Coping with demand volatility in retail pharmacies with the aid of big data exploration

Data management tools and analytics have provided managers with the opportunity to contemplate inventory performance as an ongoing activity by no longer examining only data agglomerated from ERP systems, but also, considering internet information derived from customers' online buying behaviour. The realisation of this complex relationship has increased interest in business intelligence through data and text mining of structured, semi-structured and unstructured data, commonly referred to as "big data" to uncover underlying patterns which might explain customer behaviour and improve the response to demand volatility. This paper explores how sales structured data can be used in conjunction with non-structured customer data to improve inventory management either in terms of forecasting or treating some inventory as "top-selling" based on specific customer tendency to acquire more information through the internet. A medical condition is considered - namely pain - by examining 129 weeks of sales data regarding analgesics and information seeking data by customers through Google, online newspapers and YouTube. In order to facilitate our study we consider a VARX model with non-structured data as exogenous to obtain the best estimation and we perform tests against several univariate models in terms of best fit performance and forecasting

An estimation model for hypertension drug demand in retail pharmacies with the aid of big data analytics

The unpredictability of consumer preference observed in the last few years has coincided with the global digital data explosion as consumers are increasingly relying on internet information to guide their buying behaviour. The emergence of this trend has resulted in demand volatility and uncertainty in the retail industry, leading to negative consequences on inventory control and on shareholder profits in the long-run. This paper examines whether retail pharmacies in Abuja, Nigeria may exploit the increasing availability of relevant big data (structured, semi-structured and unstructured) from different sources to anticipate the changes on demand profiles for antihypertensive medication. In order to examine this, we consider a VARX model with non-structured data as exogenous to obtain the best estimatio

IoMT Supported COVID Care – Technologies and Challenges

The Internet of Things (IoT) has sparked substantial progress in the recent days of pandemic and achieved several milestones especially in healthcare. Wearable technologies have gained in popularity as a means of ensuring the health and safety of users in medical and disaster relief activities, facilitating the evolution of the Internet of Medical Things (IoMT). The IoMT is a phenomenon in which computer networks and medical equipment are linked over the Internet to allow physicians and patients to interact in real time. This coronavirus pandemic has demonstrated how unprepared our systems are for a disaster of this magnitude, as well as the necessity for robust, computationally intelligent, and profound meddling. This study piece looks at the many IoT-enabled smart solutions that could be used to respond to various aspects of this rising epidemic, from diagnosis to treatment to prevention. The paper provides a retrospective survey and identifies several obstacles and obstructions to IoT integration as an attempt to deal with coronavirus pandemic. The work concludes with a discussion of challenges and future scope to the difficulties mentioned in the bench marked works

Deep learning for internet of underwater things and ocean data analytics

The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes

ReSCon '09, Research Student Conference: Book of Abstracts

The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Industry 4.0 perspectives in the health sector in Brazil

Health 4.0 can be understood as the set of procedures that seek to improve the efficiency and speed of health professionals with possible guidelines for combining patient data in hospitals. However, systematizing and qualitatively describing the contributions of industry 4.0 in the context of the Brazilian health sector is a complex task. The aim of this paper is to present an analysis of industry 4.0 related to the health sector and its respective characteristics in Brazil. In addition, it discusses the prospects for greater use of technology in health care. In methodological terms, an exploratory field research was conducted with a non-random and intentional sample of professionals working in the technological context of Brazilian health. The research is classified as descriptive and qualitative, exploratory. The results contribute to narrow the information gap about industry 4.0 in the Brazilian health sector. The study allowed to develop a concept map of health 4.0 regarding the professional profile, considering the adoption of technologies that may favor the sector

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model

Blockchain and Internet of Things in smart cities and drug supply management: Open issues, opportunities, and future directions

Blockchain-based drug supply management (DSM) requires powerful security and privacy procedures for high-level authentication, interoperability, and medical record sharing. Researchers have shown a surprising interest in Internet of Things (IoT)-based smart cities in recent years. By providing a variety of intelligent applications, such as intelligent transportation, industry 4.0, and smart financing, smart cities (SC) can improve the quality of life for their residents. Blockchain technology (BCT) can allow SC to offer a higher standard of security by keeping track of transactions in an immutable, secure, decentralized, and transparent distributed ledger. The goal of this study is to systematically explore the current state of research surrounding cutting-edge technologies, particularly the deployment of BCT and the IoT in DSM and SC. In this study, the defined keywords “blockchain”, “IoT”, drug supply management”, “healthcare”, and “smart cities” as well as their variations were used to conduct a systematic search of all relevant research articles that were collected from several databases such as Science Direct, JStor, Taylor & Francis, Sage, Emerald insight, IEEE, INFORMS, MDPI, ACM, Web of Science, and Google Scholar. The final collection of papers on the use of BCT and IoT in DSM and SC is organized into three categories. The first category contains articles about the development and design of DSM and SC applications that incorporate BCT and IoT, such as new architecture, system designs, frameworks, models, and algorithms. Studies that investigated the use of BCT and IoT in the DSM and SC make up the second category of research. The third category is comprised of review articles regarding the incorporation of BCT and IoT into DSM and SC-based applications. Furthermore, this paper identifies various motives for using BCT and IoT in DSM and SC, as well as open problems and makes recommendations. The current study contributes to the existing body of knowledge by offering a complete review of potential alternatives and finding areas where further research is needed. As a consequence of this, researchers are presented with intriguing potential to further create decentralized DSM and SC apps as a result of a comprehensive discussion of the relevance of BCT and its implementation.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed