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1

Predicting severe wildlife vehicle crashes (WVCs) on New Hampshire roads using a hybrid generalized additive model

Laflamme Eric M., Villamagna Amy, Kim Hyun Joong

Archives of Transport

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2024

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Vol. 69, iss. 1

39--57

EN

Across the United States, wildlife vehicle crashes (WVCs) are increasing and remain consistently deadly to drivers, despite a downward trend in fatal automobile accidents overall. That said, the factors related to severe WVCs are unclear. With this in mind, we pursued a statistical model to reveal factors associated with WVCs that result in severe injury or death to drivers. We hypothesize that there are statistically significant interactions and non-linear relation-ships between these factors and severity occurrence. We developed a generalized additive model (GAM) with linear terms, additive terms, and a binary response for severity. We surmise that our fitted model results will quantify the relationship between significant variables and severity occurrence, and ultimately help to develop countermeasures to mitigate serious injury. The model was fitted to WVC records occurring between 2002 and 2019 in the state of New Hampshire. Fitted linear terms revealed: 1) in inclement weather, there is about a 22% increase in the odds of severity for slick surface conditions compared to dry surface conditions; 2) for the warmer months (spring/summer), there is a 42% decrease in the odds of severity for straight roads compared to those with curvature/incline; 3) for highways, the odds of severity decreases by 48% for accidents occurring on NH’s two major intestates highways, and 4) for spring/summer (as compared to the fall/winter), there is more than a 3-fold increase in the odds of severity for two-way traffic. Fitted additive terms revealed: 1) the odds of severity increased in the early hours, between midnight and 6AM, and after 5PM; 2) speeds between 45 and 60 mph are associated with an increase in the odds of a severe accident, while both lower and higher speeds (those below 45 and above 60 mph) are associated with a decrease in the odds of a severe accident; and 3) low, mid-range, and high human population densities are associated with decreases, increases, and decreases in odds of severity, respectively. Cross validation and resulting ROC curves gave evidence that our model is well specified and an effective predictor. Results could be used to inform drivers of potentially dangerous roadways/conditions/times.

2

Simulation of delayed buses in a dumpling state and analysis of maximum waiting time using logistic regression

Tanamoto Tetsufumi, Sekiguchi Haruya, Orimo Souta, Kawakami Taisei

Transport Problems

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2023

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T. 18, z. 2

171--180

EN

As cities become larger and societies become more complicated, the corresponding transportation systems also become more complicated. Thus far, many important transportation models have been investigated and applied to societies. In this work, we analyze a bus transportation model that includes high randomness. By strengthening the viewpoint of the users, the bunching of buses is further explored and considered as “the dumpling bus state,” referring to cases when the next scheduled buses closely run behind a delayed bus for a while. It is described that waiting people are split into winners (people with shorter waiting times) and losers (people with longer waiting times). Waiting time is also analyzed using logistic regression to obtain the probability of people who continue to wait.

3

Application of Principle Component Analysis and logistic regression to support Six Sigma implementation in maintenance

Antosz Katarzyna, Jasiulewicz-Kaczmarek Małgorzata, Machado Jose, Relich Marcin

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 4

art. no. 174603

EN

Improving the efficiency of maintenance processes is one of the goals of companies. Improvement activities in this area require not only an appropriate maintenance strategy but also the use of a new approach to increase the efficiency of the process. This article focuses on using Six Sigma (SS) to improve maintenance processes. As an introduction, the generations of SS development are identified, and traditional and advanced analytical tools that can be useful in SS projects are reviewed. As part of the research, an example of the implementation of the SS project in the maintenance process using the DMAIC and selected advanced analytical methods, such as PCA and logistic regression, was presented. The PCA results showed that it was enough to have seven main components to keep about 84% of the information on variability. In developed logistic regression explained the impact of the individual factors affecting the availability of the machines. The identified factors and their interactions made it possible to define maintenance activities requiring improvements

4

Social and demographic factors in shaping the perception of work as a source of health and safety risks: a study on adult Europeans

