End Users Research Articles

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.

Blockchain-Based Secured Drug Traceability in Health Care Application

Despite the enormous advancements in technology, fraudsters continue to attack the healthcare industry. Numerous recent healthcare security breaches indicate that attackers are focussing on the electronic health data and the drug supply chain. Additional investigation into security problems in the healthcare sector demonstrates that the primary cause of these incidents is the centralisation of data storage. The healthcare sector also lacks immutability, adaptability, security, and privacy in addition to having insufficient documentation. It has been discovered that blockchain technology may offer a complete solution to the main issues the healthcare industry is now facing. Healthcare issues may be resolved with the aid of blockchain's intrinsic qualities, which include immutability, transparency, privacy, security, and documentation. In this work, an interactive blockchain 2.0 medication supply chain tracking system is created, and Hyperledger Calliper is used to measure the system's performance. This system makes sure that everyone involved in the drug supply chain can follow the drug's journey from the producer to the end user. The members' own inventory management is also aided by it. In addition to providing privacy and transparency, this method offers a viable remedy for the issues associated with counterfeit drugs. With one ordered peer and one endorsed peer, the system can process 25,000 transactions per second.

Điều kiện thúc đẩy sự ra đời tiền kỹ thuật số của Ngân hàng Trung ương: Đề xuất khung khổ lý thuyết hai giai đoạn

In the era of the Fourth Industrial Revolution, Central Bank Digital Currency (CBDC) emerges as a novel form of fiat currency, exerting diverse influences on the monetary system, market dynamics, and the entire economy. By synthesizing existing literature through comparative analysis and theoretical evaluation, this study presents a novel framework outlining the preconditions for CBDC successful implementation and widespread adoption. They are categorized into two phases: (i) Phase 1 encompasses four prerequisite conditions, namely: clear policy objectives, a robust legislative framework, broad stakeholder engagement, and sound technological capabilities; (ii) Phase 2 addresses the critical challenge of market feasibility, mirrored through end-user acceptance of CBDC. Our proposed two-phase theoretical model serves as a valuable guide for central banks in formulating policies and plans, aiding commercial banks in implementing CBDC related technical solutions and infrastructure, and fostering awareness among end users, both individuals and businesses.

Improving Safety and Comfort of Healthcare Workers Caring for Patients With High-Consequence Infectious Diseases in a High-Level Isolation Unit Using Innovative Approaches.

Patients with high-consequence infectious diseases (HCIDs) require high-quality care by specially trained staff in a high-level isolation unit (HLIU) that follows strict infection prevention and control (IPC) measures. Caring for patients with (suspected) HCID is challenging, mainly because of the strict personal protective equipment (PPE) and IPC protocols healthcare workers (HCW) must adhere to for protection. The Radboud University Medical Center, located in Nijmegen, the Netherlands, has been a dedicated HLIU facility since 2008. A newly built HLIU opened in May 2022, and encouraged us to review the existing PPE selection, IPC protocols, and HCID training program to improve safety and comfort for HCWs working in the HLIU. Based on a systematic search through (inter)national HCID PPE guidelines and semistructured interviews with end users, we selected an improved, more comfortable set of PPE. Additionally, we developed a more concise and easier-to-use patient care process flow and implemented a new teaching strategy. The new way of working was tested in October 2022 when the first 2 patients with suspected HCID were admitted to our unit. We used surveys to evaluate the experiences of HCWs involved in this care to further improve the workflow of the unit. When optimizing safety and comfort for HCWs, it is important to consider (inter)national guidelines as well as user preferences. By systematically evaluating recent experiences of patient admission to the HLIU and then adjusting protocols and training, we can ensure that the quality of provided healthcare and the safety of HCWs working in the HLIU remains high.

