Hearing conservation programs in selected metal fabrication industries

Noise is an undesired sound that cannot be ignored. Hearing loss caused by industrial noise has been recognized for many years, and protection of employee hearing has been made mandatory by governmental agencies. This paper presents an investigation of ongoing hearing conservation programs (HCP) in selected metal fabrication industries in the state of Tennessee and the effects of these programs on employees’ health and safety. A questionnaire has been used to collect data for this study. The questionnaire was send to 65 metal fabrication industries (MFI) which have populations of over 100 employees. The response rate was 70.77%. The majority of industries surveyed had noise problems in their plants. Hearing conservation programs are a necessity to protect employees’ health and safety in metal fabrication industries in Tennessee.     

The Local Structure of Globalization

We study the evolution of the network of foreign direct investment (FDI) in the international electricity industry during the period 1994–2003. We assume that the ties in the network of investment relations between countries are created and deleted in continuous time, according to a conditional Gibbs distribution. This assumption allows us to take simultaneously into account the aggregate predictions of the well-established gravity model of international trade as well as local dependencies between network ties connecting the countries in our sample. According to the modified version of the gravity model that we specify, the probability of observing an investment tie between two countries depends on the mass of the economies involved, their physical distance, and the tendency of the network to self-organize into local configurations of network ties. While the limiting distribution of the data generating process is an exponential random graph model, we do not assume the system to be in equilibrium. We find evidence of the effects of the standard gravity model of international trade on evolution of the global FDI network. However, we also provide evidence of significant dyadic and extra-dyadic dependencies between investment ties that are typically ignored in available research. We show that local dependencies between national electricity industries are sufficient for explaining global properties of the network of foreign direct investments. We also show, however, that network dependencies vary significantly over time giving rise to a time-heterogeneous localized process of network evolution.     

Predicting Fraud Victimization Using Classical Machine Learning

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.     

Distributions of positive correlations in sectoral value added growth in the global economic network*

International trade has grown considerably during the process of globalization. Complex supply chains for the production of goods have resulted in an increasingly connected International Trade Network (ITN). Traditionally, direct trade relations between industries have been regarded as mediators of supply and demand spillovers. With increasing network connectivity the question arises if higher-order relations become more important in explaining a national sector’s susceptibility to supply and demand changes of its trading partner. In this study we address this question by investigating empirically to what extent the topological properties of the ITN provide information about positive correlations in the production of two industry sectors. We observe that although direct trade relations between industries serve as important indicators for correlations in the industries’ value added growth, opportunities of substitution for required production inputs as well as second-order trade relations cannot be neglected. Our results contribute to a better understanding of the relation between trade and economic productivity and can serve as a basis for the improvement of crisis spreading models that evaluate contagion threats in the case of a node’s failure in the ITN.     

An interacting-agent model of financial markets from the viewpoint of nonextensive statistical mechanics

In this paper we present an interacting-agent model of stock markets. We describe a stock market through an Ising-like model in order to formulate the tendency of traders to be influenced by the other traders’ investment attitudes [Kaizoji, Physica A 287 (2000) 493], and formulate the traders’ decision-making regarding investment as the maximum entropy principle for nonextensive entropy [C. Tsallis, J. Stat. Phys. 52 (1988) 479]. We demonstrate that the equilibrium probability distribution function of the traders’ investment attitude is the q-exponential distribution. We also show that the power-law distribution of the volatility of price fluctuations, which is often demonstrated in empirical studies can be explained naturally by our model which originates in the collective crowd behavior of many interacting-agents.     

On the optimization of a CAPM portfolio using lower partial moments as measure of risk and using the possibility of safeguarding its loss

Using a portfolio built from bonds (investment without volatility) and shares (investment with volatility) corresponding to the CAPM we calculate the possible loss of this portfolio. The loss is measured by a so-called lower partial moment of the rate of return of the portfolio. Using this loss, we optimize the composition of the portfolio with respect to this loss. Also we investigate the optimization of the portfolio when the loss can be underwritten by an insurance. Concerning the premium of this insurance contract, we show that when the premium is defined inadequate, e.g. proportional to the investment or proportional to the amount of investment in shares, the optimal portfolio consists only of investment in shares. When the premium is defined more suitable, e.g. proportional to the loss, the optimal portfolio is built by an investment in bonds and shares.     