Roszko-Wójtowicz Elżbieta, Boczkowska Katarzyna, Niziołek Konrad

Economics and Environment

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2023

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No. 4

1--36

EN

The aim of this article is to assess the impact of selected social and demographic factors on the perception of European adults regarding their workplace as a health and safety risk. This aligns with the sustainable development concept, which emphasizes labor rights protection and a safe working environment. Sustainable work is defined as work that doesn't compromise employees' physical or mental health over time. Utilizing data from the 2021 European Working Conditions Survey, which covered over 70,000 individuals across 36 countries and was conducted via CATI due to the pandemic, the study employs logistic regression. It analyzes three models: one encompassing all European countries, and two focusing on Eastern and Central European countries. The findings demonstrate that factors such as company size, age, occupational group, sector, employment nature, gender, service length, and education significantly influence workplace risk perception. International comparisons highlight differences in these factors across country groups, contributing to the scientific discussion in social sciences.

PL

Celem artykułu jest ocena wpływu wybranych czynników społecznych i demograficznych na postrzeganie przez dorosłych mieszkańców Europy swojej pracy jako źródła zagrożenia dla zdrowia i bezpieczeństwa. Badanie wpisuje się w koncepcję zrównoważonego rozwoju, podkreślającą ochronę praw pracowniczych i bezpieczne środowisko pracy. Zrównoważona praca definiowana jest jako taka, która nie szkodzi zdrowiu fizycznemu czy psychicznemu pracowników w dłuższym okresie. Wykorzystując dane z Europejskiego Badania Warunków Pracy z 2021 roku, obejmującego ponad 70 000 osób w 36 krajach, przeprowadzone metodą CATI z powodu pandemii, badanie zastosowało regresję logistyczną. Analizuje ono trzy modele: obejmujący wszystkie kraje europejskie oraz dwa skoncentrowane na Europie Wschodniej i Środkowej. Wyniki pokazują, że wielkość firmy, wiek, grupa zawodowa, sektor, charakter zatrudnienia, płeć, staż pracy i wykształcenie znacząco wpływają na postrzeganie ryzyka w miejscu pracy. Międzynarodowe porównania ujawniły różnice w tych czynnikach między grupami krajów, przyczyniając się do dyskusji naukowej w naukach społecznych.

5

Automated detection of cybersecurity attacks in healthcare systems with recursive feature elimination and multilayer perceptron optimization

Firat Kilincer Ilhan, Ertam Fatih, Sengur Abdulkadir, Tan Ru-San, Rajendra Acharya U.

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

30--41

EN

Widespread proliferation of interconnected healthcare equipment, accompanying software, operating systems, and networks in the Internet of Medical Things (IoMT) raises the risk of security compromise as the bulk of IoMT devices are not built to withstand internet attacks. In this work, we have developed a cyber-attack and anomaly detection model based on recursive feature elimination (RFE) and multilayer perceptron (MLP). The RFE approach selected optimal features using logistic regression (LR) and extreme gradient boosting regression (XGBRegressor) kernel functions. MLP parameters were adjusted by using a hyperparameter optimization and 10-fold cross-validation approach was performed for performance evaluations. The developed model was performed on various IoMT cybersecurity datasets, and attained the best accuracy rates of 99.99%, 99.94%, 98.12%, and 96.2%, using Edith Cowan University- Internet of Health Things (ECU-IoHT), Intensive Care Unit (ICU Dataset), Telemetry data, Operating systems’ data, and Network data from the testbed IoT/IIoT network (TON-IoT), and Washington University in St. Louis enhanced healthcare monitoring system (WUSTL-EHMS) datasets, respectively. The proposed method has the ability to counter cyber attacks in healthcare applications.

