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1

Learning a s part of knowledge exchange processes in the context of online work, based on the example of generation z employees

Albrychiewicz-Słocińska Anna, Nejman Żaneta, Varga Erika

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2024

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z. 191

9--26

EN

Purpose: The objective of the argument in this paper is to attempt at answering the question whether learning and knowledge exchange are the key factors determining online work preferences for Generation Z employees. Design/methodology/approach: The essence of knowledge management is that all knowledge, both explicit and tacit, accumulated by an organization becomes easily accessible to each of its members. This is important for decision-making processes and allows the organization to become more agile. Knowledge management is most often associated with modern information technologies. Thanks to them, streams of various data can be processed and analyzed in many different ways. However, in the literature there is an increasingly common attitude that more attention should be paid not only to the technological but also to the human aspect of knowledge management. The processes of knowledge exchange among employees have been subject to extensive research and studies, yet the recent years have added another thread to the discussion about the matter, i.e. a significant proportion of employees switching to the online work model. Based on the findings of the studies conducted on a group of employees representing Generation Z, the Principal Component Analysis (PCA) technique was applied to organize the factors with the highest relevance for the respondents in online work. Findings: PCA demonstrated that the components recognized as most important were those relating to knowledge transfer and their impact on employee efficiency, and on the other hand employee relations as a factor that supports the learning processes. Research limitations/implications: In order to dwell upon the underlying causes of this situation, it should be recommended to proceed with further in-depth qualitative research. Practical implications: What the research communicates to the organization is that although Generation Z members are aware of the significance of the knowledge transfer and learning processes and they understand the role of peer relations in these processes, they are unable to overcome the social barriers created by the online working system due to lack of appropriate skills. Originality/value: The paper reveals new aspects that play crucial role in shaping Generation Z attitude to online work from one side. On the other hand it also helps to design synthetic tool researching this area in the future.

2

Assessment of heavy metal pollution in groundwater using a multivariate analysis approach

Sbai Khalid, Tahri Latifa, Hafiane Fatima Zahra, Halima Oumaima Iben, Fekhaoui Mohammed

Journal of Water and Land Development

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2024

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no. 61

175--182

EN

The current study aims to assess underground water pollution using an integrated approach that combines statistical methods such as principal component analysis (PCA) and water quality diagrams (Piper diagram, Schoeller-Berkalov diagram). A total of twenty water samples were collected from the Tiflet region in the Sebou basin and analysed for various physicochemical parameters, including temperature, pH, and heavy metal concentrations (Cu2+, Zn2+, Fe2+ and Pb2+). The average concentrations of Pb2+, Zn2+, Cu2+, and Fe2+ in the water samples were found to be 41.9, 14.8, 20.1, and 8.1 mg∙dm-3, respectively. These concentrations indicate a significant presence of heavy metals in the groundwater samples. Therefore, it can be concluded that the groundwater in this area is heavily polluted with heavy metals and other pollutants. This finding raises concerns regarding the use of this water for irrigation and agricultural activities in the region. This suggests that these four components play a crucial role in determining the overall water quality. The distribution patterns of the metals Pb2+, Zn2+, Cu2+, and Fe2+ in the well water within the study area are of particular environmental concern. It is recommended to establish a monitoring network to ensure the sustainable management of water resources in order to address this issue effectively.

3

Field Measurements and Machine Learning Algorithms to Monitor Water Quality in Lakes Located in Landscape Parks – A Case Study

Walczak Natalia, Walczak Zbigniew, Laks Ireneusz

Journal of Ecological Engineering

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2024

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Vol. 25, nr 1

49--64

EN

One of the greatest threats to many lakes is their accelerated eutrophication resulting from anthropogenic pressure, agricultural intensification, and climate change. A very important element of surface water protection in environmentally conserved areas is the proper monitoring of water quality and detection of potential threats by examining the physicochemical properties of water and performing statistical analyses that enable possible exposure of unfavourable trends. The article presents the analyses of the results of measurements made in three lakes located in the Sierakowski Landscape Park. As part of the measurements, water quality indicators i.e., phosphorus, nitrogen, BOD5 and COD, were determined monthly for a year at the inflows and outflows of the studied lakes. The test results of selected water quality indicators were analysed using machine learning algorithms i.e., PCA and k-means. The conducted tests enabled statistical estimation of changes in water quality indicators in the reservoirs and evaluation of their correlation.

