Search Results (278)

Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings. Full article

This study presents an integrative investigation of four rare and threatened plant species—Taraxacum kok-saghyz L.E. Rodin, Astragalus rubtzovii Boriss., Schmalhausenia nidulans (Regel) Petr., and Rheum wittrockii Lundstr.—native to the Ile Alatau and Ketmen ridges of the Northern Tian Shan in Kazakhstan. Combining chloroplast genome sequencing, geobotanical surveys, and anatomical and population structure analyses, we aimed to assess the ecological adaptation, genetic distinctiveness, and conservation status of these species. Field surveys revealed that population structures varied across species, with T. kok-saghyz and S. nidulans dominated by mature vegetative and generative individuals, while A. rubtzovii and R. wittrockii exhibited stable age spectra marked by reproductive maturity and ongoing recruitment. Chloroplast genome assemblies revealed characteristic patterns of plastid evolution, including structural conservation in S. nidulans and R. wittrockii, and a reduced inverted repeat region in A. rubtzovii, consistent with its placement in the IR-lacking clade of Fabaceae. Morphological and anatomical traits reflected habitat-specific adaptations such as tomentose surfaces, thickened epidermis, and efficient vascular systems. Despite these adaptations, anthropogenic pressures including overgrazing and habitat degradation pose significant risks to population viability. Our findings underscore the need for targeted conservation measures, continuous monitoring, and habitat management to ensure the long-term survival of these ecologically and genetically valuable endemic species. Full article

Stacking fault tetrahedra (SFTs) are typically considered improbable in high stacking fault energy metals like aluminum. Using molecular statics and dynamics simulations, we reveal the formation, growth, and transformation of SFTs in aluminum via vacancy aggregation. Three types—perfect, truncated, and defective SFTs—are characterized by their structure, formation energy, and binding energy across a range of vacancy cluster sizes. Formation energies of perfect and truncated SFTs follow a scaling relation; beyond a critical size, truncated SFTs become thermodynamically favored, indicating a size-dependent transformation pathway. Binding energy and structure evolution exhibit quasi-periodic behavior, where vacancies initially adsorb at the vertices or the midpoints of the edges of a perfect SFT, then aggregate along one facet, triggering fault nucleation and a binding energy jump as the system reconstructs into a new perfect SFT. Molecular dynamics simulations further confirm the SFT nucleation and growth via vacancy aggregation, consistent with thermodynamic predictions. SFTs exhibit notable thermal mobility, enabling coalescence and evolution into vacancy-type dislocation loops. BCC-like $V_5$ clusters are identified as potential nucleation precursors. These findings explain the nanoscale, low-temperature nature of SFTs in aluminum and offer new insights into defect evolution and control in FCC metals. Full article

Quantifying the slip rate along geometrically complex strike-slip faults is essential for understanding kinematics and strain partitioning in orogenic systems. The Karlik Tagh forms the easternmost terminus of Tian Shan and represents a critical restraining bend along the sinistral strike-slip Gobi-Tian Shan Fault System. The North Karlik Tagh Fault (NKTF) is an important fault demarcating the north boundary of the Karlik Tagh. While structurally significant, it is poorly understood in terms of its late Quaternary tectonic activity. In this study, we analyze the offset geomorphology based on interpretations of satellite imagery, field survey, and digital elevation models derived from structure-from-motion (SfM), and we provide the first quantitative constraints on the late-Quaternary slip rate using the abandonment age of deformed fan surfaces and river terraces constrained by the ¹⁰Be cosmogenic dating method. Our results reveal that the NKTF can be divided into the Yanchi and Xiamaya segments based on along-strike variations. The NW-striking Yanchi segment exhibits thrust faulting with a 0.07–0.09 mm/yr vertical slip, while the NE-NEE-striking Xiamaya segment displays left-lateral slip at 1.1–1.4 mm/yr since 180 ka. In easternmost Tian Shan, the interaction between thrust and sinistral strike-slip faults forms a transpressional regime. These left-lateral faults, together with those in the Gobi Altai, collectively facilitate eastward crustal escape in response to ongoing Indian indentation. Full article

In this study, we integrated HSQC-based DeepSAT with UPLC-MS/MS to guide the isolation of omega-3 polyunsaturated fatty acid derivatives (PUFAs) from marine resources. Through this approach, four new (1–4) and nine known (5–13) PUFA analogues were obtained from large-scale cultures of the marine dinoflagellate Prorocentrum lima, with lipidomic profiling identifying FA18:5 (5), FA18:4 (7), FA22:6 (8), and FA22:6 methyl ester (11) as major constituents of the algal oil extract. Structural elucidation was achieved through integrated spectroscopic analyses of IR, 1D and 2D NMR, and HR-ESI-MS data. Given the pivotal role of microglia in Alzheimer’s disease (AD) pathogenesis, we further evaluated the neuroprotective potential of these PUFAs by assessing their regulatory effects on critical microglial functions in human microglia clone 3 (HMC3) cells, including chemotactic migration and amyloid-β42 (Aβ42) phagocytic clearance. Pharmacological evaluation demonstrated that FA20:5 butanediol ester (1), FA18:5 (5), FA18:4 (7), FA22:6 (8), and (Z)-10-nonadecenoic acid (13) significantly enhanced HMC3 migration in a wound-healing assay. Notably, FA18:4 (7) also significantly promoted Aβ42 phagocytosis by HMC3 microglia while maintaining cellular viability and avoiding pro-inflammatory activation at 20 μM. Collectively, our study suggests that FA18:4 (7) modulates microglial function in vitro, indicating its potential to exert neuroprotective effects. Full article

Kazakhstan is recognized as one of the primary centers of origin of the wild apple Malus sieversii, concentrated mainly in the mountains like Trans-Ile and Zhongar Alatau, as well as parts of the Tarbagatay, Talas Alatau, and Karatau ranges. As the wild progenitor of Malus domestica, M. sieversii harbors a critical genetic diversity essential for apple breeding and conservation efforts. However, its natural populations are increasingly threatened by latent viral infection, which weakens trees, reduces reproduction, and hinders regeneration. In this study, the spread of apple chlorotic leaf spot virus (ACLSV) and apple stem pitting virus (ASPV) was documented in four wild apple populations, with detection rates of 50.2% and 42.2%, respectively. Mixed infections were observed in 28.8% of sampled trees. Apple stem grooving virus (ASGV) was detected exclusively in cultivated orchards, whereas apple mosaic virus (ApMV) and apple necrotic mosaic virus (ApNMV) were not found in either wild forests or cultivated orchards. Using Geographic Information System (GIS) technology, we developed the first spatial distribution maps of these viruses in wild apple forests in the Tian Shan region, revealing site-specific variation and infection rates. These results underscore the importance of monitoring viral infections in wild M. sieversii populations to preserve genetically valuable, virus-free germplasm critical for apple breeding, crop improvement, and sustainable orchard management. Full article

This paper establishes an explicable integrated machine learning model for predicting the discharge water quality in a circulating cooling water system of a power plant. The performance differences between three deep learning models, a Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN), and traditional machine learning models, namely eXtreme Gradient Boosting (XGboost) and Support Vector Machine (SVM), were evaluated and compared. The TCN model has high fitting accuracy and low error in predicting ammonia nitrogen, nitrate nitrogen, total nitrogen, chemical oxygen demand (COD), and total phosphorus in the effluent of a circulating cooling tower. Compared to other traditional machine learning models, the TCN has a larger R² (maximum 0.911) and lower Root Mean Square Error (RMSE, minimum 0.158) and Mean Absolute Error (MAE, minimum 0.118), indicating the TCN has better feature extraction and fitting performance. Although the TCN takes additional time, it is generally less than 1 s, enabling the real-time prediction of drainage water quality. The main water quality indices have the greatest causal inference relationship with those of makeup water, followed by the concentration ratio, indicating that concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen, and COD have a more decisive impact. Shapley Additive Explanations (SHAP) analysis further reveals that the concentration ratio has a weaker decisive impact on circulating cooling water drainage quality. The results of this study facilitate the optimization of industrial water resource management and offer a feasible technical pathway for water resource utilization in power plants. Full article

The pandemic of Coronavirus Disease 2019 has triggered a worldwide public health emergency. Its pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has developed multiple strategies for effectively evading the host immune defenses, including inhibition of interferon (IFN) signaling. Several viral proteins of SARS-CoV-2 are believed to interfere with IFN signaling. In this study, we found that the SARS-CoV-2 accessory protein ORF7a considerably impaired IFN-activated Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling via suppression of the nuclear translocation of IFN-stimulated gene factor 3 (ISGF3) and the activation of STAT2. ORF7a dampened STAT2 activation without altering the expression and phosphorylation of Janus kinases (JAKs). A co-immunoprecipitation (co-IP) assay was performed to gather ORF7a protein, but it failed to precipitate STAT2. Interestingly, mass spectrometry and immunoblotting analyses of the ORF7a co-IP product revealed that ORF7a interacted with an RNA-binding protein, heterogeneous nuclear ribonucleoprotein A2B1 (HNRNPA2B1), and HNRNPA2B1 was related to the inhibitory effect of ORF7a on STAT2 phosphorylation. Moreover, examination of ORF7a deletion constructs revealed that the C-terminal region of ORF7a (amino acids 96 to 122) is crucial for suppressing IFN-induced JAK/STAT signaling activation. In conclusion, we discovered that SARS-CoV-2 ORF7a antagonizes type I IFN-activated JAK/STAT signaling by interacting with HNRNPA2B1, and the C-terminal region of ORF7a is responsible for its inhibitory effect. Full article

