Search results for: golgohar iron ore mining & industrial company

Authors: Anunya Thanasrisuebwong

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This research paper aimed to find out how was the ethical climate in an organization and job performance satisfaction of employees affected employees’ engagement and commitment by using the case study of PTT Exploration and Production Public Company Limited, Thailand. The population of this research was 4,383 Thai employees of PTTEP, Thailand. From a total of 420 questionnaires sent out, 345 respondents replied. The statistics utilized was mean score and Multiple Regression Analysis. The findings revealed that the respondents had opinion towards ethical climate of their organization, job performance satisfaction and organization engagement and commitment at a high level. The test of hypothesis disclosed the determinant attributes of job performance satisfaction that affected the respondents’ overall level of organization engagement and commitment. The set of these determinant attributes consisted of employees’ responsibilities for duties, organization’s policies and practice, relationship with organization’s commanders, work security and stability, job description, career path and relationship with colleagues. These variables were able to predict the employees’ organization engagement and commitment at 50.6 percent.

Keywords: ethical climate in organization, job performance satisfaction, organization engagement, commitment

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Authors: Aminat. O Adelekan, Olawale T. Gbadebo

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Biscuit is one of the most consumed cereal foods in Nigeria and research has shown that locally available tropical crops like cassava, sweet potato can be made into flour and used in the production of biscuits and other pastries. This study investigates some quality parameters in biscuits made from blends of wheat, cassava and carrot flour. The values of result of samples increased with increasing percentage substitution of cassava and carrot flour in some quality parameter like fiber, ash, gluten content, and carbohydrate. The protein content reduced significantly (P < 0.05) with increasing percentage substitution of cassava and carrot flour which ranged from 14.80% to 11.80% compared with the control sample which had 15.60%. There was a recorded significant increase (P < 0.05) in some mineral composition such as calcium, magnesium, sodium, iron, phosphorus, and vitamin A and C composition as the percentage substitution of cassava and carrot flour increased. During storage stability test, samples stored in the fridge and freezer were found to be the best storage location to preserve the sensory attributes and inhibit microbial growth when compared with storage under the sun and on the shelf. Biscuit made with blends of wheat, cassava and carrot flour can therefore serve as an alternative to biscuits made from 100% wheat flour, as they are richer in vitamin A, vitamin C, carbohydrate, dietary fiber and some essential minerals.

Keywords: biscuit, carrot, flour blends, storage

Procedia PDF Downloads 139

Authors: D. Kurt, İ. F. Barut, Z. Ü. Yümün, E. Kam

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After development of the industrial revolution, industrial plants and settlements have spread widely on the sea coasts. This concentration also brings environmental pollution in the sea. This study focuses on the Gulf of İzmir where is located in West of Turkey and it is a fascinating natural gulf of the Eastern Aegean Sea. Investigating marine current sediment is extremely important to detect pollution. Natural radionuclides’ pollution of the marine environment which is also known as a significant environmental anxiety. Ground drilling cores (the depth of each sediment is variant) were collected from the Gulf of İzmir’s four different locations which were Karşıyaka, İnciraltı, Çeşmealtı and Bayraklı. These sediment cores were put in preserving bags with weight around 1 kg, and they were dried at room temperature in a week for moisture removal. Then, they were sieved with 1 mm sieve holes, and finally these powdered samples were relocation to polyethylene Marinelli beakers of 100 ml versions. Each prepared sediment was waited to reach radioactive equilibrium between uranium and thorium for 40 days. Gamma spectrometry measurements were settled using a HPG (High- Purity Germanium) semiconductor detector. Semiconductor detectors are very good at separating power of the energy, they are easily able to differentiate peaks that are pretty close to each other. That is why, gamma spectroscopy’s usage is common for the determination of the activities of U - 238, Th - 232, Ra - 226, Cr - 137 and K - 40 in Bq kg⁻¹. In this study, the results display that the average concentrations of activities’ values are in respectively; 2.2 ± 1.5 Bq/ kg⁻¹, 0.98 ± 0.02 Bq/ kg⁻¹, 8 ± 0.96 Bq/ kg⁻¹, 0.93 ± 0.14 Bq/ kg⁻¹, and 76.05 ± 0.93 Bq/ kg⁻¹. The outcomes of the study are able to be used as a criterion for forthcoming research and the obtained data would be pragmatic for radiological mapping of the precise areas.

