The effects of plyometric jump training on lower-limb stiffness in healthy individuals:A meta-analytical comparison

Purpose:This study aimed to examine the effects of plyometric jump training(PJT)on lower-limb stiffness.Methods:Systematic searches were conducted in PubMed,Web of Science,and Scopus.Study participants included healthy males and females who undertook a PJT programme isolated from any other training type.Results:There was a small effect size(ES)of PJT on lower-limb stiffness(ES=0.33,95%confidence interval(95%CI):0.07-0.60,z=2.47,p=0.01).Untrained individuals exhibited a larger ES(ES=0.46,95%CI:0.08-0.84,p=0.02)than trained individuals(ES=0.15,95%CI:-0.23 to 0.53,p=0.45).Interventions lasting a greater number of weeks(>7 weeks)had a larger ES(ES=0.47,95%CI:0.06-0.88,p=0.03)than those lasting fewer weeks(ES=0.22,95%CI:-0.12 to 0.55,p=0.20).Programmes with<2 sessions per week exhibited a larger ES(ES=0.39,95%CI:0.01-0.77,p=0.04)than programmes that incorporated>2 sessions per week(ES=0.20,95%CI:-0.10 to 0.50,p=0.18).Programmes with<250 jumps per week(ES=0.50,95%CI:0.02-0.97,p=0.04)showed a larger effect than programmes with250-500 jumps per week(ES=0.36,95%CI:0.00-0.72,p=0.05).Programmes with>500 jumps per week had negative effects(ES=-0.22,95%CI:-1.10 to 0.67,p=0.63).Programmes with>7.5 jumps per set showed larger effect sizes(ES=0.55,95%CI:0.02-1.08,p=0.04)than those with<7.5 jumps per set(ES=0.32,95%CI:0.01-0.62,p=0.04).Conclusion:PJT enhances lower-body stiffness,which can be optimised with lower volumes(<250 jumps per week)over a relatively long period of time(>7 weeks).

Association between ACTN3 R577X genotype and risk of non-contact injury in trained athletes:A systematic review

Purpose:The aim of this study was to review,systematically,evidence concerning the link between the ACTN3 R577X polymorphism and the rates and severity of non-contact injuries and exercise-induced muscle damage in athletes and individuals enrolled in exercise training programs.Methods:A computerized literature search was performed in the electronic databases PubMed,Web of Science,and SPORTDiscus,from inception until November 2020.All included studies compared the epidemiological characteristics of non-contact injury between the different genotypes of the ACTN3 R577X polymorphism.Results:Our search identified 492 records.After the screening of titles,abstracts,and full texts,13 studies examining the association between the ACTN3 genotypes and the rate and severity of non-contact injury were included in the analysis.These studies were performed in 6 different countries(Spain,Japan,Brazil,China,the Republic of Korea,and Italy)and involved a total participant pool of 1093 participants.Of the studies,2 studies involved only women,5 studies involved only men,and 6 studies involved both men and women.All the studies included were classified as highquality studies(≥6 points in the Physiotherapy Evidence Database(PEDro)scale score).Overall,evidence suggests there is an association between the ACTN3 R577X genotype and non-contact injury in 12 investigations.Six studies observed a significant association between A CTN3 R577X polymorphism and exercise induced muscle damage:2 with non-contact ankle injury,3 with non-contact muscle injury,and 1 with overall non-contact injury.Conclusion:The present findings support the premise that possessing the ACTN3 XX genotype may predispose athletes to a higher probability of some non-contact injuries,such as muscle injury,ankle sprains,and higher levels of exercise-induced muscle damage.

Effects of Physical Exercise and Music Therapy on Cognitive Aspects in Tunisian Patients with Mild Alzheimer’s Type Dementia

Aim: The objective of this research is to highlight the effectiveness of physical exercise and music therapy in older patients with Alzheimer’s disease (AD). Methods: Patients with a mild level of AD were included in this study, divided into the therapy group (TG;N = 30, aged 68 ± 3.2 years) and the control group (CG;N=30, aged 65 ± 2.6 years). The therapy group was enrolled in an exercise-training program (walking, resistance and balance exercises) combined with musical therapy for 10 weeks (three sessions of 60 minutes per week). The Control group was instructed to follow their daily rhythm of life (e.g., rest, reading) under the same conditions. The intervention program was enrolled under the supervision of;one psychologist;a neurologist;two music therapists, and two physiotherapists, all belonging to the same hospital unit. After 10 weeks of participation in the combined program, cognitive parameters were improved in the therapy group measured with the Behavior Pathology in Alzheimer Disease (BEHAVE-AD), (p < 0.05) for activity disturbance, diurnal rhythm disturbances, anxieties and phobias, affective disturbance. The percentage range of improvements is 1.07% to 2.96%. Results: Our results demonstrate that physical exercise combined with music therapy improves cognitive function in patients with Alzheimer’s disease. Conclusions: Physical exercise and music therapy are beneficial combined treatments for improving life quality in older patients. This approach may be useful to help patients with a mild level of Alzheimer’s disease improve their behavioral and psychological parameters.

Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such patients suffering from OLD since,after early diagnosis,breathing exercises and medical precautions can effectively improve their health state.A secure non-invasive early diagnosis of OLD is a primordial need,and in this context,digital image processing supported by Artificial Intelligence(AI)techniques is reliable and widely used in the medical field,especially for improving early disease diagnosis.Hence,this article presents an AIbased non-invasive and secured diagnosis for OLD using physiological and iris features.This research work implements different machine-learning-based techniques which classify various subjects,which are healthy and effective patients.The iris features include gray-level run-length matrix-based features,gray-level co-occurrence matrix,and statistical features.These features are extracted from iris images.Additionally,ten different classifiers and voting techniques,including hard and soft voting,are implemented and tested,and their performances are evaluated using several parameters,which are precision,accuracy,specificity,F-score,and sensitivity.Based on the statistical analysis,it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.

Performance Estimation of HEVC/h.265 Decoder in a Co-Design Flow with SADF-FSM Graphs

Multiprocessor System on Chip (MPSoC) technology presents an interesting solution to reduce the computational time of complex applications such as multimedia applications. Implementing the new High Efficiency Video Coding (HEVC/h.265) codec on the MPSoC architecture becomes an interesting research point that can reduce its algorithmic complexity and resolve the real time constraints. The implementation consists of a set of steps that compose the Co-design flow of an embedded system design process. One of the first anf key steps of a Co-design flow is the modeling phase which allows designers to make best architectural choices in order to meet user requirements and platform constraints. Multimedia applications such as HEVC decoder are complex applications that demand increasing degrees of agility and flexibility. These applications are usually modeling by dataflow techniques. Several extensions with several schedules techniques of dataflow model of computation have been proposed to support dynamic behavior changes while preserving static analyzability. In this paper, the HEVC/h.265 video decoder is modeled with SADF based FSM in order to solve problems of placing and scheduling this application on an embedded architecture. In the modeling step, a high-level performance analysis is performed to find an optimal balance between the decoding efficiency and the implementation cost, thereby reducing the complexity of the system. The case study in this case works with the HEVC/h.265 decoder that runs on the Xilinx Zedboard platform, which offers a real environment of experimentation.

Audit Quality, Timely Disclosure, and the Cost of Debt： Tunisian Evidence

The aim of this study is to investigate the effect of auditor type and eamings reporting lag on the cost of debt for the Tunisian setting. Our sample consists of 32 Tunisian companies for the period of 2003-2012. Audit quality is measured by auditor size （Big 4 versus non-Big 4） and timely disclosure is proxied by earnings reporting lag. Results show that auditor type is negatively associated with the cost of debt. By contrast, the association between earnings announcement lag and the cost of debt is positive and significant. When testing for the moderating effects of industry and listing status, we document that these associations are more pronounced for industrial companies and listed firms. Finally, the period of investigation slightly moderates the examined associations, since financial institutions become more sensitive to the tardy communication of information and less concerned with auditor type following some economic and political troubles in Tunisia between 2010 and 2012. Our findings have policy implications for managers in the Tunisian setting and other developing economies similar to Tunisia given the crucial role played by debt as an important source of external finance for companies.

IAS-IFRS Adoption Impact on Accounting Information： The Case of France

The International Accounting Standards Board （IASB） has established international standards （International Accounting Standards （lASs） and International Financial Reporting Standards （IFRSs）） to ensure more comparability and transparency and also higher-quality financial statements. The creation of such standards by the IASB aims at achieving harmonization of accounting practices among countries. The objective of this research is to show that in accordance with the expectations of international organizations, the adoption of IAS-IFRS increases the information content of financial statements and also to identify the key accounting variables that have been affected by this adoption. This article used a sample of year observations of 150 French firms which have adopted IAS-IFRS since 2005 as data to study the association relationship between accounting variables and stock returns before and after the adoption of IASs. The findings of this paper show that the application of IAS-IFRS as accounting standards increases the information content of accounting numbers. Dividends, long-term debts, equity, and revenue variables are most correlated with stock returns. Their information content has reached 80% after the adoption, while it was 30% before.

