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.

Physico-Chemical Analysis of Soils Used for Pineapple Cultivation in the Sub-Prefectures of Maferinyah and Friguiagbé (Prefectures of Forécariah and Kindia) Republic of Guinea

Over the years, pineapple production in the Republic of Guinea has become less competitive in the West African sub-region, with a world ranking of 144th. It is therefore only natural to review certain parameters in order to improve this ranking. To do this, certain physico-chemical parameters of soil samples from Friguiagbé and Maferinyah (in the Kindia and Forécariah prefectures) were taken and analysed using the following techniques: Pipette de Robinson, Anne, Bray II, Kapen HICDVITZ, Mc. Lead (1982). The analytical results show that the soils at Friguiagbé in Kindia and Maferinyah in Forécariah are acidic, with pH values of 4.4 and 4.7 (fields I and II) and 4.8 and 4.7 (fields I and II) respectively. The soils have a silty-sandy texture. This study could therefore serve as a guide for the Ministry of Agriculture of the Republic of Guinea.

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.

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.

Physico-Chemical and Nutritional Properties of a Food Broth Based on Nere (Parkia biglobosa)

Purpose: Diet and eating habits are major risk factors for the health and the development of disease, such as, for example, metabolic disorder leading to cardiovascular pathology and cancer, decreased immunity exposing to infections. This study of the physico-chemical and nutritional properties of a soumara-based food broth was carried out with the aim of promoting the consumption of organic broth made from nere seeds (soumara). That is to alleviate certain metabolic diseases, which is a matter of food safety, and also to limit the risk for the health about the consumption of some cooking stocks on the market. Methods: Several natural ingredients such as nere seeds (soumara), ginger, black pepper, parsley and garlic were used to create a nere-based stock. All these ingredients were freeze-dried and the powder obtained was used to make the broth, regarding their physical and chemical properties. Results: The broth had a good protein content of 17.41 ± 0.367 g/100g, a lipid content of 16.80 ± 0.08 g/100g and a fiber content of 8.66 ± 0.04 g/100g. In terms of nutritional values, the broth showed good levels of calcium 184.21 ± 0.09 mg/100g, potassium 50.04 ± 1.45 mg/100g and iron and zinc. In terms of antioxidant activity, the broth also showed good antioxidant activity. Conclusion: Regarding the properties of our food broth, whose composition is based on natural ingredients, could be recommended for consumption and, its properties, could play an important role in preventing and combating certain metabolic diseases.

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.

Physico-Chemical Properties of Soils from Diguel and Koudalwa Tchad,and Phytoremediation Potentials of Acasia and Kinkeliba Plants in Removing Cr and Cu from These Soils

The aim of this study to investigate the physico-chemical properties of soils from Diguel and Koudalwa Tchad,and phytoremediation potentials of acasia and kinkeliba plants in removing Cr and Cu from these soils.Soil was sampled from the tannery plant in Diguel and agricultural land around crude oil activities in Koudalwa.Soils were characterized by the following properties:pH,(Electrical Conductivity),TDS(Total Dissolved Solids),salinity,bulk density,OM(Organic Matter),nitrate,phosphate,clay,silt,sand,and textural class.The acasia and kinkeliba plants were used to decontaminate Cu and Cr from sample soils for 30 days period.The bulk density of all the soil samples from Kouldawa was higher compared to Diguil,ranging from 1.421 to 1.64 g/cm^(3)compared to 1.21 to 1.51 g/cm^(3)for Diguel.Most of the soils in Kouldawa are mineral soils,while those of Diguel are mostly mineral soil with some organics.Diguel soils are richer in nitrate(577.9 to 2,687.32 mg/kg in Diguel and 33.64 to 197.64 mg/kg in Kouldawa)and phosphate than soils from Kouldawa.Soils from the studied areas are not saline except for a subsurface soil in Diguel(salinity,4.21;EC,4.280 dS/m;TDS,3,040 mg/L).The highest amount of Cr and Cu removed by acasia is 78%and 53.12%compared to 73.8%and 52.21%by kinkeliba.Diguel soil can be very suitable for agriculture and Kouldawa soils for construction.Acasia and kinkeliba can be effectively used to decontaminate the studied soils from heavy metal pollution.

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.

