End Users Research Articles

Applying cognitive walkthrough methodology to improve the usability of an equity-focused implementation strategy.

Our research team partnered with primary care and quality improvement staff in Federally Qualified Community Health Centers (CHCs) to develop Partnered and Equity Data-Driven Implementation (PEDDI) to promote equitable implementation of evidence-based interventions. The current study used a human-centered design methodology to evaluate the usability of PEDDI and generate redesign solutions to address usability issues in the context of a cancer screening intervention. We applied the Cognitive Walkthrough for Implementation Strategies (CWIS), a pragmatic assessment method with steps that include group testing with end users to identify and prioritize usability problems. We conducted three facilitated 60-min CWIS sessions with end users (N = 7) from four CHCs that included scenarios and related tasks for implementing a colorectal cancer (CRC) screening intervention. Participants rated the likelihood of completing each task and identified usability issues and generated ideas for redesign solutions during audio-recorded CWIS sessions. Participants completed a pre-post survey of PEDDI usability. Our research team used consensus coding to synthesize usability problems and redesign solutions from transcribed CWIS sessions. Usability ratings (scale 0-100: higher scores indicating higher usability) of PEDDI averaged 66.3 (SD = 12.4) prior to the CWIS sessions. Scores averaged 77.8 (SD = 9.1) following the three CWIS sessions improving usability ratings from "marginal acceptability" to "acceptable". Ten usability problems were identified across four PEDDI tasks, comprised of 2-3 types of usability problems per task. CWIS participants suggested redesign solutions that included making data fields for social determinants of health and key background variables for identifying health equity targets mandatory in the electronic health record and using asynchronous communication tools to elicit ideas from staff for adaptations. Usability ratings indicated PEDDI was in the acceptable range following CWIS sessions. Staff identified usability problems and redesign solutions that provide direction for future improvements in PEDDI. In addition, this study highlights opportunities to use the CWIS methodology to address inequities in the implementation of cancer screening and other clinical innovations in resource-constrained healthcare settings.

A Roadmap to Industry Based Curriculum in Higher Education Through Public-Private Partnership Model

Higher education in any country plays an important role in nation building. Education system has a social agenda to develop its human resource in a better way to meet the requirements of society. Several reforms were introduced to promote quality in higher education and make education accessible to every society. The continuous amendments in education policies are required to meet the present expectations of industry. The traditional approach of rote learning and memory level teaching shall not work out in the modern times where the requirements of industry are changing frequently. There is a need of an hour for the higher institutions to shift their approach from academic based curriculum to industry-based curriculum. Many higher education institutions are struggling to provide quality, affordable education and research to its end users. Thus, a collaborative approach needs to be followed in the form of public-private partnership model for facilitating practical learning in higher institutions and preparing the future budding managers to bridge the gap between skills required to meet industry expectations and the skills they possess. The aim of this paper is to explore about the PPP model for promoting industry-based curriculum in academics. The study is purely secondary in nature followed with exploratory research-design for giving a broader view on implementation of PPP model in higher institutions. The present study provides an integrated process through which the effectiveness and working of PPP model is well-explained.

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies.

Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs. Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported. The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies. Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

FedGST: An Efficient Federated Graph Neural Network for Spatio-temporal PoI Recommendation

With the proliferation of sensor networks in urban areas, vast amounts of data from location-based social network platforms are now available, thus enabling the stakeholders to develop location-aware services for the end users. PoI (Point of Interests) recommendation is one of the most trending services. PoI recommendation models are developed by considering geographical information and historical records. More recently, with the demand for achieving PoI recommendations on mobile devices, embedded devices, or Internet of Things (IoT) devices, the challenges posed by the limited computational resources, malicious clients, and data silos, the model performance gets immensely affected. To address the above challenges and achieve efficient PoI recommendations, we propose an efficient federated graph learning-based model for mining complex spatiotemporal features to generate recommendations. We implement a GRU-based encoder-decoder to learn the temporal hidden state embeddings. Simultaneously, a 2-layer graph network is used to understand spatial embedding vectors. We also introduce an efficient contribution evaluation method to speed up the training process and improve the recommendation performance. Experiments on the PoI recommendation task based on real-life check-in data validate the effectiveness of our proposed model, and the results indicate that our recommendation model can achieve competitive results with lower computational costs.

