Towards a classification of sustainable software development process using manifold machine learning techniques

With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, I have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error.

The effect of Bonyan-Method Experiential Marathon Structured Groups (BEMSG) on ego functions

The present research has studied Bonyan-Method Experiential Marathon Structured Groups' efficacy on the nonclinical populations' ego functions. This study was a quasi-experimental trial with a control group. The trial group participated in the marathon group on three consecutive days (36 hours) and weekly sessions for three weeks. Then the ego function evaluation questionnaire was simultaneously given to both groups. All ego functions in the trial group showed significant growth compared to the control group. Among these, the most remarkable statistical effect size was related to "Adaptive Regression in Service of the Ego" and "Stimulus barrier." The relationship between improving ego functions and mental health can be anticipated, and steps can be taken to promote the community’s mental health by using these groups.

Serum Creatinine Reference Limits in Pediatric Population—A Single Center Electronic Health Record-Based Database in Taiwan

Objective: To assess age- and sex-specific serum creatinine levels in a pediatric population using a hospital-based database in Taiwan.Study Design: Data on serum creatinine levels were obtained from the National Taiwan University Hospital-integrated Medical Database (NTUH-iMD). Due to the possibility of having acute kidney injury or chronic kidney disease, individuals with multiple serum creatinine measurements were excluded, and outliers in each age- and sex-specific group were also subsequently removed. The remaining creatinine measurements in each group were analyzed, and 95% reference limits were established.Results: Serum creatinine data of individuals aged between 1 month and 18 years from May 2011 to January 2018 were retrieved. After applying the exclusion criteria, 27,911 individuals with a single corresponding serum creatinine measurement were enrolled. Creatinine level reference limits for each age- and sex-specific group were generated. The upper reference limits (URLs), which are particularly useful in clinical practice, followed the natural trend of increasing serum creatinine with age.Conclusion: We generated serum creatinine reference limits from a single hospital-integrated medical database in Taiwan for different age- and sex-specific groups of children. Our results will aid physicians in clinical practice regarding renal function evaluation, especially for patients without a recent baseline serum creatinine level.

A General Framework for Approximating Min Sum Ordering Problems

We consider a large family of problems in which an ordering (or, more precisely, a chain of subsets) of a finite set must be chosen to minimize some weighted sum of costs. This family includes variations of min sum set cover, several scheduling and search problems, and problems in Boolean function evaluation. We define a new problem, called the min sum ordering problem (MSOP), which generalizes all these problems using a cost and a weight function defined on subsets of a finite set. Assuming a polynomial time α-approximation algorithm for the problem of finding a subset whose ratio of weight to cost is maximal, we show that under very minimal assumptions, there is a polynomial time [Formula: see text]-approximation algorithm for MSOP. This approximation result generalizes a proof technique used for several distinct problems in the literature. We apply this to obtain a number of new approximation results. Summary of Contribution: This paper provides a general framework for min sum ordering problems. Within the realm of theoretical computer science, these problems include min sum set cover and its generalizations, as well as problems in Boolean function evaluation. On the operations research side, they include problems in search theory and scheduling. We present and analyze a very general algorithm for these problems, unifying several previous results on various min sum ordering problems and resulting in new constant factor guarantees for others.

How Does Stroke Affect Skeletal Muscle? State of the Art and Rehabilitation Perspective

Long-term disability caused by stroke is largely due to an impairment of motor function. The functional consequences after stroke are caused by central nervous system adaptations and modifications, but also by the peripheral skeletal muscle changes. The nervous and muscular systems work together and are strictly dependent in their structure and function, through afferent and efferent communication pathways with a reciprocal “modulation.” Knowing how altered interaction between these two important systems can modify the intrinsic properties of muscle tissue is essential in finding the best rehabilitative therapeutic approach. Traditionally, the rehabilitation effort has been oriented toward the treatment of the central nervous system damage with a central approach, overlooking the muscle tissue. However, to ensure greater effectiveness of treatments, it should not be forgotten that muscle can also be a target in the rehabilitation process. The purpose of this review is to summarize the current knowledge about the skeletal muscle changes, directly or indirectly induced by stroke, focusing on the changes induced by the treatments most applied in stroke rehabilitation. The results of this review highlight changes in several muscular features, suggesting specific treatments based on biological knowledge; on the other hand, in standard rehabilitative practice, a realist muscle function evaluation is rarely carried out. We provide some recommendations to improve a comprehensive muscle investigation, a specific rehabilitation approach, and to draw research protocol to solve the remaining conflicting data. Even if a complete multilevel muscular evaluation requires a great effort by a multidisciplinary team to optimize motor recovery after stroke.

A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image Segmentation

Masi entropy is a popular criterion employed for identifying appropriate threshold values in image thresholding. However, with an increasing number of thresholds, the efficiency of Masi entropy-based multi-level thresholding algorithms becomes problematic. To overcome this, we propose a novel differential evolution (DE) algorithm as an effective population-based metaheuristic for Masi entropy-based multi-level image thresholding. Our ME-GDEAR algorithm benefits from a grouping strategy to enhance the efficacy of the algorithm for which a clustering algorithm is used to partition the current population. Then, an updating strategy is introduced to include the obtained clusters in the current population. We further improve the algorithm using attraction (towards the best individual) and repulsion (from random individuals) strategies. Extensive experiments on a set of benchmark images convincingly show ME-GDEAR to give excellent image thresholding performance, outperforming other metaheuristics in 37 out of 48 cases based on cost function evaluation, 26 of 48 cases based on feature similarity index, and 20 of 32 cases based on Dice similarity. The obtained results demonstrate that population-based metaheuristics can be successfully applied to entropy-based image thresholding and that strengthening both exploitation and exploration strategies, as performed in ME-GDEAR, is crucial for designing such an algorithm.

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

Research on the application of myofascial induction therapy in the rehabilitation of patients with acute facial palsy: A nonrandomized controlled trial

BACKGROUND: In the healthy body, the fascial system maintains elasticity and coordination of movements. If these functions are destroyed, facial paraly appears. Myofascial induction therapy (MIT), a manual physical therapy method that focuses on restoring altered fascial tissue, is prevalently and widely used in clinical treatment. OBJECTIVE: The study aimed to observe the application of MIT in the rehabilitation of patients with acute facial palsy. METHODS: Sixty-eight patients with acute facial palsy were divided into control group and manual treatment group. The control group received drug treatments, such as prednisone, methylcobalamin, and vitamin B1, and instrumental physical therapy, such as semiconductor laser, shortwave therapy, and facial muscle training. In addition to these treatments, the manual treatment group received MIT. Both groups were treated for 4 weeks. The patients were assessed using the following methods: the House-Brackmann facial nerve function evaluation, Sunnybrook facial grading system, facial nerve electrophysiological examination compound muscle action potential (CMAP) amplitude, and blink reflex (BR) R1 latency. RESULTS: House-Brackmann and Sunnybrook scores and CMAP amplitude and BRR1 latencies were significantly different between the two groups (p < 0.05). Furthermore, the manual treatment group showed greater improvement than the control group (p < 0.05). CONCLUSIONS: Treatment with MIT promoted better recovery of acute facial palsy and thus may be considered a valid rehabilitation intervention that is worthy of clinical application.