Search results for: predictive mining

Authors: A. Asaga, N. I. Basuki

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The research area is located in Cisarua, Bogor Regency, West Java, with 12,8 km2 wide. This area belongs to mining region of PT Aneka Tambang Tbk. The purpose of this research is to study geological condition, alteration type and pattern, and type of mineralization. Geomorphology of the research area is at young to mature stage, which can be divided into Ciparigi’s Parasite Volcanic Cone Unit, Ciparigi Caldera Valley Unit, Ciparigi Caldera Rim Hill Unit, and Pongkor Volcanic Hill. Stratigraphy of the research area consist of five units, they are Laharic Breccia (Pliocene), Pyroclastic Breccia, Lapilli Tuff, Flow Tuff, Fall Tuff, and Andesite Lava (Pleistocene). Based on mineral composition, it is interpreted that there is magma composition changing from rhyolite to andesitic. Geological structures in the research area are caused by NE-SW and N-S stress direction; they are Ciparay Right Strike-Slip Fault (Pliocene), Cisarua Right Strike-Slip Fault, G. Singa Left Strike-Slip Fault, and Cinyuncung Right Strike-Slip Fault (Pleistocene). Weak to strong hydrothermal alteration can be found in the research area.They are Chlorite ± Smectite ± Halloysite Zone, Smectite - Illite - Quartz Zone, Smectite - Kaolinite - Illite - Chlorite Zone, and Smectite - Chlorite - Calcite - Quartz Zone. The distribution and assemblage of alteration minerals is controlled by lithology and geological structures in Pleistocene. Mineralization produce ore minerals, those are pyrite, marcasite, chalcopyrite, sphalerite, galena, and chalcocite. There are calcite and quartz veins that show colloform, comb, and crystalline textures. Hydrothermal alteration assemblages, ore minerals, and cavity filling textures suggest that mineralization type in research area is epithermal low sulphidation.

Keywords: Pongkor, hydrothermal alteration, epithermal, geochemistry

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Authors: Dorothy Ebere Adimora

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The study investigated the predictive power of home environment and peer pressure on disruptive behaviour and risky sexual behaviour of Secondary School Class Two Adolescents in Enugu State, Nigeria. The design of the study is a cross sectional survey of correlational study. The study was carried out in the six Education zones in Enugu state, Nigeria. Enugu State is divided into six education zones, namely Agbani, Awgu, Enugu, Nsukka, Obollo-Afor and Udi. The population for the study was all the 31,680 senior secondary class two adolescents in 285 secondary schools in Enugu State, Nigeria in 2014/2015 academic session. The target population was students in SSS.2 senior secondary class two. They constitute one-sixth of the entire student population in the state. The sample of the study was 528, a multi stage sampling technique was employed to draw the sample. Four research questions and four null hypotheses guided the study. The instruments for data collection were an interview session and a structured questionnaire of four clusters, they are; home environment, peer pressure, risky sexual behaviour and disruptive behaviour disorder questionnaires. The instruments were validated by 3 experts, two in psychology and one in measurement and Evaluation in Faculty of Education, University of Nigeria, Nsukka. The reliability coefficient of the instruments was ascertained by subjection to field trial. The adolescents were asked to complete the questionnaire on their home environment, peer pressure, disruptive behaviour disorder and risky sexual behaviours. The risky sexual behaviours were ascertained based on interview conducted on their actual sexual practice within the past 12 months. The research questions were analyzed using Pearson r and R-square, while the hypotheses were tested using ANOVA and multiple regression analysis at 0.05 level of significance. The results of this survey revealed that the adolescents are sexually active in very young ages. The mean age at sexual debut for the adolescents covered in this survey is a pointer to the fact that some of them started engaging in sexual activities long ago. It was also found that the adolescents engage in disruptive behaviour as a result of their poor home environment factors and association with negative peers. Based on the findings, it was recommended that the adolescents should be exposed to enhanced home environment such as parents’ responsiveness, organization of the environment, availability of appropriate learning materials, opportunities for daily stimulation and to offer a proper guidance to these adolescents to avoid negative peer influence which could result in risky sexual behaviour and disruptive behaviour disorder.

Keywords: parenting, peer group, adolescents, sexuality, conduct disorder

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Authors: Adisorn Banhan, Toansakul Santiboon, Prasong Saihong

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Using the Socio-Scientific Issues-Based Learning Method is to compare of the blended instruction of STEM education with a sample consisted of 84 students in 2 classes at the 11th grade level in Sarakham Pittayakhom School. The 2-instructional models were managed of five instructional lesson plans in the context of electronic wave issue. These research procedures were designed of each instructional method through two groups, the 40-experimental student group was designed for the instructional STEM education (STEMe) and 40-controlling student group was administered with the Socio-Scientific Issues-Based Learning (SSIBL) methods. Associations between students’ learning achievements of each instructional method and their science attitudes of their predictions to their exploring activities toward physics with the STEMe and SSIBL methods were compared. The Measuring Reasonable Ethics Test (MRET) was assessed students’ reasonable ethics with the STEMe and SSIBL instructional design methods on two each group. Using the pretest and posttest technique to monitor and evaluate students’ performances of their reasonable ethics on electromagnetic wave issue in the STEMe and SSIBL instructional classes were examined. Students were observed and gained experience with the phenomena being studied with the Socio-Scientific Issues-Based Learning method Model. To support with the STEM that it was not just teaching about Science, Technology, Engineering, and Mathematics; it is a culture that needs to be cultivated to help create a problem solving, creative, critical thinking workforce for tomorrow in physics. Students’ attitudes were assessed with the Test Of Physics-Related Attitude (TOPRA) modified from the original Test Of Science-Related Attitude (TOSRA). Comparisons between students’ learning achievements of their different instructional methods on the STEMe and SSIBL were analyzed. Associations between students’ performances the STEMe and SSIBL instructional design methods of their reasonable ethics and their science attitudes toward physics were associated. These findings have found that the efficiency of the SSIBL and the STEMe innovations were based on criteria of the IOC value higher than evidence as 80/80 standard level. Statistically significant of students’ learning achievements to their later outcomes on the controlling and experimental groups with the SSIBL and STEMe were differentiated between students’ learning achievements at the .05 level. To compare between students’ reasonable ethics with the SSIBL and STEMe of students’ responses to their instructional activities in the STEMe is higher than the SSIBL instructional methods. Associations between students’ later learning achievements with the SSIBL and STEMe, the predictive efficiency values of the R2 indicate that 67% and 75% for the SSIBL, and indicate that 74% and 81% for the STEMe of the variances were attributable to their developing reasonable ethics and science attitudes toward physics, consequently.

