Search Results (1,238)

This study employed a pot experiment to compare the effects of varying application rates of chili straw biochar on seed germination and seedling growth of alfalfa (Medicago sativa L.) cultivated in electrolytic manganese residue (EMR) and to elucidate the underlying mechanisms. We aimed to provide a theoretical basis for vegetation restoration and manganese pollution control at EMR disposal sites. Our results indicated that while chili straw biochar did not affect the seed germination rate, it significantly enhanced the germination energy. In addition, treatment with 5% biochar significantly increased the germination index. Biochar application increased alfalfa seedling height (6.13 cm in the control group vs. 6.63–7.20 cm in the treated groups). Concurrently, the aboveground fresh biomass significantly increased by 49–77% compared to the control. Additionally, biochar application elevated chlorophyll content and reduced malondialdehyde content in alfalfa leaves. Correlation analysis revealed that the primary mechanisms underlying biochar-mediated improvement in seed germination and seedling growth involved enhancing the organic matter, available nitrogen, and available phosphorus content in the EMR, while decreasing the available manganese content. Overall, the application of 5% biochar in EMR optimally improved alfalfa plant growth and development. Full article

Saline–alkaline water resources are globally widespread, and their rational development offers significant potential to alleviate freshwater scarcity. Saline–alkaline water aquaculture farming not only affects fish growth and survival but also impairs reproductive and developmental functions. Largemouth bass (Micropterus salmoides), an economically important fish, has demonstrated excellent high tolerance to such environments, in order to investigate the effects of alkaline water aquaculture environments on its growth performance, sex hormone levels, gonadal development, and molecular adaptation mechanisms. In this study, largemouth bass were chronically exposed to freshwater (0.55 mmol/L), low alkalinity (10 mmol/L), or high alkalinity (25 mmol/L) and cultured for 80 days. Alkalinity exposure more severely impacted the growth rate of females. High alkalinity significantly increased the hepatosomatic index and decreased the gonadosomatic index in both sexes; moreover, it induced oxidative stress in both sexes, evidenced by reduced superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (TAOC) levels and elevated malondialdehyde (MDA) content. Furthermore, the levels of sex hormones Serum estradiol (E2), 11-ketotestosterone (11-KT), and testosterone were significantly reduced, accompanied by either an elevated ratio of primary oocytes and follicular atresia, or by reduced spermatogenesis. Apoptotic signals appeared in gonadal interstitial cells, with upregulated expression of genes P53, Bax, Casp3, and Casp8. Ultrastructural damage included fewer mitochondria and cristae blurring, further indicating tissue damage causing dysfunction. Transcriptome results showed that oxidative stress damage and energy metabolism imbalance caused by carbonate alkalinity were key to the delayed gonadal development, which was mainly manifested in enrichment of the ECM–receptor interaction and PI3K-Akt signaling pathways in females exposed to low alkalinity, and the GnRH secretion and chemokine signaling pathways in males. Glycosphingolipid biosynthesis and Ferroptosis pathway were enriched in females exposed to high alkalinity, and the Cortisol synthesis and secretion pathway were enriched in males. Overall, high-alkalinity exposure significantly delayed gonadal development in both sexes of largemouth bass, leading to reproductive impairment. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

In this study, the effects of non-thermal pretreatment such as corona discharge plasma (CDP-21 kV), dielectric barrier discharge plasma (DBDP-32 kV), and ultrasonic waves of different powers (US-180 W, 210 W, 240 W) on hot-air drying of ferruginous yam were compared. The regulatory effects of ultrasonic and cold plasma pretreatment on the drying characteristics and quality of yam were systematically evaluated by determining the drying kinetic parameters, physicochemical indexes, volatile components, and energy consumption. The results showed that ultrasonic pretreatment significantly improved the drying performance of yam compared with different cold plasma treatments, with the highest drying rate and effective moisture diffusion coefficient in the US-180 W group. In terms of quality, this treatment group exhibited better color retention, higher total phenol content (366 mg/100 g) and antioxidant activity, and optimal rehydration performance. Low-field nuclear magnetic resonance (NMR) analyses showed a more homogeneous water distribution, and gas chromatography–mass spectrometry (GC-MS) identified 55 volatile components. This study confirms that the US-180 W ultrasonic pretreatment technology can effectively improve the drying efficiency and product quality of yam and at the same time reduce the energy consumption. The results of this study provide a practical solution for the optimization of a process that can be replicated in the food drying industry. Full article

