A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques

Zinc-oxide nanoparticles ameliorated the phytotoxic hazards of cadmium toxicity in maize plants by regulating primary metabolites and antioxidants activity

Cadmium stress is a major threat to plant growth and survival worldwide. The current study aims to green synthesis, characterization, and application of zinc-oxide nanoparticles to alleviate cadmium stress in maize (Zea mays L.) plants. In this experiment, two cadmium levels (0, 0.6 mM) were applied to check the impact on plant growth attributes, chlorophyll contents, and concentration of various primary metabolites and antioxidants under exogenous treatment of zinc-oxide nanoparticles (25 and 50 mg L-1) in maize seedlings. Tissue sampling was made 21 days after the zinc-oxide nanoparticles application. Our results showed that applying cadmium significantly reduced total chlorophyll and carotenoid contents by 52.87% and 23.31% compared to non-stress. In comparison, it was increased by 53.23%, 68.49% and 9.73%, 37.53% with zinc-oxide nanoparticles 25, 50 mg L-1 application compared with cadmium stress conditions, respectively. At the same time, proline, superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase contents were enhanced in plants treated with cadmium compared to non-treated plants with no foliar application, while it was increased by 12.99 and 23.09%, 23.52 and 35.12%, 27.53 and 36.43%, 14.19 and 24.46%, 14.64 and 37.68% by applying 25 and 50 mg L-1 of zinc-oxide nanoparticles dosages, respectively. In addition, cadmium toxicity also enhanced stress indicators such as malondialdehyde, hydrogen peroxide, and non-enzymatic antioxidants in plant leaves. Overall, the exogenous application of zinc-oxide nanoparticles (25 and 50 mg L-1) significantly alleviated cadmium toxicity in maize. It provides the first evidence that zinc-oxide nanoparticles 25 ~ 50 mg L-1 can be a candidate agricultural strategy for mitigating cadmium stress in cadmium-polluted soils for safe agriculture practice

Comparison of dual task specific training and conventional physical therapy in ambulation of hemiplegic stroke patients: A randomized controlled trial

Objective: To compare the effectiveness of dual task specific training and conventional physical therapy in ambulation of patients with chronic stroke. Methods: The randomised controlled trial was conducted at the Habib Physiotherapy Complex, Peshawar, Pakistan, from January to August 2017, and comprised patients with chronic stroke. The patients were randomly assigned to two treatment groups. Group A received dual task training, while Group B received conventional physiotherapy. Dual task training included activities such as slowly walking backward, sideways, and forward on a smooth surface while holding a 100gm sandbag. The conventional physiotherapy included mat activities, stretching and strengthening exercises and gait training. Pre-test and post-test data was taken for both spatial and temporal variables for both groups using Time Up and Go Test and 10-meter walk test. Step length, stride length, cycle time and cadence were also calculated before and after treatment. SPSS 23 was used to analyse the data. Results: Of the 64 patients, there were 32(50%) in each of the two groups that both had 17(53%) males and 15(47%) females. Mean age in Group A was 58.28 ± 7.13 years, while in Group B it was 58.87 ± 6.13 years. Baseline parameters had no significant differences between the groups (p>0.05). Post-treatments scores revealed significant improvement of spatial and temporal variable of gait, 10-meter walk, cadence, step length, stride and cycle time in Group A compared to Group B (p<0.05 each). Continuous..

A comprehensive skills analysis of novice software developers working in the professional software development industry

Measuring and evaluating a learner’s learning ability is always the focus of every person whose aim is to develop strategies and plans for their learners to improve the learning process. For example, classroom assessments, self-assessment using computer systems such as Intelligent Tutoring Systems (ITS), and other approaches are available. Assessment of metacognition is one of these techniques. Having the ability to evaluate and monitor one’s learning is known as metacognition. An individual can then propose adjustments to their learning process based on this assessment. By monitoring, improving, and planning their activities, learners who can manage their cognitive skills are better able to manage their knowledge about a particular subject. It is common knowledge that students’ metacognitive and self-assessment skills and abilities have been extensively studied, but no research has been carried out on the mistakes that novice developers make because they do not use their self-assessment abilities enough. This study aims to assess the metacognitive skills and abilities of novice software developers working in the industry and to describe the consequences of awareness of metacognition on their performance. In the proposed study, we experimented with novice software developers and collected data using Devskiller and a self-assessment log to analyze their use of self-regulation skills. The proposed study showed that when developers are asked to reflect upon their work, they become more informed about their habitual mistakes, and using a self-assessment log helps them highlight their repetitive mistakes and experiences which allows them to improve their performance on future tasks

An enhanced multifactor multiobjective approach for software modularization

Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED)

Applying real-time dynamic scaffolding techniques during tutoring sessions using intelligent tutoring systems

An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner’s incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy

A Novel Approach to Automate Complex Software Modularization Using a Fact Extraction System

Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques

Perception, Attitude, and Confidence of Physicians About Antimicrobial Resistance and Antimicrobial Prescribing Among COVID-19 Patients:A Cross-Sectional Study From Punjab, Pakistan

Background: Patients with coronavirus disease 2019 (COVID-19) could experience multiple coinfections, and judicial antimicrobials, including antibiotics, is paramount to treat these coinfections. This study evaluated physicians’ perception, attitude, and confidence about antimicrobial resistance (AMR) and antimicrobial prescribing in patients with COVID-19. Methods: A self-administered and validated online questionnaire comprised of six sections was disseminated among physicians working in public sector hospitals in Punjab, Pakistan, using the convenience sampling method from April to May 2021. The study also assessed the validity and reliability of the study questionnaire using exploratory factor analysis and Cronbach’s alpha. In addition, the descriptive and inferential statistics present survey results. Results: A total of 387 physicians participated in this study. The study showed that the questionnaire demonstrated good internal consistency (Cronbach’s alpha = 0.77). Most physicians (n = 221, 57.1%) believed that AMR is a considerable problem in Pakistan. Less than a quarter of respondents (n = 91, 23.5%) consulted with local antibiotic resistance data to prescribe antibiotics in COVID-19 patients. However, the respondents were confident to select a suitable antibiotic (n = 229, 59.2%). More than three-quarters of the respondents believed that advice from a senior colleague ( n = 336, 86.8%), infectious disease (ID) physician (n = 315, 81.4%), and implementing antimicrobial stewardship programs (ASPs) could facilitate appropriate prescribing of antibiotics in COVID-19 patients. Multivariate logistic regression revealed that physicians with more than 10 years of experience had higher odds of consulting local guidelines for antibiotic therapy (OR, 4.71 95% CI: 1.62–13.73, p = 0.004) than physicians with less than 5 years of experience. Similar trends were found for consulting national guidelines and local resistance data to select an empiric antibiotic therapy. Conclusion: AMR-related awareness was optimal among physicians. Only a few physicians looked up local antibiotic resistance data before prescribing antibiotics to COVID-19 patients empirically. The significant approaches advised by physicians to reduce AMR risk among COVID-19 patients were the implementation of ASPs combined with advice from ID physicians