The influence of resistance exercise training prescription variables on skeletal muscle mass,strength,and physical function in healthy adults:An umbrella review

Purpose:The aim of this umbrella review was to determine the impact of resistance training(RT)and individual RT prescription variables on muscle mass,strength,and physical function in healthy adults.Methods:Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,we systematically searched and screened eligible systematic reviews reporting the effects of differing RT prescription variables on muscle mass(or its proxies),strength,and/or physical function in healthy adults aged>18 years.Results:We identified 44 systematic reviews that met our inclusion criteria.The methodological quality of these reviews was assessed using A Measurement Tool to Assess Systematic Reviews;standardized effectiveness statements were generated.We found that RT was consistently a potent stimulus for increasing skeletal muscle mass(4/4 reviews provide some or sufficient evidence),strength(4/6 reviews provided some or sufficient evidence),and physical function(1/1 review provided some evidence).RT load(6/8 reviews provided some or sufficient evidence),weekly frequency(2/4 reviews provided some or sufficient evidence),volume(3/7 reviews provided some or sufficient evidence),and exercise order(1/1 review provided some evidence)impacted RT-induced increases in muscular strength.We discovered that 2/3 reviews provided some or sufficient evidence that RT volume and contraction velocity influenced skeletal muscle mass,while 4/7 reviews provided insufficient evidence in favor of RT load impacting skeletal muscle mass.There was insufficient evidence to conclude that time of day,periodization,inter-set rest,set configuration,set end point,contraction velocity/time under tension,or exercise order(only pertaining to hypertrophy)influenced skeletal muscle adaptations.A paucity of data limited insights into the impact of RT prescription variables on physical function.Conclusion:Overall,RT increased muscle mass,strength,and physical function compared to no exercise.RT intensity(load)and weekly frequency impacted RT-induced increases in muscular strength but not muscle hypertrophy.RT volume(number of sets)influenced muscular strength and hypertrophy.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Indirect neutron radiography experiment on dummy nuclear fuel rods for pressurized water reactors at CMRR

Nuclear energy is a vital source of clean energy that will continue to play an essential role in global energy production for future generations.Nuclear fuel rods are core components of nuclear power plants,and their safe utilization is paramount.Due to its inherent high radioactivity,indirect neutron radiography(INR)is currently the only viable technology for irradiated nuclear fuel rods in the field of energy production.This study explores the experimental technique of indirect neutron computed tomography(INCT)for radioactive samples.This project includes the development of indium and dysprosium conversion screens of different thicknesses and conducts resolution tests to assess their performance.Moreover,pressurized water reactor(PWR)dummy nuclear fuel rods have been fabricated by self-developing substitute materials for cores and outsourcing of mechanical processing.Experimental research on the INR is performed using the developed dummy nuclear fuel rods.The sparse reconstruction technique is used to reconstruct the INR results of 120 pairs of dummy nuclear fuel rods at different angles,achieving a resolution of 0.8 mm for defect detection using INCT.

Investigations of the mechanical response of dummy HTPB propellant grain under ultrahigh acceleration overload conditions using onboard flight-test measurements

In this paper,to study the mechanical responses of a solid propellant subjected to ultrahigh acceleration overload during the gun-launch process,specifically designed projectile flight tests with an onboard measurement system were performed.Two projectiles containing dummy HTPB propellant grains were successfully recovered after the flight tests with an ultrahigh acceleration overload value of 8100 g.The onboard-measured time-resolved axial displacement,contact stress and overload values were successfully obtained and analysed.Uniaxial compression tests of the dummy HTPB propellant used in the gunlaunched tests were carried out at low and intermediate strain rates to characterize the propellant's dynamic properties.A linear viscoelastic constitutive model was employed and applied in finite-element simulations of the projectile-launching process.During the launch process,the dummy propellant grain exhibited large deformation due to the high acceleration overload,possibly leading to friction between the motor case and propellant grain.The calculated contact stress showed good agreement with the experimental results,though discrepancies in the overall displacement of the dummy propellant grain were observed.The dynamic mechanical response process of the dummy propellant grain was analysed in detail.The results can be used to estimate the structural integrity of the analysed dummy propellant grain during the gun-launch process.

