Search Results (80)

This study investigated the mechanisms of soil water–salt and nitrogen transport and optimal strategies under freeze–thaw (F-T) cycles in the salinized farmlands of the Hetao Irrigation District. A combined approach of field monitoring and laboratory simulation, utilizing both undisturbed and repacked soil columns subjected to 0–15 F-T cycles and five irrigation treatments, was employed to analyze the spatiotemporal dynamics in Gleyic Solonchaks. The results demonstrated that freeze–thaw processes play an important role in salt migration in surface soil layers, driving salt redistribution through phase changes of soil moisture. Increased freeze–thaw cycles reduced surface soil moisture content while promoting upward salt accumulation, salt dynamics exhibited pronounced spatial heterogeneity and irrigation source dependency, and the surface layer exhibited lower salinity levels after irrigation compared to pre-irrigation levels. These cycles also enhanced short-term soil nitrogen transformation and facilitated inorganic nitrogen accumulation. Different irrigation regimes exhibited a significant impact on the dynamics of water–salt and nitrogen in soil, with low-salinity treatment (S2) and moderate-nitrogen irrigation (N2) effectively reducing surface salt accumulation while improving nitrogen utilization efficiency (moderate-nitrogen irrigation exhibited higher mineralization rates, which facilitated the release of inorganic nitrogen from soil). This study reveals the synergistic transport mechanisms of water–salt and nitrogen under freeze–thaw driving forces and provides a scientific basis and practical pathway for sustainable agricultural management in cold arid irrigation districts. Full article

Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image processing algorithm for snow segmentation. We benchmarked several segmentation models, including MASK R-CNN and the state-of-the-art SAM2 segmentation model. YOLOv11n-seg was selected for its optimal balance of speed and accuracy, achieving 0.99 precision and 0.80 recall. To overcome the unreliability of static thresholding under changing lighting, various dynamic methods were evaluated. Otsu’s algorithm proved most effective, reducing the absolute error of the mean in SCP estimation to just 1.1%, a significant improvement over the 13.78% error from the previous fixed-thresholding approach. The integrated system was successfully validated for real-time performance on live drone video streams, demonstrating a highly accurate and scalable solution for autonomous snow monitoring on PV systems. Full article

Obesity has emerged as a global health challenge, closely associated with multiple metabolic diseases, such as cardiovascular diseases, type 2 diabetes, and non-alcoholic fatty liver disease. The traditional “calories-in minus calories-out” paradigm is no longer sufficient to explain the heterogeneity of obesity; consequently, a growing body of research has turned its focus to epigenetic regulation—particularly chemical modifications at the RNA level. N⁶-methyladenosine (m⁶A) modification is one of the most abundant epigenetic modifications on RNA, which dynamically regulates the methylation reaction in specific sequences on mRNA through methyltransferases (writers), demethylases (erasers), and binding proteins (readers). Accumulating evidence in recent years has revealed that m⁶A modification plays a pivotal role in the pathogenesis and progression of obesity, particularly through its regulation of key biological processes, such as adipocyte differentiation, lipid metabolism, and energy homeostasis. Given its critical involvement in metabolic dysregulation, targeting m⁶A-related mechanisms may offer novel therapeutic avenues for obesity management. This review systematically summarizes the current understanding of m⁶A modification in obesity, elucidates its underlying molecular mechanisms, and evaluates its potential as a therapeutic target. By integrating recent advances in the field, we aim to provide new perspectives for the development of innovative strategies in obesity treatment. Full article

