Review on IoT Mobile App based on Rural Development in Terms of Agriculture

As India is still a developing country, it has a lot of rural areas wherein the living conditions and standards are below world standards and may even be on the underdeveloped scale of living standards. In order to achieve development in these regions the first and foremost step to initiate is to improve the agriculture standards and methodologies and bring in new technology to improve the methods used in agriculture which is the major source of income to these people. This project is a four staged project which intends on improving the agriculture standards of India. The first stage of the project is an automated humidity and moisture control for the soil, this will help the farmers in automating certain aspects and hence eliminate certain human errors and improve yield. The second stage of the project is an agriculture auction portal wherein the farmers can directly auction their products to the wholesaler without the need of a middle man/broker. The third stage of the project is an android app which conducts various surveys and suggests a new farmer the type of farming/seeds to be planted / soil information and other such relevant data in respect to agriculture which would help increase the yield for a new farmer. The last part of the project is a seed cum financial bank which helps the farmers by providing financial as well as seed aid in times of financial crisis.

Fuzzy Logic in HEART and CREAM Methods to Assess Human Error and Find an Optimum Method Using a Hierarchical Fuzzy System: A Case Study in a Steel Factory

Numerous studies have been conducted to assess the role of human errors in accidents in different industries. Human reliability analysis (HRA) has drawn a great deal of attention among safety engineers and risk assessment analyzers. Despite all technical advances and the development of processes, damaging and catastrophic accidents still happen in many industries. Human Error Assessment and Reduction Technique (HEART) and Cognitive Reliability and Error Analysis Method (CREAM) methods were compared with the hierarchical fuzzy system in a steel industry to investigate the human error. This study was carried out in a rolling unit of the steel industry, which has four control rooms, three shifts, and a total of 46 technicians and operators. After observing the work process, reviewing the documents, and interviewing each of the operators, the worksheets of each research method were completed. CREAM and HEART methods were defined in the hierarchical fuzzy system and the necessary rules were analyzed. The findings of the study indicated that CREAM was more successful than HEART in showing a better capability to capture task interactions and dependencies as well as logical estimation of the HEP in the plant studied. Given the nature of the tasks in the studied plant and interactions and dependencies among tasks, it seems that CREAM is a better method in comparison with the HEART method to identify errors and calculate the HEP.

Rise of robots in surgical territory

Robotic surgery is a rapid advancement in the scientific strata of artificial intelligence and has evolved into a refined tool for the surgeons. Over the last 30 years, this field has evolved in leaps and bounds with wide applications in the field of surgery by improving the dexterity and accessibility for the surgeons in various array of major complicated cases. The surgical armamentarium has been strengthened by evolution of robotic surgery to an extent that man may be replaced by artificial intelligence-based robots in the operation theatre, thereby eliminating the possibility of human errors and limitations.

A Novel Normalization Algorithm to Facilitate Pre-Assessment of Covid-19 Disease from Chest X-ray Image and its FPGA implementation

COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography (CT) imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus from these medical images is remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean-Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique on image processing, the algorithm is applied to chest X-ray images. Therefore, the normalized X-ray images with MVSR are used to recognize via one of the neural network models as known Convolutional Neural Networks (CNNs). At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then it is implemented on FPGA platform. All the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format. The experimental platform consists of Zynq-7000 Development Board and VGA monitor to display the both original X-ray and MVSR normalized image. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed for MVSR normalized image. The proposed MVSR normalization makes the accuracy of CNN model increase from 83.01%, to 96.16% for binary class of chest X-ray images.

Overview on the digitized production of welded steel structures

This paper presents a concept and practical topics involved in digitized production. The term “production” denotes the design, fabrication, and service life of a product, which in this case elaborates on welded steel structures. This includes the required information for guiding all the process stages of the chosen material back to its re-melting, following the material flow in a fully digitized form. This concept enables an increase in production quality at a higher level while minimizing the risk of human errors. Automation of the short-run production of steel structures for demanding applications is also a key goal, together with securing a cost-efficient process. Typically, such structures are fabricated from high- or ultra-high-strength steels. Though challenging, reaching these aims seems to be realistic by applying advanced fatigue design methods, using high-quality robotic welding and receiving information about the real loading of the structure.