Keywords: socio-scientific issues-based learning method, STEM education, science attitudes, measurement, reasonable ethics, physics classes

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Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

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Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

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Authors: Tessa E. Reid, Vanessa N. Kavamura, Maider Abadie, Adriana Torres-Ballesteros, Mark Pawlett, Ian M. Clark, Jim Harris, Tim Mauchline

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The deleterious effect of chemical fertilizer on rhizobacterial diversity has been well documented using 16S rRNA gene amplicon sequencing and predictive metagenomics. Biofertilization is a cost-effective and sustainable alternative; improving strategies depends on isolating beneficial soil microorganisms. Although culturing is widespread in biofertilization, it is unknown whether the composition of cultured isolates closely mirrors native beneficial rhizobacterial populations. This study aimed to determine the relative abundance of culturable plant growth-promoting rhizobacteria (PGPR) isolates within total soil DNA and how potential PGPR populations respond to chemical fertilization in a commercial wheat variety. It was hypothesized that PGPR will be reduced in fertilized relative to unfertilized wheat. Triticum aestivum cv. Cadenza seeds were sown in a nutrient depleted agricultural soil in pots treated with and without nitrogen-phosphorous-potassium (NPK) fertilizer. Rhizosphere and rhizoplane samples were collected at flowering stage (10 weeks) and analyzed by culture-independent (amplicon sequence variance (ASV) analysis of total rhizobacterial DNA) and -dependent (isolation using growth media) techniques. Rhizosphere- and rhizoplane-derived microbiota culture collections were tested for plant growth-promoting traits using functional bioassays. In general, fertilizer addition decreased the proportion of nutrient-solubilizing bacteria (nitrate, phosphate, potassium, iron and, zinc) isolated from rhizocompartments in wheat, whereas salt tolerant bacteria were not affected. A PGPR database was created from isolate 16S rRNA gene sequences and searched against total soil DNA, revealing that 1.52% of total community ASVs were identified as culturable PGPR isolates. Bioassays identified a higher proportion of PGPR in non-fertilized samples (rhizosphere (49%) and rhizoplane (91%)) compared to fertilized samples (rhizosphere (21%) and rhizoplane (19%)) which constituted approximately 1.95% and 1.25% in non-fertilized and fertilized total community DNA, respectively. The analyses of 16S rRNA genes and deduced functional profiles provide an in-depth understanding of the responses of bacterial communities to fertilizer; this study suggests that rhizobacteria, which potentially benefit plants by mobilizing insoluble nutrients in soil, are reduced by chemical fertilizer addition. This knowledge will benefit the development of more targeted biofertilization strategies.

Keywords: bacteria, fertilizer, microbiome, rhizoplane, rhizosphere

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Authors: S. Moustafa El Shanawany, I. Labib Salem, F. Mohamed Magdy Badr El Dine, H. Tag El Deen Abd Allah

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Thermal injuries remain a global health problem and a common issue encountered in forensic pathology. They are a devastating cause of morbidity and mortality in children and adults especially in developing countries, causing permanent disfigurement, scarring and grievous hurt. Burns have always been a matter of legal concern in cases of suicidal burns, self-inflicted burns for false accusation and homicidal attempts. Assessment of burn injuries as well as rating permanent disabilities and disfigurement following thermal injuries for the benefit of compensation claims represents a challenging problem. This necessitates the development of reliable scoring systems to yield an expected likelihood of permanent disability or fatal outcome following burn injuries. The study was designed to identify the risk factors of mortality in acute burn patients and to evaluate the applicability of FLAMES (Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex) and BOBI (Belgian Outcome in Burn Injury) model scores in predicting the outcome. The study was conducted on 100 adult patients with acute burn injuries admitted to the Burn Unit of Alexandria Main University Hospital, Egypt from October 2014 to October 2015. Victims were examined after obtaining informed consent and the data were collected in specially designed sheets including demographic data, burn details and any associated inhalation injury. Each burn patient was assessed using both BOBI and FLAMES scoring systems. The results of the study show the mean age of patients was 35.54±12.32 years. Males outnumbered females (55% and 45%, respectively). Most patients were accidently burnt (95%), whereas suicidal burns accounted for the remaining 5%. Flame burn was recorded in 82% of cases. As well, 8% of patients sustained more than 60% of total burn surface area (TBSA) burns, 19% of patients needed mechanical ventilation, and 19% of burnt patients died either from wound sepsis, multi-organ failure or pulmonary embolism. The mean length of hospital stay was 24.91±25.08 days. The mean BOBI score was 1.07±1.27 and that of the FLAMES score was -4.76±2.92. The FLAMES score demonstrated an area under the receiver operating characteristic (ROC) curve of 0.95 which was significantly higher than that of the BOBI score (0.883). A statistically significant association was revealed between both predictive models and the outcome. The study concluded that both scoring systems were beneficial in predicting mortality in acutely burnt patients. However, the FLAMES score could be applied with a higher level of accuracy.

Keywords: BOBI, burns, FLAMES, scoring systems, outcome

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Authors: Temesgen Oljira, Olugbenga Akindeji Okunlola, Akinade Shadrach Olatunji, Dereje Ayalew, Bekele A. Bedada, Tasin Godlove Bafon

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The Megele area gold-bearing Neoproterozoic rocks in the Western Ethiopian Shield has been under exploration for the last few decades. The geochemical and ore petrological characterization of the gold-bearing granite gneiss and associated quartz vein is crucial in understanding the gold's genesis. The present study concerns the ore petrological, geochemical, and stable O2 and S characterization of the gold-bearing granite gneiss and associated quartz vein. This area is known for its long history of placer gold mining. The presence of quartz veins of different generations and orientations, visible sulfide mineralization, and oxidation suggests that the Megele area is geologically fertile for mineralization. The Au and base metals analysis also indicate that Megele area rocks are characterized by Cu (2-22 ppm av. 7.83 ppm), Zn (2-53 ppm av. 29.33 ppm), Co (1-27 ppm av. 13.33 ppm), Ni (2-16 ppm av. 10 ppm), Pb (5-10 ppm av. 8.33 ppm), Au (1-5 ppb av. 2.11 ppb), Ag (0.5 ppm), As (5-12 ppm av. 7.83 ppm), Cd (0.5ppm), Li (0.5 ppm), Mo (1-4 ppm av. 1.6 ppm), Sc (5-13 ppm av. 9.3 ppm), and Tl (10 ppm). The oxygen isotope (δ18O) values of gold-bearing granite gneiss and associated quartz veins range from +8.6 to +11.5 ‰, suggesting the mixing of metamorphic water with magmatic water within the ore-forming fluid. The Sulfur isotope (δ34S) values of gold-bearing granite gneiss range from -1.92 to -0.45 ‰ (mean value of -1.13 ‰) indicating the narrow range of value. This suggests that the sulfides have been precipitated from the fluid system originating from a single source of the magmatic component under sulfur isotopic fractionation equilibrium condition. The tectonic setting of the host rocks, the occurrence of ore bodies, mineral assemblages of the host rocks and proposed ore-forming fluids of the Megele area gold prospects have similarities with features of orogenic gold deposit. The δ18O and δ34S isotopic values also suggested a metamorphic origin with the magmatic components. Thus, the Megele gold prospect could be related to an orogenic gold deposit related to metamorphism and associated intrusions.