4

Zastosowanie techniki FTIR i metod chemometrycznych do oceny procesu biologicznego oczyszczania ścieków komunalnych osadem czynnym

Piaskowski Krzysztof, Walendzik Bartosz

Gaz, Woda i Technika Sanitarna

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2024

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Nr 1

32--39

PL

Biologiczny proces osadu czynnego jest najpopularniejszą metodą stosowaną w licznych oczyszczalniach ścieków, która z reguły pozwala na uzyskanie wymaganego efektu ekologicznego. Jednakże charakteryzuje się ona również pewną niestabilnością uzyskiwanych efektów zależną od warunków i parametrów, na które częściowo eksploatator nie ma wpływu. Dlatego też poszukuje się szybkich technik analitycznych do kontroli i oceny osadu czynnego, które w przypadku pojawienia się nieprawidłowości w komorach biologicznych pozwolą na podjęcie decyzji operacyjnych korygujących proces, jak również jego optymalizację. W niniejszym artykule zaprezentowano możliwości wykorzystania analizy FTIR-DRIFT zawiesiny osadu czynnego połączonej z analizą chemometryczną wybranych parametrów osadu i ścieków do oceny procesu oczyszczania na poszczególnych etapach pracy reaktora biologicznego. Uzyskane wyniki wskazują, że zastosowanie techniki FTIR do szybkiej oceny procesu biologicznego jest możliwe, a w połączeniu z modelowaniem PLS i po odpowiednim skalibrowaniu z wartościami parametrów fizyczno-chemicznych może stanowić element kontrolny w eksploatacji oczyszczalni ścieków.

EN

The activated sludge process is the most popular method used in many sewage treatment plants, which usually allows to achieve the required ecological effect. However, it is also characterized by a certain instability of the obtained effects, depending on conditions and parameters which are partly beyond the operator's influence. Therefore, rapid analytical techniques are being sought for the control and assessment of activated sludge, which, in the event of irregularities occurring in biological tanks, will allow operational decisions to be made to correct the process as well as its optimization. This article presents the possibilities of using FTIR-DRIFT analysis of activated sludge suspension combined with chemometric analysis of selected sludge and sewage parameters to assess the course of the purification process at various stages of operation of the biological reactor. The obtained results indicate that the use of the FTIR technique for rapid assessment of a biological process is possible, and in combination with PLS modeling and after appropriate calibration with physical and chemical parameters, it can constitute a control element in the operation of sewage treatment plants.

5

Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework

Patange Abhishek, Soman Rohan, Pardeshi Sujit, Kuntoglu Mustafa, Ostachowicz Wieslaw

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 178274

EN

Tool condition affects the tolerances and the energy consumption and hence needs to be monitored. Artificial intelligence (AI) based data-driven techniques for tool condition determination are proposed. Unfortunately, the data-driven techniques are data-hungry. This paper proposes a methodology for classification based on unsupervised learning using limited unlabeled training data. The work presents a multi-class classification problem for the tool condition monitoring. The principal component analysis (PCA) is employed for dimensionality reduction and the principal components (PCs) are used as input for classification using k-means clustering. New collected data is then projected on the PC space, and classified using the clusters from the training. The methodology has been appliedforclassification of tool faults in 6 classes in a vertical milling center. The use of limited input parameters from the user makes the method ideal for monitoring a large number of machines with minimal human intervention. Furthermore, due to the small amount of data needed for the training, the method has the potential to be transferable.

