Data Requests Research Articles

BOOKER PREDICTION FROM REQUESTS FOR QUOTATION VIA MACHINE LEARNING TECHNIQUES

Purpose – Many incoming requests for quotation usually compete for the attention of accommodation service provider staff on a daily basis, while some of them might deserve more priority than others. Design – This research is therefore based on the correspondence history of a large booking management system that examines the features of quotation requests from aspiring guests in order to learn and predict their actual booking behavior. Approach – In particular, we investigate the effectiveness of various machine learning techniques for predicting whether a request will turn into a booking by using features such as the length of stay, the number and type of guests, and their country of origin. Furthermore, a deeper analysis of the features involved is performed to quantify their impact on the prediction task. Findings – We based our experimental evaluation on a large dataset of correspondence data collected from 2014 to 2019 from a 4-star hotel in the South Tyrol region of Italy. Numerical experiments were conducted to compare the performance of different classification models against the dataset. The results show a potential business advantage in prioritizing requests for proposals based on our approach. Moreover, it becomes clear that it is necessary to solve the class imbalance problem and develop a proper understanding of the domain-specific features to achieve higher precision/recall for the booking class. The investigation on feature importance also exhibits a ranking of informative features, such as the duration of the stay, the number of days prior to the request, and the source/country of the request, for making accurate booking predictions. Originality of the research – To the best of our knowledge, this is one of the first attempts to apply and systematically harness machine learning techniques to request for quotation data in order to predict whether the request will end up in a booking.

Acceptance Sampling MIL-STD 105E for Quality Control: A Case Study

The development of the garment industry in Indonesia is getting higher every year. The garment industry plays a role as a contributor to the country's foreign exchange and is relied upon to meet national clothing needs. With the increasing demand for clothing, the garment industry is competing to provide and offer good quality products at affordable prices. The company faces obstacles in products that do not comply with the specifications or standards set by the company. The finished product inspection process is carried out on each production line. Therefore, product defects that pass quality control are still found until the Final QC stage. This problem is caused by the company's absence of fixed quality standardization in each production process so that defective products enter the finishing process. The use of the sampling method at the Company is currently not effective enough to minimize found product defects at this stage, data is collected through requests for historical data on product defects in the company and also by making direct observations. One of the causes of product defects is operators who work under pressure because they have to achieve production targets. If the use of this method can be realized, there is no need to carry out 100% inspections but to adjust the number of product requests from buyers. This can help to reduce workload and the number of workers in product quality inspection thereby increasing productivity. Application of the Acceptance Sampling system based on MIL-STD 105E on products is beneficial in determining product quality. By applying this method, it is easier for companies to detect the number of product defects with large production volumes

Blockchain-Based Process Quality Data Sharing Platform for Aviation Suppliers

Given the challenge of manufacturing data silos and information credibility issues in traditional aviation suppliers’ result-oriented management approach, this paper proposes a blockchain-based aviation supplier manufacturing process quality data-sharing platform. Firstly, the paper introduces the possibility of integrating manufacturing supply chain quality management with blockchain technology. Secondly, the quality and data sharing platform architecture of the production process of new aviation suppliers is presented based on quality state and island kinds of aviation suppliers. Then, a detailed method for the implementation of quality and data security sharing is proposed to support the sharing platform’s real-time and orderly operation. Build critical technologies such as manufacturing quality data block packaging models, data storage security sharing, and supplier assessment models on this foundation. Finally, depending on data collection of supplier product production processes to shared application practices based on a specific aircraft industrial park under the supervision of the platform architecture and technology. The application platform integrates the data supply and request components, providing practical and intelligent sharing solutions for airlines’ product quality data.

Experimental Staffing Models in Inpatient Acute Mental Health Services : A Longitudinal Comparative Study of Occupational Therapy Services

Introduction: An NHS Mental Health Trust in England recently used an experimental staffing model by including Occupational Therapists (OT) in the staff numbers on ten working age adult acute inpatient wards. Aims: This study aims to compare different staffing models involving occupational therapists and make recommendations for preferred staffing models. Methods: This is a longitudinal comparative study of archived patient and multi-disciplinary electronic records and data collected from Kent and Medway NHS and Social Care Partnership Trust. Areas of analysis included quantitative data and qualitative coding and theming, covering the period February 2016-June 2019 during the experimental staffing model, and July 2019-March 2021, when the model ended and teams were Occupational Therapy-led. Full ethical approval and consent was gained in 2020 from the Trust and University to request and access data to complete this research. Results: This experimental staffing model resulted in less delivery of Occupational Therapy specific interventions, resulting in poorer retention rates and impacting on patient and student experiences. Since working in Occupational Therapy led teams, the quality of Occupational Therapy interventions, job retention, student experiences, patient care and safety have improved. Conclusions: The recommended staffing model for working age acute mental health wards has Occupational Therapists embedded in OT-led teams. Staffing tools need to be developed that involve Allied Health Professional leaders and this field needs more research.

