Popular Applications Research Articles

Success model of online food delivery system: The role of brand image in customer responses

There is a growing interest in understanding the factors affecting the success of online food delivery (OFD) systems because online food ordering has increased considerably in recent years. Hence, the purpose of this study is to investigate the effect of brand image on customer satisfaction and purchase intention based on the stimulus-organism-response (S-O-R) framework by adopting DeLone and McLean IS success model. A convenience sample of 251 respondents, who use the most popular OFD applications in 3 largest cities of Turkey, was surveyed by an online self-administered structured questionnaire. The results were first organized as descriptive statistics for observed variables and frequencies of demographic variables. In the second phase, confirmatory factor analysis (CFA) followed by structural equation modeling (SEM) was used to test the measurement and structural model. The results reveal that among OFD system success factors, only the system and service quality positively influence brand image, accounting for 46% of the variance. On the other hand, this study could not validate the proposed positive effect of information quality on brand image. For the role of brand image in customer responses, the findings evidence a significant positive effect of brand image on both customer satisfaction and intention to use. The variable explains 34% and 22% of the variance in satisfaction and purchase intention, respectively. In line with these results, this paper concludes that brand image can be introduced into the e-commerce success model as a new variable due to its partial mediating role and significant effects on customer responses.

Graftable voltage stabilizer for enhancing insulation performance of crosslinked polyethylene

AbstractWith the popular application of alternating current/direct current (AC/DC) hybrid power grid, ultra‐high‐voltage (UHV) AC/DC cables of long‐distance and large‐capacity transmission systems have been strongly demanded. Voltage stabilizers are widely used to resist AC electrical tree of cross‐linked polyethylene (XLPE) insulation materials, but poor compatibility with polymer matrix limited improvement of DC insulation performance. In this research, the maleic anhydride‐modified 2,4‐dihydroxybenzophenone as a novel voltage stabilizer 4‐(4‐benzoyl‐3‐hydroxyphenoxy)‐4‐oxobut‐2‐enoic acid (MDVS) was synthesized to improve the electrical properties of XLPE. The XLPE‐g‐MDVS with different contents of MDVS was prepared, and both AC electrical properties and DC electrical properties were tested. The results indicate that the modified XLPE retain the characteristic of voltage stabilizer to resist AC electrical tree and improve breakdown strength, and to exhibit excellent DC insulation performance in a wide temperature range. In the comparison with the pristine XLPE, the graft of MDVS can significantly suppress space charge accumulation and decrease DC conductivity. At 70°C, the maximum amount of space charge density of XLPE‐g‐1.2wt%MDVS is 56.2% lower than that of XLPE, and the DC conductivity decreases by nearly two orders of magnitude. Furthermore, the DC breakdown strength of XLPE‐g‐1.2wt%MDVS materials exhibit higher than that of the pristine XLPE. In addition to the self‐excited energy dissipation of the voltage stabilizer, quantum chemical calculations demonstrated that the improvement of DC performance attributed to the deep traps introduced by MDVS.

Event Management System for Webinars and Survey

Webinars and surveys have been widely used. Popular applications such as ezTalks, Zoom, Demio, Google Meet, LiveStorm have been used in seminars and online meetings. For every webinar activity, the organizers often carry out survey activities to see feedback from participants, google forms applications, survey monkeys, typeforms, zoho surveys, and gizmo surveys are used to see responses from all participants. In addition, several organizers also gave appreciation in the form of certificates. However, the management of many webinars is often an obstacle to seeing the results of each activity and the participants who take part in the activity. Although the use of google forms has been very good and effective, the lack of features in the management of webinar activities is a problem in itself in making reports in a short time. Therefore, we need a service application that can assist in the management of webinar activities and has survey features from registration, attendance to certificates. Where later application users (organizations, committees) can fill out webinar activities and can propose surveys for each activity carried out. In software development, Agile Development Methods (ASD) are used, Event Management System for Webinars and Survey applications that the researchers built using CodeIgniter and React-Js. In the test, we plan the data analysis to be divided into three parts, namely Demographic Analysis Results, Reliability Test Results Analysis, Validity, and Data Processing Analysis. Analysis Data processing includes Importance-Performance Analysis (IPA). So far, webinar and survey management applications have completely fulfilled needs such as making a number of questions in the form of multimedia and sharing research data, but webinar management applications with survey services in an application have not been carried out. It is hoped that the development of research on webinar and survey management applications will produce more visualitative applications and be able to manage webinar activities and reporting related to surveys, reporting participants for each activity carried out. Based on the results of the Importance Performance Analysis, it is known that the average value of the level of expectation or importance for usability is 3.97, and the average value for the level of performance is 3.68. the average value of the level of expectation or importance for information quality is 4.07, and the average value for the level of performance is 3.70. the average value of the level of expectation or importance for service interaction is 3.94, and the average value for the level of performance is 3.71.

