Search Results (1,246)

As China’s first systematic assessment of high-risk Amaranthaceae invaders, this study addresses a critical knowledge gap identified in the National Invasive Species Inventory, in which four invasive Amaranthaceae species (Dysphania ambrosioides, Celosia argentea, Amaranthus palmeri, and Amaranthus spinosus) are prioritized due to CNY 2.6 billion annual ecosystem damages in China. By coupling multi-species comparative analysis with a parameter-optimized Maximum Entropy (MaxEnt) model integrating climate, soil, and topographical variables in China under Shared Socioeconomic Pathways (SSP) 126/245/585 scenarios, we reveal divergent expansion mechanisms (e.g., 247 km faster northward shift in A. palmeri than D. ambrosioides) that redefine invasion corridors in the North China Plain. Under current conditions, the suitable habitats of these species span from 92° E to 129° E and 18° N to 49° N, with high-risk zones concentrated in central and southern China, including the Yunnan–Guizhou–Sichuan region and the North China Plain. Temperature variables (Bio: Bioclimatic Variables; Bio6, Bio11) were the primary contributors based on permutation importance (e.g., Bio11 explained 56.4% for C. argentea), while altitude (e.g., 27.3% for A. palmeri) and UV-B (e.g., 16.2% for A. palmeri) exerted lower influence. Model validation confirmed high accuracy (mean area under the curve (AUC) > 0.86 and true skill statistic (TSS) > 0.6). By the 2090s, all species showed net habitat expansion overall, although D. ambrosioides exhibited net total contractions during mid-century under the SSP126/245 scenarios, C. argentea experienced reduced total suitability during the 2050s–2070s despite high-suitability growth, and A. palmeri and A. spinosus expanded significantly in both total and highly suitable habitat. All species shifted their distribution centroids northward, aligning with warming trends. Overall, these findings highlight the critical role of temperature in driving range dynamics and underscore the need for latitude-specific monitoring strategies to mitigate invasion risks, providing a scientific basis for adaptive management under global climate change. Full article

In the era of smart agriculture, accurate identification of plant species is critical for effective crop management, biodiversity monitoring, and the sustainable use of medicinal resources. However, existing deep learning approaches often underperform when applied to fine-grained plant classification tasks due to the lack of domain-specific, high-quality datasets and the limited representational capacity of traditional architectures. This study addresses these challenges by introducing a novel, well-curated leaf image dataset consisting of 39 classes of medicinal and aromatic plants collected from the Aromatic and Medicinal Plant Research Station in Odakkali, Kerala, India. To overcome performance bottlenecks observed with a baseline Convolutional Neural Network (CNN) that achieved only 44.94% accuracy, we progressively enhanced model performance through a series of architectural innovations. These included the use of a pre-trained VGG16 network, data augmentation techniques, and fine-tuning of deeper convolutional layers, followed by the integration of Squeeze-and-Excitation (SE) attention blocks. Ultimately, we propose a hybrid deep learning architecture that combines VGG16 with Batch Normalization, Gated Recurrent Units (GRUs), Transformer modules, and Dilated Convolutions. This final model achieved a peak validation accuracy of 95.24%, significantly outperforming several baseline models, such as custom CNN (44.94%), VGG-19 (59.49%), VGG-16 before augmentation (71.52%), Xception (85.44%), Inception v3 (87.97%), VGG-16 after data augumentation (89.24%), VGG-16 after fine-tuning (90.51%), MobileNetV2 (93.67), and VGG16 with SE block (94.94%). These results demonstrate superior capability in capturing both local textures and global morphological features. The proposed solution not only advances the state of the art in plant classification but also contributes a valuable dataset to the research community. Its real-world applicability spans field-based plant identification, biodiversity conservation, and precision agriculture, offering a scalable tool for automated plant recognition in complex ecological and agricultural environments. Full article

The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10⁻⁹ m²/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R², RMSE, and MAE. The RF model demonstrated the best performance, with an R² of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R² of 0.925, and XGBoost achieved an R² of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO₂ diffusion in brine, providing a robust, data-driven framework for optimizing CO₂-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications. Full article

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (initial support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

Innovation is a strategic driver of sustainable competitive advantage and long-term economic growth. This study proposes an empirical framework to support the sustained development of national innovation capacity by examining key enabling factors. Drawing on an extensive review of the literature, the research investigates the interrelationships among governmental support (GS), innovation agents (IA), university–industry R&D collaborations (UIRD), and innovation cluster development (ICD), and their influence on two critical innovation outcomes, knowledge creation (KC) and knowledge diffusion (KD). Using panel data from G7 countries spanning 2008 to 2018, sourced from international organizations such as the World Bank, the World Intellectual Property Organization, and the World Economic Forum, the study applies regression analysis to test the proposed conceptual model. Results highlight the foundational role of GS in providing a balanced framework to foster collaborative networks among IA and enhancing the effectiveness of UIRD. Furthermore, IA emerges as a pivotal actor in advancing innovation efforts, while the development of innovation clusters is shown to selectively enhance specific innovation outcomes. These findings offer theoretical and practical contributions for policymakers, researchers, and stakeholders aiming to design supportive ecosystems that strengthen sustainable national innovation capacity. Full article

