Search results for: cluster identification

Authors: Zain Ul Abdin, Mirza Aizaz Asim, Rao Sohail Ahmad Khan, Luqman Amrao, Fiaz Hussain, Hasooba Hira, Saqi Kosar Abbas

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The success of Integrated vector management programmes mainly depends on the correct identification of insect vector species involved in vector borne diseases. Based on molecular data the most important insect species involved as vectors for Zoonotic diseases in Pakistan were identified. The precise and accurate identification of such type of organism is only possible through molecular based techniques like “DNA barcoding”. Morphological species identification in insects at any life stage, is very challenging, therefore, DNA barcoding was used as a tool for rapid and accurate species identification in a wide variety of taxa across the globe and parallel studies revealed that DNA barcoding data can be effectively used in resolving taxonomic ambiguities, detection of cryptic diversity, invasion biology, description of new species etc. A comprehensive survey was carried out for the collection of insects (both adult and immature stages) in district Faisalabad, Pakistan and their DNA was extracted and mitochondrial cytochrome oxidase subunit I (COI-59) barcode sequences was used for molecular identification of immature and adult life stage.This preliminary research work opens new frontiers for developing sustainable insect vectors management programmes for saving lives of mankind from fatal diseases.

Keywords: zoonotic diseases, cytochrome oxidase, and insect vectors, CO1

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Authors: Kais Manai

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The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%.

Keywords: nuclear emulsion, particle identification, tracking, neural network

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Authors: Hasan Vural

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Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: Prittesh Patel, Ramar Krishnamurthy

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In the present study, nine strains of C. falcatum obtained from different places and cultivars were characterized for sporulation, growth rate, and 18S rRNA gene sequence. All isolates had characteristic fast-growing sparse and fleecy aerial mycelia on potato dextrose agar with sickle shape conidia (length x width: varied from 20.0 X 3.89 to 25.52 X 5.34 μm) and blackish to orange acervuli with setae (length x width: varied from 112.37X 2.78 to 167.66 X 6.73 μm). They could be divided into two groups on the base of morphology; P1, dense mycelia with concentric growth and P2, sparse mycelia with uneven growth. Genomic DNA isolation followed by PCR amplification with ITS1 and ITS4 primer produced ~550bp amplicons for all isolates. Phylogeny generated by 18S rRNA gene sequence confirmed the variation in isolates and mainly grouped into two clusters; cluster 1 contained CoC671 isolates (cfNAV and cfPAR) and Co86002 isolate (cfTIM). Other isolates cfMAD, cfKAM, and cfMAR were grouped into cluster 2. Remaining isolates did not fall into any cluster. Isolate cfGAN, collected from Co86032 was found highly diverse of all the nine isolates. In a nutshell, we found considerable genetic divergence and morphological variation within C. falcatum accessions collected from different areas of south Gujarat, India and these can be used for the breeding program.

Keywords: Colletotrichum falcatum, ITS, morphology, red rot, sugarcane

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Authors: Bale Baidi Blaise, Gilles Marckmann, Liman Kaoye, Talaka Dya, Moustapha Bachirou, Gambo Betchewe, Tibi Beda

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This work highlights the capabilities of particles swarm optimization (PSO) method to identify parameters of hyperelastic models. The study compares this method with Genetic Algorithm (GA) method, Least Squares (LS) method, Pattern Search Algorithm (PSA) method, Beda-Chevalier (BC) method and the Levenberg-Marquardt (LM) method. Four classic hyperelastic models are used to test the different methods through parameters identification. Then, the study compares the ability of these models to reproduce experimental Treloar data in simple tension, biaxial tension and pure shear.

Keywords: particle swarm optimization, identification, hyperelastic, model

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Authors: Brandon Foggo, Nanpeng Yu

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Power distribution circuits undergo frequent network topology changes that are often left undocumented. As a result, the documentation of a circuit’s connectivity becomes inaccurate with time. The lack of reliable circuit connectivity information is one of the biggest obstacles to model, monitor, and control modern distribution systems. To enhance the reliability and efficiency of electric power distribution systems, the circuit’s connectivity information must be updated periodically. This paper focuses on one critical component of a distribution circuit’s topology - the secondary transformer to phase association. This topology component describes the set of phase lines that feed power to a given secondary transformer (and therefore a given group of power consumers). Finding the documentation of this component is call Phase Identification, and is typically performed with physical measurements. These measurements can take time lengths on the order of several months, but with supervised learning, the time length can be reduced significantly. This paper compares several such methods applied to Phase Identification for a large range of real distribution circuits, describes a method of training data selection, describes preprocessing steps unique to the Phase Identification problem, and ultimately describes a method which obtains high accuracy (> 96% in most cases, > 92% in the worst case) using only 5% of the measurements typically used for Phase Identification.

