Customized Content Research Articles

The COPILOT Raw Illumina Genotyping QC Protocol.

The Illumina genotyping microarrays generate data in image format, which is processed by the platform-specific software GenomeStudio, followed by an array of complex bioinformatics analyses that rely on various software, different programming languages, and numerous dependencies to be installed and configured correctly. The entire process can be time-consuming, can lead to reproducibility errors, and can be a daunting task for bioinformaticians. To address this, we introduce the COPILOT protocol, which has been successfully used to transform raw Illumina genotype intensity data into high-quality analysis-ready data on tens of thousands of human patient samples that have been genotyped on a variety of Illumina genotyping arrays. This includes processing both mainstream and custom content genotyping chips with over 4 million markers per sample. The COPILOT QC protocol consists of two distinct tandem procedures to process raw Illumina genotyping data. The first protocol is an up-to-date process to systematically QC raw Illumina microarray genotyping data using the Illumina-specific GenomeStudio software. The second protocol takes the output from the first protocol and further processes the data through the COPILOT (Containerised wOrkflow for Processing ILlumina genOtyping daTa) containerized QC pipeline, to automate an array of complex bioinformatics analyses to improve data quality through a secondary clustering algorithm and to automatically identify typical Genome-Wide Association Study (GWAS) data issues, including gender discrepancies, heterozygosity outliers, related individuals, and population outliers, through ancestry estimation. The data is returned to the user in analysis-ready PLINK binary format and is accompanied by a comprehensive and interactive HTML summary report file which quickly helps the user understand the data and guides the user for further data analyses. The COPILOT protocol and containerized pipeline are also available at https://khp-informatics.github.io/COPILOT/index.html. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing raw Illumina genotyping data using GenomeStudio Basic Protocol 2: COPILOT: A containerised workflow for processing Illumina genotyping data.

Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms

The accelerated development of e-commerce has been a concern for business people. Business people should be able to gain customer interest in a variety of ways so that their companies can compete with others. Analyzing click-flow data will help organizations or firms assess customer loyalty, provide advertising privileges, and develop marketing strategies through user interests. By understanding consumer preferences, clickstream data analysis may be used to determine who is participating, assist companies in evaluating customer contentment, boost productivity, and design marketing strategies. This research was performed by defining experimental user interests using Dynamic Mining and Page Interest Estimation methods. The findings of this analysis, using three algorithms at the pattern discovery page, demonstrated that the Decision Tree method excelled in both methods. It indicated that the operational performance of the Decision Tree performed well in the assessment of user interests with two different approaches. The findings of this experiment can be used as a proposal for researching the field of web usage mining, collaborating with other approaches to achieve higher accuracy values.

Application of B2C digital marketing

The purpose of this paper is to understand the application of digital B2C marketing. Companies market directly to end customers or decision-makers through B2C marketing. Most B2C customers base their decisions on how they feel about a product or service, not on facts or how useful it is. B2C marketers can take advantage of this by developing customized content that appeals to the emotions of their target audience.

An empirical analysis of mobile learning app usage experience

The study analyzed the reviews and ratings of mobile learning apps. Word frequency analysis, sentiment analysis, and content analysis were performed on 2000 reviews of four highly rated, downloaded, and reviewed mobile learning apps on the Google Play store. The results revealed that the most frequently mentioned topics included mobile applications, courses, and teaching. The majority of the reviews had a positive sentiment. Trust, anticipation, and joy were found to be the prominent emotions. Further, the text was coded to three themes (app quality, app suitability, and influence to use) and ten sub-themes. We found that seven identified sub-themes (technical quality, customer support quality, content quality, teaching quality, usefulness, compatibility, and learner influencing others), learners' sentiment, and length of review influence star ratings of apps. The star ratings and emotions of joy, trust, disgust, and sadness were moderately associated. The association was strong between the emotions trust and joy, sadness and fear. Further, there was a significant difference between star ratings based on the apps' sector (public or private) and based on the apps' education level (school or higher). The article concludes with a discussion on the learners’ experience using mobile learning apps, implications for practice, limitations, and future research directions.

