Popular Programming Research Articles

Educational Activities in the Ljubljana University Botanic Gardens

Botanic gardens have an important role in educating people about plants. Direct contact with plants has a positive effect on the perception and understanding of plants and prevents plant blindness. In botanic gardens, pupils can learn about plants through practical work. At the Ljubljana University Botanic Gardens, educational programmes for schools have existed since 1986. Every year the Ljubljana University Botanic Gardens is visited by many different groups, from the youngest to oldest (kindergarten, primary school, graduate and professional school students, and faculties). We run around 350 guided tours for groups per year. Groups can choose either a guided tour with work sheets and workshops or they can visit the botanic gardens on their own. Most of groups are children from primary school. Ljubljana University Botanic Gardens offers programmes related to the valid school curriculum in Slovenia. Our programmes include guided tours based on level of pupils and/or practical work. The most popular time to visit is spring. But, we still have visitors in winter due to our tropical greenhouse. The most popular programme at our botanic gardens is the general tour. With our pedagogical programmes we contribute to plant knowledge, since we teach our pupils about nature.

Using Brain Waves and Computer Interface Technology as a Communication System

ABSTRACT Introduction The existing methods for individual emergency alert systems often rely on physical or voice-based human intervention, which may not be practical or safe in certain emergency situations or for people with certain rare medical conditions or disabilities. Popular voice command programs such as Siri and Alexa can be loud, drawing unwanted attention. Additionally, existing devices are limited to indoor usage, lack portability, involve multiple wires, have low noise tolerance, and offer limited customization options. This study introduces a novel method for emergency alert using brain waves. Method An electroencephalography (EEG) headset device was used to capture the user’s brain waves. After calibration, the device identifies peak brain signals and stores them for future use. When a command is triggered, the device’s Bluetooth functionality communicates with a dedicated application installed on any digital device. The user can use their thoughts to select a predefined command within the application, which is then transmitted to any local WiFi network or internet connection. Results Overall, this pilot study achieved a success rate of 96–98% for receiving the brain-computer interface (BCI) commands and sending the appropriate SMS text messages. Conclusion By leveraging these technologies, disabled individuals may access and use new technologies, starting with the ability to text message using their mind.

A research brief investigating educational television and U.S. children’s interest in science and world culture

ABSTRACT Research on educational television has overwhelmingly investigated the impact of viewing on children’s knowledge acquisition. However, this content might influence other important outcomes, such as children’s interest in learning about new topics. To investigate this possibility, we invited parents of 3- to 8-year-old children (N = 83) to answer three surveys over 1 month. The first survey included a video encouraging families to view a popular science program (Hero Elementary) or cultural program (Let’s Go Luna). The remaining surveys asked about children’s exposure to science and cultural programs, as well as their engagement with science and cultural topics in everyday conversation. Although our encouragement intervention had no impact on children’s exposure to science or cultural programs, viewing that content was associated with increased science and cultural talk over time. These findings suggest that educational television might motivate children to explore new topics of interest in non-mediated contexts.

Analysis on the Enlightenment Effect of Sesame Street on Children's Personality Exploration and Self-identity

Abstract: In recent years, with the progress of social economy and the development of education, children's mental health has increasingly become the focus of attention. As an important medium of children's education, TV programs are very popular with children, and have a profound significance in the shaping of children's health. American television program Sesame Street as a popular children's program, from the plot content, role setting, theme selection, present form, etc., conforms to the preschool childrens acceptance preferences, and successfully utilizes the medium for the key period of childrens mental development, accurately grasps the mental health characteristics of preschool childrens love of imitation. The program plays a good enlightening role in guiding children to discover their own personality and construct the self identity. The concept of "personality exploration and self-identity" is related to "self". In the pursuit of personality and self-positioning, children are guided to establish self-awareness, cultivate self-management ability and self-respect feelings. This paper mainly analyzes the enlightenment effect of Sesame Street on children's personality exploration and self-identity. The program promotes children to establish confidence, form self-identity, constitute the supporting conditions of mental health, for children to lay a solid foundation, and promote the formation of children's mental health and sound personality. At the same time, as a successful TV program that is deeply loved by children and has the significance of health education, it provides examples and references for creating more high-quality children's TV programs.

