Study on the Centralization Strategy of the Blood Allocation among Different Departments within a Hospital

Hospital Management System Project

Study on the Centralization Strategy of the Blood Allocation among Different Departments within a Hospital

This paper mainly studies on how to distribute theblood items among different departments within a hospital. Theimproper allocation of blood in hospital at present could causesevere shortage and wastage of blood resource, which mayendanger patient’s lives and impose considerable costs onhospital. In order to solve this problem, we investigate the novelallocation method by centralizing the blood inventory of somedepartments. This paper illustrates the centralization principle inhospital, and formulates the integer programming model to workout the optimal allocation network scheme and the optimalinventory setting for every department. The results of thenumerical example demonstrate that this centralization methodcould considerably reduce blood shortage and wastage in hospitalby about 80% and 28% respectively. Furthermore, it coulddecrease the total cost by about 5000 Yuan in the hospital andimprove the effect of some certain surgeries by transfusing thefresh blood to patients. Code Shoppy

Human blood is a scarce resource due to its irreplaceabilityand perishability. It is paramount that blood is available forvarious departments in hospital, since the shortage of freshblood may postpone the scheduled surgeries and make thehospital fail to satisfy the requirement of the emergencydeliveries and consequently endanger patients’ lives. Thewastage of blood in hospital could also impose extraconsiderable costs on blood sampling, testing, transportationand inventory. Furthermore, recent studies show that fresherblood products are crucial for transfusion purpose, which couldbring better result for certain types of patient groups [2].Therefore, minimizing shortage and wastage of the blooditems and reducing the age of transfused blood items are themain tasks related to the management of blood. One effectiveway to solve this problem is to reshuffle the structure of bloodinventory and build appropriate blood allocation model byintegrating the blood inventory requirements of somedepartments in hospital, rather than attaining blood items fromhospital’s inventory respectively. What’s more, the amount ofblood used for a medical procedure is prone to beoverestimated for safety issues. Combining the bloodrequirements of different departments could avoid theaforementioned issue effectively, by which the shortage ofblood in some departments could be supplied by the wastage inother departments.The rest of the paper is organized as follows. Section tworeviews the related literature. Section three illustrates thecentralization principle in integrating the blood inventoryrequirements of some departments in hospital, and thenformulates the model used to work out the optimal allocationnetwork scheme and the optimal inventory set for everydepartment. Section four presents a numerical example of ahospital in Shanghai to verify the availability of the applicationof centralizing blood inventory and the final optimal bloodallocation network scheme. Section five offers conclusions andopportunities for future work.
In this section, we list the data we collected in our analysisand discuss the numerical results obtained from above modelsusing IBM ILOG CPLEX 12.1 and MATLAB 2013b softwareand numerical methods.A. DataHere we present a numerical example of a hospital inShanghai to verify the availability of the centralization methodand the final optimal blood allocation network policy. The datain this numerical example is given in table 5.We adopt the historical blood data of 24 months from 2015to 2016 on eleven departments’ the applied amount of bloodand the actual amount of blood transfused to patients in thathospital, and then calculate the average value of every item andget the data table according to the first step of centralizationprinciple
B. Numerical ResultsBy using IBM ILOG CPLEX 12.1 and MATLAB 2013bsoftware and numerical methods to solve our model, we obtainthe results as Table7 and Fig4.Table 7 compares the results of the case that alldepartments receive blood items from hospital’s inventorydirectly (case 1) with the centralized case which we attainedfrom our model (case 2).Fig4 shows that the optimal allocation network ofcentralizing the setting inventory of departments. Thedepartment labeled with a circle in figure 4 means that it holdsthe blood inventory in its group. As can be noted, the total cost z is decreased by more than5000 Yuan after centralizing some certain departments’ bloodinventory. And the centralization reduces the wastage inhospital by about 28% remarkably and the shortage by almost80% considerably, since the shortage of blood in somedepartments could be supplied by the wastage in otherdepartments. This proves that the centralization could helphospital save costs on purchasing unnecessary blood items andimprove the effect of emergency surgeries by transfusing freshblood. Therefore, the availability of the centralization methodhas been verified. https://codeshoppy.com/shop/product/hospital-management-mobile-app/

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TRACKING USING DEEP NEURAL NETWORKS

TRACKING USING DEEP NEURAL NETWORKS

Dynamic vehicle detection and tracking can provide essential data to solve the problem of road planning and traffic management. A method for real-time vehicle detection and tracking using deep neural networks is proposed in this paper and a complete network architecture is presented. Using our model, you can obtain vehicle candidates, vehicle probabilities, and their coordinates in real-time. The proposed model is trained on the PASCAL VOC 2007 and 2012 image set and tested on ImageNet dataset. By a carefully design, the detection speed of our model is fast enough to process streaming video. Experimental results show that proposed model is a real-time, accurate vehicle detector, making it ideal for computer vision application.