WISEFUSE

We characterize production workloads of serverless DAGs at a major cloud provider. Our analysis highlights two major factors that limit performance: (a) lack of efficient communication methods between the serverless functions in the DAG, and (b) stragglers when a DAG stage invokes a set of parallel functions that must complete before starting the next DAG stage. To address these limitations, we propose WISEFUSE, an automated approach to generate an optimized execution plan for serverless DAGs for a user-specified latency objective or budget. We introduce three optimizations: (1) Fusion combines in-series functions together in a single VM to reduce the communication overhead between cascaded functions. (2) Bundling executes a group of parallel invocations of a function in one VM to improve resource sharing among the parallel workers to reduce skew. (3) Resource Allocation assigns the right VM size to each function or function bundle in the DAG to reduce the E2E latency and cost. We implement WISEFUSE to evaluate it experimentally using three popular serverless applications with different DAG structures, memory footprints, and intermediate data sizes. Compared to competing approaches and other alternatives, WISEFUSE shows significant improvements in E2E latency and cost. Specifically, for a machine learning pipeline, WISEFUSE achieves P95 latency that is 67% lower than Photons, 39% lower than Faastlane, and 90% lower than SONIC without increasing the cost.

A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data

AbstractComputation of binding constants from spectrophotometric titration data is a very popular application of chemometric hard modeling. However, the calculated values are misleading if the correct binding model is not used. Given that many supramolecular systems of interest feature unknown speciation, a priori determination of binding stoichiometry constitutes an important unsolved problem in chemometrics. We present a new and reliable algorithm for accomplishing this task, implemented using a hybrid particle swarm optimization technique. Simultaneous optimization of stoichiometry ratios and binding constants allows the optimal binding model to be calculated in just a few minutes for systems with up to four reactions. Simulated data studies demonstrate that the algorithm finds the correct stoichiometry with up to nine reactions in the absence of noise, including accurately determining species with unusual stoichiometry, such as H2G5. Application to four experimental datasets shows the algorithm is robust to experimental errors for a variety of chemical systems and binding models. This algorithm will facilitate the discovery of complex binding models, increase efficiency in titration analysis, and avert incorrect stoichiometry models, thereby improving the reliability of binding constant information in spectrophotometric titrations.

Deep Reinforcement Factorization Machines: A Deep Reinforcement Learning Model with Random Exploration Strategy and High Deployment Efficiency

In recent years, the recommendation system and robot learning are undoubtedly the two most popular application fields, and the core algorithms supporting these two fields are deep learning based on perception and reinforcement learning based on exploration learning, respectively. How to combine these two fields to better improve the development of the whole machine learning field is the dream of numerous researchers. The Deep Reinforcement Network (DRN) model successfully embedded reinforcement learning into the recommendation system, which provided a good idea for subsequent researchers. However, the disadvantage is also obvious, that is, the DRN model is built for news recommendations, meaning that the DRN model is not transferable, which is also the defect of many current recommendation system models. Meanwhile, the agent learning method adopted by the DRN model is primitive and inefficient. Among many models and algorithms that have emerged in recent years, we use the newly proposed deployment efficiency to measure their comprehensive quality and found that few models focus on both efficiency and performance improvement. To fill the gap of model deployment efficiency neglected by many researchers and to create a model of reinforcement learning agents with stronger performance, we have been exploring and trying to complete research on the Gate Attentional Factorization Machines (GAFM) model. Finally, we successfully integrated the GAFM model and reinforcement learning. The Deep Reinforcement Factorization Machines (DRFM) model proposed in this paper is based on the combination of deep learning with high perception ability and reinforcement learning with high exploration ability, centered on improving the deployment efficiency and learning performance of the model. The GAFM model is modified and upgraded using multidisciplinary techniques, and a new model-based random exploration strategy is proposed to update and optimize the recommendation list efficiently. Through parallel contrast experiments on various datasets, it is proved that the DRFM model surpasses the traditional recommendation system model in all aspects. The DRFM model is far superior to other models in terms of performance and robustness, and also significantly improved in terms of deployment efficiency. At the same time, we conduct a comparative analysis with the latest deep reinforcement learning algorithm and prove the unique advantages of the DRFM model.

