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المغامرون الصغار : ملحمة عالمية : استكشاف الطاقة النفط
ليث ولين وزياد. المغامرون الصغار بانتظار المغامرات دوما لكن سرعان ما يجدون أنفسهم أمام قضية غامضة وشائقة بسبب اختفاء البروفيسور أديب. المتهم الرئيسي في قضية انفجار مصنع شركة إنيرجيز وسيسعون معا إلى كشف الحقيقة بالبحث عنه وإثبات براءته.
Book
Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks
Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.
Journal Article
Finite element analysis of percutaneous uniplanar screw fixation in the treatment of thoracolumbar fractures
Objective
To compare the biomechanical characteristics of thoracolumbar fractures treated with uniplanar screws, monoaxial screws, and polyaxial screws using finite element analysis.
Methods
CT data of the thoracolumbar spine T
12
–L
2
from a healthy volunteer were collected, and using finite element software, models of both normal and fractured spines were created. Three different fixation models were constructed with monoaxial screws (Mps group), polyaxial screws (Pps group), and uniplanar screws (Ups group), respectively. The L
2
vertebra was fixed and a compressive load of 150 N and a torque of 10 N•m were applied at the T
12
end to simulate flexion, extension, lateral bending, and rotation movements of the spine. The range of motion (ROM) and internal fixation stress within the screws and rods were recorded for each movement direction.
Results
A finite element model of the healthy human spine T
12
–L
2
was established and validated for accuracy. All three fixation models demonstrated decreased ROM in all tested movements. The UPS group showed the lowest percentage of ROM in flexion, extension, and lateral bending movements, with a mid-range percentage of ROM in rotation, and relatively the best stability. The PPS group had the highest ROM percentages in all directions of movement, with the worst relative stability. The maximum von Mises stress for pedicle screws and rods in all fixation modes occurred in flexion, with the MPS group’s screws showing significantly higher stress peaks in flexion and both rotations than those of the PPS and UPS groups. The rods of the PPS group had significantly lower stress peaks in all motion states compared to those of the MPS and UPS groups.
Conclusions
Uniplanar screws can effectively distribute stress, reduce the risk of screw and rod breakage, and ensure stability of spinal fixation.
Journal Article
Sophora japonica L. bioactives: Chemistry, sources, and processing techniques
Sophora japonica L. is an edible and medicinal woody plant of the legume family. Aside from the well‐known flower and fruit parts, the branches, leaves, roots, and its parasitic fungi contain various bioactive compounds. Besides being consumed as a ready‐to‐eat food, S. japonica is generally processed into extracts and bioactive compounds for use as food ingredients, pharmaceuticals, and cosmetic products. Various extraction and separation techniques have been applied to obtain bioactive compounds from S. japonica. However, details of the extraction and separation techniques for these compounds, such as the methods, parameters, and corresponding yields, are rarely presented. This review aims to provide a comprehensive overview of the different bioactive compounds of S. japonica, their chemistry, sources, and processing techniques to promote green advanced processing technologies for obtaining them and promote the development of S. japonica bioactive compounds as commercial products for the food and healthcare sector. Sophora japonica is an edible medicinal plant with various bioactive potentials. Bioactive compounds are abundant in different parts of S. japonica. The extraction and separation techniques of S. japonica extracts and compounds are summarized.
Journal Article
Hostile attribution bias and angry rumination: A longitudinal study of undergraduate students
Angry rumination and hostile attribution bias are important cognitive factors of aggression. Although prior theoretical models of aggression suggest that aggressive cognitive factors may influence each other, there are no studies examining the longitudinal relationship between angry rumination and hostile attribution bias. The present study used cross-lagged structural equation modeling to explore the longitudinal mutual relationship between hostile attribution bias and angry rumination; 941 undergraduate students (38.5% male) completed questionnaires assessing the variables at two time points. The results indicate that hostile attribution bias showed a small but statistically significant effect on angry rumination 6 months later, and angry rumination showed a quite small but marginally significant effect on hostile attribution bias across time. The present study supports the idea that hostile attribution bias influences angry rumination, and argue that the relationship between angry rumination and hostile attribution bias may be mutual. Additionally, the results suggest that there may be a causal relation of different aggression-related cognitive factors.
