Search Results (1,491)

Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in pattern discovery incorporating tabular data with customized 3D tensor modeling in feeding neural networks. Our findings are empirically validated using six diverse, publicly available datasets each varying in size and learning objectives. This paper proves that the proposed model based on time-sequence DL algorithms, which were generally described as inadequate when dealing with tabular data, yields satisfactory results and competes effectively with other algorithms specifically designed for tabular data. An additional benefit of this approach is its ability to preserve simplicity while ensuring fast model training also with large datasets. Even with extremely small datasets, models can be applied to achieve exceptional predictive results and fully utilize their capacity. Full article

Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images. Full article

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the [email protected] metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the [email protected] metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection. Full article

The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method’s effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length compared to the basic version of the A* algorithm. Based on the obtained results, the proposed approach also increases the path’s safety by designating narrow and risky areas as closed to vehicle movement. For this reason, the method seems suitable for use in global path planning for autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) operating in large areas. Full article

The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off. Full article

Background: Patellar dislocation is a knee injury affecting generally young, active individuals, damaging joint ligaments and structures, and impacting sports activity and quality of life. Objective: This review aimed to evaluate the role of the quadriceps femoris muscle in knee extension and to consider whether extensor strength deficits are present in patients who have suffered from a primary or recurrent patellar dislocation and have been treated surgically or conservatively. Methods: This systematic literature review with meta-analysis was performed following the PRISMA Statement criteria. The search engines consulted to select studies were MEDLINE/PubMed, Scopus, and Web of Science/ISI. The JBI Critical Appraisal Checklist tools were applied for the quality assessment based on the specific study design. The outcomes were measurements of the knee extension force of the quadriceps femoris muscle, which were objectively quantifiable with an isokinetic or mobile dynamometer. Results: Of the 891 articles initially identified through the databases, 10 studies with a total of 370 patients were included in the analysis. The results indicated a strength deficit of the quadriceps in patients who had undergone a patellar dislocation, in comparison with the control group, when examining the uninvolved limb or in comparison with the pre-operative values. The overall effect size was large, with a value of −0.99. Conclusions: Our review concluded that after a primary or recurrent patellar dislocation, strength deficits of the quadriceps femoris muscle in the knee extension of the affected limb are frequently observed in surgically or conservatively treated patients. This deficit may persist even after a protracted follow-up of up to three years after injury. Full article

The walleye pollock, Gadus chalcogrammus (Pallas, 1814), is one of the most commercially and ecologically valuable species in the Northwestern Pacific. However, combined pressures of overfishing and environmental changes have led to a substantial decline in its production in Japan and Russia since the 1990s, and a collapse in Korea since the 2000s. The objective of this study was to comprehensively examine its genetic diversity and population structure with an extensive sampling effort of 16 populations across the Northwestern Pacific including South Korea, Japan, and Russia. A multiplex PCR assay composed of seven microsatellite markers revealed a moderate level of observed heterozygosity (Ho = 0.369–0.599), which is lower than that reported in previous studies of this species. All loci were highly polymorphic, with the mean PIC ranging from 0.608 to 0.793. The structure of the 16 populations was characterized by heterozygote deficiency, a modest effective allele number (Ne = 4.551–7.969), low genetic differentiation (F_ST = 0.000–0.054), a weak population structure, a genetic admixture, and no significant correlation between the genetic and geographic distance. These characteristics are typical of pelagic marine species with large population sizes due to a consistent gene flow among populations when there are no physical boundaries in the open ocean. The seasonal and country-specific genetic structure indicated that G. chalcogrammus populations in the Northwestern Pacific region should be managed as a single management unit. The findings from this study provide critical information for future genetic monitoring, conservation management, and the development of strategies aimed at restoring the populations of this species. Full article

