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Seed germination is a key determinant of wheat seed quality, strongly affected by genetic potential, weather conditions during production, and storage duration. Although numerous studies have investigated seed viability, little is known about how the interaction between annual climatic variability and storage length affects long-term germination performance of winter wheat. The objective of this study was therefore to assess the influence of weather conditions and storage period on germination energy and germination of 50 Croatian winter wheat (Triticum aestivum L.) cultivars released between 1947 and 2010. The experiment was conducted over five consecutive production years (2013/2014–2017/2018). Seeds of each cultivar were reproduced under standardized field conditions, harvested annually, and stored under identical controlled conditions (5 °C, 30–35% RH). Germination energy (first count, day 4) and total germination (final count, day 8) were evaluated according to ISTA protocols. The results revealed significant effects of both production year and cultivar on germination performance. Seeds produced in 2016/2017 exhibited the highest germination (96.21%), which was ~15% higher than the lowest rate observed in 2013/2014 (80.48%). Germination energy of 2013/2014 seeds was 23% lower compared to 2015/2016 and 2016/2017. Unexpectedly, seeds stored for only one year (2017/2018 production) showed lower germination (90.92%) than those stored for two (96.21%) or three years (95.01%), likely due to excessive rainfall (>100% above average) during seed maturation in 2018 that impaired seed quality. Several cultivars, including Una, Tonka, Žitarka, and Kuna, consistently maintained high germination rates (>94%) even after five years of storage, demonstrating strong physiological stability and long-term viability. These findings underline the combined importance of weather conditions during seed production and storage duration for seed longevity. In practical terms, cultivars with proven stability may be recommended for long-term storage and reliable field performance. Future research should extend germination assessment to additional vigor indices (e.g., germination synchrony, vigor index, abnormal seedlings) and explore genetic mechanisms underlying superior seed longevity in modern wheat breeding. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

As the region with the earliest housing stock market and the most advanced development in China, the Pearl River Delta has experienced extensive housing demolition and construction, leading to buildings having short lifespans. The environmental pollution generated during this process has brought attention to the concept of green buildings. However, whether due to previous patterns of demolition and construction or the significant impacts of social and economic changes in the current and future housing stock contexts, the comprehensive adaptability of human-centered living spaces remains a crucial issue. This focus is strongly related to the residents’ psychological responses, such as sense of belonging, safety, and atmosphere, across different scales of physical environment. However, most housing evaluation systems regarding sustainable issues are green building evaluation systems. And their concept and practice are often accompanied by a neglect of the interrelationship between people and the built environment, as well as a lack of an appropriate methodological framework to integrate these elements in the temporal dimension. This paper primarily tries to provide new answers to old questions about housing durability by reconceptualizing evaluation systems beyond ecological metrics, while simultaneously challenging accepted answers that privilege material and energy indicators over sociocultural embeddedness. Moreover, an effective housing evaluation framework must transcend purely technical or ecological indicators to systematically integrate the temporal and sociocultural factors that sustain long-term residential quality, particularly in rapidly transforming urban contexts. Therefore, theories closely related to building longevity, such as open building and the “level” strategy, were introduced. Based on this combined methodological framework, selected cases of local traditional housing and green building evaluation systems were studied, aiming to identify valuable longevity factors and improved evaluation methods. Furthermore, two rounds of expert consultation and a data analysis were conducted. The first round helped determine the local indexes and preliminary evaluation methods, while the second round helped confirm the weighting value of each index through a questionnaire study and data analysis. This systematic study ultimately established a preliminary long-term housing evaluation system for the Pearl River Delta. Full article

This study proposes an energy-based framework for evaluating the seismic ductility of reinforced concrete (RC) structures using restoring force hysteresis curves. A custom-developed tool, the Damage Energy Calculation Program (DECP), is introduced to compute cumulative hysteretic energy and corresponding damage indices from experimental data. Seven methods for identifying yield displacement and yield load are examined, encompassing stiffness-based and energy-based techniques, including the conditional yield method, secant stiffness method, and double energy equivalence method. These methods are applied to a series of experimental restoring force curves (SP01 to SP10). Among them, the double energy equivalence method demonstrates the highest accuracy in capturing the yield state. Additionally, a novel ductility index based on the maximum energy envelope is proposed. Comparative analysis shows that this new index exhibits trends consistent with the double energy equivalence approach, highlighting its potential as a reliable alternative. The DECP tool significantly improves the consistency and efficiency of ductility assessment and offers practical support for energy-based damage evaluation in structural performance analysis. Full article

