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Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual's health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.

At present sustainable development, assessment of sustainable manufacturing practices, and prioritisation of barriers, drivers, and indicators have become complex due to the involvement of existing benchmarks like social, economical, technical, and environmental. Literature review available on sustainable manufacturing practices assessment which considers all three dimensions is relatively limited. Recently, in sustainable manufacturing decision making, approaches to evaluate sustainable manufacturing practices have used both quantitative and qualitative data. This study aims to present a systematic review of multi-criteria decision making (MCDM) applications in sustainable manufacturing. In the present study papers available in the Scopus database were reviewed on the applications of different MCDM techniques in the sustainable manufacturing area. The study highlights how the manufacturing industries can benefit from MCDM techniques in decision making. This review article develops insights into various multi-criteria decision-making techniques progress made by considering the sustainable manufacturing applications over MCDM methods. An extensive review in the sphere of sustainable manufacturing has been performed by considering the Scopus database and utilising MCDM techniques. It is found that most of the studies available in the sustainable manufacturing (SM) area are based on fuzzy-based single model approaches.

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The precise control upon each degree of freedom of a robotic arm is a great challenge in implementing industrial work. This paper aims to design a novel controller for an automated robotic arm. A discrete Proportional Integral Derivative (PID) control technique is being used to replace the complex electronic circuitry, which would greatly reduce the cost and size of the controller. DC motors will be controlled on the basis of Closed-loop System using an avr (Atmega 16/32) microcontroller. Transfer functions have been derived for mathematical modeling of the system through which the stability of the system can be evaluated prior to fabrication.

Due to the rapid growth of the number of digital media elements like image, video, audio, graphics on Internet, there is an increasing demand for effective search and retrieval techniques. Recently, many search engines have made image search as an option like Google, AlltheWeb, AltaVista, Freenet. In addition to this, Ditto, Picsearch, can search only the images on Internet. There are also other domain specific search engines available for graphics and clip art, audio, video, educational images, artwork, stock photos, science and nature [www.faganfinder.com/img]. These entire search engines are directory based. They crawls the entire Internet and index all the images in certain categories. They do not display the images in any particular order with respect to the time and context. With the availability of MPEG-7, a standard for describing multimedia content, it is now possible to store the images with its metadata in a structured format. This helps in searching and retrieving the images. The MPEG-7 standard uses XML to describe the content of multimedia information objects. These objects will have metadata information in the form of MPEG-7 or any other similar format associated with them. It can be used in different ways to search the objects. In this paper we propose a system, which can do content based image retrieval on the World Wide Web. It displays the result in user-defined order.

We propose a general framework for obtaining asymptotic distributional bounds on the stationary backlog $W^{A_1+A_2,c}$ in a buffer fed by a combined fluid process $A_1+A_2$ and drained at a constant rate $c$. The fluid process $A_1$ is an (independent) on-off source with average and peak rates $\rho_1$ and $r_1$, respectively, and with distribution $G$ for the activity periods. The fluid process $A_2$ of average rate $\rho_2$ is arbitrary but independent of $A_1$. These bounds are used to identify subexponential distributions $G$ and fairly general fluid processes $A_2$ such that the asymptotic equivalence $\bP{W^{A_1+A_2,c}>x} \sim \bP{W^{A_1,c-\rho_2}>x}$ ($x\to\infty$) holds under the stability condition $\rho_1+\rho_2

We introduce a queueing network model that allows us to capture the time-varying service delivered to a traffic stream due to the presence of random perturbations (e.g. cross-traffic in a communication network). We first present the model for a single queue and then describe how such queues may be interconnected using the operations of {\em fork (or in-synchronization)} and {\em join (or out-synchronization)}. Such networks may be seen as a generalization of stochastic event graphs, and of the class of fork-join networks. The departure processes in such a network satisfy a system of equations along with the exogenous arrival processes and the service processes of the various queues. This system can be seen as a general time-varying linear system in the $(\min,+)$ semi-field. We obtain an explicit representation of the departure process in terms of the exogenous arrival processes and the service processes. Sufficient conditions are derived for this system to have a unique solution. We also study liveness and absence of explosion in this class of networks. Under appropriate stationarity and ergodicity assumptions, we establish stability theorems for such networks. To this end we first obtain a rate result for the departure process in terms of the rates of the exogenous arrival process and the throughput of a saturated system. We then use this result to show that in a network with a single exogenous arrival, all queue lengths are finite with probability one if the arrival rate is less than the throughput of the saturated system, and to give a representation of the queue length process. This class of networks allows for a detailed description of controlled sessions in integrated service networks. We also show that it contains several earlier discrete event models of the literature pertaining to stochastic Petri nets, service curves and fork-join networks, and show how the present model unifies them in a single algebraic structure. This structure is that of a semi-ring of functions of two real variables, where the addition is the pointwise minimum and the multiplication a generalization of inf-convolution

Sustainable manufacturing (SM) has become an emerging research topic in sustainability due to strict government policies, social awareness, and rapid information and technology changes. Industries are now adopting Industry 4.0 technologies to compete in the global market competition. Very few studies discussed the evolution of SM and the related research themes that evolved in recent years. This study aims to identify the research themes and research transformation evolved in SM in the last twenty-one years. The impact of most productive authors, journals and countries is evaluated by citation analysis, while the co-occurrence of keywords is done to identify the thematic evolution and trending topics in SM. The results of this study provide a clear and detailed picture of various research themes that evolved in SM from 1999 to 2020 i.e., sustainable planning and scheduling, sustainable supply chain, lean and environmental management, sustainable machining, decision making, Industry 4.0 and Lean production systems. The number of publications related to Sustainability and Industry 4.0 has increased in the last 3 years. Cluster analysis shows the evolution of many new trending topics in SM i.e., SM in Industry 4.0. Challenges and enabling factors for the sustainable manufacturing practices have identified and role of decision making in SM implementation is discussed. The content analysis on major research themes in SM is discussed with the help of cluster analysis. Based on the literature analysis a comprehensive research framework for future research work is proposed in Industry 4.0 context. The results obtained from this study will be valuable for academia and practitioners enabling them to be updated about the latest developments in SM research.

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies.