Online Request Research Articles

WEB DEVELOPMENT REQUEST FOR INFORMATION (RFI)

The design of a Web-Based Online Request for Information (RFI) System aims to analyze issues within the verification procedures in the country of origin or loading country during the Import Technical Verification Program (VPTI) by KSO SCISI. This research also intends to evaluate the potential implementation of a web-based online Request for Information (RFI) system as a solution to enhance the efficiency and effectiveness of the verification process in the country of origin or loading country, while facilitating expedited services within VPTI. The research methodology involves initial stages of problem identification and observation of ongoing processes at KSO SCISI, supplemented by literature review to comprehend aspects of information system design and development through web-based platforms. Additionally, interviews with relevant stakeholders were conducted. The research adopts the Linear Sequential (Waterfall) methodology for system development, which translates analysis into design, coding, and testing. The primary outcome of this study is the design of a web-based online RFI system that eliminates the need for lengthy and intricate steps by enabling direct communication of physical verification location requests from the headquarters to exporters, without the involvement of branch offices in the country of origin or loading country. Implications of this research encompass procedural simplification, service efficiency enhancement, and fostering strong customer relations. An innovative facet of this study lies in the utilization of the online RFI system to streamline and expedite complex verification procedures, while also fostering more efficient connectivity between the headquarters and exporters. In conclusion, the web-based RFI system is poised to facilitate, accelerate, and fortify high-quality VPTI services by KSO SCISI.

Online Request Replication for Obtaining Fresh Information Under Pull Model

Online Request Replication for Obtaining Fresh Information Under Pull Model

An intelligent open trading system for on-demand delivery facilitated by deep Q network based reinforcement learning

On-demand delivery in urban areas has been growing rapidly in recent years. Nevertheless, on-demand delivery networks lack an efficient, sustainable, and environmentally friendly operative strategy. An open trading system equipped with on-line auctions provides an opportunity for increasing the efficiency of on-demand delivery systems. Reinforcement learning techniques that automate decision-making can facilitate the implementation of such complex and dynamic systems. This paper presents an on-line auction-based request trading platform embedded within an open trading system as a new scheme for carriers and shippers to trade on-demand delivery requests. The system is developed based on a multi-agent model, composed of carriers, shippers, and the on-line platform as autonomous agents. Deep Q network enabled reinforcement learning is used in the decision-making processes for the agents to optimise their behaviour in a dynamic environment. Numerical experiments conducted on the Melbourne metropolitan network demonstrate the effectiveness of the open trading system, which can provide benefits for all stakeholders involved in the on-demand delivery market as well as the entire system. The reinforcement learning enabled platform can gain more profit when there are more learning carriers. The results indicate that the intelligent open trading system with on-line auctions is a promising city logistics solution.

Simulated data for census-scale entity resolution research without privacy restrictions: a large-scale dataset generated by individual-based modeling

Background Entity resolution (ER) is the process of identifying and linking records that refer to the same real-world entity. ER is a fundamental challenge in data science, and a common barrier to ER research and development is that the data fields used for this fuzzy matching are personally identifiable information, such as name, address, and date of birth. The necessary restrictions on accessing and sharing these authentic data have slowed the work in developing, testing, and adopting new methods and software for ER. We recently released pseudopeople, a Python package that allows users to generate simulated datasets approaching the scale and complexity of the data on which large organizations and federal agencies, like the US Census Bureau regularly perform ER. With pseudopeople, researchers can develop new algorithms and software for ER of US population data without needing access to personal and confidential information. Methods We created the simulated population data available through pseudopeople using our Vivarium simulation platform. Our model simulates individuals and their families, households, and employment dynamics over time, which we observe through simulated censuses, surveys, and administrative data collection systems. Results Our simulation process produced over 900 gigabytes of simulated censuses, surveys, and administrative data for pseudopeople, representing hundreds of millions of simulants. A sample simulated population of thousands of simulants is now openly available to all users of the pseudopeople package, and large-scale simulated populations of millions and hundreds of millions of simulants are also available by online request through GitHub. These simulated population data are structured for use by the pseudopeople package, which includes additional affordances to add various kinds of noise to the data to provide realistic, sharable challenges for ER researchers.