Keywords: gamma, Gulf of İzmir (Eastern Aegean Sea-Turkey), natural radionuclides, pollution

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Authors: Keyur Raval, Steffen Krohn, Bruno Moerschbacher

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Chitin, the biopolymer of the N-acetylglucosamine, is the most abundant biopolymer on the planet after cellulose. Industrially, chitin is isolated and purified from the shell residues of shrimps. A deacetylated derivative of chitin i.e. chitosan has more market value and applications owing to it solubility and overall cationic charge compared to the parent polymer. This deacetylation on an industrial scale is performed chemically using alkalis like sodium hydroxide. This reaction not only is hazardous to the environment owing to negative impact on the marine ecosystem. A greener option to this process is the enzymatic process. In nature, the naïve chitin is converted to chitosan by chitin deacetylase (CDA). This enzymatic conversion on the industrial scale is however hampered by the crystallinity of chitin. Thus, this enzymatic action requires the substrate i.e. chitin to be soluble which is technically difficult and an energy consuming process. We in this project wanted to address this shortcoming of CDA. In lieu of this, we have modeled a fusion protein with CDA and an auxiliary protein. The main interest being to increase the accessibility of the enzyme towards crystalline chitin. A similar fusion work with chitinases had improved the catalytic ability towards insoluble chitin. In the first step, suitable partners were searched through the protein data bank (PDB) wherein the domain architecture were sought. The next step was to create the models of the fused product using various in silico techniques. The models were created by MODELLER and evaluated for properties such as the energy or the impairment of the binding sites. A fusion PCR has been designed based on the linker sequences generated by MODELLER and would be tested for its activity towards insoluble chitin.

Keywords: chitin deacetylase, modeling, chitin binding domain, chitinases

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

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Authors: Gyoutae Park, Seungho Han, Byungduk Kim, Youngdo Jo, Yongsop Shim, Yeonjae Lee, Sangguk Ahn, Hiesik Kim, Jungil Park

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In this paper, we presented not only development technology of an explosion proof type and portable combustible gas leak detector but also algorithm to improve accuracy for measuring gas concentrations. The presented techniques are to apply the flame-proof enclosure and intrinsic safe explosion proof to an infrared gas leak detector at first in Korea and to improve accuracy using linearization recursion equation and Lagrange interpolation polynomial. Together, we tested sensor characteristics and calibrated suitable input gases and output voltages. Then, we advanced the performances of combustible gaseous detectors through reflecting demands of gas safety management fields. To check performances of two company's detectors, we achieved the measurement tests with eight standard gases made by Korea Gas Safety Corporation. We demonstrated our instruments better in detecting accuracy other than detectors through experimental results.

Keywords: accuracy improvement, IR gas sensor, gas leak, detector

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

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Authors: Yangze Lan, Ruihua Xv, Feng Zhou, Yijia Shan, Longhao Zhang, Qinghui Xv

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Receiving and dispatching trains is an important part of railroad organization, and the risky evaluation of operating personnel is still reflected by scores, lacking further excavation of wrong answers and operating accidents. With natural language processing (NLP) technology, this study extracts the keywords and key phrases of 40 relevant risk events about receiving and dispatching trains and reclassifies the risk events into 8 categories, such as train approach and signal risks, dispatching command risks, and so on. Based on the historical risk data of personnel, the K-Means clustering method is used to classify the risk level of personnel. The result indicates that the high-risk operating personnel need to strengthen the training of train receiving and dispatching operations towards essential trains and abnormal situations.

Keywords: receiving and dispatching trains, natural language processing, risk evaluation, K-means clustering

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Authors: Adriano Araujo Carvalho, Arturo Alatrista Corrales

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This paper intends to show how electrical energy consumption and production data analysis were used to find opportunities to save energy at Taboada wastewater treatment plant in Callao, Peru. In order to access the data, it was used independent data networks for both electrical and process instruments, which were taken to analyze under an ISO 50001 energy audit, which considered, thus, Energy Performance Indexes for each process and a step-by-step guide presented in this text. Due to the use of aforementioned methodology and data mining techniques applied on information gathered through electronic multimeters (conveniently placed on substation switchboards connected to a cloud network), it was possible to identify thoroughly the performance of each process and thus, evidence saving opportunities which were previously hidden before. The data analysis brought both costs and energy reduction, allowing the plant to save significant resources and to be certified under ISO 50001.

