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刊名简称刊名全称ISSN小类名称（中文）0269-9648工程：工业PROBABILITY IN THE ENGINEERING AND INFORPROBAB ENG INFORAPPL MATH MODEL A PPLIED MATHEMATICAL MODELLING0307-904X工程：综合0927-6467工程：综合RUSS J NUMER ANARUSSIAN JOURNAL OF NUMERICAL ANALYSIS AN1239-6095环境科学ANNALES ACADEMIAE SCIENTIARUM FENNICAE-MBOREAL ENVIRON R0167-9473计算机：跨学科应用COMPUT STAT DATACOMPUTATIONAL STATISTICS & DATA ANALYSISJ COMB OPTIM JOURNAL OF COMBINATORIAL OPTIMIZATION1382-6905计算机：跨学科应用0094-9655计算机：跨学科应用J STAT COMPUT SIJOURNAL OF STATISTICAL COMPUTATION AND S1387-3954计算机：跨学科应用MATH COMP MODELMATHEMATICAL AND COMPUTER MODELLING OF DMATHEMATICAL AND COMPUTER MODELLING0895-7177计算机：跨学科应用MATH COMPUT MODEMATHEMATICS AND COMPUTERS IN SIMULATION0378-4754计算机：跨学科应用MATH COMPUT SIMUQUEUEING SYST QUEUEING SYSTEMS0257-0130计算机：跨学科应用1615-3375计算机：理论方法FOUNDATIONS OF COMPUTATIONAL MATHEMATICSFOUND COMPUT MATFUZZY SET SYSTFUZZY SETS AND SYSTEMS0165-0114计算机：理论方法COMPUT COMPLEXCOMPUTATIONAL COMPLEXITY1016-3328计算机：理论方法STAT COMPUT STATISTICS AND 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TIME SER ANAL J OURNAL OF TIME SERIES ANALYSIS0143-9782统计学与概率论LIFETIME DATA ANALYSIS1380-7870统计学与概率论LIFETIME DATA ANMATH POPUL STUD M athematical Population Studies0889-8480统计学与概率论METHODOL COMPUT1387-5841统计学与概率论METHODOLOGY AND COMPUTING IN APPLIED PROMETRIKA METRIKA0026-1335统计学与概率论PAK J STAT Pakistan Journal of Statistics1012-9367统计学与概率论0269-9648统计学与概率论PROBABILITY IN THE ENGINEERING AND INFORPROBAB ENG INFORRevista Colombiana de Estadistica0120-1751统计学与概率论REV COLOMB ESTADREVSTAT-STAT JREVSTAT-Statistical Journal1645-6726统计学与概率论SCAND ACTUAR JScandinavian Actuarial Journal0346-1238统计学与概率论Statistical Methods and Applications1618-2510统计学与概率论STAT METHOD APPLSTAT MODEL STATISTICAL MODELLING1471-082X统计学与概率论STAT NEERL STATISTICA NEERLANDICA0039-0402统计学与概率论STAT PAP STATISTICAL PAPERS0932-5026统计学与概率论STATISTICS & PROBABILITY LETTERS0167-7152统计学与概率论STAT PROBABIL LESTAT SINICA STATISTICA SINICA1017-0405统计学与概率论STATISTICS STATISTICS0233-1888统计学与概率论STOCH ANAL APPL S TOCHASTIC ANALYSIS AND APPLICATIONS0736-2994统计学与概率论STOCH DYNAM Stochastics and Dynamics 0219-4937统计学与概率论STOCH MODELS STOCHASTIC MODELS1532-6349统计学与概率论1744-2508统计学与概率论STOCHASTICS Stochastics-An International Journal ofSURV METHODOL Survey Methodology 0714-0045统计学与概率论0040-585X统计学与概率论THEORY OF PROBABILITY AND ITS APPLICATIOTHEOR PROBAB APPUTILITAS MATHEMAUTILITAS MATHEMATICA0315-3681统计学与概率论Journal of Noncommutative Geometry1661-6952物理：数学物理J NONCOMMUT GEOMJ NONLINEAR SCI J OURNAL OF NONLINEAR SCIENCE0938-8974物理：数学物理MULTISCALE MODELING & SIMULATION1540-3459物理：数学物理MULTISCALE MODELINVERSE PROBL INVERSE PROBLEMS0266-5611物理：数学物理Inverse Problems and Imaging1930-8337物理：数学物理INVERSE PROBL IMAdvances in Applied Clifford Algebras0188-7009物理：数学物理ADV APPL CLIFFOR1935-0090物理：数学物理Applied Mathematics & Information SciencAPPL MATH INFORM1559-3940物理：数学物理Communications in Applied Mathematics anCOMM APP MATH COCOMPUTATIONAL MATHEMATICS AND MATHEMATIC0965-5425物理：数学物理COMP MATH MATH PINFINITE DIMENSIONAL ANALYSIS QUANTUM 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机器学习模型在金融领域的应用研究（英文中文双语版优质文档）In recent years, the application of machine learning in the financial field has become more and more widespread. As financial markets become more complex, machine learning models can more accurately predict financial market trends and make investment decisions. This article will discuss the application research of machine learning in the field of finance.1. Financial Risk ManagementFinancial risk management is one of the most important tasks in the financial field. Machine learning can be used to predict different types of risk, such as credit risk, market risk, and operational risk. Using machine learning models, financial institutions can more accurately assess different types of risks and take appropriate measures to reduce them.2. Stock Price PredictionStock price forecasting is one of the most important problems in finance. Machine learning can be used to predict trends and changes in stock prices. For example, deep learning-based recurrent neural networks can be utilized to predict future stock prices. These models can be trained using large amounts of historical data to more accurately predict future trends.3. Quantitative investment strategyMachine learning can be used to develop quantitative investment strategies. Quantitative investment strategies are based on mathematical and statistical methods, using a large amount of historical data to formulate investment strategies. Using machine learning, you can more accurately predict stock price changes and market trends, so as to formulate more accurate investment strategies.4. Financial Fraud DetectionFinancial fraud detection is one of the important problems in the financial field. Using machine learning, financial transactions can be monitored and analyzed in real time to detect possible fraud. For example, deep learning-based neural networks can be used to identify fraudulent behavior and take corresponding measures to prevent it from happening.5. Credit EvaluationCredit assessment is another important problem in the financial field. Machine learning can be used to predict a borrower's creditworthiness and default risk. Using machine learning models, the personal information and historical data