Automatic Detection of Stalling Events using Machine Learning Algorithms

With the proliferation of new video streaming OTT service applications, content providers must guarantee an efficient, effective and satisfactory interaction between the user and the service application, to measure this they use the quality of experience (QoE). QoE in the context of telecommunications can be understood as the acceptability of a service perceived by end users. However, given the variability of the network and the different factors that can intervene during the service, this perception can be altered and one of these causes is stalling events. It is necessary to have an accurate method to detect stalling events, allowing content providers to make better decisions in the design of their OTT applications and thus reduce customer desertion and the economic losses of service or content providers. For this reason, in this paper firstly we show a comparative analysis of supervised algorithms showing its invalidity given the imbalance of the data despite the high levels of accuracy. Therfore, we propose as a contribution a novel method based on algorithms provided by SSAD (Semi Supervised Anomaly Detection) for the detection of stalling events. Finally, we observe that in our method the Isolation Forest model has the best performance, closer to 1 and we will show more detail about its performance in future works.

Utilization Patterns of Milk and Milk Products among Various End Users in Karnataka, India: A Comparative Analysis

The utilization patterns of milk and milk products in Karnataka were investigated through a study of 100 farmers and 50 consumers. The focus was on how milk was retained, consumed and transformed into various products. It was found that significant variation existed in milk production and consumption across different herd sizes. Small, medium, and large farmers were reported to produce an average of 33.80, 45.18 and 66.26 liters of milk per day, respectively, with family consumption accounting for 19.23 per cent, 17.37 per cent and 15.84 per cent of their production. Larger herds were associated with increased milk production, which allowed for a greater surplus to be sold. Utilization patterns showed that small farmers retained 6.5 liters of milk daily, consuming 32.94 per cent as liquid milk and converting 64.23 per cent into curd, with minimal ghee production. In contrast, large farmers retained 10.5 liters daily, consuming 43.80 per cent as liquid milk and converting 53.12 per cent into curd. This indicated a trend towards higher liquid milk consumption and varying product transformation depending on herd size. Consumer data indicated that rural households spent an average of ₹141.18 on 2.41 kg of milk products daily, while urban households spent ₹142.10 on 2.25 kg. Despite higher costs, urban consumers spent more on liquid milk, whereas rural consumers allocated more to curd and ghee. These findings reflected regional price differences and consumption preferences. Overall, the study highlighted the complex dynamics of milk utilization, with varying consumption patterns and product transformations observed across different farmer categories and consumer demographics. This information was deemed crucial for optimizing milk production and distribution strategies in Karnataka.

Fake Product Detection with Blockchain Technology

Consumers and brands are at serious risk due to the growth of counterfeit goods, especially in regions like Nigeria. Conventional techniques, such border inspections and market raids by the Standards Organization of Nigeria (SON), are inadequate for detecting counterfeit goods. To ensure traceability, transparency, and immutability in the supply chain, this article suggests utilizing blockchain technology. The decentralized and encrypted characteristics of blockchain, when bolstered by smart contracts, enable efficient product tracking from producers to end users, hence impeding the infiltration of fake goods. Using a permissioned blockchain network, this system attempts to confirm the legitimacy of products at every point along the supply chain—manufacturers, distributors, retailers, and end users. The Remix IDE is used to deploy and test Ethereum-based smart contracts that were created in Solidity for the proposed system. This blockchain-based strategy aims to decrease the spread of counterfeit goods, protect consumer confidence, and preserve brand reputation. To offer a user-friendly interface for wider accessibility, future advancements will link this system with decentralized apps (DApps).