Risk analysis in investment appraisal based on the Monte Carlo simulation technique by A. Hacura, M. Jadamus-Hacura and A. Kocot

Comment on Eur. Phys. J. B 20, 551 (2001) Since Hertz major work on investment appraisal using the Monte Carlo Simulation technique, the so called “Risk Analysis” has become a standard tool for supporting investment decisions [1,2]. A main problem in investment appraisal is to consider and specify the risk of investment projects in an appropriate way, for enabling consistent project evaluation. In calculating a risky project's net present value (NPV) the major difficulty is to quantify the project's risk for quantifying an appropriate risk adjusted discount rate (RADR). Theoretically not founded risk adjusted discount rates face a lot of critique. Furthermore it is discussed that the incorporation of a constant risk factor into the discount rate makes a certain assumption about the resolution of uncertainty over time [3] and finally that a single net present value could not in general reflect risk properly. Especially in consequence of the last point the proponents of simulation argue that a whole distribution of net present values shows a project's risk better than a single number. In the special issue “Econophysics” of this journal Hacura et al. tried to describe the methodology and use of Monte Carlo Simulation in investment appraisal [4]. The purpose of this comment is to point out three fundamental flaws in that article. Received 29 April 2002 Published online 19 December 2002 RID="a" ID="a"e-mail: robert.obermaier@wiwi.uni-regensburg.de     

Socio-Economic Impact of the Covid-19 Pandemic in the U.S.

This paper proposes a dynamic cascade model to investigate the systemic risk posed by sector-level industries within the U.S. inter-industry network. We then use this model to study the effect of the disruptions presented by Covid-19 on the U.S. economy. We construct a weighted digraph G = (V,E,W) using the industry-by-industry total requirements table for 2018, provided by the Bureau of Economic Analysis (BEA). We impose an initial shock that disrupts the production capacity of one or more industries, and we calculate the propagation of production shortages with a modified Cobb–Douglas production function. For the Covid-19 case, we model the initial shock based on the loss of labor between March and April 2020 as reported by the Bureau of Labor Statistics (BLS). The industries within the network are assigned a resilience that determines the ability of an industry to absorb input losses, such that if the rate of input loss exceeds the resilience, the industry fails, and its outputs go to zero. We observed a critical resilience, such that, below this critical value, the network experienced a catastrophic cascade resulting in total network collapse. Lastly, we model the economic recovery from June 2020 through March 2021 using BLS data.     

Near-Infrared Spectroscopy: Applications in the Analysis of Tablets and Solid Pharmaceutical Dosage Forms

Introduction

The popularity of near-infrared (near-IR) spectroscopy is rapidly increasing for many reasons. Availability of inexpensive yet powerful computers and chemometric software for spectral data analysis is fostering the growth of new applications of the technique. The development of rapid-scanning spectrometers offering very high signal-to-noise ratios, an increased understanding and acceptance of the method in a variety of industries, and the need to maintain real-time process control in an era of total quality management are other reasons this method has begun to receive such attention. Near-IR spectroscopy has been used for a wide range of analyses in industries as diverse as biomedicine and petrochemicals. Although the pharmaceutical industry has been relatively slow to embrace this technique, a variety of pharmaceutical applications of near-IR have been identified and investigated. This review will discuss the development of near-IR spectroscopy for the analysis of pharmaceutical dosage forms, specifically solid dosage from matrices, capsules, and tablets. The chemometric techniques used extensively in these analyses will also be discussed briefly.     

Study of memory effects in international market indices

Long term memory effects in stock market indices that represent internationally diversified stocks are analyzed in this paper and the results are compared with the S&P 500 index. The Hurst exponent and the Detrended fluctuation analysis (DFA) technique are the tools used for this analysis. The financial time-series data of these indices are tested with the Normalized Truncated Levy Flight to check whether the evolution of these indices is explained by the TLF.Some features that seem to be specific for international indices are discovered and briefly discussed. In particular, a potential investor seems to be faced with new investment opportunities in emerging markets during and especially after a crisis.     