The construction industry, characterized by high energy consumption and carbon emissions, plays a pivotal role in climate change mitigation. This paper employs bibliometric analysis, based on 282 articles from the SCIE and SSCI in the Web of Science spanning 1992–2022, to explore research trends and themes in Carbon Emissions of Construction Industry (CECI). A manual review was conducted to identify challenges and possibilities concerning accounting scales, objects, boundaries, and methods in CECI research. Key findings include (1) temporal and thematic evolution, with a notable increase in research activity since 2015, primarily focusing on energy efficiency, sustainable development, green building technologies, and policy evaluation; (2) scale-specific gaps, as 80.7% of studies are conducted at macro (national/regional) or micro (building/material) levels, while city-scale analyses are significantly underrepresented, with only 13 articles identified; (3) object granularity deficiencies, with 74.8% of studies not distinguishing between building types, resulting in rural residential, educational, and office buildings being significantly underrepresented; (4) system boundary limitations, as few studies account for emissions from building demolition or the disposal and recycling of construction waste, indicating a substantial gap in life-cycle carbon assessments. Furthermore, the predominant reliance on the carbon emission factor method, along with embedded assumptions in accounting processes, presents challenges for improving carbon accounting accuracy. This review synthesizes insights into prevailing research scales, object classifications, system boundaries, and methodological practices, and highlights the urgent need for more granular, lifecycle-based, and methodologically diverse approaches. These findings provide a foundation for advancing CECI research toward more comprehensive, accurate, and context-sensitive carbon assessments in the construction sector. Full article

For the guide vane adjusting mechanism, precision represents the primary design requirement. Meanwhile, due to the presence of aerodynamic loads under actual operating conditions, stagnation forces emerge that affect the mechanism motion characteristics, including the response speed and precision. This paper establishes kinematic and static analysis models of the guide vane adjusting mechanism through analytical modeling methods, investigates analytical approaches for mechanism adjustment precision and stagnation force, and conducts error and sensitivity analyses of the mechanism parameters based on these analytical models. Building upon this foundation, an optimization design method integrating adjustment precision and force transmission performance is proposed using a multi-objective genetic algorithm. Optimizing the critical design parameters, such as the mechanism dimensions and positions, can enhance both the adjustment precision and force transmission performance. Through case studies, significant reductions in motion precision errors and the peak stagnation force and maximum differences in stagnation force were achieved, validating the feasibility of this optimization design approach. Full article

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and Shape-IoU to improve detection accuracy while employing depthwise separable convolution to reduce model complexity. The proposed architecture was evaluated systematically on the DOTAv1.0 dataset, and our results show that YOLOv5s-CACSD achieved a 91.0% mAP@0.5, marking a 2% improvement over the original YOLOv5s. Additionally, it reduced model parameters and computational complexity by 0.9 M and 2.9 GFLOPs, respectively. These results demonstrate the enhanced detection performance and efficiency of the YOLOv5s-CACSD model, making it suitable for practical applications in land target detection for remote sensing imagery. Full article

Peanut yield and quality are often threatened by soil degradation under continuous cropping. Biochar has been known to improve the soil microbial community and plant resistance. However, studies on its functions to reduce soil degradation losses and improve the peanut yield are limited. A field peanut experiment was conducted in an Alfisol soil and biochar was applied at a rate of 20 t ha⁻¹ in 2022. The biochar was prepared from woodchip (WB) and maize straw (MB) feedstocks alone, as well as with co-composted biochar of the same feedstocks with pig manure labeled as WBSC and MBSC amendment, respectively. The conventional organic manure was applied as a control treatment (OM). All plots were base-fertilized with a mineral compound fertilizer of N-P₂O₅-K₂O (16-16-16, %) at 600 kg ha⁻¹. Topsoil (20 cm) and plant samples were collected at the time of peanut harvest. Soil quality, enzyme function, peanut growth traits, microbial abundance, and community composition were analyzed. Compared to OM, peanut yields increased by 22%, 23%, and 18% under WB, WBSC, and MBSC, respectively. The content of oleic acid increased by 4–5%, while the content of linoleic acid decreased by 7–9%, respectively, under biochar–compost treatments. However, biochar amendment alone showed non-significant changes in these fatty acids. The soil extracellular enzyme activity increased by 3.7–5.5% with biochar amendments and 6.4–10.1% with biochar–compost application. The enzyme activity ratio of hydrolase to non-hydrolase, of C cycling to N cycling, and of P cycling increased by 11.4–15.9%, 20.9–33.8%, and 14.7–23.5% under biochar amendments and by 20.5–25.0%, 17.4–39.0%, and 23.5–32.3% under biochar–compost, respectively. Overall, crop residue biochar enhanced peanut yield and quality by improving soil aggregation, enzyme functionality, and fungal community in line with the soil nutrient supply. Full article

