Provision conditions for increasing the reliability of trace analysis of toxic metals

Introduction. Heavy metals are included in one of the significant groups of ecotoxicants. Determining heavy metals at low levels is one of the main problems in analytical chemistry, which depends on various factors. Ignoring the contribution of these factors to the total uncertainty can increase the probability of distortion of the results due to an erroneous decision on the compliance of the obtained data with a particular standard. The most significant influences include the purity of reagents, dishes, and air in laboratory rooms. Purpose of the work. Search for ways of reducing the listed influences provided that the expense of time, material and labour resources are minimized. Materials and methods. In the course of the work, many experiments were carried out, including the analysis of nitric acid for the content of metals before and after cleaning by distillation, the analysis of washes from new fluoroplastic laboratory glassware and glassware that had been cleaned by steaming, an assessment of the effect of air pollution in the laboratory room based on a study of calibration curves, which were constructed at the analysis of standard iron solutions prepared in the Clean Workplace and in a conventional fume hood. All measurements were performed by inductively coupled plasma mass spectrometry on Agilent 7800 ICP-MS mass spectrometer. Results. The efficiency of the proposed methods for eliminating interfering influences on the analysis has been experimentally proved. Conclusion. The technical and analytical problem, which consists of finding optimal conditions for preparation of reagents, glassware cleaning and decreasing the influence of laboratory air pollution, allows to increase reliability of the obtained results and prevent distortion of information about the observed degree of environmental pollution.

Problem Gambling ‘Fuelled on the Fly’

Problem gambling is a gambling disorder often described as continued gambling in the face of increasing losses. In this article, we explored problem gambling behaviour and its psychological determinants. We considered the assumption of stability in risky preferences, anticipated by both normative and descriptive theories of decision making, as well as recent evidence that risk preferences are in fact ‘constructed on the fly’ during risk elicitation. Accordingly, we argue that problem gambling is a multifaceted disorder, which is ‘fueled on the fly’ by a wide range of contextual and non-contextual influences, including individual differences in personality traits, hormonal and emotional activations. We have proposed that the experience of gambling behaviour in itself is a dynamic experience of events in time series, where gamblers anchor on the most recent event—typically a small loss or rare win. This is a highly adaptive, but erroneous, decision-making mechanism, where anchoring on the most recent event alters the psychological representations of substantial and accumulated loss in the past to a representation of negligible loss. In other words, people feel better while they gamble. We conclude that problem gambling researchers and policy makers will need to employ multifaceted and holistic approaches to understand problem gambling.

Odpowiedzialność Skarbu Państwa wobec akcjonariuszy (udziałowców) upadłego za szkodę wyrządzoną przez sąd upadłościowy poprzez niesłuszne orzeczenie o upadłości

This study addresses the issues related to the State Treasury’s liability for damages towards the debtor’s shareholders due to an erroneous decision on the bankruptcy option instead of the restructuring option. The proceedings are initiated at the request of the entitled person. The court decides about opening the proceedings. In practice, there may be a situation in which competing applications are submitted, either aimed at liquidating the debtor’s assets or at its restructuring. The study refers to two legal states: the legal state in force in 2003–2015 and the legal state in force since 2016. The study analyzes the statutory criteria for resolving the conflict between the liquidation and restructuring option, and the premises of the State Treasury’s liability for damages. In the event of such a conflict, the insolvency law gives priority to restructuring over liquidation.

X-ray image based pneumonia classification using convolutional neural networks

Pneumonia is an infectious disease of the lungs. About one third to one half of pneumonia cases are caused by bacteria. Early diagnosis is a critical factor for a successful treatment process. Typically, the disease can be diagnosed by a radiologist using chest X-ray images. In fact, chest X-rays are currently the best available method for diagnosing pneumonia. However, the recognition of pneumonia symptoms is a challenging task that relies on the availability of expert radiologists. Such “human” diagnosis can be inaccurate and subjective due to lack of clarity and erroneous decision. Moreover, the error can increase more if the physician is requested to analyze tens of X-rays within a short period of time. Therefore, Computer-Aided Diagnosis (CAD) systems were introduced to support and assist physicians and make their efforts more productive. In this paper, we investigate, design, implement and assess customized Convolutional Neural Networks to overcome the image-based Pneumonia classification problem. Namely, ResNet-50 and DenseNet-161 models were inherited to design customized deep network architecture and improve the overall pneumonia classification accuracy. Moreover, data augmentation was deployed and associated with standard datasets to assess the proposed models. Besides, standard performance measures were used to validate and evaluate the proposed system.