Liver Disease Detection using Machine Learning

Abstract: Liver disease is a serious worldwide health issue, and prompt diagnosis and treatment are essential for successful outcomes. Traditional diagnostic techniques, however, may be expensive and time-consuming and can require intrusive procedures. In this work, we suggest a machine learning-based method for liver disease identification that makes use of the Support Vector Machine (SVM) and Random Forest Decision Tree algorithms. Our approach uses a large dataset with pertinent clinical characteristics including biochemical signs and patient demographics to categorize people into liver disease-positive or - negative groups. Furthermore, we incorporate an intuitive UI with Streamlit, improving accessibility and usability for end users and healthcare providers alike. By means of meticulous testing and assessment, we exhibit the efficiency and functionality of our suggested framework. In terms of precision, specificity, and sensitivity, our model performs admirably, demonstrating its promise as a trustworthy instrument for the identification of liver illness. Moreover, the incorporation of Streamlit improves the system's practicality and usability, making it easier to install and use in actual healthcare environments.

Enhancement of Power Quality in Grid Network Using Compensator and Modified Power Filters

Abstract: Now-a-days with the advancement in technology there is a drastic improvement in the semi-conductor devices. With this development and advantages, the semi-conductor devices got a permanent place in the power sector helping to ease the control of overall system. Moreover, most of the loads are also semi-conductor-based equipment. But the semi-conductor devices are non-linear in nature and draws non-linear current from the source. And also, the semi-conductor devices are involved in power conversion, which is either AC to DC or from DC to AC. This power conversion contains lot of switching operations which may introduce discontinuity in the current. Due to this discontinuity and non-linearity, harmonics are present which affect the quality of power delivered to the end user. To maintain the quality of power delivered, the harmonics should be filtered out. Thus, a device named Filter is used which serves this purpose.

TrafPS: A shapley-based visual analytics approach to interpret traffic

AbstractRecent achievements in deep learning (DL) have demonstrated its potential in predicting traffic flows. Such predictions are beneficial for understanding the situation and making traffic control decisions. However, most state-of-the-art DL models are considered “black boxes” with little to no transparency of the underlying mechanisms for end users. Some previous studies attempted to “open the black box” and increase the interpretability of generated predictions. However, handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging. To overcome these challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirements from domain experts, we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

Analyzing User Satisfaction of the SMP Negeri 1 Sungai Lilin Website Using the End User Computing Satisfaction (EUCS) Method

Education plays a central role in society, with schools serving as crucial institutions in providing access to relevant information for all stakeholders. With advancements in technology, many schools have established an online presence through their websites. SMP Negeri 1 Sungai Lilin is one such school that provides various information related to school activities on its website. However, overall user engagement and feedback, particularly from students, have been limited, and the level of user satisfaction remains unknown. This study aims to analyze the user satisfaction of the school website using a quantitative descriptive approach to test specific hypotheses. Data were collected from 250 respondents selected through purposive sampling, conducted at SMP Negeri 1 Sungai Lilin between February and April 2024. The End User Computing Satisfaction (EUCS) method was employed to assess user satisfaction and the influencing factors. EUCS evaluates satisfaction based on five parameters: content, accuracy, format, ease of use, and timeliness. Data analysis was conducted using multiple linear regression and classical assumption tests, processed with SPSS version 23. Initial validity and reliability tests revealed inadequacies, but subsequent tests confirmed that all instruments were valid and reliable. The results confirm that all research hypotheses are accepted. The F-test results indicate that the regression coefficients of content, accuracy, format, ease of use, and timeliness significantly affect user satisfaction (sig < 0.05). The T-test results further reveal that each variable positively influences user satisfaction individually (sig < 0.05). An R Square value of 0.297 suggests that 29.7% of user satisfaction is explained by the variables of content, accuracy, format, ease of use, and timeliness, indicating a weak relationship, while the remaining 70.3% is influenced by other factors not included in this study. The findings indicate that respondents generally exhibit low levels of satisfaction when using the SMP Negeri 1 Sungai Lilin website, as evidenced by the influence of content, accuracy, format, ease of use, and timeliness on user satisfaction.