Measuring colorant fading within raster regions of printed scanned customer content using a novel unsupervised clustering method

Colorant fading defects in raster region of interest (ROI) are among the most common printing issues in electrophotographic printers. Colorant fading manifests as faint print or customer content and is usually caused by low-level ink/cartridge. This paper presents an accurate method to detect the colorant fading defects and classify the defects based on their severity. There are two modules for this method. The first module uses the SLIC super-pixel method to separate the raster ROI and extract the smooth super-pixels. We design a novel unsupervised clustering algorithm to automatically extract the three or four main colors from the smooth super-pixels. Unsupervised data clustering is an important problem that arises in many image processing applications. We propose a new approach to unsupervised data clustering called Data Inter-Distance MEdiated Clustering (DIDMEC). Our new method is based on analyzing the matrix of Euclidean distances between each pair of points in the data set. Based on three simple properties, we devise an approach that effectively yields the same accuracy as the K-Means algorithm but at a much lower computational cost for a moderate number of sample points. The second module extracts feature vectors for each main color clustered by DIDMEC to classify the colorant fading defects based on their severity.

Measuring CMYK color plane misregistration from scanned printed customer content image

Print defect detection and measurement are critical for designing and improving high-quality printing systems. Color plane misregistration is considered to be among the most serious and common defects affecting the overall image quality within the printing industry. There has been some research on it, but mostly focused on measuring the defect on uniform color pages or specific test images. In this paper, we designed and developed a processing pipeline, and two measurement strategies for measuring color plane misregistration. The processing pipeline can automatically determine the direction, magnitude, and identity of the misregistered color plane for a scanned printed customer content image. The entire design pipeline can be directly used on the printed output without prior information on image content, allowing it to be widely used for printer troubleshooting.

Measuring margin and skew errors in scanned printed customer content

Print margin and skew describe an image placed crookedly on the printed page. It is one of the most common defects in electrophotographic printers and dramatically affects print quality. It primarily might occur when using a two-sided printing module on the printer. To solve or correct the print margin and skew error, we should first accurately detect the print margin and skew on the printed page. This paper proposes a method to accurately detect the print margin and skew based on the Hough Lines Detection algorithm. There are three steps of this print margin and skew detection method. We first project the digital master images into the scanned test image with master image edges. The second step is the most challenging part of designing this method because all of our scanned test pages had only two or three and barely visible edges of the printed paper to the naked eye. We use an image processing method and Hough Lines Detection algorithm to extract the paper edges. The third step uses the projected master images edge and the extracted paper edges to calculate the print margin and skew result. Our algorithm is an efficient and accurate method to detect print margin and skew errors based on factual scanned image verification.

A Counseling Mobile App to Reduce the Psychosocial Impact of Human Papillomavirus Testing: Formative Research Using a User-Centered Design Approach in a Low-Middle-Income Setting in Argentina.