6

Monitoring a metabolic profile of wheat by using FTIR spectroscopy and chemometric methods — concept studies

Borek-Dorosz Aleksandra, Kurczab Rafał

Science, Technology and Innovation

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2023

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Vol. 17, no. 1-2

41--75

EN

Changes in plants under the influence of a variety of chemical and physical factors are reflected in metabolomic changes. To date, there are very few methods that would allow studying metabolic changes occurring in single cells. Spectroscopic methods especially combined with the chemometrics methods are a very good tool to investigate such changes in metabolomics. Tracking changes in plants is of particular importance in industry, especially when studying how the use of fertilizers affects plants. In this paper, we present preliminary research as concept of proof to examine whether the use of FTIR (Fourier Transform Infrared Spectroscopy) helps to monitor the changes in the metabolomic profile of the plants. For preliminary research, four species of cereals and cuckooflower were used. In this step, it was possible to verify the differences in metabolites that are produced by plants belonging to different families. Then one species of grain was selected and subjected to eleven different physical and chemical factors. Next, the research was expanded to determine the optimal concentration of hydrogen peroxide. FTIR spectra of leaves and extracts of the plants were obtained for all experimental groups and then analyzed with the use of chemometric methods: HCA (Hierarchical Component Analysis) and PCA (Principal Component Analysis). Those methods were used to help in the interpretation of metabolic changes resulting in the plant in response to external factors.

7

Relationships between Wellbeing and Sustainable Development in a Group of Selected Developed Countries

Drastichová Magdaléna, Filzmoser Peter, Gajanin Rastko

Problemy Ekorozwoju

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2023

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Vol. 18, nr 2

49--77

EN

This work evaluates the crucial aspects of sustainable development (SD) related to wellbeing and quality of life, which were measured by twenty-two relevant indicators (indices) in a sample of 31 countries over the period 2010 – 2019. All the pillars of SD are reflected, while the indicators applied either reflect one of these dimensions, i.e. the economic, social or environmental pillar of SD, or two/all of them. Several of these indicators also measure specific aspects encompassed by the particular pillars, which are of great importance for SD and have to be included. These include especially health and inequality, which belong to the social pillar of SD, and are reflected in several indicators used. Furthermore, the indicator of subjective happiness is included as well. Principal component analysis (PCA) and parallel factor analysis (PARAFAC) are the main methods used to analyse relationships between twenty-two indicators (composite indices) reflecting crucial aspects of SD, wellbeing, and quality of life in the sample. Three stages of both analyses were carried out. For both of them similar results were identified. Principal component 1 (for PCA)/component 1 (for PARAFAC) divided the sample into the less and the more developed countries, since the positive contribution was predominantly determined by the socioeconomic, wellbeing and the more complex environmental or SD indicators, which are predominantly the highest (high) in the more developed countries. On the contrary, the negative contribution was determined by the pollution damage indicators, which are the highest in the less developed countries. Principal component 2 (for PCA)/component 2 (for PARAFAC) divided the sample according to a crucial aspect of the social pillar of SD, i.e. quality of health, particularly reflected in Healthy life years at birth (HLY), which has also poor results in the many developed countries. At the third stage this component is determined by the environmental indicators reflecting resource depletion/consumption and also pollution damages in monetary values, being crucial for SD, since a number of them had the highest values in the developed countries.

PL

Niniejsza praca ocenia kluczowe aspekty zrównoważonego rozwoju (SD) związane z dobrostanem i jakością życia, które zostały zmierzone za pomocą dwudziestu dwóch odpowiednich wskaźników (wskaźników) w próbie 31 krajów w latach 2010-2019. Uwzględniono wszystkie filary zrównoważonego rozwoju, natomiast zastosowane wskaźniki odzwierciedlają albo jeden z tych wymiarów, tj. filar ekonomiczny, społeczny lub środowiskowy ZR, albo dwa/wszystkie z nich. Niektóre z tych wskaźników mierzą również konkretne aspekty objęte poszczególnymi filarami, które mają ogromne znaczenie dla zrównoważonego rozwoju i muszą zostać uwzględnione. Wśród nich wyróżnić należy zwłaszcza zdrowie i nierówności, które należą do społecznego filaru zrównoważonego rozwoju i znajdują odzwierciedlenie w przyjętych wskaźnikach. Ponadto uwzględniono również wskaźnik subiektywnego szczęścia. Analiza głównych składowych (PCA) i równoległa analiza czynnikowa (PARAFAC) to główne metody stosowane do analizy relacji między dwudziestoma dwoma wskaźnikami (wskaźnikami złożonymi) odzwierciedlającymi kluczowe aspekty SD, dobrostanu i jakości życia. Przeprowadzono trzy etapy obu analiz. Zidentyfikowano podobne wyniki. Komponent główny 1 (w przypadku PCA)/komponent 1 (w przypadku PARAFAC) podzielił próbę na kraje słabiej i bardziej rozwinięte, ponieważ pozytywny wkład był determinowany głównie przez wskaźniki społeczno-ekonomiczne, dobrobyt i bardziej złożone wskaźniki środowiskowe lub zrównoważonego rozwoju, które są przeważnie najwyższe (wysokie) w krajach bardziej rozwiniętych. O ujemnym wkładzie zadecydowały wskaźniki szkód powodowanych przez zanieczyszczenia, które są najwyższe w krajach słabiej rozwiniętych. Komponent główny 2 (dla PCA)/komponent 2 (dla PARAFAC) podzielił próbę według kluczowego aspektu społecznego filaru SD, jakim jest zdrowie, w szczególności Healthy life years at birth (HLY), który wypadł słabo także w wielu krajach rozwiniętych. W trzecim etapie składnik ten jest określany przez wskaźniki środowiskowe odzwierciedlające wyczerpywanie się/konsumpcję zasobów, a także szkody spowodowane zanieczyszczeniami w wartościach pieniężnych, które są kluczowe dla zrównoważonego rozwoju, gdyż wiele z nich miało najwyższe wartości w krajach rozwiniętych.

8

The impact of water quality on the availability of phytoplankton and growth of Litopenaeus vannamei

Musa Muhammad, Thoyibah Auliarifka A., Puspitaningtyas Dyah A., Arsad Sulastri, Mahmudi Mohammad, Lusiana Evellin D., Maftuch Maftuch, Huda Agus S.

Journal of Water and Land Development

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2023

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no. 56

127--135

EN

This research analysed the availability of phytoplankton and the growth rate of Vannamei shrimp in relation to water quality changes. The research was carried out in February-March 2021 for a half cycle of shrimp cultivation in two ponds of the Brackish Water Fish Culture Probolinggo Laboratory in Probolinggo, East Java, Indonesia. The research used a descriptive method and included a survey. Sampling was made every two weeks for two months. Nine parameters were measured and ten shrimps were taken for a specific growth rate (SGR) measurement once per sampling. Data were analysed using the principal component analysis (PCA) and canonical correspondence analysis (CCA). Secondary data of water quality were added for the PCA. The results show that the phytoplankton found in the first pond consisted of Chlorophyta, Chrysophyta, and Cyanophyta, whereas the phytoplankton in the other pond included Chlorophyta, Chrysophyta, Cyanophyta, and Dinophyta. The abundance of phytoplankton ranged from 12-80∙103 cell∙cm-3, which indicated eutrophic waters. The PCA demonstrated that pH, nitrate, and total organic matter (TOM) significantly influenced phytoplankton abundance in the pond. In addition, water quality parameters, such as temperature, transparency, salinity, nitrite and phosphate levels, were tolerable in both ponds for the growth of shrimps. However, the level of pH was lower than the aquaculture quality standard, whereas those of nitrate, ammonia, and TOM were higher. The growth rate of Vannamei shrimp increased by 0.76–7.34%∙day-1.

9

Multivariate Statistical Analysis of Groundwater Quality of Hassi R'mel, Algeria

Mehdi Metaiche, Hakim Djafer Khodja, Amina Aichour, Nourredine Gaci

Journal of Ecological Engineering

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2023

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Vol. 24, nr 5

22--31

EN

The quality of Groundwater is characterized by physico-chemical parameters. They determine the way in which this water is used (water supply, irrigation, industry, etc.). This present study gives the highlighting of the hydrogeological and physico-chemical characteristics of aquifer waters in question resulting from the various wells, which aims to; gather, exploit and analyze the data, in order to determine their conformity with potability standards and their suitability for irrigation. Using multivariate statistical techniques including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (ACH) and Diagram Analysis. They are applied to a dataset composed of 17 boreholes with 12 chemical variables over the entire study area, they were sampled in 2020. These boreholes are the principal water resources suppling Hassi R'mel w. Laghouat region in terms of drinking water and irrigation. Obtained results showed that the majority of groundwater in the Hassi R’mel region is hard; where approximately 20% of boreholes are characterized by fairly soft water, and approximately 5% are characterized by very hard water.