Identifying enablers for the successful deployment of prescriptive analytics – a multiple case study

Purpose Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics. Design/methodology/approach The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management. Findings The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes. Originality/value The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

EDNAqua-Plan—Standardisation Overview for eDNA Sequencing of Aquatic Organisms and the Downstream Data Ecosystem

The eDNAqua-Plan project stands as a beacon of innovation in the biomonitoring of marine and freshwater ecosystems, propelled by the urgent need to integrate DNA-based approaches in aquatic bioassessment and monitoring frameworks. The broad utilisation of cutting-edge environmental DNA (eDNA) and DNA barcoding methodologies is dependent on complete, reliable, and accessible reference DNA sequence data (Rimet et al. 2021). Complete and interoperable metadata is crucial to allow a broad reuse of (e)DNA data and analysis outputs, and for a broader uptake of results by end users. The eDNAqua-Plan project aims to address key limitations to the routine implementation of eDNA-based monitoring methods in Europe by developing plans for federated DNA barcode reference libraries and eDNA data repositories to support DNA-based environmental monitoring. This will ensure a sustainable and reliable infrastructure to underpin its broad use, thereby paving the way for more effective conservation and management strategies. The project is working towards creating a comprehensive overview of standardisation efforts and data workflows, through collaborations with other projects, initiatives and infrastructures for aquatic monitoring across the European Union (EU) and associated countries. We are analysing existing archives (e.g., International Nucleotide Sequence Database Collaboration (INSDC), Barcode of Life Data System (BOLD), Global Biodiversity Information Facility (GBIF), Ocean Biodiversity Information System (OBIS)), portals, and papers to determine current and best practices through the use of questionnaires, manual evaluation of repositories, and machine learning methods (LLMs). This includes an overview of the usage of existing metadata and data standards (e.g., Minimum Information about any (X) Sequence Specifications from the Genomics Standards Consortium (GSC), Darwin Core standard). The results are being integrated by a team of experts in marine and freshwater biomonitoring. With a diverse consortium comprising 18 partner institutions from 11 countries and one international institute, eDNAqua-Plan brings together experts in marine and freshwater monitoring, eDNA analysis, and data science. The collective effort by this consortium will lay the groundwork for the creation of a digital ecosystem of eDNA repositories and an integrated reference library of marine and freshwater species, adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles.

Comparative Study of Bacillus-Based Plant Biofertilizers: A Proposed Index

The market for bacteria as agricultural biofertilizers is growing rapidly, offering plant-growth stimulants; biofungicides; and, more recently, protectors against extreme environmental factors, such as drought. This abundance makes it challenging for the end user to decide on the product to use. In this work, we describe the isolation of a strain of Bacillus velezensis (belonging to the operational group Bacillus amyloliquefaciens) for use as a plant-growth-promoting rhizobacterium, a biofungicide, and a protector against drought. To compare its effectiveness with other commercial strains of the same operational group, Bacillus amyloliquefaciens, we analyzed its ability to promote the growth of pepper plants and protect them against drought, as well as its fungicidal activity through antibiosis and antagonism tests, its ability to solubilize potassium and phosphates, and its ability to produce siderophores. Finally, we used a probit function, a type of regression analysis used to model the outcomes of analyses, to quantify the biostimulatory effectiveness of the different plant-growth-promoting rhizobacteria, developing what we have called the Agricultural Protection Against Stress Index, which allowed us to numerically compare the four commercial strains of the operational group Bacillus amyloliquefaciens, based on a Delphi method—a type of regression analysis that can be used to model a cumulative normal distribution—and integrate the results from our panel of tests into a single value.

Women's experiences with continuity for effective coordination during maternal and neonatal continuum in Kenya: An interpretive phenomenology.