Keywords: energy and production data analysis, energy management, ISO 50001, wastewater treatment plant energy analysis

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Authors: Francesco Pandolfi, Georgios Baltzopoulos, Iunio Iervolino

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The impact of extreme winds on industrial assets and the built environment is gaining increasing attention from stakeholders, including the corporate insurance industry. This has led to a progressively more in-depth study of building vulnerability and fragility to wind. Wind vulnerability models are used in probabilistic risk assessment to relate a loss metric to an intensity measure of the natural event, usually a gust or a mean wind speed. In fact, vulnerability models can be integrated with the wind hazard, which consists of associating a probability to each intensity level in a time interval (e.g., by means of return periods) to provide an assessment of future losses due to extreme wind. This has also given impulse to the world- and regional-scale wind hazard studies.Another approach often adopted for the probabilistic description of building vulnerability to the wind is the use of fragility functions, which provide the conditional probability that selected building components will exceed certain damage states, given wind intensity. In fact, in wind engineering literature, it is more common to find structural system- or component-level fragility functions rather than wind vulnerability models for an entire building. Loss assessment based on component fragilities requires some logical combination rules that define the building’s damage state given the damage state of each component and the availability of a consequence model that provides the losses associated with each damage state. When risk calculations are based on numerical simulation of a structure’s behavior during extreme wind scenarios, the interaction of component fragilities is intertwined with the computational procedure. However, simulation-based approaches are usually computationally demanding and case-specific. In this context, the present work introduces the ExtReMe wind risk assESsment prototype Software, ERMESS, which is being developed at the University of Naples Federico II. ERMESS is a wind risk assessment tool for insurance applications to industrial facilities, collecting a wide assortment of available wind vulnerability models and fragility functions to facilitate their incorporation into risk calculations based on in-built or user-defined wind hazard data. This software implements an alternative method for building-specific risk assessment based on existing component-level fragility functions and on a number of simplifying assumptions for their interactions. The applicability of this alternative procedure is explored by means of an illustrative proof-of-concept example, which considers four main building components, namely: the roof covering, roof structure, envelope wall and envelope openings. The application shows that, despite the simplifying assumptions, the procedure can yield risk evaluations that are comparable to those obtained via more rigorous building-level simulation-based methods, at least in the considered example. The advantage of this approach is shown to lie in the fact that a database of building component fragility curves can be put to use for the development of new wind vulnerability models to cover building typologies not yet adequately covered by existing works and whose rigorous development is usually beyond the budget of portfolio-related industrial applications.

Keywords: component wind fragility, probabilistic risk assessment, vulnerability model, wind-induced losses

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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Authors: Shreya Asthana

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Testers know that the feature they tested on stage is working perfectly in production only after release went live. Sometimes something breaks in production and testers get to know through the end user’s bug raised. The panic mode starts when your staging test results do not reflect current production behavior. And you started doubting your testing skills when finally the user reported a bug to you. Testers can deploy their confidence on release day by testing on production. Once you start doing testing in production, you will see test result accuracy because it will be running on real time data and execution will be a little faster as compared to staging one due to elimination of bad data. Feature flagging, canary releases, and data cleanup can help to achieve this technique of testing. By this paper it will be easier to understand the steps to achieve production testing before making your feature live, and to modify IT company’s testing procedure, so testers can provide the bug free experience to the end users. This study is beneficial because too many people think that testing should be done in staging but not in production and now this is high time to pull out people from their old mindset of testing into a new testing world. At the end of the day, it all just matters if the features are working in production or not.

Keywords: bug free production, new testing mindset, testing strategy, testing approach

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Authors: A. O. David, A. A. Akaho, M. A. Abah, J. O. Ogunjimi

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This study focuses on the growing concerns about the quality of groundwater found around cement factories, which have caused several health issues for residents located within two (2) kilometer radius. The qualities of groundwater were determined by an investigative study that involved the determination of some heavy metals and physicochemical properties in drinking water samples. Eight (8) samples of groundwater were collected from the eight sampling sites. The samples were analysed for the following parameters; iron, copper, manganese, zinc, lead, color, dissolved solids, electrical conductivity, pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), temperature, turbidity and total hardness using standard methods. The test results showed the variation of the investigated parameters in the samples as follows: temperature 26-31oC, pH 5.9-7.2, electrical conductivity (EC) 0.37 – 0.78 µS/cm, total hardness 181.8 – 333.0 mg/l, turbidity 0.00-0.05 FTU, colour 5-10 TCU, dissolved oxygen 4.31-5.01 mg/l, BOD 0.2-1.0 mg/l, COD 2.0 -4.0 mg/l, Cu 0.04 – 0.09 mg/l, Fe 0.006-0.122 mg/l, Zn 0.016-0.306 mg/l, Mn 0.01-0.05 mg/l and Pb < 0.001 mg/l. The World Health Organization's standard for drinking water quality guidelines was exceeded in several of the analyzed parameters' amounts in the drinking water samples from the study area. The dissolved oxygen was found to exceed 5.0 mg/l, which is the WHO permissible limit; also, Limestone was found to exceed the WHO maximum limit of 170 mg/l. All the above results confirmed the high pollution of the groundwater sources, and hence, they are not suitable for consumption without any prior treatment.