Approximately one-third of global food production is wasted annually, which contributes significantly to greenhouse gas emissions and economic costs. Anaerobic digestion (AD) is an effective method for converting food waste into biogas, but its efficiency depends on factors such as temperature and substrate composition. This study compared mesophilic and thermophilic AD of selectively collected fruit and vegetable waste, quantifying process efficiency and identifying factors leading to collapse. Studies were performed in 1 dm³ reactors with gradually increasing organic loading rates until process collapse. Process dynamics, stability, and gas yields were assessed through daily biogas measurements and analyses of pH, FOS/TAC ratio, sCOD, ammonia, volatile fatty acids, alcohols, total and volatile solids, and C/N ratio. Research has shown that peak methane yields occurred at OLR = 0.5–1.0 kg VS·m⁻³·d⁻¹, with thermophilic systems producing 0.63–5.48% more methane during stable phases. Collapse occurred at OLR = 3.0 in thermophilic and 4.0 in mesophilic reactors, accompanied by sharp increases in methanol, acetic acid, butyric acid, propionic acid, and FOS/TAC. The pH dropped to 5.49 and 6.09. While thermophilic conditions offered higher methane yields, they were more susceptible to rapid process destabilization due to intermediate metabolite accumulation. Full article

This article develops an innovative and intelligent method for analysing the criminal community’s influence on risk-forming parameters based on an analysis of regional economic processes. The research motivation was the need to create an intelligent method for quantitative assessment and risk control arising from the interaction between regional economic processes and criminal activity. The method includes a three-level mathematical model in which the economic activity dynamics are described by a modified logistic equation, taking into account the criminal activity’s negative impact and feedback through the integral risk. The criminal activity itself is modelled by a similar logistic equation, taking into account the economic base. The risk parameter accumulates the direct impact and delayed effects through the memory core. To numerically solve the spatio-temporal optimal control problem, a neural network based on the convolutional architecture was developed: two successive convolutional layers (N₁ with 3 × 3 filters and N₂ with 3 × 3 filters) extract local features, after which two 1 × 1 convolutional layers (FC₁ and FC₂) form a three-channel output corresponding to the control actions U_E, U_C, and U_I. The loss function combines the supervised component and the residual terms of the differential equations, which ensures the satisfaction of physical constraints. The computational experiment showed the high accuracy of the model: accuracy is 0.9907, precision is 0.9842, recall is 0.9983, and F1-score is 0.9912, with a minimum residual loss of 0.0093 and superiority over alternative architectures in key metrics (MSE is 0.0124, IoU is 0.74, and Dice is 0.83). Full article

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

Helium (He) accumulation in tungsten—widely used as a plasma-facing material in fusion reactors—can lead to clustering, trap mutation, and eventual formation of helium bubbles, critically impacting material performance. To clarify the atomic-scale mechanisms governing this process, we conducted systematic molecular statics and molecular dynamics simulations across a wide range of vacancy cluster sizes (n = 1–27) and temperatures (500–2000 K). We identified the onset of trap mutation through abrupt increases in tungsten atomic displacement. At 0 K, the critical helium-to-vacancy (He/V) ratio required to trigger mutation was found to scale inversely with cluster size, converging to ~5.6 for large clusters. At elevated temperatures, thermal activation lowered the mutation threshold and introduced a distinct He/V stability window. Below this window, clusters tend to dissociate; above it, trap mutation occurs with near certainty. This critical He/V ratio exhibits a linear dependence on temperature and can be described by a size- and temperature-dependent empirical relation. Our results provide a quantitative framework for predicting trap mutation behavior in tungsten, offering key input for multiscale models and informing the design of radiation-resistant materials for fusion applications. Full article