644. CUSTOMIZING A LARGE LANGUAGE MODEL TO PROVIDE CLINICALLY TAILORED ADVICE FOR EMERGENCY ESOPHAGEAL IMPACTIONS

Abstract Background There is interest in applying Large Language Models (LLMsclinically, but they are not trained for medical purposes. Equally, the development of LLMsrequires significant time and financial resources. Customizing an existing LLM for clinical purposes may overcome these limitations and would be of interest to clinicians, patients, hospital managers, and government/policy makers. Methods Thirty patient cases with nonbony esophageal impactions were developed using guidance from UpToDate. Standardized prompts were engineered for physicians as the end user. The customization tool was applied to ChatGPT-4.0 to create the Esophageal Impaction Tool (GPT-EIT), which provides advice for managing patients with esophageal impactions. Both GPT-EIT and the generic ChatGPT-4.0 models were queried February 28th, 2024. Their performance was evaluated based on guidelines for the removal of foreign bodies from the European Society of Gastrointestinal Endoscopy. Outcome data was presented using descriptive statistics in the form of counts and percentages. Results The GPT-EIT provided accurate recommendations for 30/30 (100.0%) cases. Each case was managed appropriately, recommending that stable patients received endoscopy within 24 hours, while patients with drooling were recognized as a surgical emergency and advice was given to provide endoscopy within 2 hours, but no more than 6 hours. GPT-EIT also advised to consider the appropriateness of treatment for patients presenting at 97 years of age based on their comorbidities and health status. The generic ChatGPT-4.0 model produced accurate guidance for 14/30 (46.7%) of cases. Patients with signs of significant obstruction would not have received timely endoscopy (Table 1). Conclusions ChatGPT can be customized to overcome limitations with its training dataset to develop clinically relevant, accurate advice for esophageal impactions. Clinicians, patients, hospital managers, and policymakers should take note that this methodology may be applied for other clinical purposes to avoid limitations related to resource constraints with developing LLMsfrom scratch.

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).

The role of public-sector-led inclusive instant payment systems for growing the digital payments industry in Africa

Digital payments are the most popular financial service in Africa, having been adopted by half of all adults. Nevertheless, there remain significant barriers to access in Africa’s digital payment sector, stymieing growth. Cost and distance to a branch or agent are chief among them, exacerbated by the fact that many economies in Africa have small financial services markets, with payment sectors dominated by private sector financial service and payment system providers that have invested in proprietary, closed-loop digital payments processing infrastructure. These closed-loop systems can be inefficient and therefore expensive to operate, leaving payment services out of reach for millions of end users. With the goal of calling out fruitful approaches for expanding and maturing Africa’s payments market, this paper highlights lessons from four public or public–private instant payment systems (IPS) initiatives that aimed to build affordable and inclusive digital public infrastructure to enable broader access and therefore growth in Africa’s digital payments sector. Using case studies from Ghana, Malawi, Zambia and the Central African Economic and Monetary Community, this paper highlights lessons from these initiatives, including the need for enabling regulations, the need to craft a compelling business case for incumbent payment service providers, central bank involvement to ensure a focus on inclusive access, and customer-centricity to ensure the IPS can process high-demand payment types through the channels that end users prefer.

Unlocking Local Private Capital to Finance the Productive Use of Renewable Energy (PURE) Sector – a Look at East African Local Financial Institutions (LFIs)

This paper explores opportunities in Ethiopia, Kenya, Uganda, and Tanzania to mobilize and scale up financing for the fast-growing PURE sector, which drives energy access and economic growth. It highlights the critical role of local financial institutions (LFIs) in accelerating PURE lending to small- and medium-sized enterprises (SMEs) and end users in East Africa. Recommended interventions in this paper focus on eliminating existing barriers and galvanizing interest from investors, entrepreneurs, development partners, and local governments.

Bargaining on price on behalf of price-insensitive downstream consumers

There are settings in which prices are negotiated by procurement agents and final consumption decisions are made by end users who are indifferent to negotiated prices. For example, a patient seeking medical treatment is indifferent to the treatment's cost if it is covered by his insurance program. We study bargaining for per-unit prices between suppliers and an intermediary who represents price-insensitive consumers. Under the commonly used simultaneous bargaining framework we show that, if suppliers have sufficiently high bargaining power, the resulting prices will exceed the value of the good (or service) being delivered. This overpricing is solved if simultaneous negotiations are replaced by sequential ones. The theoretical problem with sequential negotiations is that they necessitate treating the suppliers asymmetrically, even if they are symmetric; the empirical problem with sequential negotiations is that the negotiations-order is unobservable. We propose a multi-period model that resolves these issues: overpricing is prevented and all suppliers are treated the same. In this model, the result about sequential negotiations is utilized in order to produce (asymmetric) off-path threats. These threats sustain symmetric on-path play.

Leveraging Collaborative Partnerships to Enhance NWS and Emergency Management Communications through Exercising

Abstract Tabletop exercises examining weather-related hazards are not uncommon but are often built around somewhat generic scenarios that only touch on the meteorological communication environment at a very shallow level. A recent exercise in central Oklahoma sought to change that. A local emergency manager, personnel from a National Weather Service (NWS) forecast office, and a severe weather researcher with a background in exercise design and facilitation worked together to create and deliver a realistic severe weather simulation. Exercise participants were exposed to detailed forecast information via NWSChat—a dedicated communication tool used to connect NWS forecasters, emergency managers, and media members for real-time information sharing. NWS forecasters were able to both actively play in the exercise due to the use of NWSChat and observe how local decision-makers interpreted and utilized the impact-based decision support service (IDSS) graphics and short-term forecast updates. The collaborative approach of developing a detailed scenario with numerous real-world IDSS graphics, along with the use of NWSChat for real-time delivery, resulted in overwhelmingly positive feedback from the participants. The local emergency management office identified numerous areas for improvement in communicating real-time forecast information across their jurisdiction, along with gaps in current plans and resources. Meanwhile, the NWS forecast office had the opportunity to experiment with using the new NWSChat platform in a high-impact severe weather environment before a real-world event took place. Forecasters also gained insight into current IDSS graphic interpretation, noting areas for improved messaging to end users, such as adding storm motion to existing severe weather graphics.