6

Measuring the effects of passengers’ socio-demographic factors on satisfaction with public bus services in a developing city: a case study in Semarang, Indonesia

Haryadi Bambang

Transport Problems

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2022

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T. 17, z. 2

151--160

EN

The present study aimed to assess passenger satisfaction with bus transit services based on passengers’ socio-demographic characteristics, given the service quality. An ordinal logistic regression analysis was conducted to relate passengers’ sociodemographic characteristics to their satisfaction with public bus services. The sociodemographic characteristics studied were age, gender, marital status, occupation, income, housing type, family size, and motor vehicle ownership. Data were collected by administering an onboard survey to public bus passengers. In total, 580 completed a questionnaire asking about their socio-demographic characteristics and their satisfaction with bus transit services. The study reveals that significant differences exist in the levels of passengers’ satisfaction depending on their socio-demographic characteristics. Greater satisfaction was significantly associated with being married, unemployed, and young. The delivery of public bus services needs to consider different segments of passengers.

7

Application of PCA with logistic regression in embankment drainage

Kozłowski Edward, Król Krzysztof, Rymarczyk Tomasz

Przegląd Elektrotechniczny

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2022

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R. 98, nr 4

127--130

EN

The article presents a method using deep-sea probes, which were used to collect measurements in electrical tomography on the leakage of flood embankments. For this purpose, the main components analysis and elasticnet in logistic regression were used. The results of research on the method of spatial analysis of object moisture are presented. Research focused on the development and comparison of algorithms and models for data analysis and reconstruction using electrical tomography. The presented algorithms were used in the process of converting the input electrical values into the conductance represented by the pixels of the output image. The article presents PCA methods in logistic regression and elastic network in logistic regression to identify leakages in shafts. Deep probes were used to collect data in electrical impedance tomography.

PL

W artykule została zaprezentowana metoda wykorzystująca sondy głębinowe, które posłużyły do zbierania pomiarów w tomografii elektrycznej na temat przesiąkania wałów przeciwpowodziowych. W tym celu została wykorzystana analiza głównych składowych oraz elasticnet w regresji logistycznej. Przedstawiono wyniki badań nad metodą przestrzennej analizy zawilgocenia obiektów. Badania koncentrowały się na opracowaniu i porównaniu algorytmów i modeli do analizy i rekonstrukcji danych z wykorzystaniem tomografii elektrycznej. Przedstawione algorytmy zostały wykorzystane w procesie konwersji wejściowych wartości elektrycznych na konduktancję reprezentowaną przez piksele obrazu wyjściowego. W artykule przedstawiono metody PCA w regresji logistycznej oraz sieci elastycznej w regresji logistycznej do identyfikacji wycieków w szybach. Do zbierania danych w tomografii impedancji elektrycznej wykorzystano sondy głębinowe.

8

Predicting the stability of open stopes using Machine Learning

Szmigiel Alicja, Apel Derek B.

Journal of Sustainable Mining

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2022

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Vol. 21, iss. 3

241--248

EN

The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database that consisted of 176 historical case studies was passed to the two most popular Machine Learning algorithms: Logistic Regression and Random Forest, to compare their predicting capabilities. The results obtained showed that those algorithms can indicate the stability of underground openings, especially Random Forest, which, in examined data, performed slightly better than Logistic Regression.

9

Revisiting strategies for fitting logistic regression for positive and unlabeled data

Wawrzeńczyk Adam, Mielniczuk Jan

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

299--309

EN

Positive unlabeled (PU) learning is an important problem motivated by the occurrence of this type of partial observability in many applications. The present paper reconsiders recent advances in parametric modeling of PU data based on empirical likelihood maximization and argues that they can be significantly improved. The proposed approach is based on the fact that the likelihood for the logistic fit and an unknown labeling frequency can be expressed as the sum of a convex and a concave function, which is explicitly given. This allows methods such as the concave-convex procedure (CCCP) or its variant, the disciplined convex-concave procedure (DCCP), to be applied. We show by analyzing real data sets that, by using the DCCP to solve the optimization problem, we obtain significant improvements in the posterior probability and the label frequency estimation over the best available competitors.