The isle of Gavdos, and its wider area, is one of the few places worldwide where the calibration and validation of altimetric satellites has been carried out during the last, more than, two decades using dedicated techniques at sea and on land. The sea-surface calibration employed for the determination of the bias in the satellite altimeter’s sea-surface height relies on the use of a gravimetric geoid in collocation with data from tide gauges, permanent global navigation satellite system (GNSS) receivers, as well as meteorological and oceanographic sensors. Hence, a high-accuracy and high-resolution gravimetric geoid model in the vicinity of Gavdos and its surrounding area is of vital importance. The existence of such a geoid model resides in the availability of reliable, in terms of accuracy, and dense, in terms of spatial resolution, gravity data. The isle of Gavdos presents varying topographic characteristics with heights larger than 400 m within small spatial distances of ~7 km. The small size of the island and the significant bathymetric variations in its surrounding marine regions make the determination of the gravity field and the geoid a challenging task. Given the above, the objective of the present work was two-fold. First, to collect new land gravity data over the isle of Gavdos in order to complete the existing database and cover parts of the island where voids existed. Relative gravity campaigns have been designed to cover as homogenously as possible the entire island of Gavdos and especially areas where the topographic gradient is large. The second focus was on the determination of a high-resolution, $1\prime \times 1\prime$, and high-accuracy gravimetric geoid for the wider Gavdos area, which will support activities on the determination of the absolute altimetric bias. The relative gravity campaigns have been designed and carried out employing a CG5 relative gravity meter along with geodetic grade GNSS receivers to determine the geodetic position of the acquired observations. Geoid determination has been based on the newly acquired and historical gravity data, GNSS/Leveling observations, and topography and bathymetry databases for the region. The modeling was based on the well-known remove–compute–restore (RCR) method, employing least-squares collocation (LSC) and fast Fourier transform (FFT) methods for the evaluation of the Stokes’ integral. Modeling of the long wavelength contribution has been based on EIGEN6c4 and XGM2019e global geopotential models (GGMs), while for the contribution of the topography, the residual terrain model correction has been employed using both the classical, space domain, and spectral approaches. From the results achieved, the final geoid model accuracy reached the ±1–3 cm level, while in terms of the absolute differences to the GNSS/Leveling data per baseline length, 28.4% of the differences were below the $1\mathrm{c}\mathrm{m}\sqrt{S_{ij} \left[\mathrm{k}\mathrm{m}\right]}$ level and 55.2% below the $2\mathrm{c}\mathrm{m}\sqrt{S_{ij} \left[\mathrm{k}\mathrm{m}\right]}$. The latter improved drastically to 52.8% and 81.1%, respectively, after deterministic fit to GNSS/Leveling data, while in terms of the relative differences, the final geoid reaches relative uncertainties of 11.58 ppm (±1.2 cm) for baselines as short as 0–10 km, which improves to 10.63 ppm (±1.1 cm) after the fit. Full article

Geoffroy’s spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission–fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (−45°, −90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was “substantial” (Fleiss’ kappa coefficient = 0.61–0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a −90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model. Full article

The Chinese government has made the realization of sustainable development in grassland animal husbandry an important policy objective, and achieving a reasonable input of production factors is the key to realizing that goal. Based on the assumption of “rational economic man”, this study measures the economically optimal inputs and actual input bias of production factors, and constructs an econometric model focusing on analyzing the impact of operation scale on the factor input bias. The results indicate that herdsmen deviate from the economically optimal production input levels in forage, labor, and machinery, with the degree of bias decreasing as the livestock size or pasture size expands. Furthermore, it is established that market risk plays a role in mediating the impact of operation scale on the bias of variable production factors. Overall, large-scale herding households have a smaller bias in factor inputs, and should be promoted to operate on an appropriate scale, while paying attention to the prevention of market risk and the enhancement of information symmetry between herders and factor markets. Full article

Lepidium meyenii Walp (LmW) or Maca, including its bioactive components such as macamides, among others, has demonstrated antioxidant effects. However, the effect size (ES) of LmW on oxidative stress has not been qualitatively described and calculated. The primary objective of this systematic review and meta-analysis was to review and qualitatively describe the studies published up to 2023 that supplemented LmW to control cellular oxidative stress; the secondary objective was to calculate the ES of the different interventions. The search was designed following the PRISMA^® guidelines for systematic reviews and meta-analyses and performed in the Web of Science, Scopus, SPORTDiscus, PubMed, and MEDLINE until 2023. The selection of studies included randomized controlled trials, with tests and post-tests, both in vitro and in vivo in animals and humans. The methodological quality and risk of bias were evaluated with the CAMARADES tool. The main variables were reduced glutathione, glutathione peroxidase, superoxide dismutase, and malondialdehyde. The analysis was conducted with a pooled standardized mean difference (SMD) through Hedges’ g test (95% CI). Eleven studies were included in the systematic review and eight in the meta-analysis. They revealed a small effect for reduced glutathione (SMD = 0.89), a large effect for glutathione peroxidase (SMD = 0.96), a moderate effect for superoxide dismutase (SMD = 0.68), and a moderate effect for malondialdehyde (SMD = −0.53). According to the results, the phytochemical compounds of LmW effectively controlled cellular oxidative stress, mainly macamides. It was also determined that a higher dose of LmW generated a greater antioxidant effect. However, information concerning humans is scarce. Full article

