Online Requests Research Articles

Enhancing e-KYC Security and Privacy: Harnessing Quantum Computing and Blockchain in Web 3.0

By implementing a successful and effective Electronic- Know Your Customer (e-KYC) infrastructure, conventional defects and expenses may be greatly reduced. Strong authentication procedures are used by existing e-KYC solutions to guarantee safety and confidentiality. However, these technologies have shortcomings like as key administration expenses and dependency on encryption. We suggest a revolutionary strategy that integrates blockchain technology, Web 3.0, and quantum computing to get around these restrictions. In this research, we show how current e-KYC processes could possibly be improved, making them faster and more reliable, by integrating Web 3.0 with quantum computing. Our solution makes it simple to quickly verify several consumers, increasing productivity. We provide improved safety for online requests, transcending the weaknesses inherent in traditional encryption techniques by utilizing quantum computing technology and applying the concept of quantum encryption key methodology. We contribute to the development of safe and private solutions by addressing the shortcomings of the present e-KYC systems. The results of our research show how Web 3.0 and quantum computer technology have the power to completely alter the e-KYC environment

“A day in the life” – telemedicine in family medicine and its relationship with practicing physicians’ satisfaction: a cross-sectional study

BackgroundTelemedicine has expanded rapidly in recent years, and many encounters that were conducted in person now take place remotely. This study aimed to assess primary care physicians’ (PCPs) attitudes towards the different modalities of patient care.MethodsThis is a cross-sectional nationwide descriptive study conducted in Israel. We asked PCPs to document an entire workday and answer a short questionnaire after each visit. The questions addressed the type of visit (face-to-face, remote synchronous [telephone/video], or remote asynchronous [online requests]), the perceived quality of the visit, and the physicians’ feelings at the end of each visit. Before documenting their working day, we asked the participants to answer a questionnaire about their general attitudes toward different modalities of medical visits and how they affect their well-being and burnout.ResultsSixty physicians documented 2,025 visits, of which 39% took place in person, 36% stemmed from online patient requests, 18% were telephone meetings, < 1% were video meetings, and 6% consisted of other types of contact. Mixed effects logistic regressions were used to model the visits’ evaluation. The odds ratios (ORs) for perceived medical quality of visits focused on medical tasks were lower for non-face-to-face visits: OR = 0.39, 95% CI 0.25–0.59 for remote synchronous, and OR = 0.14, 95% CI 0.09–0.23 for remote asynchronous. The perceived medical quality of visits focused on administrative tasks was lower for remote asynchronous than for face-to-face visits (OR = 0.31, 95% CI 0.14–0.65). We found no association between medical quality and patients, physicians, or clinic characteristics. The inappropriateness of the visit modality was also associated with lower medical quality (OR = 0.13, 95% CI 0.09–0.18). We found a correlation between perception of medical quality and physicians’ feelings at the end of the visits, Spearman’s r = 0.82 (p < 0.001).ConclusionsA substantial portion of the visits was dedicated to administrative tasks and remote medicine. In comparison, physicians rated face-to-face visits’ quality higher than remote visits. Policymakers should intervene to minimize administrative work, reduce PCPs’ administrative workload, and direct patients to the optimal visit modality for their complaints. These steps would increase medical quality, reduce burnout, and mitigate the shortage of PCPs.

Integrated Quality of Service for Offline and Online Services in Edge Networks via Task Offloading and Service Caching.

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.

The role of a dedicated Oncology Nurse Navigator in the surgical oncology pathways: 1 year experience from a General Surgery Unit in central Italy with follow up.

