Search Results (35,763)

Investigating the influence of women’s family status on farmers’ adoption of digital and intelligent production transformation holds significant value in bridging the gender gap in research on modern agricultural production transformation and in facilitating the digital and intelligent transformation of the agricultural sector. Drawing on survey data from Henan Province collected through a household survey conducted in July 2024 by the research team, which employed a combination of stratified and random sampling, and focusing on farmers’ adoption of plant protection drone technology, this paper employs the Triple-Hurdle model to examine the impact of women’s family status on farmers’ digital and intelligent production transformation decisions and the underlying mechanisms. The baseline regression results show that the improvement of women’s family status facilitates farmers’ digital and intelligent production transformation decisions. Specifically, it enhances farmers’ willingness to adopt digital and intelligent production transformation, promotes their adoption behavior of plant protection drone technology, and increases the degree of adoption of such technology. The mechanism analysis reveals that the improvement of women’s family status promotes farmers’ digital and intelligent production transformation decisions by increasing their satisfaction with the institutional environment. The heterogeneity analysis of household characteristics indicates that women’s family status has a greater facilitating effect on the willingness of farmers with lower female labor force participation and those with heavier child or elderly dependency burdens to undergo digital and intelligent production transformation. The heterogeneity analysis of village environmental characteristics shows that women’s family status has a greater facilitating effect on the willingness and behavior of farmers in villages with a larger number of technical personnel to undergo digital and intelligent production transformation. Additionally, it has a greater facilitating effect on the willingness of farmers in villages with a stronger culture of gender equality to undergo such transformation. Using plant protection drone adoption as an example, this paper provides preliminary evidence of the positive impact of women’s family status on the digital and intelligent transformation of agriculture. However, due to the inherent limitations of cross-sectional data, our exploration of the dynamic process of transformation remains inadequate. Therefore, future research is warranted to employ longitudinal panel data to further validate the findings of this study. Full article

Tocosh, an ancestral fermented potato product, relies on spontaneous processes near freshwater springs under extreme high-altitude conditions and represents an underexplored reservoir of microbial diversity with significant potential for the discovery of probiotics. This study provides, for the first time, a comprehensive probiogenomic characterization of 19 lactic acid bacteria (LAB) isolated from tocosh, in the Peruvian Andes, at three distinct altitudes—2992, 3882, and 4451 m above sea level (m.a.s.l.)—using whole genome sequencing (WGS) and bioinformatic profiling. A total of six species were identified: Lactiplantibacillus plantarum and Levilactobacillus brevis at all three study sites, Lacticaseibacillus paracasei and Lentilactobacillus buchneri at the lowest altitude (2992 m.a.s.l.), and Latilactobacillus curvatus and Latilactobacillus sakei at the highest altitudes (3882 and 4451 m.a.s.l.). Our results reveal that the extreme Andean environment is associated with stability in L. plantarum (genome sizes from 3.36 to 3.38 Mb) across all altitudinal levels. Functional analysis using CAZymes determined that L. brevis and L. buchneri act as primary degraders (high percentage of glycosyl hydrolases/carbohydrate binding) while L. curvatus and L. sakei function as primary builders through exopolysaccharide biosynthesis, likely a cryoprotective adaptation preventing cell damage during cold temperatures at high altitudes. Additionally, L. sakei and L. plantarum exhibited unique auxiliary activity (AA) enzymes, suggesting an oxidative mechanism to breach recalcitrant starch surfaces. All isolates were confirmed as genomically safe, lacking transferable antibiotic resistance genes and virulence factors. Pathogenic risk potential scores (PPRS) were consistently ≤ 2.0, fulfilling qualified presumption of safety (QPS) criteria. These findings provide the first genomic characterization of tocosh-associated LAB, establishing a basis for tocosh standardization, enabling the rational design of starter cultures that preserve ancestral traits and ensure microbiological safety in modern food applications. Full article

As global shipping expands, Automated Container Terminals (ACTs) are vital for port competitiveness. However, modern three-stage yard layouts often suffer from spatio-temporal conflicts between dual yard cranes during relay operations, while uncoordinated container placement causes localized overloads and safety hazards. To address these issues, this study proposes a multi-objective mixed-integer linear programming (MILP) model integrating three-stage operations with spatio-temporal mutual exclusion constraints. The model minimizes makespan, external truck waiting time, and inventory disparities across landside bays. To solve this NP-hard problem, an Improved Octopus Optimization Algorithm (IOOA) is developed, featuring discrete space mapping, Euclidean-based state determination, integer flight steps, and local fine-tuning. Numerical experiments demonstrate that this approach significantly reduces the total makespan and truck waiting times while ensuring a highly uniform container distribution across bays. Ultimately, this study mitigates safety risks associated with space overloads and isolated stack collapses, providing a robust decision-making framework to enhance the efficiency and safety of next-generation ACTs. Full article