Keywords: fluid source, gold mineralization, oxygen isotope, stable isotope, sulfur isotope

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Authors: Debjit Ray

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Horizontal gene transfer (HGT) and recombination leads to the emergence of bacterial antibiotic resistance and pathogenic traits. HGT events can be identified by comparing a large number of fully sequenced genomes across a species or genus, define the phylogenetic range of HGT, and find potential sources of new resistance genes. In-depth comparative phylogenomics can also identify subtle genome or plasmid structural changes or mutations associated with phenotypic changes. Comparative phylogenomics requires that accurately sequenced, complete and properly annotated genomes of the organism. Assembling closed genomes requires additional mate-pair reads or “long read” sequencing data to accompany short-read paired-end data. To bring down the cost and time required of producing assembled genomes and annotating genome features that inform drug resistance and pathogenicity, we are analyzing the performance for genome assembly of data from the Illumina NextSeq, which has faster throughput than the Illumina HiSeq (~1-2 days versus ~1 week), and shorter reads (150bp paired-end versus 300bp paired end) but higher capacity (150-400M reads per run versus ~5-15M) compared to the Illumina MiSeq. Bioinformatics improvements are also needed to make rapid, routine production of complete genomes a reality. Modern assemblers such as SPAdes 3.6.0 running on a standard Linux blade are capable in a few hours of converting mixes of reads from different library preps into high-quality assemblies with only a few gaps. Remaining breaks in scaffolds are generally due to repeats (e.g., rRNA genes) are addressed by our software for gap closure techniques, that avoid custom PCR or targeted sequencing. Our goal is to improve the understanding of emergence of pathogenesis using sequencing, comparative genomics, and machine learning analysis of ~1000 pathogen genomes. Machine learning algorithms will be used to digest the diverse features (change in virulence genes, recombination, horizontal gene transfer, patient diagnostics). Temporal data and evolutionary models can thus determine whether the origin of a particular isolate is likely to have been from the environment (could it have evolved from previous isolates). It can be useful for comparing differences in virulence along or across the tree. More intriguing, it can test whether there is a direction to virulence strength. This would open new avenues in the prediction of uncharacterized clinical bugs and multidrug resistance evolution and pathogen emergence.

Keywords: genomics, pathogens, genome assembly, superbugs

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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Authors: Temitope L. Baiyegunhi, Christopher Baiyegunhi, Kuiwu Liu, Oswald Gwavava

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Diagenesis is the most important factor that affects or impact the reservoir property. Despite the fact that published data gives a vast amount of information on the geology, sedimentology and lithostratigraphy of the Ecca Group in the Karoo Basin of South Africa, little is known of the diagenesis of the potentially feasible shales and sandstones of the Ecca Group. The study aims to provide a general account of the diagenesis of sandstones and mudstone of the Ecca Group. Twenty-five diagenetic textures and structures are identified and grouped into three regimes or stages that include eogenesis, mesogenesis and telogenesis. Clay minerals are the most common cementing materials in the Ecca sandstones and mudstones. Smectite, kaolinite and illite are the major clay minerals that act as pore lining rims and pore-filling cement. Most of the clay minerals and detrital grains were seriously attacked and replaced by calcite. Calcite precipitates locally in pore spaces and partly or completely replaced feldspar and quartz grains, mostly at their margins. Precipitation of cements and formation of pyrite and authigenic minerals as well as little lithification occurred during the eogenesis. This regime was followed by mesogenesis which brought about an increase in tightness of grain packing, loss of pore spaces and thinning of beds due to weight of overlying sediments and selective dissolution of framework grains. Compaction, mineral overgrowths, mineral replacement, clay-mineral authigenesis, deformation and pressure solution structures occurred during mesogenesis. During rocks were uplifted, weathered and unroofed by erosion, this resulted in additional grain fracturing, decementation and oxidation of iron-rich volcanic fragments and ferromagnesian minerals. The rocks of Ecca Group were subjected to moderate-intense mechanical and chemical compaction during its progressive burial. Intergranular pores, matrix micro pores, secondary intragranular, dissolution and fractured pores are the observed pores. The presence of fractured and dissolution pores tend to enhance reservoir quality. However, the isolated nature of the pores makes them unfavourable producers of hydrocarbons, which at best would require stimulation. The understanding of the space and time distribution of diagenetic processes in these rocks will allow the development of predictive models of their quality, which may contribute to the reduction of risks involved in their exploration.

Keywords: diagenesis, reservoir quality, Ecca Group, Karoo Supergroup

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Authors: Shazia Almas, Jaffar Iqbal, Nazia Almas

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In most of the South Asian countries like Pakistan medical profession is one the most valued and respectful professions yet being a medical professional requires an enormous amount of responsibilities and work overload at the same time which possibly can be in contrast with family role of a doctor. Job and family are two primary spheres of a person's life no matter whatever the profession one adopts and the type of family one is running. There is a bi-directional relationship between job and family. The type and nature of work, time schedules, working shifts in medical profession are very demanding in the countries like Pakistan where number of patients is far more higher than the number of doctors available. The work life also have significant impact on family life and vice versa. Because of the sensitivity and interdependency of these relations, today’s overarching and competing demands remain dissatisfactory. The main objective of the current research is to investigate how interpersonal relationships affect work and work affects interpersonal relationships of medical professionals. In line with identifying these facts, the current study aimed to examine the predictive role of psychological capital (self-efficacy, hope, optimism, and resilience), in organizational outcome (job satisfaction) and personal outcome (family satisfaction) amongst male and medical professionals. A total of 350 participants from public and private sector hospitals of Pakistan were recruited through simple random and stratified sampling techniques, with age ranges from 26-50 years. The questionnaire including established and certified self-report measures of Psychological Capital Questionnaire, Job Satisfaction, and Family Satisfaction were adopted to collect the data. The reliability and validity of mentioned instruments were established through Cronbach’s alpha and factor analyses (exploratory and confirmatory) respectively using Structural Equation Modeling (SEM) by AMOS. The proposed hypotheses were tested using Pearson’s Correlation and Regression analyses for predicting effect whereas, t-Test was deployed to verify the difference between male and female health professionals. The results revealed that self-efficacy and optimism predicted job satisfaction while, self-efficacy, hope, and resilience predicted family satisfaction. Moreover, the results depicted significant gender differences in job satisfaction where females were higher on job satisfaction as compared to male medical professionals but no significant differences were observed in levels of family satisfaction between both genders. The study has implications for social, organizational and work policy designers. The study also paves for more researches with positive psychological approach to promote work-family harmony.

