Search Results (24,284)

The escalating challenge of resistance to insecticides among agricultural and public health pests poses a significant threat to global food security and vector-borne disease control. This review synthesizes current understanding of the molecular mechanisms underpinning resistance, including well-characterized pathways such as target-site mutations affecting nicotinic acetylcholine receptors (nAChRs), acetylcholinesterase (AChE), voltage-gated sodium channels (VGSCs), and γ-aminobutyric acid (GABA) receptors, and metabolic detoxification mediated by cytochrome P450 monooxygenases (CYPs), esterases, and glutathione S-transferases (GSTs). Emerging resistance mechanisms are also explored, including protein sequestration by odorant-binding proteins and post-transcriptional regulation via non-coding RNAs, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Focused case studies on Aedes aegypti and Spodoptera frugiperda illustrate the complex interplay of genetic and biochemical adaptations driving resistance. In Ae. aegypti, voltage-gated sodium channel (VGSCs) mutations (V410L, V1016I, F1534C) combined with metabolic enzyme amplification confer resistance to pyrethroids, accompanied by notable fitness costs and ecological impacts on vector populations. In S. frugiperda, multiple resistance mechanisms, including overexpression of cytochrome P450 genes (e.g., CYP6AE43, CYP321A8), target-site mutations in ryanodine receptors (e.g., I4790K), and behavioral avoidance, have rapidly evolved across global populations, undermining the efficacy of diamide, organophosphate, and pyrethroid insecticides. The review further evaluates integrated pest management (IPM) strategies, emphasizing the role of biopesticides, biological control agents, including entomopathogenic fungi and parasitoids, and molecular diagnostics for resistance management. Taken together, this analysis underscores the urgent need for continuous molecular surveillance, the development of resistance-breaking technologies, and the implementation of sustainable, multifaceted interventions to safeguard the long-term efficacy of insecticides in both agricultural and public health contexts. Full article

Photosynthesis in higher plants is highly sensitive to drought and salinity. While studies have examined the individual effects of drought or salt stress on photosynthesis, their combined impact remains poorly understood. In this study, we investigated the diurnal dynamics and primary limiting factors (stomatal, mesophyll, and biochemical) affecting the net photosynthetic rate (A_n) in Ginkgo (G.) biloba under drought, salt, and combined drought–salt stress. The results revealed that G. biloba exhibited a bimodal pattern of A_n under control conditions, primarily driven by mesophyll conductance (g_m). Under drought, this pattern shifted, with stomatal limitations dominant in the late afternoon. In contrast, salt and combined stress induced a unimodal A_n pattern due to a flattened g_m curve and reduced correlation between g_m and A_n. Interestingly, combined stress caused significantly lower mesophyll limitations than salt stress alone, compensating for increased stomatal limitations and leading to a higher A_n. Our findings reveal a dynamic shift in the limiting factors over time and stress types, suggesting that G. biloba has mechanisms to mitigate combined drought–salt stress. These insights deepen our understanding of plant resilience under complex environmental conditions. Full article

Polycystic ovary syndrome (PCOS) is a hormonal disorder with complex, multifactorial and still not fully explained etiopathogenesis. It is believed that the cause is a combination of genetic and environmental factors. Background: With the aim to better understand PCOS etiology, the study examines body composition and compares the occurrence of lipid disorders and visceral adipose tissue depending on the adopted Rotterdam phenotypes. Methods: The study included 242 patients classified into four classic Rotterdam phenotypes. Clinical data from patients were collected and carefully analyzed to determine the relationship between the occurrence of lipid disorders and the visceral adiposity index (VAI). Results: The results obtained after assessing the differences between the Rotterdam phenotypes were not statistically significant. Differences in the levels of coefficients included in the VAI equation in the given phenotypes were also analyzed, as follows: waist circumference (p-value = 0.3415), BMI (p-value = 0.7112), TG [mmol/L] (p-value = 0.5341) and HDL [mmol/L] (p-value = 0.2302). None of the differences were statistically significant. Conclusions: Although the results did not show a clear association between VAI and the individual Rotterdam PCOS phenotypes, this coefficient can be used in the assessment of cardiometabolic risk in women with PCOS regardless of the adopted classification. Full article

