Non-thermal plasma as a new food preservation method, Its present and future prospect

      Atmospheric pressure plasma (APP) is an emerging non thermal technology for the improvement of food  safety. Non-thermal plasma (NTP) is a neutral ionized gas that comprises highly reactive spices including, positive ions, negative ions, free radicals, electrons, excited or non excited molecules and photons at or near room temperature. NTP can be generated at atmospheric pressure that makes it more applicable. Moreover, it could be employed in inactivation of microorganisms on the surface of fresh and processed foods. However, for the reason that there are few studies on the application of this technology in real food systems, the effects of non-thermal plasma on nutritional and chemical properties of food is not known well. Furthermore, the studies which explore the safety and cost aspects of this technology could help it become widespread in food industry. This paper will attempt to provide a review of atmospheric pressure cold plasma, its application in microbial inactivation,food preservation and future prospect of this new technology.

Content wizard: Concept-based recommender system for instructors of programming courses

Authoring an adaptive educational system is a complex process that involves allocating a large range of educational content within a fixed sequence of units. In this paper, we describe Content Wizard, a concept-based recommender system for recommending learning materials that meet the instructor's pedagogical goals during the creation of an online programming course. Here, the instructors are asked to provide a set of code examples that jointly re.ect the learning goals that are associated with each course unit. The Wizard is built on top of our course-authoring tool, and it helps to decrease the time instructors spend on the task and to maintain the coherence of the sequential structure of the course. It also provides instructors with additional information to identify content that might be not appropriate for the unit they are creating. We conducted an o.- line study with data collected from an introductory Java course previously taught at the University of Pittsburgh in order to evaluate both the practicality and effectiveness of the system. We found that the proposed recommendation's performance is relatively close to the teacher's expectation in creating a computer-based adaptive course

Knowledge Maximizer: Concept-Based Adaptive Problem Sequencing for Exam Preparation

To support introductory Java programming students in preparing for their exams, we developed Knowledge Maximizer as a concept-based problem sequencing tool that considers a fine-grained concept-level model of student knowledge accumulated over the semester and attempts to bridge the possible knowledge gaps in the most efficient way. This paper presents the sequencing approach behind the Knowledge Maximizer and its classroom evaluation

Exploring Problem Solving Paths in a Java Programming Course

Assessment of students’ programming submissions has been the focus of interest in many studies. Although the final submissions capture the whole program, they often tell very little about how it was developed. In this paper, we are able to look at intermediate programming steps using a unique dataset that captures a series of snapshots showing how students developed their program over time. We assessed each of these intermediate steps and performed a fine-grained concept-based analysis on each step to identify the most common programming paths. Analysis of results showed that most of the students tend to incrementally build the program and improve its correctness. This finding provides us with evidence that intermediate programming steps are important, and need to be taken into account for not only improving user modelling in educational programming systems, but also for providing better feedback to students

Program Construction Examples in Computer Science Education: From Static Text to Adaptive and Engaging Learning Technology

My dissertation is situated in the field of computer science education research, specifically, the learning and teaching of programming. This is a critical area to be studied, since, primarily, learning to program is difficult, but also, the need for programming knowledge and skills is growing, now more than ever. This research is particularly focused on how to support a student's acquisition of program construction skills through worked examples, one of the best practices for acquiring cognitive skills in STEM areas. While learning from examples is superior to problem-solving for novices, it is not recommended for intermediate learners with sufficient knowledge, who require more attention to problem-solving. Thus, it is critical for example-based learning environments to adapt the amount and type of assistance given to the student's needs. This important matter has only recently received attention in a few select STEM areas and is still unexplored in the programming domain. The learning technologies used in programming courses mostly focus on supporting student problem-solving activities and, with few exceptions, examples are mostly absent or presented in a static, non-engaging form. To fill existing gaps in the area of learning from programming examples, my dissertation explores a new genre of worked examples that are both adaptive and engaging, to support students in the acquisition of program construction skills. My research examines how to personalize the generation of examples and how to determine the best sequence of examples and problems, based on the student's evolving level of knowledge. It also includes a series of studies created to assess the effectiveness of the proposed technologies and, more broadly, to investigate the role of worked examples in the process of acquiring programming skills. Results of our studies show the positive impact that examples have on student engagement, problem-solving, and learning. Adaptive technologies were also found to be beneficial: The adaptive generation of examples had a positive impact on learning and problem-solving performance. The adaptive sequencing of examples and problems engaged students more persistently in activities, resulting in some positive effects on learning

Investigating Automated Student Modeling in a Java MOOC

With the advent of ubiquitous web, programming is no longer a sole prerogative of computer science schools. Scripting languages are taught to wider audiences and programming has become a flag post of any technology related program. As more and more students are exposed to coding, it is no longer a trade of the select few. As a result, students who would not opt for a coding class a decade ago are in a position of having to learn a rather difficult subject. The problem of assisting students in learning programming has been explored in several intelligent tutoring systems. The key component of such systems is a student model that keeps track of student progress. In turn, the foundation of a student model is a domain model – a vocabulary of skills (or concepts) that structures the representation of student knowledge. Building domain models for programming is known as a complicated task. In this paper we explore automated approaches for extracting domain models for learning programming languages and modeling student knowledge in the process of solving programming exercises. We evaluate the validity of this approach using large volume of student code submission data from a MOOC on introductory Java programming

Longitudinal Machine Learning Model for Predicting Systolic Blood Pressure in Patients with Heart Failure

Objective: Systolic blood pressure (SBP) is a powerful prognostic factor in heart failure (HF) patients, which is associated with death and readmission. Therefore, control of blood pressure is an important element for managing these patients. The goal of this study was to compare the performance of classical and machine learning models for predicting SBP and identify important variables related to SBP changes over time. Methods: The information of 483 HF patients was analyzed in this retrospective cohort study. These patients were hospitalized at least twice in Farshchian Heart Center Hamadan province, the west of Iran, between October 2015 and July 2019. We applied a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR) for predicting SBP. The performance of both models was assessed by mean absolute error, and root mean squared error. Results: Based on LMM results, there was a significant association between sex, body mass index (BMI), sodium, time, and history of hypertension with SBP changes over time (P-value <0.05). Also, MLS-SVR indicated that the four most important variables were history of hypertension, sodium, BMI, and triglyceride. The performance of MLS-SVR compared to LMM was better in both training and testing datasets. Conclusions: According to our results, BMI, sodium, and history of hypertension were the important variables on SBP changes in both LMM and MLS-SVR models. Also, it seems that MLS-SVR can be used as an alternative for classical longitudinal models for predicting SBP in HF patients