Search Results (739)

This study investigates weekend price gaps in three major stock market indices—the Dow Jones Industrial Average (DJIA), NASDAQ, and Germany’s DAX—from 2013 to 2023, using high-frequency (5 min) data to explore whether gap movements arise from random volatility or reflect systematic market tendencies. We examine 205 weekend gaps in the DJIA, 270 in NASDAQ, and 406 in the DAX. Two principal hypotheses guide our inquiry as follows: (i) whether price movements into the gap are primarily driven by increased volatility and (ii) whether larger gaps are associated with heightened volatility. Employing Chi-square tests for the independence and linear regression analyses, our results show no strong, universal bias towards closing gaps at shorter distances across all three indices. However, at medium-to-large distances, significant directional patterns emerge, particularly in the DAX. This outcome challenges the assumption that weekend gaps necessarily “fill” soon after they open. Moreover, larger gap sizes correlate with elevated volatility in both the DJIA and NASDAQ, underscoring that gaps can serve as leading indicators of near-term price fluctuations. These findings suggest that gap-based anomalies vary by market structure and geography, raising critical questions about the universality of efficient market principles and offering practical insights for risk management and gap-oriented trading strategies. Full article

The spatial distribution of vegetation across metropolitan areas is important for wildlife habitat, air quality, heat mitigation, recreation, and other ecosystem services. This study investigated relationships between vegetation patterns and parcel characteristics at multiple scales of the Austin Metropolitan Statistical Area (MSA), a rapidly growing region in central Texas characterized by diverse biophysical and socioeconomic landscapes. We used LiDAR data to map vegetation types and distributions across a 6000 km² study area. Principal component analysis (PCA) and regression models were employed to explore tree, shrub, and grass cover across parcels, cities, and the MSA, considering home value, age, size, and distance to the city center. At the MSA scale, tree and shrub cover were higher in the Edwards Plateau than in the Blackland Prairie ecoregion. Tree cover increased with parcel size and home value, especially in suburban areas. Older parcels had more mature trees, though less so in the grass-dominated Blackland Prairie. Shrub cover was higher on larger parcels in the Edwards Plateau, while the Blackland Prairie showed the opposite trend. PCA explained 60% of the variance, highlighting links between vegetation and urban development. Our findings reveal how biophysical and socioeconomic factors interact to shape vegetation, offering considerations for land use, housing, and green infrastructure planning. Full article

The archaeological materials from the Velika Humska Čuka site on the northern fringe of the Niš Basin in southeastern Serbia were analyzed to reveal the provenance of ceramics and other artifacts. This study focused on the elemental analysis of 61 samples, including local clay pits, potsherds, and whole vessels. Samples were chosen based on stylistic and typological characteristics to distinguish local and “foreign” pottery. Elemental analysis was conducted using energy-dispersive X-ray fluorescence (EDXRF) spectrometry, complemented by principal component analysis (PCA) for data interpretation. Results indicated that the majority of pottery samples, over 80%, were produced using local clay from deposits near the site. However, approximately 20% of the analyzed vessels were made using clay from deposits near the Bubanj site, 8 km south of Velika Humska Čuka. A vessel on a hollow high foot combining stylistic elements of the Bubanj-Hum I group and Early Eneolithic Pannonian groups was made of clay not sourced from any identified local deposits, suggesting its non-local origin. While the predominance of local materials suggests self-sufficient production, the use of non-local clays and stylistic influences highlights long-distance connections and exchanges. The study emphasizes the importance of Velika Humska Čuka in understanding the development of ceramic traditions and the cultural dynamics of the Early Eneolithic in the Central Balkans. Full article

Litsea species have been used for herbal medicine by many ethnic groups. However, defining the morphological characteristics of the species remains difficult, leading to confusion and misuse of Litsea names. We examined Litsea classification, focusing on folk taxonomy. A field survey revealed that Litsea cubeba, L. elliptica, L. mollis, L. glutinosa, and L. martabanica have the highest use values. Using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) analysis and multivariate statistical methods, we examined metabolites from these species to assess consistency across plant parts. Principal coordinate analysis (PCoA) and cluster analysis revealed distinct metabolite patterns, grouping species into four significant clusters: Group I (L. elliptica and L. martabanica), Group II (L. martabanica roots), Group III (L. cubeba and L. mollis bark and roots), and Group IV (L. glutinosa and L. cubeba and L. mollis leaves). Chemical compounds are clustered by species rather than by plant parts. Our study reveals a significant correlation (p < 0.05) between phylogenetic distances and chemical differences among Litsea species, elucidating the evolutionary links through metabolite variations. This predictive approach could help with more efficient selection for traditional medicine discovery and should be the first to be pharmacologically tested for drug development. Full article