Introduction

In today’s society, more and more vehicles are taking to the highways every year, which makes a push to monitor and control the traffic more efficiently. The real-time vehicle detection and tracing is essential for intelligent road routing, road traffic control, road planning and so on. Therefore, it is important to know the road traffic density real time, especially in mega cities for signal control and effective traffic management. For a long time, several approaches[1,2] in the literature have been proposed to resolve the problem of various moving vehicles; Nevertheless, the aim of real-time fully-automatic detection of vehicle is far from being attained as it needs improvement in detection and tracking for accurate prediction with faster processing speed. Zheng et al. use brake lights detection through color segmentation method to generate vehicle candidates and verify them through a rule-based clustering approach. A tracking-by-detection scheme based on Harris-SIFT feature matching is then used to learn the template of the detected vehicle on line, localize and track the corresponding vehicle in live video [2]. It is a good measure to extract vehicle areas, however, it needs a relatively ideal background. Wei Wang et al. have presented a method of multi-vehicle tracking and counting using a fisheye camera based on simple feature points tracking, grouping and association. They integrates low level feature-point based tracking and higher level “identity appearance” and motion based real-time association [1]. However, the average processing time of it is around 750ms, which is not fast enough to achieve the real-time processing. System based Convolutional Neural Networks (CNN) can provide the solution of many contemporary problems in vehicle detection and tracing. CNN currently outperform other techniques by a large margin in computer vision problems such as classification [3] and detection [4]. 

The training procedure of CNN automatically learn the weights of the filters, so that they are able to extract visual concepts from raw image content. Using the knowledge obtained through the analysis of the training set containing labelled vehicle and non-vehicle examples, vehicle can be identified in given images. In general, Convolutional Neural Networks show more promising results. In this paper, we propose a method of real-time vehicles detection and tracking using Convolutional Neural Networks. We present a network architecture, which create multiple vehicle candidates and predict vehicle probabilities in one evaluation. Our architecture uses features from the entire image to create vehicle candidates. Firstly, we use convolutional layers of the system to extract features from the raw image. Secondly, we use four kinds of inception modules. Thirdly, we add Spatial Pyramid Pooling (SPP) layer between convolutional layers and fully connected layers, which is able to resize any images into fixed size. Lastly, the fully connected layers predict the probability and coordinates of vehicles.

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Touch Based Digital Ordering System on Android using GSM and Bluetooth for Restaurants

Touch Based Digital Ordering System on Android using GSM and Bluetooth for Restaurants

With rapid economic and technology development people’s living standard has improved. In almost every area, technology has changed the traditional ways of doing things and has made the life easier and more convenient. However, food industry still lags behind other industries in adopting new technology, especially automation in various processes. Even today many restaurants follow completely manual process of pen and paper in food ordering. In traditional pen and paper method, the waiter notes down the orders from customers, takes these orders to the kitchen, updates them in records, delivers the ordered items at the appropriate table and then makes the bill. This system is conventional and too sluggish. It requires more manpower and thus is prone to human errors. Apart from errors, it consumes a lot of time. It disturbs the serenity of a eating and hangout place and results in chaos. So, this system often leads to dissatisfaction among the customers, as sometimes time taken by waiter for taking order is very long. In recent past, some systems like PDA based system, KIOSK technology, and multi touchable restaurants management systems were developed to automate the food ordering process. However, the results of these systems were not up to the expectation. They provided uninformative and unattractive menu details, and they were also costly to adopt. To overcome these limitations of the system, a touch based digital ordering using an android application is proposed to automate the food-ordering process. This system provides an attractive user interface through the android application. It also provides the images of all the menu items along with their prices so that it becomes easier for the customer to order. It has the facility to give customized personal message to the manager for the food order. It allows the customer to give feedback to the manager. It also allows the customer to call a waiter, using the android application, for help.

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