Keywords: family satisfaction, job satisfaction, medical professionals, psychological capital

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Authors: Jose Flores, Nadia Gamboa

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A proper water quality management requires the establishment of a monitoring network. Therefore, evaluation of the efficiency of water quality monitoring networks is needed to ensure high-quality data collection of critical quality chemical parameters. Unfortunately, in some Latin American countries water quality monitoring programs are not sustainable in terms of recording historical data or environmentally representative sites wasting time, money and valuable information. In this study, multivariate statistical techniques, such as principal components analysis (PCA) and hierarchical cluster analysis (HCA), are applied for identifying the most significant monitoring sites as well as critical water quality parameters in the monitoring network of the Jequetepeque River basin, in northern Peru. The Jequetepeque River basin, like others in Peru, shows socio-environmental conflicts due to economical activities developed in this area. Water pollution by trace elements in the upper part of the basin is mainly related with mining activity, and agricultural land lost due to salinization is caused by the extensive use of groundwater in the lower part of the basin. Since the 1980s, the water quality in the basin has been non-continuously assessed by public and private organizations, and recently the National Water Authority had established permanent water quality networks in 45 basins in Peru. Despite many countries use multivariate statistical techniques for assessing water quality monitoring networks, those instruments have never been applied for that purpose in Peru. For this reason, the main contribution of this study is to demonstrate that application of the multivariate statistical techniques could serve as an instrument that allows the optimization of monitoring networks using least number of monitoring sites as well as the most significant water quality parameters, which would reduce costs concerns and improve the water quality management in Peru. Main socio-economical activities developed and the principal stakeholders related to the water management in the basin are also identified. Finally, water quality management programs will also be discussed in terms of their efficiency and sustainability.

Keywords: PCA, HCA, Jequetepeque, multivariate statistical

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Authors: M. S. Nikitenko, S. A. Kizilov, D. Y. Khudonogov

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The paper considers an approach to a machine vision operating system combined with using a grid of light markers. This approach is used to solve several scientific and technical problems, such as measuring the capability of an apron feeder delivering coal from a lining return port to a conveyor in the technology of mining high coal releasing to a conveyor and prototyping an autonomous vehicle obstacle detection system. Primary verification of a method of calculating bulk material volume using three-dimensional modeling and validation in laboratory conditions with relative errors calculation were carried out. A method of calculating the capability of an apron feeder based on a machine vision system and a simplifying technology of a three-dimensional modelled examined measuring area with machine vision was offered. The proposed method allows measuring the volume of rock mass moved by an apron feeder using machine vision. This approach solves the volume control issue of coal produced by a feeder while working off high coal by lava complexes with release to a conveyor with accuracy applied for practical application. The developed mathematical apparatus for measuring feeder productivity in kg/s uses only basic mathematical functions such as addition, subtraction, multiplication, and division. Thus, this fact simplifies software development, and this fact expands the variety of microcontrollers and microcomputers suitable for performing tasks of calculating feeder capability. A feature of an obstacle detection issue is to correct distortions of the laser grid, which simplifies their detection. The paper presents algorithms for video camera image processing and autonomous vehicle model control based on obstacle detection machine vision systems. A sample fragment of obstacle detection at the moment of distortion with the laser grid is demonstrated.

Keywords: machine vision, machine vision operating system, light markers, measuring capability, obstacle detection system, autonomous transport

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Authors: Daria Karasalihovic Sedlar, Lucija Jukic, Ivan Smajla, Marija Macenic

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This paper analyses possible geothermal energy production from a mature oil reservoir based on exploitation of underlying aquifer thermal energy for the purpose of heating public buildings. Research was conducted based on the case study of the City of Ivanic-Grad public buildings energy demand and Ivanic oil filed that is situated in the same area. Since the City of Ivanic is one of the few cities in the EU where hydrocarbon exploitation has been taking place for decades almost entirely in urban area, decommissioning of oil wells is inevitable; therefore, the research goal was to investigate how to extend the life-time of the reservoir by exploiting geothermal brine beneath the oil reservoir in an environmental friendly manner. This kind of a project is extremely complex in all segments, from documentation preparation, implementation of technological solutions, and providing ecological measures for environmentally acceptable geothermal energy production and utilization. New mining activities that will be needed for the development of geothermal project at the observed Hydrocarbon Exploitation Field Ivanic will be carried out in order to prepare wells for increasing geothermal brine production. These operations involve the conversion of existing wells (well completion for conversion of the observation wells to production ones) along with workover activities, installation of new heat exchangers, and pipelines. Since the wells are in the urban area of the City of Ivanic-Grad in high density populated area, the inhabitants will be exposed to the different environmental impacts during preparation phase of the project. For the purpose of performing workovers, it will be necessary to secure access to wellheads of existing wells. This paper gives guidelines for describing potential impacts on environment components that could occur during geothermal production preparation on existing mature oil filed, recommends possible protection measures to mitigate these impacts, and gives recommendations for environmental monitoring.

Keywords: geothermal energy production, mature oil filed, environmental impact assessment, underlying aquifer thermal energy

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Authors: Liang Cheng Chang, Wei Ju Huang, You Cheng Chen

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Investigation and research are keys for conjunctive use of surface and groundwater resources. The hydrogeological structure is an important base for groundwater analysis and simulation. Traditionally, the hydrogeological structure is artificially determined based on geological drill logs, the structure of wells, groundwater levels, and so on. In Taiwan, groundwater observation network has been built and a large amount of groundwater-level observation data are available. The groundwater level is the state variable of the groundwater system, which reflects the system response combining hydrogeological structure, groundwater injection, and extraction. This study applies analytical tools to the observation database to develop a methodology for the identification of confined and unconfined aquifers. These tools include frequency analysis, cross-correlation analysis between rainfall and groundwater level, groundwater regression curve analysis, and decision tree. The developed methodology is then applied to groundwater layer identification of two groundwater systems: Zhuoshui River alluvial fan and Pingtung Plain. The abovementioned frequency analysis uses Fourier Transform processing time-series groundwater level observation data and analyzing daily frequency amplitude of groundwater level caused by artificial groundwater extraction. The cross-correlation analysis between rainfall and groundwater level is used to obtain the groundwater replenishment time between infiltration and the peak groundwater level during wet seasons. The groundwater regression curve, the average rate of groundwater regression, is used to analyze the internal flux in the groundwater system and the flux caused by artificial behaviors. The decision tree uses the information obtained from the above mentioned analytical tools and optimizes the best estimation of the hydrogeological structure. The developed method reaches training accuracy of 92.31% and verification accuracy 93.75% on Zhuoshui River alluvial fan and training accuracy 95.55%, and verification accuracy 100% on Pingtung Plain. This extraordinary accuracy indicates that the developed methodology is a great tool for identifying hydrogeological structures.

Keywords: aquifer identification, decision tree, groundwater, Fourier transform

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Authors: Precious Ehiomogue, Ifechukwude Israel Ahuchaogu, Isiguzo Edwin Ahaneku

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Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Several works have verified the adsorption of toxic metals using vermicompost but the application is still scarce for the retention of organic compounds. This research brings to knowledge the effectiveness of earthworm waste (vermicompost) for the remediation of crude oil contaminated soils. The remediation methods adopted in this study were two soil washing methods namely, batch and column process which represent laboratory and in-situ remediation. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental results. This study also investigated the application of machine learning models [Artificial neural network (ANN), Adaptive neuro fuzzy inference system (ANFIS). ANN and ANFIS were evaluated using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency obtained from the Box-Behnken design experiment ranged from 29% to 98.9% for batch process remediation. Optimization of the experimental factors carried out using numerical optimization techniques by applying desirability function method of the response surface methodology (RSM) produce the highest removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively, showing the agreement between experimental and predicted results. For batch and column precess, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between experimental and projected findings. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. It can be concluded that machine learning models can predict the removal of crude oil from polluted soil using vermicompost. Therefore, it is recommended to use machines learning models to predict the removal of crude oil from contaminated soil using vermicompost.