Co-infections pose significant challenges not only clinically, but also in terms of simultaneous diagnoses. The development of sensitive, multiplexed analytical platforms is critical for accurately detecting viral co-infections, particularly in complex biological environments. In this study, we present a mass spectrometry (MS)-based detection strategy employing a target-triggered hybridization chain reaction (HCR) to amplify signals and in situ photocleavable mass tags (PMTs) for the simultaneous detection of multiple targets. Hairpin DNAs modified with PMTs and immobilized loop structures on magnetic particles (Loop@MPs) were engineered for each target, and their hybridization and amplification efficiency was validated using native polyacrylamide gel electrophoresis (PAGE) and laser desorption/ionization MS (LDI-MS), with silica@gold core–shell hybrid (SiAu) nanoparticles being employed as an internal standard to ensure quantitative reliability. The system exhibited excellent sensitivity, with a detection limit of 415.12 amol for the hepatitis B virus (HBV) target and a dynamic range spanning from 1 fmol to 100 pmol. Quantitative analysis in fetal bovine serum confirmed high accuracy and precision, even under low-abundance conditions. Moreover, the system successfully and simultaneously detected multiple targets, i.e., HBV, human immunodeficiency virus (HIV), and hepatitis C virus (HCV), mixed in various ratios, demonstrating clear PMT signals for each. These findings establish our approach as a robust and reliable platform for ultrasensitive multiplexed detection, with strong potential for clinical and biomedical research. Full article

The COVID-19 pandemic has highlighted critical vulnerabilities in urban logistics systems, particularly in last-mile delivery. To enhance logistics resilience and efficiency, the Korean government has initiated an innovative project that repurposes idle spaces in subway vehicle bases within the Seoul Metropolitan Area into logistics centers. This study proposes a comprehensive multi-criteria evaluation framework combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the suitability of ten candidate sites. The evaluation criteria span four dimensions, facility, geographical, environmental, and social factors, derived from the literature and expert consultations. AHP results indicate that geographical factors, especially proximity to urban centers and major logistics facilities, hold the highest weight. Based on the integrated analysis using TOPSIS, the most suitable locations identified are Sinnae, Godeok, and Cheonwang. The findings suggest the strategic importance of aligning infrastructure development with spatial accessibility and stakeholder cooperation. Policy implications include the need for targeted investment, public–private collaboration, and sustainable logistics planning. Future research is encouraged to incorporate dynamic data and consider social equity and environmental impact for long-term urban logistics planning. Full article

Background/Objectives: Syphilis, a re-emerging global public health issue, has shown increasing incidence over the past decade, particularly among key populations such as men who have sex with men (MSM), people living with HIV, and pregnant women. This narrative review aimed to synthesize global epidemiological trends of syphilis from 2015 to 2025, with a focus on surveillance gaps, regional disparities, and structural determinants. Methods: A broad narrative approach was used to collect and analyze epidemiological data from 2015 to 2025. The literature was retrieved from databases (PubMed, Scopus) and official reports from the WHO, CDC, and ECDC. Included materials span observational studies, surveillance reports, and modeling data relevant to global trends and public health responses. Results: Globally, syphilis incidence has increased, with notable surges in North America, Europe, and Asia. MSM remain disproportionately affected, while congenital syphilis is resurging even in high-income countries. Low- and middle-income countries report persistent burdens, especially among women of reproductive age, often exacerbated by limited screening and surveillance infrastructure. The COVID-19 pandemic disrupted syphilis-related services and further exacerbated underreporting, hindering timely detection and response efforts. Surveillance systems vary widely in their completeness and quality, which significantly hinders global data comparability and coordinated public health responses. Conclusions: Despite its curability, syphilis continues to spread due to fragmented prevention strategies, inequities in access to care, and insufficient surveillance. Strengthening diagnostic access, integrating prevention efforts into broader health systems, and addressing social determinants are essential. Improved surveillance, equitable access, and innovation—including diagnostics and potential vaccine research—are critical to controlling the global syphilis epidemic. Full article

