Search Results (181)

Objective: Vitamin D deficiency (VDD) is common in pregnancy and may affect lipid metabolism. The underlying mechanisms are multifactorial, but most evidence so far comes from non-pregnant populations. This study aims to identify metabolites and metabolic patterns associated with VDD in early pregnancy and to evaluate their relationships with maternal lipid profiles. Methods: A nested case–control research was carried out in the Zhoushan Pregnant Women Cohort (ZPWC). Cases were defined as women with VDD (25(OH)D < 20 ng/mL), and controls (≥20 ng/mL) were matched 1:1 using propensity scores based on age, pre-pregnancy BMI, gestational week, and calendar year at blood sampling. The untargeted metabolomics of first-trimester maternal plasma were measured. Metabolic profiles were analyzed using partial least squares-discriminant analysis (PLS-DA). Principal component analysis (PCA) was applied to visualize group separation, and metabolite set enrichment analysis (MSEA) was performed to reveal biologically relevant metabolic patterns. Associations between VDD-related metabolite components in early pregnancy and lipid levels in mid-pregnancy were assessed using linear regression models. Results: 44 cases and 44 controls were selected for the study. There were 60 metabolites identified as being connected to VDD. Among these, 26 metabolites, primarily glycerophospholipids and fatty acyls, exhibited decreased levels in the VDD group. In contrast, 34 metabolites showed increased levels, mainly comprising benzene derivatives, carboxylic acids, and organooxygen compounds. PCA based on these metabolites explained 52.8% of the total variance (R²X = 0.528) across the first six principal components (PC1: 16.4%, PC2: 10.6%, PC3: 9.2%, PC4: 6.3%, PC5: 5.7%, PC6: 4.6%). PC2, dominated by lineolic acids and derivatives, was negatively associated with total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) (all p < 0.01). PC3, dominated by glycerophosphocholines, was negatively associated with TC, TG, and high-density lipoprotein cholesterol (HDL-C) (all p < 0.05). MSEA revealed significant enrichment of the pantothenate and CoA biosynthesis pathway after multiple testing correction (FDR < 0.05). Conclusions: This study reveals distinct metabolic alterations linked to VDD and suggests potential mechanisms underlying its association with maternal lipid metabolism in early pregnancy. Full article

Wireless sensor networks (WSNs) are key enablers of efficient communication in the Internet of Things (IoT) ecosystem. These networks comprise numerous sensor nodes that collaboratively collect and transmit data, requiring adaptive and energy-efficient management. However, high node density and resource limitations introduce challenges such as control overhead, packet collisions, interference, and energy inefficiency. To mitigate these issues, this paper adopts the Hybrid Wireless Mesh Protocol (HWMP), standardized under IEEE 802.11s for wireless mesh networks (WMNs), as the routing protocol in WSNs. HWMP’s hybrid design combining reactive and proactive routing is well-suited for dynamic and mobile environments, making it applicable to WSNs operating under similar conditions. Building on this foundation, we propose a community-aware topology control mechanism that constructs a Connected Dominating Set (CDS) to serve as the network’s energy-efficient backbone. Node selection is guided by centrality metrics and detected community structures to enhance routing efficiency and network longevity. The mechanism is evaluated across six mobility scenarios characterized by realistic movement patterns. Comparative results show that incorporating community structure significantly improves routing performance and reduces energy consumption, validating the approach’s effectiveness in real-world WSN deployments. Full article