This study assessed the impact of planting techniques and short-term organic additions on soil quality, enzyme activity, and bacterial community composition. Biochar (BC) amendment substantially enhanced the ACE, Chao 1, and Shannon indices in direct-seeded rice (DS). Principal coordinate analysis (PCoA) and dissimilarity distances confirmed significant differences in the rhizosphere bacterial community composition associated with planting methods and organic applications. At the phylum level, transplanting (TT) significantly increased the abundance of Proteobacteria, Planctomycetes, Bacteroidetes, Firmicutes, and Verrucomicrobia, whereas DS significantly reduced the abundance of Acidobacteria, Chloroflexi, Actinobacteria, Gemmatimonadetes, and WPS-2. Rice straw (RS) application was associated with increased Proteobacteria, Acidobacteria, Chloroflexi, and Gammaproteobacteria, while BC application improved Bacteroidetes, Firmicutes, and Verrucomicrobia. Planting methods and organic amendments were also observed to affect soil enzyme activities and physicochemical properties. DS was associated with an increase in microbial biomass nitrogen (MBN) and carbon (MBC), cellulase activities (CA), total phosphorus (TP), available nitrogen (AN), and available potassium (AK), while TT significantly increased urease activities (UA). Compared to BC and the control (CK), RS significantly increased CA, AN, and available phosphorus (AP). RDA ordination plots were used to examine the interactions between soil bacterial communities and soil physicochemical properties; planting techniques and organic additions had different effects on soil bacterial communities. Compared to RS and CK, BC enhanced MBN, MBC, UA, and AK. According to Pearson’s correlation analysis, Chloroflexi levels were positively associated with those of organic carbon (OC), MBN, and MBC. OC, TP, MBN, and CA positively correlated with gemmatimonadetes. In conclusion, these data reveal that planting practices and short-term organic inputs alter soil’s physicochemical parameters, enzyme activity, and microbial community composition. Full article

Long-distance sewerage network systems have serious vulnerabilities, specifically pipeline blockage, leakage, sedimentation, mixed connection, and other problems. A vulnerability evaluation system for a sewage network was established in this study with the comprehensive consideration of three aspects: basic attributes of the sewage network, operation and maintenance (O&M) drivers, and structural level. First, we obtained vulnerability indicators for the sewage pipeline network system through data collection and the preliminary selection and screening of indicators. The extent of the importance of each criterion level to the vulnerability was clarified through principal component analysis (PCA), with the basic attribute indicators being the per capita GDP (X3) and the urbanization rate (X5), the O&M-driven indicators being the daily per capita wastewater treatment volume (X7) and the industrial wastewater discharge volume (X8), and the structural-level indicators being the pipe diameter (X13) and the flow capacity (X15). Qingshanhu District, Jiangxi province, was taken as an example for diagnosing and evaluating vulnerability. Using the ranking size of PCA indicators as the evaluation level of the importance for the analytic hierarchy process (AHP) indicators, a hierarchical structure model was established. The evaluation value was obtained by weighting the hierarchical structure model results with the scores of each indicator. The comprehensive evaluation values of basic attributes, operation and maintenance drivers, and structural level were 58.38, 68.67, and 73.17, which corresponded to vulnerability levels of III, II, and II, respectively. Full article

Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. The proposed method employs principal component transformation ($PCT$) as its foundational framework, similar to existing techniques. A running average filter is applied to reduce noise in the input data, which reduces the number of principal components ($PCs$) necessary to represent the data. Subsequently, a k-d tree is employed to identify a relatively similar spectrum, which efficiently constrains the search space. Additionally, fine search strategies leveraging precomputed distances enhance the existing pilot search method by dynamically updating candidate spectra, thereby improving search efficiency. Experimental results demonstrate that the proposed method achieves accuracy comparable to exhaustive search methods while significantly reducing computational complexity relative to existing approaches. Full article

Background: The characterization of facial sexual dimorphic patterns in healthy populations serves as valuable normative data to tailor functionally effective surgical treatments and predict their aesthetic outcomes and to identify dysmorphic facial traits related to hormonal disorders and genetic syndromes. Although the analysis of facial sexual differences in juveniles of different ages has already been investigated, few studies have approached this topic with three-dimensional (3D) geometric morphometric (GMM) analysis, whose interpretation may add important clinical insight to the current understanding. This study aims to investigate the location and extent of facial sexual variations in juveniles through a spatially dense GMM analysis. Methods: We investigated 3D stereophotogrammetric facial scans of 304 healthy Italians aged 3 to 18 years old (149 males, 155 females) and categorized into four different age groups: early childhood (3–6 years), late childhood (7–12 years), puberty (13–15 years), and adolescence (16–18 years). Geometric morphometric analyses of facial shape (allometry, general Procrustes analysis, Principal Component Analysis, Procrustes distance, and Partial Least Square Regression) were conducted to detail sexually dimorphic traits in each age group. Results: The findings confirmed that males have larger faces than females of the same age, and significant differences in facial shape between the two sexes exist in all age groups. Juveniles start to express sexual dimorphism from 3 years, even though biological sex becomes a predictor of facial soft tissue morphology from the 7th year of life, with males displaying more protrusive medial facial features and females showing more outwardly placed cheeks and eyes. Conclusions: We provided a detailed characterization of facial change trajectories in the two sexes along four age classes, and the provided data can be valuable for several clinical disciplines dealing with the craniofacial region. Our results may serve as comparative data in the early diagnosis of craniofacial abnormalities and alterations, as a reference in the planning of personalized surgical and orthodontic treatments and their outcomes evaluation, as well as in several forensic applications such as the prediction of the face of missing juveniles. Full article

In order to solve the wall damage problem of roadways with deep and high stress in methane explosion accidents, mathematical-physical analysis models for the dynamic response damage of roadway walls were established by LS-Dyna software in this paper, and the models were validated to be effective. The roadway wall displacement, stress, and deformation characteristics under the methane explosion impact load were numerical simulated and the response and damage evolution process of the roadway wall was studied. The results indicate that the model established in this study can reflect the dynamic response damage characteristics of the roadway wall. The damage of the roadway wall caused by the methane explosion impact load was mainly concentrated in the methane accumulation section, but the maximum principal stress of the roadway wall near the methane accumulation section was still high, and the damage possibility was also high. The dynamic response damage of the roadway wall decreased with the increase in the distance from the initiation explosion point. The stress response of the curved part of the roadway roof was the most severe, and the stress response of the side part was second to that of the roof. The stress changes at the corners were significant, but the deformation was small. The bottom plate was minimally affected by the methane explosion impact loads. The arch top and two sides of the roadway were first subjected to significant impact, resulting in a high-pressure zone. The peak pressure of the side part was relatively high, and the difference in peak pressure between the corner and the bottom plate was not significant. Full article

Background: The human oral microbiome is a critical reservoir for antibiotic resistance; however, subgingival peri-implant biofilms remain underexplored in this context. We aimed to explore the prevalence and distribution of antibiotic resistance genes (ARGs) in metagenomes derived from saliva and subgingival peri-implant biofilms. Methods: A total of 100 metagenome datasets from 40 individuals were retrieved from the Sequence Read Archive (SRA) database. Of these, 20 individuals had exclusively healthy implants and 20 had both healthy and affected implants with peri-implantitis. ARGs and their taxonomic assignments were identified using the ABRicate tool, and plasmid detection was performed with PlasmidFinder. Results: Four plasmid replicons were identified in 72 metagenomes, and 55 distinct ARGs from 13 antibiotic classes were detected in 89 metagenomes. ARGs conferring resistance to macrolides–lincosamides–streptogramins, tetracyclines, beta-lactams, and fluoroquinolones were the most prevalent. The msr(D) and mef(A) genes showed the highest prevalence, except in saliva samples from individuals with healthy implants, where mef(A) ranked fourth. A pairwise PERMANOVA of principal coordinate analysis based on Jaccard distances revealed that saliva samples exhibited significantly greater ARG diversity than subgingival biofilm samples (p < 0.05). However, no significant differences were observed between healthy and peri-implantitis-affected subgingival biofilm groups (p > 0.05). The taxonomic origins of ARGs were also analyzed to understand their distribution and potential impact on oral microbial communities. Conclusions: Resistome profiles associated with both peri-implant health and disease showed no significant differences and higher salivary abundance of ARGs compared to subgingival biofilm samples. Full article