BackgroundHuman papillomavirus (HPV) testing detects sexually transmitted infections with oncogenic types of HPV. For many HPV-positive women, this result has negative connotations. It produces anxiety, fear of cancer or death, and disease denial. Face-to-face counseling could present many difficulties in its implementation, but a counseling mobile app could be practical and may help HPV-positive women reduce the psychosocial impact of the result, improve their knowledge of HPV and cervical cancer, and increase adherence to follow-up.ObjectiveThis study aims to understand HPV-tested women’s perceptions about an app as a tool to receive information and support to reduce the emotional impact of HPV-positive results. We investigated their preferences regarding app design, content, and framing.MethodsWe conducted formative research based on a user-centered design approach. We carried out 29 individual online interviews with HPV-positive women aged 30 years and over and 4 focus groups (FGs) with women through a virtual platform (n=19). We shared a draft of the app's potential screens with a provisional label of the possible content, options menus, draft illustrations, and wording. This allowed us to give women understandable triggers to debate the concepts involved on each screen. The draft content and labels were developed drawing from the health belief model (HBM) and integrative behavioral model (IBM) variables and findings of mobile health literature. We used an FG guide to generate data for the information architecture (ie, how to organize contents into features). We carried out thematic analysis using constructs from the HBM and IBM to identify content preferences and turn them into app features. We used the RQDA package of R software for data processing.ResultsWe found that participants required more information regarding the procedures they had received, what HPV-positive means, what the causes of HPV are, and its consequences on their sexuality. The women mentioned fear of the disease and stated they had concerns and misconceptions, such as believing that an HPV-positive result is a synonym for cancer. They accepted the app as a tool to obtain information and to reduce fears related to HPV-positive results. They would use a mobile app under doctor or health authority recommendation. The women did not agree with the draft organization of screens and contents. They believed the app should first offer information about HPV and then provide customized content according to the users’ needs. The app should provide information via videos with experts and testimonies of other HPV-positive women, and they suggested a medical appointment reminder feature. The app should also offer information through illustrations, or infographics, but not pictures or solely text.ConclusionsProviding information that meets women’s needs and counseling could be a method to reduce fears. A mobile app seems to be an acceptable and suitable tool to help HPV-positive women.

The Effect of Content Marketing and Social Media Influencer on Customer Satisfaction and Their Impact On Online Repurchase Intention During The Covid-19 Pandemic

This study aims to examine the effect of content marketing and social media influencer on customer satisfaction and their impact on online repurchase intention during the COVID-19 pandemic. This study was conducted on the people of Banda Aceh City, Boomers Sub Group, Generation X & Y. The sampling technique used a non-probability sampling technique through purposive sampling. The number of samples was 200 respondents following the requirements for using the SEM statistical method using AMOS software. Data were collected by interview method and distributing questionnaires. The results showed that of the 6 direct influence hypotheses tested, there was one that was not significant, namely the social media influencer effect on online repurchase intention, while the results of testing other hypotheses were significant, namely content marketing and social media influencer affect customer satisfaction, content marketing and customer satisfaction affect repurchase intention, and the mediation testing which proves that customer satisfaction partially mediates the content marketing effect on online repurchase intention during the covid 19 pandemic, and Customer Satisfaction fully mediates the social media influencer effect on online repurchase intention during the Covid 19 pandemic. These findings prove that the model for increasing online repurchase intention during the covid 19 pandemic in Banda Aceh is a function of the accuracy of social media influencer and increased content marketing which plays a role in increasing customer satisfaction.

The importance of content marketing for achieving customer brand engagement

In the last decade, with the development of information and communication technologies, content marketing has become one of the leading marketing techniques in marketing communication, which seeks to create a relationship with customers by creating and sharing interesting content. Activities related to content marketing are theoretically synthesized in different ways, but regardless of the chosen way of their theoretical definition, they should intuitively lead to building customer brand engagement. Customer brand engagement is characterized by different cognitive, emotional and behavioral brand-related responses caused by specific brand interactions. In the modern economy, where it is very difficult to attract and retain the attention of customers, building creative, consistent and accurate content is becoming one of the basic challenges for gaining customer engagement. The research subject focuses on the growing popularity of content marketing and customer brand engagement, and systematizes the existing findings on the importance of the effects that content marketing has on customer brand engagement. Therefore, the main goal is to review the literature on existing knowledge about customer brand engagement and content marketing, which is most often shared through social media, and critically analyze the findings of researchers and practitioners on the relationship between content marketing and customer brand engagement.

