Use of process modelling in product development integration within manufacturing environment

The impact of supply chain on new product development (NPD) and product introduction is particularly important in a time when (i) there are pressures for growing product proliferation in order to meet varied demands and constraints, (ii) the research and development pipeline is a key focus in companies, and (iii) technology life cycles have shortened so much that obsolete inventories and time to market are crucial for companies’ margin performance. This research focuses on the early stages of the collaborative product development process in the extended enterprise and shows it is a contribution to the business world. The output of the research includes the functional requirements of a framework and a developed prototype methodology with tools and technologies that are tested with case studies in the industrial environment. The study also focuses on using modern modelling tools to represent the product development processes of an Original Equipment Manufacturer (OEM) and its suppliers

An overview of change management within manufacturing environment

In this competitive world, where features like time to market, new technology and change management are the features that can affect the performance of New Product Development (NPD). Companies tend to use a conventional approach to NPD by assigning representatives from support functions to review and recommend changes as projects evolve. This approach has, in recent years, been questioned since it is a costly and time-consuming approach due to its iterative nature. It is argued that the change management process can reduced the negative effect and has the ability to support the functions of a supply chain to a greater extent and also earlier in the NPD process. This paper focuses on different change management techniques to support the required changes by management to integrate the NPD within supply chain (SC)

Estimation of organ absorbed dose in pediatric chest X-ray examination: a phantom study

Children have a greater risk of developing lifetime cancer and other biological effects from ionizing radiation exposure than adults. The aim of this study was to measure the absorbed dose received by lungs and heart in pediatric chest X-ray examination using nanoDot optically stimulated luminescent dosimeter (OSLD). The X-ray system, Siemens Multix Top was used. A pediatric phantom developed by using beeswax and polyurethane foam was exposed at 50 kVp, 52 kVp, 55 kVp, 57 kVp and 60 kVp, with fixed tube current-exposure time (3 mAs), which is normally used in pediatric clinical chest X-ray examinations. The nanoDot OSLDs were placed in different parts in the thorax of the phantom according to the position of organs in the chest area, which are lungs and heart. For lungs, absorbed dose measurement nanoDot OSLDs were placed in the apex and base at three different depths. The phantom was exposed three times for each kVp value, and the absorbed doses were measured in mGy. The findings show that the measured absorbed dose to the heart increased with the increase in kVp. Overall, a 22% increase in absorbed dose to heart and a 29% increase in lungs with the increase in kVp was recorded. In addition, absorbed dose to the base of left and right lungs was recorded higher up to 9% as compared to the apex of lungs. In conclusion, the absorbed dosage increases with exposure, while the absorbed dose decreases with depth. It is necessary for the radiographer to select an appropriate exposure setting based on the physical characteristics of the pediatric patient

Performance Evaluation of Mutual Funds in Pakistan

In Pakistan Mutual Funds were introduced in 1962, when the public offering of National Investment (Unit) Trust (NIT) was introduced which is an open-end mutual fund. In 1966 another fund that is Investment Corporation of Pakistan (ICP) was establishment. ICP subsequently offered a series of closed-end mutual funds. Up to early 1990s, twenty six (26) closed-end ICP mutual funds had been floated by Investment Corporation of Pakistan. After considering the option of restructuring the corporation, government decided to wind up ICP in June, 2000. In 2002, the Government started Privatisation of the Investment Corporation of Pakistan. 25 Out of 26 closed-end funds of ICP were split into two lots. There had been a competitive bidding for the privatisation of funds. Management Right of Lot-A comprising 12 funds was acquired by ABAMCO Limited. Out of these 12, the first 9 funds were merged into a single closed-end fund and that was named as ABAMCO Capital Fund, except 4th ICP mutual fund as the certificate holders of the 4th ICP fund had not approved the scheme of arrangement of Amalgamation into ABAMCO capital fund in their extra ordinary general meeting held on December 20, 2003. The fund has therefore been reorganised as a separate closedend trust and named as ABAMCO Growth Fund. Rest of the three funds were merged into another single and named as ABAMCO Stock Market Fund. So far as the Lot-B is concerned, it comprised of 13 ICP funds, for all of these thirteen funds, the Management Right was acquired by PICIC Asset Management Company Limited. All of these thirteen funds were merged into a single closed-end fund which was named as “PICIC Investment Fund”. Later on the 26th fund of ICP (ICP-SEMF) was also acquired by PICIC Asset Management Company Limited

BARRIERS IN IMPLEMENTATION OF E-BUSINESS TECHNOLOGIES IN SMALL AND MEDIUM ENTERPRISES (SMEs) IN PAKISTAN

The current research investigates the Barriers in implementation of E-Business Technologies in Small and Medium enterprises (SMEs) in Pakistan. Data were collected from 2000 respondents by using simple random technique. A structural questionnaire was developed for the data collection and reliability and validity of data. It was revealed that most of the SMEs business owners are not familiar in using internet and in many cases they are not computer literate. It was further revealed that Government should provide some basic computer training to the Small and Medium Enterprises so they will able to use computer. The proper implementation of E-Business technologies in SMEs in Pakistan, Government and other related agencies can initiate E-Business in SMEs to achieve competitive edge

Investigating the intersections of vulnerability detection and IoMTs in healthcare, a scoping review protocol for remote patient monitoring

Due to the rapid and ubiquitous development and acceptance of IoT, healthcare providers have changed their locational settings from solely based in clinics to extend more broadly into the reach of patients’ domestic homes. This IoMT focus extends to various medical devices and applications within the healthcare domain, such as any form of smartphones, surveillance cameras, wearable sensors, and actuators, that hold the capability to access IoT technologies. The aim of this scoping review has two important objectives. The first is to understand the best approaches towards acquisition and refinement of data in favour of an optimised cyber security posture for remote patient monitoring. The second is to understand how best to detect cyberattacks and vulnerabilities in Medical IoTs using automated reasoning. The review will be carried out according to the Joanna Briggs Institute (JBI) scoping review methodology. The key information sources are Springer Link, IEEE Xplore, Science Direct, SCOPUS, and ACM databases. The search is limited to studies written in English. The initial step in the review uses keywords and index terms to identify literature from the selected database information sources. The second step then takes the identified elements and searches each of the databases. The third step involves a search of the references to determine literature inclusion using a full-text screening process. Medical IoT devices, specifically designed for patient monitoring and diagnosis, excel in their ability to collect, transfer, and interact with real-time data. It focuses on intersections between IoMTs, cyberattacks and vulnerabilities, knowledge graph detection, and automated reasoning

An analysis of RDF view maintenance using Jena

Resource Description Framework is a next generation technique to create web content. This has given rise to the need to develop efficient and effective techniques to manage high volume RDF structures. This paper deals with Semantic Web technologies and presents an analysis of JENA based updation of RDF structures. The view maintenance of RDF structures (varying sizes), i.e. updating RDF structures using views is performed through JENA constructs and performance of insertion and deletion operations is measured. After analysis, Insert operation time was observed to increase proportionally however time remained the same for delete operations performed on the RDF dat

Correlation of foraminal area and response to cervical nerve root injections

Ray et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 3.0., which permits unrestricted use, distribution, and reproduction in any medium

Random neural network based epileptic seizure episode detection exploiting electroencephalogram signals

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation