Online Applications Research Articles

Integration of Islamic Values in Learning Methods: Building Character and Spirituality in the Digital Era

Islamic education is more than just a knowledge transfer process; it is a profound effort to shape individual character and spirituality. In this context, learning methods are crucial in applying religious and moral values in daily life. This research employs a qualitative approach through literature analysis, demonstrating that integrating Islamic values as a fundamental basis for forming the personalities of the young Muslim generation is vital. Learning methods that focus on the Quran, Hadith, and scientific literature not only enable students to deeply understand religious teachings but also to develop strong moral and spiritual character. The role of technology in Islamic education is also discussed, highlighting the use of online platforms and applications that broaden access and deepen understanding of Islamic values in today's digital era. The implications of this research for educators emphasize the need for developing teaching methods aligned with Islamic values, while for education policymakers, it indicates the necessity of inclusive policies that accommodate moral values within the national education system. These steps are expected to foster a more ethical society that respects religious values in various aspects of life, in line with the holistic and sustainable vision of Islamic education. Keywords: Islamic Education, Learning Methods, Character, Technology.

Multi-stage Team Projects in the Management Classroom: Using a Double-loop Peer Presentation Feedback to Cultivate Reflective Learning

Reflection empowers practicing managers to learn from their exposure to experiences in business organizations, and reflective learning is a powerful tool to apply in the classroom to train competent managers. Through reflection, managers and students can learn how to learn from their experiences. This article proposes using a pedagogical approach based on a double-loop peer presentation feedback cycle in multi-stage team projects focusing on complex and ill-defined managerial challenges to cultivate reflective learning in the management classroom. We suggest adopting a semester-long team project with several work-in-progress presentations to incorporate reflection practices in the course design. We introduce a comprehensive process using an online application suite to deploy the double-loop peer presentation feedback approach in class. In the first loop, students in the audience provide feedback to the presenting team after each presentation. For the second loop, the presenting team rates the feedback received to stimulate individual and group reflection. We illustrate the use of this double-loop feedback process in three different undergraduate management courses and offer empirical evidence that the approach may improve reflective learning outcomes.

PENGARUH FASILITAS DAN HARGA TERHADAP KEPUTUSAN PEMESANAN KAMAR MELALUI SITUS TRAVELOKA DI HOTEL REDLINK PERIODE 2023

This research is motivated by the growth and development of companies engaged in providing services such as hotels in the city of Batam, the development of the hotel and tourism business in the city of Batam itself can be said to have experienced quite a significant increase. This of course has a big influence on the number of visitors staying at the hotel. Hospitality service providers must prepare the best strategy to attract consumers both from the facilities, quality of service and prices provided. There are many easy ways to make hotel reservations, one of the conveniences is by using an online application which can be accessed anytime and anywhere; therefore, researchers are very interested in choosing one of the companies that provides online services, namely the TRAVELOKA application. Redlink Hotel is a service provider company that has quite a lot of interest both from direct bookings and bookings via online sites because of its very strategic location. Researchers use Redlink Hotel to complete data and assessments from the public or guests who use the Traveloka online application. And in more depth to find out how the price and facilities provided by the hotel are affected when guests use the online application. The population of this study were guests from the Redlink hotel who used the Traveloka site to stay at the Redlink hotel, with a sample of 170 respondents taken by sampling. The data collection technique in this research uses the questionnaire method. Data processed using SPSS V20 program.

Design and Implementation of the Marsh Climate Monitoring System Using the Internet of Things

The Internet of Things (IoT) has become a major enabler for sustainable development and has begun to have an impact on society as a whole. Marshes are significant ecosystems for the environment that are essential to biodiversity support and ecological equilibrium. However, environmental changes and human activity are posing an increasing threat to these fragile ecosystems. An Internet of Things (IoT)-based marsh monitoring system was created and put into operation to gather data in real-time on a variety of environmental factors, such as wind speed, CO2 and hydrogen levels, temperature, humidity, voltage, and current. The system makes use of a network of sensors spread out throughout the marsh, which may promote sustainable development, send data to a central node for processing before sending it to a platform hosted in the cloud. After that, an interactive online application is used to visualize the data, giving stakeholders important information about the condition and health of the marsh. Because the suggested system makes it possible to monitor and manage marsh ecosystems effectively, it may promote sustainable development.