of borrowers can be automatically analyzed and evaluated to more accurately predict their default risk.In general, machine learning is widely used in the financial field, which can help financial institutions more accurately assess risks, predict market trends and formulate investment strategies, as well as detect fraudulent behavior and assess the credit level of borrowers. With the continuous development of technology and the continuous growth of data, the application prospect of machine learning in the financial field is very broad.In addition to the above-mentioned applications, there are other emerging applications, such as using machine learning to predict exchange rate changes, analyze market sentiment, and predict financial crises. These applications can help investors better understand the market and make more informed investment decisions.However, machine learning models are not perfect and there are some challenges and issues. For example, the interpretability of the model is insufficient, there may be problems with the quality and reliability of the data, overfitting and underfitting, etc. Therefore, when applying a machine learning model, it is necessary to carefully evaluate and adjust the model, and to continuously optimize and improve it in practice.To sum up, the application of machine learning in the financial field is very important and promising. With the continuous development of technology and the continuous growth of data, machine learning models will be able to more accurately predict market trends and make investment decisions, helping investors and financial institutions gain an advantage in the highly competitive market.近年来，机器学习在金融领域的应用越来越广泛。
119近年来，随着新型采油管理区的建设，油气生产信息化手段更加丰富，为稠油油藏的管理提供了强有力的技术支持。
依托四化等信息辅助系统，实时提取功图、地面等多项参数，结合现场管理经验，利用判别分析等统计学方法建立油井正常工况与异常工况预警模型[1]。
目前针对稠油油井，利用多参数统计方法建立工况预警模型的研究相对较少，且研究时将各影响参数孤立。
面对稠油开发易出现出砂、汽窜、油稠、断脱等异常工况问题，如何降低工况异常造成的产量损失，保持油田稳产已成为当前的重要工作，有必要利用信息化手段，对稠油井况预警模型进行深入研究。
1 稠油井况参数统计分析1.1 示功图参数示功图是载荷随位移的变化关系曲线所构成的封闭曲线图，主要包括最小载荷、最大载荷、面积、功图形状等参数，除功图形状之外均可以量化。
A区块最小载荷分布以11~20kN和21~30kN 这两个区间为主，合计占81%。
最大载荷分布以91kN以上这一区间为主，占42%。
71~80kN以及81~90kN这两个区间合计占比50%。
功图面积分布以201~300区间为主，占比达43%；0~100区间分布较小，占比为12%。
1.2 示功图形状将工况异常井的示功图进行分类统计，提取问题井典型示功图，对稠油井常出现的出砂、汽窜、泵漏、断脱、油稠等异常工况进行描述，建立其相应图版。
（1）出砂。
示功图左下为尖镰刀状，表现为泵筒内无液柱，载荷在下死点附近才卸载，液面接近泵深。
如果泵的入口受到阻塞或有流体供应不足，会导致泵筒内无液柱形成，液位接近泵深。
这可能是由于管道堵塞、阀门关闭或进水源出现问题等原因引起的。
如果泵的装置不正确，例如进口管道截面积太小或泵的位置不正确，会导致泵无法充分吸入液体形成液柱，使载荷在下死点附近才卸载。
这可能需要重新检查和调整泵的安装[2]。
（2）泵漏。
泵漏井示功图整体图形与正常时变化不大，最大载荷变小，最小载荷变大，形状稠油井工况判断智能预警模型的应用武杰中国石化胜利油田石油开发中心 山东 东营 257000摘要：对于蒸汽吞吐开发的稠油油藏，易出现出砂、汽窜、套坏等异常工况问题，如何利用油井工况参数建立有效的预警模型，并智能判断处置从而降低异常工况造成的产量损失已成为油田稳产的关键。
数学与科学如何将两门学科结合起来数学和科学是两个密切相关且相辅相成的学科，它们之间存在着紧密的联系与依存关系。
数学提供了科学研究所需的工具和方法，而科学则为数学提供了实际应用的场景和问题。
通过将数学与科学结合起来，我们能够更加全面地理解和解决现实世界中的问题。
下面将从几个方面阐述数学与科学的结合。
一、数学在科学中的应用科学研究需要数据的收集、处理和分析，而这些过程中离不开数学的应用。
数学提供了丰富的统计学方法和模型，可以对实验数据进行有效的整理和分析。
例如，科学家可以使用数学上的概率和统计模型来推断和预测实验结果，从而更好地解释和理解观察到的现象。
此外，数学还为科学实验中的测量提供了精确和可靠的方法，如误差分析和不确定性计算。
另一方面，数学在科学建模中也扮演着重要的角色。
科学家通过建立各种数学模型来描述和解释自然现象，从而揭示其背后的规律和原理。
这些数学模型可以是微分方程、线性代数、概率论等的数学表达式，通过求解这些方程，科学家能够预测和控制自然现象的发展变化。
例如，数学模型在物理学中的应用非常广泛，它们可以解释光的传播、电磁波的干涉和衍射现象等，为物理学的研究提供了理论基础。
二、科学启发数学的发展科学的研究对象通常是实际现象和问题，而这些问题往往能够启发和激发数学的发展。
科学家在研究各种现象时，往往需要将问题转化为数学形式，从而应用已有的数学理论和方法进行分析。
这种实际问题的需求推动了数学的发展和创新。
例如，微积分的发展正是受到物理学中运动和变化问题的启发，而概率论的发展则是源于赌局和游戏中的随机事件分析。
科学问题为数学提供了实际应用的场景和挑战，推动了数学理论的不断完善和深化。
数学与科学的结合也在实际应用中起到了重要的作用。
例如，现代工程中的计算机模拟和优化设计往往需要大量的数学计算，通过对科学问题的数学建模和分析，可以得到最优解、最快速度和最低成本的方案。
在金融领域，数学模型和算法被广泛应用于风险评估、投资组合优化等问题的解决。
【bioinfo】⽣物信息学——代码遇见⽣物学的地⽅注：从进⼊⽣信领域到现在，已经过去快8年了。
⽣物信息学包含了我最喜欢的三门学科：⽣物学、计算机科学和数学。
但是如果突然问起，什么是⽣物信息学，我还是⽆法给出⼀个让⾃⼰满意的答案。
于是便有了这篇博客。
起源据说在1970年，荷兰科学家Paulien Hogeweg和Ben Hesper最早在荷兰语中创造了"bioinformatica"⼀词，英语中的"bioinformatics" 在1978年⾸次被使⽤。
这两位科学家当时使⽤该词来表⽰：The study of information processes in biotic systems.该定义中有两个关键词：⽣物系统（biotic systems）和信息过程（information processes）。
但是这⾥的"信息过程"不太好理解。
此外，从该领域的著名期刊——"bioinformatics"期刊名称的变化也可以从另⼀个⾓度来考证"⽣物信息学"这个词的接受程度。
"bioinformatics"创⽴于1985年，改名前的期刊名为：Computer Applications in the Biosciences (CABIOS)同时也是国际计算⽣物学会（the International Society for Computational Biology, ISCB）的会刊，在1998年改为现在的名字。
各个不同时期的定义wiki【定义1】⾸先看⼀下维基百科对⽣物信息学的解释：Bioinformatics /ˌbaɪ.oʊˌɪnfərˈmætɪks/ (About this soundlisten) is an interdisciplinary field that develops methods and softwaretools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines biology, computerscience, information engineering, mathematics and statistics to analyze and interpret biological data. Bioinformatics has beenused for in silico analyses of biological queries using mathematical and statistical techniques.Bioinformatics and computational biology involve the analysis of biological data, particularly DNA, RNA, and proteinsequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the HumanGenome Project and by rapid advances in DNA sequencing technology.The primary goal of bioinformatics is to increase the understanding of biological processes.这⾥的定义强调交叉学科以及对⽣物学数据的理解，认为最主要的⽣物学数据是DNA、RNA和蛋⽩质的序列数据。