Keywords: ANFIS, ANN, crude-oil, contaminated soil, remediation and vermicompost

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Authors: Sohan Sengupta, Arnab Pramanik, Abhrajyoti Ghosh, Maitree Bhattacharyya

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Newly emerged phyto-pathogens and multi drug resistance have been threating the world for last few decades. Actinomycetes, the most endowed group of microorganisms isolated from unexplored regions of the world may be the ultimate solution to these problems. Thus the aim of this study was to isolate several bioactive actinomycetes strains capable of producing antimicrobial secondary metabolite from Sundarbans, the only mangrove tiger land of the world. Fifty four actinomycetes were isolated and analyzed for antimicrobial activity against fifteen test organisms including three phytopathogens. Nine morphologically distinct and biologically active isolates were subjected to polyphasic identification study. 16s rDNA sequencing indicated eight isolates to reveal maximum similarity to the genus streptomyces, whereas one isolate presented only 93.57% similarity with Streptomyces albogriseolus NRRL B-1305T. Seventy-one carbon sources and twenty-three chemical sources utilization assay revealed their metabolic relatedness. Among these nine isolates three specific strains were found to have notably higher degree of antimicrobial potential effective in a broader range including phyto-pathogenic fungus. PCR base whole genome screen for PKS and NRPS genes, confirmed the occurrence of bio-synthetic gene cluster in some of the isolates for novel antibiotic production. Finally the strain SMS_SU21, which showed antimicrobial activity with MIC value of 0.05 mg ml-1and antioxidant activity with IC50 value of 0.242±0.33 mg ml-1 was detected to be the most potential one. True prospective of this strain was evaluated utilizing GC-MS and the bioactive compound responsible for antimicrobial activity was purified and characterized. Rare bioactive actinomycetes were isolated from unexplored heritage site. Diversity of the biosynthetic gene cluster for antimicrobial compound production has also been evaluated. Antimicrobial compound SU21-C has been identified and purified which is active against a broad range of pathogens.

Keywords: actinomycetes, sundarbans, antimicrobial, pks nrps, phyto-pathogens, GC-MS

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Authors: Addin Osman, Anwar Ali Yahya, Mohammed Basit Kamal

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Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.

Keywords: ABET, accreditation, benchmark collection, machine learning, program educational objectives, student outcomes, supervised multi-class classification, text mining

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Authors: E. M. Rifqi Wilda Pradana, Bagus Bayu Prabowo, Meida Riski Pujiyati, Efraim Maykhel Hagana Ginting, Virgiawan Arya Hangga Reksa

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The VLF (Very Low Frequency) method is an electromagnetic method that uses low frequencies between 10-30 KHz which results in a fairly deep penetration. In this study, the VLF method was used for zonation of disaster-prone areas by identifying geological structures in the form of faults. Data acquisition was carried out in Trimulyo Region, Jetis District, Bantul Regency, Special Region of Yogyakarta, Indonesia with 8 measurement paths. This study uses wave transmitters from Japan and Australia to obtain Tilt and Elipt values that can be used to create RAE (Rapat Arus Ekuivalen or Current Density) sections that can be used to identify areas that are easily crossed by electric current. This section will indicate the existence of a geological structure in the form of faults in the study area which is characterized by a high RAE value. In data processing of VLF method, it is obtained Tilt vs Elliptical graph and Moving Average (MA) Tilt vs Moving Average (MA) Elipt graph of each path that shows a fluctuating pattern and does not show any intersection at all. Data processing uses Matlab software and obtained areas with low RAE values that are 0%-6% which shows medium with low conductivity and high resistivity and can be interpreted as sandstone, claystone, and tuff lithology which is part of the Semilir Formation. Whereas a high RAE value of 10% -16% which shows a medium with high conductivity and low resistivity can be interpreted as a fault zone filled with fluid. The existence of the fault zone is strengthened by the discovery of a normal fault on the surface with strike N550W and dip 630E at coordinates X= 433256 and Y= 9127722 so that the activities of residents in the zone such as housing, mining activities and other activities can be avoided to reduce the risk of natural disasters.

Keywords: current density, faults, very low frequency, zonation

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Authors: Kibrom Hadush

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Countries like Ethiopia, whose economy is mainly rain-fed dependent agriculture, are highly vulnerable to climate variability and weather extremes. Sub-seasonal (monthly) and dekadal forecasts are hence critical for crop production and water resource management. Therefore, this paper was conducted to study the predictability and variability of Kiremt rainfall over the northern half of Ethiopia on monthly and dekadal time scales in association with global Sea Surface Temperature (SST) at different lag time. Trends in rainfall have been analyzed on annual, seasonal (Kiremt), monthly, and dekadal (June–September) time scales based on rainfall records of 36 meteorological stations distributed across four homogenous zones of the northern half of Ethiopia for the period 1992–2017. The results from the progressive Mann–Kendall trend test and the Sen’s slope method shows that there is no significant trend in the annual, Kiremt, monthly and dekadal rainfall total at most of the station's studies. Moreover, the rainfall in the study area varies spatially and temporally, and the distribution of the rainfall pattern increases from the northeast rift valley to northwest highlands. Methods of analysis include graphical correlation and multiple linear regression model are employed to investigate the association between the global SSTs and Kiremt rainfall over the homogeneous rainfall zones and to predict monthly and dekadal (June-September) rainfall using SST predictors. The results of this study show that in general, SST in the equatorial Pacific Ocean is the main source of the predictive skill of the Kiremt rainfall variability over the northern half of Ethiopia. The regional SSTs in the Atlantic and the Indian Ocean as well contribute to the Kiremt rainfall variability over the study area. Moreover, the result of the correlation analysis showed that the decline of monthly and dekadal Kiremt rainfall over most of the homogeneous zones of the study area are caused by the corresponding persistent warming of the SST in the eastern and central equatorial Pacific Ocean during the period 1992 - 2017. It is also found that the monthly and dekadal Kiremt rainfall over the northern, northwestern highlands and northeastern lowlands of Ethiopia are positively correlated with the SST in the western equatorial Pacific, eastern and tropical northern the Atlantic Ocean. Furthermore, the SSTs in the western equatorial Pacific and Indian Oceans are positively correlated to the Kiremt season rainfall in the northeastern highlands. Overall, the results showed that the prediction models using combined SSTs at various ocean regions (equatorial and tropical) performed reasonably well in the prediction (With R2 ranging from 30% to 65%) of monthly and dekadal rainfall and recommends it can be used for efficient prediction of Kiremt rainfall over the study area to aid with systematic and informed decision making within the agricultural sector.