The Relationship between Digital Marketing, Customer Engagement, and Purchase Intention via OTT Platforms

The Internet and digital technologies have grown exponentially and have become a part of billions of people’s daily lives around the world. Over‐the‐top (OTT) services are delivered directly over the Internet, providing customers access to films and TV series. In India, there has been a tremendous surge in the consumption of video material on OTT platforms. The significant growth in the digital marketing application in online businesses and its ability to engage customers has enhanced its importance. The emergence of the Internet and digital technologies has provided a modernized setting and a fertile ground for business organizations to reach millions of customers at affordable prices with effective and customized promotion content. The present study aims to investigate the relationship between digital media marketing (DMM), consumer engagement, brand image, and OTT platform purchase intention in the Indian context. The researchers will also investigate the mediating role of consumer engagement and brand image in mediating the relationship between digital marketing practices and OTT platform purchase intention. In a survey of 417 Indian consumers, it was found that there was no direct effect of DMM on the purchase intention of OTT platforms. In addition, there is a strong indirect effect through brand image and consumer engagement, supporting the hypothesis that brand image and consumer engagement mediate the relationship between DMM practices and the purchase intention of OTT platforms. Some of the managerial implications, limitations, and scope of future research are also presented in the study.

Kano and QFD analyses for autonomous electric car: Design for enhancing customer contentment

With the development of new technologies, the services in the automobile sector are rapidly changing. In the ancient times, the manually-driven wheel laid the foundation of transport; while now the world is heading towards self-driving vehicles. These vehicles provide contentment to the customer behaviour through the means of their features like voice-bot and intelligent interface. Moreover, with the introduction of micro-computers, large databases for maintaining such autonomous vehicles can be handled easily. This paper presents a collective study of Kano model analysis and quality function deployment (QFD) in a detailed manner. Different attributes were provided to the potential customers regarding an autonomous electric car in a survey form. The consumers were asked to express their feelings regarding the product if a particular feature is provided and not provided in the product for conducting Kano analysis. Furthermore, QFD was implemented to determine the priority for making improvements in the product profile design. The attributes were mapped to technical metrics/parameters and ranked based on the target customers’ priorities. The output is displayed in a typical house of quality which also consisted of a benchmarking on market competitors. Overall, the paper provides a scientific and engineered framework regarding the attributes which may help the manufacturing organizations re-evaluate their services and come up with efficient and new technological features in the automobile domain.

Increasing Applicant Engagement During the 2020-2021 Virtual Residency Interview Cycle and Beyond: The Dartmouth-Hitchcock Radiology Residency Video Project

To explore the effectiveness of the video medium and YouTube platform in conveying residency program and community information to prospective applicants during the 2020-2021 virtual residency interview cycle. To garner insights on the virtual-only residency interview experience and our video-centered approach through survey data collected from interviewing candidates for potential implementation in future application cycles. 13 custom content videos were produced highlighting our radiology residency program and uploaded onto a newly created YouTube channel and the institutional website during the late summer through fall of the 2020-2021 residency interview cycle. Feedback on the videos was generated using YouTube analytics and an anonymous 10-question survey sent to interviewing candidates. As of September 29, 2021, the date when residency programs could begin reviewing ERAS applications for the 2021-2022 cycle, the videos amassed 4487 views with over 149 hours' worth of material viewed. 57 of 114 interviewed candidates responded to the survey. Feedback was overwhelmingly positive with 84% of respondents agreeing that the videos positively influenced their decision to apply to our program, 77% of respondents judging our video-based format to be overall more effective in delivering residency program information than text-based media (website, email etc.), and 86% feeling adequately informed about the program through the virtual interview process and provided electronic resources. 79% of respondents felt our videos to be more informative than the resources from other radiology programs encountered during the interviewing cycle. Finally, a majority 72% of respondents are open to virtual interviewing in the future. Our program's video-centered approach was one of many creative media solutions employed by residency programs across the nation to help make virtual interviewing an adequate replacement to the traditional in-person interviewing experience. While further investigation is needed to determine which methods communicate residency program information best in the virtual setting, our initial experience and data show the video-centered approach to be overwhelmingly positive with applicants and at a minimum, we've established a baseline process and aesthetic for others to improve upon.

Reduced failure rates associated with playing a new online game developed to support learning of core content in human systems physiology.

