End Users Research Articles

Supporting Carers: Study Protocol of a Meta-Review of Psychosocial Interventions for Carers of People With Cancer.

While there is a clear need for psychosocial interventions that promote the well-being of carers of patients with cancer, the corresponding evidence base is disparate, complex, and difficult for end users to navigate and interpret. Carers remain undersupported with a lack of dedicated, effective, evidence-based programs. We will conduct a meta-review to synthesize this evidence and determine the state of science in this field. This study aims to address the question, "what psychosocial interventions are available to promote the well-being of carers for people with cancer?" A meta-review will synthesize the relevant reviews of psychosocial interventions that have been developed and evaluated with carers for people with cancer. A total of 4 electronic databases (PsycInfo, MEDLINE, CINAHL, and Cochrane Database of Systematic Reviews) will be searched for reviews published between January 2013 and December 2023. A team-based approach will be taken for screening and assessment of the returned records against the eligibility criteria to determine inclusion. Included reviews will be critically appraised using the Joanna Briggs Institute Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. Relevant data on study characteristics, carer and patient populations, intervention details, and psychosocial outcomes will be extracted, synthesized, and the findings will be presented in a narrative format. It is anticipated that the study will be completed by October 2024. Ensuring that carers have access to evidence-based programs that promote their well-being as they care for loved ones is critical. This meta-review will contribute to program development and translation efforts by providing a clear picture of the intervention evidence base of carers of patients with cancer and identifying notable strengths, weaknesses, and gaps across the literature. The findings are anticipated to offer future directions to advance research in the field. PROSPERO (CRD42023403219); https://tinyurl.com/4tnzv49s. DERR1-10.2196/56403.

Cocreative Development of Robotic Interaction Systems for Health Care: Scoping Review.

Robotic technologies present challenges to health care professionals and are therefore rarely used. Barriers such as lack of controllability and adaptability and complex control functions affect the human-robot relationship. In addition to educational opportunities, the possibility of individual adaptation can improve the usability and practical implementation of robotics. Previous work has focused on developments from a technology-centered perspective and has included user interests too late in the process. This study addresses the following research question: What cocreative research approaches are used in the field of nursing robotics to improve the usability, intended use, and goal-directed application of robotic developments for nurses and to support the nursing process? This scoping review provides an overview of the topic and the research activities taking place within it. Five databases and the reference lists of the identified publications were searched for studies without further restrictions. Studies were included if they developed and evaluated interaction and control platforms for robotic systems in health care in a cocreative way with end users. The search resulted in 419 hits, of which 3 publications were included. All publications were feasibility or user studies that were mainly carried out in the European Union. The 3 interaction and control platforms presented were all prototypes and not commercially available. In addition to those in need of care, all studies also included family carers and health care professionals. Robotic interaction and control platforms in health care are rarely, if ever, developed and evaluated with feasibility or user studies that include prototypes and end users. While the involvement of end users is crucial, this review emphasizes that all stakeholders, including health care professionals, should participate in the development process to ensure a holistic understanding of application needs and a focus on user experiences and practical health care needs. It is emphasized that the active involvement of end users in the development process is critical to effectively meeting the needs of the target group.

D-Tour: Semi-Automatic Generation of Interactive Guided Tours for Visualization Dashboard Onboarding.

Onboarding a user to a visualization dashboard entails explaining its various components, including the chart types used, the data loaded, and the interactions available. Authoring such an onboarding experience is time-consuming and requires significant knowledge and little guidance on how best to complete this task. Depending on their levels of expertise, end users being onboarded to a new dashboard can be either confused and overwhelmed or disinterested and disengaged. We propose interactive dashboard tours (D-Tours) as semi-automated onboarding experiences that preserve the agency of users with various levels of expertise to keep them interested and engaged. Our interactive tours concept draws from open-world game design to give the user freedom in choosing their path through onboarding. We have implemented the concept in a tool called D-TOUR PROTOTYPE, which allows authors to craft custom interactive dashboard tours from scratch or using automatic templates. Automatically generated tours can still be customized to use different media (e.g., video, audio, and highlighting) or new narratives to produce an onboarding experience tailored to an individual user. We demonstrate the usefulness of interactive dashboard tours through use cases and expert interviews. Our evaluation shows that authors found the automation in the D-Tour Prototype helpful and time-saving, and users found the created tours engaging and intuitive. This paper and all supplemental materials are available at https://osf.io/6fbjp/.