Utilization, Expenditure, and Treatment Patterns Associated With Calcitonin Gene-Related Peptide Monoclonal Antibodies Reimbursed Subject to a Managed Access Protocol in Ireland

ObjectivesCalcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) are novel high-cost treatments for the prevention of migraine. This study presents data on utilization, expenditure, and treatment patterns with CGRP mAbs available under a managed access protocol in Ireland, to a cohort of treatment refractory patients (failed 3 or more previous treatments) with chronic migraine. MethodsData were extracted from the Primary Care Reimbursement Service High Tech claims database and special drug request online system and analyzed using Microsoft Excel and SAS. Treatment persistence was evaluated by refill patterns, and adherence was evaluated using the proportion of days covered method. Expenditure data were extracted directly from the database. ResultsBetween September 1, 2021 and April 30, 2023, 1517 applications for reimbursement approval for a CGRP mAb were received; 1458 (96.1%) were approved for reimbursement. Total expenditure on CGRP mAbs in year 1 (September 1, 2021 to August 31, 2022) was €3.2 million. The majority of patients initiated treatment with fremanezumab (60.8%) or erenumab (37.1%). Almost 90% of patients were considered adherent, and treatment persistence was high, with more than 75% of patients receiving more than 12 months of treatment in our 18-month study time frame. ConclusionsThis study demonstrates the importance of active health technology management, after reimbursement, in enabling cost-effective use of high-cost treatments while providing budget certainty for the healthcare payer. High levels of adherence and persistence suggest that treatment is successfully targeted in situations which unmet clinical need is greatest.

Spatial pattern and associated factors of timely vaccination in Ethiopia using EDHS-2016 data: A multilevel and spatial analysis.

Age-appropriate vaccination or vaccine timeliness is the administering of vaccines on the specified schedule of immunization. One of the qualities of the immunization program is an age-appropriate vaccine, it has become an ignored indicator of program performance. Even though age-appropriate vaccination is critical for child health, there are no national-level studies to generate conclusive and tangible evidence about the determination of timely vaccination in Ethiopia. To assess the spatial pattern and associated factors of timely vaccination in Ethiopia using EDHS-2016 data: A multilevel and spatial analysis. Community based cross-sectional study design was employed from 18 January to 27 June 2016. To select the participants, two-stage cluster sampling was employedin the Ethiopian Demographic Health Survey 2016 data. Permission was obtained via online request by explaining the aim of this particular study from DHS international. A statistical package for social science-21 software was used for data cleaning, recoding, and analysis. Arc GIS 10.3 software was used to show the spatial variation of age-appropriate vaccination practices. A generalized linear mixed-effect model was used. For all models, intra-class correlation, a proportional change in variance, the log-likelihood test, and the Akaike information criterion were calculated. The best model was selected by the lowest value Akaike information criterion. Variables with a p-value less than 0.05 and a 95% confidence level were considered for the statistically significant association. The spatial distribution of age-appropriate vaccination practice in Ethiopia was non-randomly distributed with the global Moran's I value of 0.22 (p-value <0.001). The prevalence of age-appropriate vaccination practice in Ethiopia was 33.8%. Women who receive ≥ 2 TT vaccines (AOR = 1.48; 1.22, 1.79), women who lived in rural residences (AOR = 0.77; 0.62, 0.96), gave birth at a health facility (AOR = 1.2; 1.12, 1.37), ANC follow up (AOR = 174; 1.45, 2.1), richest (AOR = 1.65; 1.15, 2.37), richer (AOR = 1.72; 1.3, 2.22), middle-level income (AOR = 1.65; 1.16, 2.36), poorer (AOR = 1.47; 1.11, 1.96) were the factors of age-appropriate vaccination practice. The spatial distribution of age-appropriate vaccination practice in Ethiopia was non-randomly distributed across the regions. Age-appropriate vaccination practice was low in Ethiopia. Wealth index, usual caretaker of the child, ANC utilization, history of TT vaccination, place of delivery, and residence were associated with age-appropriate vaccination practice.

FEBench: A Benchmark for Real-Time Relational Data Feature Extraction

As the use of online AI inference services rapidly expands in various applications (e.g., fraud detection in banking, product recommendation in e-commerce), real-time feature extraction (RTFE) systems have been developed to compute the requested features from incoming data tuples in ultra-low latency. Similar to relational databases, these RTFE procedures can be expressed using SQL-like languages. However, there is a lack of research on the workload characteristics and specialized benchmarks for RTFE, especially in comparison with existing database workloads and benchmarks (e.g., concurrent transactions in TPC-C). In this paper, we study the RTFE workload characteristics using over one hundred real datasets from open repositories (e.g. Kaggle, Tianchi, UCI ML, KiltHub) and those from 4Paradigm. The study highlights the significant differences between RTFE workloads and existing database benchmarks in terms of application scenarios, operator distributions, and query structures. Based on these findings, we propose to develop a realtime feature extraction benchmark named FEBench based on the four important criteria for a domain-specific benchmark proposed by Jim Gray. FEBench consists of selected representative datasets, query templates, and an online request simulator. We use FEBench to evaluate the effectiveness of feature extraction systems including OpenMLDB and Flink and find that each system exhibits distinct advantages and limitations in terms of overall latency, tail latency, and concurrency performance.