In response to China’s dual-carbon strategy, this study proposes a comprehensive analytical framework to identify the evolutionary pathways of key policy tasks in developing a new-type power system. A dual-channel data acquisition process was designed to extract, standardize, and segment policy documents and online texts into a unified corpus. A multi-label BERT classification model was then developed, incorporating domain-specific terminology injection, label-wise attention, dynamic threshold scanning, and imbalance-aware weighting. The model was trained and validated on 200 energy news articles, 100 official policy releases, and 10 strategic planning documents. By the 10th epoch, it achieved convergence with a Macro-F1 of 0.831, Micro-F1 of 0.849, and Samples-F1 of 0.855. Ablation studies confirmed the significant performance gain over simplified configurations. Structural label analysis showed “Build system-friendly new energy power stations” was the most frequent label (107 in plans, 80 in news, 24 in policies) and had the highest co-occurrence (81 times) with “Optimize and strengthen the main grid framework.” The label co-occurrence network revealed multi-layered couplings across generation, transmission, and storage. The Priority Evaluation Index (PEI) further identified “Build shared energy storage power stations” as a structurally central task (centrality = 0.71) despite its lower frequency, highlighting its latent strategic importance. Within the domain of national-level public policy and planning documents, the proposed framework shows reliable and reusable performance. Generalization to sub-national and project-level corpora is left for future work, where we will extend the corpus and reassess robustness without altering the core methodology. Full article

To enhance the structural performance of concrete-filled steel tube (CFST) columns, various strengthening techniques have been proposed, including the use of internal steel stiffeners, external wrapping with carbon fiber-reinforced polymer (CFRP) sheets, and embedded steel elements. However, the behavior of concrete-filled stainless-steel tube (CFSST) columns remains insufficiently explored. This study numerically investigates the axial performance of square CFSST columns internally strengthened with embedded I-section steel profiles under biaxial eccentric loading. Finite element (FE) simulations were conducted using ABAQUS v. 6.2, and the developed models were validated against experimental results from the literature. A comprehensive parametric study was performed to evaluate the effects of several variables, including concrete compressive strength (f_cu), stainless-steel yield strength (f_y), the depth ratio between the stainless-steel tube and the internal I-section (D_st/D_si), biaxial eccentricities (e_x and e_y), and tube thickness (t). The results demonstrated that the axial performance of CFSST columns was most significantly influenced by increasing the D_st/D_si ratio and load eccentricities. In contrast, increasing the concrete strength and steel yield strength had relatively modest effects. Specifically, the ultimate axial capacity increased by 9.97% when the steel yield strength rose from 550 MPa to 650 MPa and by 33.72% when the tube thickness increased from 3.0 mm to 5.0 mm. A strength gain of only 10.23% was observed when the concrete strength increased from 30 MPa to 60 MPa. Moreover, the energy absorption index of the strengthened columns improved in correlation with the enhanced axial capacities. Full article

While the corporate adoption of artificial intelligence (AI) is accelerating, its environmental consequences remain insufficiently understood, particularly in absolute firm-level energy consumption. The main objective of this study is to empirically determine the causal impact of AI adoption on absolute firm-level energy consumption in Chinese publicly listed companies, with a particular focus on the mediating role of green innovation and the moderating role of digital capabilities. This study provides the first large-scale micro-level evidence on how AI adoption shapes corporate energy use, drawing on panel data from Chinese non-financial listed firms during 2011–2022. We construct a novel AI adoption index via Word2Vec-based textual analysis of annual reports and estimate its impact using firm fixed effects, instrumental variables, mediation models, and multiple robustness checks. Results show that AI adoption significantly reduces total energy consumption, with a 1% increase in AI intensity associated with an estimated 0.48% decrease in energy use. Green innovation emerges as a key mediating channel, while the energy-saving benefits are amplified in firms with advanced digital transformation and IT-oriented executive teams. Heterogeneity analyses indicate more substantial effects among large firms, private enterprises, non-energy-intensive sectors, and firms in digitally lagging regions, suggesting capability-driven and context-dependent dynamics. This study advances the literature on digital transformation and corporate sustainability by uncovering the mechanisms and boundary conditions of AI’s environmental impact and offers actionable insights for aligning AI investments with carbon reduction targets and industrial upgrading in emerging economies. Full article

The karst inorganic carbon sink is crucial for carbon neutrality, but its trends and drivers in subtropical humid zones remain unclear. This study selected subtropical humid zones in China with significant forest greening, quantified the carbonate rock weathering carbon sink (CCS) using a thermodynamic dissolution model, and explored the effects of climate, vegetation, hydrology, and radiation energy on CCS through importance analysis. The results showed that from 1982 to 2020, the CCS flux was 12.40 t C km⁻² yr⁻¹, and the total carbon sink was 1188.54 × 10⁴ t C yr⁻¹. Normalized difference vegetation index, leaf area index, and CCS exhibited an increasing trend, with growth rates of 0.002, 0.01 m² m⁻², and 0.05 t C km⁻² yr⁻¹, respectively. Surface available water, precipitation, and evapotranspiration were the dominant factors affecting CCS. This study found that forest greening caused precipitation to increase faster than evapotranspiration, driving an increase in available surface water and ultimately promoting the karst carbon sink in subtropical humid zones. Our findings highlight forest greening as a vital strategy for carbon neutrality. Full article