Involving lead users in firm’s standardization strategy within action groups: evidence from smart robotics

PurposeThis research explores how firms manage the complex technologies standardization in action groups. It considers the strategic issues that technology producers face when involving lead users in architecture design. Drawing on the multi-mode standardization literature, this study addresses two dilemmas regarding value creation and appropriation by technology producers within coalitions. The first dilemma is how to create value by developing solutions in compliance with industry standards. The second one is how to appropriate value while ensuring the technology sharing with action groups. The answers to these two dilemmas contribute to filling the research gap on value creation and appropriation in multi-mode standardization.Design/methodology/approachThe research focuses on technology producers participating in action groups where lead users play a crucial role. We conducted a qualitative analysis based on the standardization experience of a Japanese company specializing in smart robotics. Data are collected through semi-structured interviews with key actors. Action groups are defined operationally as a set of stakeholders including competitors of the technology producers, component suppliers, end users, services providers, research centers and academia. The case study is suitable for highlighting specific aspects of the standardization process during its manifestation. It reveals how firms create and appropriate value, providing details about its standardization strategy.FindingsOur findings show that smart robotics standardization is drivn by collaborative models, where the two dilemmas of value creation and appropriation are evident. Firstly, the case revealed that standardization is lead users oriented. Secondly, lead users’ involvement is crucial to customize technologies. Thirdly, the firm’s position is to share a part of the value with the members. The IPR policy is a matter of interest within action groups, since the collaboration is based on open innovation models to share patents and licenses related knowledge.Research limitations/implicationsThis research has some limitations attributable to the limited generalizability of the results due to the qualitative analysis. In addition, this study considers the perspective of technology producers, but should also take into account the perspective of both collective actions itself and the lead users. Findings have some implications in the strategy negotiation. Participating in action groups is not enough to ensure a competitive advantage. Involving lead users is of strategic importance to acquire a competitive advantage. Lead users contribute to the producers’ technology design, helping firms to differentiate solutions from the industry standard and create value from customized technologies.Practical implicationsThis study helps practitioners understand the competitive side of collective actions, clarifying the value capture and appropriability in standardization. The research provides insights to policymakers and standard development organizations committees when they are called to harmonize standards considering the fallouts on the sector’s competitiveness. Findings suggest appropriate property rights policies to manage the issues related to the value appropriability and technology sharing, recognizing action groups members for their contribution in value creation.Originality/valueThis study shows how firms deal within action groups with the two dilemmas of variety versus technology conformity and property rights versus technology sharing. It fills the research gap in collective actions, emphasizing the perspective of the individual firm in the group rather than the coalition strategy itself. This topic highlights the crucial role of lead users within action groups in managing the two dilemmas, offering a new perspective for understanding critical issues of multi-mode standardization. Reflecting on mechanisms and tools to manage the two dilemmas allows firms to protect their competitive advantage in coalitions.

Improving preparation in the emergency trauma room: the development and impact of real-time data transfer and dashboard visualization system.

This study examines, with clinical end users, the features of a visualization system in transmitting real-time patient data from the ambulance to the emergency trauma room (ETR) to determine if the real-time data provides the basis for more informed and timely interventions in the ETR before and after patient arrival. We conducted a qualitative in-depth interview study with 32 physicians in six German and Swiss hospitals. A visualization system was developed as prototype to display the transfer of patient data, and it serves as a basis for evaluation by the participating physicians. The prototype demonstrated the potential benefits of improving workflow within the ETR by providing critical patient information in real-time. Physicians highlighted the importance of features such as the ABCDE scheme and vital signs that directly impact patient care. Configurable and mobile versions of the prototype were suggested to meet the specific needs of each clinic or specialist, allowing for the transfer of only essential information. The results highlight on the one hand the potential need for adaptable interfaces in medical communication technologies that balance efficiency with minimizing additional workload for emergency medical services and show that the use of pre-notification systems in communication between ambulance and hospital can be supportive. Further research is recommended to assess practical application and support in clinical practice, including a re-evaluation of the enhanced prototype by professionals.