Household energy consumption is one of the major drivers of carbon emissions, and an in-depth analysis of its influencing factors, along with forecasting carbon emission trajectories, is crucial for achieving China’s carbon emission targets. This study reviews the research progress on urban household energy-related carbon emissions (HErC) in China since 2000, with a focus on the latest developments in influencing factors. The study categorizes these factors into five major groups: household characteristics, economic attributes, energy consumption features, awareness and norms, and policies and interventions. The findings indicate that income levels, energy efficiency, and household size are the key determinants of urban HErC of China and are commonly used as core assumptions in scenario-based forecasts of emission trends. In addition, although environmental awareness and government services have increasingly garnered attention, their specific effects require further investigation due to the challenges in quantification. A synthesis of existing forecasting studies suggests that, without the implementation of effective measures, HErC will continue to rise, and the peak emission period will be delayed. Enhancing building and energy efficiency, promoting low-carbon consumption and clean energy applications, and implementing multidimensional coordinated policies are considered the most effective pathways for emission reduction. Full article

In the chlorination industry, the reactor is a crucial equipment in which the chlorination reaction takes place. However, when the reactor is subjected to complex conditions such as high temperatures (e.g., >200 °C) and high pressures (e.g., >10 MPa), its structural integrity is significantly compromised, leading to severe safety issues. In this study, the fatigue life of a reactor is analyzed, with particular focus on the fatigue behavior of the cyclone separator under varying working conditions, such as changes in the temperature, pressure, and chemical environment. Using finite element simulations under steady-state conditions and the S-N curve from fatigue testing, the fatigue life and potential weak points of the reactor under different amplitudes and vibration frequencies are analyzed and predicted. This analysis is conducted using a combined simulation approach with ABAQUS and Fe-Safe software, v 6.14. This work also considers the periodic vibrations at the base of the cyclone separator within the reactor. Fatigue simulations under different vibration conditions are performed to further assess the fatigue life of the reactor, providing a theoretical basis for the optimization of design and ensuring operational safety. In addition, the influence of welding zones on the fatigue life is discussed. The results indicate that the welding defects and stress concentration may cause the welded joint to become a critical weak point for fatigue failure. Therefore, the fatigue performance of the welding zone should be carefully considered during the design phase. Full article

To clarify the compensatory effect of increasing density on the yield of extremely late-sown wheat and screen the best combination of the sowing date and density of extremely late-sown wheat in the wheat area of northern Xinjiang, this study set three extremely late-sown dates of October 25 (D1), November 4 (D2), and November 14 (D3) and four densities of 337.5 (M1), 450 (M2), 562.5 (M3), and 675 kg·hm⁻² (M4). Additionally, the effects of the sowing date and density combinations on the formation process of the yield element spike number, spike grain number, and 1000-grain weight were analyzed in detail using the local conventional sowing date and density (25 September, 270 kg·hm⁻²) as the control (CK). The results showed that compared to the CK, increasing the planting density of extremely late-sown wheat compensated for the reduction in the number of harvested spikes due to low emergence rates. The young spikes were stunted due to a reduction in the number of grains per spike, and the grain grouting rate caused a reduction in the defects of the 1000-grain weight in order to increase the number of harvested spikes to improve yield. Under extremely late sowing conditions, D2M2 had the highest post-spring emergence rates, the highest number of harvested spikes, better development of young spikes and grain-filling, and non-significant declines in the number of grains per spike and 1000-grain weight, which balanced the contribution of the number of harvested spikes, number of grains per spike, and 1000-grain weight to the yield and gave the highest yield. After comprehensive yield factor analysis, sowing 450 kg·hm⁻² (1.00 × 10⁶ seeds·hm⁻²) on 4 November (pre-winter cumulative temperature of 47.5 °C) was determined to be the best combination for planting extremely late-sown wheat in the northern Xinjiang wheat area, and the results of this study can provide important theoretical and technical references for guaranteeing the yield of winter wheat in extremely late-sown winter wheat areas. Full article