英文原文Optimize the reliability of mechanical structure designIt is now generally recognized that structural and mechanical problems are nondeterministic and, consequently, engineering optimum design must cope with un-certainties，Reliability technology provides tools for formal assessment and analysis of such uncertainties，Thus, the combination of reliability-based design procedure sand optimization promises to provide a practical optimum design solution, i，e，, a de-sign having an optimum balance between cost and risk，However, reliabilty-based structural optimization programs have not enjoyed the name popularity as their deterministic counterparts，Some reasons for this are suggested，First, reliability analysis can be complicated even for simple systems，There are various methods for handling the uncertainty in similar situations (e，g，, first order second moment methods, full distribution methods)，Lacking a single method, individuals are likely to adopt separate strategies for handling the uncertainty in their particular problems，This suggests the possibility of different reliability predictions in similar structural design situations，Then, there are diverging opinions on many basic issues, from the very definition of reliability-based optimization, including the definition of the optimum solution, the objective function and the constraints, to its application in structural design practice，There is a need to formally consider these itess in the merger of present structural optimization research with reliability-based design philosophy。
定量分析的英文名词解释Quantitative Analysis: An English Term ExplanationIntroductionThe field of quantitative analysis is a methodical approach that involves the examination and interpretation of data using mathematical and statistical techniques. It plays a crucial role in various disciplines, including finance, economics, business management, and scientific research. This article aims to provide a comprehensive explanation of the English term "quantitative analysis" by exploring its definitions, applications, and key components.Defining Quantitative AnalysisQuantitative analysis refers to the systematic process of analyzing numerical data to uncover patterns, relationships, and trends. Unlike qualitative analysis, which focuses on subjective observations and interpretations, quantitative analysis utilizes objective measurements and mathematical calculations to derive meaningful insights. By quantifying data through statistical models and mathematical formulas, this analytical approach enables researchers and decision-makers to make informed judgments based on empirical evidence.Applications of Quantitative Analysis1. Financial Analysis:Quantitative analysis plays a vital role in the field of finance. Analysts utilize various quantitative techniques to assess investments, evaluate risk, and make strategic decisions. For instance, by using financial ratios and mathematical models, analysts can analyze the performance and stability of companies, determine the fair value of stocks, and predict future market trends.2. Economics:Economists heavily rely on quantitative analysis to study economic phenomena and formulate economic policies. By analyzing economic indicators such as GDP, inflation rates, and unemployment rates, economists can assess the health of economies, predict future trends, and propose effective strategies for economic growth.3. Market Research:Quantitative analysis is widely used in market research to gather and interpret consumer data. Surveys and questionnaires are designed to collect quantitative data, which is then analyzed to understand consumer preferences, behavior patterns, and market trends. Statistical techniques, such as regression analysis and hypothesis testing, enable researchers to identify correlations, test hypotheses, and make predictions.Components of Quantitative Analysis1. Data Collection:The first step in quantitative analysis involves collecting relevant data. This can be done through various methods, such as surveys, experiments, or secondary data sources. It is crucial to ensure the accuracy, reliability, and representativeness of the data collected, as the quality of the analysis heavily relies on the quality of the data.2. Data Analysis:Once the data is collected, it is processed and analyzed using statistical techniques and mathematical models. Descriptive statistics, such as mean, median, and standard deviation, provide insights into the central tendencies and variability of the data. Inferential statistics allow researchers to draw conclusions and make predictions based on a sample of data.3. Data Interpretation:The final step of quantitative analysis involves interpreting the results. This requires critically evaluating the findings, identifying patterns or relationships, and drawing meaningful conclusions. Proper interpretation of quantitative analysis is essential to ensure that the insights gained from the data are relevant, valid, and actionable.ConclusionQuantitative analysis is a valuable tool used across various disciplines to analyze numerical data and derive meaningful insights. Its applications extend to finance, economics, market research, and beyond. By utilizing mathematical and statistical techniques, researchers and decision-makers can make informed judgments based on empirical evidence. Understanding the components and applications of quantitative analysis is essential for those who seek to effectively analyze and interpret numerical data.。