The decarbonization process of the carbon emissions in the Chinese building sector exerts a profound impact on the achievement of the national goals of carbon peak and carbon neutrality. Currently, there is limited literature quantifying the impact of laws and policies on the achievement of carbon peak in the Chinese building sector and further utilizing deep learning technology to characterize the carbon emissions peak path under uncertainty in the Chinese building sector. To address this issue, a quantitative framework of legal and policy incentive intensity is constructed to capture both the immediate effects and the long-term evolution of laws and policies, and the index of legal and policy incentive intensity for carbon emissions in the building sector in China from 2010 to 2022 is calculated. Based on this, a dynamic scenario forecasting model for carbon emissions in the Chinese building sector is developed by integrating a CNN-BiLSTM-AM model with the Monte Carlo simulation algorithm, embedded within the scenario analysis method. The model projects the dynamic trajectories of carbon emissions in the Chinese building sector under different scenarios from 2023 to 2050 and identifies effective schemes for controlling carbon emissions in the Chinese building sector. Results indicate that the growth in legal and policy incentive intensity was most significant during the 12th Five-Year Plan period in China. During the 13th Five-Year Plan in China, the legal and policy system became increasingly mature, leading to a diminishing marginal effect of newly issued policies. A negative growth in legal and policy incentive intensity was observed in 2020 due to the impact of the COVID-19 pandemic. From 2021 to 2022, the annual growth rate of policy intensity began to rebound. Under the current scenario, carbon emissions in the Chinese building sector are projected to reach its carbon peak in 2036 (±1), with a peak level of 28.617 (±1.047) × 10⁸ t CO₂. Energy consumption per unit floor space, population size, legal and policy incentive intensity, integrated carbon emission factor, and floor space per capita are identified as the most critical factors influencing the timing and value of carbon peaking. The research methodology employed in this study not only provides scientific insights for the emission reduction efforts in the building sector but is also applicable to related studies in other industries’ energy conservation and emission reduction. It holds universal value for environmental policymakers and strategic planners. Full article

With rapid global urbanization development, impermeable surface increase, urban population growth, building area expansion, and rising energy consumption, the urban heat island (UHI) effect is becoming increasingly serious. However, the spatial distribution of the UHI cannot be accurately extracted. Therefore, we focused on Luoyang City as the research area and combined the Getis-Ord-Gi* statistic and the greenest image to extract the UHI based on the Google Earth Engine using land surface temperature–spatial autocorrelation characteristics and seasonal changes in vegetation. As bare land considerably influenced the UHI extraction results, we combined the greenest image with the initial extraction results and applied the maximum normalized difference vegetation index threshold method to remove this effect on UHI distribution extraction, thereby achieving improved UHI extraction accuracy. Our results showed that the UHI of Luoyang continuously expanded outward, increasing from 361.69 km² in 2000 to 912.58 km² in 2023, with a continuous expansion rate of 22.95 km²/year. Furthermore, the urban area had a higher UHI area growth rate than the county area. Analysis indicates that the UHI effect in Luoyang has increased in parallel with the expansion of the building area. Intensive urban construction is a primary driver of this growth, directly exacerbating the UHI effect. Additionally, rising temperatures, population growth, and gross domestic product accumulation have collectively contributed to the ongoing expansion of this phenomenon. This study provides scientific guidance for future urban planning through the accurate extraction of the UHI effect, which promotes the development of sustainable human settlements. Full article