Strategic fleet renewal represents a fundamental challenge for small and medium-sized enterprises (SMEs) and public entities seeking to align their operational objectives with sustainable mobility practices. This paper proposes a hybrid decision support model based on fuzzy logic, combining the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with the Fleet Renewal Priority Index (FRPI). The model evaluates and prioritizes different vehicle alternatives based on multiple economic, environmental, and operational criteria, including total cost of operation, CO₂ emissions, maintenance, autonomy, infrastructure compatibility, and energy independence. The criteria are evaluated by linguistic judgments converted into triangular fuzzy numbers (TFN), allowing uncertainty and subjectivity to be addressed. A simulated case study illustrates the application of the model, identifying the vehicles most aligned with a sustainability and efficiency strategy, as well as those that present a greater urgency for replacement. The results demonstrate the potential of the approach to support rational, transparent and sustainable decisions in fleet modernization. Full article

Cellulose aerogel is a promising thermal insulation material with terrific thermal insulation and environmental friendliness. However, the intrinsic flammability of polysaccharide molecules and dependence on freeze-drying have limited its application in flame-retardant and thermal management systems. Here, we develop a flame-retardant biomass aerogel based on a dual-network matrix of bacterial cellulose and sodium alginate. This innovative material enables high-efficiency and low-cost preparation via ambient pressure drying technology (only ~3.5% volume shrinkage), while achieving flame retardancy by introducing an inorganic nanosheet microstructure within a polymer matrix. The resulting dual-network flame-retardant cellulose aerogel demonstrates thermal performance superior to that of most polymer foams and conventional cellulose aerogels, featuring an ultra-low thermal conductivity of ~0.04 W m⁻¹ K⁻¹ and a high limiting oxygen index (LOI) of ~69%. This research provides a novel strategy for simultaneous flame-retardant modification and energy-efficient manufacturing of biomass-derived aerogels. Full article

Regarding the impact of hull roughness on ship resistance and propulsive performance, most existing studies rely heavily on numerical hulls or simplified models, while systematic analysis focusing on the heterogeneous roughness of actual ships remains insufficient. Taking the 2433 TEU container ship SITC CAGAYAN as the research object, this study adopts a method that combines CFD numerical simulation with actual ship operation data. It employs a resistance prediction model based on the “roughness influence factor” to explore the mechanism by which local roughness affects ship resistance. Meanwhile, this study innovatively proposes the index of “fuel consumption increment per unit wetted surface area” and the concept of “fuel consumption factor,” thereby realizing the quantitative characterization of the impact of local rough areas on fuel consumption. The purpose of this study is to provide theoretical support and technical pathways for the optimization of ship energy efficiency and the development of green shipping. Full article

Blast furnace (BF) ironmaking remains one of the most efficient countercurrent processes; however, achieving further CO₂ emission reductions through conventional methods is increasingly challenging. Currently, BF ironmaking emits approximately 2.33 tonnes of fossil-derived CO₂ per tonne of crude steel cast. Integrating briquettes composed of biochar and in-plant fines into the BF process offers a promising short- to medium-term strategy for lowering emissions. This approach enables efficient recycling of fine residues and the substitution of fossil reductants with bio-based alternatives, thereby improving productivity while reducing energy and carbon intensity. This study investigates the reduction behavior of (i) biochar mixed with pellet fines, (ii) various in-plant residues individually, and (iii) briquettes composed of biochar and in-plant fines. The reduction rate of biochar–pellet fine mixtures was found to depend on biochar type, with pyrolyzed pine sawdust exhibiting the highest reactivity, and pyrolyzed contorta wood chips the lowest. A correlation between reduction rate and the alkali index of each char was established, although other factors such as char origin and physical properties also influenced reactivity. The effect of biochar addition (0, 5, and 10 wt.%) on the reduction of steelmaking residues was also studied. In general, biochar enhanced the reduction degree and shifted the reaction onset to lower temperatures. The produced briquettes maintained high mechanical integrity during and after reduction, regardless of biochar origin. Thermogravimetric and XRD analyses revealed that mass loss initiates with the dehydroxylation of cement phases and release of volatiles, followed by carbonate decomposition and reduction of higher oxides above 500 °C. At temperatures ≥ 850 °C, the remaining iron oxides were further reduced to metallic iron. Full article