Keywords: groundwater, quality, heavy metals, parameters

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: Fnu Asina, Ivana Brzonova, Keith Voeller, Yun Ji, Alena Kubatova, Evguenii Kozliak

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Lignin, a heterogeneous three-dimensional biopolymer, is one of the building blocks of lignocellulosic biomass. Due to its limited chemical reactivity, lignin is currently processed as a low-value by-product in pulp and paper mills. Among various industrial lignins, Kraft lignin represents a major source of by-products generated during the widely employed pulping process across the pulp and paper industry. Therefore, valorization of Kraft lignin holds great potential as this would provide a readily available source of aromatic compounds for various industrial applications. Microbial degradation is well known for using both highly specific ligninolytic enzymes secreted by microorganisms and mild operating conditions compared with conventional chemical approaches. In this study, the degradation of Indulin AT lignin was assessed by comparing the effects of Basidiomycetous fungi (Coriolus versicolour and Trametes gallica) and Actinobacteria (Mycobacterium sp. and Streptomyces sp.) to two commercial laccases, T. versicolour ( ≥ 10 U/mg) and C. versicolour ( ≥ 0.3 U/mg). After 54 days of cultivation, the extent of microbial degradation was significantly higher than that of commercial laccases, reaching a maximum of 38 wt% degradation for C. versicolour treated samples. Lignin degradation was further confirmed by thermal carbon analysis with a five-step temperature protocol. Compared with commercial laccases, a significant decrease in char formation at 850ºC was observed among all microbial-degraded lignins with a corresponding carbon percentage increase from 200ºC to 500ºC. To complement the carbon analysis result, chemical characterization of the degraded products at different stages of the delignification by microorganisms and commercial laccases was performed by Pyrolysis-GC-MS.

Keywords: lignin, microbial degradation, pyrolysis-GC-MS, thermal carbon analysis

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Authors: Chiara Mansanta, Daniela Sani

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The present paper presents the further development of the application of a standard methodology to boost innovation inside real case studies of manufacturing companies. The proposed methodology provides a viable solution for manufacturing companies that have to evaluate new business ideas. The study underlined the concept of entrepreneurship and how a manager can use it to promote innovation inside their companies. Starting from a literature study on entrepreneurship, this paper examines the role of the manager in supporting a company’s development. The empirical part of the study is based on two manufacturing companies that used the proposed methodology to favour entrepreneurship through an alternative approach. The research demonstrated the need for companies to have a structured and well-defined methodology to achieve their goals. The purpose of this article is to understand the significance of business models inside companies and explore how they affect business strategy and innovation management. The idea is to use business models to support entrepreneurs in their decision-making processes, reducing risks and avoiding errors.

Keywords: entrepreneurship, manufacturing companies, solution validation, strategic management

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Authors: Husam Abdulmuttaleb Hashim

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The environmental pollution problems represent a great challenge to the world, threatening to destroy all the evolution that mankind has reached, the organizations and associations that cares about environment are trying to warn the world from the forthcoming danger resulted from excessive use of nature resources and consuming it without looking to the damage happened as a result of unfair use of it. Most of the urban centers suffers from the environmental pollution problems and health, economic, and social dangers resulted from this pollution, and while the land use planning is responsible for distributing different uses in urban centers and controlling the interactions between these uses to reach a homogeneous and perfect state for the different activities in cities, the occurrence of environmental problems in the shade of existing land use planning operation refers to the disorder or insufficiency in this operation which leads to presence of such problems, and this disorder lays in lack of sufficient importance to the environmental considerations during the land use planning operations and setting up the master plan, so the research start to study this problem and finding solutions for it, the research assumes that using accurate and scientific methods in early stages of land use planning operation will prevent occurring of environmental pollution problems in the future, the research aims to study and show the importance of the environmental impact assessment method (EIA) as an important planning tool to investigate and predict the pollution ranges of the land use that has a polluting pattern in land use planning operation. This research encompasses the concept of environmental assessment and its kinds and clarifies environmental impact assessment and its contents, the research also dealt with urban planning concept and land use planning, it also dealt with the current situation of the case study (Al-Garma district) and the land use planning in it and explain the most polluting use on the environment which is the industrial land use represented in the tannery industries and then there was a stating of current situation of this land use and explaining its contents and environmental impacts resulted from it, and then we analyzed the tests applied by the researcher for water and soil, and perform environmental evaluation through applying environmental impact assessment matrix using the direct method to reveal the pollution ranges on the ambient environment of industrial land use, and we also applied the environmental and site limits and standards by using (GIS) and (AUTOCAD) to select the site of the best alternative of the industrial region in Al-Garma district after the research approved the unsuitability of its current site location for the environmental and site limitations, the research conducted some conclusions and recommendations regard clarifying the concluded facts and to set the proper solutions.