The Nurse Navigator is a highly specialized nurse with technical and non-technical skills that offers individualized assistance to cancer patients, their family and caregivers to overcome health system barriers and facilitate access to care. This role was introduced in the General Surgery Unit of the Madonna del Soccorso Hospital in San Benedetto del Tronto from 1st January 2023. The primary endpoint is to compare the times taken for each step of the diagnostic-therapeutic pathway comparing the study group followed by Oncology Nurse Navigator (ONN) and the group not followed by this role. The secondary endpoints, only for the study group, were the number of patient contacts with the ONN and the time slots; the number of examinations and consultations organized by ONN; the evaluation of patient satisfaction at discharge; the number and type of problems noted during follow-up contact at 7 and 30days after discharge. A prospective court study with historical control was conducted from 1st January 2023 in Madonna del Soccorso Hospital, Italy. The study group consists of all cancer patients cared for by ONN. The control group was created by selecting the same number of patients as the study group but taken care of in the previous 3years (from 2020 to 2022) and, therefore, without the presence of the Nurse Navigator. The control group data come from clinical documentation. The number and time slots of contact with the ONN were recorded through the use of a company mobile phone active 24/7 through phone calls and messages. The number of examinations and consultations is known through online requests. The satisfaction assessment was carried out through the use of externally validated questionnaire Patient Satisfaction with Cancer Care (PSCC). The follow-up was performed by telephone and recorded on documentation according to established parameters. A total of 200 patients were analyzed. Both the study and control groups included 100 patients each. The average time between the first contact with the patient and the execution of the diagnostic test was 7days in the cases compared to 28days in the control group. The waiting time for the Multi-Disciplinary Team discussion (MDT) was 3 days for the study group compared to 6days in the control group. The average time taken for the first oncological visit was 3days in the study group compared to 18days in the controls. The time from first contact to the operating session was 20days compared to 45 in controls. Each patient had an average of 10 phone calls with the ONN. For all patients accompanied at the first diagnosis, at least 2 radiological and laboratory tests were organized. Oncology appointment for treatment evaluations after delivery of the histological report was communicated within a maximum of 3 working days. A patient satisfaction questionnaire achieved a response rate of 100%, with an average score of 87.0/90. The telephone follow-up had a response rate of 100% of patients and revealed a decrease in problems at the 30-day check-up compared to that of 7days after discharge. (Activity of Daily Living 20% vs 8%; nutritional problems 40% vs 21%, pain 18% vs 2%; surgical wounds 45% vs 1%; mobilization 8% vs 0%). The data demonstrate that ONN service improves the quality and outcomes of surgical oncology patients' pathway. The professional role of the ONN, with predefined technical and non-technical skills, should also be officially recognized by the healthcare system and hospital administration.

Monitoring emergency and urgent surgery: an improvement in a Healthcare Management Unit at a third-level hospital in Italy.

At San Martino IRCCS Hospital in Genoa, Italy, emergency and urgent surgery is classified according to the National Confidential Enquiry into Patient Outcome and Death (NCEPOD) classification, whereby cases are divided into two categories and colour-coded accordingly: - EMERGENCY - (Colour-code: RED) "Surgery to be performed within minutes"; - URGENCY - (colour-code: YELLOW) "Surgery to be performed within hours". In this context, it is essential that the emergency surgical team gets clear and complete information from the proposing surgeon, in order to complete the surgical list. Between 14th April 2023 and 23th July 2023, a new method of requesting urgent and emergency surgery by filling out an online form was tested. A total of 406 online requests were made, resulting in 367 E/U surgeries. The greatest number of emergency operations concerned thoracic-abdominal surgery (45%), followed by urology (19%). The requests analysed classified 18% of cases as red and 71% as yellow. The remaining 11% contained compilation errors. Moreover, 11% of the interventions were not performed within the time limits defined according to the severity code. By means of this new tool, San Martino IRCCS Hospital's Healthcare Management Unit can monitor requests for surgery in real time, thereby achieving greater efficiency and implementing corrective measures in the use of Operating Room resources.

A scalable combinatorial benders decomposition for the Stochastic Dial-a-Ride Problem

Nowadays, Demand-Responsive Transport companies can help the elderly or disabled citizens in their everyday journeys as well as provide an important link between big cities and their outer suburbs. In practice, many of those trips are scheduled in advance but many others are asked on the fly (online) by the users while the drivers are already on their routes. To answer those online requests, dispatching centers often rely on fast heuristics based on some myopic objectives. However, this deteriorates the flexibility of the dispatch over time. This paper shows the potential of a thorough optimization of the planning of the vehicles at the start of the day under the hypothesis that the transport company is able to forecast the requests that will arrive during the service. Our objective is to design an initial set of routes for the vehicles that maximizes the expected acceptance rate of future requests, since the ones scheduled in-advance are mandatory. In this prospect, we propose a Combinatorial Benders Decomposition framework to solve the so-called Stochastic Dial-a-Ride Problem. A key ingredient for the success of this solver is the use of a clustering-based initialization of the Master Problem. The computational results show the effectiveness of this approach when applied to instances extracted from the literature as well as from actual services provided by Padam Mobility, an international company in Shared Mobility Systems. The proposed method provides a substantial performance gain though traditional heuristics are kept to manage online insertions afterward.