Keywords: distribution network, machine learning, network topology, phase identification, smart grid

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Authors: Christo Februanto Putra

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This paper presents risk identification that affects investment feasibility on toll road infrastructure in Indonesia using qualitative methods survey based on the expert practitioner in investor, contractor, and state officials. The problems on infrastructure investment in Indonesia, especially on KPBU model contract, is many risk factors in the investment plan is not calculated in detail thoroughly. Risk factor is a value used to provide an overview of the risk level assessment of an event which is a function of the probability of the occurrence and the consequences of the risks that arise. As results of the survey which is to show which risk factors impacts directly to the investment feasibility and rank them by their impacts on the investment.

Keywords: risk identification, indonesia toll road, investment feasibility

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Authors: Madhat Abu Al-Naeem, Ismail Yusoff, Ng Tham Fatt, Yatimah Alias

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Groundwater in Gaza strip is increasingly being exposed to anthropic and natural factors that seriously impacted the groundwater quality. Physiochemical data of groundwater can offer important information on changes in groundwater quality that can be useful in improving water management tactics. An integrative hydrochemical and statistical techniques (Hierarchical cluster analysis (HCA) and factor analysis (FA)) have been applied on the existence ten physiochemical data of 84 samples collected in (2000/2001) using STATA, AquaChem, and Surfer softwares to: 1) Provide valuable insight into the salinization sources and the hydrochemical processes controlling the chemistry of groundwater. 2) Differentiate the influence of natural processes and man-made activities. The recorded large diversity in water facies with dominance Na-Cl type that reveals a highly saline aquifer impacted by multiple complex hydrochemical processes. Based on WHO standards, only (15.5%) of the wells were suitable for drinking. HCA yielded three clusters. Cluster 1 is the highest in salinity, mainly due to the impact of Eocene saline water invasion mixed with human inputs. Cluster 2 is the lowest in salinity also due to Eocene saline water invasion but mixed with recent rainfall recharge and limited carbonate dissolution and nitrate pollution. Cluster 3 is similar in salinity to Cluster 2, but with a high diversity of facies due to the impact of many sources of salinity as sea water invasion, carbonate dissolution and human inputs. Factor analysis yielded two factors accounting for 88% of the total variance. Factor 1 (59%) is a salinization factor demonstrating the mixing contribution of natural saline water with human inputs. Factor 2 measure the hardness and pollution which explained 29% of the total variance. The negative relationship between the NO3- and pH may reveal a denitrification process in a heavy polluted aquifer recharged by a limited oxygenated rainfall. Multivariate statistical analysis combined with hydrochemical analysis indicate that the main factors controlling groundwater chemistry were Eocene saline invasion, seawater invasion, sewage invasion and rainfall recharge and the main hydrochemical processes were base ion and reverse ion exchange processes with clay minerals (water rock interactions), nitrification, carbonate dissolution and a limited denitrification process.

Keywords: dendrogram and cluster analysis, water facies, Eocene saline invasion and sea water invasion, nitrification and denitrification

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Authors: Haythem H. Abdullah, Hesham Elkady

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With the fast increase in radio frequency identification (RFID) applications such as medical recording, library management, etc., the limitation of active tags stems from its need to external batteries as well as passive or active chips. The chipless RFID tag reduces the cost to a large extent but at the expense of utilizing the spectrum. The reduction of the cost of chipless RFID is due to the absence of the chip itself. The identification is done by utilizing the spectrum in such a way that the frequency response of the tags consists of some resonance frequencies that represent the bits. The system capacity is decided by the number of resonators within the pre-specified band. It is important to find a solution to enhance the spectrum utilization when using chipless RFID. Target identification is a process that results in a decision that a specific target is present or not. Several target identification schemes are present, but one of the most successful techniques in radar target identification in the oscillatory region is the extinction pulse technique (E-Pulse). The E-Pulse technique is used to identify targets via its characteristics (natural) modes. By introducing an innovative solution for chipless RFID reader and tag designs, the spectrum utilization goes to the optimum case. In this paper, a novel capacity enhancement scheme based on the E-pulse technique is introduced to improve the performance of the chipless RFID system.