10

Consumer Adoption of Fast-Fashion, Differences of Perceptions, and the Role of Motivations Across the Adoption Groups

Okur Nazan

Autex Research Journal

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2022

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Vol. 22, no. 3

365--372

EN

The purpose of this study was to determine the adoption groups of the fast-fashion consumers, evaluate the consumers’ perceptions of the fast-fashion in different groups, and model the role of “social or status image”, “uniqueness”, and “conformity” on the level of fast-fashion consumer adoption. The consumer adoption groups were determined as “innovators”, “early adopters”, “early majority”, “late majority”, and “laggards” by using a domain-specific innovativeness (DSI) scale. Consumers’ perceptions of fast-fashion were evaluated from cognitive and emotional aspects and the differences across the consumer groups were investigated by using Kruskal-–Wallis test and Mann-–Whitney U test. The roles of “social or status image”, “uniqueness”, and “conformity” on consumer groups were modeled by using ordinal logistic regression analysis. As a result of the research, consumers’ perceptions of fast-fashion were found to vary across different consumer adoption groups in terms of “being in-style products”, “expressing self-image”, “imitating the luxury fashion products”, and “frequent renewal of the collections”. Further, the findings revealed that the probabilistic relationship between different levels of consumer adoption based on innovativeness could be modeled based on the motivations of “social or status image” and “uniqueness”.

11

Machine learning prediction of future peripheral neuropathy in type 2 diabetics with percussion entropy and body mass indices

Xiao Ming-Xia, Lu Chang-Hua, Ta Na, Wei Hai-Cheng, Haryadi Bagus, Wu Hsien-Tsai

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

1140--1149

EN

This study was designed to evaluate the clinical applications of body mass index (BMI) and a percussion-entropy-based index (PEINEW) for predicting the development of diabetic peripheral neuropathy (DPN) in a group of type 2 diabetes mellitus (DM) patients. The study population comprised a sample of 90 subjects with diabetics (aged 37–86 years), who went through a blood test and photoplethysmography (PPG) measurement and were then followed for 5.5 years. Conventional parameters, including the small-scale multiscale entropy index (MEISS), pulse wave velocity with electrocardiogram located (PWVmean), and PEIoriginal, were computed and compared. A logistic regression model with PEINEW and a single categorical variable (BMI) showed a graded association between the diabetics, with a high BMI (i.e., ‘‘high” category) associated with a 12.53-fold greater risk of developing DPN relative to the diabetics with a low BMI (i.e., ‘‘low” category) (p = 0.001). The odds ratio for PEINEW was 0.893. The Kaplan-Meier survival analysis showed that the diabetic patients with BMI ＞ 30 had a significantly higher cumulative incidence of PN on follow-up than those with BMI [...] 30 (log-rank test, p < 0.001). These findings suggest that BMI and PEINEW are both important risk and protective factors for new-onset DPN from diabetes mellitus and, thus, BMI and percussion entropy calculation can provide valid information that may help to identify diabetics with a high BMI and a low PEINEW as being at increased risk of future DPN.

12

Improvement of Story-telling Advertisement According to Screenwriting Techniques

Uehara Daiki, Harada Fumiko, Shimakawa Hiromitsu

Annals of Computer Science and Information Systems

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2021

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Vol. 26

33--42

EN

The study proposes a method for enterprisers to enhance their scenarios for storytelling marketing. The research focuses on the storyline and the scene development of the scripting technique. Toward creation of an advertisement that resonates with the reader, the method uses logistic regression to detect expressions lacking in each scene of the scenario. Through an experiment, it turns out the proposed method can support the creation of scenarios whose scene development are close to the model ones. The method enables enterprisers to refine the scenario into advertising documents that attain empathy from readers.

13

Determinants of the quality of life in municipalities of Kielce district

Brzozowska-Rup Katarzyna, Czaja Roksana, Piotrowska-Piątek Agnieszka

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2020

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z. 146

21--35

EN

Identifying the factors that significantly affect the quality of life of the residents of municipalities of the Kielce poviat based on a synthetic indicator. Design/methodology/approach: The method used in the paper is create a synthetic indicator designed using a taxonomic method and the estimation of the logistic regression model. Data of the Central Statistical Office concerning the years 2014-2018 were used in the calculations. Findings: The obtained values of the TMR (Total Material Requirement) indicator suggest that in the Kielce poviat the quality of life of the residents of individual municipalities is very diverse. At the same time, a common positive tendency is observed, manifested in the trend indicating an improvement in the quality of life in each of the municipalities in the examined period. The constructed indicator was used to estimate the regression model for cross-sectional data from 2018. Originality/value: The estimated models allowed to formulate conclusions concerning the impact of particular explanatory variables on the diagnosed level of quality of life in the surveyed municipalities.