Aplikasi Pengolahan Bantuan Sosial Berbasis Web dan Mobile di Kapenewon Kokap Kulon Progo

The web and mobile-based social assistance processing application is designed to address issues in Kapanewon Kokap, Kulon Progo, which currently lacks an integrated web, mobile, and digital database application. As a result, the community must go to the Kapanewon office to apply for social assistance, which consumes time, energy, and costs due to the manual processes still in place, leading to slow service. To tackle this problem, the application is developed using the System Development Life Cycle (SDLC) prototype method, which allows for rapid development, low costs, and flexibility to user needs. The outcome of this application development facilitates the community in applying for social assistance quickly, accelerates administration, and enhances the transparency of aid distribution. With this application, the community no longer needs to visit the Kapanewon Kokap office, saving costs, time, and energy. In conclusion, this application improves the service process, provides better comfort for the community, and simplifies the online application process for social assistance, making the service more optimal and timely.

Effects of Pigai.org on College English Writing Competency: The Complexity, Accuracy and Fluency

Online applications in teaching and learning have been widely used in recent years. Pigai.org has become extremely popular for second language learning in China, and the study aims to examine its application to enhance students’ college English writing competency. Despite numerous studies on the use of Pigai.org, there has been rare research on its effects on college English writing competency in Chinese polytechnics. The current study investigates the effectiveness of its application over five weeks in a polytechnic college. Pigai.org has been applied in a College English Test-oriented writing class with 30 second-year students majoring in English. Sixty writing samples are collected in the first and fifth weeks, respectively, as the data analysis to evaluate writing complexity, accuracy, and fluency in pre-and post-tests. The study utilises a quantitative research approach, incorporating descriptive statistics, paired samples t-tests, and repeated measures ANOVA. The results show that the complexity, accuracy, and fluency significantly increased after applying Pigai.org in writing. Despite the decreases in very few items, most items measured in the study had higher scores in the post-test than in the pretest. The findings indicate that the use of Pigai.org positively affected college English writing and shed light on the pedagogical implications.

Exploring the Application of Online and Offline Integrated Teaching Mode in the Digital Human Resource Management Practice Course

Exploring the Application of Online and Offline Integrated Teaching Mode in the Digital Human Resource Management Practice Course

Optimizing contact tracing for avian influenza in poultry flocks.

Contact tracing is commonly used to manage infectious diseases of both humans and animals. It aims to detect early and control potentially infected individuals or farms that had contact with infectious cases. Because it is very resource-intensive, contact tracing is usually performed on a pre-defined time window, based on previous knowledge of the duration of the incubation period. However, pre-defined time windows may not be always relevant, reducing the efficiency of contact tracing. In this study, we estimated the day when farms were first infected with highly pathogenic avian influenza viruses, a devastating pathogen causing severe socio-economic damage in domestic poultry. The estimation was performed by fitting a stochastic mechanistic model to observed daily mortality data from 63 infected poultry farms in France and The Netherlands, using approximate Bayesian computation. Independent of the poultry species or country, the estimates of the time of first infection ranged between 3.4 (95% credible interval-CrI: 2.6, 4.6) and 19.9 (95% CrI: 11.9, 31.3) days prior to the last observation. We developed an online application to provide real-time support to policymakers by estimating realistic ranges of dates of first infection to inform contact tracing and improve its efficiency.

A novel three-dimensional fast thermal modeling strategy of lithium-ion batteries for online applications

A novel three-dimensional fast thermal modeling strategy of lithium-ion batteries for online applications

Surrogate modeling and optimization of Pressure/Vacuum Swing Adsorption (P/VSA) processes for carbon capture from post-combustion CO2 point sources