Tabu search is a well-known local search-based metaheuristic, widely used for tackling complex combinatorial optimization problems. As with other metaheuristics, its performance is sensitive to parameter configurations, requiring careful tuning. Among the critical parameters of tabu search is the tabu tenure. This study aims to identify key search attributes and instance characteristics that can help establish comprehensive guidelines for a robust tabu tenure policy. First, a review different tabu tenure policies is provided. Next, critical baselines to understand the fundamental relationship between tabu tenure settings and solution quality are established. We verified that generalizable parameter selection rules provide value when implementing metaheuristic frameworks, specifically showing that a more robust tabu tenure policy can be achieved by considering whether a move is improving or non-improving. Finally, we explore the integration of machine learning techniques that exploits both dynamic search attributes and static instance characteristics to obtain effective and robust tabu tenure policies. A statistical analysis confirms that the integration of machine learning yields statistically significant performance gains, achieving a mean improvement of 12.23 (standard deviation 137.25, $n=$ 10,000 observations) when compared to a standard randomized tabu tenure selection (p-value < 0.001). While the integration of machine learning introduces additional computational overhead, it may be justified in scenarios where heuristics are repeatedly applied to structurally similar problem instances, and even small improvements in solution quality can accumulate to large overall gains. Nonetheless, our methods have limitations. The influence of the tabu tenure parameter is difficult to detect in real time during the search process, complicating the reliable identification of when and how tenure adjustments impact search performance. Additionally, the proposed policies exhibit similar performance on the chosen instances, further complicating the evaluation and differentiation of policy effectiveness. Full article

Fed-batch cultivation is a widely used strategy for microbial lipid production, offering flexibility in nutrient control and the potential for high lipid productivity. However, optimizing feeding strategies remains a complex challenge, as it depends on multiple factors, including strain-specific metabolism and process limitations. In this study, we developed a computational framework based on dynamic flux balance analysis and small-scale metabolic models to evaluate and optimize lipid production in Rhodosporidium toruloides strains. We proposed equations to estimate both the carbon and energy source mass feed rate ($F_{in}·s_r$) and its concentration in the feed ($s_r$) based on lipid accumulation targets, and defined minimum feeding flow rate ($F_{in}$) according to process duration. We then assessed the impact of these parameters on commonly used bioprocess metrics—lipid yield, titer, productivity, and intracellular accumulation—across wild-type and engineered strains. Our results showed that the selection of $F_{in}·s_r$ was strongly strain-dependent and significantly influenced strain performance. Moreover, for a given $F_{in}·s_r$, the specific values of $s_r$, and the resulting $F_{in}$, had distinct and non-equivalent effects on performance metrics. This methodology enables the rational pre-selection of feeding strategies and strains, improving resource efficiency and reducing the probability of failed experiments. Full article

Marine microalgae from the genus Nannochloropsis are promising candidates for the photoautotrophic production of eicosapentaenoic acid (EPA, C20:5), a polyunsaturated fatty acid known for its numerous health benefits. A recent study demonstrated that Microchloropsis salina can accumulate high amounts of EPA when cultivated in flat-plate gas-lift photobioreactors. This study aimed to characterize an alternative strain, Nannochloropsis oceanica, and compare its biomass and EPA productivity to M. salina. Applying simulated dynamic climate conditions of a repeated sunny summer day in Eastern Australia, N. oceanica was cultivated in LED-illuminated flat-plate gas-lift photobioreactors. The results showed significantly higher biomass growth and EPA contents compared to M. salina. An EPA productivity of 33.0 ± 0.6 mg_EPA L⁻¹ d⁻¹ has been achieved in batch processes with N. oceanica. Scaling up the photoautotrophic process to 8 m² thin-layer cascade photobioreactors resulted in doubled concentrations of N. oceanica biomass compared to laboratory-scale batch processes. This improvement was likely due to the reduced fluid layer depth, which enhanced light availability to the microalgal cells. Using urea instead of nitrate as a nitrogen source further improved the EPA production of N. oceanica in thin-layer cascade photobioreactors, achieving CDW concentrations of up to 17.7 g L⁻¹ and thus a high EPA concentration of 843 mg L⁻¹. These findings highlight N. oceanica as an alternative to M. salina for sustainable EPA production, offering potential for further industrial applications. Full article