Cable force distribution in cable-stayed bridges critically impacts structural safety and efficiency, yet traditional optimization methods struggle with unconventional designs due to nonlinear mechanics and computational inefficiency. This study proposes a hybrid approach combining Response Surface Methodology (RSM) and Multi-Objective Particle Swarm Optimization (MOPSO) to overcome these challenges. RSM constructs surrogate models for strain energy and mid-span displacement, reducing reliance on finite element analysis, while MOPSO optimizes Pareto solution sets for rapid cable force adjustment. Validated through an engineering case, the method reduces the main girder’s max bending moment by 8.7%, mid-span displacement by 31.2%, and strain energy by 7.1%, improving stiffness and mitigating stress concentrations. The response surface model demonstrates prediction errors of 0.35% for strain energy and 5.1% for maximum vertical mid-span deflection. By synergizing explicit modeling with intelligent algorithms, this methodology effectively resolves the longstanding efficiency–accuracy trade-off in cable force optimization for cable-stayed bridges. It achieves over 80% reduction in computational costs while enhancing critical structural performance metrics. Engineers are thereby equipped with a rapid and reliable optimization framework for geometrically complex cable-stayed bridges, delivering significant improvements in structural safety and construction feasibility. Ultimately, this approach establishes both theoretical substantiation and practical engineering benchmarks for designing non-conventional cable-stayed bridge configurations. Full article

Span-wise homogeneous supersonic cavity flows display complicated structures due to shear layer breakdown, flow acoustic resonance, and even non-linear hydrodynamic-acoustic interactions. In practical applications, such as aircraft bays, the cavity is of finite width and has doors, both of which introduce distinctive phenomena that couple with the shear layer at the cavity lip, further modulating shear layer bifurcations and tonal mechanisms. In particular, asymmetric states manifest as ‘tornado’ vortices with significant practical consequences on the design and operation. Both inward- and outward-facing leading-wedge doors, resulting in leading edge shocks directed into and away from the cavity, are examined at select opening angles ranging from $22.5°$ to $90°$ (fully open) at Mach 1.6. The computational approach utilizes the Reynolds-Averaged Navier–Stokes equations with a one-equation model and is augmented by experimental observations of cavity floor pressure and surface oil-flow patterns. For the no-doors configuration, the asymmetric results are consistent with a long-time series DDES simulation, previously validated with two experimental databases. When fully open, outer wedge doors (OWD) yield an asymmetric flow, while inner wedge doors (IWD) display only mildly asymmetric behavior. At lower door angles (partially closed cavity), both types of doors display a successive bifurcation of the shear layer, ultimately resulting in a symmetric flow. IWD tend to promote symmetry for all angles observed, with the shear layer experiencing a pitchfork bifurcation at the ‘critical angle’ ($67.5°$). This is also true for the OWD at the ‘critical angle’ ($45°$), though an entirely different symmetric flow field is established. The first observation of pitchfork bifurcations (‘critical angle’) for the IWD is at $67.5°$ and for the OWD, $45°$, complementing experimental observations. The back wall signature of the bifurcated shear layer (impingement preference) was found to be indicative of the 3D cavity dynamics and may be used to establish a correspondence between 3D cavity dynamics and the shear layer. Below the critical angle, the symmetric flow field is comprised of counter-rotating vortex pairs at the front and back wall corners. The existence of a critical angle and the process of door opening versus closing indicate the possibility of hysteresis, a preliminary discussion of which is presented. Full article

This study investigates the impact of rural digital economy development on agricultural carbon emission efficiency, aiming to elucidate the intrinsic mechanisms and pathways through which digital technology enables low-carbon transformation in agriculture, thereby contributing to the achievement of agricultural carbon neutrality goals. Based on provincial-level panel data from China spanning 2011 to 2022, this study examines the relationship between the rural digital economy and agricultural carbon emission efficiency, along with its underlying mechanisms, using bidirectional fixed effects models, mediation effect analysis, and Spatial Durbin Models. The results indicate the following: (1) A significant N-shaped-curve relationship exists between rural digital economy development and agricultural carbon emission efficiency. Specifically, agricultural carbon emission efficiency exhibits a three-phase trajectory of “increase, decrease, and renewed increase” as the rural digital economy advances, ultimately driving a sustained improvement in efficiency. (2) Industrial integration acts as a critical mediating mechanism. Rural digital economy development accelerates the formation of the N-shaped curve by promoting the integration between agriculture and other sectors. (3) Spatial spillover effects significantly influence agricultural carbon emission efficiency. Due to geographical proximity, regional diffusion, learning, and demonstration effects, local agricultural carbon emission efficiency fluctuates with changes in neighboring regions’ digital economy development levels. (4) The relationship between rural digital economy development and agricultural carbon emission efficiency exhibits a significant inverted N-shaped pattern in regions with higher marketization levels, planting-dominated areas of southeast China, and digital economy demonstration zones. Further analysis reveals that within rural digital economy development, production digitalization and circulation digitalization demonstrate a more pronounced inverted N-shaped relationship with agricultural carbon emission efficiency. This study proposes strategic recommendations to maximize the positive impact of the rural digital economy on agricultural carbon emission efficiency, unlock its spatially differentiated contribution potential, identify and leverage inflection points of the N-shaped relationship between digital economy development and emission efficiency, and implement tailored policy portfolios—ultimately facilitating agriculture’s green and low-carbon transition. Full article