The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), dynamic line ratings (DLRs), and flexible loads is reshaping modern power systems while introducing significant operational uncertainties. Reinforcement learning (RL) has gained attention as a data-driven solution for optimal coordination and control under uncertainty. However, existing studies that used RL for optimal coordination reviewed in this research primarily address uncertainties from DERs and load variability, largely neglecting DLRs and EVs as a time-varying network constraint. Moreover, long training times and limited interpretability hinder the practical deployment of RL-based controllers. This paper presents a comprehensive review of RL applications in power system operational control, categorizing approaches based on uncertainty sources, control objectives, and learning architectures. The review highlights the operational advantages of incorporating DLR uncertainty, including improved line utilization, congestion mitigation, enhanced renewable hosting capacity, and increased system flexibility. A critical research gap is identified in the absence of integrated RL frameworks that jointly consider DLRs and learning efficiency. To address this gap, a future research direction integrating a Belief–Desire–Intention (BDI) framework within RL is proposed, enabling faster convergence, constraint-aware decision-making, improved transparency, and enhanced resilience in modern power system coordination and control. Full article

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) represent some of the most complex and therapeutically challenging disorders in modern medicine. Despite decades of research, the traditional one drug–one target paradigm has largely failed to deliver disease-modifying therapies. Increasing evidence suggests that these complex diseases arise from interconnected pathological networks involving protein aggregation, oxidative stress, mitochondrial dysfunction, neuroinflammation, and synaptic loss. In this context, natural products (NPs) have re-emerged as a promising pipeline for next-generation therapeutics. Unlike conventional small molecules, NPs inherently exhibit polypharmacology, targeting multiple pathways simultaneously. Recent advances (2019–2026) demonstrate a paradigm shift, from crude NPs and single-mechanism compounds toward engineered derivatives, network pharmacology, and multi-target drug design. Using AD and PD as case studies, this review critically evaluates how NPs are redefining drug discovery by highlighting key emerging NPs, translational strategies, and future directions. Full article

Background: Tectoridin is a prominent isoflavone glycoside found in herbs such as Belamcanda chinensis (L.) DC and Iris tectorum Maxim. It has drawn increasing research interest due to its promising pharmacological activities. However, no critical review to date has determined whether its broad pharmacological activity stems from binding to specific targets or from the non-specific, broad-spectrum activity commonly associated with flavonoids. This paper provides a comprehensive review of tectoridin, covering its plant sources, pharmacological effects, pharmacokinetics, and toxicity, alongside an in-depth analysis of the mechanisms underlying its pharmacological effects and strategic recommendations for advancing its clinical translation. Methods: A systematic literature search was conducted in PubMed, Web of Science, Google Scholar, SciFinder, and CNKI for publications from 1968 to 2025 using keywords including tectoridin, tectorigenin 7-O-glucoside, traditional uses, ethnopharmacology, pharmacology, bioactive compounds, biological activity, pharmacokinetics and toxicity. Results: Tectoridin exhibits a broad spectrum of pharmacological activities, including anticancer, anti-inflammatory, hepatoprotective, antidiabetic, antioxidant, cardiovascular, and estrogenic effects. Pharmacokinetic studies have shown rapid tissue distribution and slow elimination; the aglycone metabolite tectorigenin often displays enhanced bioactivity, and chemical modifications may further improve efficacy. Toxicity data suggest relative safety in medicinal food contexts, but comprehensive in vivo studies remain limited. Tectoridin shows promise for treating cancer and inflammatory diseases; however, further research is needed to elucidate its molecular mechanisms, clarify toxicity, and optimize bioactivity. Conclusions: This review bridges natural products and modern therapeutics by focusing on tectoridin, highlighting its therapeutic potential, addressing challenges, and offering new perspectives for treating various diseases. Full article