This article was downloaded by: [Wayne State University]On: 10 April 2015, At: 08:18Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UKJournal of Environmental Science and Health, PartA: T oxic/Hazardous Substances and EnvironmentalEngineeringPublication details, including instructions for authors and subscription information:/loi/lesa20Mathematical-Statistical Models of GeneratedHazardous Hospital Solid WasteA. R. Awad a , M. Obeidat b & M. Al-Shareef ba Department of Environmental Engineering , Tishreen University , Lattakia , Syriab Department of Civil Engineering , Jordan University of Science & T echnology , Irbid ,JordanPublished online: 28 Mar 2012.PLEASE SCROLL DOWN FOR ARTICLEJOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTHPart A—Toxic/Hazardous Substances &Environmental EngineeringVol.A39,No.2,pp.315–327,2004Mathematical-Statistical Models of GeneratedHazardous Hospital Solid WasteA.R.Awad,1,*M.Obeidat,2and M.Al-Shareef 21Department of Environmental Engineering,Tishreen University,Lattakia,Syria 2Department of Civil Engineering,Jordan University of Science &Technology,Irbid,JordanABSTRACTThis research work was carried out under the assumption that wastes generatedfrom hospitals in Irbid,Jordan were hazardous.The hazardous and non-hazardous wastes generated from the different divisions in the three hospitalsunder consideration were not separated during collection process.Threehospitals,Princess Basma hospital (public),Princess Bade’ah hospital (teaching),and Ibn Al-Nafis hospital (private)in Irbid were selected for this study.Theresearch work took into account the amounts of solid waste accumulated fromeach division and also determined the total amount generated from each hospital.The generation rates were determined (kilogram per patient,per day;kilogramper bed,per day)for the three hospitals.These generation rates were comparedwith similar hospitals in Europe.The evaluation suggested that the currentsituation regarding the management of these wastes in the three studied hospitalsneeds revision as these hospitals do not follow methods of waste disposals thatwould reduce risk to human health and the environment practiced in developedcountries.Statistical analysis was carried out to develop models for the prediction*Correspondence:A.R.Awad,Department of Environmental Engineering,Tishreen University,P.O.Box 1385,Lattakia,Syria;E-mail:adelawad@ or adel-a@.315DOI:10.1081/ESE-1200275241093-4529(Print);1532-4117(Online)Copyright &2004by Marcel Dekker, D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015of the quantity of waste generated at each hospital (public,teaching,private).In these models number of patients,beds,and type of hospital were revealed to be significant factors on quantity of waste generated.Multiple regressions were also used to estimate the quantities of wastes generated from similar divisions in the three hospitals (surgery,internal diseases,and maternity).Key Words:Evaluation;Generation rate;Hospital solid waste;Prediction models.INTRODUCTIONFor many years the World Health Organization [1,2]has advocated that hospital wastes should be regarded as special wastes and a Working Group on Hospital Waste Management was held in Bergen in 1983to produce recommendations on this subject.It is now commonly acknowledged that certain categories of medical waste are among the most hazardous and potentially dangerous of all wastes arising in the community.As the volume and complexity of health care waste increase,the risk of transmitting disease through unsatisfactory handling and disposal practices also increases.The recent rise in the incidence of diseases such as AIDS and Hepatitis B opens up the possibility of infection of personnel handling these wastes,and the widespread,unlawful use of drugs makes the need for proper disposal of used hypodermics and syringes imperative.The greatest risks,however,are associated with the handling and disposal of medical wastes,which may remain potent for considerable periods of time after being removed from their source.The dangers from health care wastes may be significantly increased in situations where the wastes are disposed of in conjunction with other municipal solid waste and not either sterilized or incinerated at source.The health hazard potential rises with secondary handling of the waste.For example,when a recycling process such as composting,refuse derived fuel,or sorting for the reclamation of glass,plastics,metal,paper,fabrics etc.,is used within the community,there will always be a risk of infection if medical wastes are not disposed of separately.[3]Since all wastes accumulated in hospitals in Irbid City (Jordan)are not separated into hazardous or nonhazardous wastes,our study assumed all wastes to be hazardous according to the EPA guide to pollution prevention which states:‘‘by current law,any waste mixture of non-hazardous and hazardous or infections and hazardous wastes must be handled all as hazardous wastes.’’