In the present work, a surrogate-based modeling framework has been developed to simulate and optimize Pressure/Vacuum Swing Adsorption (P/VSA) processes, for the efficient separation of CO2 from post-combustion carbon point sources. A single-stage 4-step P/VSA process is considered, using zeolite 13X as adsorbent, aiming to minimize the energy requirements with minimum CO2 purity and recovery targets of 95% and 90%, respectively. The developed P/VSA surrogate model consists of simple non-linear algebraic expressions developed by the ALAMO algorithm, which describe the key process performance indicators as functions of several process variables. In all cases examined, the optimization results from the P/VSA surrogate model are in excellent agreement with the ones obtained from the respective mechanistic model, with a relative deviation of less than 1% between the optimal solutions of the mechanistic and the surrogate model. On the other hand, the computational time for the optimization of the surrogate model is 3-4 orders of magnitude lower compared to that required for the optimization of the mechanistic model, which renders surrogate models particularly suitable for online applications that involve rapid and continuous responses to various input fluctuations.

Development of a high-resolution, high-dynamic-range charge detector for ion beam monitoring

We present an innovative charge detector with high resolution and wide dynamic range designed to fulfill the requirements of a monitoring system for a high energy ion beam. The detector prototype, constructed using Si photodiodes and a custom readout electronics, underwent extensive testing during HERD and AMS beam tests at CERN SPS facilities. Initial testing showcased the detector's exceptional performance, emphasizing both high resolution and a dynamic range capable of measuring nuclei with atomic numbers ranging from 1 to 80. The prototype's compatibility with fast, quasi real-time data analysis qualifies it as an ideal candidate for online applications. This article presents the results from the testing phase of the prototype, highlighting its capabilities and performance. Ongoing detector development, potential applications, and future developments aimed at enhancing the detector's functionality and versatility are also discussed.

An IoT Prototype for Water Quality Monitoring System with Case Study in NPIC Campus

Water is one of the natural resources that may be found in the environment. It is the most essential element of life. All living things, including humans, depend on water to survive, as do other species. Regretfully, water contamination has become a significant worldwide issue because of industrialization and irresponsible consumption. Moreover, low-quality water is poisonous to entire ecosystems due to its hazardous chemical and microbial contents. The issue is made much more dangerous by the lack of accessible tools for monitoring water quality other than costly laboratory tests. In this study, we developed an integrated system to measure water quality using the Internet of Things. Among the sensors, the system makes use of are Turbidity, pH (potential of hydrogen), TDS (Total Dissolved Solids), and water temperature. The embedded system is involved in this project as well. It will help with the establishment of wireless data transmission and sensor device detection. We employed LoRa technology alongside a cellular network ensuring effective communication. Firebase was utilized as the backend platform to securely store and manage the sensor data. The information can be tracked via a mobile or online application. Based on the sensor data, the water environment's quality was assessed and problems with the water's quality were anticipated to prevent the spread of contamination.

Usability and optimization of online apps in user's context.

The OptiFlow framework introduces a novel approach for enhancing usability evaluation and optimization known as OptiFlow. This framework combines heuristic evaluation with a web-based platform to provide a comprehensive method for assessing and optimizing user experiences in online applications. The architecture of OptiFlow incorporates key components, including the user, website, web service, and library, enabling seamless interaction and data exchange. A set of 240 usability guidelines, derived from a multidisciplinary expert collaboration, are systematically categorized into 15 usability categories, aligned with established design principles. Guidelines within OptiFlow are assigned implementation levels: "Green" for easily implementable guidelines, "Amber" for moderately complex ones, and "Red" for highly complex guidelines. These levels prioritize tasks based on complexity and feasibility. The framework's integration of guidelines into a structured SQL database simplifies implementation challenges, and the "execute" function systematically assesses website adherence to guidelines, resulting in True, False, or Null outcomes. Usability assessment outcomes are presented through categorized and prioritized data views for each implementation level, allowing stakeholders to address high-priority concerns efficiently. The OptiFlow framework represents an innovative approach to usability evaluation, fostering enriched user experiences and finely tuned digital interfaces. Future advancements may include additional rule types and the integration of advanced technologies for tackling intricate usability challenges. Ultimately, OptiFlow paves the way for proactive user experience enhancement and digital interface optimization in an ever-evolving digital landscape.

Cadê o Kauê? Co-design and acceptability testing of a chat-story aimed at enhancing youth participation in the promotion of mental health in Brazil.