Aiming at the lightweight design of a bridge-shed integration structure, this paper presents a three-layered absorbing system in which a part of the sand cushion is replaced by expanded polystyrene (EPS) geofoam and the reinforced concrete (RC) protective slab is arranged above the sand cushion to enhance the composite system’s safety. A three-dimensional Smoothed Particle Hydrodynamics–Finite Element Method (SPH-FEM) coupled numerical model is developed in LS-DYNA (Livermore Software Technology Corporation, Livermore, CA, USA, version R13.1.1), with its validity rigorously verified. The dynamic response of rockfall impacts on the shed slab with composite cushions of various thicknesses is analyzed by varying the thickness of sand and EPS materials. To optimize the cushion design, a specific energy dissipation ratio (SEDR), defined as the energy dissipation rate per unit mass (η/M), is introduced as a key performance metric. Furthermore, the complicated interactional mechanism between the rockfall and the optimum-thickness composite system is rationally interpreted, and the energy dissipation mechanism of the composite cushion is revealed. Using logistic regression, the ultimate stress state of the reactive powder concrete (RPC) slab is methodically analyzed, accounting for the speed and mass of the rockfall. The results are indicative of the fact that the composite cushion not only has less dead weight but also exhibits superior impact resistance compared to the 90 cm sand cushions; the impact resistance performance index SEDR of the three-layered absorbing system reaches 2.5, showing a remarkable 55% enhancement compared to the sand cushion (SEDR = 1.61). Additionally, both the sand cushion and the RC protective slab effectively dissipate most of the impact energy, while the EPS material experiences relatively little internal energy build-up in comparison. This feature overcomes the traditional vulnerability of EPS subjected to impact loads. One of the highlights of the present investigation is the development of an identification model specifically designed to accurately assess the stress state of RPC slabs under various rockfall impact conditions. Full article

The coastal sandy land in northeast Hainan Province is typical for this land type, also exhibiting strong sand activity. This study is based on wind speed, wind direction, and sediment transport data obtained at a field meteorological station using an omnidirectional sand accumulation instrument from 2020 to 2024, studying the coastal aeolian environment and sediment transport distribution characteristics in the region. Its findings provide a theoretical basis for comprehensively analyzing the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results show the following: (1) The annual average threshold wind velocity for sand movement in the study area is 6.84 m/s, and the wind speed frequency (frequency of occurrence) is 51.54%, dominated by easterly (NE, ENE) and southerly (S, SSE) winds. (2) The drift potential (DP) refers to the potential amount of sediment transported within a certain time and spatial range, and the annual drift potential (DP) and resultant drift potential (RDP) of Mulan Bay from 2020 to 2024 were 550.82 VU and 326.88 VU, respectively, indicating a high-energy wind environment. The yearly directional wind variability index (RDP/DP) was 0.59, classified as a medium ratio and indicating blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 249.45°, corresponding to a WSW direction, indicating that the sand in Mulan Bay is generally transported in the southwest direction. (3) When the measured data extracted from the sand accumulation instrument in the study area from 2020 to 2024 were used for statistical analysis, the results showed that the total sediment transport rate (the annual sediment transport of the observation section) in the study area was 110.87 kg/m·a, with the maximum sediment transport rate in the NE direction being 29.26 kg/m·a. These results suggest that when sand fixation systems are constructed for relevant infrastructure in the region, the construction direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

This study presents a detailed analysis of the combustion dynamics of stoichiometric H₂–air and CH₄–air mixtures in a 20 L closed vessel over an initial temperature range of 298–423 K. We integrate experimental pressure–time P(t) measurements with numerical analysis to extract laminar burning velocity (LBV) and deflagration index (K_G) values, and we assess three independent kinetic mechanisms (KiBo_MU, University of San Diego, Lund University) via simulations. For H₂–air, LBV increases from 0.50 m/s at 298 K to 0.94 m/s at 423 K (temperature exponent α ≈ 1.79), while for CH₄–air, LBV rises from 0.36 m/s to 0.96 m/s (α ≈ 2.82). In contrast, the deflagration index K_G decreases by ca. 20% for H₂–air and ca. 30% for CH₄–air over the same temperature span. The maximum explosion pressure (P_max) and peak pressure rise rate ((dP/dt)_max) also exhibit systematic increases with temperature. A comparison with model predictions shows agreement within experiments, providing data for safety modeling and kinetic mechanism validation in H₂- and CH₄-based energy systems. Full article