[4]The objectives of the research work are:(a)to evaluate the systems applied in the collection,storage,transportation,and disposal of hospital wastes in the city of Irbid;(b)to determine the waste generation rate in hospitals as classified (public,teaching,private);(c)to develop a sound and representative mathematical,statistical models that describe the relationship between the quantity of waste generated and the effective factors (number of beds,patients,and type of hospitals).316Awad,Obeidat,and Al-ShareefD o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015MATERIALS AND METHODSThe three hospitals studied represented various types of hospitals in Irbid City.These hospitals are:(i)Princess Basma hospital;(ii)Princess Bade’ah hospital;and (iii)Ibn Al-Nafis hospital.All the solid wastes generated at these hospitals were weighed during two different periods of time.The first period for all three hospitals was 38days (March–April)while the second period consisted of 10days (June)of the same year.The generation rates for each department as well as for all the hospitals were determined.An investigation of the components of solid waste generated at various departments of Princess Basma hospital was also undertaken as a case study.A sample was taken randomly from each patient department through the period of observation to determine the components of the following solid wastes:(i)paper;(ii)plastics;(iii)needles and syringes;(iv)garbage;(v)glass;(vi)metals;and (vii)textiles.Moisture content was determined for each component by placing a sample weighing 500g in an oven and gradually increasing the temperature to100 C.The difference in weight divided by the wet weight (100%)was taken as the moisture content.By weighing the plastic bag and then placing the component of the plastic bag in a graduated container for volume reading,density of each plastic bag is determent as well.RESULTS AND DISCUSSIONEvaluation of Handling,Storage,Transport,and Disposal MethodsEvaluation of solid waste system is based on four environmental factors:sanitation,safety,security,and aesthetics that are considered to affect the health of hospital occupants and the general public.[3]Based on these factors and solid waste system applied at the hospitals in Irbid,the following observation were made.No separation of contaminated wastes is practiced at any of the hospitals under study.Therefore all solid wastes generated from the hospitals are considered contaminated wastes.They are not disposed off separately,which is not acceptable from the sanitation and security points of view.In general,the situation is considered very bad at the three hospitals.There are no storage containers available for on-site storage and all the plastic bags are placed in unsecured area causing odor problems at all the time.The plastic bags,which have to be loaded manually into the collection vehicle,expose the workers to various hazards from leakage,spillage,sharp objects,and needle pricks.The problem of manual loading of the plastic bags is the same at all the hospitals included in this study.All wastes from residential,commercial,and other areas are collected together with hospital wastes.No special handling of hospital wastes at landfill site (Al-akaider dumping site)is practiced and landfill workers and scavengers are generally unaware of the type of wastes they are dealing.Mathematical-Statistical Models of Hospital Waste 317D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015A summary of generation rates at different departments at the three hospitals is shown in Tables 1–3Generation rate at the three hospitals was found to be6.904kg/pat/day (4.315kg/bed/day)at Princess Basma hospital,5.718kg/pat/day (3.212kg/bed/day)at Princess Bade’ah hospital,and 4.532kg/pat/day (2.556kg/bed/day)at Ibn Al-Nafis hospital.The higher generation rate at Princess Basma hospital is due to the fact that the hospital is a general public teaching facility,which serves a large number of patients from Irbid city’s government.Princess Basma hospital accommodates a large number of patients compared with other hospitals in the study,which has a significant effect on generation rate.Also contributing to generation rate at hospitals is kitchen waste generated by staff whose shifts necessitate that they stay late at the hospital.Princess Bade’ah hospital has a generation rate close to that found at Princess Basma hospital.The hospital is a general public,teaching facility,and it is the largest maternity hospital in Irbid,serving a large number of patients close to the numbers served by Princess Basma hospital.The third hospital in this study,Ibn Al-Nafis hospital,is a private hospital with a few numbers of specialties.In addition it accommodates the lowest number of patients among the hospitals in this study.These two factors explain the low generation rate at this hospital.Table 1.Summary of generation rates at various departments at Princess Basma hospital.Department Average totalweight generated (kg/day)Average number of patients Generation rate (kg/pat/day)Percentage byweight (%)General surgery [men Àwomen]101.1055 1.83610.46Neurology 10.155 2.180 1.05Internal þCCU [men þwomen]83.6553 1.5908.65Orthopedic 8.3150.5680.86Nose,ear,throat 5.53120.4510.57Operating theatre 83.3017 4.8018.26Dialysis unit 31.5020 1.575 3.26Emergency 121.23110.38912.54Outpatient clinics 133.85460.24513.84X-ray unit 45–– 4.66Laboratory and blood bank49.98–– 5.17Kitchen 293.05140 2.103 3.032Total 966.560140 6.904100Total waste generated ¼966.56kg/day.Average number of patients ¼140patient.Number of beds ¼224bed.Generation rate ¼6.904kg/pat/day.Generation rate ¼4.315kg/bed/day.318Awad,Obeidat,and Al-ShareefD o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015A comparison has been made between the generation rates reached in hospitals under study and those reported in the literature.