The investment timing game in petroleum production: An econometric model

This paper uses a structural econometric model to analyze an investment timing game that takes place during petroleum production. The model I develop enables one to estimate the structural parameters governing petroleum-producing firms’ investment timing decisions and therefore to assess the net effect of the information and extraction externalities they face. The econometric methodology presented in this paper can be employed to analyze any problem of dynamic multi-stage strategic decision-making in the presence of externalities.     

Optimal investment horizons

In stochastic finance, one traditionally considers the return as a competitive measure of an asset, i.e., the profit generated by that asset after some fixed time span Δt, say one week or one year. This measures how well (or how bad) the asset performs over that given period of time. It has been established that the distribution of returns exhibits “fat tails” indicating that large returns occur more frequently than what is expected from standard Gaussian stochastic processes [1-3]. Instead of estimating this “fat tail” distribution of returns, we propose here an alternative approach, which is outlined by addressing the following question: What is the smallest time interval needed for an asset to cross a fixed return level of say 10%? For a particular asset, we refer to this time as the investment horizon and the corresponding distribution as the investment horizon distribution. This latter distribution complements that of returns and provides new and possibly crucial information for portfolio design and risk-management, as well as for pricing of more exotic options. By considering historical financial data, exemplified by the Dow Jones Industrial Average, we obtain a novel set of probability distributions for the investment horizons which can be used to estimate the optimal investment horizon for a stock or a future contract. Received 20 February 2002 Published online 25 June 2002     

A review on hybrid techniques for the degradation of organic pollutants in aqueous environment

The creation of the modern world requires many industrial sectors, however, sustainability needs to be considered while developing industries. In particular, organic pollutants generated by many of these industries contaminate the environment leading to health and other issues. Advanced oxidation processes (AOPs) have been introduced to remove organic pollutants present in wastewater. Sonolytic degradation of organic pollutants is considered as one of the AOPs, however, this process has its limitations. In order to overcome the limitations, hybrid techniques involving ultrasound and other AOPs have been developed. That is, ultrasound combined with heterogeneous AOPs (ultrasound/metal ions, ultrasound/metal oxides, and ultrasound/photocatalysis) and homogeneous AOPs (ultrasound/ozone, ultrasound/H₂O₂, and ultrasound/persulfate) for the degradation/mineralization of organic pollutants. This review highlights the advantages of using hybrid techniques involving ultrasound for the degradation of organic pollutants in aqueous solutions.     

Thoron (220Rn) in the indoor environment and work places

Ever since studies on uranium miners established the presence of a positive risk coefficient for the occurrence of lung cancer in miners exposed to elevated levels of ²²²Rn and its progeny, there was a great upsurge of interest in the measurement of ²²²Rn in the environment. Subsequently, considerable data is being generated on the levels of ²²²Rn in the environment across the worlds and is being periodically reported by UNSCEAR reports. In contrast to this, data pertaining to ²²⁰Rn in indoors and workplace environment is scaree due to the genral perception that its levels are negligible due to its shorter half life, and subsequently its contribution to the total inhalation dose is ignored, in the presence of other significant sources of natural radiation. This may not be true. Globally many locations have higher levels of natural background radiation due to elevated levels of primordial radio nuclides in the soil and their decay products like radon (²²²Rn), and thoron (²²⁰Rn) in the environment. Of late, technologically enhanced naturally occurring radioactive material has also contributed to the burden of background radiation. It is estimated that inhalation of ²²²Rn, ²²⁰Rn and their short lived progenies contribute more than 54% of the total natural background radiation dose received by the general population. ²²⁰Rn problem exists in industries which use thorium nitrate. Including India, lamps using thoriated gas mantles are still being used for indoor and outdoor lighting and by hawkers in rural as well as urban areas. Considering the fact that large amount of thorium nitrate is being handled by these industries, contribution to the inhalation dose of workers from ²²⁰Rn gas emanated and build up of the progeny in ambient air may also be quite significant. In this paper current status of ²²⁰Rn levels in the indoor environment and workplaces as well as in other industries where large amount of ²³²Th is being handled is being summarized. Methods of measurement and reported levels are also summarized.     