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract: Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development & Financial Analysis and then describe packages that are popular in that sector

A large-scale analysis of the effectiveness of publicly reported security patches

Public vulnerability reports assist developers in mitigating recurring threats caused by software vulnerabilities. However, security patches that lack effectiveness (1) may fail to completely resolve target vulnerabilities after application (i.e., require supplementary patches), or (2) cannot be directly applied to the codebase without modifying the patch code snippets. In this study, we systematically assessed the effectiveness of security patches from the perspective of their reliability and flexibility. We define a security patch as reliable or flexible, respectively, if it can resolve the vulnerability (1) without being complemented by additional patches or (2) without modifying the patch code snippets. Unlike previous studies that relied on manual inspection, we assess the reliability of a security patch by determining the presence of supplementary patches that complement the security patch. To evaluate flexibility, we first locate vulnerable codes in popular open-source software programs and then determine whether the security patch can be applied without any modifications. Our experiments on 8,100 security patches obtained from the National Vulnerability Database confirmed that one in ten of the collected patches lacked effectiveness. We discovered 476 (5.9%) unreliable patches that could still produce security issues after application; for 84.6% of the detected unreliable patches, the fact that a supplementary patch is required is not disclosed through public security reports. Furthermore, 377 (4.6%) security patches were observed to lack flexibility; we confirmed that 49.1% of the detected vulnerable codes required patch modifications owing to syntax diversity. Our findings revealed that the effectiveness of security patches can directly affect software security, suggesting the need to enhance the vulnerability reporting process.

Algorithm XXXX: UltimateKalman, Flexible Kalman Filtering and Smoothing Using Orthogonal Transformations

UltimateKalman is a flexible linear Kalman filter and smoother implemented in three popular programming languages: MATLAB, C, and Java. UltimateKalman is a slight simplification and slight generalization of an elegant Kalman filter and smoother that was proposed in 1977 by Paige and Saunders. Their algorithm appears to be numerically superior and more flexible than other Kalman filters and smoothers, but curiously has never been implemented or used before. UltimateKalman is flexible: it can easily handle time-dependent problems, problems with state vectors whose dimensions vary from step to step, problems with varying number of observations in different steps (or no observations at all in some steps), and problems in which the expectation of the initial state is unknown. The programming interface of UltimateKalman is broken into simple building blocks that can be used to construct filters, single or multi-step predictors, multi-step or whole-track smoothers, and combinations. The paper describes the algorithm and its implementation as well as a test suite of examples and tests.

Detecting and Explaining Python Name Errors

Python has become one of the most popular programming languages nowadays but has not received enough attention from the software engineering community. Many errors, either fixed or not yet, have been scattered in the lifetime of Python projects, including popular Python libraries that have already been reused. NameError is among one of those errors that are widespread in the Python community, as confirmed in our empirical study. Yet, our community has not put effort into helping developers mitigate its introductions. To fill this gap, we propose in this work a static analysis-based approach called DENE (short for Detecting and Explaining Name Errors) to automatically detect and explain name errors in Python projects. To this end, DENE builds control-flow graphs for Python projects and leverages a scope-aware reaching definition analysis to locate identifiers that may cause name errors at runtime and report their locations. Experimental results on carefully crafted ground truth demonstrate that DENE is effective in detecting name errors in real-world Python projects. The results also confirm that unknown name errors are still widely presented in popular Python projects and libraries, and the outputs of DENE can indeed help developers understand why the name errors are flagged as such.