Journal Article
Enhanced bone cement distribution in percutaneous vertebroplasty using a curved guide wire: a propensity score matching analysis
Background
Osteoporotic vertebral compression fractures (OVCF) severely affect the quality of life in the aged population. Percutaneous vertebroplasty (PVP) alleviates pain and stabilizes vertebrae, but suboptimal bone cement distribution can cause complications. Hence, this study aimed to clarify whether a new technique for PVP, using a curved guide wire, enhances the distribution of bone cement and improves clinical outcomes in patients with OVCF.
Methods
Patients with single-segment OVCF underwent PVP or curved guide wire percutaneous vertebroplasty (C-PVP). Propensity score matching (PSM) was employed to balanced the baseline characteristics. The primary outcomes were the visual analog scale (VAS) and Oswestry disability index (ODI) scores. The secondary outcomes included assessments of bone cement distribution, bone cement injection volume, radiological parameters, and general clinical results. Additionally, Complications and adverse events were documented.
Results
After PSM analysis, each group comprised 54 patients, which significantly reduced baseline differences. The C-PVP group showed better clinical outcomes compared to the traditional PVP group. One month after surgery, the C-PVP group had significantly lower VAS and ODI scores (
p
< 0.001). These improvements persisted at six months and the final follow-up. Additionally, bone cement distribution scores were better (
p
< 0.001), injection volume was higher (
p
= 0.03), leakage was less frequent (
p
= 0.02), and adjacent vertebral fractures occurred less frequently (
p
= 0.04) in the C-PVP group. Radiological parameters and overall clinical outcomes revealed no significant differences between the two groups.
Conclusion
The use of curved guide wire in PVP significantly improves bone cement distribution and injection volume, resulting in better clinical efficacy in patients with OVCF.
Journal Article
Lipid accumulation product and cardiometabolic index as indicators for sarcopenia: A cross-sectional study from NHANES 2011–2018
Although evidence suggests that lipid accumulation product (LAP) and cardiometabolic index (CMI) may be associated with the pathogenesis of sarcopenia, their relationship remains unclear. This study aims to investigate their association with sarcopenia. This cross-sectional study analyzed data from 4,172 adults aged 20–59 years from the NHANES 2011–2018 cycles. Log-transformed LAP and CMI were the primary exposure variables. Sarcopenia was defined based on the appendicular skeletal muscle mass divided by body mass index (ASM/BMI) according to FNIH guidelines (< 0.789 in males, < 0.512 in females). Weighted analyses examined the associations between LAP, CMI, and sarcopenia. Multivariable logistic regression, restricted cubic spline (RCS), and threshold analysis were used to assess associations, nonlinear patterns, and potential cutoff points. Subgroup analyses explored associations in specific populations. The dataset was randomly split into training (70%) and validation (30%) sets. LASSO regression was applied to identify key associated factors, followed by a nomogram for estimating the probability of sarcopenia. Model performance was evaluated in both the training and validation sets. Sensitivity analyses were performed using untransformed LAP and CMI to assess the robustness of the findings. Ln-transformed LAP (Ln LAP) and Ln-transformed CMI (Ln CMI) were significantly associated with sarcopenia. Threshold effect analysis identified inflection points (Ln LAP: 4.64, Ln CMI: -0.14) beyond which associations weakened. Individuals in the top quartiles of Ln LAP and Ln CMI exhibited significantly higher odds of sarcopenia (Ln LAP: OR = 8.78, 95% CI: 4.92–15.67; Ln CMI: OR = 4.44, 95% CI: 2.41–8.21). Subgroup analyses revealed stronger associations among adults aged 20–29 and 50–59 years, individuals with higher education levels, and drinkers. Classification models with Ln LAP and Ln CMI performed robustly (AUC: 0.780, 0.768) with high accuracy. Sensitivity analyses confirmed consistent nonlinear associations and dose–response relationships for untransformed LAP and CMI. LAP and CMI showed a positive association with sarcopenia in U.S. adults aged 20–59 years. The developed models highlight this relationship, offering potential guidance for identifying and managing high-risk populations.