The Acute Effects of Aerobic Dance Exercise with and without Face Mask Use on Attention, Perceived Exertion and Mood States

The present study aimed to determine the effect of wearing a face mask during aerobic dance exercise on cognitive function,more specifically on attention,as well as on perceived exertion and mood states.Thirteen healthy college students(9 males and 4 females:mean age=17.5 years,height=1.72 m,weight=71.00 kg)volunteered to participate in this study.They were randomized to perform aerobic dance exercise while wearing a cloth face mask or no mask or a control condition(sitting on a comfortable chair and reading information about the health benefits of aerobic dance exercise)on three separate occasions(with at least one week of interval).Rate of perceived exertion(RPE),the Brunel Mood Scale(BRUMS)and d2 Attention assessment were assessed before and immediately after each condition.The results demonstrated higher concentration performance for the aerobic dance exercise without face mask than the control condition(p=0.05).Post RPE and BRUMS fatigue subscale values were significantly higher in the aerobic dance exercise with face mask as compared to the aerobic dance exercise without face mask and control condition(all,p<0.05).BRUMS vigor subscale value significantly differed across conditions(F=113.84,p<0.001,ES=0.86)and was significantly higher in the aerobic dance exercise group without face mask as compared to the aerobic dance exercise with face mask and the control conditions(both,p<0.001).This study suggests that face mask use during aerobic dance exercise with moderate intensity did not affect attention.Practitioners,students and athletes should avoid wearing face mask while practicing physical activity or aerobic dance exercise with moderate intensity to improve its acute effect on cognitive function.

Landslide Cartography at the Region of Nabeul-Hammamet Based on Geographic Information System and Geomatic

Landslides are one of the most significant natural damaging disasters in hilly environment [1]. The location of our study area is to the north of Tunisia, home to several manifestations of land instabilities, we bring to study this area of interest by GIS and geomatic approach to reduce social economic losses due to landslides. The performance of a cartographic data base for the landslide study in the Cap-Bon region was realized through studying geologic 1/50,000 and topographic 1/25,000 maps, aster optic Remote Sensing, land observation, and climatologic seismic data. These data will be digitalized, georeferenced, vectorized, spatially analyzed, classified and geotreated in order to produce a landslides card. The findings have shown that fields with smooth and friable lithology which are located at rather important seismic zones (more than 4 at Richter’s scale) have some stability. However, the most endangered zones are in the North-West around Oued El Kbir and El Ain. Realizing this work helps to determine the most hazardous zones so that policy makers have an effective field intervention.

Effects of 12 weeks of back-squat training program on jump performances and bone markers in female students

Background:The training program promoted improvements of jump abilities throughout the musculoskeletal system including bone markers.The aim of this study is to examine both the acute and chronic response of bone markers to resistance training program.Methods:Ten female students(age:18±0.7 years,body mass:63±3.6 kg;height:164±5.2 cm)participated in this study.They were recruited for a back-squat training program for 12 weeks,two days/week.The full-back squat protocol consisted of 3–5 sets×3–8 repetitions at 45–55%one repetition maximum.Testing sessions included a 5 jump test(5JT),standing long jump(SLJ),drop jump(DJ),and vertical jump(VJ).Results:Substantial improvements in all testing jumps(5JT:∆10%;P=0.000;ES=1.72;SLJ:∆7%;P=0.000;ES=1.33;DJ:∆11%;P=0.000;ES=0.72;VJ:∆20%;P=0.000;ES=1.84)were found during post program in comparison to pre-program results.Moreover,a significant change(P≤0.05)of bone markers during post-exercise compared to pre-exercise either before or after the training program.Only collagen type I carboxy-terminal peptide(CICP)levels elevated after the training program(pre-exercise only)compared to former levels.Conclusion:12 weeks of back-squat training program resulted greater acute improvements of jump abilities with adaptation in all musculoskeletal system including bone formation.

Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures

This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

Context Aware Wireless Sensor Network Suitable for Precision Agriculture

Using the Wireless Sensor Networks WSNs in a wide variety of applications is currently considered one of the most challenging solutions. For instance, this technology has evolved the agriculture field, with the precision agriculture challenge. In fact, the cost of sensors and communication infrastructure continuously trend down as long as the technological advances. So, more growers dare to implement WSN for their crops. This technology has drawn substantial interests by improving agriculture productivity. The idea consists of deploying a number of sensors in a given agricultural parcel in order to monitor the land and crop conditions. These readings help the farmer to make the right inputs at the right moment. In this paper, we propose a complete solution for gathering different type of data from variable fields of a large agricultural parcel. In fact, with the in-field variability, adopting a unique data gathering solution for all kinds of fields reveals an inconvenient approach. Besides, as a fault-tolerant application, precision agriculture does not require a high precision value of sensed data. So, our approach deals with a context aware data gathering strategy. In other words, depending on a defined context for the monitored field, the data collector will decide the data gathering strategy to follow. We prove that this approach improves considerably the lifetime of the application.