A digital mobile card-matching game called eFlip was developed to assist second-year undergraduate medical science students to learn core content and understand key associations in physiology. Our team developed customized content of core physiological associations, of increasing difficulty, for upload on a generic card-matching platform. The generic game was extended with add-ons coded to allow identification and access of student usage data for students who consented to have their game usage linked to academic performance such as final course marks and grades. Here, we describe the development of the eFlip game content, the student usage profile, and the game's impact on learning. Students were invited to use eFlip prior to and during the final examination period. Of those who used eFlip, 152 students consented to participate in a study of game use on learning performance outcomes. Within the students who consented, 74 played the game and 78 did not play the game (nonplayers). The mean course mark of the students who played the game [69.57%; 95% confidence level (CI) (67.22, 71.92)] was higher than that of the nonplayers [65.33%; 95% CI (62.67, 67.99)] (P = 0.02). Playing eFlip was also associated with reduced failure rates in students who played the game (1% failure rate) compared with the students who did not play the game (10% failure rate; P = 0.02). The number of games played by students peaked just prior to the course final examination. Overall, students who chose to play eFlip demonstrated improved grades that were associated with a higher probability of passing the physiology course.

Content Service Platform for Providing User-Centric Content in Smart Environments

Smart environments, such as smart cities and streets, contain various heterogeneous devices and content that provide information to users and interact with each other. In a smart environment, appropriate content should be provided based on the situations of users. Additionally, when a user is in motion, it is necessary to provide content in a seamless manner without interruption. A method for systematically controlling the delivery of such content is required. Therefore, we propose a content service platform to meet the needs discussed above. The content service platform supports the delivery of content and events between different devices, as well as the control of content. Context-aware technology can also be applied to support customized content. In this paper, we present an architectural model, a contextual reasoning process, and case study on applying content service platform to a smart street environment. The proposed content service platform applied as a base model to support the provision of user-specific content in smart environments.

Scaling Agile Adoption Motivators from Management Perspective: An Analytical Hierarchy Process Approach

Agile methodology has been noted as the mostly used method for software development over the past few years. This methodology guarantees the rapid delivery of software products with lower cost and greater customer contentment. The adoption of agile methods was initially applied in the software development industry for single, small project teams following the principles of Agile Manifesto. The intense need of the time is to identify from management perspective the motivators that can be focused on to adopt agile methodologies in large-scale projects. Therefore, the main purpose of the study is to develop from management perspective a priority-based taxonomy of motivators that helps in scaling agile methodologies in large-scale projects. The taxonomy is developed in two steps. In the first step, data are collected from a survey and open literature. In the second step, the analytical hierarchy process (AHP) is implemented on the collected data that produced the proposed priority-based taxonomy of the motivators. With the help of AHP model, the 21 motivators of successful project management are categorized into four main categories, i.e., “organization management,” “team,” “customer and technology,” and “process.” Furthermore, the AHP analysis is used to rank the motivators and their respective categories in order of their importance. The findings of this study are further reviewed and validated by the agile experts in other organizations. The proposed priority-based taxonomy of motivators can be used as a handbook that gives guidelines on scalability of agile methodologies to large projects, which helps in successful management of large-scale projects.

Entertain Me Well: An Entertaining, Tailorable, Online Platform Delivering CBT for Depression