Omnidirectional soft pneumatic actuators: a design and optimization framework

IntroductionSoft pneumatic actuators (SPAs) play a pivotal role in soft robotics due to their unique characteristics of compliance, flexibility, and adaptability. There are plenty of approaches that examine the modeling parameters of SPAs, aiming to optimize their design and, thus, achieve the most advantageous responses. Current optimization methods applied to SPAs are usually performed individually for each design parameter without considering the simultaneous effect all parameters can have on the output performance. This modeling shortcoming is essential to be addressed since customized SPAs are used in a variety of applications, each with different output requirements.MethodsThis study provides a generalized design optimization framework for modeling the SPA performance for their motion profiles, the produced strain energy while being deformed, and their stiffness characteristics. Utilizing experimentally validated finite element methods, all geometrical and material parameters of the models are investigated in response surface methodology optimization using the central composite design approach.ResultsThe results showcase the entire design space of omnidirectional SPAs, along with their output performance, providing guidelines to the end user for design optimization.DiscussionThe offering of this modeling process for SPAs can be adapted to the demands of any potential application and ensure the best performance with respect to the required output responses.

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance. We suggested a proof of concept to define an ensemble approach useful for integrating different algorithms proposed to solve the same clinical task. The proposed approach was developed starting from a public database consisting of radiomic features extracted from CT images relating to 535 patients suffering from lung cancer. Seven algorithms were trained independently by participants in the AI4MP working group on Artificial Intelligence of the Italian Association of Physics in Medicine to discriminate metastatic from non-metastatic patients. The classification scores generated by these algorithms are used to train SVM classifier. The Explainable Artificial Intelligence approach is applied to the final model. The ensemble model was validated following an 80-20 hold-out and leave-one-out scheme on the training set. Compared to individual algorithms, a more accurate result was achieved. On the independent test the ensemble model achieved an accuracy of 0.78, a F1-score of 0.57 and a log-loss of 0.49. Shapley values representing the contribution of each algorithm to the final classification result of the ensemble model were calculated. This information represents an added value for the end user useful for evaluating the appropriateness of the classification result on a particular case. It also allows us to evaluate on a global level which methodological approaches of the individual algorithms are likely to have the most impact. Our proposal represents an innovative approach useful for integrating different algorithms that populate the literature and which lays the foundations for future evaluations in broader application scenarios.

Improving transparency and quality assurance: Operationalising semi-automated data provenance tracking in a Trusted Research Environment

We present a prototype solution for improving transparency and quality assurance of the data linkage process through a data provenance dashboard designed to assist data analysts, researchers and information governance teams in authenticating and auditing data workflows within a trusted research environment (TRE). Building on prior research (Scheliga, et al., 2022), this work describes our first operationalised prototype tested in a real-world setting. The prototype development involved four stages: (1) Co-design of interfaces with end users for provenance data collection and visualisation, producing a low-fidelity design; (2) Extension and refinement of the Safe Haven Provenance (SHP) ontology (https://tre-provenance.github.io/SHP-ontology/releases/v0.2/index-en.html); (3) Design and implementation of mechanisms for semi-automated collection of data linkage provenance using the SHP ontology; (4) Implementation and user evaluation of the dashboard. A participatory design process with data analysts, researchers and information governance teams resulted in a low-fidelity prototype and was validated via public consultations to ensure it met public trust. The resulting prototype dashboard (https://tre-provenance.github.io/) was built as an offline desktop app to match the deployment TRE requirements. The interactive dashboard displays the data linkage information extracted from a knowledge graph described using the SHP ontology (e.g., modifications of datasets, data release) and results of rule-based validation checks (e.g., checking extracted data against researchers' specification). User evaluations confirmed the dashboard would contribute to better quality of data linkage. This project demonstrates the next stage in advancing transparency and quality assurance within TREs by semi-automating and systematising data tracking from ingress to egress in a single tool.

Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams

IntroductionOverdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows. MethodsWe utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation. ResultsEleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data. ConclusionsWe refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users.

Energy performance certificate calculation methodologies across Europe and accommodating new performance indicators

Energy Performance Certificates (EPC) are used across Europe to communicate the energy performance of buildings to a range of different end users. However, as new approaches are introduced and guided at the European level via the Energy Performance Buildings Directive (EPBD), the ability of assessment methodologies and assessors to accommodate these new forms is likely to differ due to the significantly different frameworks in each country. The Horizon-funded crossCert project investigates this by identifying how EPC methodologies differ across countries, highlighting the implications of these differences on the performance gap and what that means for future innovations. Results indicate that methodologies vary considerably regarding general approaches, from highly standardised to highly tailored and assessor qualifications. This paper will look at such differences and investigate how these differences can affect future efforts towards improving EPCs.

Installation of Clean Water Piping for Resident on Jl. Arjuna, RW 08 and 09, East Labuh Baru, Pekanbaru

Water is a crucial resource for sustaining life; however, access to it through the Regional Drinking Water Company (Indonesian: Perusahaan Daerah Air Minum [PDAM]) remains limited in certain communities. As a result, residents frequently depend on groundwater sourced through independent pumping by households, places of worship, public facilities, private and government offices, and others. An assessment of the buildings on Jl. Arjuna RW 08 and 09, East Labuh Baru District, Pekanbaru, indicates a significant lack of adequate clean water installation systems, highlighting a need for targeted community service efforts. This initiative aims to rectify these deficiencies by drilling wells to establish reliable sources of clean water and installing a comprehensive clean water piping system to ensure effective distribution from the source to the end users, thus aligning with Indonesian National Standards (SNI). The project involves faculty members as part of their commitment to the tri-dharma of higher education, offering both a practical learning opportunity for students and a valuable service to the community.

Software System Architecture for Estimating Software Development Time

The article is devoted to the issues of estimating the duration of software development, the solution of which can significantly improve the efficiency of software projects. The relevance of the topic is conditioned by the rapid development of software development methodologies, on which the existing algorithms of such estimation depend, and, as a consequence, by the rapid obsolescence of approaches to solving this problem. A systematic analysis of the main algorithms for estimating software development duration is carried out. The classification of such algorithms is presented. The algorithm of software development duration estimation and the idea of the system implementing it, which will allow end users to automate this process, are proposed. The approaches to implementation of algorithms of retrospective (historical) estimation, PERT, expert estimation are considered. The prospectivity of researches in the field of implementation of methods of expert evaluation using neural networks is shown. The architecture of the proposed system is described. A prototype of the system implementing the method of retrospective evaluation based on the evaluation of the speed of the development team is developed.

Modelling user notification scenarios in privacy policies

The processing of personal data gives a rise to many privacy concerns, and one of them is to ensure the transparency of data processing to end users. Usually this information is communicated to them using privacy policies. In this paper, the problem of user notification in case of data breaches and policy changes is addressed, besides an ontology-based approach to model them is proposed. To specify the ontology concepts and properties, the requirements and recommendations for the legislative regulations as well as existing privacy policies are evaluated. A set of SPARQL queries to validate the correctness and completeness of the proposed ontology are developed. The proposed approach is applied to evaluate the privacy policies designed by cloud computing providers and IoT device manufacturers. The results of the analysis show that the transparency of user notification scenarios presented in the privacy policies is still very low, and the companies should reconsider the notification mechanisms and provide more detailed information in privacy policies.

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

BackgroundOur 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.MethodsWe 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.ResultsUsability 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.ConclusionsUsability 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.

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care

The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

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.