Signal processing and thermal performance analysis of motor heat recovery system

In order to realize the condition monitoring of the motor ?anytime, anywhere?, improve the detection accuracy and shorten the detection time, the author proposes a fault signal processing and diagnosis system for the motor heat recovery system based on the IoT. That is, based on the IoT technology, a mobile terminal oriented motor remote monitoring and fault diagnosis system, the sensing layer of the system collects real-time motor operation status data, and the transmission layer realizes data transmission, cloud storage and response to data requests from the application layer, finally, at the mobile end, the motor running status and diagnosis results are displayed through charts and text, so as to realize remote monitoring and fault diagnosis of the motor. The experimental results show that the accuracy of fault diagnosis test of GA-SVM in mobile terminal is more than 90%, and the running time is less than 30 ms, and the running time is very short. It proves that the mobile terminal uses the fault detection method based on GA-SVM model with high accuracy and short detection time, that is, the fault signal processing and diagnosis accuracy of the motor heat recovery system of the IoT is high and the detection time is short.

A Secure Big Data Storage Framework Based on Blockchain Consensus Mechanism With Flexible Finality

Data security and integrity are becoming increasingly important as the volume of data being created and stored grows. A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks. By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, blockchain technology plays a vital role and has a significant potential to safeguard personal information. Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach. This study introduces a brand-new approach to big data storage security that leverages blockchain technology and applies highway protocol to generate new blocks. The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models. The highway allows blocks to run the consensus mechanism to configure security thresholds more freely. The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence. Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process. A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models. The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.

High Priority Request List cross-section measurements: 7Li(d,x)7Be/3H and 39K(n,p)39Ar

The Nuclear Energy Agency’s High Priority Nuclear Data Request List is a compilation of the highest priority nuclear data requirements. The U-120M cyclotron of the Nuclear Physics Institute of the Czech Academy of Sciences is a suitable tool for studies of several reactions from this list. In this paper, we present the measurements of the 7Be and 3H production in the lithium-7 after the irradiation with deuterons and the validation measurement for the 39Ar production in 39K after irradiation with neutrons .

Телемедицинские консультации в педиатрии — структура и анализ

Aim: To analyze the content of requests for telemedicine consultations (TMC), received by the National Medical Research Center for Children's Health, and suggest possible steps to perfect it. Design: Retrospective study. Material and methods. We analyzed the content of 167 TMC requests for patients with “general pediatric profile” received in 2021 and 2022. Results. Among requests for TMC the majority (43.1%) were for “non-explainable” fever in adolescents 9–17 years of age — with clear signs of a fictitious fever. Similar number of requests were for children with a low grade fever (37,0–37,5°C) that had been vainly investigated to find out the disease causing it. Of 4 children with Kawasaki syndrome only in one it had been suspected at the late stage. Requests for respiratory disease (37.1%) were mostly for their chronic forms. Pediatricians seem not to be conversant with chronic food aspiration syndromes’ diagnosis and management, including that in patient with neurologic conditions. Rare disease and/or unusual symptoms, as well as management of problem patients with known disease were less often the subject of TMC (12 и 7.8% respectively). Conclusion. We could state that pediatric hospitals have extensive diagnostic potential — except bacteriology that has to be upgrade. At the same time there is a tendency of pediatricians to conduct in problem cases multiple non-directional analyses and investigations, incorrectly interpret their results, as well as voluntarily formulate diagnoses. There is a need to do away with excessive and non-recommended use of antibiotics, IV infusion of “desintoxication” solutions, as well as polypharmacy. For the improvement of TMC it seems important to foresee the inclusion into TMS requests data on the disease progression as well as on the dynamics of relevant clinical and paraclinical parameters; the results of analyses and instrumental investigations should be put into addendum. There should be a feedback mechanism — a response to TMC with an appraisal of its effectivity. Keywords: telemedicine, telemedicine consultations in pediatrics.

