Anti-inflammation Activities of Essential Oil and Anti-microbial Activities Of Ethanol Extraction from China's Rosemary

Rosemary(Rosmarius officinalis L.), an endemic plant species in south region of China, is traditionally used as a spice. In this research, the anti-inflammatory activities of essential oil and the antibacterial activities of ethanol extraction were determined, respectively. Results showed that based on the GC-MS analysis there were 35 kinds of active ingredients in the essential oil in totally, mainly including D-limonene(24.158 ml/L), α-Pinene(23.325 ml/L), Camphor(9.855 ml/L),Camphene(7.076 ml/L), Verbenone(6.685 ml/L), Borneol(5.580 ml/L), etc. The LCUV determination indicated that the main components in the ethanol extractionwere rosmarinic acid(3 910 mg/kg) and carnosic acid(2 970 mg/kg). By mice peritoneal macrophage phagocytosis of chicken erythrocytes experiment, the essential oil of rosemary was shown having a significant role in anti-inflammation. And the ethanol extraction had broad-spectrum antibacterial effects, but had no effect on mold by the agar diffusion method of 8 bacteria. As a result, both rosemary essential oil and ethanol extraction had good potential medicinal values.

基于LDA主题模型的GitHub Actions工作流项目推荐算法

在CI/CD实践中,自动化已成为软件开发实践中的一种规范。GitHub引入GitHub Actions为软件维护者提供自动化的持续集成工作流方案,尽管其为开发者提供了诸多便利,GitHub社区也提供了许多第三方的GitHub Actions服务,但仍然只有极少的项目在使用。为了满足开发人员对工作流自动化的需求,减少非开发任务工作量,提出一种基于隐含狄利克雷分布(LDA)主题模型和Jensen-Shannon距离的GitHub Actions工作流项目推荐算法。通过对GitHub Actions存储库的README文件进行主题建模,将GitHub的事件描述文本和用户输入作为模型输入,为正在开发的代码仓库推荐GitHub Actions服务。将该推荐模型与标准的基于余弦相似度的方法进行比较的实验结果表明,该方法能有效改善开源软件仓库的推荐精度。

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Unveiling health rights:A call to action for sex workers'HIV care in the Philippines

We are writing in response to the article titled“Addressing the needs and rights of sex workers for HIV healthcare services in the Philippines”[1].The article calls for attention on the significant challenges faced by sex workers in the Philippines in accessing HIV healthcare.We appreciate the article’s effort to examine these issues in depth.We would like to present a constant flow of thoughts in this letter while highlighting the positive aspects,potential obstacles,and additional points that contribute to this ongoing discussion.

Impact of Action Observation Therapy along with Usual Physiotherapy Intervention of Individual with Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairments in the initial stage, which lead to severe cognitive dysfunction in the later stage. Action observation therapy (AOT) is a multisensory cognitive rehabilitation technique where the patient initially observes the actions and then tries to perform. The study aimed to examine the impact of AOT along with usual physiotherapy interventions to reduce depression, improve cognition and balance of a patient with AD. A 67 years old patient with AD was selected for this study because the patient has been suffering from depression, dementia, and physical dysfunction along with some other health conditions like diabetes and hypertension. Before starting intervention, a baseline assessment was done through the Beck Depression Inventory (BDI) tool, the Mini-Cog Scale, and the Berg Balance Scale (BBS). The patient received 12 sessions of AOT along with usual physiotherapy interventions thrice a week for four weeks, which included 45 minutes of each session. After four weeks of intervention, the patient demonstrated significant improvement in depression, cognition, and balance, whereas the BDI score declined from moderate 21/63 to mild 15/63 level of depression. The Mini-Cog score improved from 2/5 to 4/5, and the BBS score increased from 18/56 to 37/56. It is concluded that AOT along with usual physiotherapy intervention helps to reduce depression, improve cognition and balance of people with AD.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

HgaNets:Fusion of Visual Data and Skeletal Heatmap for Human Gesture Action Recognition

Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual andskeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data,failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility toefficiently process both uniformand disparate input patterns.Thus, in this paper, an attention-enhanced pseudo-3Dresidual model is proposed to address the GAR problem, called HgaNets. This model comprises two independentcomponents designed formodeling visual RGB (red, green and blue) images and 3Dskeletal heatmaps, respectively.More specifically, each component consists of two main parts: 1) a multi-dimensional attention module forcapturing important spatial, temporal and feature information in human gestures;2) a spatiotemporal convolutionmodule that utilizes pseudo-3D residual convolution to characterize spatiotemporal features of gestures. Then,the output weights of the two components are fused to generate the recognition results. Finally, we conductedexperiments on four datasets to assess the efficiency of the proposed model. The results show that the accuracy onfour datasets reaches 85.40%, 91.91%, 94.70%, and 95.30%, respectively, as well as the inference time is 0.54 s andthe parameters is 2.74M. These findings highlight that the proposed model outperforms other existing approachesin terms of recognition accuracy.