Adolescent mental health is vital for public health, yet many interventions fail to recognise adolescents as proactive community contributors. This paper discusses the co-design and acceptability testing of a chat-story intervention to enhance Brazilian adolescents' participation in the promotion of mental health in their peer communities. We specifically highlight the iterative process of co-creating this intervention with community stakeholders. The co-design was led by researchers, a youth collaborative group, and health-tech experts. Part 1 included quantitative (n = 1,768) and qualitative (n = 46) studies with Brazilian adolescents aged 15-18 for priority-setting. Part 2 involved co-creation and technical production, with input from youth advisors (n = 24), school staff (n = 11), and policy experts (n = 3). In Part 3, the chat-story was user tested (n = 32). Parts 4 and 5 assessed acceptability through a qualitative study in schools (n = 138) and initial efficacy during an online campaign (n = 795). Participants aspired to support their peers' mental health in schools, both one-to-one and collectively, but felt unprepared. This informed the chat-story's goal of enhancing peer support and collective action skills. Themes identified during Part 1, such as prejudice and academic pressure, were woven into the narrative to raise awareness of the social determinants of mental health, drawing from real-life stories. In the final story, players search for their missing best friend at school, uncovering his anxiety struggles and practicing skills such as empathic listening and partnership building. A manual for teachers was collaboratively designed for use within school settings, supplementing direct-to-user online applications. Acceptability testing showed participants found the tool authentic and user-friendly. Online users perceived the tool as preparing and motivating them to offer peer support and engage in collective action. The immersive co-creation model, enriched by input from key stakeholders, yielded a relevant and well-received intervention for Brazilian adolescents. Co-designed creative tools like chat-stories hold promise as digital mental health tools, fostering awareness, critical reflection, and inspiring adolescents to drive positive social change.

Bridging minds and machines: Unmasking the limits in text-based automatic personality recognition for enhanced psychology-AI synergy.

Text-based automatic personality recognition (APR) operates at the intersection of artificial intelligence (AI) and psychology to determine the personality of an individual from their text sample. This covert form of personality assessment is key for a variety of online applications that contribute to individual convenience and well-being such as that of chatbots and personal assistants. Despite the availability of good quality data utilizing state-of-the-art AI methods, the reported performance of these recognition systems remains below expectations in comparable areas. Consequently, this work investigates and identifies the source of this performance limit and attributes it to the flawed assumptions of text-based APR. These insights are obtained via a large-scale comprehensive benchmark and analysis of text data from five corpora with diverse characteristics and complementary personality models (Big Five and Dark Triad) applied to an assortment of AI methods ranging from hand-crafted linguistic features to data-driven transformers. Finally, the work concludes by identifying the open problems that can help navigate the limitations in text-based automatic personality recognition to a great extent.

IOT IN CONTAINER SHIPPING INDUSTRY: APPLICATIONS AND EXAMPLES OF SOLUTIONS

The study analyzes the participation of technologies related to the development of the Internet of Things (IoT) network concept in the intermodal container transportation system. Since the beginning of the XXI century, IoT-related technologies have been dynamically spreading in almost all areas of international business, and this has changed the understanding of cargo owners about the quality of transportation services, among other things. Since transparency in monitoring the location of high-value goods is extremely important for transport companies, cargo owners, and all other stakeholders, and the intermodal transportation system is extremely complex due to the number and diversity of communications, the need to use IoT is obvious. As analysts predict that the introduction of IoT technologies in containerization will become widespread in the coming years, a study to assess the level of research on the introduction of such technologies and implementation in the practical activities of transport enterprises is relevant. The aim of the proposed review is to determine the degree of IoT implementation in the container transportation industry, areas of research on the topic, and identify factors that may impede the predicted rapid development of IoT in containerization. The proposed study includes an analysis of scientific publications related to the use of the Internet of Things in the field of international container transportation, identifies the main priority areas of such publications, establishes the areas of current implementation of IoT technology and determines the prospects for its use. Since the solutions that can potentially be obtained with the help of IoT are quite heterogeneous, the literature analysis is organized by grouping such publications into the following areas: container monitoring and tracking, enhancing supply chain transparency and visibility, optimization of operational efficiency, security and risk management, environmental impact and sustainability, challenges and barriers. The paper provides examples of online platforms and applications currently offered by major container operators that are related to this technology.

