Search results

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Some studies have been reported in the past on diaphragmatic hernia (DH) surgery techniques using additive manufacturing (AM) technologies, symptoms of a hernia and post-surgery complications. But hitherto little has been reported on bibliographic analysis (BA) for health monitoring of bovine post-DH surgery for long-term management. Based on BA, this study aims to explore the sensor fabrication integrated with innovative AM technologies for health monitoring assistance of bovines post-DH surgery.

A BA based on the data extracted through the Web of Science database was performed using bibliometric tools (R-Studio and Biblioshiny).

After going through the BA and a case study, this review provides information on various 3D-printed meshes used over the sutured site and available Internet of Things-based solutions to prevent the recurrence of DH.

Research gaps exist for 3D-printed conformal sensors for health monitoring of bovine post-DH surgery.

This paper aims to highlight how the six sigma methods helped the medical equipment manufacturing company in finding and analysing the root causes that lead to the reduction in production rate, rejection rates, quality and other major causes that lead to the reduction in productivity of the blood bags manufacturing unit.

Given the critical nature of blood bag manufacturing Six Sigma was chosen as the primary methodology for this research since Six Sigma’s data-driven approach provides a structured framework to identify, analyse and rectify inefficiencies in the production processes. This study proposes the Six Sigma DMAIC (D-Define, M-Measure, A-Analyse, I-Improve, C-Control) encompassing rigorous problem definition, precise measurement, thorough analysis, improvement and vigilant control mechanisms for effectively attaining predetermined objectives.

The paper demonstrates how the Six Sigma principles were executed in a blood bag manufacturing unit. After a detailed and thorough data analysis, it was found that a total of 40 critical-to-quality factors under the five drivers such as Machine, Components, Inspection and Testing, People and Workspace were influential factors affecting the manufacturing of blood bags. From the study, it is identified that the drivers such as inspection and testing, components and machines contribute significantly to increasing productivity.

The paper offers valuable strategic insights into implementing Six Sigma methodologies within the specific context of a blood bag manufacturing unit. The Six Sigma tools and techniques used by the project team to solve issues within the blood bag manufacturing unit can be used for similar healthcare organizations to successfully deploy Six Sigma. The insights from this research might not be directly applicable to other manufacturing facilities or industries but can be used as a guiding reference for researchers and managers.

The current state of scholarly literature indicates a significant absence in the examination of Six Sigma methodologies designed specifically to improve production output in healthcare equipment manufacturing. This paper highlights the application of Six Sigma principles to enhance efficiency in the specific context of blood bag manufacturing.

There is evidence that both hypnotherapy and virtual reality (VR) can be helpful in reducing perceived stress in the general population. This is a feasibility and acceptability trial of an intervention combining hypnotherapy and VR to establish its acceptability in students. This study aims to establish whether students found the experience acceptable, described any adverse effects and whether they reported feeling calmer after the experience.

The study was testing the hypothesis that students would attend the sessions and find the experience acceptable. A secondary hypothesis was that preliminary qualitative and quantitative evaluation of measures of stress and wellbeing would signal potential improvements.

All participants completed all three sessions. No side effects were reported. Visual analogue scales recorded each day assessing the immediate effect improved. At the end of the intervention, there was an increase in wellbeing of 2.40 (95% CI: 1.33, 3.53, p = 0.006), and a decrease in depression of 0.73 (95% CI: 0.40, 1.07, p = 0.010), reflecting large effect sizes of 0.76 and 0.83, respectively. Qualitative feedback was generally very positive.

This study is small with just 15 students and was over a short period of time. The recruitment method meant there was no way to establish whether the volunteer students were representative of the general student population in terms of mental wellbeing. There was no control arm.

The preliminary results suggest that a larger controlled trial is justified.

This VR experience may have benefit to university students and to the wider population.

This described the evaluation of a novel intervention for perceived stress combining hypnotherapy and virtual reality in a group of healthcare students, with promising results suggesting further evaluation is needed.

This research aims to examine the impact of friction stir processing (FSP) treatment on an aluminum alloy, especially the AD31T alloy derived from the Al-Fe-Mg-Si system. The aim is to assess the influence of different processing techniques on the microstructure and physical and mechanical characteristics of the material, with a specific focus on structural and bulk imperfections inside the stir zone (SZ).

The study demonstrates that augmenting the linear velocity of the tool within the 25–100 mm/min range results in significant enhancements. The enhancements include a decrease in the heat-affected zone (HAZ), a reduction in the extent of volume defects inside the SZ and a more uniform deformation. The microstructural analysis results are corroborated by data acquired from microhardness and electrical conductivity studies, confirming the beneficial influence of modifying the tool’s linear velocity on the material parameters.

This study provides significant observations on the changes in microstructure and the generation of flaws throughout the process of FSP of AD31T alloy. These results have practical implications for improving the characteristics of the alloy and optimizing the production conditions.