Mid-infrared Period-Luminosity Relations of Gaia DR3 Long Period Variables

Long period variable(LPV)stars are very promising distance indicators in the infrared bands.We selected asymptotic giant branch(AGB)stars in the Large and Small Magellanic Cloud(LMC and SMC)from the Gaia Data Release 3 LPV catalog,and classified them into oxygen-rich(O-rich)and carbon-rich(C-rich)AGB stars.Using the Wide-field Infrared Survey Explorer database,we determined the W1-and W2-band period-luminosity relations(PLRs)for each pulsation-mode sequence of AGB stars.The dispersion of the PLRs of O-rich AGB stars in sequences C'and C is relatively small,around 0.14 mag.The PLRs of LMC and SMC are consistent in each sequence.In the W2 band,the PLR of large-amplitude C-rich AGB stars is steeper than that of small-amplitude C-rich AGB stars,due to their more circumstellar dust.By two methods,we find that some PLR sequences of O-rich AGB stars in the LMC are dependent on metallicity.The coefficients of the metallicity effect areβ=-0.533±0.213 mag dex~1andβ=-0.767±0.158 mag dex~1for sequence C in W1 and W2 bands,respectively.The significance of the metallicity effect in W1 band for the four sequences is 2.2-3.5σ.Both of these imply that distance measurements using O-rich Mira may need to take the metallicity effect into account.

Responses of Annual Variability of Vegetation NPP to Climate Variables Using Satellite Techniques in Gadarif State, Sudan

Plants play an essential role in matter and energy transformations and are key messengers in the carbon and energy cycle. Net primary productivity (NPP) reflects the capability of plants to transform solar energy into photosynthesis. It is very sensible for factors affecting on vegetation variability such as climate, soils, plant characteristics and human activities. So, it can be used as an indicator of actual and potential trend of vegetation. In this study we used the actual NPP which was derived from MODIS to assess the response of NPP to climate variables in Gadarif State, from 2000 to 2010. The correlations between NPP and climate variables (temperature and precipitation) are calculated using Pearson’s Correlation Coefficient and ordinary least squares regression. The main results show the following 1) the correlation Coefficient between NPP and mean annual temperature is Somewhat negative for Feshaga, Rahd, Gadarif and Galabat areas and weakly negative in Faw area;2) the correlation Coefficient between NPP and annual total precipitation is weakly negative in Faw, Rahd and Galabat areas and somewhat negative in Galabat and Rahd areas. This study demonstrated that the correlation analysis between NPP and climate variables (precipitation and temperature) gives reliably result of NPP responses to climate variables that is clearly in a very large scale of study area.

Fermions: Spin, Hidden Variables, Violation of Bell’s Inequality and Quantum Entanglement

Using real fields instead of complex ones, it was recently claimed, that all fermions are made of pairs of coupled fields (strings) with an internal tension related to mutual attraction forces, related to Planck’s constant. Quantum mechanics is described with real fields and real operators. Schrodinger and Dirac equations then are solved. The solution to Dirac equation gives four, real, 2-vectors solutions ψ1=(U1D1)ψ2=(U2D2)ψ3=(U3D3)ψ4=(U4D4)where (ψ1,ψ4) are coupled via linear combinations to yield spin-up and spin-down fermions. Likewise, (ψ2,ψ3) are coupled via linear combinations to represent spin-up and spin-down anti-fermions. For an incoming entangled pair of fermions, the combined solution is Ψin=c1ψ1+c4ψ4where c1and c4are some hidden variables. By applying a magnetic field in +Z and +x the theoretical results of a triple Stern-Gerlach experiment are predicted correctly. Then, by repeating Bell’s and Mermin Gedanken experiment with three magnetic filters σθ, at three different inclination angles θ, the violation of Bell’s inequality is proven. It is shown that all fermions are in a mixed state of spins and the ratio between spin-up to spin-down depends on the hidden variables.

Complete Convergenceand Complete Moment Convergence for Weighted Sums of ANA Random Variables

In this paper,we investigate the complete convergence and complete moment conver-gence for weighted sums of arrays of rowwise asymptotically negatively associated(ANA)random variables,without assuming identical distribution.The obtained results not only extend those of An and Yuan[1]and Shen et al.[2]to the case of ANA random variables,but also partially improve them.

Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Wave propagation of a functionally graded plate via integral variables with a hyperbolic arcsine function

Several studies on functionally graded materials(FGMs)have been done by researchers,but few studies have dealt with the impact of the modification of the properties of materials with regard to the functional propagation of the waves in plates.This work aims to explore the effects of changing compositional characteristics and the volume fraction of the constituent of plate materials regarding the wave propagation response of thick plates of FGM.This model is based on a higher-order theory and a new displacement field with four unknowns that introduce indeterminate integral variables with a hyperbolic arcsine function.The FGM plate is assumed to consist of a mixture of metal and ceramic,and its properties change depending on the power functions of the thickness of the plate,such as linear,quadratic,cubic,and inverse quadratic.By utilizing Hamilton’s principle,general formulae of the wave propagation were obtained to establish wave modes and phase velocity curves of the wave propagation in a functionally graded plate,including the effects of changing compositional characteristics of materials.

The Determination Method of Product Engineering Features Based on Linguistic Variables

To overcome the problem of imprecise and unclear information in the development of quality functions,a method for determining the priority of engineering features based on mixed linguistic variables is proposed.First,the evaluation member uses the determined linguistic variable to give the correlation strength evaluation matrix of customer requirements and engineering features.Secondly,the relative importance of the evaluation member and customer requirements are aggregated.Finally,the priority of engineering features is obtained by calculating the deviation.The feasibility and practicability of this method are proven by taking the design of a new product of a long bag low-pressure pulse dust collector as an example.

基于自适应滑模控制器的PMSM调速策略研究

为了提高永磁同步电机的调速性能,滑模控制逐渐取代PI控制被引入控制系统中。但是传统滑模控制中趋近律的趋近速度慢与固有抖振问题制约着滑模控制的发展,为此提出一种改进的滑模趋近律。首先,为了解决传统等速趋近律中趋近速度和抖振难以平衡的问题,在所提趋近律中引入系统状态变量,并在滑模增益中引入指数项,使其在保证趋近速度的同时抑制滑模抖振。其次,由于开关函数在零点附近具有不连续的特性,会引起滑模抖振,为此采用sat函数代替常规开关函数,能够在保证响应速度的前提下抑制滑模抖振。然后,基于所提趋近律,设计永磁同步电机的滑模速度控制器。最后,仿真和实验结果表明,所提趋近律可以提高永磁同步电机调速系统的控制性能。

端粒长度与10种常见肌肉骨骼疾病的关系孟德尔随机化分析

背景:多项观察性研究表明,端粒长度与肌肉骨骼疾病之间存在潜在的关联,然而它们之间的潜在机制仍不清楚。目的:利用两样本孟德尔随机化分析来探索端粒长度与肌肉骨骼疾病之间的遗传因果关系。方法:从英国生物银行中获得端粒长度的全基因组关联研究汇总数据。从FinnGen财团中获得了关于10种常见肌肉骨骼疾病(骨坏死、骨髓炎、骨质疏松、类风湿关节炎、腰痛、椎管狭窄、痛风、肩周炎、强直性脊柱炎和下肢深静脉血栓)的全基因组关联研究汇总数据。使用逆方差加权、孟德尔随机化-Egger和加权中位数方法评估端粒长度与10种肌肉骨骼疾病的因果关系,逆方差加权作为主要的孟德尔随机化分析方法,并进行敏感性分析探讨结果稳健性。结果与结论:①逆方差加权法结果表明,遗传预测的端粒长度与类风湿关节炎(OR=0.78,95%CI:0.64-0.95,P=0.015)和骨坏死(OR=0.56,95%CI:0.36-0.90,P=0.016)风险之间存在负向因果关系,但未发现端粒长度与其他8种肌肉骨骼疾病之间存在因果关系(P均>0.05)。②敏感性分析结果表明因果关系稳健,孟德尔随机化-Egger截距分析未检测到潜在的水平多效性(P均>0.05)。③此项孟德尔随机化研究支持端粒长度对类风湿关节炎和骨坏死的保护作用的结论,然而,未来将需要更多的基础和临床研究来验证。