Clarifying to whom, where, and when payments are made : Recommendations from the ERPB Working Group on Transparency for Retail Payment End Users

It has become increasingly difficult for consumers to identify to whom, where, and when they have made payment transactions, as the descriptions provided on account statements do not always match the expectations of the consumer. Given that multiple actors in the payment chain can influence the information provided to the consumer, this problem can only be addressed with the involvement of multiple stakeholders. This paper discusses the recommendations made by the working group established by the Euro Retail Payments Board (ERPB) to address this issue. As regards the name of the party to whom payment has been made, the working group recommended that the commercial trade name of the payee (defined as the name used by the merchant to identify itself to the consumer) should be used along the entire payments chain. Regarding the where element, the working group recommended that the account statement should indicate the precise geographical location of the transaction. In the case of online commerce, the commercial trade name displayed on the website, or the commercial trade name of the payee’s online platform should be mentioned. Related to the when element, the working group recommended that the account statement should indicate clearly the date and time of the transaction as it would be known by the consumer. The working group called upon all actors involved to create and maintain quality transaction data and to ensure that the underlying technical standards being used are capable of supporting the collection, preservation and safe transmission of the data required by the consumer. The industry was expected to implement these voluntary recommendations by June 2024, but although progress has been made, the overall progress seems to be lagging slightly as the various stakeholders are addressing the issue in isolation. In the meantime, the European Commission has adopted some of the working group’s recommendations into proposed new payments legislation.

Talk Through It: End User Directed Manipulation Learning

Talk Through It: End User Directed Manipulation Learning

GRAPHENE AS AN ANTIOXIDANT AND ANTIOZONANT IN TIRE SIDEWALL COMPOUNDS

ABSTRACT Tire sidewalls have an important function in achieving optimized tire performance. The sidewall compound must show good abrasion resistance, aging resistance, and tear strength; low hysteresis and minimal contribution to whole tire rolling resistance; and good adhesion to adjacent components in the tire. In addition, in both the original equipment market and the premium performance market, appearance is of considerable importance. The current system of waxes, antioxidants, and phenyl diamine antiozonants is very effective in meeting the need of the tire end user. However, recent concerns centered on the environmental impact of N′-phenyl-p-phenylenediamine, specifically N-(1,3-dimetylbutyl)-N′-phenyl-p-phenylenediamine, and staining and discoloration of the tire sidewall surface raise issues that should be addressed. Specialized grades of graphene can offer a mechanism by which tire appearance and environmental concerns can be overcome. Pristine graphene can improve compound resistance of ozonolysis and oxidation by replacing the current antiozonants and antioxidants used in tire compounds; improve tire sidewall appearance; and improve tire sidewall resistance to scuffing, abrasion, and tearing.

Multi-criteria Decision Making Methods and sustainable Appliations in the Digital Age

Abstract: Designing a sustainable electrification system for remote places in developing countries is very complex while considering various factors such as technical, economic, environmental and social (TEES). Development of an efficient, rational, coherent and a translucent framework is required to address issues related to the requirement of energy needs based on the end user. The main goal of this research is to design a methodological framework based on decision analysis and optimized model with locally available energy sources. The new optimal renewable energy options are required to be used to minimize the fossil fuel dependency of electrical generation. Initially, suitable energy alternatives are selected by considering different criterion with multiple scenarios for decision analysis. The further optimal combination of energy sources evaluated for different possibilities. The new methodology of hybrid renewable optimization is applied to a rural place, Himachal Pradesh to illustrate its effectiveness based on metrological data

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

Recent 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.

Crowdsourcing Projekt „Reden Sie mit!“

Crowdsourcing project “Have your say!” – Practical research priorities for occupational medicine in Austria The aim of the AAMP project “Unresolved issues in occupational medicine. Have your say!” was to find out which topics should be prioritized with the limited research resources available in Austria. With the help of crowdsourcing, where, in contrast to traditional research practices, end users rather than scientists set the research topics, a practical research agenda with eight key topics was developed for Austria.