The development of China’s National Modern Agricultural Industrial Parks (NMAIPs) has provided valuable knowledge to guide regional agricultural structural adjustment. To systematically analyze and scale up the successful practices of crop–livestock spatial layouts, this study examines 335 NMAIPs established between 2017 and 2024. Based on seven natural environmental variables, a deep clustering model (VAE-GMM) was applied to classify the parks into representative environmental types, establishing a standardized spatial reference frame. Crucially, the study introduces a spatial discrepancy (Gap) metric—calculated as the difference between model-predicted theoretical suitability and actual occurrence frequencies—to evaluate industrial expansion potential. Results reveal the parks form five distinct environmental types with clear regional patterns. The LightGBM prediction (micro-average AUC = 0.75, macro-average AUC = 0.63; range: 0.37–0.86) effectively captures natural constraints. Discrepancy analysis exposes a structural divergence between environmental suitability and actual agricultural allocation. Quantifying this divergence highlights suitable yet underrepresented industries, offering a pathway for sustainable resource management. By treating existing parks as reference baselines, this AI-driven forecasting framework provides transferable decision support for preliminary ecological screening and early-stage option identification in newly established parks. Full article

Det Norske Misjonsselskap (Norwegian Missionary Society, NMS) was founded in Stavanger, Norway, in 1842. Having established its first mission field in Africa, it then made plans to work in Asia. In 1902, the first missionaries were sent out to Hunan, an inland, culturally isolated, and conservative Chinese province that experienced particularly strong anti-foreigner and anti-Christian waves. This article argues that the NMS developed a distinctive, pragmatic strategy of political accommodation—rooted in its Pietistic Lutheran social ethos and a Norwegian pioneering spirit—to ensure its institutional survival in Hunan. Examining the NMS’s responses to two major political turning points, the Anti-Christian Movement (1924–1927) and the New Life Movement (1934–1937), the article reveals three key findings: First, the NMS’s proclaimed “neutrality” was not merely a passive stance but an active survival tactic, evolving from a claim grounded in Norway’s geopolitical neutrality into a strategic rhetoric for navigating local political risks. Second, the missionaries’ “excessive expectations” of the Nationalist government, particularly during the New Life Movement, stemmed from a structural cognitive bias shaped by their deep institutional embedding in the KMT-governed local order. Third, their ultimate withdrawal was less a simple political misjudgement than the logical endpoint of a survival model that lacked a contingency plan for revolutionary change. By tracing this specific case, the article contributes to the historiography of Christianity in modern China by illuminating the diversity of missionary strategies beyond the dominant Anglo-American coastal narratives. Full article

Astrocytes are now recognized as active and essential participants in neural circuit function, extending far beyond their traditional roles as passive support cells. Emerging evidence highlights their critical involvement in synaptic modulation, information processing, and complex behaviors, making them key targets for understanding cognitive dysfunction in psychiatric disorders. This narrative review synthesizes current findings from rodent models to elucidate the relationship between astrocytic networks and multidomain cognitive performance. We first outline the morphological and physiological features of astrocytes, followed by a comprehensive overview of the modern experimental toolkit, including observational markers and advanced interventional strategies. Next, we evaluate commonly used behavioral assays that capture distinct cognitive domains, ranging from basic spatial and recognition memory to higher-order executive functions, cognitive flexibility, and social cognition. By integrating recent experimental evidence, we detail the specific mechanistic pathways, such as intracellular calcium signaling, gliotransmission, and neuroinflammatory reactivity, through which astrocytes directly govern these cognitive processes. Finally, we highlight critical knowledge gaps stemming from methodological limitations, arguing for the integration of more ethologically relevant, high-throughput behavioral tasks alongside highly specific targeting tools to better capture the functional heterogeneity of astrocytes in cognitive health and disease. Full article

Receiver nonlinear distortion severely impacts modern wireless systems. Traditional power series polynomial models suffer from numerical instability in parameter estimation, especially at high orders or with memory. This paper investigates orthogonal memory polynomial models from the perspectives of memory depth, nonlinear order, input signal distribution, and temporal correlation of the input signal, focusing on effective methods for improving the condition number. Comprehensive analysis reveals that the condition number of the Gram matrix grows rapidly with polynomial order and memory depth for the conventional polynomial, while orthogonal polynomials remain well-conditioned due to their inherent orthogonality and normalization. Notably, orthogonal polynomials maintain robust performance even when the input distribution does not perfectly match the basis weight function. Experiments using OFDM and 3-carrier WCDMA signals confirm that orthogonal polynomials achieve condition numbers orders of magnitude lower than those of power series, along with superior fitting accuracy. Full article