Keywords: EIA, pollution, tannery industry, land use planning

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Authors: Javier Sanchez Vidal, Ciarán Mac An Bhaird, Brian Lucey

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This study examines the decision by firm owners not to apply for intermediated debt due to a perception that their application will be rejected. Based on a sample of SMEs in 9 European countries over the period 2009-2011, we examine potential explanatory factors for borrower discouragement, including firm, macroeconomic, regulatory and banking industry variables. Compared with firms that applied for bank loans, discouraged borrowers are smaller, younger, have declining turnover and an increasing debt/assets ratio. Perceived willingness of banks to lend rather than the company’s own credit history is more important to encourage applications. Perceptions of refusal are procyclical and may be self-perpetuating. Increased concentration in the banking sector reduces discouragement, indicating the importance of relationship banking. Transmission of macro effects through the banking system and economic environment may also lead to higher levels of discouragement. A good regulatory scheme is also advisable, either for the lenders or the borrowers (overall the good ones).

Keywords: entrepreneurial finance, discouraged borrowers, banking, financial crisis, eurozone

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Authors: Jessica Pinheiro Silva, Brenda Rabello De Camargo, Daniel Gusmao De Morais, Eliane Ferreira Noronha

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The utilization of lignocellulosic biomass as source of polysaccharides for industrial applications requires an arsenal of enzymes with different mode of action able to hydrolyze its complex and recalcitrant structure. Clostridium thermocellum is gram-positive, thermophilic bacterium producing lignocellulosic hydrolyzing enzymes in the form of multi-enzyme complex, termed celulossomes. This complex has several hydrolytic enzymes attached to a large and enzymically inactive protein known as Cellulosome-integrating protein (CipA), which serves as a scaffolding protein for the complex produced. This attachment occurs through specific interactions between cohesin modules of CipA and dockerin modules in enzymes. The present work aims to construct celulosomes in vitro with the structural protein CipA, a xylanase called Xyn10D and a cellulose called CelJ from C.thermocellum. A mini-scafoldin was constructed from modules derived from CipA containing two cohesion modules. This was cloned and expressed in Escherichia coli. The other two genes were cloned under the control of the alcohol oxidase 1 promoter (AOX1) in the vector pPIC9 and integrated into the genome of the methylotrophic yeast Pichia pastoris GS115. Purification of each protein is being carried out. Further studies regarding enzymatic activity of the cellulosome is going to be evaluated. The cellulosome built in vitro and composed of mini-CipA, CelJ and Xyn10D, can be very interesting for application in industrial processes involving the degradation of plant biomass.

Keywords: cellulosome, CipA, Clostridium thermocellum, cohesin, dockerin, yeast

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Authors: Abdul Kadir, L. Meily Kurniawidjaja

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Batik Madura workers are exposed to many Musculoskeletal Disorders risk factors, particularly Low Back Pain (LBP). This study was conducted as an early detection of ergonomic risk level on Workers Industrial Sentra Batik Madura in Dusun Banyumas, Klampar Subdistrict, Proppo Pamekasan, Madura, East Java. This study includes 12 workers who 11 workers had pain in the upper and lower part of the neck, back, wrist right hand, also 10 workers had pain in the right shoulder. This is a descriptive observational study with cross-sectional approach. Qualitative research by observing workers activity such as draw and putting the wax motif, fabric dyeing, fabric painting, discoloration, washing, and drying. The results are workers have identified ergonomic hazards such as awkward postures, twisting movements, repetitive, and static work postures. Using the method of REBA and QEC, the results get a very high-risk level of activity in each of Madura batik making process is the draw and putting the wax motif, coloring, painting, discoloration, washing, and drying. The level of risk can be reduced by improvement of work equipment include the provision of seats, strut fabric, high settings furnaces, drums, coloring basin, and washing tub.