10

Interrelationship and Determining Factors of Water Quality Dynamics in Whiteleg Shrimp Ponds in Tropical Eco-Green Aquaculture System

Musa Muhammad, Mahmudi Mohammad, Arsad Sulastri, Lusiana Evellin Dewi, Sunadji, Wardana Wisnu Angga, Ompusunggu Magdalena Florensia, Damayanti Dhea Novita

Journal of Ecological Engineering

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2023

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Vol. 24, nr 1

19--27

EN

Whiteleg shrimp (Litopenaeus vannamei) farming is a major activity in the coastal areas of many tropical countries. To meet the demand in this market, the culture system has expanded using intensive technology, which has resulted in the emission of effluents that threaten the surrounding aquatic ecosystem. Therefore, proper aquaculture management is needed to ensure both economic and ecological benefits. This led to the emergence of eco-green aquaculture. Water quality monitoring is a critical part of aquaculture management and when performed regularly, it yields a large and complex dataset. In this study, the authors aimed to analyse the dynamics of water quality characteristics and the relationships between these variables in whiteleg shrimp ponds in a tropical eco-green aquaculture system from 2020 to 2022. Since the data includes nine parameters and is quite complex, the principal component analysis (PCA) approach was used. This method enables to identify the factors that determine water quality, which will help ensure effective and efficient aquaculture management. Consequently, the water quality variables in the studied area were reduced to five dimensions and salinity, ammonia, and pH were found to be the key factors responsible for the changes in water quality characteristics. Hence, these variables should be the focus of farming management systems.

11

Clustering Analysis of Soil Environmental Quality for Perennial Crop Recommendations in Vinh Long Province, Vietnam

Minh Vo Quang, Vu Pham Thanh, Khoa Le Van, Tinh Tran Kim, Dang Pham Cam

Journal of Ecological Engineering

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2023

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Vol. 24, nr 8

343--352

EN

The study aimed to evaluate the soil environmental characteristics of Vinh Long Province’s perennial crop-growing area using principal component analysis (PCA) and cluster analysis (CA). Soil environmental quality data were collected in eight districts of Vinh Long province for 27 physical and chemical parameters. CA and PCA analysis was used to group and identify critical parameters affecting perennial crops’ soil environment. The findings demonstrate low to moderate soil compaction porosity, buffering capacity, and structure for perennial crops. In addition, the soil has a low pH, electrical conductivity, total soluble salts, aluminum, and cation exchange capacity. Although rich in nutrients, the content of organic matter, available phosphorus, cations, and trace elements is only low to moderate. CA results showed three districts suitable for strongly developing perennial crops: Tra On, Mang Thit, and Vung Liem. The PCA results showed that except for density, the buffer capacity of the soil, and dissolved Al3+, the upcoming monitoring program must incorporate all remaining criteria. The study’s findings offer crucial information to help the management organization devise strategies for enhancing and sustainably expanding perennial crops in the province. It is necessary to further evaluate the soil’s environmental quality over time and soil depth and determine the frequency of monitoring in the study area.

12

Classification of Parkinson’s disease and other neurological disorders using voice features extraction and reduction techniques

Majdoubi Oumaima, Benba Achraf, Hammouch Ahmed

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2023

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T. 13, nr 3

16--22

EN

This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications.