Background and Objectives: The extracellular volume fraction (fECV) of the liver, as measured by contrast-enhanced computed tomography (CT), has been shown to correlate closely with the histological stages of hepatic fibrosis. This study aimed to investigate the diagnostic performance of a liver extracellular volume fraction derived from dual-energy CT (DECT) for evaluating liver functional reserve based on the Child–Pugh class in cirrhotic patients, compared with other noninvasive markers. Materials and Methods: This retrospective study included 258 patients with liver cirrhosis who underwent contrast-enhanced DECT. The fECV was measured using iodine maps derived from equilibrium phase images obtained 3 min after contrast injection at 100/140 Sn kVp. Statistical analyses included Welch’s ANOVA with post hoc tests, Spearman’s correlation, and ROC analysis. The area under the curve (AUC) was compared among fECV and other noninvasive markers (aspartate transaminase to platelet ratio index [APRI], Fibrosis-4 [FIB-4], and model for end-stage liver disease [MELD]) using DeLong’s test. Intra- and interobserver reliability of fECV was assessed with the intraclass correlation coefficient (ICC). The area under the receiver operating characteristic curve (AUC) for differentiating Child–Pugh classes was compared among the fECV and other noninvasive markers (aspartate transaminase to platelet ratio index [APRI], Fibrosis-4 [FIB-4], and model for end-stage liver disease [MELD]). Results: The fECV increased significantly with advancing Child–Pugh classes (p < 0.001), showing a moderate correlation with Child–Pugh class (r = 0.53). The mean differences in fECV among the Child–Pugh classes were 8.88 between A and B (95% confidence interval [CI], 5.85–11.92; p < 0.001) and 7.42 between B and C (95% CI, 1.92–12.91: p < 0.001). The AUC for differentiating Child–Pugh classes A and B demonstrated no significant differences among the fECV (0.84), APRI (0.83, p > 0.99) and FIB-4 (0.83, p > 0.99), except for MELD, which had a significantly higher AUC (0.94, p = 0.047). For differentiating Child-Pugh classes B and C, the fECV demonstrated a significantly higher AUC (0.78), compared with FIB-4 (0.50, p = 0.038) and APRI (0.49, p = 0.037), whereas no significant difference was observed between fECV and MELD (0.92, p = 0.12). The intra- and interobserver reliabilities of the fECV measurements were excellent (ICC, 0.93; 95% CI, 0.91–0.95 and 0.91; 95% CI, 0.88–0.92, respectively). Conclusions: DECT derived fECV is a useful noninvasive marker for assessing liver functional reserve based on the Child–Pugh classification. Full article

Protein-rich microalgae have been increasingly recognized as viable alternatives to fish meal (FM) in aquaculture diets. In this study, we evaluated the effects of partial or total replacement of FM with the microalga Arthrospira platensis (Spirulina, SM) on the growth performance, reproductive parameters, and transcriptomic profile of zebrafish. Six isoproteic, isoenergetic experimental diets were formulated with increasing levels of SM (0, 10, 20, 30, 40, and 50 g kg⁻¹ feed) replacing FM. Fish were randomly assigned to six groups (five replicates each) and fed for 60 days. The diet containing 50 g kg⁻¹ SM resulted in the highest final body weight, weight gain, specific growth rate, and protein efficiency, as well as increased gonadosomatic index, eggs per female, fertilization rate, and hatching rate compared to the results for the control group (0 g kg⁻¹ SM). RNA-Seq transcriptomic analysis identified 2299 differentially expressed genes in the SM50 group, mainly associated with muscle development and energy metabolism. These findings offer new insights into the underlying molecular mechanisms and underscore the potential of Spirulina as a sustainable alternative for cultured fish nutrition. Full article

The rise in energy prices due to global uncertainties and risks is accelerating the transition to renewable energy in countries. It is expected that embracing energy diversity instead of dependence on a single energy source, such as oil, will curb energy-related increases in inflation. In this study, the impact of the transition to renewable energy on inflation is investigated using the energy diversification index. For this purpose, the Chinese economy is analyzed with the Augmented ARDL method. According to the long-term results of the analysis covering the 1991–2023 period, the effect of energy diversity on inflation is negative. The study also examined the effect of composing an energy portfolio consisting of various renewable energy sources rather than a single renewable energy source on inflation. According to the results obtained, renewable energy diversity has a negative effect on inflation, too. As a result, inflation is expected to decrease as renewable energy diversification and overall energy diversification increase. In sum, inflation can be expected to fall when authorities increase both renewable energy diversity and overall energy diversity instead of solely depending on oil or any renewable energy source. Full article