Look after gardens and they look after you: Cultivating wellbeing in a pandemic through a popular TV gardening programme

Over the past few years there has been intense policy interest in the idea that nature-related activities such as gardening are good for wellbeing. Several studies have indeed found links between such activities and self-reported wellbeing. However, there are also sympathetic critiques of the limited conceptions of what kinds of activities are considered beneficial. Therefore, more attention should be paid to the social and cultural roots of the assumption that nature is good for us rather than treating connection to nature as an innate need. This article contributes to this task by showing how commonly held ideas about gardening and wellbeing were reproduced in the long-running and popular UK TV gardening programme Gardeners’ World ( GW) during 2020. As an exemplar of lifestyle programming, GW provides both information and entertainment and appeals to viewers to try out the techniques they observe on screen in their own gardens. Within the programme there is an emphasis on gardens as secluded havens from the outside world and on the attractive characteristics of individual plants. However, in many cases it is the work involved and the challenge that interests gardeners, as much as the qualities of the plants. Gardeners design a space and use it to express their identity. But the results of their actions are not guaranteed. Gardening can therefore be understood through a political ecology lens as a process whereby the social and nature constitute each other.

DrYFiT: An Excel Freeware Tool to Describe Thin Layer Drying of Foods

ABSTRACTWe introduced DrYFiT (drying data fitting tool), a Microsoft Excel freeware tool to be used for modeling thin‐layer drying of foods, which is available at https://drive.google.com/drive/folders/1ouompmNkmdmw1KMTJUnY0t8dJqSJ9iKv?usp=drive_link. There are 12 models in the tool and it can be used without any modeling and programming skills. Time and moisture ratio data can be entered and one of models available (one at a time) can be selected to describe the drying data. Parameter values, standard error of the parameters, p value and a statement that indicates whether the parameter is statistically significant or not (α = 0.05) are reported. Moreover, R2, adjusted R2 and root mean square error values are calculated for each model. Users can instantaneously observe the experimental data and the model fit on the same graph. Residual plot is given next to this graph. It is possible for the users to have the results of all models applied to drying data within a couple of minutes. The results of DrYFiT were compared with some popular software programs used for nonlinear regression and identical values (parameters, standard errors, p values, goodness‐of‐fit statistics) were obtained for 40 datasets.

Descriptive Analysis Of K-Means and Apriori Methods To Find Promotion Strategies For University Bhayangkara.

The increasing number of higher education institutions in Indonesia has intensified competition between universities. Universitas Bhayangkara Jakarta Raya (Ubhara Jaya) must develop an effective marketing plan to stand out. This study used segmentation and associative analysis on 2023 student enrolment data. The Apriori algorithm identified patterns in student preferences for study programmes, while the K-Means method categorized students based on demographics and family income. Three income-based clusters were identified: C0 ‘Already stable’ (IDR 1,000,000 - 2,500,000), C1 ‘Focus on promotion strategy’ (IDR 20,000,000), and C2 ‘Maximise promotion again’ (IDR 5,000,000 - 10,000,000). The Davies-Bouldin Index (DBI) indicated k=5 as the optimal cluster number, but k=3 was adequate with a minimal score difference. The most popular programmes were Communication Science and Management, with high support and confidence values. This data helps Ubhara Jaya manage study programme demand and room availability. Combining K-Means and Apriori algorithms is expected to enhance data segmentation[1] and support effective marketing strategies, aiding strategic decisions in higher education marketing.

Computational linguistics and natural language processing techniques for semantic field extraction in Arabic online news

The research aimed to extract semantic fields from Arabic online news and advance Natural Language Processing (NLP) applications in understanding and managing news information effectively. It provides a comprehensive approach to processing and analyzing large volumes of Arabic news data by integrating semantic field analysis, NLP, and computational linguistics. Using quantitative methods, Arabic news articles were collected and processed with Python, a popular programming language in data analysis, and applied various NLP techniques and machine learning models to accurately extract semantic fields. The primary objective was to evaluate the effectiveness of different classification models in categorizing Arabic news and to identify the most suitable model for semantic field extraction. The research evaluated five classification models: Naive Bayes, Support Vector Machine (SVM), Logistic Regression, Random Forest, and Gradient Boosting. Among these, SVM achieves the highest overall accuracy of 90%. Specifically, SVM demonstrated exceptional performance in categorizing sports-related news, with a 99% probability and an F1-Score of 98%. However, it faced challenges in categorizing health and science news, achieving a lower F1-Score of 79%. Overall, the study demonstrated the effectiveness of computational methods, particularly SVM, in classifying Arabic news and extracting semantic fields, thereby advancing NLP and computational linguistics. The findings highlighted the potential of SVM for accurate news analysis and the need for further enhancement of NLP techniques to address multilingual and domain-specific challenges.