THE DESIGNING OF ANDROID-BASED SMART RT-RW INFORMATION SYSTEM PERUMAHAN PERMATA DEPOK REGENCY, KELURAHAN RATU JAYA, DEPOK, INDONESIA

The monograph is a detailed administration of statistics on government and human resources. As the smallest form of government, the RT/RW performs administrative services in the form of RT/RW cover letters, monographic population data when needed to collect data on potential citizens, economic status, assistance and health data. For RT/RW cover letter service facilities, every citizen certainly expects a fast and precise service. For this reason, the RT/RW administrators need supplements to make it easier for them to provide services to the community, namely the development of an application that can support the performance of the RT/RW especially in citizen administration facilities. By developing a citizen information system then all needs related to citizens can be accommodated online, especially in the most basic matter, namely the management of citizen data. The main problems faced by RT 01 and RT 03, RW 11 Permata Depok Regency are data that is not properly updated, financial reports that are not transparent, it is difficult to request data from residents, some data in hardcopy form is easily lost, various information is not conveyed to residents , the mailing service can not be fast. The main goal of the Community Service Team for the Kegiatan Kampung Bangkit (KKB) of Universitas Satya Negara Indonesia is to provide solutions to partners problems by creating a prototype of a smart RT/RW management system in the form of an Android-based Smart RT-RW Information System Design (SIAR). The activities were carried out in order to observe the needs of the residents, develop the SIAR (Citizen Administration Information System) application, socialize and train the residents to use the application. The results of this Community Service activity worked well. The residents were enthusiastic and satisfied with these activities based on the testimonials that were carried out. Applications are very useful for managing citizen data in a detailed and sustainable manner.

Excitation functions of neutron-induced threshold reactions in Au, Bi, Ta measured using 30–94 MeV quasi mono-energetic neutron sources

Activation measurements of neutron-induced threshold reactions were studied employing 7Li(p,n) quasi mono-energetic neutron sources in the neutron energy range of 30–94 MeV, where scarce experimental and evaluated data exist. A novel iterative neutron background subtraction approach is presented for the first time and its successful application is demonstrated on the analysis of the excitation functions of (n,xn) reactions in Au, Bi and Ta with x up to 10. Some of them are on the High Priority Nuclear Data Request List (HPRL). A comprehensive comparison of the measured data with theoretical predictions using the TALYS code is presented. While the calculated cross-sections agree with the measurements for lower-threshold reactions, severe discrepancies are observed for higher (n,xn) reactions for any of the TALYS model parameters.

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.

A Cloud-based Mobile Privacy Protection System with Efficient Cache Mechanism

People increasingly rely on their mobile devices and use them to store a lot of data. Some of the data are personal and private, whose leakage leads to users' privacy harm. Meanwhile, mobile apps and services over-collect users' data due to the coarse-grained access control approach utilized by the mobile operating system. We propose a cloud-based approach to provide fine-grained access control toward data requests. We add privacy level, as a new metadata, to data and manage the storage using different policies correspondingly. However, the proposed approach leads to performance decreases because of the extra communication cost. We also introduce a novel cache mechanism to eliminate the extra cost by storing non-private and popular data on the mobile device. As part of our cache mechanism, we design a user-preference-based ordering method along with the principle of locality to determine how popular some data are. We also design a configurable refresh policy to improve the overall performance. Finally, we evaluate our approach using a real phone in a simulated environment. The results show that our approach can keep the response time of all data requests within a reasonable range and the cache mechanism can further improve the performance.

CureSCi Metadata Catalog-Making sickle cell studies findable.

To adopt the FAIR principles (Findable, Accessible, Interoperable, Reusable) to enhance data sharing, the Cure Sickle Cell Initiative (CureSCi) MetaData Catalog (MDC) was developed to make Sickle Cell Disease (SCD) study datasets more Findable by curating study metadata and making them available through an open-access web portal. Study metadata, including study protocol, data collection forms, and data dictionaries, describe information about study patient-level data. We curated key metadata of 16 SCD studies in a three-tiered conceptual framework of category, subcategory, and data element using ontologies and controlled vocabularies to organize the study variables. We developed the CureSCi MDC by indexing study metadata to enable effective browse and search capabilities at three levels: study, Patient-Reported Outcome (PRO) Measures, and data element levels. The CureSCi MDC offers several browse and search tools to discover studies by study level, PRO Measures, and data elements. The "Browse Studies," "Browse Studies by PRO Measures," and "Browse Studies by Data Elements" tools allow users to identify studies through pre-defined conceptual categories. "Search by Keyword" and "Search Data Element by Concept Category" can be used separately or in combination to provide more granularity to refine the search results. This resource helps investigators find information about specific data elements across studies using public browsing/search tools, before going through data request procedures to access controlled datasets. The MDC makes SCD studies more Findable through browsing/searching study information, PRO Measures, and data elements, aiding in the reuse of existing SCD data.