Keywords: dekadal, Kiremt rainfall, monthly, Northern Ethiopia, sea surface temperature

Procedia PDF Downloads 143

Authors: Walid A. M. Ghoneim, Hamdy A. Ashour, Asmaa E. Abdo

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In this paper, linear analysis of a Switched Reluctance Generator (SRG) model is applied on the most common configurations (4/2, 6/4 and 8/6) for both conventional short-pitched and fully-pitched designs, in order to determine the optimum stator/rotor pole angles at which the maximum output voltage is generated per unit excitation current. This study is focused on SRG analysis and design as a proposed solution for renewable energy applications, such as wind energy conversion systems. The world’s potential to develop the renewable energy technologies through dedicated scientific researches was the motive behind this study due to its positive impact on economy and environment. In addition, the problem of rare earth metals (Permanent magnet) caused by mining limitations, banned export by top producers and environment restrictions leads to the unavailability of materials used for rotating machines manufacturing. This challenge gave authors the opportunity to study, analyze and determine the optimum design of the SRG that has the benefit to be free from permanent magnets, rotor windings, with flexible control system and compatible with any application that requires variable-speed operation. In addition, SRG has been proved to be very efficient and reliable in both low-speed or high-speed applications. Linear analysis was performed using MATLAB simulations based on the (Modified generalized matrix approach) of Switched Reluctance Machine (SRM). About 90 different pole angles combinations and excitation patterns were simulated through this study, and the optimum output results for each case were recorded and presented in detail. This procedure has been proved to be applicable for any SRG configuration, dimension and excitation pattern. The delivered results of this study provide evidence for using the 4-phase 8/6 fully pitched SRG as the main optimum configuration for the same machine dimensions at the same angular speed.

Keywords: generalized matrix approach, linear analysis, renewable applications, switched reluctance generator

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Authors: Lubna M. A. Hassan, Nasir Brema, Abdallah Mamy, Insaf Yahya, Tanzil A. G., Ahmed M. M. Hasoba, Omer A. Suliman

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The Nubian ibex species has been categorized as vulnerable by the International Union for Conservation of Nature (IUCN) due to a lack of population data in specific regions within their habitat. This species faces numerous challenges, including habitat loss caused by agricultural practices, livestock rearing, mining activity, and infrastructure development. Additionally, competition with non-native species and hunting pose significant threats to their survival. Unfortunately, studies on the distribution, conservation status, ecology, and health of the ibex are limited and primarily descriptive in nature. In order to bridge this knowledge gap, recent surveys were conducted in the Red Sea State of Sudan during specific periods in 2015, 2016, 2019, and 2021. These surveys have provided valuable insights into the distribution, habitats, and conservation status of the Nubian ibex in the Red Sea State. The findings indicate that the Capra nubiana ibex can be found across more than 17 mountains in the Red Sea State. However, the total population estimate from recent years suggests that there are fewer than 250 individuals remaining. The study has also identified the highest altitude at which the Nubian ibex habitats existed in Sudan's Red Sea State, measuring 1675 m. This area harbors a diverse array of Nubian ibex habitats, encompassing a total of 21 wild plant species from 10 distinct families. The region experiences an average annual temperature ranging from 20.64°C in January to 33.30°C in August. Precipitation occurs in November and December, although it is characterized by unreliability and erratic patterns. It is important to note that these population estimates were obtained through surveys conducted in collaboration with rangers and local communities, and adjustments to survey methods are necessary to accommodate the challenging mountainous terrain, such as utilizing aerial surveys. To effectively address these threats, it is imperative to establish comprehensive long-term monitoring programs.

Keywords: Nubian ibex, distribution, population, habitats

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Authors: Arun Joshi

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The present study deals with the report of Gangamopteriscyclopteroides from the Barakar Formation of Simlong Open Cast Mine, Rajmahal Area, Rajmahal Basin, Jharkhand, India. The genus Gangamopteriscomprises leaves which are simple, entire, symmetrical or asymmetrical, linear, lanceolate, elliptical, obovate in shape, apex broadly rounded, obtuse, acute, acuminate or mucronate, base petiolate or contracted, midrib absent. Median region occupied by subparallel veins with anastomoses of elongate or hexagonal outline. Secondary veins arise from median veins by repeated dichotomy, arched, bifurcating and anasotomosing network. The present work is significant as it represents the presence of Glossopteris flora (250- 290 ma) which is mainly responsible for the formation of coal. Coal is one of the major fuels for power production through thermal power plants. The Glossopteris flora is one of the major floras that occupied the southern continent during Carboniferous- Permian time. This southern continent is also known as Gondwana comprising Australia, South Africa, Antarctica, Madagascar and India. There is a vast geological reserve of coal with favorable stripping ratio available at the Simlong Block but the area comes under the most naxalite prone area and thus the mine has been running in an unplanned manner. It has got the potential of becoming a big project with higher capacity and is well suited for enhancing production which can be helpful in the economic growth of the country. Though, the present record is scanty, it shows the presence of Glossopteris flora responsible for the formation of coal in the Coalmine. However, there are fears of fossils disappearing from this area as the state government of Jharkhand has given out a mining lease in the area to private companies. Therefore, it is very necessary to study such coal forming vegetation and their systematic study from the area to generate a new palaeobotanical database, palaeoenvironmental interpretation, basinal correlation and for the understanding of evolutionary perspectives.

Keywords: Barakar formation, coal, Glossopteris flora, Gondwana, India, Naxalite, Rajmahal Basin

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Authors: Ahmad S. Darwish, Tarek Lemaoui, Hanifa Taher, Inas M. AlNashef, Fawzi Banat

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This research reports a systematic top-down approach for designing neoteric hydrophobic solvents –particularly, deep eutectic solvents (DES) and ionic liquids (IL)– as furfural extractants from aqueous media for the application of sustainable biomass conversion. The first stage of the framework entailed screening 32 neoteric solvents to determine their efficacy against toluene as the application’s conventional benchmark for comparison. The selection criteria for the best solvents encompassed not only their efficiency in extracting furfural but also low viscosity and minimal toxicity levels. Additionally, for the DESs, their natural origins, availability, and biodegradability were also taken into account. From the screening pool, two neoteric solvents were selected: thymol:decanoic acid 1:1 (Thy:DecA) and trihexyltetradecyl phosphonium bis(trifluoromethylsulfonyl) imide [P₁₄,₆,₆,₆][NTf₂]. These solvents outperformed the toluene benchmark, achieving efficiencies of 94.1% and 97.1% respectively, compared to toluene’s 81.2%, while also possessing the desired properties. These solvents were then characterized thoroughly in terms of their physical properties, thermal properties, critical properties, and cross-contamination solubilities. The selected neoteric solvents were then extensively tested under various operating conditions, and an exceptional stable performance was exhibited, maintaining high efficiency across a broad range of temperatures (15–100 °C), pH levels (1–13), and furfural concentrations (0.1–2.0 wt%) with a remarkable equilibrium time of only 2 minutes, and most notably, demonstrated high efficiencies even at low solvent-to-feed ratios. The durability of the neoteric solvents was also validated to be stable over multiple extraction-regeneration cycles, with limited leachability to the aqueous phase (≈0.1%). Moreover, the extraction performance of the solvents was then modeled through machine learning, specifically multiple non-linear regression (MNLR) and artificial neural networks (ANN). The models demonstrated high accuracy, indicated by their low absolute average relative deviations with values of 2.74% and 2.28% for Thy:DecA and [P₁₄,₆,₆,₆][NTf₂], respectively, using MNLR, and 0.10% for Thy:DecA and 0.41% for [P₁₄,₆,₆,₆][NTf₂] using ANN, highlighting the significantly enhanced predictive accuracy of the ANN. The neoteric solvents presented herein offer noteworthy advantages over traditional organic solvents, including their high efficiency in both extraction and regeneration processes, their stability and minimal leachability, making them particularly suitable for applications involving aqueous media. Moreover, these solvents are more environmentally friendly, incorporating renewable and sustainable components like thymol and decanoic acid. This exceptional efficacy of the newly developed neoteric solvents signifies a significant advancement, providing a green and sustainable alternative for furfural production from biowaste.

Keywords: sustainable biomass conversion, furfural extraction, ionic liquids, deep eutectic solvents

Procedia PDF Downloads 73

Authors: Tammeen Siraj, Wenxing Zhou, Terry Huang, Mohammad Al-Amin

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The high resolution inline inspection (ILI) tool is used extensively in the pipeline industry to identify, locate, and measure metal-loss corrosion anomalies on buried oil and gas steel pipelines. Corrosion anomalies may occur singly (i.e. individual anomalies) or as clusters (i.e. a colony of corrosion anomalies). Although the ILI technology has advanced immensely, there are measurement errors associated with the sizes of corrosion anomalies reported by ILI tools due limitations of the tools and associated sizing algorithms, and detection threshold of the tools (i.e. the minimum detectable feature dimension). Quantifying the measurement error in the ILI data is crucial for corrosion management and developing maintenance strategies that satisfy the safety and economic constraints. Studies on the measurement error associated with the length of the corrosion anomalies (in the longitudinal direction of the pipeline) has been scarcely reported in the literature and will be investigated in the present study. Limitations in the ILI tool and clustering process can sometimes cause clustering error, which is defined as the error introduced during the clustering process by including or excluding a single or group of anomalies in or from a cluster. Clustering error has been found to be one of the biggest contributory factors for relatively high uncertainties associated with ILI reported anomaly length. As such, this study focuses on developing a consistent and comprehensive framework to quantify the measurement errors in the ILI-reported anomaly length by comparing the ILI data and corresponding field measurements for individual and clustered corrosion anomalies. The analysis carried out in this study is based on the ILI and field measurement data for a set of anomalies collected from two segments of a buried natural gas pipeline currently in service in Alberta, Canada. Data analyses showed that the measurement error associated with the ILI-reported length of the anomalies without clustering error, denoted as Type I anomalies is markedly less than that for anomalies with clustering error, denoted as Type II anomalies. A methodology employing data mining techniques is further proposed to classify the Type I and Type II anomalies based on the ILI-reported corrosion anomaly information.

Keywords: clustered corrosion anomaly, corrosion anomaly assessment, corrosion anomaly length, individual corrosion anomaly, metal-loss corrosion, oil and gas steel pipeline

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Authors: Gautier Girard, Marion Martiny, Sebastien Mercier, Mohamad Jrad, Mohamed-Slim Bahi, Laurent Bodin, Francois Lechleiter, David Nevo, Sophie Dareys

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Reliability of electronic devices has always been of highest interest for Aero-MIL and space applications. In any electronic device, Printed Circuit Board (PCB), providing interconnection between components, is a key for reliability. During the last decades, PCB technologies evolved to sustain and/or fulfill increased original equipment manufacturers requirements and specifications, higher densities and better performances, faster time to market and longer lifetime, newer material and mixed buildups. From the very beginning of the PCB industry up to recently, qualification, experiments and trials, and errors were the most popular methods to assess system (PCB) reliability. Nowadays OEM, PCB manufacturers and scientists are working together in a close relationship in order to develop predictive models for PCB reliability and lifetime. To achieve that goal, it is fundamental to characterize precisely base materials (laminates, electrolytic copper, …), in order to understand failure mechanisms and simulate PCB aging under environmental constraints by means of finite element method for example. The laminates are woven composites and have thus an orthotropic behaviour. The in-plane properties can be measured by combining classical uniaxial testing and digital image correlation. Nevertheless, the out-of-plane properties cannot be evaluated due to the thickness of the laminate (a few hundred of microns). It has to be noted that the knowledge of the out-of-plane properties is fundamental to investigate the lifetime of high density printed circuit boards. A homogenization method combining analytical and numerical approaches has been developed in order to obtain the complete elastic orthotropic behaviour of a woven composite from its precise 3D internal structure and its experimentally measured in-plane elastic properties. Since the mechanical properties of the resin surrounding the fibres are unknown, an inverse method is proposed to estimate it. The methodology has been applied to one laminate used in hyperfrequency spatial applications in order to get its elastic orthotropic behaviour at different temperatures in the range [-55°C; +125°C]. Next; numerical simulations of a plated through hole in a double sided PCB are performed. Results show the major importance of the out-of-plane properties and the temperature dependency of these properties on the lifetime of a printed circuit board. Acknowledgements—The support of the French ANR agency through the Labcom program ANR-14-LAB7-0003-01, support of CNES, Thales Alenia Space and Cimulec is acknowledged.

Keywords: homogenization, orthotropic behaviour, printed circuit board, woven composites

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Authors: Larissa Bolliger, Junoš Lukan, Mitja Lustrek, Dirk De Bacquer, Els Clays

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Introduction and Aim: Chronic workplace stress and its health-related consequences like mental and cardiovascular diseases have been widely investigated. This project focuses on the sources and context of psychosocial daily workplace stress in a real-world setting. The main objective is to analyze and model real-time relationships between (1) psychosocial stress experiences within the natural work environment, (2) micro-level work activities and events, and (3) physiological signals and behaviors in office workers. Methods: An Ecological Momentary Assessment (EMA) protocol has been developed, partly building on machine learning techniques. Empatica® wristbands will be used for real-life detection of stress from physiological signals; micro-level activities and events at work will be based on smartphone registrations, further processed according to an automated computer algorithm. A field study including 100 office-based workers with high-level problem-solving tasks like managers and researchers will be implemented in Slovenia and Belgium (50 in each country). Data mining and state-of-the-art statistical methods – mainly multilevel statistical modelling for repeated data – will be used. Expected Results and Impact: The project findings will provide novel contributions to the field of occupational health research. While traditional assessments provide information about global perceived state of chronic stress exposure, the EMA approach is expected to bring new insights about daily fluctuating work stress experiences, especially micro-level events and activities at work that induce acute physiological stress responses. The project is therefore likely to generate further evidence on relevant stressors in a real-time working environment and hence make it possible to advise on workplace procedures and policies for reducing stress.