[2]On one hand,in Princess Basma hospital which is a teaching hospital,the generation rate was 4.315kg/bed/day,in Princess Bade’ah hospital,a maternity hospital,the generation rate was 3.212kg/bed/day.On the other hand in teaching hospitals in Europe [2]the generation rates were 3.9kg/bed/day in Norway,4.4kg/bed/day in Spain,3.3kg/bed/day in UK and France,while in maternity hospital they were 3.4kg/bed/day in Spain,and 3kg/bed/day in UK.It will be noted that the rates in both are approximately same.Another Table 3.Summary of generation rates at various departments at Ibn Al-Nafis hospital.Department Average total weight generated (kg/day)Averagenumber of patients Generation rate (kg/pat/day)Percentage byweight (%)Surgery þinternal [men]11.15120.907 5.68Surgery þinternal [women]þpediatric37.9518 2.17519.03Maternity 43.914 3.15822.02Operating theatre 23.26 4.25711.63Laboratory þX-ray unit 5.25–– 2.63Kitchen 77.9544 1.78039.09Total 199.4044 4.532100Total waste generated ¼199.4kg/day.Average number of patients ¼44patient.Number of beds ¼78bed.Generation rate ¼4.532kg/pat/day.Generation rate ¼2.556kg/bed/day.Table 2.Summary of generation rates at various departments at Princess Bade’ah hospital.Department Average totalweight generated (kg/day)Average number of patients Generation rate (kg/pat/day)Percentage byweight (%)Pediatric 68.730 2.30512.01Maternity 154.170 2.20626.95Emergency 76.51600.47913.38Out patient clinics 26.75930.287 4.68X-ray unit 6.00–– 1.05Laboratory 20.00–– 3.50Kitchen 219.75100 2.20538.43Total 571.8100 5.718100Total waste generated ¼571.8kg/day.Average number of patients ¼100patient.Number of beds ¼178bed.Generation rate ¼5.718kg/pat/day.Generation rate ¼3.212kg/bed/day.Mathematical-Statistical Models of Hospital Waste 319D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015comparison between the above mentioned generation rates in Princess Basma hospital and Princess Bade’ah hospital and the generation rate of 4.42kg/bed/day (by size of hospital:100–299number of beds)in American hospitals [2]showed even closer values.According to the previous discussions,the generation rate of solid waste at the hospitals,expressed as kg/pat/day,ranged from 4.532kg/pat/day at Ibn Al-Nafis hospital to 6.904kg/pat/day at Princess Basma hospital,such generation rates can be used to estimate the solid waste generation at all hospitals in Irbid.Moisture Content,Density,and Composition of Solid WasteA study was made to determine the moisture content,density,and composition of solid waste generated at the patients departments at Princess Basma hospital.All data are summarized in Table 4.The results of this study show that the percentage of paper product is the highest;the average percentage of paper product is 38.54%.Plastic item was the next highest percentage;the average percentage of plastic product is 27.25%.The glass and textiles item are the third in highest percentage,10.50%for glass,and 10.86%for textiles.The garbage percentage is 8.55%,metals and needles percentages are 2.8and 1.66%,respectively.Moisture content of paper ranged from 22to 57%for hospital departments,moisture content for plastic ranged from 11to 54%,moisture content for textiles ranged from 37to 68%,moisture content for garbage ranged from 37to 57%.The density of solid waste generated from hospital departments ranged from 174.43kg/m 3(Internal department)to 112.5kg/m 3(Operating theater).Development of Waste Quantity Prediction ModelsTo develop a sound and predictable model that can be used to describe or estimate the quantity of waste generated at any hospital,complete andTable 4.Summary of percentage,moisture content for thecomponents of solid wastes generated at all departments atPrincess Basma hospital without kitchen wastes.Component Percentage (%)Moisture content (%)Paper 38.5422–57Plastic 27.2511–54Textiles 10.8637–68Garbage 8.5537–57Needles 1.66–Metals 2.80–Glass 10.50–Total 100–320Awad,Obeidat,and Al-Shareef D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015comprehensive database must by gathered.In addition,all variables and factors that could affect the quantity of waste generated should be investigated and analyzed.Data related to the quantity of waste variable were collected throughout the two periods.It was assumed that the data collected in each period represents a separate population,therefore t -test was applied to check whether the two populations are identical or not.It was found that for all populations in all hospitals the absolute value of t was less than the value t (//2,n À2)obtained from t -distribution tables,which means that the two populations are identical and the data are representative,and thus are considered as if extracted from one population.The data can also be used for developing representative and predictable sound models.Establishing the simple correlation matrices between different variables is the first step in model development.This step is crucial for investigating the strength and form of the relationship between the variables included in the analysis.Next,a scattergram should be plotted to determine the ranges and the general trends of the included variables.This can be used in determining the suitable transformation for the variables included in the analysis.In this study,the important variables that effect the quantity of wastes generated from the hospitals have been identified,then simple linear and nonlinear,regression analysis was applied in order to develop predictable models that can be used in estimating or predicting the generated waste in Irbid hospitals.To accomplish this goal the statistical analysis system (SAS)version 6.07at Jordan University of Science and Technology computer center was used to develop these models.