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.

Reveal: Robustness-aware VNF placement and request scheduling in edge clouds

In the edge cloud network, service providers place virtual network functions (VNFs) in edge clouds to serve users’ requests. Thus, it is essential to consider VNF placement and request scheduling in edge clouds. Existing works often focus on minimizing request completion time or maximizing network throughput to utilize network resources and ensure users’ QoS efficiently. However, they ignore two practical factors: malicious users and failed VNFs, leading to poor network robustness. To this end, this paper studies robustness-aware VNF placement and request scheduling, named Reveal. Specifically, we limit the number of VNFs each user can access and the number of users each VNF can serve to control the influence scope of malicious users and VNF failures. Since placing VNFs is time-consuming and requests arrive dynamically, we solve this problem through two phases: robust VNF placement and assignment, and online request scheduling. For the first phase, we design an efficient knapsack-based rounding algorithm with bounded approximation factors. For online request scheduling, we propose a primal–dual based algorithm with a competitive ratio of 1−ϵ,O(log1/ϵ) where ϵ∈(0,1). Experiment and simulation results show that Reveal can achieve better performance and robustness than other alternatives.

Throughput Maximization of Delay-Aware DNN Inference in Edge Computing by Exploring DNN Model Partitioning and Inference Parallelism

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to overwhelming explosions of mobile applications, by offloading the compute-intensive tasks to an MEC network for processing. The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. In this paper, we study a novel delay-aware DNN inference throughput maximization problem by accelerating each DNN inference through jointly exploring DNN partitioning and multi-thread parallelism. Specifically, we consider the problem under both offline and online request arrival settings: a set of DNN inference requests is given in advance, and a sequence of DNN inference requests arrives one by one without the knowledge of future arrivals, respectively. We first show that the defined problems are NP-hard. We then devise a novel constant approximation algorithm for the problem under the offline setting. We also propose an online algorithm with a provable competitive ratio for the problem under the online setting. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.

Multi-agent deep reinforcement learning for online request scheduling in edge cooperation networks

Edge computing as a complementary paradigm of cloud computing has gained more attention by providing mobile users with diversified services at the network edge. However, the increasingly complex mobile applications put a heavier load on edge networks. It is challenging to provide concurrency requests with high-quality service processing, especially when the edge networks are dynamically changing. To address the above issues, this paper investigates the online concurrent user requests scheduling optimization problem in edge cooperation networks. We model it as an online multi-stage decision-making problem, where requests are divided into a group of independent and logically related sub-tasks. We proposed a centralized training distributed execution based multi-agent deep reinforcement learning technique to realize the implicit cooperation scheduling decision-making policy learning among edge nodes. At the centralized training stage of the proposed mechanism, a value-decomposition-based policy learning technique is adopted to improve the long-term system performance, while at the distributed execution stage, only local environment status information is needed for each edge node to make the request scheduling decision. Extensive experiments are conducted, and simulation results demonstrate that the proposed mechanism outperforms other request scheduling mechanisms in reducing the long-term average system delay and energy consumption while improving the throughput rate of the system.

Profit Driven Service Provisioning in Edge Computing via Deep Reinforcement Learning

With the integration of Mobile Edge Computing (MEC) and Network Function Virtualization (NFV), service providers are able to provide low-latency services to mobile users for profit. In this paper, we study the online service placement and request assignment problem in an MEC network, where service requests arrive one by one without the knowledge of future arrivals, and each arrived request demands a specific service with a tolerable service delay requirement with the aim to maximize the profit of the service provider, through admitting as many service requests as possible for a given monitoring period. This optimization objective is achieved by assigning service requests to appropriate cloudlets in the MEC network, pre-installing service instances into cloudlets to shorten service delays, and accommodating new services by revoking some idle service instances from cloudlets due to limited computing resources in MEC networks. In this paper, we first show that the problem is NP-hard. We then devise an efficient deep reinforcement learning algorithm for the online service placement and request assignment problem that consists of a deep reinforcement learning-based prediction mechanism for dynamic service placement, followed by a dynamic request assignment procedure to assign requests to cloudlets. We finally evaluate the performance of the proposed algorithms by conducting experiments through simulations. Simulation results demonstrate that the proposed algorithm is promising, improving performance by 46.8% compared with that of the comparison algorithms.