PL

Przedstawione badanie miało na celu różnicowanie osób z chorobą Parkinsona (PD) od osób z innymi zaburzeniami neurologicznymi poprzez analizę próbek głosowych, biorąc pod uwagę związek między zaburzeniami głosu a PD. Próbki głosowe zostały zebrane od 76 uczestników przy użyciu różnych urządzeń i warunków nagrywania, a uczestnicy byli instruowani, aby wydłużyć samogłoskę /a/ w wygodnym tempie. Oprogramowanie PRAAT zostało zastosowane do ekstrakcji cech, takich jak autokorelacja (AC), krzyżowa korelacja (CC) i współczynniki cepstralne Mel (MFCC) z próbek głosowych. Analiza składowych głównych (PCA) została wykorzystana w celu zmniejszenia wymiarowości cech. Jako techniki nadzorowanego uczenia maszynowego wykorzystano drzewa decyzyjne (CT), regresję logistyczną, naiwny klasyfikator Bayesa (NB), maszyny wektorów nośnych (SVM) oraz metody zespołowe. Każda z tych metod posiadała swoje unikalne mocne strony i charakterystyki, umożliwiając kompleksową ocenę ich skuteczności w rozróżnianiu pacjentów z PD od osób z innymi zaburzeniami neurologicznymi. Naiwny klasyfikator Bayesa, wykorzystujący siedem składowych PCA, osiągnął najwyższy wskaźnik dokładności na poziomie 86,84% wśród przetestowanych metod klasyfikacji. Należy jednak zauważyć, że wydajność klasyfikatora może się różnić w zależności od zbioru danych i konkretnych cech próbek głosowych. Podsumowując, to badanie wykazało potencjał analizy głosu jako narzędzia diagnostycznego do rozróżniania pacjentów z PD od osób z innymi zaburzeniami neurologicznymi. Poprzez zastosowanie różnych technik analizy głosu i wykorzystanie różnych algorytmów uczenia maszynowego, takich jak drzewa decyzyjne, regresja logistyczna, naiwny klasyfikator Bayesa, maszyny wektorów nośnych i metody zespołowe, osiągnięto znaczący poziom dokładności. Niemniej jednak, konieczne są dalsze badania i walidacja na większych zbiorach danych w celu skonsolidowania i uogólnienia tych wyników dla przyszłych zastosowań klinicznych.

13

Utilization of ASTER data in lithological and lineament mapping of the southern flank of the Central High Atlas in Morocco

Errami Maryam, Algouti Ahmed, Algouti Abdellah, Farah Abdelouhed, Agli Saloua

Geologos

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2023

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Vol. 29, No. 1

1--20

EN

Geological mapping undoubtedly plays an important role in several studies and remote sensing data are of great significance in geological mapping, particularly in poorly mapped areas situated in inaccessible regions. In the present study, Principal Component Analysis (PCA), Band Rationing (BR) and Minimum Noise Fraction (MNF) algorithms are applied to map lithological units and extract lineaments in the Amezri-Amassine area, by using multispectral ASTER image and global digital elevation model (GDEM) data for the first time. Following preprocessing of ASTER images, advanced image algorithms such as PCA, BR and MNF analyses are applied to the 9ASTER bands. Validation of the resultant maps has relied on matching lithological boundaries and faults in the study area and on the basis of pre-existing geological maps. In addition to the PCA image, a new band-ratio image, 4/6–5/8–4/5, as adopted in the present work, provides high accuracy in discriminating lithological units. The MNF transformation reveals improvement over previous enhancement techniques, in detailing most rock units in the area. Hence, results derived from the enhancement techniques show a good correlation with the existing litho-structural map of the study area. In addition, the present results have allowed to update this map by identifying new lithological units and structural lineaments. Consequently, the methodology followed here has provided satisfactory results and has demonstrated the high potential of multispectral ASTER data for improving lithological discrimination and lineament extraction.

14

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

15

Physicochemical Characterization and Metallic Contamination of the Waters of Oued Nfifikh (Morocco)

Merbouh Chaimaa, Kabriti Mohamed, Nahli Abdelmottalib, Rihane Abdeslam, Iounes Nadia

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 1

1--11

EN

Oued Nfifikh is a coastal stream of the Bouregreg and Chaouia watershed (Morocco). It passes through many rural and urban areas and receives different types of liquid and solid discharges from anthropogenic activities adopted along the watercourse. This study aims to evaluate the physicochemical quality of the water from the most accessible sites upstream and downstream of Oued Nfifikh, along with highlighting the impact of human activities on these waters’ quality. For this purpose, water samples were collected and analyzed within normalized methods. Statistical analysis of the collected data shows significant spatial variations (pvalue < 0.05) for pH, electrical conductivity, nitrate and chloride, and for metallic trace elements (Zn, Fe, Ba, Mn, Cr and Al). Unlike (temperature, Pb, Ni, Cu and Cd), whose values do not present statistically significant variations (pvalue > 0.05). The study of the physicochemical quality reveals that the waters at the upstream are classed as good quality, except for site (S2), located at the upstream part of the river, it is affected by human activities. Consequently, its physicochemical composition is quite similar to that of waters of poor quality at the downstream sites. The Principal Component Analysis of the results followed by the Ascending Hierarchical Classification on the same data matrix allowed to regroup the sampling sites with similar characteristics into three distinct groups. A group of highly mineralized waters, a second group dominated by elements indicating urban pollution, and a group of waters with low mineral content and low metallic contamination indicating agricultural pollution.