Analyzing QoS factor in 5 G communication using optimized data communication techniques for E-commerce applications

The fifth generation (5 G) communication is characterized using gigabyte data speeds and ultra-low latency features. Therefore, service providers and mobile networks exploit this technology for real-time applications, including E-commerce. The peak data request in an E-commerce application/ website increases with consumer density and demands. The prime requirement in such environments is data optimization for controlling network traffic and sustaining Quality of Service (QoS). For providing a stabilized data handling procedure for E-commerce applications, this article introduces a Contingent Data Optimization Technique (CDOT). This technique provides multi-resource allocation rather than data compression, depending on the peak utilization. The application-specific data generation and communication are identified for network radio-resource allocation that satisfies the 5 G compliance. The decisions on application data peak and distributions are assured using federated learning. In this process, the network devices are monitored for the least and peak data movements for which the radio resources that deform stagnancy are allocated. The federated learning tunes the allocation recommendation using the resource to data mapping during the peak data span. This technique's performance is validated using data handling rate, application response, delay, backlogs, and peak utilization.

Cohort profile: the Swedish National Quality Register for bipolar disorder(BipoläR)

PurposeThe Swedish National Quality Register for bipolar affective disorder, BipoläR, was established in 2004 to provide nationwide indicators for quality assessment and development in the clinical care of individuals with...

Study within a review (SWAR).

Study within a review (SWAR).

Trustworthy and Efficient Crowdsensed Data Trading on Sharding Blockchain

With the development of communications, networking, and information technology, Crowdsensed Data Trading (CDT) becomes a novel data trading paradigm. In CDT, the data requesters publish crowdsensing tasks with specific data requirements, and then workers complete these tasks, upload the data and obtain corresponding rewards. To efficiently deal with data trading, most of the existing CDT systems assume a trusted centralized platform. However, we argue that the platform may collude with workers or requesters to trick others for achieving more benefits. For example, according to the workers’ uploaded data, the platform can modify the reward functions by colluding with the requester. Similarly, the platform might collude with workers to let them know the reward function, then workers could forge data. Meanwhile, requesters and workers may also be malicious. For example, requesters may post tasks but fail to pay and workers can upload wrong data to mislead the system. To solve the above problems, we combine the Crowdsensed Data Trading system with intelligent Blockchain (CDT-B), which contains a smart contract called CDToken. As a credible third-party, the CDToken is used to record the requesters’ reward function and workers’ data uploading function to avoid targeted trick. At the same time, we not only design a Data Uploading and Preprocessing (DUP) mechanism in CDToken to collect and process the workers’ sensed data, but also propose a Grouping Truth Discovery (GTD) to evaluate their data quality for determining the payments. Moreover, to hold a large number of requesters and workers in CDT-B, we propose a Layered Sharding blockchain based on Membership Degree (LSMD) to solve the blockchain inefficiency problem. Finally, we deploy CDToken to an experimental environment based on Ethereum and demonstrate its efficient performance and practicability.

Quantum Machine Learning Driven Malicious User Prediction for Cloud Network Communications

This letter proposes a novel malicious user prediction model based on quantum machine learning that estimates the vicious entity present in the communication system precedently before allocating the data in the distributed environments. The proposed model scrutinizes the behavior of each user and estimates probable data breaches using a developed malicious user predictor unit. The model computes essential scores associated with each user request for the learning process of the prediction unit by generating training samples. The predictor unit exploits the computational and behavioral properties of Qubits and Quantum gates for the accurate prediction of the malicious user with high precision to grant access to non-malicious data requests only. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it significantly improves the security of the system up to 33.28%.

A Blockchain-Based Data Sharing System with Enhanced Auditability

Cloud platforms provide a low-cost and convenient way for users to share data. One important issue of cloud-based data sharing systems is how to prevent the sensitive information contained in users’ data from being disclosed. Existing studies often utilize cryptographic primitives, such as attribute-based encryption and proxy re-encryption, to protect data privacy. These approaches generally rely on a centralized server which may cause a single point of failure problem. Blockchain is known for its ability to solve such a problem. Some blockchain-based approaches have been proposed to realize privacy-preserving data sharing. However, these approaches did not fully explore the auditability provided by the blockchain. The dishonest cloud server can share data with a requester without notifying the data owner or being logged by the blockchain. In this paper, we propose a blockchain-based privacy-preserving data sharing system with enhanced auditability. The proposed system follows the idea of hybrid encryption to protect data privacy. The data to be shared are encrypted with a symmetric key, and the symmetric key is encrypted with a joint public key which is the sum of multiple blockchain nodes’ public keys. Only if a data requester is authorized, the blockchain nodes will be triggered to execute a verifiable key switch protocol. By using the output of the protocol, the data requester can get the plaintext of the symmetric key. The blockchain nodes participate in both the authorization process and the key switch process, which means the behavior of the data requester is witnessed by multi-parties and is auditable. We implement the proposed system on Hyperledger Fabric. The simulation results show that the performance overhead is acceptable.

Revised service metrics partially explain variation in outcomes across facilities in a state-wide advanced practice public musculoskeletal service.

Revised service metrics partially explain variation in outcomes across facilities in a state-wide advanced practice public musculoskeletal service.