(SAS)Version 6.07is a powerful system,capable of handling regular or simple nonlinear and stepwise regression analysis.[5]Development of Waste Quantity Prediction Models for all HospitalsTo investigate the effect of different independent variables on the quantity of generated wastes,the correlation matrix between the dependent variable (quantity of waste generated)and different independent variables were established.It was found that quantity of waste has a strong correlation with the number of patients (R ¼0.973),number of beds (R ¼0.956),and type of hospital (R ¼0.368)variables.To investigate the effect of every single independent variable on the dependent variable,scatterplots of waste vs.number of patients,number of beds,and type of hospital were plotted as shown in Figs.1–3,respectively.Figure 1shows that the relationship between quantity of waste and the number of patients is approximately linear.The relationship between the quantity of waste and number of beds was also approximately linear.In spite of this many transformations,which may fit the data,were tried to develop the best model that can fit the data properly.After the application of these transformations,it was possible to develop the following model:WASTE ¼À17:77þ1:049ðPAT Þþ0:818ðBED Þþ12:22ðTYPE Þ:ð1Þwhere TYPE ¼0for public and teaching hospitals.¼1for private hospitals.Mathematical-Statistical Models of Hospital Waste 321D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015Figure 1.Scatterplot of quantity of waste vs.number ofpatients.Figure 2.Scatterplot of quantity of waste vs.number of beds.322Awad,Obeidat,and Al-ShareefD o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015It is worth mentioning herein that type of hospital was considered qualitative (dummy)variables.The other variables in this model (number of patients and number of beds)were considered quantitative.The developed model in Eq.(1)was found to be the best fit model.The statistical characteristics of this model are presented in Table 5.As shown in this table,the model has R 2value ¼0.943which indicates a good predictable model with high level of significance.Also,this model shows that the variables representing the number of patients (PAT)and number of beds (BED)are highly significant predictors of quantity of wastes in Irbid hospitals (level of /less then 0.10).Furthermore,Eq.(1)shows that the type of hospital has significant role in the model.The positive sign for type variable indicated that the quantity of wastes generated of private hospitals were higher than that from public hospitals.Development of Waste Quantity Prediction Model by Hospital TypeIn this study,data pertaining to each hospital type were analyzed separately,since each hospital type has it’s own environmental and operational characteristics.The adoption of this procedure was suggested to develop statistical models with high accuracy.Models representing the relationship between quantity of waste and both number of patients and number of beds variables were developed for public hospitals and private hospitals as follows:Public and teaching hospitalsWASTE ¼À17:22þ1:035ðPAT Þþ0:816ðBED Þð2ÞFigure 3.Scatterplot of quantity of waste vs.hospital type.Mathematical-Statistical Models of Hospital Waste 323D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015Private hospitalsWASTE ¼1:04Â10À3ðPAT Þ00:645ðBED Þ2:6:ð3ÞThe models in Eq.(2)for the public hospitals and the model in Eq.(3)forprivate hospitals were selected to be the best fit models.The model in Eq.(2)was found to have value of R 2equal to 0.955and high levelof significance for both the model and the included independent variables( ¼0.0001)while the model in Eq.(3)was found to have an R 2equal to 0.800and high level of significance ( ¼0.0001)for the model and the independentvariables.Development of Waste Quantity Prediction Model by HospitalSince each hospital within the same type,differs from others in terms ofoperational characteristics,and medical services provided.It is suggested that anempirical model for each hospital must be developed for predicting or estimating thequantity of waste generated in each hospital.This idea was adopted to establish amore realistic and accurate model that gives accurate and acceptable estimations.Table 5.Statistical characteristics of the model in Eq.(1).