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The purpose of this paper is to convert real-world raster data into vector format and evaluate loss of accuracy in the conversion process. Open-source Geographic Information System (GIS) is used in this process and system resource utilizations were measured for conversion and accuracy analysis methods. Shape complexity attributes were analyzed in co-relation to the observed conversion errors.

The paper empirically evaluated the challenges and overheads involved in the format conversion algorithms available in open-source GIS with real-world land use and land cover (LULC) map data of India. Across the different LULC categories, geometric errors of varying density were observed in Quantum GIS (QGIS) algorithm. Area extents of original raster data were compared to the vector forms and the shape attributes such as average number of vertices and shape irregularity were evaluated to explore the possible correlation.

The results indicate that Geographic Resources Analysis Support System provides near error-free conversion algorithm. At the same time, the overall time taken for the conversion and the system resource utilizations were optimum as compared to the QGIS algorithm. Higher vector file sizes were generalized and accuracy loss was tested.

Complete shape complexity analysis could not be achieved, as the weight factor for the irregularity of the shapes is to be varied based on the demography as well as on the LULC category.

Because of the higher system resource requirements of topological checker tool, positional accuracy checks for the converted objects could not be completed.

This paper addresses the need of accuracy analysis of real-world spatial data conversions from raster to vector format along with experimental setups challenges and impact of shape complexity.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

This study aims to identify and prioritize barriers to the implementation of medical equipment (ME) marketing strategies using the analytic hierarchy process (AHP).

This was an applied, cross-sectional and descriptive-analytic study conducted in 2017 in Iran. The study population included 30 medical equipment experts working in the Ministry of Health and Medical Education. A researcher made pair-wise comparison questionnaire was used for collecting the required data. The validity and reliability of this questionnaire were confirmed through getting five related experts’ opinions and inconsistency ratio (IR = 0.04). The AHP technique and Expert Choice 10.0 software were used to prioritize the barriers to the implementation of ME marketing strategies.

The results showed that among four categories of barriers to the implementation of ME marketing strategies, “managerial and strategic barriers” (FW = 0.339) and “structural barriers” (FW = 0.139) were the most important and the least important barriers, respectively.

This study, for the first time, has identified and prioritized barriers to the implementation of medical equipment marketing strategies using the AHP.

The purpose of this study is to determine the effect of olive cake (CO) on glycaemia and lipemia and lipid peroxidation and antioxidant enzymes activities in erythrocytes and tissues, in streptozotocin (STZ)-induced diabetic rats.

Diabetes was induced by a single intraperitoneal injection of STZ (55 mg/kg BW). In total, 12 diabetic D rats, weighing 260 ± 20 g, were divided into two groups fed a casein diet supplemented (D-OC) or not (D) with OC (7.5 per cent), for four weeks.

In D-OC compared with D, glycaemia, total cholesterol and triglycerides values (−40 per cent; p = 0.007, 27 per cent; p = 0.007 and −27 per cent; p = 0.0019). In erythrocyte, liver, kidney, heart, muscle and brain, thiobarbituric acid reactive substances contents were respectively, (−19 per cent; p = 0.03, −32 per cent; p = 0.002, −20 per cent; p = 0.04, −68 per cent; p = 0.003, −74 per cent; p = 0.0003 and −38 per cent; p = 0.04). In erythrocyte, SOD, GSH-Px and CAT activities were respectively, (+14 per cent; p = 0.01, +74 per cent; p = 0.012 and +34 per cent; p = 0.0009). In the liver, kidney, heart and muscle, SOD activity was respectively, (+31 per cent; p = 0.004, +12 per cent; p = 0.038, +43 per cent; p = 0.001 and +23 per cent; p = 0.18). GSH-Px activity was respectively, (+121 per cent; p = 0.0009, 89 per cent; p = 0.0006, + 95 per cent; p = 0.008, +71 per cent; p = 0.02 and +26 per cent; p = 0.01), in the liver, kidney, heart, muscle and brain. Catalase activity was (+21 per cent; p = 0.008) in the liver, (+88 per cent; p = 0.0002 in the kidney, +53 per cent; p = 0,002 in the heart and 83 per cent; p = 0.00004 in the muscle).

In diabetic rats, OC reduces hyperglycaemia induced by STZ and attenuates triglyceridemia and cholesterolemia. This residue is able to decrease the oxidative stress by increasing the antioxidant enzymes activity in erythrocytes and tissues. The high contents of phytoconstituents present in OC are considered to be responsible for this effect.

Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC).

The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework.

The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth.

This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability.

The current industrial revolution is powered by data, which is also referred as Industry 4.0. The Industry 4.0 has attracted significant attention from academia and the industry professionals. The supply chain integration (SCI) has played a significant role in enhancing supply chain performance and organizational performance. This study explores the relationship between Industry 4.0 and SCI via an extensive literature review to understand the various levels of integration with the supply chain processes and to identify missing links, through a framework, and suggest further research directions.