A novel recommender framework with chatbot to stratify heart attack risk

Cardiovascular diseases are a major cause of mortality and morbidity. Fast detection of life-threatening emergency events and an earlier start of the therapy would save many lives and reduce successive disabilities. Understanding the specific risk factors associated with heart attack and the degree of association is crucial in the clinical diagnosis. Considering the potential benefits of intelligent models in healthcare, many researchers have developed a variety of machine learning (ML)-based models to identify patients at risk of a heart attack. However, the common problem of previous works that used ML concepts was the lack of transparency in black-box models, which makes it difficult to understand how the model made the prediction. In this study, an automated smart recommender system (Explainable Artificial Intelligence) for heart attack prediction and risk stratification was developed. For the purpose, the CatBoost classifier was applied as the initial step. Then, the SHAP (SHapley Additive exPlanation) explainable algorithm was employed to determine reasons behind high or low risk classification. The recommender system can provide insights into the reasoning behind the predictions, including group-based and patient-specific explanations. In the final step, we integrated a Large Language Model (LLM) called BioMistral for chatting functionally to talk to users based on the model output as a digital doctor for consultation. Our smart recommender system achieved high accuracy in predicting a patient risk level with an average AUC of 0.88 and can explain the results transparently. Moreover, a Django-based online application that uses patient data to update medical information about an individual’s heart attack risk was created. The LLM chatbot component would answer user questions about heart attacks and serve as a virtual companion on the route to heart health, our system also can locate nearby hospitals by applying Google Maps API and alert the users. The recommender system could improve patient management and lower heart attack risk while timely therapy aids in avoiding subsequent disabilities.

Surfing beta burst waveforms to improve motor imagery-based BCI

Abstract Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during, and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non-invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient, and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article, we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors, thus being suitable for online applications. By adopting a time-resolved decoding approach, we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.

The Impact of Using Virtual Vocabulary Learning Applications on Enhancing Law Students’ Lexical Competence at the Tertiary Level

Technological advancements have transformed foreign language learning and teaching practices in the 21st century. Correspondingly, numerous online applications have arisen to meet the needs of English learners, one of which is Quizlet, a virtual vocabulary learning initiative. Having seen the growing popularity of Quizlet, the researcher investigated its effectiveness in developing 60 law students’ lexical competence within 12 weeks in the spring semester of the 2023-2024 academic year at a private university in Iraq. The participants were chosen via convenience sampling to represent the control and experimental groups. Control group students’ vocabulary lessons were based on a paper-based format through coursebooks and worksheets; however, experimental group students’ lessons were enriched with Quizlet to learn legal terminology through flashcards, games, tests, and self-study folders in an Academic English course. Vocabulary exams, questionnaires, and interviews were used to collect data using mixed methods research design principles. Quantitative data were analyzed by SPSS via descriptive statistics or independent samples t-test, respectively; however, qualitative data gathered by interviews were categorized under specific themes through content analysis using MAXQDA. The findings revealed that Quizlet-enriched instruction paid off with higher vocabulary exam marks, satisfaction, and motivation rates. On the other hand, control group students did not progress noticeably. The findings of this study can offer specific implications for educators interested in integrating technological tools into vocabulary lessons.

Analisis Perilaku Agresi Pengguna Aplikasi Kencan Online pada Gen Z

Technological developments have given birth to various cutting-edge applications, including online chatting applications, online motorcycle taxis, shopping, and online dating applications. Social media and online applications can be easily accessed by various groups of society. Aggressive behavior is behavior that aims to hurt or harm other people who do not want it. This research uses a qualitative phenomenological method. A phenomenological approach was used to understand generation Z's subjective experience of open relationships through the use of online dating applications. The formulation of the problem in this research is, what are the characteristics of aggressive behavior that appear among online dating application users among Generation Z and what factors influence this. This research aims to understand the dynamics of aggressive behavior that occurs among online dating application users in Generation Z. In addition, this research aims to explore and understand various types of aggression, including physical, verbal and relational.