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy dissipation.The water decoupling coefficients can adjust the contributions of the stress wave and quasi-static pressure.However,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling blasting.In this study,a theoretical model of blasting fracturing partitioning is established.The mechanical mechanism and determination method of the optimal decoupling coefficient are obtained.The reliability is verified through model experiments and a field test.The results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then decreases.The ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed zone.The quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling coefficient.The optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical model.Compared with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.

Experimental study on the movement of oil spill under freeze-thaw action

Oil leakages cause environmental pollution,economic losses,and even engineering safety accidents.In cold regions,researchers urgently investigate the movement of oil spill in soils exposed to freeze-thaw cycles.In this study,a series of laboratory model experiments were carried out on the migration of oil leakage under freeze-thaw action,and the distributions of the soil temperature,unfrozen water content,and displacement were analyzed.The results showed that under freeze-thaw action,liquid water in soils migrated to the freezing front and accumulated.After the pipe cracked,oil pollutants first gathered at one side of the leak hole,and then moved around.The pipe wall temperature affected the soil temperature field,and the thermal influence range below and transverse the pipe wall(35–40 cm)was larger than that above the pipe wall(8 cm)owing to the soil surface temperature.The leaked oil's temperature would make the temperature of the surrounding soil rise.Oil would inhibit the cooling of the soils.Besides,oil migration was significantly affected by the gravity and water flow patterns.The freeze-thaw action would affect the migration of the oil,which was mainly manifested as inhibiting the diffusion and movement of oil when soils were frozen.Unfrozen water transport caused by freeze-thaw cycles would also inhibit oil migration.The research results would provide a scientific reference for understanding the relationship between the movement of oil pollutants,water,and soil temperature,and for establishing a waterheat-mass transport model in frozen soils.

Research Progress on Purification Process, Content Determination and Pharmacological Action of Atractylodin

Atractylodis Rhizoma comes from the dry rhizome of Atractylis lancea or Atractylodes chinensis in the Compositae family,and it is suitable for preventing and treating diseases such as cold,edema,night blindness and rheumatic arthralgia.Atractylodin is the main active component extracted and isolated from Atractylodis Rhizoma.A large number of studies have found that atractylodin has excellent drug activity in improving gastrointestinal emptying,anti-inflammation,inhibiting malignant tumor and reducing blood lipid.In this paper,the purification process and pharmacological activity of Atractylodin were summarized to provide a theoretical basis for basic research,clinical application and further development and utilization of atractylodin.

Study on the Mechanism of Action of Glyasperin A in the Treatment of Atherosclerosis Based on Network Pharmacology and Molecular Docking

[Objectives] This study was conducted to investigate the mechanism of action of glyasperin A in the treatment of atherosclerosis using a network pharmacology approach. [Methods] Targets related to atherosclerosis were searched in GeneCards database. An active ingredient-disease-target network was constructed by Cytoscape 3.7.1. A target protein interaction network was constructed by String database. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the DAVID database. [Results] Glyasperin A acted on 36 atherosclerosis-related targets, and the biofunctional and pathway enrichment analyses showed that it was mainly involved in response to xenobiotic stimulus, drug transport across blood-brain barrier, lipid oxidation, barrier, and lipid oxidation, etc. The results showed that glyasperin A acted on 36 atherosclerosis-related targets. The biofunctional and pathway enrichment analyses showed that it was mainly involved in response to xenobiotic stimulus, drug transport across blood-brain barrier, lipid oxidation, positive regulation of protein localization to nucleus, and hepoxilin biosynthetic process, and it played an anti-fatigue role through signal pathways such as serotonergic synapse, efferocytosis, arachidonic acid metabolism, chemical carcinogenesis-receptor activation and platelet activation. [Conclusions] Glyasperin A has multi-target and multi-pathway effects in the treatment of atherosclerosis. This study provides reference for further research on glyasperin A in the treatment of atherosclerosis.

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.

Tangled web: Animal-human interaction and the rise of antibiotic resistance

Introduction Egypt is a Low-Middle Income Country(LMIC)with a population of 106,472,382 people in August 2022(Worldometers&Department of Economic and Social Affairs,2022).Available evidence suggests that there are misuse/overuse of antibiotic in Egypt[1].On recognizing the global threat of antibiotic resistance(AMR),Egypt launched the National Action Plan(NAP)in 2018.This comprehensive plan,inspired by the Global Action Plan(GAP),united diverse stakeholders like ministries,universities,and international organizations.The NAP tackled AMR through four key strategies:boosting public understanding of antibiotics,optimizing their use across humans and animals,embracing a“One Health”approach,and implementing effective infection prevention practices[2].