In this paper, we have used systematic literature review approach to identify the building blocks of the conceptual framework, which is the main contribution of the present study. We have used Scopus database to search literature using keywords.

The study offers some interesting insights that may help scholars to advance theoretical debates. Moreover, the study also provides interesting direction to the practitioners engaged in supply chain management in data-driven environment. In this study, we have proposed a conceptual framework for the adoption of Industry 4.0 and SCI.

In this study we have proposed a conceptual framework. However, the framework is yet to be empirically tested. Hence, we caution readers to evaluate the findings of the present study in context to its limitations. This is an attempt to develop a conceptual framework which may be tested using longitudinal data.

The present work helps in integrating two independent subjects', i.e. Industry 4.0 and SCI. The theoretical framework presented here integrates Industry 4.0 and SCI which can be useful to the practitioners and policymakers engaged in implementing Industry 4.0.

The construction industry's application of supply chain management (SCM) principles is confronted with numerous Gordian Knots ranging from late delivery, fragmentation and others. The challenges could be attributed to multiple factors. The most crucial amongst them are adopting management ideas rooted in the second and third industrial revolution without taking consideration of the present industrial revolution. Evidence from literature and practice revealed that we are presently in the fourth industrial revolution (4IR). This chapter calls for developing a model that supports construction supply chain management (CSCM) in tandem with the principles of 4IR. This chapter presents the idea behind the conception, development and benefit of this research book to construction stakeholders and academia. The various shortcomings in the existing model for CSCM were also discussed in this chapter extensively.

Purpose: There have been continuous developments in the production industry to meet the increasing customer demand from the past to the present. At this point, supply chain management (SCM) systems emerge as an important topic. SCM is a set of systems that manages the entire process from the production of a product to its delivery to the end user. Industry 4.0 aims to improve the production industry by increasing the quality, efficiency, and performance of the production process. Therefore, in this chapter, the authors highlight the challenges, benefits, and future trends of the combination of Industry 4.0 and SCM systems.

Methodology: In this chapter, the integration of Industry 4.0 and SCM systems was investigated. For this purpose, the Industry 4.0 position of the countries and the current status of SCM systems have been examined. In addition, the key technologies in the Industry 4.0 transformation, the possible problems encountered in the transformation, the deficiencies encountered in SCM systems, and how these deficiencies can be solved with Industry 4.0 were investigated.

Findings: The results of this study show that companies that use an SCM system can separate themselves from their competitors by using Industry 4.0 technologies.

Significance: This can allow them to achieve their strategic goals and to ensure the maintenance of their competitive advantage.

In the age of the Fourth Industrial Revolution, Industry 4.0 can increase the productivity and competitiveness of companies in the international marketplace. The purpose of this article is to investigate the drivers for and outcomes of the adoption of Industry 4.0 technologies in the case of a food processing company located in Italy.

The present work adopted a case study approach by investigating an Italian food processing company to investigate the drivers, challenges and outcomes of Industry 4.0 adoption in the agri-food sector.

This research highlighted drivers and challenges related to the adoption of different Industry 4.0 technologies. Secondly, it underlined the impacts of Industry 4.0 in terms of firm performance, operations management, human resource management and strategy.

Industry 4.0 technologies remain underexplored from the strategic perspective in the agri-food sector. This article provides preliminary evidence on the digital transformation of food processing companies, with a focus on Industry 4.0. Practical implications for managers, CEOs and entrepreneurs are discussed.

The unpredictable distribution of globalization has directed the world economy and revealed the need to establish cooperation in all business processes. This global structure also highlights the necessity of designing an agile supply chain that is capable of continuous information sharing during end-to-end transportation for the purpose of creating sustainable connections. In this process, various strategies enhanced with contemporary information and communication technologies have been developed to create a fast and accurate data-sharing network between logistics service providers and supply chain parties. However, unlike the short-term interruptions experienced in the past, COVID-19 has caused unprecedented problems in the dynamics of most economies. In eliminating these problems, it has been seen that information communication technologies, which provide the advantage of digital visibility in normal operations, need to be redesigned with the technology of the autonomous age. To meet this requirement, Industry 4.0, a revolutionary conceptual trend that started roughly a decade ago but that has become considerably more widespread during COVID-19, has been integrated into business models as a key concept that governs intelligent transformation in the chain. This process, which is today considered a compulsory intelligent transformation rather than an alternative method, has however led to some conflicts, especially in adaptation, expertise, and security subjects. This chapter of the book evaluates, within the scope of cybersecurity and workforce in logistics services, the conflicts created by the digital solution methods that have been integrated into business models to reduce the negative effects of COVID-19.