496-P: Biomedical Expertise in the Face of Social Media: How Is Diabetes-Related Information That Stems from Social Media Discussed in Clinical Encounters?

Background and Aim: In today’s digital world, people with type 1 and 2 diabetes turn to peers on social media to exchange information and support. However, little is known about what the online sharing of diabetes-related information implies for the clinical encounter. This study examines how information that stems from social media is discussed in clinical encounters between healthcare professionals (HCPs) and people with diabetes (PWD) . Methods: We conducted semi-structured interviews with 25 PWD aged 22-76 (type 1: n= 17, type 2: n=8) and HCPs (nurses n=9, medical doctors n=7, dieticians n=3) . The participants were recruited from the diabetes outpatient care setting in Denmark. Two researchers independently developed a thematic coding framework that was compared, revised, and then applied to analyze each interview. Subsequently, overarching themes were identified across the whole data set. Results: The first theme suggests that HCPs tend to have a ‘blind spot’ towards information that stems from social media: Most HCPs have not asked whether PWD retrieve information from social media and most PWD present information that is accessed on social media without naming their source. The second theme reflects that clinical dialogues about online information rest upon trustful relationships: PWD find online peer exchanges valuable and request for HCPs to acknowledge this. Still, PWD navigate large amounts of information and treatment advice online, and while PWD may seek to discuss their information in clinical encounters, initiating these dialogues is sensitive to the reactions of HCPs. Discussion: The results suggest that HCPs should encourage curious dialogues about how PWD retrieve diabetes-related information. This serves for HCPs to learn what PWD gain from peers online and for HCPs to be able to offer their expertise to support PWD who are navigating large flows of information and advice online. Disclosure E.M.Kjærulff: None. M.A.Nexo: None. U.Bjerre-christensen: None. H.Langstrup: None. Funding Innovation Fund Denmark (Ref.no. 9163-00016B)

The new customisable electronic administration user interface in Hungary

The Act No. CCXXII. of 2015 on general rules of electronic administration and trust services,(hereinafter ‘eAdministration Act’), defined the concept of customisable electronic administration user interface (hereinafter ‘SZÜF’).&#x0D; The purpose of establishing the SZÜF portal was to create a single gate entry point (as a starting page) for electronic administration of central and local government, linking the various IT systems of different institutions.&#x0D; The new customisable electronic administration user interface was launched in January 2018. According to the plans, it will shortly replace the former magyarorszag.hu portal as the point of single contact portal of Hungary. The new SZÜF portal has a more modern and pure design as well as a life-situation based approach to publish existing eGovernment services.&#x0D; The electronic administration services are available to the client after electronic identification and authentication by the Central Authentication Agent. Services can be used by natural persons (citizens) and organisations (including public administrations, businesses). The identified client can access their digital post-box, can manage their personal calendar, and can save their favourite services among the eGovernment services available on the portal.&#x0D; The SZÜF portal provides infrastructure and applications services to the connected organisations supporting the electronic administration process. For the connected service providers (public administration bodies designated by eAdministration Act) the SZÜF provides specific content and service management solutions. These services may be integrated in the SZÜF or be outside the SZÜF. Currently there are big differences in the quality of services. As a first step, the collection of e-administration information and services was completed. The next task is to ensure uniformity, service-oriented platform and interoperability.&#x0D; The purpose of this study is to present the areas for further development of the services of the SZÜF portal while presenting the results achieved. Achieved goals: a single gate entry point for electronic services for natural persons and organizations on a customisable interface with new online request submission options (e-Paper, iForm). Further development is needed: unification, interoperability and integration of services, connection of additional organizations, possibility of situation-based administration.