16

Assessment of Groundwater Quality in the Berrechid Aquifer, Central Morocco, Using Multivariate Statistics and Water Quality Indices

Moukhliss Mohammed, Taleb Abdslam, Ouallali Abdessalam, Spalevic Velibor, Mazigh Nouhaila, El Fathi Badr, Souabi Salah

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 4

187--199

EN

Groundwater salinity is a serious problem for water quality in the irrigated parts of arid and semi-arid regions, especially in the aquifers of Berrechid, Morocco. This study used a variety of techniques, including the Water Quality Index (WQI) and World Health Organization (WHO) recommended limits, Principal Component Analysis (PCA), and Geographic Information System (GIS) to evaluate the quality of the groundwater for irrigation and domestic use in the Berrechid region in central Morocco. The goal of this study was to evaluate the quality of groundwater for irrigation and human consumption. The collection and analysis of twenty-two samples for ions was carried out, including, EC, Cl-, NO3-, NH4+, NO2-, Ca2+, Mg2+, pH, SO42-, Na+, K+, CO3-, HCO3-, and Mn2+. The Water Quality Index (WQI) was used to classify the water quality vis: excellent, good, average, poor and very poor. The research area’s water quality index (WQI) ranges from 43.89 to 439.34, with around 40.90% of samples having excellent water quality, 45.45% having poor water quality, 4.54% showing extremely bad water quality, and 9.09% having unsuitable quality for human consumption. The principal component analysis reveals that the average concentration of cations in groundwater was Na+> Mg2+> Ca2+> K+> Mn2+> NH4+, whereas the concentration of anions was Cl-> HCO3-> SO42-> NO3-> NO2-> CO32-. The correlation matrix was created and analyzed to determine its significance in groundwater quality assessment. The primary sources of pollution are household waste, exposed septic tanks, landfill leachate, and excessive fertilizer usage in agriculture and industrial operations. The current analysis demonstrates that the deteriorating groundwater quality in the region needs pre-consumption treatment and contamination risk prevention.

17

Waveform and resistivity data fusion imaging method based on the refection coefficient

Su Maoxin, Han Min, Xue Yiguo, Zhao Ying, Wang Peng, Li Guangkun

Acta Geophysica

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2023

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Vol. 71, no. 1

175--192

EN

To achieve comprehensive analyses, the presentation of comprehensive geophysical results usually involves the use of separate imaging and the combination of various results. At present, few studies have considered the correlation degree and unified imaging of different types of geophysical data. We establish a set of data fusion imaging methods for multiple geophysical data based on their refection coefficients. As geophysical exploration results are primarily provided through waveform and resistivity sections, waveform and resistivity data were selected for fusion and were converted into refection coefficients, and ground-penetrating radar (GPR) and surface electrical resistivity tomography (ERT) were taken as examples. Re-sampling and feature reconstruction were performed to unify the data in space and resolution. Finally, principal component analysis was used to calculate the correlation of the reconstructed refection coefficient and to perform data fusion; this led to unified imaging based on the refection coefficient of the considered geophysical data. Numerical simulation analyses and field experiments proved the efficacy of this method for producing unified imaging of multiple geophysical data. In summary, we provide a novel method for the unified interpretation of multiple geophysical data and enhance the identification ability of geological interfaces and anomaly distribution.