Embracing women's experiences in decision-making is imperative for continuity in effective coordination of maternal and neonatal health (MNH); women are the end users within the care ecosystem. Through women's continuous feedback, skilled birth attendants (SBAs) and the healthcare system get to understand emerging issues based on their needs and preferences. The purpose of this article is to describe women's experiences of continuity for effective coordination of care through the transitions in the MNH continuum in Kenya. The study was conducted in selected counties of Kenya based on birth rates per woman as follows: Wajir (7.8) Narok (6.0) Kirinyaga (2.3) and Nairobi (2.7) (1). The clients were interviewed concerning their experiences of the MNH continuum of care in English and Kiswahili. An interpretive hermeneutic phenomenological approach was used to construct the experiences of women of continuity during transitions in the MNH continuum for effective care coordination. Twelve participants were interviewed between January and April 2023. Atlas ti 22 software was used for data analysis. Four women experiences were highlighted: Women unawareness of preconception care, use of prenatal care, labour, birthing and postpartum flow and the women's view on the MNH continuum. The women reported their segmental and transitional experience of the MNH continuum as one that did not consistently meet their needs and preferences in order for them to fully agree that the continuum enhanced continuity for effective coordination. They felt that they experienced continuity in some segments while in some they did not.Contribution:The embrace of women's experience of their needs and preferences through the MNH continuum (segments and transitional segments) through the lens of continuity for effective coordination is timely towards the improvement of maternal and neonatal care by 2030.

Predicting Tourist Arrivals: A Google Trends-Based Model for Destination Management

In this paper, a review of the scientific literature on the importance and knowledge of the tourist destination for the formation of a correct strategy for its development and promotion is made. The possibility of managing the tourist destination by modeling with Google Trends data is represented. A review of studies using the mentioned tool in the field of tourism is conducted. The potential benefits of using Google Trends data on searches for a city as a keyword in combination with archival data are summarized. The possibilities for modeling the visits of tourists using data from Google Trends are investigated, as a result of which a model is built by applying polynomial regression to predict the overnight stays of foreign citizens in the city of Veliko Tarnovo based on archival data from the National Statistical Institute and Google Trends data. Through data analytics with Google Trends, it is concluded that key value is created for the external and internal environment, influencing the destination. The data creates conditions for the development of management strategies for attracting tourists based on primary and secondary data for analysis. The application of the received data sets through data analysis are also important for tourism organizations and institutional bodies in tourism (macro- and meso-level). The resulting analysis affects issues related to the management and marketing of the destination, and on the other hand, it directly affects the segmentation of the market and the distribution of products and services to the end user (micro level).

Development of the Guide to Disseminating Research (GuiDiR): A consolidated framework

BackgroundLess than one third of research evidence is translated into policy or practice. Knowledge translation requires effective dissemination, adoption and finally implementation. These three stages are equally important, however, existing knowledge translation models and frameworks provide little and disparate information about the steps and activities required for effective dissemination. ObjectiveThis study aimed to empirically develop a consolidated framework of evidence-based steps and activities for disseminating research evidence. MethodsWe identified models and frameworks from a scoping review and dissemination and implementation webtool. We synthesised them into a prototype dissemination framework. Models and frameworks were eligible to inform steps in our framework if they fulfilled at least one of three elements of dissemination: intending to generate awareness of a message, incorporates targeting an audience: tailoring communication. An initial coding framework was created to organise data into dissemination steps.Drawing on ‘co-approach’ principles, authors of the included models and frameworks (dissemination experts) and health service researchers (end users) were invited to test and refine the prototype framework at a workshop. ResultsFrom 48 models and frameworks reviewed, only 32 fulfilled one or more of the three dissemination elements. The initial coding framework, upon refinement, yielded the Guide to Disseminating Research (GuiDiR) comprising five steps.1) Identify target audiences and dissemination partners.2) Engage with dissemination partners.3) Identify barriers and enablers to dissemination.4) Create dissemination messages.5) Disseminate and evaluate.Multiple activities were identified for each step and no single model or framework represents all steps and activities in GuiDiR. ConclusionsGuiDiR unifies dissemination components from knowledge translation models and frameworks and harmonises language into a format accessible to non-experts. It outlines for researchers, funders and practitioners the expected structure of dissemination and details the activities for executing an evidence-based dissemination strategy.