Exposure to Food Marketing via Social Media and Obesity among University Students in Saudi Arabia.

This study investigated the associations between obesity and unhealthy food/drink intake with both the frequency of social media platform usage and food/drink marketing exposure on social media. Data were obtained from 316 university students aged 18–29 years at two universities in Jeddah, Saudi Arabia. These participants completed online questionnaires with sections on demographics, anthropometric measurements, social media platform usage, food marketing exposure via social media, and unhealthy food consumption. All of the participants, 20.3% and 13.6% were overweight and obese, respectively. Snapchat was the most popular application (85.8%), followed by Instagram (75%), YouTube (61%), Twitter (51%), and TikTok (50%). The obese participants were more likely to purchase foods/drinks after watching relevant social media advertisements than their non-obese counterparts (p < 0.04). Moreover, those who purchased foods/drinks more frequently after watching such advertisements consumed higher amounts of potato chips (p < 0.01) and fast foods (p < 0.03). Finally, those who used Snapchat, TikTok, and Instagram tended to have higher consumption rates for potato chips (p < 0.02), fast foods (p < 0.01), sweets (p < 0.02), and sugary drinks (p < 0.04). Public health policymakers in Saudi Arabia should consider regulating unhealthy food and drink advertisements on social media platforms, especially those targeted at younger generations.

Non-Fungible Token: A Systematic Review and Research Agenda

The popularity of the Non-Fungible Token (NFT) has risen rapidly since 2020, becoming one of the most popular applications in the Fintech field. However, there has so far been no attempt to perform a systematic review in this new area. Considering the items of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this paper conducts a systematic review of the research work on NFT, published in journals indexed at the Web of Science and ScienceDirect until April 2022. The results reveal that there are 13 published articles in the targeted journals and they are mainly focused on the asset pricing area. The research gaps identified in the literature also can be the opportunity for future study. Thus, we lay down the research agenda for the future in several important but unanswered fields related to asset pricing, tokenomics, and risk and regulation.

Usability Testing Pada Software Line Messenger

This study aims to perform Usability Testing on software or freeware applications, namely LINE Messenger. Usability Testing itself is a method of evaluating the usability of software (Software) which aims to increase the usability of an application / product by analyzing, identifying problems, measure efficiency, measure convenience and determine user satisfaction. Usability testing for the LINE Messenger application is carried out to determine the level of ease of use of the Line application, such as ease of understanding features, memory location of features, and also assessing the User Interface (UI).&#x0D; to the application, preparing test scenarios, looking for respondents or testing subjects, making questionnaires with the System Usability Scale (SUS) standard, and conducting usability analysis on the results of respondents and questionnaires. Then the final output is in the form of reports and conclusions regarding the results of the analysis. LINE Messenger itself is a popular social media application, an application that has many features such as LINE Today, Share screen and also has a LINE Shop. LINE itself is an easy-to-use application, with the results of the questionnaire showing positive Usability Testing results for LINE Messenger.

A comprehensive state‐of‐the‐art review of power conditioning systems for energy storage systems: Topology and control applications in power systems

Energy storage systems are pivotal for maximising the utilisation of renewable energy sources for smart grid and microgrid systems. Among the ongoing advancements in energy storage systems, the power conditioning systems for energy storage systems represent an area that can be significantly improved by using advanced power electronics converter designs and control techniques. Normally, the battery, flywheel, ultracapacitor and superconducting magnetic energy storage are the types of energy storage systems that typically require power conditioning systems for efficient bidirectional power flows. Compared to the previous review papers, this paper critically reviews the power conditioning system configurations and control techniques from the perspective of energy storage system applications in the power systems. First, the power conditioning systems are categorised into DC bus power conditioning systems and AC bus power conditioning systems based on the output voltage types at the connected point. Next, the topologies and control configurations of popular power electronics are comparatively analysed to highlight their advantages and power system applications. Moreover, the control configurations are discussed in terms of the popular applications of energy storage systems, that is, power backup smoothing, frequency regulation, voltage regulation and power quality applications. In addition, the latest developments in the energy storage system such as multi-functional energy storage system stacking, artificial intelligence for power conditioning system of energy storage systems and security of control of energy storage systems are critically analysed. Finally, the review is concluded by discussing industrial applications and future research trends for the power conditioning systems of energy storage systems.