Depression prevalence is high, impacting approximately 20% of Americans during their lifetime, and on the rise due to stress and loss associated with the COVID-19 pandemic. Despite the high prevalence of depression, unacceptable treatment access disparities persist. When depression goes untreated, it leads to substantial negative impacts in multiple life domains. Cognitive behavioral therapy (CBT), the gold-standard psychosocial treatment for depression, remains largely unavailable to individuals living with depression, particularly individuals who are members of underrepresented groups in our society. Digital mental health interventions (DMHI) have led to important advances in extending the reach of CBT for depression; however, they are underutilized and treatment engagement remains low. We sought to address some of the current gaps in DMHI by developing an online platform for delivering CBT for depression that is entertaining, simple and straightforward, and tailorable. First, this article introduces our online platform, Entertain Me Well (EMW) and its key innovations, including the use of an engaging, character-driven storyline presented as “episodes” within each session, as well as customizable content that allows for tailoring of text, images, and examples to create content most relevant to the target client population, context, or setting. Next, we describe two EMW depression treatment programs that have been tailored: one for delivery in the rural church setting, called Raising Our Spirits Together, and one tailored for delivery in dialysis centers, called Doing Better on Dialysis. Finally, we discuss future directions for the EMW platform, including the ability to create programs for other common mental health and health conditions, the development of additional character-driven storylines with greater treatment personalization, translation of content in multiple languages, and the use of additional technological innovation, such as artificial intelligence like natural language processing, to enhance platform interactivity.

TPMT and NUDT15 Variants Predict Discontinuation of Azathioprine for Myelotoxicity in Patients with Inflammatory Disease: Real-World Clinical Results.

Azathioprine is used frequently to treat several inflammatory conditions. However, treatment is limited by adverse events-in particular, myelotoxicity. Thiopurine-S-methyltransferase (TPMT) and nudix hydrolase-15 (NUDT15) are enzymes involved in azathioprine metabolism; variants in the genes encoding these enzymes increase the risk for azathioprine myelotoxicity. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has recommended dose adjustments based on the results of TPMT and NUDT15 genotyping. However, little is known about the importance of this genetic information in routine clinical care. We hypothesized that in patients with inflammatory diseases, TPMT and NUDT15 genotype data predict the risk of discontinuing azathioprine due to myelotoxicity. This was a retrospective cohort study in 1,403 new adult azathioprine users for the management of inflammatory conditions for whom we had genetic information and clinical data. Among patients who discontinued azathioprine, we adjudicated the reason(s). Genotyping was performed using the Illumina Infinium Expanded Multi-Ethnic Genotyping Array plus custom content. We used CPIC guidelines to determine TPMT and NUDT15 metabolizer status; patients were grouped as either: (i) poor/intermediate, or (ii) normal/indeterminate metabolizers. We classified 110 patients as poor/intermediate, and 1,293 patients as normal/indeterminate metabolizers. Poor/intermediate status was associated with a higher risk for azathioprine discontinuation due to myelotoxicity compared to normal/indeterminate metabolizers (hazard ratio (HR)=2.90, 95% confidence interval (CI): 1.58-5.31, P=0.001). This association remained significant after adjustment for race, age at initiation, sex, primary indication, and initial daily dose of azathioprine (adjusted HR (aHR)=2.67, 95% CI: 1.44-4.94, P=0.002). In conclusion, TPMT and NUDT15 metabolizer status predicts discontinuation due to myelotoxicity for patients taking azathioprine for inflammatory conditions.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend. Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century. As the competitiveness between supply chains intensifies day by day, companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits. Excessive inventory (overstock) and stock outs are very significant issues for suppliers. Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory. Excess inventory can also lead to increased storage, insurance costs and labor as well as lower and degraded quality based on the nature of the product. Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store. If clients are unable to find the right products on the shelves, they may switch to another vendor or purchase alternative items. Demand forecasting is valuable for planning, scheduling and improving the coordination of all supply chain activities. This paper discusses the use of neural networks for seasonal time series forecasting. Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

Multimodal Emotion Recognition from Art Using Sequential Co-Attention.

In this study, we present a multimodal emotion recognition architecture that uses both feature-level attention (sequential co-attention) and modality attention (weighted modality fusion) to classify emotion in art. The proposed architecture helps the model to focus on learning informative and refined representations for both feature extraction and modality fusion. The resulting system can be used to categorize artworks according to the emotions they evoke; recommend paintings that accentuate or balance a particular mood; search for paintings of a particular style or genre that represents custom content in a custom state of impact. Experimental results on the WikiArt emotion dataset showed the efficiency of the approach proposed and the usefulness of three modalities in emotion recognition.