Hydrocephalus is a chronic neurological condition caused by abnormal cerebrospinal fluid (CSF) accumulation, significantly impacting patients’ quality of life. Its causes remain poorly understood, making neurosurgery the primary treatment. Research suggests that hydrocephalus may result from impaired macromolecular clearance, leading to increased osmotic load in the ventricles. Macromolecules are cleared via processes such as transcytosis, involving caveolae- and clathrin-dependent pathways, soluble N-ethylmaleimide-sensitive factor activating protein receptor (SNARE) proteins, and vesicular trafficking. Abnormalities in transcytosis components, such as mutations in alpha-SNAP (α-soluble NSF attachment protein) and SNARE complexes, disrupt membrane organization and vesicle fusion, potentially contributing to hydrocephalus. Other factors, including alpha-synuclein and Rab proteins, may also play roles in vesicle dynamics. Insights from animal models, such as hyh (hydrocephalus with hop gait) mice, highlight the pathological consequences of these disruptions. Understanding transcytosis abnormalities in hydrocephalus could lead to novel therapeutic strategies aimed at enhancing macromolecular clearance, reducing ventricular fluid buildup, and improving patient outcomes. Full article

Background: Rose processing faces critical challenges in preserving bioactive compounds and aroma profiles during thermal treatments, particularly given the growing demand for natural ingredients in the food and cosmetic industries. Methods: Using widely targeted metabolomics, we first characterized volatile profiles of four major commercial cultivars (Hetian, Damask, Bulgarian, and Fenghua; n = 6 replicates per cultivar), identifying terpenoids as dominant components (p < 0.05). Subsequent thermal optimization focused on Hetian rose, where WGCNA and K-means analyses revealed temperature-dependent dynamics (40–55 °C, triplicate drying trials per temperature). Results: Hetian rose exhibited significantly higher accumulation (p < 0.05) of a unique sesquiterpene marker, 4-(1,5-dimethyl-1,4-hexadienyl)-1-methyl-cyclohexene. Systematic drying optimization identified 50 °C as the thermal threshold for optimal color, bioactive retention, and sensory quality. Mechanistic analysis identified 193 temperature-responsive metabolites (VIP > 1, FC < 0.25 or >4, p < 0.01), with terpenoid biosynthesis (MVA/MEP pathways) and esterification dynamics emerging as critical control points. Conclusions: This study establishes the first cultivar-specific processing framework for roses, demonstrating that metabolic signature-guided drying improves product quality. The findings advance our understanding of thermal impacts on aroma biochemistry while providing actionable protocols for natural product industries. Full article

The effects of plant configuration modes on soil organic carbon fractions are mainly reflected in plant species, root structure, apoplastic input, and microbial activity, and different plant configuration modes affect the accumulation and stability of soil organic carbon by changing the input and decomposition processes of organic matter. Considering the common use of local species in ecological restoration and their diverse ecological functions, we selected five different plant configuration modes in the lakeshore zone of Hongze Lake (Metasequoia glyptostroboides-Amorpha fruticosa L. (M-Af), Metasequoia glyptostroboides-Acorus calamus L. (M-Ac), Salix babylonica L.-Amorpha fruticosa L. (S-Af), Magnolia grandiflora L.-Nandina domestica Thunb. (Mg-N), and Pterocarya stenoptera C. DC.-Nandina domestica Thunb. (P-N)) in this study. The objective of the present study was to analyze the carbon content in the vegetation, the content of soil organic carbon and its components in the understorey, and the activity of the soil carbon pool and their interrelationships under different plant configuration modes in the lakeshore zone of Hongze Lake to reveal the dynamic change law in the carbon pool under different plant configuration modes. The findings demonstrated that within the Metasequoia glyptostroboides mode, M-Ac exhibited notable benefits in accumulating soil organic carbon and enhancing the stability of carbon fractions. The soil organic carbon (SOC) content was recorded at 3.93 g·kg⁻¹, the total carbon (TC) content at 4.73 g·kg⁻¹, and the mineral-associated organic carbon (MAOC) content of 2.20 g·kg⁻¹ in the soil layer of 0–20 cm, which were 23.4%–71.6%, 9%–24.5%, and 18.9%–54.3% (p < 0.05), respectively, and were higher than the other configuration modes. Regarding the percentage of inactive carbon (NLC/SOC), the corresponding values for M-Ac and M-Af were 74.21% and 70.33%, respectively, which were significantly higher than the other modes. Redundancy analysis further showed that the soil whole carbon and arbor layer branch carbon content were the pivotal factors driving the accumulation of soil organic carbon fractions (with a cumulative explanation of 71.26%). This study has the potential to provide a theoretical basis and practical reference for optimizing plant allocation and enhancing the carbon sink function in the ecological restoration of the lakeshore zone. Full article