Identification and evaluation of human errors of Epoxy control room operators of a pipe Mill company using HEC technique

Introduction: In many workplaces today, the incidence of human error can lead to catastrophic accidents in which human error is the main cause of accidents. Due to the vital role of the control room in guiding and controlling various sites of the pipe industry, especially the outer coating sector, the incidence of any error can lead to human accidents, damage to machinery, interruption in production. This study aimed to identify and evaluate human error by Human Error Calculator (HEC) method in the epoxy control room of a pipe mill company. Materials and Methods: In the present descriptive cross-sectional study, the HEC method was used to identify and evaluate human errors. The HEC technique is provided by Risk Map Company, in which the probability of human error is based on five factors affecting the occurrence of human error, including a degree of urgency, complexity, importance, degree of individual skill, and task repetition, using a disk-shaped tool called Risk Disk is determined through direct observation, available instructions and interview with the head of the mentioned unit. Results: According to the results of this study, out of 11 identified tasks, five job tasks with a risk number of 70% have a high probability of human error, four job tasks with a risk number of 50%, and one job task with a number There is a 40% risk of moderate human error, And a job task with a 20% risk number has an increased chance of human error. Conclusion: The results of the present study showed that the HEC method is easy to use and is a simple and useful tool for professionals to calculate the probability of human error. In addition, HEC is a practical, effective and beneficial method for managers to reduce human error.

The Cybersecurity Awareness Training Model (CATRAM)

This chapter presents the outcome of one empirical research study that assess the implementation and validation of the cybersecurity awareness training model (CATRAM), designed as a multiple-case study in a Canadian higher education institution. Information security awareness programs have become unsuccessful to change people's attitudes in recognizing, stopping, or reporting cyberthreats within their corporate environment. Therefore, human errors and actions continue to demonstrate that we as humans are the weakest links in cybersecurity. The chapter studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the author compiled recent awareness methodologies, frameworks, and approaches. The cybersecurity awareness training model (CATRAM) has been created to deliver training to different corporate audiences, each of these organizational units with peculiar content and detached objectives. They concluded their study by addressing the necessity of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.

The role of accounting information systems in firms’ performance during the COVID-19 pandemic

This paper investigates the influence of accounting information systems (AIS) on firms’ performance during the COVID-19 pandemic and how they help enhance employees’ performance and the external auditing process. This paper is qualitative in nature using the inductive approach. In-depth primary data were gathered through semi-structured interviews conducted in the year 2020. Due to the pandemic, the interviews with ten auditors were done online through the Zoom software application. The empirical findings of this paper show a positive impact of AIS on firms’ performance and a more significant influence on employees’ performance and the auditing process. AIS reduces costs and human errors, eases operations, speeds up work tasks, and increases employees’ productivity during the COVID-19 pandemic. The findings also show that there is no direct impact on firms’ overall cash flow/revenues. This paper increases our understanding of how AIS can influence and improve firms’ performance and the significance of implementation factors such as training. It provides practical guidelines for regulators and managers to utilize accounting information systems to perform better.

Knowledge Based Engineering for Hydrogen Gas Turbines and Burners Design: a review

Hydrogen gas turbines and burners need high attention and their appropriate realization, yet during their design, can lead important benefits for the whole sector. Realizing the best design, the first time, reduces reworks and requests of design changes from the manufacturing departments. In this field, Knowledge Based Engineering is a good strategy for embedding, in an automatic way, experts’ knowledge into CAD models during the design of a component. It enables a reduction of human errors and costs in several design tasks and improving the final quality of a component model. With these premises, the aim to the study is to lead improvements and appropriate actions in the design and re-configuration of hydrogen power generation systems (i.e. gas turbines and burners) by means of KBE, leading improvements yet in this early phase of the global race for hydrogen. A systematic literature review is carried out to explore the current state of art for the application of KBE for the design of turbines and burners in different industrial sectors. Evidences from the practice are collected in a structured classification and elaborated and summarized for application in the design of gas turbines and burners for the hydrogen production.