(a)—Analysis of varianceVariable DF Sum of squares Mean square F -Value -LevelModel 3429,962.728143,320.9091,097.0250.0001Error 19625,606.426130.645C Total 199455,569.155R -square ¼0.9438ADJ R -SQ ¼0.9429(b)—Regression parameter estimatesVariables DF Parameter estimate Standard error T -Value -LevelIntercep 1À17.772 1.797À9.8890.0001PAT 1 1.0490.1298.1150.0001BED 10.8180.0918.9710.0001TYPE 112.228 1.930 6.3340.0001Acceptable -level (level of significance)¼0.100.F represents—general linearity test.R 2represents—coefficient of multiple determination.DF—degree of freedom.ADJ R 2represents—adjustment of R 2.T represents—importance of model variables.324Awad,Obeidat,and Al-ShareefD o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015Based on this,it was possible to develop an empirical model for each hospital in thisstudy.The best-fit model for each hospital was found to be as follows:Princess Basma hospitalWASTE ¼0:356ðPAT Þ0:633ðBED Þ0:640:ð4ÞPrincess Bade’ah hospitalLog WASTE ¼0:533þ0:862Log ðPAT Þþ0:00057ðBED Þ:ð5ÞIbn Al-Nafis hospitalWASTE ¼1:04Â10À3ðPAT Þ0:645ðBED Þ2:6:ð6ÞThe statistical characteristics of these models already determined,indicated thatthe models reached high R 2values equal 0.781,0.996,and 0.80respectively.Allindependent variables listed in these models were highly significant.For application,Fig.4shows the estimated quantity of waste generated in publichospital plotted against number of patients at three levels of number of beds usingthe model developed in this study.As shown in Fig.4,the quantity of waste variessignificantly at the same value of number of patients and at different values ofnumber of beds.For example,at number of patients equal to 80,the quantityofFigure 4.Estimated quantity of waste at three levels of number of beds for public hospitals.Mathematical-Statistical Models of Hospital Waste 325D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015waste equal to 75,105,and 155kg/day at number of beds equal to 10,50,and 110,respectively.The application of the model developed for private hospitals given by Eq.(3)ispresented in Fig.5.It shows that the generated quantity of waste differs significantlyin values according to the number of patients and the number of beds.In addition,Figs.4and 5show the effect of independent variables (patients and beds)onquantity of waste,where the number of patients and number of beds are inter-correlated proportionally in the models.Development of Waste Quantity Prediction Model by DivisionData collected from all hospital were analyzed by division (surgery,internal,maternity).This procedure was adopted because each division generates differentquantities of waste and type due to the different type and nature of the operationshandled by each division.On the trend of the scattered data related to each divisionand after performing the necessary transformation.It was possible to develop apredictable statistical model that represent the best fit for the data related to eachdivision,the development models for each division were as follows:Surgery þInternalWASTE ¼0:295ðPAT Þ0:531ðBED Þ0:825:ð7ÞFigure 5.Estimated quantity of waste at three levels of number of beds for private hospitals.326Awad,Obeidat,and Al-ShareefD o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015MaternityWASTE ¼6:92ðPAT Þ0:948ðBED ÞÀ0:196:ð8ÞThe models in Eqs.(7)and (8)were found to have R 2values of 0.9111,0.9993,and R 2—adj.of 0.9088,0.9992respectively,and high level of significance( ¼0.0001)for the models and the included independent variables.CONCLUSIONSAs a result of this research work the following can be concluded:the handling,storage,transport,and disposal method of solid wastes practiced by the hospitals inIrbid city need major improvements.The generation rate of solid wastes for general,public,and teaching hospitals in Irbid ranged from 5.718kg/pat/day to 6.904kg/pat/day,and for the private hospital 4.535kg/pat/day.The generation rate of solidwastes at hospitals is related to the following factors:(i)number of bed/patientavailable at the hospital,it was found that when number of beds increased,generation rate will be increased for the general hospitals;(ii)type of specialization(type and range of care);and (iii)number,kind,and size of departments.As thenumber and kinds of specialties available at the hospital increased,the generationrate of solid waste was increased.Kitchen waste was the highest percentage of thetotal weight of solid wastes generated from hospital.It ranged from 30to 39%.Careful sorting,handling,and storage of waste inside hospital is the key to hospitalhygiene.Normal wastes should be kept separate from hazardous wastes and eachtype of hazardous waste should be kept in appropriate containers.There is a greatneed for establishing a proper hazardous hospital waste management program tocontrol the existing situation in Irbid hospitals.This can be achieved by setting up anofficial national agency to regulate,control,and plans the procedures of handlingthese wastes.REFERENCES1.World Health Organization Occupational Hazardous in Hospitals ,Report on aWHO Meeting;Copenhagen,(EURO Reports and Studies:80),1983.2.World Health Organization Management of Wastes from Hospitals ,Report on aWHO Meeting;Copenhagen,(EURO Reports and Studies:97),1985.EPA Solid Waste Handling and Disposal in Multistory Buildings andHospitals ;EPA Office of Research and Development:Washington,DC,1972.EPA Guides to Pollution Prevention,Selected Hospital Waste Stream ;EPAOffice of Research and Development:Washington,DC,USA,1990.5.Hewling,J.T.Statistical Analysis System (SAS),Introductory Guide ,3rd Ed.;SAS Institute Inc.:Cary,North Carolina,USA,1985.Mathematical-Statistical Models of Hospital Waste 327D o w n l o a d e d b y [W a y n e S t a t e U n i v e r s i t y ] a t 08:18 10 A p r i l 2015。