不同施肥效应处理时长白落叶松干形变化

通过建立通用性树干削度方程,探究不同施肥处理对长白落叶松(Larix olgensis)人工林干形的影响。依据黑龙江省佳木斯市孟家岗林场不同施肥处理时(施用N肥、NP肥、NPK肥、空白对照)落叶松人工林解析木数据,在林业常用的削度方程模型中筛选最优基础模型,利用哑变量法和非线性混合效应模型法优化模型,分析不同施肥处理对不同等级木的影响。结果表明:基础模型拟合中,Kozak(2004)方程的拟合效果最好,引入哑变量模型、施肥水平、样木水平及施肥样木两水平的混合效应模型均能提高模型拟合效果。其中,依据施肥和样木嵌套两水平的混合效应模型预估精度最高(R^(2)=0.991),且消除了大部分观测值间的自相关性。树干模拟时发现,施肥对优势木(胸径(D_(dh))≥18 cm)干形的影响最大,其次是平均木(11 cm≤D_(dh)<18 cm)、被压木(D_(dh)<11 cm)。施用NP肥明显增加优势木和平均木树干上部(相对高度0.6~1.0)直径;N肥及NPK肥的施用效果相似,但均略低于施用NP肥。依据施肥和样木嵌套两水平的混合效应模型在预测树干不同位置直径时精度最高。不同施肥处理对人工落叶松干形的影响更多集中在树干中上部分。可以对优势木施用氮磷肥,对平均木和被压木施用氮肥。

Hybrid-Ⅲ型Dummy刚性有限元模型的研究改进

Hybrid-Ⅲ型Dummy刚性有限元模型因具有独特的优点已被广泛地用于评定汽车乘员安全系统有效性的模拟研究中。在汽车安全带的模拟中发现现有模型存在较大的缺陷。本文提供了一个改进模型,利用实验结果验证,改进模型是合理有效的。该模型可为乘员安全保护系统的评估提供更为精确的模拟结果。

Plant Traits and Soil Chemical Variables During a Secondary Vegetation Succession in Abandoned Fields on the Loess Plateau

Species dynamics in terms of both plant biological traits, ecological strategies and species richness as well as soil chemical variables during a secondary succession in abandoned fields on the Loess Plateau along a temporal sere from 3 a to 149 a were studied. The results indicated that (I) Soil total C and N increased while soil pH, total K and Na decreased with years since abandonment. No noticeable trend was found in the case of soil P along the successional sere. On the other hand, total CaO of the surface layer (0 - 10 cm) decreased, but that of the two deeper layer, (20 - 30 cm, 40 - 50 cm) increased with years since abandonment. Soil C, N, K and P decreased, while Na, CaO and soil pH increased with increasing soil depth. (2) Species richness peaked at both mid-stage of the successional sere and the intermediate portion of soil chemical variables gradient. (3) An ideal dominant species in the early successional stage were annuals with stable seed pool, CR-life strategy, S-regeneration strategy, and strong competitive ability on relatively poor soil, while perennials capable of intensive lateral spread and colonal ability, requiring high nutrient supply, and having Clife strategy would be the dominant species in the subsequent stages. Plant traits, such as perennial-life history, C-, CR-, SC-, SR-, S- and R-life strategies, W-, S-, Bs- VBs- and V-regeneration strategies, were over- represented throughout the whole sere among the other species. (4) Some traits, such as C-, SC-life strategies, ability of clonality, perennial-life history, well-developed lateral spread ability, V- and VBs-regeneration strategies, seed animal. dispersal mode, flowering time of autumn, fruit types of legumen and nut, were more or less correlated with increased soil total C, N and K, while S-, SR-, R-, CR-life strategies, annual-, biannual-life history, non-clonal ability, S-regeneration strategy, poor lateral spread ability, and fruit types of utricle, capsule were associated with increased soil total Na, CaO and pH. The results suggested that steppes should be the dominant native vegetation coinciding with the large-scaled eco-climatic conditions on the Loess Plateau.

Multiple attribute decision making method based on trapezoid fuzzy linguistic variables

The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variables（TFLV）, is studied. The formula of the degree of possibility between two TFLVs is defined, and some of its characteristics are studied. Based on the degree of possibility of fuzzy linguistic variables, an approach to ranking the decision alternatives in multiple attribute decision making with TFLV is developed. The trapezoid fuzzy linguistic weighted averaging （TFLWA） operator method is utilized to aggregate the decision information, and then all the alternatives are ranked by comparing the degree of possibility of TFLV. The method can carry out linguistic computation processes easily without loss of linguistic information, and thus makes the decision results reasonable and effective. Finally, the implementation process of the proposed method is illustrated and analyzed by a practical example.