Italian ryegrass (Lolium multiflorum, IRG) and perennial ryegrass (Lolium perenne L., PRG) are widely cultivated as forage grasses in Korea using heterogeneous and polycross techniques, which promote genetic diversity within varieties. However, their genetic diversity patterns in Korea remain underexplored. This study evaluated the genetic diversity of IRG (eight varieties, including one exotic) and PRG (two exotic varieties) using 66 simple sequence repeat (SSR) markers. Across 87 samples (nine IRG and two PRG varieties), 655 alleles were identified, averaging 9.9 per locus. Key genetic parameters included heterozygosity (0.399), observed heterozygosity (0.675), fixation index (0.4344), and polymorphic informative content (0.6428). The lowest within-variety genetic distance was observed in ‘Hwasan 104ho’ (0.469), while ‘IR901’ had the highest (0.571). Between varieties, the closest genetic distance was between ‘Greencall’ and ‘Greencall 2ho’ (0.542), and the furthest was between ‘Kowinmaster’ and ‘Aspire’ (0.692). Molecular variance analysis showed 90% variation within varieties and 10% among varieties. Five clusters (I–V) were identified, with cluster I primarily including diploid IRG varieties and the tetraploid ‘Hwasan 104ho.’ Structural analysis differentiated diploid from tetraploid varieties (K = 2) and further separated tetraploid IRG and PRG (K = 3). Principal component analysis confirmed these groupings, with ‘Greencall’ and ‘Greencall 2ho’ exhibiting the closest genetic distance (0.227) and ‘Greencall’ and ‘Aspire’ the furthest (0.384). These findings provide a foundational resource for marker-assisted breeding to improve agronomic traits and enhance the efficiency of ryegrass breeding programs. Full article

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction. Full article

As renewable energy resources are increasingly deployed on a large scale in remote areas, their share within the power grid continues to expand, rendering direct current (DC) transmission essential to the stability and efficiency of power systems. However, existing transmission line protection principles are constrained by limited fault feature quantities and insufficient correlation exploration among features, leading to operational refusals under remote and high-resistance fault conditions. To address these limitations in traditional protection methods, this study proposes an innovative single-ended protection principle based on Principal Component Analysis (PCA) and Kernel Density Estimation (KDE). Initially, PCA is employed for multidimensional feature extraction from fault data, followed by KDE to construct a joint probability density function of the multidimensional fault features, allowing for fault type identification based on the joint probability density values of new samples. In comparison to conventional methods, the proposed approach effectively uncovers intrinsic correlations among multidimensional features, integrating them into a comprehensive feature set for fault diagnosis. Simulation results indicate that the method exhibits robustness across various transition resistances and fault distances, demonstrates insensitivity to sampling frequency, and achieves 100% accuracy in fault identification across sampling time windows of 0.5 ms, 1 ms, and 2 ms. Full article

Leymus chinensis, a major component of the plant community in the eastern Eurasian grasslands with a wide distribution, provides stability to grassland ecosystems and supports animal husbandry. This study aimed to bridge the gap between the molecular breeding and industrial application of L. chinensis by conducting a comprehensive simple sequence repeat (SSR) analysis. A total of 973,129 SSRs were identified in the L. chinensis whole genome, which was used to design 20 polymorphic pairs of SSR primers to further assess 105 L. chinensis accessions. On average, 33.55 alleles were detected per locus, with an average Shannon index of 2.939 and a polymorphic information content value of 0.910. Principal coordinate, maximum likelihood, and structure analyses consistently showed that all samples were coincidentally divided into four subclasses. In addition, Mantel test data indicated a weak correlation between genetic and geographical distances in L. chinensis, whose variability may be related to the pollination mode and natural selection pressures. Finally, we used the 20 pairs of selected markers to scan 105 accessions, constructing a fingerprint for them. These findings provide new foundations for identifying superior varieties, improving the management of genetic resources, and constructing a germplasm resource database for L. chinensis. Full article

As a principal insulating component in power transmission systems, the integrity of the insulator is of paramount importance for ensuring the safe and reliable operation of transmission lines. While the deployment of aerial photography technology has markedly enhanced the efficacy of power facility inspections, the intricate backgrounds, multifarious viewpoint alterations, and erratic lighting circumstances inherent in the captured images present novel challenges for the algorithmic detection of insulator defects. To address these issues, this study proposes an enhanced version of the YOLOV7 detection algorithm. The introduction of the contextual transformer network (CoTNet) structure and an EMA attention mechanism enhances the model’s capacity to perceive global contextual information in images and to model long-distance feature dependencies. Experiments based on a real aerial photography dataset demonstrate that the proposed algorithm outperforms the benchmark model in all key performance indicators, including accuracy, recall, and F1 score, which improved by 0.6%, 1.8%, and 0.8%, respectively. Additionally, the average precision (mAP@[0.5]) and mAP@[0.5:0.95] improved by 0.6% and 4.4%, respectively. The superiority of the algorithm in feature extraction and target localization is verified through Grad-CAM visual analysis, which provides a high-precision detection method for intelligent inspection of power transmission systems. Full article