Permanent Make-Up (PMU) Inks Decolorization Using Plant Origin Materials

Permanent make-up (PMU) has become a very popular application over the last few years. The ingredients of PMU inks, used over the face area, are organic and inorganic substances very close to the chemical composition of tattoo inks. As the application rates increase, the demand for PMU removal rises. The aim of this study is to assess the decolorization of PMU inks using preparations originating from different plant sources. The leaves of Pelargonium zonale (PE) were extracted with water for 48 h. The Total Phenolic Content (TPC) of the extract was determined using the Folin–Ciocalteu technique reaching 201.34 ± 4.57 μg Gallic Acid Equivalents (GAE)/mL of extract. The antioxidant activity of the extract was 20.87 ± 0.36 μg of Trolox equivalents (TE)/mL and 3.56 ± 0.43 mg FeSO4×7H2O mL of extract when assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) or ferric reducing antioxidant power (FRAP) assay respectively. The decolorization potential of PE leaf extract on five commercially available PMU inks of different hues was assessed by UV-Vis spectrophotometry in comparison to polyphenol oxidases enzyme (PPO). The results demonstrated higher absorption reduction that indicates decolorization potential for the inks that have mainly ferrous oxides as colorants.

Gold Nanorod-Assisted Photothermal Therapy and Improvement Strategies.

Noble metal nanoparticles have been sought after in cancer nanomedicine during the past two decades, owing to the unique localized surface plasmon resonance that induces strong absorption and scattering properties of the nanoparticles. A popular application of noble metal nanoparticles is photothermal therapy, which destroys cancer cells by heat generated by laser irradiation of the nanoparticles. Gold nanorods have stood out as one of the major types of noble metal nanoparticles for photothermal therapy due to the facile tuning of their optical properties in the tissue penetrative near infrared region, strong photothermal conversion efficiency, and long blood circulation half-life after surface modification with stealthy polymers. In this review, we will summarize the optical properties of gold nanorods and their applications in photothermal therapy. We will also discuss the recent strategies to improve gold nanorod-assisted photothermal therapy through combination with chemotherapy and photodynamic therapy.

Design and implementation of multiplexed amplicon sequencing panels to serve genomic epidemiology of infectious disease: A malaria case study.

Multiplexed PCR amplicon sequencing (AmpSeq) is an increasingly popular application for cost-effective monitoring of threatened species and managed wildlife populations, and shows strong potential for the genomic epidemiology of infectious disease. AmpSeq data from infectious microbes can inform disease control in multiple ways, such as by measuring drug resistance marker prevalence, distinguishing imported from local cases, and determining the effectiveness of therapeutics. We describe the design and comparative evaluation of two new AmpSeq assays for Plasmodium falciparum malaria parasites: a four-locus panel ("4CAST") composed of highly diverse antigens, and a 129-locus panel ("AMPLseq") composed of drug resistance markers, highly diverse loci for inferring relatedness, and a locus to detect Plasmodium vivax co-infection. We explore the performance of each panel in various public health use cases with in silico simulations as well as empirical experiments. The 4CAST panel appears highly suitable for evaluating the number of distinct parasite strains within samples (complexity of infection), showing strong performance across a wide range of parasitaemia levels without a DNA pre-amplification step. For relatedness inference, the larger AMPLseq panel performs similarly to two existing panels of comparable size, despite differences in the data and approach used for designing each panel. Finally, we describe an R package (paneljudge) that facilitates the design and comparative evaluation of genetic panels for relatedness estimation, and we provide general guidance on the design and implementation of AmpSeq panels for the genomic epidemiology of infectious disease.

Enhancing E-Commerce Applications with Machine Learning Recommendation Systems

In today’s times everything has moved to a digital platform. Even commerce has moved to a digital mode with people now preferring to buy things online rather than going to a physical store. Recommendation Systems are used in such platforms to help users. Recommendation System is one of the most popular application of Machine Learning with various techniques and algorithms to implement it. We have researched these algorithms and have presented an analysis by taking various factors into consideration.