Logical methods of forensic expertise of Android devices

Mobile devices are now so ubiquitous that they have revolutionized the way we do most of our activities. As a result, the mobile device is now a huge repository of confidential and personal information about its owner. This has led to the development of mobile forensics, which focuses on the recovery and investigation of mobile data. The purpose of the diagnostic process is to retrieve and recover any information from a mobile device without changing the data on the device. Over the years, digital forensics has expanded with the rapid growth of mobile devices. There are different branches of digital forensics depending on the type of digital device, such as computer forensics, network forensics, mobile forensics, etc. Data obtained from telephones becomes an invaluable source of evidence for investigations in criminal and civil cases. It's rare to conduct digital forensics without using a phone. Third-party applications are an integral part of the investigation mobile device. This requires understanding where app data is stored on the device, what app data is stored for that platform, and which tool best helps uncover the evidence. Although some commercial tools, such as Magnet IEF, are known for supporting application parsing, no tool is perfect, and it is nearly impossible for tools to keep up with the frequent updates that are released for each application. The most commonly available commercial tools analyze the most popular programs on the market. For example, when Facebook acquired WhatsApp, Cellebrite, IEF, and Oxygen Forensics started supporting the app. This is where all programs differ.

Overcoming Sampling Issues and Improving Computational Efficiency in Collective-Variable-Based Enhanced-Sampling Simulations: A Tutorial.

This tutorial is designed to help users overcome sampling challenges and improve computational efficiency in collective-variable (CV)-based enhanced-sampling, or importance-sampling, simulations. Toward this end, we introduce well-tempered metadynamics-extended adaptive biasing force (WTM-eABF) and its integration with Gaussian accelerated molecular dynamics (GaMD). Additionally, use will be made of a method for identifying the least-free-energy pathway (LFEP) and multiple concurrent pathways on high-dimensional free-energy surfaces. We illustrate these sampling techniques with the conformational equilibria of trialanine and chignolin in aqueous solution as test cases. This tutorial assumes that the user has prior experience with molecular dynamics (MD) simulations, in general, with the popular program NAMD, and to some extent with Colvars, the module for CV-based calculations. This tutorial can, however, in large measure be used in conjunction with alternate MD engines that support the Colvars module such as GROMACS, LAMMPS, and Tinker-HP.

AI Image Generator Utilizing OpenAI and Replit

In this research, we leverage OpenAI's DALL- E 3 API in the assistance of generating images from a web interface which has been built on Replit. DALL-E 3 is a very advanced generative AI model due to its ability of generating complex and creative images from textual inputs. We intend to make use of the capabilities of DALL-E 3 and have it available to users in the generation of high-quality, custom images. Replit-the popular cooperative programming platform-secures the setup of the development environment, API request and response management, and user interfaces which allow users to interact with these AI models. The following apparently consider these factors at the implementation of AI in graphic design: better creativity, effective usage of time, and chance of creating unique aesthetic contents. Key Words: DALL-E 3 API, Generative AI, Web interface, Replit, Custom images, Graphic design.