Keywords: ecological momentary assessment, real-time, stress, work

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Authors: Hiromi Takafuji

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It is a well-known fact that health care and welfare are the foundation of human activities, and human service professionals such as nurses and child care workers support these activities. COVID-19 pandemic has made the severity of the working environment in these fields even more known. It is high time to discuss the work of human service workers for the sustainable development of the human environment. Early turnover has been recognized as a long-standing issue in these fields. In Japan, the attrition rate within three years of graduation for these occupations has remained high at about 40% for more than 20 years. One of the reasons for this is Reality Shock: RS, which refers to the stress caused by the gap between pre-employment expectations and the post-employment reality experienced by new workers. The purpose of this study was to academically elucidate the mechanism of RS among human service professionals and to contribute to countermeasures against it. Firstly, to explore the structure of the relationship between professionalism and workers' RS, an exploratory interview survey was conducted and analyzed by text mining and content analysis. The results showed that the expectation of professionalism influences RS as a pre-employment job expectation. Next, the expectations of professionalism were quantified and categorized, and the responses of a total of 282 human service work professionals, nurses, child care workers, and caregivers; were finalized for data analysis. The data were analyzed using exploratory factor analysis, confirmatory factor analysis, multiple regression analysis, and structural equation modeling techniques. The results revealed that self-control orientation and authority orientation by qualification had a direct positive significant impact on RS. On the other hand, interpersonal helping orientation and altruistic orientation were found to have a direct negative significant impact and an indirect positive significant impact on RS.; we were able to clarify the structure of work expectations that affect the RS of welfare professionals, which had not been clarified in previous studies. We also explained the limitations, practical implications, and directions for future research.

Keywords: human service professional, new hire turnover, SEM, reality shock

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Authors: Anne-Margré C. Vink

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Background: Food-related allergies and intolerances are frequently occurring in dogs. Diagnosis and monitoring according to ‘Golden Standard’ of elimination efficiency are time-consuming, expensive, and requires expert clinical setting. In order to facilitate rapid and robust, quantitative testing of intolerance, and determining the individual offending foods, a serological test is implicated. Method: As we developed Medisynx IgG Human Screening Test ELISA before and the dog’s immune system is most similar to humans, we were able to develop Medisynx IgG Dog Screening Test ELISA as well. In this study, 47 dogs suffering from Canine Atopic Dermatitis (CAD) and several secondary induced reactions were included to participate in serological Medisynx IgG Dog Screening Test ELISA (within < 0,02 % SD). Results were expressed as titers relative to the standard OD readings to diagnose alimentary induced diseases and monitoring the efficacy of an individual eliminating diet in dogs. Split sample analysis was performed by independently sending 2 times 3 ml serum under two unique codes. Results: The veterinarian monitored these dogs to check dog’ results at least at 3, 7, 21, 49, 70 days and after period of 6 and 12 months on an individual negative diet and a positive challenge (retrospectively) at 6 months. Data of each dog were recorded in a screening form and reported that a complete recovery of all clinical manifestations was observed at or less than 70 days (between 50 and 70 days) in the majority of dogs(44 out of 47 dogs =93.6%). Conclusion: Challenge results showed a significant result of 100% in specificity as well as 100% positive predicted value. On the other hand, sensitivity was 95,7% and negative predictive value was 95,7%. In conclusion, an individual diet based on IgG ELISA in dogs provides a significant improvement of atopic dermatitis and pruritus including all other non-specific defined allergic skin reactions as erythema, itching, biting and gnawing at toes, as well as to several secondary manifestations like chronic diarrhoea, chronic constipation, otitis media, obesity, laziness or inactive behaviour, pain and muscular stiffness causing a movement disorders, excessive lacrimation, hyper behaviour, nervous behaviour and not possible to stay alone at home, anxiety, biting and aggressive behaviour and disobedience behaviour. Furthermore, we conclude that a relatively more severe systemic candidiasis, as shown by relatively higher titer (class 3 and 4 IgG reactions to Candida albicans), influence the duration of recovery from clinical manifestations in affected dogs. These findings are consistent with our preliminary human clinical studies.

Keywords: allergy, canine atopic dermatitis, CAD, food allergens, IgG-ELISA, food-incompatibility

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Authors: Mareike Dornhoefer, Madjid Fathi

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Water supply and water quality are key problems of mankind at the moment and - due to increasing population - in the future. Management disciplines like water, environment and quality management therefore need to closely interact, to establish a high level of water quality and to guarantee water supply in all parts of the world. Groundwater remediation is one aspect in this process. From a knowledge management perspective it is only possible to solve complex ecological or environmental problems if different factors, expert knowledge of various stakeholders and formal regulations regarding water, waste or chemical management are interconnected in form of a knowledge base. In general knowledge management focuses the processes of gathering and representing existing and new knowledge in a way, which allows for inference or deduction of knowledge for e.g. a situation where a problem solution or decision support are required. A knowledge base is no sole data repository, but a key element in a knowledge based system, thus providing or allowing for inference mechanisms to deduct further knowledge from existing facts. In consequence this knowledge provides decision support. The given paper introduces an environmental knowledge system in water management. The proposed environmental knowledge system is part of a research concept called Green Knowledge Management. It applies semantic technologies or concepts such as ontology or linked open data to interconnect different data and information sources about environmental aspects, in this case, water quality, as well as background material enriching an established knowledge base. Examples for the aforementioned ecological or environmental factors threatening water quality are among others industrial pollution (e.g. leakage of chemicals), environmental changes (e.g. rise in temperature) or floods, where all kinds of waste are merged and transferred into natural water environments. Water quality is usually determined with the help of measuring different indicators (e.g. chemical or biological), which are gathered with the help of laboratory testing, continuous monitoring equipment or other measuring processes. During all of these processes data are gathered and stored in different databases. Meanwhile the knowledge base needs to be established through interconnecting data of these different data sources and enriching its semantics. Experts may add their knowledge or experiences of previous incidents or influencing factors. In consequence querying or inference mechanisms are applied for the deduction of coherence between indicators, predictive developments or environmental threats. Relevant processes or steps of action may be modeled in form of a rule based approach. Overall the environmental knowledge system supports the interconnection of information and adding semantics to create environmental knowledge about water environment, supply chain as well as quality. The proposed concept itself is a holistic approach, which links to associated disciplines like environmental and quality management. Quality indicators and quality management steps need to be considered e.g. for the process and inference layers of the environmental knowledge system, thus integrating the aforementioned management disciplines in one water management application.

Keywords: water quality, environmental knowledge system, green knowledge management, semantic technologies, quality management

Procedia PDF Downloads 224