A hybrid model for VNF deployment capable of responding to online requests at network edge

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the Whale Optimization Algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

Optimization of Social Media for Advertising Brown Sugar Products in Jelatang Village, Hulu Sungai Selatan

The digital era is an inevitability that must be faced by business people. Changes in spending patterns of the wider community to digital markets force UMKM to continue to adapt. Even so, there are several challenges for UMKM to digitize products. The first challenge, which is also the main challenge, comes from digital literacy. Coupled with the number of Human Resources (HR) who have not been able to adapt to online requests that require quick responses. In fact, the key to do online selling is a fast response coupled with fast production of goods too. Within community service in the form of optimizing social media for advertising brown sugar products in Jelatang Village, Hulu Sungai Selatan, it is hoped that UMKM who have been familiar with online sales can be more optimizing their social media so that productivity and opportunities from the market are better.

Online Predictions for Online TSP on the Line

With a rapidly growing attention on the development of learning-augmented algorithms, we revisit the classical online traveling salesman problem (OLTSP) from the perspective of such learning approaches. A learning-augmented online algorithm, or simply an online algorithm with predictions, considers how to improve its competitive performance with theoretical guarantees by forecasting the information of future requests, e.g., their release time or locations in the OLTSP. In this study, we investigate the OLTSP on the real line, motivated by the parcel picking problem in a smart warehouse, which aims at scheduling a route for a server (saying, a Kiva robot), starting at the origin, serving all online requests and returning to the origin such that the completion time is minimized. Each online request is released over time on the real line and the server moves back and forth along the line with unit speed. We mainly focus on zealous algorithms, a special type of online algorithms for the OLTSP which never allow the sever to wait if there are still unserved requests, and exploit a specific forecasting strategy, called online predictions, which makes a sequence of predictions one by one in an online manner. In order to ensure better competitive performance, we particularly explore the worst-case scenarios that restrict the power of such predictions. Based on the discussion, we make an assumption in which online predictions are guaranteed to be useful, and devise a learning-augmented algorithm with [Formula: see text]-robustness and [Formula: see text]-consistency, [Formula: see text], comparing to the previous lower bound of [Formula: see text], where [Formula: see text] and [Formula: see text] which indicate prediction errors are sufficiently small constants. Moreover, we also conduct numerical experiments to demonstrate the practical effectiveness of the proposed algorithm.

Pharmacovigilance in the Caribbean Countries: an Overview.

The constant surge in accessing essential medicines creates a greater need for continuous monitoring of usage. The inability to source active pharmaceutical ingredients during the COVID-19 pandemic resulted in drug shortages that increased online requests for medications. E-commerce and social sites have opened the floodgate for the marketing of falsified, substandard, and unregistered pharmaceuticals, making them easily accessible to consumers with the click of a button. A high prevalence of such products with compromised quality highlights further the need for enhanced post-marketing vigilance of safety and quality within the pharmaceutical industry. This review aims to assess the extent to which pharmacovigilance (PV) systems in selected Caribbean countries conform to the minimum World Health Organization (WHO) requirements, highlight the importance of PV in ensuring the safer use of medicines across the Caribbean region, and identify opportunities and challenges in building comprehensive PV systems. The review finds that while major advancements in PV and adverse drug reaction (ADR) monitoring have occurred in Europe and other parts of the Americas, little has been done in the Caribbean region. Only a few countries in the region are active members of the WHO's global PV network, and ADR reporting is minimal. The reason for low reporting includes a lack of awareness, commitment, and participation of healthcare professionals, manufacturers, authorized distributors, and the general consumers. Nearly all established national PV systems do not fully conform to the minimum PV requirements by the WHO. Legislation, regulatory framework, political commitment, adequate funding, strategies, and incentives to encourage reporting of ADRs are needed to build sustainable PV systems in the Caribbean.

Direct Election, Bureaucratic Appointment and Local Government Responsiveness in Taiwan

AbstractDoes local democracy induce better service to citizens? While elected officials can be punished at the ballot box if they fail to address citizens’ needs, appointed bureaucrats may have policy knowledge that enables them to better serve citizens. Employing a multimethod design, this paper uses variation in local political institutions in Taiwan to assess the relative merits of direct election and bureaucratic appointment for local government responsiveness. While democratic institutions are often thought to induce responsiveness, I find that in Taiwan, with its historically strong bureaucracy and relatively new democratic institutions, the picture is somewhat more complicated. Elected and appointed officials face different incentives that motivate the latter to respond more quickly and effectively to online requests for help.