Spectra of the exponential potential with the supposition of minimal length

In the framework of different theories of quantum gravity, such as string theory, dual special relativity, and the physics of black holes, the existence of a minimal length is proposed. It is necessary to modify and generalize the usual Heisenberg principle of uncertainty to incorporate the minimal length into the ordinary quantum mechanics. In this scenario, the momentum of the system is modified, and the Hamiltonian obtains additional terms. It is expected that the energy spectrum of any physical system is modified under the hypothesis of minimal length. In this article, these effects on the spectra of the exponential potential for the ground state are studied by applying a perturbation method. As a popular application of the exponential potential, the deuteron bound state can be explained by using this potential. Here, the modification of the energy spectrum of deuteron under the supposition of the existence of a minimal length is calculated.

Multi-class Classification of Twitter Sentiments using Frequency based, LSTM and BERT Methods

Abstract: Twitter is one of the most popular social media applications used by people of diverse age groups. Tweet has become an important mode of communication. Tweets are unstructured and often consist of different kinds of data tokens like mentions, hash tags, emoticons etc. Analysing the sentiment of this kind of unstructured data is challenging. In this paper, the experimentation of multiclass twitter sentiment analysis is presented, using three different approaches. Starting with the simple approach of frequency based feature extraction method, that helps capture the features from a text document by counting the frequency of the occurrence of words, to more advanced approaches like LSTM and BERT have been used. Different preprocessing techniques and different classifiers are explored in this paper. The performance of all the three methods is evaluated using metrics (precision, recall and f1 score). At the end the results of all three approaches are discussed and compared against one another. Keywords: NLP, BERT, LSTM, MLP Classifier, Multiclass Classification, Embeddings, Tweets.

Ultrasound as a modern preservation technique in food safety: a review

Ultrasonic methods are increasingly being used in the food industry for both food analysis and food modification. Ultrasound technology is relatively inexpensive, simple and cost-effective in terms of energy and time and is a non-destructive technique compared to other conventional technologies. The ultrasonic spectrum based on frequency and intensity is divided into two types, including low frequency (less than 100 kHz)-high-power (more than 1 W/cm2 ) ultrasound and high frequency (more than100 kHz) low-power (less than 1 W/cm2 ) ultrasound. The effect of ultrasound on various materials is caused by the formation, growth, and collapse of bubbles, which is referred to as cavitation. The ultrasonic spectrum based on frequency and intensity is divided into two types, including low frequency (less than 100 kHz)-high-power (more than 1 W/cm2 ) ultrasound and high frequency (more than 100 kHz) low-power (less than 1 W/cm2 ) ultrasound. Ultrasound applications in the food industry are very diverse. This review summarizes the major popular applications of low and high-power ultrasound in food science and technology.

Using Identity-Based Cryptography as a Foundation for an Effective and Secure Cloud Model for E-Health.

Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients' confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations.

Implementation of Social Media Site for College

In today’s life, social networking sites has become one of the most popular Internet applications and has a large number of users. The public social networking systems have meet the communication demand of people, there is insufficient assistance to get connected to the users learning, professional life, and cultural life on campus with their real life activities. We have seen in the pandemic that college struggled lot to get connect with their students, so this website will give solution if something like pandemic ever happen again. Also colleges always rely on the platform such as whatsapp to connect with their student online but on such platform only limited featurs are given. Therefore, this project design made a new campus social networking site for users, which is very closely connected to the user requirements of real environment of campus and users real activities. It will also give a platform to college where college can add as many features as they want according to the requirement. In this paper, a design and implementation of social media site for college campus is achieved. This website enables interaction of all the users like students, teachers, professors etc. It has all the features the user can expect plus more exciting and useful additional features which will help users in their college life. It has been implemented using ReactJs, MongoDb, CSS, Nodejs. Details of the implementation are below in this paper.

Anomalous distributed traffic: Detecting cyber security attacks amongst microservices using graph convolutional networks

Currently, microservices are trending as the most popular software application design architecture. Software organisations are also being targeted by more cyber-attacks every day and newer security measures are in high demand. One available measure is the application of anomaly detection, which is defined as the discovery of irregular or unusual activity that occurs to a greater or lesser degree than normal occurrences in a data series. In this paper, we continue existing work where various real-world cyber-attacks are executed against a running microservices application, and the application traffic is logged and returned in the form of distributed traces. A Diffusion Convolutional Recurrent Neural Network is used to model the set of distributed traces and learn the spatial and temporal dependencies of the application traffic. Subsequently, the model is used to make predictions for ongoing microservice activity and threshold-based anomaly detection is applied to detect irregular microservice activity indicating the presence of seeded cyber security attacks, or anomalies. The cyber-attacks used to evaluate this approach include a brute force attack, a batch registration of bot accounts and a distributed denial of service attack.