scholarly journals Effectiveness of the “Sterius 60” SHF Radiation Installation for Disinfection of Objects Contaminated with PBA of Groups I–IV, when Working with Infected Biomodels

2021 ◽  
pp. 53-58
Author(s):  
V. G. Germanchuk ◽  
A. P. Semakova ◽  
O. A. Lobovikova ◽  
M. V. Gordeeva ◽  
N. Yu. Shavina ◽  
...  
Keyword(s):  
Model Test ◽  
Bacillus Stearothermophilus ◽  
Alcaligenes Faecalis ◽  
Medical Waste ◽  
Study Data ◽  
Laboratory Animals ◽  
Waste Generation ◽  
Microwave System ◽  
Bacillus Subtilus ◽  
Radiation Installation

The aim was to evaluate the effectiveness of using the “Sterius 60” microwave disinfection system (Russia) for decontamination of objects infected with PBA of groups I–IV emerging as a result of working with infected laboratory animals.Materials and methods. Effectiveness verification of disinfection of biological waste generated as a result of the life of laboratory animals by SHF radiation was carried out in the microwave system “Sterius 60”, recommended by the manufacturer for disinfection of epidemiologically hazardous and extremely dangerous medical waste, including biological ones (classes B and C), by volumetric SHF heating. Carcasses of uninfected laboratory animals (white mice, Guinea pigs, suckling rabbits), granulated feed and bedding material (wood shavings), which are objects directly in contact with biomodels, were used as vivarium waste to be decontaminated. The following microorganisms were utilized as model test ones: Bacillus subtilus VKM B-911, Bacillus stearothermophilus VKM B-718, Bacillus licheniformis G VKM B-1711-D, Alcaligenes faecalis 415, Yersinia pestis EV, Bacillus anthracis STI. Laboratory utensils (plastic Petri dishes, porcelain mortars and pestles) were used as a mock-up chamber filler for model test microorganisms.Results and discussion. As a result of the study, data were obtained indicating that the microwave system for disinfection of medical waste “Sterius 60” is ineffective for decontamination of biological waste in laboratories working with biomodels infected with PBA of groups I–II. The established standard mode of disinfection of this system was effective only for non-spore forms of microorganisms, pathogenicity groups III–IV. Therefore, in our opinion, it is advisable to use it for decontamination of laboratory utensils infected with PBA of groups III–IV, directly at sites of waste generation.

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania

2018 ◽  
Vol 36 (5) ◽  
pp. 454-462 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Tomas Ruzgas ◽  
Gintaras Denafas
Keyword(s):  
Time Series ◽  
Mathematical Modelling ◽  
Error Rate ◽  
Medical Waste ◽  
Waste Generation ◽  
Prediction Horizon ◽  
Modelling Method ◽  
Forecasting Method ◽  
Hybrid Forecasting

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used ‘pure’ time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%–4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models’ abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

2016 ◽  
pp. 224-251 ◽  
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

2020 ◽  
pp. 990-1012
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Structure Development for Effective Medical Waste and Hazardous Waste Management System

2020 ◽  
pp. 830-854
Author(s):  
Nilgün Cılız ◽  
Hacer Yıldırım ◽  
Şila Temizel
Keyword(s):  
Waste Management ◽  
Hazardous Waste ◽  
Management System ◽  
Solid Waste Management ◽  
Medical Waste ◽  
Study Data ◽  
Point Of View ◽  
Hazardous Waste Management ◽  
Waste Management System ◽  
Statistical Institute

Management of medical and hazardous wastes is a serious problem especially for developing countries. People are not aware of possible threats and/or they are afraid of the cost of application. Rapid population growth leads municipalities towards proper solid waste management applications. In this study, data were collected from the Turkish Statistical Institute and a general framework was drawn for medical and hazardous waste amounts and disposal methods. Starting from this point of view, the authors analyzed both the Regulation on Control of Hazardous Waste and the Regulation on Control of Medical Waste applied in Turkey. Taking into account all of these factors, this chapter is intended to develop the medical and hazardous waste management system economically and environmentally including waste generation, collection, transportation, disposal and treatment activities. Additionally, it investigates the reasons for lack of proper application of the regulations in light of the statistical data.

Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

2020 ◽  
pp. 808-829
Author(s):  
J. Jagan ◽  
Yıldırım Dalkiliç ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
System Modeling ◽  
Medical Waste ◽  
Least Square ◽  
Support Vector ◽  
Waste Generation ◽  
Nonlinear System Modeling ◽  
Hospital Capacity ◽  
Regression Techniques ◽  
Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

Structure Development for Effective Medical Waste and Hazardous Waste Management System

2016 ◽  
pp. 303-327 ◽  
Author(s):  
Nilgün Cılız ◽  
Hacer Yıldırım ◽  
Şila Temizel
Keyword(s):  
Waste Management ◽  
Hazardous Waste ◽  
Management System ◽  
Solid Waste Management ◽  
Medical Waste ◽  
Study Data ◽  
Point Of View ◽  
Hazardous Waste Management ◽  
Waste Management System ◽  
Statistical Institute

Management of medical and hazardous wastes is a serious problem especially for developing countries. People are not aware of possible threats and/or they are afraid of the cost of application. Rapid population growth leads municipalities towards proper solid waste management applications. In this study, data were collected from the Turkish Statistical Institute and a general framework was drawn for medical and hazardous waste amounts and disposal methods. Starting from this point of view, the authors analyzed both the Regulation on Control of Hazardous Waste and the Regulation on Control of Medical Waste applied in Turkey. Taking into account all of these factors, this chapter is intended to develop the medical and hazardous waste management system economically and environmentally including waste generation, collection, transportation, disposal and treatment activities. Additionally, it investigates the reasons for lack of proper application of the regulations in light of the statistical data.

Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR

2016 ◽  
pp. 1-18
Author(s):  
Jagan J. ◽  
Pijush Samui ◽  
Barnali Dixon
Keyword(s):  
Bayesian Learning ◽  
Piecewise Linear ◽  
Regression Function ◽  
Relevance Vector Machine ◽  
Medical Waste ◽  
Waste Generation ◽  
Learning Framework ◽  
Adaptive Regression ◽  
Regression Techniques

The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.

scholarly journals STUDY OF BIO-MEDICAL WASTE GENERATION AND MANAGEMENT IN VARIOUS HOSPITALS IN DAVANGERE CITY OF KARNATAKA, INDIA

2013 ◽  
Vol 03 (03) ◽  
pp. 022-024
Author(s):  
S. Thirumala
Keyword(s):  
Medical Waste ◽  
Survey Result ◽  
Waste Generation ◽  
Biomedical Waste ◽  
Research Article

AbstractThis research article is to survey the practice of biomedical waste such as collection, storage, transportation and disposal along with the amount of generated biomedical waste in various hospitals in Davangere city, and create awareness among the staff and patient about biomedical wastes. The survey result on biomedical waste generation, disposal and methods adopted in various hospitals of Davangere city are discussed.

Forecasting medical waste generation using short and extra short datasets: Case study of Lithuania

2016 ◽  
Vol 34 (4) ◽  
pp. 378-387 ◽  
Author(s):  
Aistė Karpušenkaitė ◽  
Tomas Ruzgas ◽  
Gintaras Denafas
Keyword(s):  
Medical Waste ◽  
Waste Generation
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