This study quantified the Habitat Connectivity Index (DCI) of cascade dams in the mainstream of the Dongjiang River, revealing the non-linear relationship between dam passability (p) and connectivity restoration. Results showed that DCI increased slowly when p < 0.6 (with the magnitude of increase not exceeding 10.41), whereas an exponential response emerged when p > 0.8 (specifically, DCI rose by 16.53 as p increased from 0.8 to 0.9). Time-series analysis indicated that the number of dams increased from 3 to 16 between 1970 and 2020, which plunged the natural-state DCI (set at 100) to 9.01 (representing a 90.99% decrease); notably, 78.14% of the total connectivity loss occurred during the 2000–2010 period. Spatial heterogeneity analysis demonstrated that enhancing the passability of Jiantan Dam increased DCI by 4.68 (under the baseline condition of p = 0.8), whereas the same intervention on Sulei Dam only led to a 0.58 increase in DCI. This finding highlights the importance of key nodes for connectivity restoration and provides a scientific basis for prioritizing the enhancement of connectivity at such nodes in subsequent ecological governance. A 2024 fish community survey found that 84.2% of the recorded species were native (64 out of 76), while only 18.8% of the total individuals (617 individuals) were migratory; the dominant species were identified as generalist residents, including Oreochromis zillii, Cirrhinus molitorella, and Hemiculter leucisculus. This study identifies 0.8 as a critical threshold for connectivity restoration and provides a spatial decision-making framework for prioritizing the restoration of key dams. Full article

Background: The global rise in the aging population presents significant challenges to healthcare systems, especially with increasing rates of chronic illnesses, mental health issues, and functional decline among older adults. In response, holistic and tech-driven approaches, such as telehealth and remote virtual interactive agents (VIAs), are potential emerging solutions to support the physical, cognitive, and emotional well-being of older adults. VIAs are multimodal digital tools that provide interactive and immersive experiences to users. Despite its promise, gaps still exist in the insights that explore ways of delivering geriatric healthcare remotely. Objective: This systematic review examines the existing literature on remote virtual interventions for older adults, focusing on bibliometrics, study purposes, outcomes, and network analysis of studies extracted from major databases using selected keywords and managed using the Covidence application. Methods and Results: Following five stages, namely, problem identification, a literature search, data evaluation, data analysis, and presentation, the review found that the studies on remote VIAs for older adults (2013–2025) were mostly from a positivist perspective, multi-authored, and U.S.-led, mainly showing positive outcomes for most studies (n = 13/15) conducted in home settings with healthy older participants. The dominance of positivist, US-led studies reflect an epistemological stance that emphasizes objectivity, quantification, and generalizability. VIAs, often pre-programmed and internet-based, supported health promotion and utilized visual humanoid avatars on personal devices. Keyword and network analysis additionally revealed four themes resulting from the review: Health and Clinical, Holistic and Cognitive, Home and Caring, and Hybrid and Connection. Conclusions: The review provides innovative insights and illustrations that may serve as a foundation for future research on VIAs and remote healthcare delivery for older adults. Full article

Most astrophysical systems (except for very compact objects such as, e.g., black holes and neutron stars) in our Universe are characterized by shallow gravitational potentials, with dimensionless compactness $|\Phi |\equiv r_s/R\ll 1$, where $r_s$ and R are their Schwarzschild radius and typical size, respectively. While the existence and characteristic scales of such virialized systems depend on gravity, we demonstrate that the value of $|\Phi |$—and thus the non-relativistic nature of most astrophysical objects—arises from microphysical parameters, specifically the fine structure constant and the electron-to-proton mass ratio, and is fundamentally independent of the gravitational constant, G. In fact, the (generally extensive) gravitational potential becomes ‘locally’ intensive at the system boundary; the compactness parameter corresponds to the binding energy (or degeneracy energy, in the case of quantum degeneracy pressure-supported systems) per proton, representing the amount of work that needs to be done in order to allow proton extraction from the system. More generally, extensive properties of gravitating systems depend on G, whereas intensive properties do not. It then follows that peak rms values of large-scale astrophysical velocities and escape velocities associated with naturally formed astrophysical systems are determined by electromagnetic and atomic physics, not by gravitation, and that the compactness, $|\Phi |$, is always set by microphysical scales—even for the most compact objects, such as neutron stars, where $|\Phi |$ is determined by quantities like the pion-to-proton mass ratio. This observation, largely overlooked in the literature, explains why the Universe is not dominated by relativistic, compact objects and connects the relatively low entropy of the observable Universe to underlying basic microphysics. Our results emphasize the central but underappreciated role played by dimensionless microphysical constants in shaping the macroscopic gravitational landscape of the Universe. In particular, we clarify that this independence of the compactness, $|\Phi |$, from G applies specifically to entire, virialized, or degeneracy pressure-supported systems, naturally formed astrophysical systems—such as stars, galaxies, and planets—that have reached equilibrium between self-gravity and microphysical processes. In contrast, arbitrary subsystems (e.g., a piece cut from a planet) do not exhibit this property; well within/outside the gravitating object, the rms velocity is suppressed and G reappears. Finally, we point out that a clear distinction between intensive and extensive astrophysical/cosmological properties could potentially shed new light on the mass hierarchy and the cosmological constant problems; both may be related to the large complexity of our Universe. Full article