A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations

This paper contributes to the intersection of operations research and machine learning in the context of autonomous ridesharing. In this work, autonomous ridesharing operations are reproduced through an event-based simulation approach and are modeled as a sequence of static subproblems to be optimized. The optimization framework consists of a novel data-driven metaheuristic within a two phase approach. The first phase consists of a greedy insertion heuristic that assigns new online requests to vehicles. The second phase consists of a local-search based metaheuristic that iteratively revisits previously-made vehicle-trip assignments through intra- and inter-vehicle route exchanges. These exchanges are performed by selecting from a pool of destroy–repair operators using a machine learning approach that is trained offline on a large dataset composed of more than one and a half million examples of previously-solved autonomous ridesharing subproblems.Computational results are performed on multiple dynamic instances extracted from real ridesharing data published by Uber Technologies Inc. Results show that the proposed machine learning-based optimization approach outperforms benchmark state-of-the-art data-driven metaheuristics by up to about nine percent, on average. Managerial insights highlight the correlation between selected vehicle routing features and the performance of the metaheuristics in the context of autonomous ridesharing operations.

Does the crying baby always get the milk? An analysis of government responses for online requests

One thing that has frequently been overlooked in studies of how a local government responds to its citizens is the text itself. When a petitioner drafts a post, they can pick up words and choose how the demand or question is conveyed. Thus, how a post is composed may also influence local government responsiveness. In this study, we investigated whether a delayed response is due to the way the message is drafted or the actual content by separating content-related and content-agnostic text features. Based on posts retrieved from two websites, we found that the response pattern varies by location, time, and type of queried agency. Our results also indicated that lengthy and low positive sentiment posts generally result in longer waiting times. However, more research is needed to gauge the possibility of a false negative and the meaning of contents extracted from computational tools. In addition, our work scrutinized several methodological issues and sought practical solutions to analyzing big data.

Unified Route Planning for Shared Mobility: An Insertion-based Framework

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning . Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion . In addition, previous route planning solutions fail to exploit the appearance patterns of future requests hidden in historical data for optimization. In this paper, we present a unified formulation of route planning called URPSM. It has a well-defined parameterized objective function which eliminates the contradicted objectives in previous studies and enables flexible multi-objective route planning for shared mobility. We propose two insertion-based frameworks to solve the URPSM problem. The first is built upon the plain-insertion widely used in prior studies, which processes online requests only, whereas the second relies on a new insertion operator called prophet-insertion that handles both online and predicted requests. Novel dynamic programming algorithms are designed to accelerate both insertions to only linear time. Theoretical analysis shows that no online algorithm can have a constant competitive ratio for the URPSM problem under the competitive analysis model, yet our prophet-insertion-based framework can achieve a constant optimality ratio under the instance-optimality model. Extensive experimental results on real datasets show that our insertion-based solutions outperform the state-of-the-art algorithms in both effectiveness and efficiency by a large margin (e.g., up to 30 \( \times \) more effective in the objective and up to 20 \( \times \) faster).

EmcFIS: Evolutionary multi-criteria Fuzzy Inference System for virtual network function placement and routing

With the increasing demands for low-delay network services, mobile edge computing (MEC) has emerged as an appealing solution to provide computing resources in close to the end users. Network function virtualization (NFV) is a new network architecture which replaces dedicated hardware middleboxes with software instances to run network functions via software virtualization on general-purpose servers deployed at edge clouds. Because of the resource limitation at network edges, efficient placement and routing for online virtual network function requests (VNF-PRO) is a challenging task. The VNF-PRO has proven to be NP-hard, and thus, metaheuristic algorithms are the best choice in term of the solution quality. However, metaheuristics suffer from high computational complexity, and cannot be performed for online requests in the VNF-PRO. In this paper, a combined model based on fuzzy logic and genetic algorithm is proposed to achieve proper solution quality-speed trade-off in the VNF-PRO. In this method, a multi-criteria fuzzy inference system (named mcFIS) is used for the online VNF placement and routing. To achieve the best performance, a multi-objective evolutionary algorithm based on genetic algorithm (GA) is utilized in an offline procedure for automatic rule tuning of the mcFIS, once before applying it for online applications. Simulation results on two NFV benchmark instances demonstrate the efficiency of the proposed model against the existing techniques.