Due to widespread use of silver nanoparticles (AgNPs), the assessment of their potential harm to microalgal photosynthesis is crucial, as microalgae, together with cyanobacteria, contribute to approximately 50% of global oxygen production. This study investigated photosynthetic pigments, photosynthetic rate, chlorophyll a fluorescence, and the expression of photosynthesis-related genes and proteins in green alga Chlorella vulgaris after 72 h exposure to citrate- and cetyltrimethylammonium bromide (CTAB)-stabilized AgNPs, as well as silver ions (AgNO₃), at concentrations allowing 75% cell survival (EC₂₅). All treatments impaired photosynthetic performance. The most pronounced decreases in chlorophyll fluorescence parameters and photosynthetic rate, alongside elevated energy dissipation, were observed after exposure to AgNP-CTAB and AgNO₃. AgNP-citrate had milder effects and induced compensatory responses, reflected in an increased performance index and upregulation of photosynthesis-related proteins. AgNP-CTAB induced the strongest downregulation of gene and protein expression, likely due to its higher EC₂₅ concentration and cationic surface promoting interaction with photosynthetic structures. Although AgNO₃ caused fewer molecular changes, it significantly disrupted photosynthetic function, suggesting a direct effect of Ag⁺ ions on photosynthesis-related proteins. Overall, the results highlight the role of AgNPs’ surface coatings and dosage in determining their phytotoxicity, with photosystem disruption and oxidative stress emerging as key mechanisms of action. Full article

Background: The introduction of cystic fibrosis transmembrane conductance regulator modulators, especially the triple therapy elexacaftor, tezacaftor, ivacaftor (ETI), has improved outcomes in people with cystic fibrosis (pwCF), reducing underweight but increasing overweight rates. Objectives: This study investigates the effect of ETI on appetite control, body composition, and energy balance during a 3-week inpatient rehabilitation programme with regular exercise. Methods: In 54 pwCF (38 on ETI, 16 without ETI), changes in body composition (fat mass index, FMI; fat-free mass index, FFMI) and energy balance (calculated from body composition changes) were assessed. Appetite control was evaluated via plasma peptide YY (PYY) levels and post-exercise meal energy intake. Results: The programme significantly increased BMI (+0.3 ± 0.1 kg/m²; CI 0.1–0.4) and energy balance (+4317 ± 1976 kcal/3 weeks), primarily through FFMI gains (+0.3 ± 0.1 kg/m²; CI 0.1–0.4). Despite higher post-exercise meal energy intake and a tendency towards lower PYY levels in the ETI group, changes in body composition and energy balance did not differ between groups. This is explained by a higher prevalence of exocrine pancreatic insufficiency in the ETI group (92% vs. 50%, p < 0.001). Small sample sizes limit the interpretation of data on appetite control and energy intake. Conclusions: A 3-week inpatient rehabilitation programme improved body composition in pwCF, without resulting in a more positive energy balance with ETI therapy. This is due to a higher prevalence of pancreatic insufficiency in this group. Full article

The behavior of brine solution in the porous media of the strata is of great significance for geological environment regulation. In this study, a molecular dynamics model with silicon dioxide walls was constructed to reveal the regulatory mechanism of the natural potential of the electric field on cluster aggregation. It was found that the critical electric field intensity was 7 V/m. When the electric field intensity was lower than this value, the aggregation rate was only increased by 0.73 times due to thermal motion; when it was higher than this value, the rate increased sharply by 3.2 times due to the dominant effect of electric field force. The microscopic structure analysis indicated that the strong electric field induced the transformation of clusters from fractal structure into an amorphous structure (the index of the order degree increased by 58%). The directional regulation experiments confirmed that the axial electric field led to anisotropic growth (the index of uniformity increased by 0.58 ± 0.04), and the rotational electric field could achieve a three-dimensional uniform distribution (the index of uniformity increased by 42%). This study provides theoretical support for the regulation of brine behavior and the optimization of geological energy storage. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article