Keywords: chipless RFID, E-pulse, natural modes, resonators

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Authors: Hyun-Woo Cho

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It is quite important to utilize right time and intelligent production monitoring and diagnosis of industrial processes in terms of quality and safety issues. When compared with monitoring task, fault diagnosis represents the task of finding process variables responsible causing a specific fault in the process. It can be helpful to process operators who should investigate and eliminate root causes more effectively and efficiently. This work focused on the active use of combining a nonlinear statistical technique with a preprocessing method in order to implement practical real-time fault identification schemes for data-rich cases. To compare its performance to existing identification schemes, a case study on a benchmark process was performed in several scenarios. The results showed that the proposed fault identification scheme produced more reliable diagnosis results than linear methods. In addition, the use of the filtering step improved the identification results for the complicated processes with massive data sets.

Keywords: diagnosis, filtering, nonlinear statistical techniques, process monitoring

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Authors: Gangmin Li, Fan Yang

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Personalized recommendation is crucial for any recommendation system. One of the techniques for personalized recommendation is to identify the intention. Traditional user intention identification uses the user’s selection when facing multiple items. This modeling relies primarily on historical behaviour data resulting in challenges such as the cold start, unintended choice, and failure to capture intention when items are new. Motivated by recent advancements in Large Language Models (LLMs) like ChatGPT, we present an approach for user intention identification by embracing LLMs with Chain-of-Thought (CoT) prompting. We use the initial user profile as input to LLMs and design a collection of prompts to align the LLM's response through various recommendation tasks encompassing rating prediction, search and browse history, user clarification, etc. Our tests on real-world datasets demonstrate the improvements in recommendation by explicit user intention identification and, with that intention, merged into a user model.

Keywords: personalized recommendation, generative user modelling, user intention identification, large language models, chain-of-thought prompting

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Authors: Bjornar Kjellstadli, Shaher A. I. Shalfawi

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The aim of this study was to investigate the effect of strength training Cluster set-method compared to traditional set-method 30 m sprinting time and maximum strength in squats and bench-press. Thirteen Physical Education students, 7 males and 6 females between the age of 19-28 years old were recruited. The students were random divided in three groups. Traditional set group (TSG) consist of 2 males and 2 females aged (±SD) (22.3 ± 1.5 years), body mass (79.2 ± 15.4 kg) and height (177.5 ± 11.3 cm). Cluster set group (CSG) consist of 3 males and 2 females aged (22.4 ± 3.29 years), body mass (81.0 ± 24.0 kg) and height (179.2 ± 11.8 cm) and a control group (CG) consist of 2 males and 2 females aged (21.5 ± 2.4 years), body mass (82.1 ± 17.4 kg) and height (175.5 ± 6.7 cm). The intervention consisted of performing squat and bench press at 70% of 1RM (twice a week) for 8 weeks using 10 repetition and 4 sets. Two types of strength-training methods were used , cluster set (CS) where the participants (CSG) performed 2 reps 5 times with a 10 s recovery in between reps and 50 s recovery between sets, and traditional set (TS) where the participants (TSG) performed 10 reps each set with 90 s recovery in between sets. The pre-tests and post-tests conducted were 1 RM in both squats and bench press, and 10 and 30 m sprint time. The 1RM test were performed with Eleiko XF barbell (20 kg), Eleiko weight plates, rack and bench from Hammerstrength. The speed test was measured with the Brower speed trap II testing system (Brower Timing Systems, Utah, USA). The participants received an individualized training program based on the pre-test of the 1RM. In addition, a mid-term test of 1RM was carried out to adjust training intensity. Each training session were supervised by the researchers. Beast sensors (Milano, Italy) were also used to monitor and quantify the training load for the participants. All groups had a statistical significant improvement in bench press 1RM (TSG 1RM from 56.3 ± 28.9 to 66 ± 28.5 kg; CSG 1RM from 69.8 ± 33.5 to 77.2 ± 34.1 kg and CG 1RM from 67.8 ± 26.6 to 72.2 ± 29.1 kg), whereas only the TSG (1RM from 84.3 ± 26.8 to 114.3 ± 26.5 kg) and CSG (1RM from 100.4 ± 33.9 to 129 ± 35.1 kg) had a statistical significant improvement in Squats 1RM (P < 0.05). However, a between groups examination reveals that there were no marked differences in 1RM squat performance between TSG and CSG (P > 0.05) and both groups had a marked improvements compared to the CG (P < 0.05). On the other hand, no differences between groups were observed in Bench press 1RM. The within groups results indicate that none of the groups had any marked improvement in the distances from 0-10 m and 10-30 m except the CSG which had a notable improvement in the distance from 10-30 m (-0.07 s; P < 0.05). Furthermore, no differences in sprinting abilities were observed between groups. The results from this investigation indicate that traditional set strength training at 70% of 1RM gave close results compared to Cluster set strength training at the same intensity. However, the results indicate that the cluster set had an effect on flying time (10-30 m) indicating that the velocity at which those repetitions were performed could be the explanation factor of this this improvement.