Keywords: activities of Madura's batik, ergonomic risk level, equipment, QEC (Quick Exposure Checklist), REBA (Rapid Entire Body Assessment)

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Authors: Fadimatou Ngounouno Yamgouota, Isaac Bertrand Gbambié Mbowoub, Ismaila Ngounounob

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Barium and titanium bearing biotite from alkaline trachyte of Upper Benue valley, Northern Cameroon is studied. The iron enrichment index of mica (average I.E.=0.40) is intermediate between annite and phlogopite. The biotite phenocrysts contain up to 6.2 wt. % BaO and 9.8 wt. % TiO2. The BaO content of electron-microprobe mica is positively correlated with the Al2O3, TiO2, and FeO contents, and negatively correlated with the SiO2, K2O, and MgO contents. Ba and Ti rich micas are generally found in in SiO2 deficient rocks, whereas Ba and Ti bearing mica in this study occur in silica-saturated rocks. Most of the phenocrysts analysed have deficiencies in their octahedral and interlayer sites. Deficiencies in the octahedral sites may arise from the Ti vacancy and partly the Ti tschermakite substitution. On the other hand, deficiencies in the interlayer-site are due to the replacement of K by Ba. The substitution mechanism in the Upper Benue valley mica is characterized by Ba + 2Ti + 3Al =(K + Na + Ca) + 3(Mg + Fe + Mn) + 3Si, with an excellent correlation coefficient. Biotite compositions from the Upper Benue valley area fall between the quartz-fayalite-magnetite (QFM) and nickel-nickel-oxide (NNO) oxygen fugacity buffers. All these show that Upper Benue valley mica with high Ba and Ti contents may be formed from magmas rich in these elements.

Keywords: Benue valley, trachyte, biotite, mineral chemistry, enrichment

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Authors: Monica Lia

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This article presents a customer data analysis model using business intelligence tools for data modelling, transforming, data visualization and dynamic reports building. Economic organizational customer’s analysis is made based on the information from the transactional systems of the organization. The paper presents how to develop the data model starting for the data that companies have inside their own operational systems. The owned data can be transformed into useful information about customers using business intelligence tool. For a mature market, knowing the information inside the data and making forecast for strategic decision become more important. Business Intelligence tools are used in business organization as support for decision-making.

Keywords: customer analysis, business intelligence, data warehouse, data mining, decisions, self-service reports, interactive visual analysis, and dynamic dashboards, use cases diagram, process modelling, logical data model, data mart, ETL, star schema, OLAP, data universes

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Lali Khurtsia, Vano Tsertsvadze

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Georgian drug policy is directed to reduce the supply of drugs. Retrospective analysis has shown that law enforcement activities have been followed by the expulsion of particular injecting drugs. The demand remains unchanged and drugs are substituted by the hand-made, even more dangerous homemade drugs entered the market. To find out expected new trends on the Georgian drug market, qualitative study was conducted with Georgian drug users to determine drug supply routes. It turned out that drug suppliers and consumers for safety reasons and to protect their anonymity, use Skype to make deals. IT in illegal drug trade is even more sophisticated in the worldwide. Trading with Bitcoins in the Darknet ensures high confidentiality of money transactions and the safe circulation of drugs. In 2014 largest Bitcoin mining enterprise in the world was built in Georgia. We argue that the use of Bitcoins and Darknet by Georgian drug consumers and suppliers will be an incentive to response adequately to the government's policy of restricting supply in order to satisfy market demand for drugs.

Keywords: bitcoin, darknet, drugs, policy

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Authors: Hotlan Siagian, Han Tae Hee

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This study aims to examine the effect of supplier trust and top management involvement on the supply chain risk management through buyer-supplier relationship. The population of the research is 44 Korean companies domiciled in East and Central Java of Indonesia. The respondent consists of a top management level from each company. Data collection used a questionnaire designed with five-item Likert scale. Collected data were analyzed using structural equation modeling (SEM) technique with SmartPLS software version 3.0 to examine the hypotheses. The result revealed that supplier trust has an effect on supply chain risk management, top management involvement affects supply chain risk management, supplier trust influences buyer-supplier relationship, top management involvement affects the buyer-supplier relationship, and buyer-supplier relationship affects supply chain risk management. The last finding is that buyer-supplier relationship empirically mediates the effect of supplier trust and top management involvement.