14

Application of the logistic regression model to assess the risk of death in road traffic accidents in the Mazowieckie voivodeship

Borucka Anna, Grzelak Małgorzata, Świderski Andrzej

Transport Problems

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2020

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T. 15, z. 4, cz. 1

125--136

EN

Mortality caused by road accidents is a significant problem for most countries, including Poland, where approximately 2,900 people die each year, and another 37,359 are injured. Research in this area has been conducted on a large scale. One of the most important elements is the evaluation of factors leading to fatalities in road accidents, which is also the goal of this article. The analysis was based on data on road accidents from the Mazowieckie Voivodeship, which is characterized by one of the highest mortality rates gathered for the period 2016-2018. Owing to the dichotomous form of the studied variable, logistic regression was used. Estimated model parameters and calculated odds ratios allowed to assess the effect of selected factors on road traffic mortality rate. As significant, the type of the perpetrator and the traffic participant, sex and age of the victim, road lighting, and the driver’s experience were selected. It was assessed that pedestrians are the group most exposed to death in a road accident, both as perpetrators and victims. It was also pointed out that the risk of death for women is 1.8 times higher than men. In addition, the effect of driving experience is also important, and the risk of death is 0.64 times lower for drivers with longer practice. It was also assessed that with each subsequent year of life, the risk of death in a road accident increased by 2%. Furthermore, according to incident site lighting, the study demonstrated that the risk of death was greatest when driving at night on an unlit road. The results obtained may support public safety and law enforcement authorities in carrying out preventive actions and also can be helpful in shaping the overall strategy on road safety.

15

Logistic regression in image reconstruction in electrical impedance tomography

Kozłowski Edward, Rymarczyk Tomasz, Kłosowski Grzegorz, Cieplak Tomasz

Przegląd Elektrotechniczny

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2020

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R. 96, nr 5

95--98

EN

The problem of image reconstruction in electrical impedance tomography (EIT) consists in both performing measurements using a set of sensors and creating of reconstruction based on these measurements. The image reconstruction requires accurate modeling of area, which presents field of view. To determine the inclusion in analyzed area the logistic regression has been applied. Additionally to select the predictors in logistic regression the elasticnet method has been used.

PL

Problem rekonstrukcji obrazu w elektrycznej tomografii impedancyjnej (EIT) polega zarówno na wykonywaniu pomiarów przy użyciu zestawu czujników, jak i na tworzeniu rekonstrukcji na podstawie tych pomiarów. Rekonstrukcja obrazu wymaga dokładnego modelowania obszaru, który przedstawia pole widzenia. Do określenia wtrąceń w analizowanym obszarze zastosowano regresję logistyczną. Dodatkowo do wyboru predyktorów w regresji logistycznej zastosowano metodę elasticnet.

16

Application of the logistic regression for determining transition probability matrix of operating states in the transport systems

Kozłowski Edward, Borucka Anna, Świderski Andrzej

Eksploatacja i Niezawodność

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2020

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Vol. 22, no. 2

192--200

EN

Transport companies can be regarded as a technical, organizational, economic and legal transport system. Maintaining the quality and continuity of the implementation of transport requisitions requires a high level of readiness of vehicles and staff (especially drivers). Managing and controlling the tasks being implemented is supported by mathematical models enabling to assess and determine the strategy regarding the actions undertaken. The support for managing processes relies mainly on the analysis of sequences of the subsequent activities (states). In many cases, this sequence of activities is modelled using stochastic processes that satisfy Markov property. Their classic application is only possible if the conditional probability distributions of future states are determined solely by the current operational state. The identification of such a stochastic process relies mainly on determining the probability matrix of interstate transitions. Unfortunately, in many cases the analyzed series of activities do not satisfy Markov property. In addition, the occurrence of the next state is affected by the length of time the system remains in the specified operating state. The article presents the method of constructing the matrix of probabilities of transitions between operational states. The values of this matrix depend on the time the object remains in the given state. The aim of the article was to present an alternative method of estimating the parameters of this matrix in a situation where the studied series does not satisfy Markov property. The logistic regression was used for this purpose.