Simple Baseline has become a dominant benchmark in human pose estimation (HPE) due to its excellent performance and simple design. However, its “strong encoder + simple decoder” architectural paradigm suffers from two core limitations: (1) its non-branching, linear deconvolutional path prevents it from leveraging the rich, fine-grained features generated by the encoder at multiple scales and (2) the model lacks explicit prior knowledge of both the absolute positions and structural layout of human keypoints. To address these issues, this paper introduces AFJ-PoseNet, a new architecture that deeply enhances the Simple Baseline framework. First, we restructure Simple Baseline’s original linear decoder into a U-Net-like multi-scale fusion path, introducing intermediate features from the encoder via skip connections. For efficient fusion, we design a novel Attention Fusion Module (AFM), which dynamically gates the flow of incoming detailed features through a context-aware spatial attention mechanism. Second, we propose the Joint-Aware Positional Encoding (JAPE) module, which innovatively combines a fixed global coordinate system with learnable, joint-specific spatial priors. This design injects both absolute position awareness and statistical priors of the human body structure. Our ablation studies on the MPII dataset validate the effectiveness of each proposed enhancement, with our full model achieving a mean PCKh of 88.915, a 0.341 percentage point improvement over our re-implemented baseline. On the more challenging COCO val2017 dataset, our ResNet-50-based AFJ-PoseNet achieves an Average Precision (AP) of 72.6%. While this involves a slight trade-off in Average Recall for higher precision, this result represents a significant 2.2 percentage point improvement over our re-implemented baseline (70.4%) and also outperforms other strong, publicly available models like DARK (72.4%) and SimCC (72.1%) under comparable settings, demonstrating the superiority and competitiveness of our proposed enhancements. Full article

Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of predicting extreme flash floods of a runoff-dominated hydrological system. A fully connected multilayer perceptron with a shallow structure was used to reduce complexity and limit overfitting. The inputs of the ANN were determined by rainfall–water level cross-correlation analysis. For each optimizer, the hyperparameters of the ANN were selected using a grid search and the cross-validation score on a novel criterion (PERS PEAK) mixing the persistency (PERS) and the quality of flood-peak restitution (PEAK). For an extreme and unseen event used as a test set, LM outperformed AD by 25% on all performance criteria. The peak water level of this event, 66% greater than that of the training set, was predicted by 92% after more training iterations were done by the LM optimizer. This shows that the ANN can predict beyond the ranges of the training set, given the right optimizer. Nevertheless, the LM training time was up to five times longer than that of AD during grid search. Full article

The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality. Full article

This study examines the evolution of public debt among the 27 EU member states using Graph Theory tools; the Threshold Weighted–Minimum Dominating Set (TW–MDS) and the k-core decomposition method, alongside a standard network quantitative metric, the density. By separating the data into three distinct periods, pre-crisis (2000–2007), European sovereign debt crisis (2008–2015), and post-crisis (2016–2023), we examine the potential synchronization of the debt ratios among EU countries through cross-correlations of the public debts. The findings reveal that public debt correlation was at its highest level during the 2008–2015 period, reflecting the universal impact of the crisis and the subsequent synchronized fiscal and monetary policy measures taken within EU. A significantly lower network density is observed in both the pre- and post-crisis periods. These results contribute to the overall debate on fiscal stability and policy coordination by showing how EU countries tend to align their fiscal behaviors during periods of crisis while behaving more independently during stable times. In addition, we yield a deeper insight into how economic shocks reorganize public debt interconnections within the crisis period. Finally, this analysis highlights to what extent European economic integration strengthens connections between the fiscal positions (through public debt) of the European Union member countries. Full article

This study systematically investigates the pore–fracture architecture of deep coal seams in the JiaTan (JT) block of the Ordos Basin using an integrated suite of advanced techniques, including nuclear magnetic resonance (NMR), high-pressure mercury intrusion, low-temperature nitrogen adsorption, low-pressure carbon dioxide adsorption, and micro-computed tomography (micro-CT). These complementary methods enable a quantitative assessment of pore structures spanning nano- to microscale dimensions. The results reveal a pore system overwhelmingly dominated by micropores—accounting for more than 98% of the total pore volume—which play a central role in coalbed methane (CBM) storage. Microfractures, although limited in volumetric proportion, markedly enhance permeability by forming critical flow pathways. Together, these features establish a dual-porosity system that governs methane transport and recovery in deep coal reservoirs. The multiscale characterization employed here proves essential for resolving reservoir heterogeneity and designing effective stimulation strategies. Notably, enhancing methane desorption in micropore-rich matrices and improving fracture connectivity are identified as key levers for optimizing deep CBM extraction. These insights offer a valuable foundation for the development of deep coalbed methane (DCBM) resources in the Ordos Basin and similar geological settings. Full article

This study evaluated the chemical composition and subcutaneous tissue biocompatibility of C-Point, a root canal filling material, compared to ProTaper gutta-percha cones (control). Material characterization was conducted using scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS). For biocompatibility assessment, both materials were implanted subcutaneously in the dorsal connective tissue of sixteen albino rats (n = 8 per group). Histological evaluation of inflammatory infiltrate intensity was performed at 30 and 60 days post-implantation, with statistical analysis (significance set at p < 0.05). SEM-EDS analysis revealed distinct elemental compositions: C-Point primarily contained zirconium and cobalt ions, while gutta-percha cones demonstrated a strong zinc signature with trace amounts of barium, aluminum, and sulfur. Both materials exhibited similar particulate morphology with radiopaque inclusions. Histologically, no significant difference in inflammatory response was observed between C-Point and gutta-percha at any time point (p > 0.05). All specimens developed a fibrous encapsulation. The inflammatory profile showed temporal dynamics, with lymphocyte predominance during early stages that progressively diminished by the study endpoint. These findings demonstrate that while C-Point possesses a unique elemental profile dominated by zirconium, its tissue biocompatibility parallels that of conventional gutta-percha obturation materials. However, due to the absence of mechanical testing and the limited in vivo follow-up period, the long-term stability of the material remains uncertain. Full article

Land use and land cover change (LULCC) can influence giant panda distributions by altering landscape structure and configuration. However, the spatial impacts and potential time lag effects of landscape pattern changes on giant pandas remain underexplored. In this study, we applied a random forest classification method to analyze LULCC in 1990, 2000, and 2010, alongside calculating a set of landscape metrics to assess changes in landscape fragmentation, connectivity, and diversity. Random forest regression models were then used to evaluate the spatial relationships between landscape metrics and giant panda density, with the aim of identifying whether a time lag effect exists. The results revealed the following: (1) The random forest classification achieved high land use classification accuracy. Forests remained the dominant land cover, occupying approximately 97% of the study area throughout the period, with only minor fluctuations observed among other land use types. (2) Landscape metrics indicated increasing landscape fragmentation, connectivity, and diversity. While increased landscape fragmentation can negatively impact giant panda habitat, improvements in landscape connectivity and diversity could mitigate these effects by preserving movement corridors and enhancing habitat accessibility. (3) The strongest correlations between giant panda density and landscape metrics were observed when the time points aligned. Landscape metrics from 2010 showed the highest correlation with the 4th NGPS (around 2010), and landscape metrics from 2000 had the highest correlation with the 3rd NGPS (around 2000). The results revealed that giant panda density responded most strongly to contemporary landscape pattern changes, suggesting an immediate response. However, correlations with earlier landscape metrics also suggest that a relatively weak time lag effect may be present. All landscape metrics were derived from remote sensing data, enabling scalable and repeatable GIS-based analysis. These findings highlight the utility of spatial landscape indicators for monitoring species distribution patterns and underscore the importance of maintaining and enhancing habitat connectivity within giant panda conservation efforts. Full article

