In the era of industrialization, automatic machines become an integral part of human life. These machines help to reduce the time needed to do a specific task. Nowadays, human life becomes more competitive and faster than the previous. Automation brought about by technology has saved human effort and time to a large extent. Slicing vegetables are a risky and time-consuming task in our busy life. This project is aimed at solving above stated problems by introducing a special product named Automatic Vegetable Chopper. This chopper is mainly designed to reduce human effort and make the job of chopping vegetables much easier and faster. Its main features are fully automated, easily portable, less power consumption and changeable stainless, sharp blade, etc. This product is designed and established by following a structured product design process and with the help of a board of design engineers. Product planning, customer needs identifying, product specification, concept screening, concept scoring and bill of materials are tools that mainly used to accomplish this task. Finally, this paper also suggests various techniques and opportunities of product planning in manufacturing industries as future recommendations.
Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child’s body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child’s current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child’s growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child’s obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.
Risk is a basic term for any decision making framework which can be defined as a probability or threat of damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities. For having a wide range of risks, Ready Made Garments (RMG) sector faces different difficulties in accomplishing their missions and goals. This paper has identified different risks associated with internal supply chain of the RMG sector. To achieve the expected outcome from this study, a renowned apparel factory named TALISMAN (BD), a branch of FCI GROUP, has been visited. 58 risks in 11 different departments have been identified by discussing about the process with the experts and experienced personnel of those departments along with the previous historical data of that factory. Analytical Hierarchy Process (AHP) has been used to identify the most severe risk of each department. Some corrective actions have also been suggested to eliminate the most severe risks of all department to enhance the overall efficiency of the supply chain.
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