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

Microseismic monitoring has become a critical technology for hydraulic fracturing in unconventional oil and gas reservoirs, owing to its high temporal and spatial resolution. It plays a pivotal role in tracking fracture propagation and evaluating stimulation effectiveness. However, the automatic picking of first-arrival times and accurate source localization remain challenging under complex noise conditions, which constrain the reliability of fracture parameter inversion and reservoir assessment. To address these limitations, we propose a hybrid approach that combines optimized variational mode decomposition (OVMD), wavelet thresholding denoising (WTD), and an adaptive fractal box-counting dimension algorithm for enhanced first-arrival picking and source localization. Specifically, OVMD is first employed to adaptively decompose seismic signals and isolate noise-dominated components. Subsequently, WTD is applied in the multi-scale frequency domain to suppress residual noise. An adaptive fractal dimension strategy is then utilized to detect change points and accurately determine the first-arrival time. These results are used as inputs to a particle swarm optimization (PSO) algorithm for source localization. Both numerical simulations and laboratory experiments demonstrate that the proposed method exhibits high robustness and localization accuracy under severe noise conditions. It significantly outperforms conventional approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT). The proposed framework offers reliable technical support for dynamic fracture monitoring, detailed reservoir characterization, and risk mitigation in the development of unconventional reservoirs. Full article

The gas reservoir of the Sinian Dengying Formation (Member 4) in Sichuan Basin exhibits extensive development of inter-clast dissolution pores and vugs within its carbonate reservoirs, characterized by low porosity (average 3.21%) and low permeability (average 2.19 mD). With the progressive development of the Moxi (MX)structure, the existing stimulation techniques require further optimization based on the specific geological characteristics of these reservoirs. Through large-scale true tri-axial physical simulation experiments, this study systematically evaluated the performance of three principal acid systems in reservoir stimulation: (1) Self-generating acid systems, which enhance etching through the thermal decomposition of ester precursors to provide sustained reactive capabilities. (2) Gelled acid systems, characterized by high viscosity and effectiveness in reducing breakdown pressure (18%~35% lower than conventional systems), are ideal for generating complex fracture networks. (3) Diverting acid systems, designed to improve fracture branching density by managing fluid flow heterogeneity. This study emphasizes hybrid acid combinations, particularly self-generating acid prepad coupled with gelled acid systems, to leverage their synergistic advantages. Field trials implementing these optimized systems revealed that conventional guar-based fracturing fluids demonstrated 40% higher breakdown pressures compared to acid systems, rendering hydraulic fracturing unsuitable for MX reservoirs. Comparative analysis confirmed gelled acid’s superiority over diverting acid in tensile strength reduction and fracture network complexity. Field implementations using reservoir-quality-adaptive strategies—gelled acid fracturing for main reservoir sections and integrated self-generating acid prepad + gelled acid systems for marginal zones—demonstrated the technical superiority of the hybrid system under MX reservoir conditions. This optimized protocol enhanced fracture length by 28% and stimulated reservoir volume by 36%, achieving a 36% single-well production increase. The technical framework provides an engineered solution for productivity enhancement in deep carbonate gas reservoirs within the G-M structural domain, with particular efficacy for reservoirs featuring dual low-porosity and low-permeability characteristics. Full article

Picea neoveitchii Mast., a critically endangered spruce species endemic to China, is classified as a national second-level key protected wild plant and listed as critically endangered (CR) on the International Union for Conservation of Nature (IUCN) Red List. Its habitat features complex forest light environments, and global climate change coupled with environmental pollution has increased regional nitrogen deposition, posing significant challenges to its survival. This study explores the effects of light–nitrogen interactions on the leaf functional traits of Picea neoveitchii Mast. seedlings by simulating combinations of light intensities (100%, 70%, and 40% full sunlight) and nitrogen application levels (0, 10, and 20 g N·m ⁻²·a⁻¹, where g N·m⁻²·a⁻¹ denotes grams of nitrogen applied per square meter per year). We examined changes in morphological traits, anatomical structures, photosynthetic physiology, and stress resistance traits. Results indicate that moderate shading (70% full sunlight) significantly enhances leaf morphological traits (e.g., leaf length, leaf area, and specific leaf area) and anatomical features (e.g., mesophyll tissue area and resin duct cavity area), improving light capture and stress resistance. Medium- to high-nitrogen treatments (10 or 20 g N·m⁻²·a⁻¹) under moderate shading further increase photosynthetic efficiency, stomatal conductance, and antioxidant enzyme activity. According to the comprehensive membership function evaluation, the L2N0 (70% full sunlight, 0 g N·m⁻²·a⁻¹) treatment exhibits the most balanced performance across both growth and stress-related traits. These findings underscore the critical role of light–nitrogen interactions in the growth and adaptability of Picea neoveitchii Mast. leaves, offering a scientific foundation for the conservation and ecological restoration of endangered plant populations. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article