Keywords: buyer supplier relationship, supplier trust, supply chain risk management, top management involvement

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Authors: Azhar Naila, Steuer Benjamin

Abstract:

Over-exploitation of resources and the burden on natural systems have provoked worldwide concerns about the potential resource as well as supply risks in the future. It has been estimated that the consumption of materials and resources will double by 2060, substantially mounting the amount of waste and emissions produced by individuals, organizations, and businesses, which necessitates sustainable technological innovations to address the problem. Therefore, there is a need to design products and services purposefully for material resource efficiency. This directs us toward the conceptualization and implementation of the ‘Circular Economy (CE),’ which has gained considerable attention among policymakers, researchers, and businesses in the past decade. A large amount of literature focuses on the concept of CE. However, contextual empirical research on the need to embrace CE in an emerging economy like Pakistan is still scarce, where the traditional economic model of take-make-dispose is quite common. Textile exports account for approximately 61% of Pakistan's total exports, and the industry provides employment for about 40% of the country's total industrial workforce. The industry provides job opportunities to above 10 million farmers, with cotton as the main crop of Pakistan. Consumers, companies, as well as the government have explored very limited CE potential in the country. This gap has motivated us to carry out the present study. The study is based on a mixed method approach, for which key informant interviews have been conducted to get insight into the present situation of the ecosystem readiness for the adoption of CE in 20 textile manufacturing industries. The subject study has been conducted on the following areas i) the level of understanding of the CE concept among key stakeholders in the textile manufacturing industry ii) Companies are pushing boundaries to invest in circularity-based initiatives, exploring the depths of risk-taking iii) the current national policy framework support the adoption of CE. Qualitative assessment has been undertaken using MAXQDA to analyze the data received after the key informant interviews. The data has been transcribed and coded for further analysis. The results show that most of the key stakeholders have a clear understanding of the concept, whereas few consider it to be only relevant to the end-of-life treatment of waste generated from the industry. Non-governmental organizations have been observed to be key players in creating awareness among the manufacturing industries. Maximum companies have shown their consent to invest in initiatives related to the adoption of CE. Whereas a few consider themselves far behind the race due to a lack of financial resources and support from responsible institutions. Mostly, the industries have an ambitious vision for integrating CE into the company’s policy but seem not to be ready to take any significant steps to nurture a culture for experimentation. However, the government is not playing any vital role in the transition towards CE; rather, they have been busy with the state’s uncertain political situation. Presently, Pakistan does not have any policy framework that supports the transition towards CE. Acknowledging the present landscape a well-informed CE transition is immediately required.

Keywords: circular economy, textile supply chain, textile manufacturing industries, resource efficiency, ecosystem readiness, multi-case study analysis

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Authors: Bashistha Kumar Kanth, Seung Pil Pack

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Carbonic anhydrase is ubiquitously distributed in organisms, and is fundamental to many eukaryotic biological processes such as photosynthesis, respiration, CO2 and ion transport, calcification and acid–base balance. However, CA occurs across the spectrum of prokaryotic metabolism in both the archaea and bacteria domains and many individual species contain more than one class. In this review, various roles of CA involved in cellular mechanism are presented to find out the CA functions applicable for industrial use.

Keywords: carbonic anhydrase, mechanism, CO2 sequestration, respiration

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Authors: Muhammad Shahzad Shifa, Amna Shifa, Muhammad Omar, Aamir Shahzad, Rahmat Ali Khan

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Many applications of remote sensing to vegetation and crop response depend on spectral properties of individual leaves and plants. Vegetation indices are usually determined to estimate crop biophysical parameters like crop canopies and crop leaf area indices with the help of remote sensing. Cotton crops assessment is performed with the help of vegetative indices. Remotely sensed images from an optical multispectral radiometer MSR5 are used in this study. The interpretation is based on the fact that different materials reflect and absorb light differently at different wavelengths. Non-normalized and normalized forms of these datasets are analyzed using two complementary data mining algorithms; K-means and K-nearest neighbor (KNN). Our analysis shows that the use of normalized reflectance data and vegetative indices are suitable for an automated assessment and decision making.