The concept of domination is introduced within the context of signed fuzzy graphs (signed-FGs), along with examples, as a novel metric to evaluate graph stability under varying conditions. This metric particularly focuses on dominant sets and integrity measures, providing a well-rounded approach to assessing the structural stability of signed- FGs. The necessity of fulfilling the domination integrity condition in evaluating the performance of signed-FGs is highlighted through a discussion on its formulation and an analysis of its upper and lower bounds. An algorithm for identifying strong arcs and classifying them is presented, along with an algorithm for identifying signed fuzzy trees. Furthermore, the role of symmetry in signed-FGs is explored, revealing that symmetrical structures often correspond to higher domination integrity, thus contributing to the improved stability and predictability of the graphs. The paper also establishes important connections with classical graph varieties, such as complete graphs and their variations, demonstrating that changes in domination integrity increase with certain parameters. Additionally, real-life scenarios where these concepts are applicable serve to complement the theoretical results. The case study findings illustrate the significance of domination integrity in practical contexts by emphasizing various instances where it can be determined and utilized. Such instances include identifying independent dominant sets in path and cycle diagrams, as well as estimating the lower bounds of domination integrity in these structures. The estimation of domination integrity using block graph methods is underscored as crucial for enhancing the efficiency of signed-FG applications. Full article

Let $\Lambda$ denote a commutative ring with unity and $\mathfrak{D}\left(\Lambda \right)$ denote a collection of all annihilating ideals from $\Lambda$. An annihilator intersection graph of $\Lambda$ is represented by the notation $\mathcal{AIG}\left(\Lambda \right)$. This graph is not directed in nature, where the vertex set is represented by $\mathfrak{D}{\left(\Lambda \right)}^{*}$. There is a connection in the form of an edge between two distinct vertices $\varsigma$ and $\varrho$ in $\mathcal{AIG}\left(\Lambda \right)$ iff $Ann\left(\varsigma \varrho \right)\ne Ann\left(\varsigma \right)\cap Ann\left(\varrho \right)$. In this work, we begin by categorizing commutative rings $\Lambda$, which are finite in structure, so that $\mathcal{AIG}\left(\Lambda \right)$ forms a star graph/2-outerplanar graph, and we identify the inner vertex number of $\mathcal{AIG}\left(\Lambda \right)$. In addition, a classification of the finite rings where the genus of $\mathcal{AIG}\left(\Lambda \right)$ is 2, meaning $\mathcal{AIG}\left(\Lambda \right)$ is a double-toroidal graph, is also investigated. Further, we determine $\Lambda$, having a crosscap 1 of $\mathcal{AIG}\left(\Lambda \right),$ indicating that $\mathcal{AIG}\left(\Lambda \right)$ is a projective plane. Finally, we examine the domination number for the annihilator intersection graph and demonstrate that it is at maximum, two. Full article

Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study area under risk of land cover transition in northern Ontario, Canada. To enhance the prediction of soil modeling, integrated approaches combining encoder-decoder (ED) with dense neural network (DNN) and convolutional neural network (CNN) formulations suitable for smaller target data sets were developed. These methods were able to effectively extract dominant features within predictor data and augment modeling accuracy. The obtained results were compared with those attained from structural equation modeling (SEM) and random forest (RF), as well as basic DNN and CNN models. A model ensemble based on all approaches was also considered, from which standard deviations were calculated to gauge the prediction uncertainty. Quantile mappings with respect to deciles were also derived from the model ensemble to provide additional insights with prediction. Validation accuracies for the ED-CNN model attained a coefficient of determination (R²) of 0.59. The greatest deviations with predicting C contents corresponded to the wetlands. However, when quantified by decile mapping, forested localities within river valleys encountered the highest uncertainties with prediction, indicting a need for better modeling of sites with intermediate concentrations of soil C. Full article