Enteroviruses initiate genomic replication via a highly conserved mechanism that is controlled by an RNA platform, also known as the 5’ cloverleaf (5’CL). Here, we present a biophysical analysis of the 5’CL conformation of three enterovirus serotypes under various ionic conditions, utilizing CD spectroscopy, size-exclusion chromatography, and small-angle X-ray scattering. In general, a tendency toward a smaller monomeric hydrodynamic radius in the presence of salts was observed, but the exact structural signature of each 5’CL varied depending upon the serotype. Rhinovirus B14 (RVB14) exhibited at least two monomeric conformations and a low propensity for dimerization, while poliovirus 1 (PV1) showed a high propensity for dimerization, which was enhanced by the presence of salts. Enterovirus D70 was observed to be somewhat intermediate, with primarily a monomeric structure, but possessing some potential for dimerization. The equilibrium between the two monomeric and the dimeric conformations is also discussed. These results indicate that the 5’CL conformation may be more complex than the current literature suggests, thus underscoring the need for a combined crystal and solution approach for the accurate representation of the 5’CL conformation, and the conformation of other RNA structural elements, under native conditions. Full article

Traditional hydraulic-fracturing models are restricted by low computational efficiency, insufficient field data, and complex physical mechanisms, causing evaluation delays and failing to meet practical engineering needs. To address these challenges, this study innovatively develops a dynamic hydraulic-fracturing monitoring method that integrates machine learning with numerical simulation. Firstly, this study uses GOHFER 9.5.6 software to generate 12,000 sets of fracture geometry data and constructs a big dataset for hydraulic fracturing. In order to improve the efficiency of the simulation, a macro command is used in combination with a Python 3.11 code to achieve the automation of the simulation process, thereby expanding the data samples for the surrogate model. On this basis, a parameter sensitivity analysis is carried out to identify key input parameters, such as reservoir parameters and fracturing fluid properties, that significantly affect fracture geometry. Next, a neural-network surrogate model is established, which takes fracturing geological parameters and pumping parameters as inputs and fracture geometric parameters as outputs. Data are preprocessed using the min–max normalization method. A neural-network structure with two hidden layers is chosen, and the model is trained with the Adam optimizer to improve its predictive accuracy. The experimental results show that the efficiency of automated numerical simulation for hydraulic fracturing is significantly improved. The surrogate model achieved a prediction accuracy of over 90% and a response time of less than 10 s, representing a substantial efficiency improvement compared to traditional fracturing models. Through these technical approaches, this study not only enhances the effectiveness of fracturing but also provides a new, efficient, and accurate solution for oilfield fracturing operations. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

Reliability analysis of complex engineering products often involves high-dimensional nonlinear state functions, with random variable distributions hard to determine due to limited samples, restricting the fourth-order moment method that fails to link moments of variables and state functions. This study proposes a method combining a back propagation (BP) neural network and a fourth-order moment method: a BP neural network surrogates the mapping between the model approximation variables and the state function, generating samples for estimating the first-fourth-order moments of the state function, and thus performing reliability analyses based on the fourth-order moment method. Validation shows the BP model outperforms Kriging in predicting high-dimensional nonlinear functions; it aligns with Monte Carlo simulation (MCS) results in rolling bearing reliability analysis with higher efficiency and applies to time-varying fatigue analysis. This method overcomes limitations of the fourth-order moment method, offers higher accuracy than existing surrogate-based methods, and retains the efficiency of moment methods, suitable for limited-sample and time-varying scenarios. Full article