Filtering Tort Accidents

Conventional wisdom in the economic analysis of tort law holds that legal errors distort incentives, causing behavior to depart from the optimum. If potential injurers know that courts err, they may engage in less or more than optimal precaution. This article revisits the effect of judicial error on the incentives of potential injurers by identifying a heretofore-neglected filtering effect of uncertainty in settings of imperfect judicial decision-making. We show that when courts make errors in the application of the liability standards, uncertainty about erroneous decision-making filters out the most harmful torts but leaves unaffected less harmful accidents. Our insight applies to various procedural and institutional aspects of legal adjudication, including the randomization of case assignment, the strength of precedent, and the use of standards versus rules.

Measuring capability of a binomial process

Many product characteristics are qualitative in nature, e.g. colour, brightness, surface finish etc. The manufacturing process of such products is usually described in terms of fraction nonconforming or conforming which is assumed to follow binomial distribution. Measuring capability of a binomial process implies assessing to what extent the fraction nonconforming or conforming in the continuous stream of lots conform to the specification limits. The Cp or Cpl of a binomial process can be estimated using several approaches. However, these approaches generally give widely varying assessment about the capability of a given binomial process. Consequently, a user of the index may inadvertently be led to erroneous decision making based on an inaccurate estimate of the index. In this paper, a procedure is proposed for assessing accuracies of estimates of Cpu or Cpl obtained by different methods. Subsequently, the best method for evaluating capability of a binomial process is identified based on analysis of multiple case studies, and also the methods giving inaccurate estimates are highlighted. Keywords: Process capability index, binomial process, fraction nonconforming, nonconforming lot (NL), predicted NL%, prediction error

Triangular Single Valued Neutrosophic Data Envelopment Analysis: Application to Hospital Performance Measurement

The foremost broadly utilized strategy for the valuation of the overall performance of a set of identical decision-making units (DMUs) that use analogous sources to yield related outputs is data envelopment analysis (DEA). However, the witnessed values of the symmetry or asymmetry of different types of information in real-world applications are sometimes inaccurate, ambiguous, inadequate, and inconsistent, so overlooking these conditions may lead to erroneous decision-making. Neutrosophic set theory can handle these occasions of data and makes an imitation of the decision-making procedure with the aid of thinking about all perspectives of the decision. In this paper, we introduce a model of DEA in the context of neutrosophic sets and sketch an innovative process to solve it. Furthermore, we deal with the problem of healthcare system evaluation with inconsistent, indeterminate, and incomplete information using the new model. The triangular single-valued neutrosophic numbers are also employed to deal with the mentioned data, and the proposed method is utilized in the assessment of 13 hospitals of Tehran University of Medical Sciences of Iran. The results exhibit the usefulness of the suggested approach and point out that the model has practical outcomes for decision-makers.

AUC Optimization with a Reject Option

Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classification with a reject option, i.e., abstain rather than classify an instance of low confidence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classification. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.

Do Investors’ Exhibit Cognitive Biases: Evidence From Indian Equity Market

The current article examines the influence of cognitive biases on the process of decision making among equity investors of India. The research is being directed by conducting a survey on a sample of 400 investors investing in Indian capital market. This study measures behavioural biases of individual investors' using a structured questionnaire as a research instrument of the study. By means of cross section data analysis, this analysis steadily provides evidence that behavioural biases adversely affect rational decision-making of an investor. This research indicates that a statistically significant relationship exists between behavioural biases among investors and the process of rational decision-making. The findings of the study are imperative to investors investing Indian capital market, brokers, financial consultants and investment advisors; responsible for managing assets and constructing portfolios for investment clients, as they can alter the investment decision by accessing the susceptibility of investors towards cognitive biases, which often lead to erroneous decision.