Journal Article
Spectra of Self-Similar Measures
This paper is devoted to the characterization of spectrum candidates with a new tree structure to be the spectra of a spectral self-similar measure μN,D generated by the finite integer digit set D and the compression ratio N−1. The tree structure is introduced with the language of symbolic space and widens the field of spectrum candidates. The spectrum candidate considered by Łaba and Wang is a set with a special tree structure. After showing a new criterion for the spectrum candidate with a tree structure to be a spectrum of μN,D, three sufficient and necessary conditions for the spectrum candidate with a tree structure to be a spectrum of μN,D were obtained. This result extends the conclusion of Łaba and Wang. As an application, an example of spectrum candidate Λ(N,B) with the tree structure associated with a self-similar measure is given. By our results, we obtain that Λ(N,B) is a spectrum of the self-similar measure. However, neither the method of Łaba and Wang nor that of Strichartz is applicable to the set Λ(N,B).
Journal Article
Application of Feature Selection Based on Elastic Network and Random Forest in the Evaluation of Sports Effects
With the rapid development of data mining and machine-learning technology and the outbreak of big sports data mining development challenges, sports data mining cannot simply use data statistical methods such as how to combine machine learning and data mining technology for effective mining and analysis of sports data, to provide useful advice for public physical exercise, and this is an urgent need to study. It is a kind of efficient sports data mining study through the feature selection algorithm. Around the difficult problems existing in the study of sports effect, given the limitations of existing data sets and traditional research methods, this paper starts from the data mining algorithm, builds the sports effect evaluation database, based on feature selection idea, using elastic network algorithm, random forest algorithm, and the influence of sports on the effect of physical indicators. The evaluation algorithm introduces machine learning algorithm and feature selection algorithm to guide the sports effect evaluation research. When studying the evaluation problem of sports effect, according to the constructed sports effect evaluation database, elastic network algorithm is added to regularize, optimize, and realize feature selection. When selecting the characteristics of different sports ability, using information gains indicators to rank the importance of characteristics, which can scientifically and accurately obtain the influence degree of sports on different physical indicators, make the physical fitness research more scientific, and can reveal the effect of sports as far as possible. Experimental results show that the selected features and ground-truth have good accuracy, good evaluation performance, and high accuracy compared with the baseline method.
Journal Article
Identifying the Groundwater Sources of Huangtupo Landslide in the Three Gorges Reservoir Area of China
Groundwater plays a crucial role in triggering and reactivating deep-seated landslides. However, classical hydrogeological investigations have limitations in their applicability to deep-seated landslides due to anisotropic and heterogeneous media. The Huangtupo landslide in the Three Gorges Reservoir area has garnered significant attention due to its high hazard potential. Of particular interest is the NO.1 Riverside Sliding Mass (HTP-1), which has shown notable deformation and has become the primary focus of landslide research. The study aims to investigate the sources of water in the HTP-1 landslide through hydrochemical analysis. This was achieved by monitoring the major ion content in the groundwater within the landslide for one year. Furthermore, stable isotope investigations were conducted on the groundwater in and around the landslide area, and an analysis of the mineral composition of the landslide soil was also performed. The results indicate that the groundwater in the landslide area (LGW) is a mixture of karst groundwater (KGW) from the adjacent upslope and local precipitation (LP). The karst groundwater is a major contributor to the recharge of the landslide groundwater system, causing a high component of groundwater that can easily exceed the critical level that causes landside failure during heavy rainfall events. Furthermore, prior to the relocation of residents from the Huangtupo landslide, the landslide groundwater was also impacted by human sewage, which not only affected the chemical composition of groundwater, but also had potential implications for slope stability. These findings provide a more scientific basis for the design and implementation of interception and drainage measures for the Huangtupo landslide and other large-scale landslides with similar geological conditions in the Three Gorges Reservoir area.
Journal Article