Reliable e-nose for air toxicity monitoring by filter diagonalization method

This paper introduces a compact, affordable electronic nose (e-nose) device devoted to detect the presence of toxic compounds that could affect human health, such as carbon monoxide, combustible gas, hydrogen, methane, and smoke, among others. Such artificial olfaction device consists of an array of six metal oxide semiconductor (MOS) sensors and a computer-based information system for signal acquisition, processing, and visualization. This study further proposes the use of the filter diagonalization method (FDM) to extract the spectral contents of the signals obtained from the sensors. Preliminary results show that the prototype is functional and that the FDM approach is suitable for a later classification stage. Example deployment scenarios of the proposed e-nose include indoor facilities (buildings and warehouses), compromised air quality places (mines and sanitary landfills), public transportation, mobile robots, and wireless sensor networks.

Beyond Programming: A Computer-Based Assessment of Computational Thinking Competency

Computational Thinking (CT ), entailing both domain-general and domain-specific skills, is a competency fundamental to computing education and beyond. However, as a cross-domain competency, appropriate assessment design and method remain equivocal. Indeed, the majority of the existing assessments have a predominant focus on measuring programming proficiency and neglecting other contexts in which CT can also be manifested. To broaden the promotion and practice of CT, it is necessary to integrate diverse problem types and item formats using a competency-based assessment method to measure CT. Taking a psychometric approach, this article evaluates a novel computer-based assessment of CT competency, Computational Thinking Challenge. The assessment was administered to 119 British upper secondary school students ( M = 16.11; SD = 1.19) with a range of prior programming experiences. Results from several reliability analyses, a convergent validity analysis, and a Rasch analysis, provided evidence to support the quality of the assessment. Taken together, the study demonstrated the feasibility to expand from traditional assessment methods to integrating multiple contexts, problem types, and item formats in measuring CT competency in a comprehensive manner.

Assessing Students’ Approaches and Perceptions to Learning in Physics Experiments Based on Simulations and At-Home Lab Kits

Our study evaluates students’ approaches to and perceptions of the use of hands-on at-home laboratory kits (HALK) experiments, open-source computer-based simulations (OSCBS), and their combination (OSCBS-HALK) in undergraduate introductory asynchronous online physics courses. Anonymous survey data from students who had completed online physics courses with labs based on simulations, at-home lab kits, or both were collected using a modified version of the Learn Questionnaire (MVLQ). Findings in this study indicate that among the six scales (interest and relevance; peer support; staff enthusiasm and support; teaching for understanding; alignment; and constructive feedback) used to measure students’ perceptions of the teaching and learning environments, interest and relevance, peer support, and teaching for understanding had statistically significant different means across the three lab types. Post-hoc comparisons using the Tukey HSD test for the interest and relevance scale indicated that students viewed using a combination approach of OSCBS and HALK labs (M = 3.98, SD = 0.61) more significantly positive than using computer-simulated labs only (M = 3.56, SD = 0.75). Compared to other labs, computer-simulated labs were perceived to lead to a deep approach to learning. However, they had the lowest interest and relevance, peer support, and alignment ranking among the three lab groups. Thus, developing strategies to improve students’ engagement and ability to translate the simulations into physical processes is recommended for OSCBS.

PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

Novel Molecular Targets of Antidepressants

Antidepressants target a variety of proteins in the central nervous system (CNS), the most important belonging to the family of G-protein coupled receptors and the family of neurotransmitter transporters. The increasing number of crystallographic structures of these proteins have significantly contributed to the knowledge of their mechanism of action, as well as to the design of new drugs. Several computational approaches such as molecular docking, molecular dynamics, and virtual screening are useful for elucidating the mechanism of drug action and are important for drug design. This review is a survey of molecular targets for antidepressants in the CNS and computer based strategies to discover novel compounds with antidepressant activity.

E-learning: Introducing Computer Use in Mathematics Lessons in Primary Education

The aim of this paper is to identify and analyze the role that the use of the computer has in stimulating the logical thinking of young schoolchildren. Through this, the purpose of the activity of solving operations with natural numbers, is to develop logical thinking, properly combining intuitive elements with abstract ones. Solving arithmetic problems, we can activate young students in the formation of skills and abilities to analyze the given situation, to intuit and discover the way to get what is required in the mathematical problem. This paper aims to prove that, if both traditional methods and computer-based teaching methods are used in the instructive-educational process, then school performance will register a significant increase in terms of quantity and quality. This experimental study started from the premise that solving arithmetic problems with the help of computer, using e-learning platforms is an important activity in the mathematics lesson in primary school through which we stimulate young students’ logical thinking.

Water management in wheat using non-traditional techniques

Water is one of the most limiting resources for agricultural production. Due to uneven distribution of rainfall, supplemental irrigation is often required to produce sustainable yield level. Timing and frequency of irrigation is one of the most important tactical decisions, which a farmer has to make to maximize profit from limited water availability. Computer based dynamic simulation models have the capability to assess management options under different environments to help in decision making. In this study, CRESS-Wheat Model V-3.5 has been utilized to quantify the optimum utilization of limited water for popular wheat genotypes of NW India for operational use in Agrometeorological Advisory services with routinely measured weather parameters.