18

The study of the interdependencies of areas and aspects of smart city in Polish cities

Owczarek Tomasz, Sojda Adam, Wolny Maciej

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

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2022

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z. 161

203--214

EN

Purpose: To determine the interdependencies between Smart City areas as well as the aspects and areas between resident-oriented IT areas of the city. Design / methodology / approach: The data for the study was collected during a survey of 287 cities for Smart City. The study of interdependence was based on a correlation analysis using: Pearson's correlation coefficient, Cramér's V coefficient, and Kendall's tau. In addition, a PCA analysis was used to reduce variable dimensions. Findings: The results of the research indicate that the scope of using services within e-office services is more strongly related to functionality than to IT equipment. In turn, the economic area plays a fundamental role in the perception of the city as a Smart City. There was also a clear difference in self-evaluation regarding Smart City areas and IT aspects of the city depending on the size of the city. However, this difference does not translate into declarations regarding the readiness for evaluation in Smart City categories. Originality/value: presentation of the relationship between the areas defining the concept of Smart City dependence on the basis of an original study addressed to city representatives. The results of the study allow us to look at the Smart City concept from the perspective of the city. The results of the analysis, in addition to scientists dealing with Smart City, may be of interest to city managers in Poland. They show the way of understanding and dependencies between individual areas. They also show those dependencies that need to be strengthened in the context of sustainable development.

19

Effects of anthropogenic activities on nutrient parameters in the North Eastern Mediterranean Sea (Bay of Sığacık/Aegean Sea)

Çulha Saniye Türk, Karaduman Fatma Rabia, Gündoğdu Ayşe, Barış Kamil Emre

Oceanological and Hydrobiological Studies

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2022

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Vol. 51, No. 2

189--202

EN

This study aims to examine the physicochemical parameters of seawater (temperature, pH, salinity, dissolved oxygen, suspended particulate matter, ammonium-nitrogen, nitrite-nitrogen, nitrate-nitrogen, and phosphate-phosphorus, burnable organic matter in the sediment), and possible effects of pollution in Sığacık Bay where different anthropogenic activities are carried out. Samples of surface seawater (0 m), bottom seawater (2 m), and sediment were collected monthly from four sampling sites between September 2013 and September 2014. Annual mean nutrient values were determined as 1.6 ± 0.14 μg.at.NH4+-N l-1, 0.1 ± 0.01 μg.at.NO2--N l-1, 0.8 ± 0.08 μg.at.NO3--N l-1, 0.7 ± 0.08 μg.at.PO43--P l-1, SPM 21.4 ± 0.33 mg l-1. The BOM content in the sediment was 5.6 ± 0.39%. As a result of the study, it was determined that Sığacık Bay was polluted by anthropogenic point and non-point source pollution. According to the water quality criteria, the bay was found to be in the group of polluted seawater in terms of phosphate phosphorus.

20

Zastosowanie sieci neuronowych i metody komponentów głównych PCA do kompresji wyników przemieszczeń obiektów budowlanych

Mrówczyńska Maria

Materiały Budowlane

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2022

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nr 11

16--18

PL

W artykule zaproponowano wykorzystanie metody transformacji PCA (Principal Component Analysis), realizowanej z wykorzystaniem sieci neuronowej do kompresji wielowymiarowych danych, uzyskanych w wyniku prowadzonych pomiarów geodezyjnych. Jako przykład możliwości zastosowania prezentowanego podejścia wykorzystano wyniki pomiarów przemieszczeń pionowych obiektu budowlanego. Testy oceny skuteczności zaproponowanego podejścia wykonano z wykorzystaniem współczynnika korelacji oraz błędu średniokwadratowego, który nie przekroczył dwukrotności błędu średniego pomiaru. Wyniki analiz numerycznych porównano z wartościami przemieszczeń pionowych punktów sieci pomiarowo-kontrolnej, uzyskanymi z rzeczywistych pomiarów. Wyniki sugerują, że podejście może znaleźć zastosowanie do kompresji, a następnie rekonstrukcji danych z monitoringu geodezyjnego bez zmniejszenia dokładności identyfikacji przemieszczeń.

EN

The article proposes using the PCA (Principal Component Analysis) transformation method carried out using a neural network to compress multidimensional data obtained from geodetic measurements. As an example of the possibility of using the presented approach, the results of measurements of vertical displacements of a construction object were used. Tests to assess the effectiveness of the proposed method were performed using a correlation coefficient and a mean-square error that did not exceed twice the error of the average measurement. The results of numerical analyses were compared with the values of vertical displacements of the measuring and control network points obtained from actual measurements. The results suggest that the approach can be applied to the compression and subsequent reconstruction of geodetic monitoring data without compromising the accuracy of displacement identification.