schemes in shaping agritourism development in Albania, with particular emphasis on farm diversification, rural resilience, and implementation constraints. The study adopts a mixed-methods case study approach, combining secondary policy and statistical analysis with primary field research. The fieldwork involved 60 semi-structured, face-to-face interviews with agritourism farm owners across the regions of Korçë, Berat, Vlorë, Shkodër, and Lezhë, conducted during September–October 2025. Data were analyzed using descriptive statistics and thematic analysis to assess investment patterns, perceived socio-economic impacts, and institutional barriers associated with agricultural support schemes. The results show that agricultural support schemes primarily function as catalysts for investment and diversification rather than stand-alone drivers of sustainable rural transformation. However, persistent structural constraints were identified, including administrative complexity, limited marketing capacity, infrastructure deficiencies, and a strong dependence on seasonal tourism demand. These factors reduce the long-term sustainability and scalability of agritourism enterprises. The paper contributes to the literature on agritourism and rural development by providing empirical evidence from the context of a transition economy and demonstrating that financial support alone is insufficient to ensure sustainable rural transformation. Full article

Resource-exhausted cities face intertwined economic, social, and ecological pressures during transition, yet the dynamic evolution and pathway-specific responses of their socio-ecological resilience remain insufficiently understood. Using Jiaozuo, China, a nationally designated resource-exhausted coal-mining city, this study develops an interpretable-machine-learning framework that integrates resilience assessment, XGBoost-SHAP interpretation, spatial statistical validation, scenario simulation, and sensitivity analysis. A multidimensional resilience index was constructed for 2012–2022, and alternative development pathways were projected for 2030 and 2035. The results reveal stage-dependent resilience evolution, with model-explained drivers shifting from economy- and policy-related factors in 2012–2017 to a more ecology-oriented and multidimensional structure in 2017–2022. SHAP dependence and interaction analyses further identify nonlinear response patterns and conditional interactions among key social, economic, and ecological indicators. Scenario simulations show that green transformation produces the strongest model-predicted gains and remains the highest-ranked pathway under alternative subsystem-weighting schemes. These findings suggest that resilience enhancement in resource-exhausted cities depends on coordinated ecological restoration, industrial upgrading, economic vitality, and social adaptive capacity. The proposed framework provides a transferable approach for diagnosing resilience evolution and comparing transition pathways in resource-exhausted urban systems. Full article

With the increasing penetration of renewable energy, frequency stability issues in new-type power systems have become increasingly prominent due to reduced system inertia and weakened primary frequency regulation capability. To address the insufficient frequency response capability of power systems in high-renewable regions, this paper proposes a coordinated multi-type load frequency control strategy based on controllable load damping factors. First, an improved system frequency response model considering renewable penetration is established to analyze the impacts of renewable penetration on maximum frequency deviation, rate of change of frequency (RoCoF), and quasi-steady-state frequency deviation. Subsequently, coordinated frequency control strategies are designed for feeder voltage-sensitive loads, distributed constant power loads, and energy-intensive industrial loads. Finally, an electromagnetic transient simulation model based on an actual Yunnan power grid is established to verify the effectiveness of the proposed method. The results show that increasing renewable penetration deteriorates frequency response by reducing the frequency nadir, increasing RoCoF, and enlarging quasi-steady-state frequency deviation. Under a −0.1 p.u. active power disturbance, the studied grid triggers under-frequency load shedding when renewable penetration exceeds approximately 44% without load-side frequency regulation, whereas the proposed strategy enables the system to satisfy the 49.2 Hz UFLS constraint at 70% renewable penetration, increasing the allowable renewable accommodation level by about 26 percentage points. Economic analysis further indicates that the proposed load-side control has lower regulation cost than renewable curtailment-based frequency support. Full article

Associated gas in the Permian Basin is a methane-rich but spatially fragmented and intermittently available feedstock. Methane pyrolysis can convert hydrocarbons to hydrogen and solid carbon without forming process CO₂ in the reactor, but its practical value depends on the captured-gas capacity factor, feed composition, high-temperature heat supply, product purification, continuous carbon withdrawal, carbon offtake, and transparent greenhouse-gas accounting. This study presents an implemented screening model for a modular 1 million standard cubic feet per day (MMSCFD) Permian associated-gas unit. A representative Permian composition is evaluated with hydrocarbon cracking stoichiometry, catalytic and thermal conversion envelopes, a net hydrogen recovery assumption, an energy-duty allocation, a levelized-cost model, and a well-to-gate carbon-intensity model. The catalytic base case produces 3.78 t/d of saleable H₂ after 90% pressure-swing adsorption (PSA) recovery and 14.27 t/d of solid carbon; the thermal near-complete conversion bound produces 4.31 t/d of saleable H₂ and 16.15 t/d of solid carbon. At a 0.85 capacity factor, $10 million installed capital expenditure (CAPEX), 8% real discount rate, 20-year life, 10 kWh per kg H₂ energy intensity, and $0.06 per kWh electricity, the deterministic plant-gate levelized cost of hydrogen (LCOH) is $1.81 per kg H₂ at zero carbon value and $1.05 per kg H₂ at a net realized carbon value of $0.20 per kg C. Monte Carlo analysis over capacity factor, CAPEX, energy intensity, electricity price, carbon value, feed/capture cost, and yield uncertainty gives levelized cost of hydrogen values at the 10th, 50th, and 90th percentiles (P10/P50/P90) of $1.32/$1.91/$2.57 per kg H₂. The corresponding screening carbon-intensity distribution is 2.34/4.11/5.89 kg carbon dioxide equivalent (CO₂e) per kg H₂, dominated by electricity carbon intensity and upstream methane loss. Geothermal or waste-heat preheat is treated quantitatively as a partial offset to low- and mid-temperature duties, not as a replacement for high-grade 900–1200 °C trim heat. The pathway is benchmarked against steam methane reforming, autothermal reforming with carbon capture and storage, electrolysis, small-scale liquefied natural gas, and gas-to-liquids conversion. Reported LCOH values are plant-gate production costs; separate hydrogen-logistics and negative-carbon-value stress tests identify conditions under which remote delivery or carbon disposal can erode the apparent economic advantage. Full article