Associations between PACS symptoms and risk factors among Long Haulers in the Rutgers Post-COVID Recovery Program

At least 2/3 of people with mild to moderate COVID-19 infection will experience long-haul COVID symptoms that persist for weeks or months, however, risk factors that modify the likelihood that one develops these symptoms are unknown. Patients referred to the Post-COVID Recovery Program at Rutgers in New Brunswick (n= 108) through primary care referral or self-submitted online request and experiencing a wide variety of Post-Acute COVID-19 Syndrome (PACS) symptoms were stratified by those without self-reported cognitive complaints (n=54), those with self-reported cognitive complaints who scored well on cognitive testing (n=29), and those with self-reported cognitive complaints who scored poorly on cognitive testing (n=25). Comparisons between groups were made using ANOVAs and Chi Squared: for COVID-19 disease severity, COVID-19 disease treatment, comorbid COVID-19 symptoms during infection, comorbid PACS symptoms post-infection, pre-existing health conditions, levels of depression and anxiety, level of fatigue, and social determinants of health (access to healthcare, economic stability, housing stability). Preliminary analyses indicated that whereas people without complaints were normally distributed according to age (p>0.200 for Kolmogorov–Smirnov test), people with complaints and deficits were skewed towards the older age group (p<0.001 for K-S test) suggesting age to be a risk factor for cognitive impairment in PACS. Participants that reported cognitive complaints also reported increased symptoms of depression, anxiety, and fatigue, compared to participants without cognitive complaints. These data provide insight into associations between PACS symptoms and risk factors relevant in understanding this novel disease.

Boston biorepository, recruitment and integrative network (BBRAIN): A resource for the Gulf War Illness scientific community

AimsGulf War Illness (GWI), a chronic debilitating disorder characterized by fatigue, joint pain, cognitive, gastrointestinal, respiratory, and skin problems, is currently diagnosed by self-reported symptoms. The Boston Biorepository, Recruitment, and Integrative Network (BBRAIN) is the collaborative effort of expert Gulf War Illness (GWI) researchers who are creating objective diagnostic and pathobiological markers and recommend common data elements for GWI research. Main methodsBBRAIN is recruiting 300 GWI cases and 200 GW veteran controls for the prospective study. Key data and biological samples from prior GWI studies are being merged and combined into retrospective datasets. They will be made available for data mining by the BBRAIN network and the GWI research community. Prospective questionnaire data include general health and chronic symptoms, demographics, measures of pain, fatigue, medical conditions, deployment and exposure histories. Available repository biospecimens include blood, plasma, serum, saliva, stool, urine, human induced pluripotent stem cells and cerebrospinal fluid. Key findingsTo date, multiple datasets have been merged and combined from 15 participating study sites. These data and samples have been collated and an online request form for repository requests as well as recommended common data elements have been created. Data and biospecimen sample requests are reviewed by the BBRAIN steering committee members for approval as they are received. SignificanceThe BBRAIN repository network serves as a much needed resource for GWI researchers to utilize for identification and validation of objective diagnostic and pathobiological markers of the illness.

Online data caching in edge computing

SummaryData caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

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.

An Evaluation of eVisits at an Academic Medical Center

An eVisit is a form of asynchronous telehealth whereby the patient submits an online request for medical advice and receives a written response from a health care provider. While thought to be an efficient way to resolve low-acuity medical issues, there is limited information on whether eVisits lead the avoidance of in-person care. We reviewed 8627 eVisits that occurred at our institution from July 2017 to March 2020 and found that 23.1% of eVisits required follow-up medical care within 14 days (22.6% with primary care physician, 0.3% with emergency department, 0.2% both). Our results indicate that eVisits are a feasible alternative to in-person care for low-complexity medical issues.

Less is More: Service Profit Maximization in Geo-Distributed Clouds

Nowadays cloud providers purchase a good deal of bandwidth from Internet service providers to satisfy the growing requests from corporate customers for the exclusive use of inter-datacenter bandwidth. For exclusive bandwidth services, neither maximizing the revenue nor minimizing the cost can bring the maximal profit to cloud providers. The diversity of bandwidth prices and the random arrival time of user requests further increase the difficulty in economically scheduling the services to meet user requests from cloud providers. In this article, we propose to help cloud providers maximize their service profits by properly selecting user requests to serve rather than satisfying them all. We formulate the problem of service profit maximization and prove its NP-hardness. To handle offline request submission, we propose a solution that maximizes the service profit by alternately maximizing the service revenue and minimizing the service cost. To maximize service profit under online request submission, we propose an online scheduling algorithm that carefully handles the risk of not being able to pay off the incremental service cost and makes scheduling decisions in real time. Our extensive evaluations demonstrate that our solutions can achieve more than 1.6x the service profits of existing solutions.