The accumulation of sugar beet (Beta vulgaris L.) root yield across Central and Eastern Europe typically occurs mostly from July to September but can vary substantially depending on precipitation patterns. When summer rainfall is insufficient, the period of intensive yield accumulation may be delayed, often affecting the technological quality of the roots. Conversely, as light and thermal conditions deteriorate in autumn, growth processes slow down, and each cultivar’s response to late-season conditions may vary. To investigate these dynamics, we examined nine sugar beet cultivars (Zeltic, Pacific, Mariza, Everest, BTS 2205N, Jaromir, Jantar, Eliska KWS, and Klara) under three harvest dates (8–10 September—first date; 7–8 October—second date; and 3–5 November—third date) during the 2020–2021 growing seasons. Both cultivar and harvest timing had a significant impact on root yield, sucrose content, and the concentrations of molasses-forming elements (K, Na, and α-amino N), though the magnitude of these effects strongly depended on weather conditions. In 2020, which was characterized by very high precipitation in June and August, harvesting in early September resulted in optimal yield for most cultivars, with no significant benefit from delaying harvest. However, in 2021, when precipitation was moderate and more evenly distributed, later harvest dates enhanced both yield and sucrose content in several cultivars (e.g., Eliska KWS and Jantar). Among all cultivars tested, Eliska KWS consistently demonstrated high root yield and sucrose content. The sucrose content in the roots was strongly influenced by weather conditions in each study year. In 2021, which had average water availability, sucrose content was high, and delaying the harvest led to an increase in sucrose content while reducing molasses-forming elements in the roots. In contrast, in 2020, during summer rainfall, the effect of harvest date on quality traits was significantly weaker and largely dependent on the cultivar. These findings underscore that choosing the optimal harvest date is highly site- and season-dependent, shaped by precipitation distribution, late-season temperatures, and cultivar genotype. In practical terms, these results can help producers and sugar processors align harvest schedules with local conditions to optimize both root yield and technological quality. Full article

Achieving simultaneous anammox and denitrification is a feasible approach for enhancing nitrogen removal in mainstream anammox processes. Nevertheless, the optimal C/N range and microbial dynamics driving this process are still not fully understood. In this study, three mainstream anammox reactors were operated with varying influent C/N ratios. The results demonstrated a remarkable nitrogen removal of 92.6% achieved by combining partial denitrification and anammox with the C/N ratio set at 1.0. However, the nitrogen removal efficiency decreased when the C/N ratio was either 0.5 or 2.0, causing the accumulation of nitrate and ammonium in the effluent, respectively. These results suggest a narrow optimal range of the influent C/N for mainstream anammox processes. Additionally, a transition in the predominant denitrifier population from Denitratisoma to Thauera was noted when the C/N ratio increased. The denitrifying phenotype of Thauera was significantly influenced by the C/N ratio. Thauera can effectively collaborate with anammox bacteria only at a suitable C/N ratio, where it partially reduces the nitrate generated in the anammox reaction. With a high influent C/N, Thauera primarily performed nitrite reduction, notably inhibiting anammox activity. The results of this study are valuable for the optimal design of the mainstream anammox process. Full article