PL

Przedsiębiorstwa transportowe mogą być traktowane jako wyodrębniony pod względem technicznym, organizacyjnym, ekonomicznym i prawnym system transportowy. Zachowanie jakości i ciągłości realizacji zleceń przewozowych wymaga wysokiego poziomu gotowości pojazdów oraz personelu (szczególnie kierowców). Kontrolowanie i sterowanie realizowanymi zadaniami wspierane jest modelami matematycznymi, umożliwiającymi ocenę i określenie strategii dotyczącej podejmowanych działań. Wsparcie procesów zarządzania polega głównie na analizie sekwencji kolejnych, realizowanych czynności (stanów). W wielu przypadkach taki ciąg czynności jest modelowany za pomocą procesów stochastycznych, spełniających własność Markowa. Ich klasyczne zastosowanie możliwe jest tylko w przypadku, gdy warunkowe rozkłady prawdopodobieństwa przyszłych stanów są określone wyłącznie przez bieżący stan eksploatacyjny. Identyfikacja takiego procesu stochastycznego polega głównie na wyznaczeniu macierzy prawdopodobieństw przejść międzystanowych. Niestety w wielu przypadkach analizowane ciągi czynności nie spełniają własności Markowa. Dodatkowo, na wystąpienie kolejnego stanu wpływa długość interwału czasowego pozostania systemu w określonym stanie eksploatacyjnym. W artykule przedstawiono metodę konstrukcji macierzy prawdopodobieństw przejść pomiędzy stanami eksploatacyjnymi. Wartości tej macierzy zależą od czasu przebywania obiektu w danym stanie. Celem artykułu było zaprezentowanie alternatywnej metody estymacji parametrów tej macierzy w sytuacji, gdy badany szereg nie spełnia własności Markowa. Wykorzystano w tym celu regresję logistyczną.

17

Assessment model of cutting tool condition for real-time supervision system

Kozłowski Edward, Mazurkiewicz Dariusz, Żabiński Tomasz, Prucnal Sławomir, Sęp Jarosław

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 4

679--685

EN

Further development of manufacturing technology, in particular machining requires the search for new innovative technological solutions. This applies in particular to the advanced processing of measurement data from diagnostic and monitoring systems. The increasing amount of data collected by the embedded measurement systems requires development of effective analytical tools to efficiently transform the data into knowledge and implement autonomous machine tools of the future. This issue is of particular importance to assess the condition of the tool and predict its durability, which are crucial for reliability and quality of the manufacturing process. Therefore, a mathematical model was developed to enable effective, real-time classification of the cutting blade status. The model was verified based on real measurement data from an industrial machine tool.

PL

Dalszy rozwój inżynierii produkcji, w szczególności obróbki skrawaniem, wymaga poszukiwania nowych innowacyjnych rozwiązań technologicznych. Dotyczy to w szczególności zaawansowanego przetwarzania danych pomiarowych pochodzących z systemów diagnostycznych i monitorujących. Rosnąca ilość danych gromadzonych przez wbudowane systemy pomiarowe wymaga opracowania skutecznych narzędzi analitycznych, aby efektywnie przekształcać dane w wiedzę i wdrażać autonomiczne obrabiarki przyszłości. Kwestia ta ma szczególne znaczenie dla oceny stanu narzędzia i przewidywania jego trwałości, które są kluczowe dla niezawodności i jakości procesu produkcyjnego. Dlatego opracowano nowy model matematyczny, którego zadaniem jest skuteczna klasyfikacja stanu ostrza narzędzia skrawającego realizowana w czasie rzeczywistym. Opracowany model został zweryfikowany na podstawie rzeczywistych danych pomiarowych z przemysłowej obrabiarki.