The present study aimed to investigate the relationship between the physical capabilities of young soccer players and their performance in game-related variables as assessed through the Nomination Scale for Identifying Football Talent (NSIFT) questionnaire. A total of 80 young soccer players, with an average age of 10.70 ± 1.02 years, participated in the research. Each player underwent a comprehensive assessment session that included the 5-0-5 Change of Direction (COD) test, the Illinois Agility Test, and the Countermovement Jump (CMJ) test. These assessments were selected to evaluate critical physical attributes essential for soccer performance such as agility, explosive strength, and the ability to change direction rapidly. To analyze the data, Principal Component Analysis (PCA), a statistical technique that reduces the dimensionality of large datasets while retaining as much variance as possible, was employed. The PCA results indicated strong sample validity as confirmed by the Kaiser–Meyer–Olkin (KMO) measurement index, which assesses the adequacy of the sample size for factor analysis. The analysis revealed two principal components: development and disposition, which together accounted for 73% of the total variance in the data. The development component encompasses various physical attributes that contribute to a player’s growth and improvement, including strength, speed, and agility. Conversely, the disposition component reflects innate qualities and cognitive skills that predispose players to excel in soccer such as decision making and game awareness. This research highlights the importance of incorporating physical assessments into talent identification processes, providing objective measures that complement subjective evaluations. This study contributed to the literature on talent identification in soccer, emphasizing the need for a multidisciplinary approach to nurture young athletes effectively. Future research should continue to explore the interplay between physical and cognitive skills in soccer to enhance player development and success in competitive environments. Full article

Autonomous vehicle technology is advancing, with 3D object detection based on point clouds being crucial. However, point clouds’ irregularity, sparsity, and large data volume, coupled with irrelevant background points, hinder detection accuracy. We propose a two-stage multi-scale 3D object detection network. Firstly, considering that a large number of useless background points are usually generated by the ground during detection, we propose a new ground filtering algorithm to increase the proportion of foreground points and enhance the accuracy and efficiency of the two-stage detection. Secondly, given that different types of targets to be detected vary in size, and the use of a single-scale voxelization may result in excessive loss of detailed information, the voxels of different scales are introduced to extract relevant features of objects of different scales in the point clouds and integrate them into the second-stage detection. Lastly, a multi-scale feature fusion module is proposed, which simultaneously enhances and integrates features extracted from voxels of different scales. This module fully utilizes the valuable information present in the point cloud across various scales, ultimately leading to more precise 3D object detection. The experiment is conducted on the KITTI dataset and the nuScenes dataset. Compared with our baseline, “Pedestrian” detection improved by 3.37–2.72% and “Cyclist” detection by 3.79–1.32% across difficulty levels on KITTI, and was boosted by 2.4% in NDS and 3.6% in mAP on nuScenes. Full article

Data structures such as sets, lists, and arrays are fundamental in mathematics and computer science, playing a crucial role in numerous real-life applications. These structures represent a variety of entities, including solutions, conditions, and objectives. In scenarios involving large datasets, eliminating duplicate elements is essential to reduce complexity and enhance performance. This paper introduces a novel algorithm that uses logarithmic prime numbers to efficiently sort data structures and remove duplicates. The algorithm is mathematically rigorous, ensuring correctness and providing a thorough analysis of its time complexity. To demonstrate its practicality and effectiveness, we compare our method with existing algorithms, highlighting its superior speed and accuracy. An extensive experimental analysis across one thousand random test problems shows that our approach significantly outperforms two alternative techniques from the literature. By discussing the potential applications of the proposed algorithm in various domains, including computer science, engineering, and data management, we illustrate its adaptability through two practical examples in which our algorithm solves the problem more than $3\times {10}^4$ and $7\times {10}^4$ times faster than the existing algorithms in the literature. The results of these examples demonstrate that the superiority of our algorithm becomes increasingly pronounced with larger problem sizes. Full article

Depth image has been widely involved in various tasks of 3D systems with the advancement of depth acquisition sensors in recent years. Depth images suffer from serious distortions near object boundaries due to the limitations of depth sensors or estimation methods. In this paper, a simple method is proposed to rectify the erroneous object boundaries of depth images with the guidance of reference RGB images. First, an RGB–Depth boundary inconsistency model is developed to measure whether collocated pixels in depth and RGB images belong to the same object. The model extracts the structures of RGB and depth images, respectively, by Gaussian functions. The inconsistency of two collocated pixels is then statistically determined inside large-sized local windows. In this way, pixels near object boundaries of depth images are identified to be erroneous when they are inconsistent with collocated ones in RGB images. Second, a depth image rectification method is proposed by embedding the model into a simple weighted mean filter (WMF). Experiment results on two datasets verify that the proposed method well improves the RMSE and SSIM of depth images by 2.556 and 0.028, respectively, compared with recent optimization-based and learning-based methods. Full article