Design,preparation,application of advanced array structured materials and their action mechanism analyses for high performance lithium-sulfur batteries

Lithium-sulfur battery(LSB)has brought much attention and concern because of high theoretical specific capacity and energy density as one of main competitors for next-generation energy storage systems.The widely commercial application and development of LSB is mainly hindered by serious“shuttle effect”of lithium polysulfides(Li PSs),slow reaction kinetics,notorious lithium dendrites,etc.In various structures of LSB materials,array structured materials,possessing the composition of ordered micro units with the same or similar characteristics of each unit,present excellent application potential for various secondary cells due to some merits such as immobilization of active substances,high specific surface area,appropriate pore sizes,easy modification of functional material surface,accommodated huge volume change,enough facilitated transportation for electrons/lithium ions,and special functional groups strongly adsorbing Li PSs.Thus many novel array structured materials are applied to battery for tackling thorny problems mentioned above.In this review,recent progresses and developments on array structured materials applied in LSBs including preparation ways,collaborative structural designs based on array structures,and action mechanism analyses in improving electrochemical performance and safety are summarized.Meanwhile,we also have detailed discussion for array structured materials in LSBs and constructed the structure-function relationships between array structured materials and battery performances.Lastly,some directions and prospects about preparation ways,functional modifications,and practical applications of array structured materials in LSBs are generalized.We hope the review can attract more researchers'attention and bring more studying on array structured materials for other secondary batteries including LSB.

Unstable evolution of railway slope under the rainfall-vibration joint action

Understanding the unstable evolution of railway slopes is the premise for preventing slope failure and ensuring the safe operation of trains.However,as two major factors affecting the stability of railway slopes,few scholars have explored the unstable evolution of railway slopes under the joint action of rainfall-vibration.Based on the model test of sandy soil slope,the unstable evolution process of slope under locomotive vibration,rainfall,and rainfall-vibration joint action conditions was simulated in this paper.By comparing and analyzing the variation trends of soil pressure and water content of slope under these conditions,the change laws of pore pressure under the influence of vibration and rainfall were explored.The main control factors affecting the stability of slope structure under the joint action conditions were further defined.Combined with the slope failure phenomena under these three conditions,the causes of slope instability resulting from each leading factor were clarified.Finally,according to the above conclusions,the unstable evolution of the slope under the rainfall-vibration joint action was determined.The test results show that the unstable evolution process of sandy soil slope,under the rainfall-vibration joint action,can be divided into:rainfall erosion cracking,vibration promotion penetrating,and slope instability sliding three stages.In the process of slope unstable evolution,rainfall and vibration play the roles of inducing and promoting slide respectively.In addition,the deep cracks,which are the premise for the formation of the sliding surface,and the violent irregular fluctuation of soil pressure,which reflects the near penetration of the sliding surface,constitute the instability characteristics of the railway slope together.This paper reveals the unstable evolution of sandy soil slopes under the joint action of rainfall-vibration,hoping to provide the theoretical basis for the early warning and prevention technology of railway slopes.

Actionable Genotypes and Their Association with Life Span in Iceland

Background:In 2021,the American College of Medical Genetics and Genomics(ACMG)recommended reporting actionable genotypes in 73 genes associated with diseases for which preventive or therapeutic measures are available.Evaluations of the association of actionable genotypes in these genes with life span are currently lacking.Methods:We assessed the prevalence of coding and splice variants in genes on the ACMG Secondary Findings,version 3.0(ACMG SF v3.0),list in the genomes of 57,933 Icelanders.We assigned pathogenicity to all reviewed variants using reported evidence in the ClinVar database,the frequency of variants,and their associations with disease to create a manually curated set of actionable genotypes(variants).We assessed the relationship between these genotypes and life span and further examined the specific causes of death among carriers.

Quantization of Action for Elementary Particles and the Principle of Least Action

The uncertainty principle is a fundamental principle of quantum mechanics, but its exact mathematical expression cannot obtain correct results when used to solve theoretical problems such as the energy levels of hydrogen atoms, one-dimensional deep potential wells, one-dimensional harmonic oscillators, and double-slit experiments. Even after approximate treatment, the results obtained are not completely consistent with those obtained by solving Schrödinger’s equation. This indicates that further research on the uncertainty principle is necessary. Therefore, using the de Broglie matter wave hypothesis, we quantize the action of an elementary particle in natural coordinates and obtain the quantization condition and a new deterministic relation. Using this quantization condition, we obtain the energy level formulas of an elementary particle in different conditions in a classical way that is completely consistent with the results obtained by solving Schrödinger’s equation. A new physical interpretation is given for the particle eigenfunction independence of probability for an elementary particle: an elementary particle is in a particle state at the space-time point where the action is quantized, and in a wave state in the rest of the space-time region. The space-time points of particle nature and the wave regions of particle motion constitute the continuous trajectory of particle motion. When an elementary particle is in a particle state, it is localized, whereas in the wave state region, it is nonlocalized.

Impact of Earthquake Action on the Design and Sizing of Jointed Masonry Structures in South Kivu, DRC

This article deals with the investigation of the effects of seismic impacts on the design and dimensioning of structures in South Kivu. The starting point is the observation of an ambivalence that can be observed in the province, namely the non-consideration of seismic action in the study of structures by both professionals and researchers. The main objective of the study is to show the importance of dynamic analysis of structures in South Kivu. It adopts a meta-analytical approach referring to previous researches on South Kivu and proposes an efficient and optimal method. To arrive at the results, we use Eurocode 7 and 8. In addition, we conducted static analysis using the Coulomb method and dynamic analysis using the Mononobe-Okabe method and compared the results. At Nyabibwe, the results showed that we have a deviation of 24.47% for slip stability, 12.038% for overturning stability and 9.677% for stability against punching through a weight wall.