18

Use of data mining techniques to classify length of stay of emergency department patients

Sariyer Görkem, Taşar Ceren Öcal, Cepe Gizem Ersoy

Bio-Algorithms and Med-Systems

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2019

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Vol. 15, no. 1

art. no. 20180044

EN

Emergency departments (EDs) are the largest departments of hospitals which encounter high variety of cases as well as high level of patient volumes. Thus, an efficient classification of those patients at the time of their registration is very important for the operations planning and management. Using secondary data from the ED of an urban hospital, we examine the significance of factors while classifying patients according to their length of stay. Random Forest, Classification and Regression Tree, Logistic Regression (LR), and Multilayer Perceptron (MLP) were adopted in the data set of July 2016, and these algorithms were tested in data set of August 2016. Besides adopting and testing the algorithms on the whole data set, patients in these sets were grouped into 21 based on the similarities in their diagnoses and the algorithms were also performed in these subgroups. Performances of the classifiers were evaluated based on the sensitivity, specificity, and accuracy. It was observed that sensitivity, specificity, and accuracy values of the classifiers were similar, where LR and MLP had somehow higher values. In addition, the average performance of the classifying patients within the subgroups outperformed the classifying based on the whole data set for each of the classifiers.

19

Model zysków nadzwyczajnych dla przemysłu chemicznego

Staszkiewicz P.

Przemysł Chemiczny

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2018

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T. 97, nr 2

228--229

PL

Przedstawiono wyniki badań modelowania zysków nadzwyczajnych w przemyśle chemicznym w Europie. Zastosowano regresję panelową i logistyczną na próbie 74345 rocznych sprawozdań finansowych w okresie 2012-2016 dla 14869 przedsiębiorstw w 45 europejskich krajach. Zidentyfikowano istotną zależność nadzwyczajnej stopy zwrotu z kapitałów w europejskim przemyśle chemicznym i oceny ryzyka dostawców oraz efektywności cyklu produkcyjnego.

EN

On a sample of 74345 yearly reports for period 2012-2016, the logit regression was applied for 14869 companies from 45 European countries. The primary drivers for abnormal return were creditors and stock days. The model showed abnormal results of European chem. industry driven by the prodn. efficiency and supplier risk assessment.

20

Segregation of waste at source reduces the environmental hazards of municipal solid waste in Patna, India

Singh A., Raj P.

Archives of Environmental Protection

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2018

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Vol. 44, no. 4

96--110

EN

Though Municipal Solid Waste (MSW) is a worldwide problem, the collected wastes are dumped in open dumping at landfilling sites while the uncollected wastes remain strewn on the roadside, many-a-time clogging drainage. Such unsafe and inadequate management of MSW causes spread of bacteria, viruses, particulate matter, dioxins and other harmful pollutants in the surroundings and atmosphere. Hence, due to the repeated exposure of population to these pollutants can lead to serious health problems such as Diarrhea/Dysentery, Acute Respiratory Infection (ARI), and Asthma/Chronic Respiratory Diseases (CRD). Therefore, two-phase study included secondary data on diseases caused due to environmental pollution and primary data on MSW and lack of MSW management from 127 households in urban Patna, India. The random sampling method was used for collection of primary survey data, conducted during 2015–16 in selected areas of Patna. Logistic regression model odds ratios and their 95% confidence intervals were used to show the strength of the associations among segregation of wastes at source, segregation behavior, collection bins in the area, distance of collection bins from a residential area, and transportation of MSW. The ROC is a statistical technique to validate the logistic regression method that predicts the occurrence of an event through the comparison of probability picture of an event occurrence observed by probability and the predicted probability of the same event. The area under the ROC curve is up to 0.889 extent, which reveals that the ‘segregation of waste at source’ has a very strong scope to accomplish sustainable recycling at urban Patna in order to manage waste with the overall accuracy of 92.126%, which proves a better fi t logistic regression model. Hence, this paper concludes that ‘segregation of waste at source’ helps to attain sustainable recycling which would be the most viable approach to manage MSW in Patna and would eventually reduce environmental pollutants for the public health safety.