Public crowdsourcing: Analyzing the role of government feedback on civic digital platforms

AbstractGovernment organizations increasingly use crowdsourcing platforms to interact with citizens and integrate their requests in designing and delivering public services. Drawing on attribution theory, this study asks how the causal attributions of the government response to a citizen request affect continued participation in crowdsourcing platforms. To test our hypotheses, we use a 7‐year dataset of both online requests from citizens to government and government responses to citizen requests. We focus on citizen requests that are denied by government, and find that the reasoning for denying a request is related with continued participation behavior. Citizens are less willing to collaborate further with government via the platform, when their requests are denied although the locus of causality is with the government. This study contributes to research on the role of responsiveness in digital interaction between citizens and government and highlights the importance of rationale transparency to sustain citizen participation.

Do squeaky wheels get the grease? Understanding when and how municipalities respond to online requests

Citizens often attempt to interact with government through online modes of communication such as email and social media. Using an audit study, we examine when and how American municipalities with populations of over 50,000 respond to online requests for information. We develop baselines for municipal responsiveness, including the average rate, time, and quality of responses, and examine whether these response attributes vary based on the mode of communication or the tone of the request. Overall, municipalities responded to 54% of email requests and 38% of Twitter requests. A majority of responses were received on the same business day. Responses are slightly faster on Twitter, but of higher quality on email. Governments are more likely to respond to frustrated constituents on email, but respond faster to frustrated queries on Twitter, though with lower quality responses. These findings contribute to our understanding of local government responsiveness and have significant implications for democratic accountability and resident compliance with and the effectiveness of local government policies. Furthermore, our scholarly understanding of local government communications with residents, and particularly the promise of social media as a tool of two-way communication, may be underdeveloped.

Asymptotically Optimal Online Caching on Multiple Caches With Relaying and Bypassing

Motivated by practical scenarios in areas such as Mobile Edge Computing (MEC) and Content Delivery Networks (CDNs), we study online file caching on multiple caches, where a file request might be relayed to other caches or bypassed directly to the memory when a cache miss happens. We can also choose to fetch files from the memory to caches and conduct file replacement if necessary. We take the relaying, bypassing and fetching costs altogether into consideration. We first show the inherent difficulty of the problem even when the online requests are of uniform costs. We propose an O(logK)-competitive randomized online multiple caching algorithm (named Camul) and an O(K)-competitive deterministic algorithm (named Camul-Det), where K is the total number of slots in all caches. Both of them achieve asymptotically optimal competitive ratios. Moreover, our algorithms can be implemented efficiently such that each request is processed in amortized constant time. We conduct extensive simulations on production data traces from Google and a benchmark workload from Yahoo. It shows that our algorithms dramatically outperform state-of-the-art schemes, i.e., reducing the total cost by 85% and 43% respectively compared with important baselines and their strengthened versions with request relaying. More importantly, Camul achieves such a good total cost without sacrificing other performance measures, e.g., the hit ratio, and performs consistently well on various settings of experiment parameters.

The conceptualization of processes for handling online requests of information resources in academic libraries in South Africa: Experience from an Open Distance e-Learning (ODeL) environment

This article aims to present the Open Distance eLearning library processes for requesting information resources using the Online Public Access Catalogue (OPAC). OPAC is known as a platform used by library clients to check the availability of information resources from the entire library collection. ODeL libraries also use the OPAC, not as a library catalogue, but as a portal for requesting information resources. The processes presented in this articles starts from when the library clients request information resources when the requests reach the library/library staff, how the online requests are handled when received, how they are sent to the requesters, how the information resources are sent back to the library after use by the requesters and the process of receiving the returned information resources.

ECC: Edge Collaborative Caching Strategy for Differentiated Services Load-Balancing

Due to the explosion of network data traffic and IoT devices, edge servers are overloaded and slow to respond to the massive volume of online requests. A large number of studies have shown that edge caching can solve this problem effectively. This paper proposes a distributed edge collaborative caching mechanism for Internet online request services scenario. It solves the problem of large average access delay caused by unbalanced load of edge servers, meets users’ differentiated service demands and improves user experience. In particular, the edge cache node selection algorithm is optimized, and a novel edge cache replacement strategy considering the differentiated user requests is proposed. This mechanism can shorten the response time to a large number of user requests. Experimental results show that, compared with the current advanced online edge caching algorithm, the proposed edge collaborative caching strategy in this paper can reduce the average response delay by 9%. It also increases the user utility by 4.5 times in differentiated service scenarios, and significantly reduces the time complexity of the edge caching algorithm.