Skill Enactment Among University Students Using a Brief Video-Based Mental Health Intervention: Mixed Methods Study Within a Randomized Controlled Trial.

Mental health problems are common among university students, yet many students do not seek professional help. Digital mental health interventions can increase students' access to support and have been shown to be effective in preventing and treating mental health problems. However, little is known about the extent to which students implement therapeutic skills from these programs in everyday life (ie, skill enactment) or about the impact of skill enactment on outcomes. This study aims to assess the effects of a low-intensity video-based intervention, Uni Virtual Clinic Lite (UVC-Lite), in improving skill enactment relative to an attention-control program (primary aim) and examine whether skill enactment influences symptoms of depression and anxiety (secondary aim). The study also qualitatively explored participants' experiences of, and motivations for, engaging with the therapeutic techniques. We analyzed data from a randomized controlled trial testing the effectiveness of UVC-Lite for symptoms of depression and anxiety among university students with mild to moderate levels of psychological distress. Participants were recruited from universities across Australia and randomly assigned to 6 weeks of self-guided use of UVC-Lite (243/487, 49.9%) or an attention-control program (244/487, 50.1%). Quantitative data on skill enactment, depression, and anxiety were collected through baseline, postintervention, and 3- and 6-month follow-up surveys. Qualitative data were obtained from 29 intervention-group participants through open-ended questions during postintervention surveys (n=17, 59%) and semistructured interviews (n=12, 41%) after the intervention period concluded. Mixed model repeated measures ANOVA demonstrated that the intervention did not significantly improve skill enactment (F3,215.36=0.50; P=.68). Skill enactment was also not found to influence change in symptoms of depression (F3,241.10=1.69; P=.17) or anxiety (F3,233.71=1.11; P=.35). However, higher levels of skill enactment were associated with lower symptom levels among both intervention and control group participants across time points (depression: F1,541.87=134.61; P<.001; anxiety: F1,535.11=73.08; P<.001). Inductive content analysis confirmed low levels of skill enactment among intervention group participants. Participants were motivated to use techniques and skills that were perceived to be personally relevant, easily integrated into daily life, and that were novel or had worked for them in the past. The intervention did not improve skill enactment or mental health among students with mild to moderate psychological distress. Low adherence impacted our ability to draw robust conclusions regarding the intervention's impact on outcomes. Factors influencing skill enactment differed across individuals, suggesting that it may be necessary to tailor therapeutic skills and engagement strategies to the individual user. Theoretically informed research involving collaboration with end users is needed to understand the processes underlying skill enactment in digital mental health interventions. Australian New Zealand Clinical Trials Registry ACTRN12621000375853; https://tinyurl.com/7b9ar54r.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Software supply chain security: a systematic literature review

ABSTRACT In recent years, software supply chain security has attracted significant research attention. This research subject is concerned both with the security of infrastructures used to build software and deliver it to end users and with the security of software that contains external dependencies such as third-party packages and libraries, and its chief goal is to ensure that no vulnerabilities are introduced in the path between developers and users. This paper presents a Systematic Literature Review to identify knowledge gaps in software supply chain security. For this, we considered studies published between 2012 and 2023 in the search engines of IEEE Xplore, ACM Digital Library, Engineering Village, Scopus, and arXiv. Of the 2051 studies obtained in the primary survey, only 85 are relevant for this research. Analyzing the studies, we observed gaps such as little discussion of software supply chains that involve cloud components, few proposals focused on the software distribution process, and a lack of use of threat modeling frameworks to help identify threats to the supply chain and validate the results.