For high-growth firms, designing and implementing strategies to ensure the long-term sustainability of business models is a key priority. Although these strategies are carefully planned to achieve specific outcomes, these firms also encounter contextual factors inherent to entrepreneurship, as well as the potential negative consequences of operating as small- and medium-sized enterprises (SMEs). Consequently, they adapt emergent outcomes to secure positive scaling-up processes. A comprehensive analysis of 69 studies from 1978 to 2023 revealed that 34.8% used sales as the main indicator of high-growth outcomes, 18.8% considered employment to be the most important outcome, and 37.7% incorporated both. The assessment period for these studies spanned three to seven consecutive years. A subsequent review of the existing literature yielded 56 potential new outcomes, emphasising the existence of a diverse array of concepts and metrics with which to assess high-growth performance. The study confirmed sales and positive profits arising during the planning process as strategic outcomes. However, it was also demonstrated that geographical expansion and innovation become emergent outcomes in critical situations. The research also identified that external factors, including an adverse public environment, business context difficulties, and a favourable business environment, may influence the effect of the firm’s high growth. Full article

Children and adolescents with type 1 diabetes (T1D) often experience abnormalities in bone health. Studies have consistently demonstrated that youth with T1D have lower bone mineral density (BMD) compared to their healthy peers. Additionally, children with T1D show impaired bone microarchitecture and reduced bone turnover. These factors collectively contribute to an increased risk of fractures across the life span of this population. To optimize bone accrual and reduce fracture risk, several strategies can be employed during childhood and adolescence. First, maintaining good glycemic control is critical, as poor glycemic control has been associated with lower BMD and an increased risk of fractures. Second, specific nutritional recommendations can help improve bone health, including a balanced diet, adequate calcium and vitamin D intake, and careful monitoring of both macronutrient and micronutrient intake. Third, regular physical activity plays a vital role. A systematic review and meta-analysis have shown that youth with T1D are generally less physically active, more sedentary, and have lower cardiorespiratory fitness levels than their non-diabetic peers. This review emphasizes targeted strategies aimed at optimizing skeletal health in the pediatric population with T1D, with a particular focus on the critical roles of glycemic control, nutritional adequacy, and regular physical activity. These modifiable factors may contribute to the reduction of fracture risk across the life span in individuals with T1D. Full article

Brain tumours challenge the structural and functional integrity of the brain, yet remarkable neuroplastic adaptations often preserve critical functions. This review synthesises the current knowledge of the molecular events underlying neuroplasticity in the context of tumoural growth, spanning from early genetic and protein alterations to macroscopic functional reorganisation. We discuss the roles of stress-regulated molecules, synaptic proteins, trophic factors, and morphological changes in driving adaptive responses. Furthermore, we bridge the gap between microscopic molecular events and large-scale network adaptations, emphasising clinical implications for glioma surgery and patient outcomes. Despite advances, knowledge gaps persist regarding the dynamics, predictors, and therapeutic modulation of plasticity, underscoring the need for future longitudinal and translational research. Understanding and leveraging these molecular mechanisms holds promise for improving functional recovery and quality of life in patients with brain tumours. Full article

We examined the associations between individual differences in intensive longitudinal data-derived affective dynamics (i.e., positive and negative affect variability and inertia and positive affect–negative affect bipolarity) and concurrent stress, drinking levels, and affect-regulation drinking motives across three time points spanning early adulthood. This allowed us to evaluate the stability of the affective dynamics and whether their associations with alcohol outcomes varied across this critical developmental period. Moderate-to-heavy college drinkers (N = 1139, 51% women) reported on their affective states, stress, drinking levels, and drinking motives daily for 30 days using a web-based daily diary in three assessment waves: during college and at two post-college waves, approximately 5 and 10 years after the initial assessment. Findings indicated moderate stability of the affect dynamic indicators, except for inertia. Negative affect variability showed the strongest positive association with mean daily stress. Individuals who demonstrated stronger affect bipolarity had lower drinking levels and higher enhancement motivation. None of the other dynamic indicators were consistently related to the drinking outcomes in the predicted direction after controlling for mean affect levels, and we found little evidence for changes in these effects across time. Our results add to the inconsistent literature regarding the associations between affective dynamics and alcohol-related outcomes. Full article