Physico-Chemical and Structural Assessment of Akono Riparian Forest Watershed, Tributary of the Nyong Basin (Centre-Cameroon) at Different Stages of Degradation

Freshwater bodies are natural resources that should be exploited to the fullest, while maintaining the sustainability of ecosystems and ecosystem services which they support. Riparian forests are more important as they contain rivers which are vital sources of fresh water for local populations. However, the quality and quantity of water issued from the watershed depend on the structural state of these forests. The aim of this work was to assess the physico-chemical and structural state of the Akono gallery forest. To achieve this, fieldwork consisted of selecting six major streams of the watershed including Ndjolong, Menyeng adzap, Emomodo, Mvila, Negbe and Ossoé kobok. On each of these, two stations, one intact and one degraded, were marked by transects. The method involved measuring Hydrometric parameters (depth, length, width) of the stream and Physico-chemical parameters of water in the streams while dendrometric parameters were measured along 100 m-transects laid using the point-centred quarter method modified for water bodies to collect tree, shrub and palm variables such as trunk diameter, crown diameter and height. Macrophytes and species identification were carried out using standard botanical procedures. Results showed that, the majority of physico-chemical parameters measured differed significantly between intact and degraded stations (P Pentachletra mancrophylla, whereas on degraded sites, this index was low and characterized by the relative dominance of species Piptadeniastrum africanum. Sorensen’s index (0.56) and CFA showed that the different stands were homogeneous. We can affirm that the riparian forests of Akono watershed are towards a state of stability notwithstanding the perpetuation of anthropological actions.

Assessment of Groundwater Physico-Chemical Quality in the Ouémé Delta (Southern-Benin)

Groundwater resources are the main sources of water used to supply drinking water to the population of the Ouémé Delta via the Continental Terminal aquifer. Urbanization, population growth, and agricultural and industrial activities have resulted in a deterioration in the quality of these resources. To assess the quality of the delta’s groundwater and its suitability for human consumption and irrigation, a total of fourteen (14) physico-chemical parameters were analyzed in some forty existing water points between September 2020 and March 2021, using standard water analysis techniques. The values obtained were compared with the potability standards recommended by the World Health Organization (WHO) and the Republic of Benin and were subjected to statistical analysis (principal component analysis (PCA)). In addition, methods for determining the suitability of water for irrigation were used. The results showed that the waters are acidic to slightly neutral and influenced by ambient temperature. In addition, the waters are moderately mineralized, with conductivities (24 - 1205 μS/cm) in line with WHO standards. A comparison of the analytical results of the WHO (2017) and Benin (2001) standards indicates that the majority of the waters studied are of good quality for all the chemical parameters considered. Nevertheless, some samples show levels of nitrates (21%), potassium (14% to 16%), calcium (13%), ammonium (12%), nitrites (8%) and bicarbonates (10%) over their respective standards. The Wilcox and Riverside diagrams indicate that the majority of waters (90%) have excellent suitability for irrigation and no negative effect on soil fertilization.

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.

Physico-Chemical Characterization of Some Honeys from the Savannah-Gallery Forest Complex and Primary Forest in Gabon

Honey harvesting throughout beekeeping is a technology recently introduced in Gabon comparing to the other African countries well known in international honey trade. The purpose of this study is to contribute to the knowledge of the quality of multi-flower honeys from the savannah-gallery forest complex and primary forest not well documented in Gabon. Physical and chemical parameters of different honeys were analyzed using the official methods of analysis of the Association of Official Analytical Chemists. The results of physico-chemical analysis show variations in pH (3.4 to 3.9), free acidity (37.12 to 76.65 meq∙kg−1), water content (17.49% to 21.21%), electrical conductivity (0.64 to 1.24 mS∙cm−1), density (1.02 to 1.03) and total sugar content (77% to 82%). These variations were significantly different (p < 0.05) between certain honeys, but independent of the type of ecosystem considered. The pH values confirmed the botanical origin of the five honeys. The physico-chemical parameters of the honeys show significant correlations (p < 0.05). Moisture content is negatively correlated with conductivity (r = −0.628), pH (r = −0.631), density (r = −0.552) and total sugar content (r = −0.890). Conductivity is positively correlated with free acidity (r = 0.688) and total sugar content (r = 0.776), and negatively correlated with water content (r = −0.628). All honeys were in line with Codex Alimentarius standards, except for 3 types of honey (M1, M2 and M4) for free acidity and electrical conductivity, showing possible fermentation of these honeys.

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.

Prediction of microstructure evolution of ZK61 alloy during hot spinning by internal state variable model

An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.

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.