Current cases of and motivations for second screen use by generation Z: university students

ABSTRACT Second screen use is becoming increasingly popular among young people. Türkiye can be expressed as a place where the use of devices such as TV, phone, tablet and computer is widespread. This study, which is based on the uses and gratifications approach, seeks answers to the following questions: a) which devices are used as second screens, b) with which TV programs they are used, c) with what kind of motivations they are used by Generation Z members who still watch TV. We collected data from university students using a questionnaire and the convenience sampling method, which is a type of non-probabilistic sampling, through SPSS. The data showed that Generation Z primarily uses smartphones as second screens, and only a few use second screen applications on their mobile phones. Music programs are the most popular thematic programs watched together with the second screen. Women tend to use their second screens for watching series and shopping. Generation Z prefers using a ‘smartphone with computer’ as a second screen. Future studies could investigate the motivations of students and academics to use second screens in distance education. Additionally, future studies can investigate the usage habits of second screens in second language learning.

Online Therapy to Treat Mental Health Concerns Among Police and Other Public Safety Personnel

PSPNET is a research unit at the University of Regina in Saskatchewan, Canada, that provides free online therapy programs to Canadian first responders and other public safety personnel (PSP). The first 560 clients (33% police) in PSPNET’s most popular therapy program—an internet-delivered cognitive behavioural therapy (ICBT) program called the PSP Wellbeing Course—have shown large improvements in mental health, as well as good satisfaction and engagement with the program. Treatment outcomes were similar for different groups of clients (e.g., men and women, clients in different occupational groups), suggesting that PSPNET’s online therapy program is effective for various groups of PSP.

CONCEPTUAL METAPHORS OF ARTIFICIAL INTELLIGENCE AND AI DEVELOPMENT IN THE GUARDIAN NEWSPAPER

The whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.

The Limits to Food and Beverage Industry Influence over Fiscal and Regulatory Policy in Latin America.

Little is known about the political, institutional, and social contexts contributing to a decline in food and beverage industry power and influence over fiscal (soda taxes) and regulatory (sales/advertising restrictions and food labels) policy. This article addresses this issue by exploring why Mexico and Chile eventually saw such a decline in the food and beverage industry's influence whereas Brazil was not as successful. I argue that in Mexico and Chile, these outcomes are explained by shifts in presidential, congressional, and bureaucratic interests in pursuing policies that went against industry preferences. This article took a qualitative methodological approach to comparative historical research. Policymakers' interest in pursuing stronger food and beverage regulations were shaped by economic and public health concerns, new electoral contexts, epidemiological information, and normative beliefs. In Mexico, the infiltration of nutrition researchers within government facilitated this process. In contrast, Brazil's government was divided about pursuing regulatory policies, with presidents favoring partnerships with industry to implement a popular anti-hunger program; industry's power endured there with limited progress in policy reforms. Governments can eventually overcome industry power and policy influence, but it depends on a whole government commitment to reform.

Using Media to Stimulate Babies’ Language Activities Development

As the digital age continues to reshape educational paradigms, it is essential to investigate the purposeful integration of media into infants’ lives to enhance language acquisition “Harmonizing Voices” explores how age-appropriate media, such as videos, apps, and interactive content, can complement traditional methods, providing infants with a multi-sensory and engaging platform for language exploration. Today, various strategies are employed to stimulate babies from pregnancy through infancy, with mothers increasingly using media as a tool. This includes playing music during pregnancy and introducing nursery rhymes through media post-birth to create a sensory -rich environment. This study investigates the synergy between the melodic allure of English nursery rhymes and intricate process of a baby’s linguistic development. Digital platforms like YouTube and Netflix offer numerous nursery rhymes, such as Cocomelon and Bebefinn, which are popular among infants. This research aims to evaluate which of these nursery rhymes programs is more effective in stimulating infants and which elicits more positive reactions. Caregivers, educators, and researchers will gain insights into the connections between musicality, language and early cognitive development through this exploration. The study addresses the identification of high quality media content, strategies for joint engagement between caregivers and infants, and the potential impact of media on various facets of language development. By fostering an environment where technology harmonizes with natural language acquisition processes, this research aspires to contribute valuable insights for educators, caregivers, and researchers. The objective of this study is to observe and compare infants’ reactions to two popular nursery rhyme programs, Cocomelon and Bebefinn. The research aims to identify which program stimulates more positive engagement, thereby providing a deeper understanding of infants’ preferences and the effectiveness of media content in early language development.