一个描述金融投资项目演化的量子力学状态方程

本文提出了一个描述金融投资项目演化的量子力学状态方程,该方程的参数比较好地模拟了金融市场的基本要素,包括投资(输入)、资产损耗(缩水)、资产增益、和收益(输出)等,方程中的量子力学算符也能够反映该项目的动力学过程与特征,所以能代表一类金融投资项目(其状态用Dirac符号表示)在市场中的演化模式.通过引入纠缠态表象得出了该方程的量子解(算符之和形式),该量子解给出了初态与终态的联系,也就给出了投资项目的动态过程.作为例子,求出单一投资项目在金融市场中的演化,不但得出了符合市场演化趋势的解,而且提出了二项-负二项纠缠态.在推导过程中,充分利用了Dirac符号和有序算符内的积分技术(IWOP).     

Ensuring Quality of Experience in the Connected Home: A New Battleground for Quality of Experience Differentiation

ABSTRACT

The connected home is a critical part of the network—but one that has seen little in the way of true investment in recent years. With customer loyalty and churn directly linked to quality of experience, this is a part of the network that no players in the supply chain can afford to ignore any longer. Beyond broadband experience, the addition of smart technology to the connected home also represents one of the biggest revenue growth areas in the consumer telecoms market for some time. From both a churn-reduction and a revenue-growth perspective, investment in the connected home network must accelerate. This article outlines the key trends identified by Ovum in the connected home space for 2016 and beyond.     

Real-time decision support and information gathering system for financial domain

The challenge of the investment domain is that a large amount of diverse information can be potentially relevant to an investment decision, and that, frequently, the decisions have to be made in a timely manner. This presents the potential for better decision support, but poses the challenge of building a decision support agent that gathers information from different sources and incorporates it for timely decision support.These problems motivate us to investigate ways in which the investors can be equipped with a flexible real-time decision support system to be practical in time-critical situations. The flexible real-time decision support system considers a tradeoff between decision quality and computation cost. For this purpose, we propose a system that uses the object oriented Bayesian knowledge base (OOBKB) design to create a decision model at the most suitable level of detail to guide the information gathering activities, and to produce an investment recommendation within a reasonable length of time. The decision models our system uses are implemented as influence diagrams.We validate our system with experiments in a simplified investment domain. The experiments show that our system produces a quality recommendation under different urgency situations. The contribution of our system is that it provides the flexible decision recommendation for an investor under time constraints in a complex environment.     

Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH-EVT-Copula model

This paper introduces GARCH-EVT-Copula model and applies it to study the risk of foreign exchange portfolio. Multivariate Copulas, including Gaussian, t and Clayton ones, were used to describe a portfolio risk structure, and to extend the analysis from a bivariate to an n-dimensional asset allocation problem. We apply this methodology to study the returns of a portfolio of four major foreign currencies in China, including USD, EUR, JPY and HKD. Our results suggest that the optimal investment allocations are similar across different Copulas and confidence levels. In addition, we find that the optimal investment concentrates on the USD investment. Generally speaking, t Copula and Clayton Copula better portray the correlation structure of multiple assets than Normal Copula.     

The risks and returns of stock investment in a financial market

The risks and returns of stock investment are discussed via numerically simulating the mean escape time and the probability density function of stock price returns in the modified Heston model with time delay. Through analyzing the effects of delay time and initial position on the risks and returns of stock investment, the results indicate that: (i) There is an optimal delay time matching minimal risks of stock investment, maximal average stock price returns and strongest stability of stock price returns for strong elasticity of demand of stocks (EDS), but the opposite results for weak EDS; (ii) The increment of initial position recedes the risks of stock investment, strengthens the average stock price returns and enhances stability of stock price returns. Finally, the probability density function of stock price returns and the probability density function of volatility and the correlation function of stock price returns are compared with other literatures. In addition, good agreements are found between them.     

The impact of background risk

In this paper, we introduce an incomplete-market dynamic investment model with a correlated background risk. In so doing, we show the impact of background risk on the investment decisions.