All samples exhibit a distinct reduction in electrical conductivity within the initial third of the sample, aligning with the transitional region between the base metal (BM) and the HAZ. This underscores the importance of understanding the transitional zones during FSP.

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

Three-way decision (3WD) and probabilistic rough sets (PRSs) are theoretical tools capable of simulating humans' multi-level and multi-perspective thinking modes in the field of decision-making. They are proposed to assist decision-makers in better managing incomplete or imprecise information under conditions of uncertainty or fuzziness. However, it is easy to cause decision losses and the personal thresholds of decision-makers cannot be taken into account. To solve this problem, this paper combines picture fuzzy (PF) multi-granularity (MG) with 3WD and establishes the notion of PF MG 3WD.

An effective incomplete model based on PF MG 3WD is designed in this paper. First, the form of PF MG incomplete information systems (IISs) is established to reasonably record the uncertain information. On this basis, the PF conditional probability is established by using PF similarity relations, and the concept of adjustable PF MG PRSs is proposed by using the PF conditional probability to fuse data. Then, a comprehensive PF multi-attribute group decision-making (MAGDM) scheme is formed by the adjustable PF MG PRSs and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. Finally, an actual breast cancer data set is used to reveal the validity of the constructed method.

The experimental results confirm the effectiveness of PF MG 3WD in predicting breast cancer. Compared with existing models, PF MG 3WD has better robustness and generalization performance. This is mainly due to the incomplete PF MG 3WD proposed in this paper, which effectively reduces the influence of unreasonable outliers and threshold settings.

The model employs the VIKOR method for optimal granularity selections, which takes into account both group utility maximization and individual regret minimization, while incorporating decision-makers' subjective preferences as well. This ensures that the experiment maintains higher exclusion stability and reliability, enhancing the robustness of the decision results.

The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

Apprenticeships in England, while defined by level and typical duration, are not quantified regarding the number of learning hours required to achieve the outcomes specified, as with other regulated qualifications and accredited programmes. This paper proposes an approach to ascribe credit to apprenticeships recognising both on-and-off-the-job learning to remove some of the existing barriers to accessing higher education (HE) and the professions.

A mixed methodological approach resulting in a total learning hours/credit value was proposed.

There is significant HE-wide confusion regarding the amount of learning/training that is required to complete apprenticeships in England. Whilst sector guidance made it clear that there was no prescribed method to ascribe credit to qualifications, programmes, modules, units or apprenticeships by drawing out the core principles within current practice, a key outcome of this project was the development of a method to ascribe a credit value to apprenticeships.

There is potential to support further research into the recognition of prior learning as a specialised pedagogy and for reflecting on apprenticeship practice in other roles and sectors.

Whilst the project underpinning this paper focused on the healthcare sector, the method used to ascribe credit to the level-3 healthcare support worker apprenticeship was not sector specific and can therefore be applied to apprenticeships within other contexts providing more widespread benefits to workforce development.

Policy makers must ensure that employers and providers are clear that the minimum statutory off-the-job hours constitute an apprentice employment entitlement, which must not be conflated with total apprenticeship learning hours requirements. This recommended policy clarification could assist in simplifying the process required for ascribing credit to apprenticeships and at the same time support a move towards better and more consistent recognition of the value of apprenticeship learning.

It is a first attempt to ascribe a credit value to an apprenticeship in England for the specific purpose of facilitating progression to HE.

After completion of the case study, the participants will be able to understand the significance of quality as a pivotal domain within project management and to analyze the issues related to quality and offer logical solutions.

In this case, the Bharat Bijlee Construction Limited (BBCL) group, with a proven track record of over five decades in the transmission and distribution business in India, decided to venture into international projects, considering the prevailing stagnant domestic power sector. They secured contracts worth $85m from the “Shariket Karhaba Koudiet Eddraouch Spa,” a state-owned company responsible for power generation, transmission and distribution in Algeria. However, during the execution phase of these projects, BBCL encountered significant challenges related to product and service quality. These challenges arose due to the tight schedule constraints and cost considerations, as well as a lack of understanding of the dynamics involved in executing international projects, especially in the demanding conditions of the sub-Saharan desert. This case study addresses the complex issue of ensuring and maintaining high-quality standards in large-scale substation projects situated in the challenging environment of the sub-Saharan desert, highlighting the importance of effective project management and international project execution expertise. The case study is from quality management knowledge area and focuses on identification of root cause of quality noncompliance and for better decision-making in projects.

The teaching case is designed for undergraduate and postgraduate courses in project management, civil engineering and architecture domain. The participants will be able to understand the application of various quality tools, statistical process tools and control charts in problem identification, categorization, root cause identification and decision-making.

Teaching notes are available for educators only.

CSS2: Built environment