Dynamic Task Migration in Multi-processor Systems On-chip： Modelization and Performance Evaluation

MPSoC （multi-processor systems on-chip） are going to be the leading hardware platform featured in embedded systems, The complexity of multimedia applications implemented on MPSoC and the requirements of users complicate the designer＇s job to conceive and deliver efficient systems in the shortest time. MPSoC dedicated to specific embedded applications are facing constraints of performance and cost. In this context, in order to improve the performance of these systems, we propose a methodology for dynamic task migration in dedicated MPSoC. This methodology aims to improve the performance of execution and also reduction of energy consumption. We propose in this paper, an estimation of the real-time energy （line）. This approach is structured on four steps： （1） modeling software; （2） mapping and execution; （3） performance evaluation; （4） migration. We have using the Soclib tool to determine performance estimation of migrating software task to hardware component. Experiments on MJPEG decoder are made to illustrate the efficiency of our approach in performance estimation.

Search-Based Cost-Effective Software Remodularization

Software modularization is a technique used to divide a software system into independent modules （packages）that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new require-ments, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automaticallyoptimize the classes＇ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a newapproach to optimize software modularization by moving classes to more suitable packages. In addition to improving designquality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective initself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm （NSGA-Ⅱ）of Deb et al. to find the best sequence of refactorings that 1） maximize structural quality, 2） maximize semantic cohesivenessof packages （evaluated by a semantic measure based on WordNet）, and 3） minimize the refactoring effort. We report theresults of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce acoherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer＇s pointsof view.

Extracts from Marine Macroalgae and Opuntia ficus-indica Cladodes Enhance Halotolerance and Enzymatic Potential of Diazotrophic Rhizobacteria and Their Impact on Wheat Germination Under Salt Stress

Soil salinity, which affects more than 6% of the earth’s land surface and more than 20% of its irrigated areas, is a major threat to agriculture. Diazotrophic bacteria are among the functional groups of soil microbiota that are threatened by this abiotic stress, as their activity is mostly inhibited by salt stress. Seventy bacterial strains with distinct characteristics were isolated from soils by using N-free Jensen’s selective medium. Based on their ability to produce metabolites of agricultural interest, four strains were selected and identified as Flavobacterium johnsoniae, Pseudomonas putida, Achromobacter xylosoxidans, and Azotobacter chroococcum. The selected strains were grown at different NaCl concentrations (0–600 mmol L^(-1) in N-free broth and 0–2 000 mmol L^(-1) in Luria-Bertani medium) in the presence and absence of glycine betaine (GB), aqueous and hydro-alcoholic extracts from marine macroalgae, Ulva lactuca and Enteromorpha intestinalis, and Opuntia ficus-indica cladodes. The selected bacterial strains, GB, and the aforementioned extracts were tested for their ability to promote the germination of wheat (Triticum durum) seeds at 0–300 mmol L^(-1) NaCl. Compared with the results obtained with the synthetic osmoprotectant GB, the extracts from O. ficus-indica, U. lactuca, and E. intestinalis significantly promoted bacterial growth and seed germination under salt stress.

Exogenous approach to improve topic segmentation

Purpose-Topic segmentation is one of the active research fields in natural language processing.Also,many topic segmenters have been proposed.However,the current challenge of researchers is the improvement of these segmenters by using external resources.Therefore,the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.Design/methodology/approach-New topic segmenters(TSS-Onto and TSB-Onto)are proposed based on the two well-known segmenters C99 and TextTiling.The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource.Subsequently,an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.Findings-Based on this study,the authors showed that adding semantic knowledge,which is extracted from a domain ontology,improves the quality of topic segmentation.Moreover,TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.Research limitations/implications-The main limitation of this study is the used test corpus for the evaluation which is not a benchmark.However,we used a collection of scientific papers from well-known digital libraries(ArXiv and ACM).Practical implications-The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.Originality/value-The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge.This knowledge is extracted from an ontological external resource.

Sparse Representations-based depth images quality assessment

The conventional 2D metrics can be used for measuring the quality of depth maps,but none of them is considered to be efficient and is not accurate when used for evaluating 3D quality.In this paper,we propose a new full reference objective metric,called Sparse Representations-Mean Squared Error(SR-MSE),which efficiently evaluates the depth maps compression distortions.It adaptively models the reference and compressed depth maps in a mixed redundant transform domain dedicated to depth features.Then,it computes the mean squared error between the sparse coefficients issued from this modeling.As a benchmark of quality assessment,we perform a subjective evaluation test for depth maps compressed using the latest 3D High Efficiency Video Coding standard at various bitrates.We compare the subjective results with the proposed and conventional objective metrics.Experimental results demonstrate that the proposed SR-MSE,compared to the conventional image quality assessment metrics,yields the highest correlated scores to the subjective ones.