Damping modeling significantly influences the numerical seismic response of buildings, something that, despite being repeatedly emphasized in earthquake engineering research, is still overlooked even by seismic codes. It is a fact that, for simplification and ease of application, modern seismic design provisions assume damping for buildings entirely composed of a single material, e.g., reinforced concrete or structural steel. The current codes offer no guidance on damping assumptions for so-called mixed buildings comprising a lower part (stories) of reinforced concrete and an upper part (stories) of structural steel. Despite the growing use of mixed reinforced concrete-structural steel buildings, damping modeling of their seismic response remains almost unexplored. This study aims to contribute to this field by investigating the effect of different damping models on the elastic and inelastic seismic response of realistic three-dimensional mixed buildings. Modal response spectrum and time-history analyses served for this purpose. Key seismic response parameters, including interstory drift ratios, floor accelerations, and base shear demands, are extracted and systematically compared for the examined damping models. The results highlight the sensitivity of computed seismic demands to the assumed damping model. Guidance on selecting a damping model for the seismic analysis of mixed reinforced concrete-structural steel buildings is provided. Full article

Self-collision detection has become the dominant computational bottleneck in GPU-accelerated cloth simulation, as modern parallel solvers such as XPBD have drastically reduced the cost of position updates while leaving collision resolution largely unoptimized. Existing spatial partitioning methods treat all cloth regions uniformly, saturating GPU memory bandwidth despite the fact that the vast majority of the mesh surface remains geometrically flat and collision-free at any given frame. We propose a hierarchical self-collision culling framework built upon a resolution-independent discrete curvature metric derived from the $h^2$-normalized Laplace-Beltrami operator, integrated with a discrete Kirchhoff–Love shell model combining distance and dihedral bending constraints within XPBD. Unlike prior cache-dependent acceleration strategies, our method tightly couples curvature-driven geometric pruning with a fused GPU kernel design and shows that this stateless formulation is both faster and physically more reliable. Evaluated on meshes of $512\times 512$ and $1024\times 1024$ particles, our method achieves a $5.5\%$ and $9.7\%$ FPS improvement alongside a $34.9\%$ and $28.4\%$ reduction in active collision pairs, respectively, with qualitative validation via high-fidelity rendering confirming artifact-free self-contact and strict ground-plane non-penetration. Ablation results further reveal that temporal coherence, conventionally regarded as an optimization standard, strictly degrades both performance (FPS decrease of $1.4\%p$ to $1.9\%p$) and physical accuracy (penetration depth increase of $36.1\%$ to $100.0\%$ relative to the curvature-only stage) on RTX 3070 GPU, advocating for stateless per-frame geometric evaluation as the preferred design paradigm. Full article

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals. Full article

Parasitic plants represent a unique group of angiosperms that extract nutrients from host plants through specialized structures called haustoria. With over 4750 recognized species, these plants vary in their dependence on hosts, classified as holoparasites (completely non-photosynthetic) or hemiparasites (partially photosynthetic). Despite their parasitic lifestyle, these plants contribute significantly to ecological stability by regulating plant communities. Some parasitic species, such as Striga and Orobanche, are major agricultural pests, while others, including Cistanche and Cynomorium, are valued for their medicinal properties. Parasitic plants in general are rich in secondary metabolites with potential pharmacological significance. These compounds, including alkaloids, phenolics, and terpenoids, display antimicrobial, anticancer, and immunomodulatory effects. Mistletoe (Viscum album L.) produces lectins and viscotoxins, which exhibit cytotoxic and immune-stimulating properties. Traditional medicine has long utilized parasitic plants, and modern pharmacological research continues to uncover their potential in drug development. However, an intriguing question arises: whether they are superior in any way to their non-parasitic counterparts, or just received more attention due to their unique appearance. Understanding the unique chemistry of parasitic plants provides insights into their ecological role and offers opportunities for advancements in medicine and agriculture. Full article

In power engineering, various mathematical models are used, for example, to study stability, forecasting, etc., obtained using analytical methods, machine learning, and artificial intelligence. The present authors pursue a novel direction in modeling the development of the power system as an intelligent control system using data from 1990–2024 under conditions including a growing level of renewable energy production and an increased level of electrical energy saving. As a result of the modeling carried out in the MATLAB and Simulink environment, two types of highly accurate development models were obtained: a regression machine learning ARX model and a multilayer perceptron (MLP) neural network. For the neural model, MAPE errors ranged from 0.73% to 3.37%, and the coefficient of determination R² ranged from 0.9478 to 0.9868. The accuracy of the ARX models was close to 100%. Using an ARX model converted into a state-space (SS) model, it was observed that the subsystems of conventional electricity production and renewable energy were observable and controllable. The presented methodology is modern, enabling the study of large development systems using development models in terms of control and systems theory and artificial intelligence methods. Full article