To address the insufficient utilization of network model features and low search efficiency in kill chain analysis for Weapon System of Systems (WSoS), a complex network model of WSoS based on OODA loop was constructed, which converts the indicator system into attribute features embedded in network nodes, and analyzes the kill chain mode through the metapath. Subsequently, a Depth First Search (DFS) algorithm combined with Graph Attention Network (GAT) is proposed for kill chain search evaluation. The algorithm utilizes GAT to extract topological information and node attribute features from graph data to obtain node-embedding vectors, and optimizes the DFS algorithm process by computing the cosine similarity of node-embedding vectors. Simulation results demonstrated that the proposed algorithm achieves high search efficiency and accuracy, providing robust support for combat decision-making. Full article

Urban tree planting on single-family-home lots represents a critical yet underexplored component of municipal greening strategies. This study examines residents’ perceptions of tree planting in Westpointe, a diverse neighborhood in Salt Lake City, Utah, as part of the city’s Reimagine Nature Public Lands Master Plan development effort. Through a mixed-methods approach combining qualitative interviews (n = 24) and a tree signup initiative extended to 86 residents, with 51 participating, this research explores the complex interplay of demographic, economic, social, and infrastructure factors influencing residents’ willingness to plant trees on single-family-home lots. The findings reveal significant variations based on gender, with women expressing more positive environmental and aesthetic motivations, while men focused on practical concerns including maintenance and property damage. Age emerged as another critical factor, with older adults (65+) expressing concerns about long-term maintenance capabilities, while younger families (25–44) demonstrated future-oriented thinking about shade and property values. Property characteristics, particularly yard size, significantly influenced receptiveness, with owners of larger yards (>5000 sq ft) showing greater willingness compared to those with smaller properties, who cited space constraints. Additional barriers, i.e., maintenance, financial, and knowledge barriers, included irrigation costs, lack of horticultural knowledge, pest concerns, and proximity to underground utilities. Geographic analysis revealed that Spanish-speaking social networks were particularly effective in promoting tree planting. The study contributes to urban forestry literature by providing nuanced insights into single-family homeowners’ tree-planting decisions and offers targeted recommendations for municipal programs. These include gender-specific outreach strategies, age-appropriate support services, sliding-scale subsidy programs based on property size, and comprehensive education initiatives. The findings inform evidence-based approaches to increase urban canopy coverage through private property plantings, ultimately supporting climate resilience and environmental justice goals in diverse urban neighborhoods. Full article

The peach landrace ‘Liuyefeitao’ exhibits the unique reproductive trait of self-compatibility combined with cross-incompatibility, contrasting with typical Prunus species in this way. In preliminary studies involving controlled pollination assays, we showed complete pollen tube arrest in cross-pollinated styles, whereas self-pollination enabled full tube elongation. S-genotyping identified a homozygous S₂S₂ genotype with intact S₂-RNase but a truncated PpSFB2 due to a frameshift mutation. Transcriptome profiling of the styles revealed 7937 differentially expressed genes (DEGs) between self- and cross-pollination treatments, with significant enrichment in plant MAPK signaling, plant–pathogen interactions, and plant hormone signaling transduction pathways (|Fold Change| ≥ 2, FDR < 0.01). Notably, PpSLFL3 (a pollen F-box gene) showed down-regulation in cross-pollinated styles, as validated by means of qRT-PCR. Protein interaction assays revealed direct binding between PpSLFL3 and S₂-RNase via Y2H and BiFC analysis, suggesting its role in mediating SCF complex-dependent degradation. We propose that insufficient PpSLFL3 expression during cross-pollination disrupts SCF ubiquitin ligase complex-mediated degradation of non-self S₂-RNase, leading to the toxic degradation of RNA in pollen tubes by S₂-RNase. This mechanism is mechanistically similar to unilateral reproductive barriers in Solanaceae but represents a novel regulatory module in Rosaceae. Our findings provide critical insights into the evolution of cross-incompatibility systems and molecular breeding strategies for Prunus species. Full article