Keywords: cotton, condition assessment, KNN algorithm, clustering, MSR5, vegetation indices

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Authors: Kyriaki Kalaitzidou, Athanasia Tolkou, Christina Raptopoulou, Manassis Mitrakas, Anastasios Zouboulis

Abstract:

Phosphate rock is the main source of phosphorous (P) in fertilizers and is essential for high crop yield in agriculture; currently, it is considered as a critical element, phasing scarcity. Chemical precipitation, which is a commonly used method of phosphorous removal from wastewaters, finds its significance in that phosphates may be precipitated in appropriate chemical forms that can be reused-recovered. Most often phosphorous is removed from wastewaters in the form of insoluble phosphate salts, by using salts (coagulants) of multivalent metal ions, most frequently iron, aluminum, calcium, or magnesium. The removal degree is affected by various factors, such as pH, chemical agent dose, temperature, etc. In this study, phosphate precipitation from the secondary (biologically treated) effluent of a municipal wastewater treatment plant is examined. Using chlorosulfate (FeClSO4) it was attempted to either remove and/or recover PO43-. Results showed that the use of Fe3+ can achieve residual concentrations lower than the commonly applied legislation limit of PO43- (i.e. 3 mg PO43-/L) by adding 7.5 mg/L Fe3+ in the secondary effluent with an initial concentration of about 10 mg PO43-/L and at pH range between 6 to 9. In addition, the formed sediment has a percentage of almost 24% PO43- content. Therefore, simultaneous removal and recovery of PO43- as ferric phosphate can be achieved, making it possible for the ferric phosphate to be re-used as a possible (secondary) fertilizer source.

Keywords: ferric phosphate, phosphorus recovery, phosphorus removal, wastewater treatment

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Authors: L. Kutateldze, T. Urushadze, R. Khvedelidze, N. Zakariashvili, I. Khokhashvili, T. Sadunishvili

Abstract:

Microbial cellulase/xylanase have shown their potential application in various industries including pulp and paper, textile, laundry, biofuel production, food and feed industry, brewing, and agriculture. Extremophilic micromycetes and their enzymes that are resistant to critical values of temperature and pH, and retaining enzyme activity for a long time are of great industrial interest. Among strains of microscopic fungi from the collection of S. Durmishidze Institute of Biochemistry and Biotechnology, strains isolated from different ecological niches of Southern Caucasus-active producers of cellulase/xylanase have been selected by means of screening under deep cultivation conditions. Extremophilic micromycetes and their enzymes that are resistant to critical values of temperature and pH, and retaining enzyme activity for a long time are of great industrial interest. Among strains of microscopic fungi from the collection of S. Durmishidze Institute of Biochemistry and Biotechnology, strains isolated from different ecological niches of Southern Caucasus-active producers of cellulase/xylanase have been selected by means of screening under deep cultivation conditions. Representatives of the genera Aspergillus, Penicillium and Trichoderma are outstanding by relatively high activities of these enzymes. Among the producers were revealed thermophilic strains, representatives of the genus Aspergillus-Aspergillus terreus, Aspergillus versicolor, Aspergillus wentii, also strains of Sporotrichum pulverulentum and Chaetomium thermophile. As a result of optimization of cultivation media and conditions, activities of enzymes produced by the strains have been increased by 4 -189 %. Two strains, active producers of cellulase/xylanase – Penicillium canescence E2 (mesophile) and Aspergillus versicolor Z17 (thermophile) were chosen for further studies. Cellulase/xylanase enzyme preparations from two different genera of microscopic fungi Penicillium canescence E2 and Aspergillus versicolor Z 17 were obtained with activities 220 U/g /1200 U/g and 125 U/g /940 U/g, correspondingly. Main technical characteristics were as follows: the highest enzyme activities were obtained for mesophilic strain Penicillium canescence E2 at 45-500C, while almost the same enzyme activities were fixed for the thermophilic strain Aspergillus versicolor Z 17 at temperature 60-65°C, exceeding the temperature optimum of the mesophile by 150C. Optimum pH of action of the studied cellulase/xylanases from mesophileic and thermophilic strains were similar and equaled to 4.5-5.0 It has been shown that cellulase/xylanase technical preparations from selected strains of Penicillium canescence E2 and Aspergillus versicolor Z17 hydrolyzed cellulose of untreated wheat straw to reducible sugars by 46-52%, and to glucose by 22-27%. However the thermophilic enzyme preparations from the thermophilic A.versicolor strains conducted the process at 600C higher by 100C as compared to mesophlic analogue. Rate of hydrolyses of the pretreated substrate by the same enzyme preparations to reducible sugars and glucose conducted at optimum for their action 60 and 500C was 52-61% and 29-33%, correspondingly. Thus, maximum yield of glucose and reducible sugars form untreated and pretreated wheat straw was achieved at higher temperature (600C) by enzyme preparations from thermophilic strain, which gives advantage for their industrial application.

Keywords: cellulase/xylanase, cellulose hydrolysis, microscopic fungi, thermophilic strain

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