Keywords: physical performance, 1RM, pushing velocity, velocity based training

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Authors: Dagim Dessalegn Haile

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This study investigated the interaction between the drivers' socio-demographic background information (age, gender, and driving experience) and the risky events score in the i-DREAMS platform. Further, the relationship between the participants' background driving behavior and the i-DREAMS platform behavioral output scores of risky events was also investigated. The i-DREAMS acronym stands for Smart Driver and Road Environment Assessment and Monitoring System. It is a European Union Horizon 2020 funded project consisting of 13 partners, researchers, and industry partners from 8 countries. A total of 25 Belgian car drivers (16 male and nine female) were considered for analysis. Drivers' ages were categorized into ages 18-25, 26-45, 46-65, and 65 and older. Drivers' driving experience was also categorized into four groups: 1-15, 16-30, 31-45, and 46-60 years. Drivers are classified into two clusters based on the recorded score for risky events during phase 1 (baseline) using risky events; acceleration, deceleration, speeding, tailgating, overtaking, and lane discipline. Agglomerative hierarchical clustering using SPSS shows Cluster 1 drivers are safer drivers, and Cluster 2 drivers are identified as risky drivers. The analysis result indicated no significant relationship between age groups, gender, and experience groups except for risky events like acceleration, tailgating, and overtaking in a few phases. This is mainly because the fewer participants create less variability of socio-demographic background groups. Repeated measure ANOVA shows that cluster 2 drivers improved more than cluster 1 drivers for tailgating, lane discipline, and speeding events. A positive relationship between background drivers' behavior and i-DREAMS platform behavioral output scores is observed. It implies that car drivers who in the questionnaire data indicate committing more risky driving behavior demonstrate more risky driver behavior in the i-DREAMS observed driving data.

Keywords: i-dreams, car drivers, socio-demographic background, risky events

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Authors: Jo-Ann Müller

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The integration-responsiveness (IR) framework presents four distinct internationalization strategies which differ depending on the extent of pressure the company faces for local responsiveness and global integration. This study applies the framework to standards by examining differences in the relative importance of three types of standards depending on the role the subsidiary plays within the corporate group. Hypotheses are tested empirically in a two-stage procedure. First, the subsidiaries are grouped performing cluster analysis. In the second step, the relationship between cluster affiliation and subsidiary strategy is tested using multinomial Probit estimation. While the level of local responsiveness of a firm relates to the relative importance of national and international formal standards, the degree of vertical integration is associated with the application of internal company.

Keywords: FDI, firm-level data, standards, subsidiary strategy

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Authors: Seyeh Hassan Eftekhar Afzali, Hamid Mohammadi

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Pistachio is like almond but the second cycle of growth (third phase) has rather fast growth. This is caused to add final mass of product. When the germ grows, it and its cover are reached to the final size during six week period. As starting the second phase, the lignifications of pericarp is begun and continued for 4 or 6 weeks. Physiological maturity or easy separation of green from scutum is specified. This test was done according to random blocks of 6 orchards in the type of Ahmad Aghaie with 4 iterations. Vegetative properties of branch are investigated. The results of the bunch numbers on the growth of branch in current year are shown that the most growth of branch is happened by trimming of one and two bunches of the branch and the most diameter of the branch is happened by trimming of one to four bunches of branch. Trimming of a bunch is caused the most number of pistachio products in the bunch.

Keywords: pistachio, cluster, bud, fruit, branch

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Authors: Chad Goldsworthy, B. Rajeswari Matam

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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation

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Authors: Fawad Ali

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Toxic heavy metal contamination has a negative impact on soil quality which ultimately pollutes the agriculture system. In the current work, we analyzed uptake of various heavy metals by dietary vegetables grown in wastewater irrigated areas of Swabi city. The samples of soil and vegetables were analyzed for heavy metals viz Cd, Cr, Mn, Fe, Ni, Cu, Zn and Pb using Atomic Absorption Spectrophotometer. High levels of metals were found in wastewater irrigated soil and vegetables in the study area. Especially the concentrations of Pb and Cd in the dietary vegetable crossed the permissible level of World Health Organization. Substantial positive correlation was found among the soil and vegetable contamination. Transfer factor for some metals including Cr, Zn, Mn, Ni, Cd and Cu was greater than 0.5 which shows enhanced accumulation of these metals due to contamination by domestic discharges and industrial effluents. Linear regression analysis indicated significant correlation of heavy metals viz Pb, Cr, Cd, Ni, Zn, Cu, Fe and Mn in vegetables with concentration in soil of 0.964 at P≤0.001. Abelmoschus esculentus indicated Health Risk Index (HRI) of Pb >1 in adults and children. The source identification analysis carried out by Principal Component Analysis (PCA) and Cluster Analysis (CA) showed that ground water and soil were being polluted by the trace metals coming out from industries and domestic wastes. Hierarchical cluster analysis (HCA) divided metals into two clusters for wastewater and soil but into five clusters for soil of control area. PCA extracted two factors for wastewater, each contributing 61.086 % and 16.229 % of the total 77.315 % variance. PCA extracted two factors, for soil samples, having total variance of 79.912 % factor 1 and factor 2 contributed 63.889 % and 16.023 % of the total variance. PCA for sub soil extracted two factors with a total variance of 76.136 % factor 1 being 61.768 % and factor 2 being 14.368 %of the total variance. High pollution load index for vegetables in the study area due to metal polluted soil has opened a study area for proper legislation to protect further contamination of vegetables. This work would further reveal serious health risks to human population of the study area.

Keywords: health risk, vegetables, wastewater, atomic absorption sepctrophotometer

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Authors: N. C. Ghosh, Niladri Das, Prolay Mondal, Ranajit Ghosh

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Groundwater quality deterioration due to human activities has become a prime factor of modern life. The major concern of the study is to access spatial variation of groundwater quality and to identify the sources of groundwater chemicals and its impact on human health of the concerned area. Multivariate statistical techniques, cluster, principal component analysis, and hydrochemical fancies are been applied to measure groundwater quality data on 14 parameters from 107 sites distributed randomly throughout the Birbhum district. Five factors have been extracted using Varimax rotation with Kaiser Normalization. The first factor explains 27.61% of the total variance where high positive loading have been concentrated in TH, Ca, Mg, Cl and F (Fluoride). In the studied region, due to the presence of basaltic Rajmahal trap fluoride contamination is highly concentrated and that has an adverse impact on human health such as fluorosis. The second factor explains 24.41% of the total variance which includes Na, HCO₃, EC, and SO₄. The last factor or the fifth factor explains 8.85% of the total variance, and it includes pH which maintains the acidic and alkaline character of the groundwater. Hierarchical cluster analysis (HCA) grouped the 107 sampling station into two clusters. One cluster having high pollution and another cluster having less pollution. Moreover hydromorphological facies viz. Wilcox diagram, Doneen’s chart, and USSL diagram reveal the quality of the groundwater like the suitability of the groundwater for irrigation or water used for drinking purpose like permeability index of the groundwater, quality assessment of groundwater for irrigation. Gibb’s diagram depicts that the major portion of the groundwater of this region is rock dominated origin, as the western part of the region characterized by the Jharkhand plateau fringe comprises basalt, gneiss, granite rocks.

Keywords: correlation, factor analysis, hydrological facies, hydrochemistry

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Authors: Matthew C. Best

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Linear Multi-Input Multi-Output (MIMO) dynamic models can be identified, with no a priori knowledge of model structure or order, using a new Generalised Identifying Filter (GIF). Based on an Extended Kalman Filter, the new filter identifies the model iteratively, in a continuous modal canonical form, using only input and output time histories. The filter’s self-propagating state error covariance matrix allows easy determination of convergence and conditioning, and by progressively increasing model order, the best fitting reduced-order model can be identified. The method is shown to be resistant to noise and can easily be extended to identification of smoothly nonlinear systems.

Keywords: system identification, Kalman filter, linear model, MIMO, model order reduction

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Authors: Zaky Musyarof, Dwi Yono Sutarto, Dwima Rindy Atika, R. B. Fajriya Hakim

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Each country should be responsible for the safety of people, especially responsible for the safety of people living in disaster-prone areas. One of those services is provides evacuation route for them. But all this time, the selection of evacuation route is seem doesn’t well organized, it could be seen that when a disaster happen, there will be many accumulation of people on the steps of evacuation route. That condition is dangerous to people because hampers evacuation process. By some methods in Statistical analysis, author tries to give a suggestion how to prepare evacuation route which is organized and based on people habit. Those methods are association rules, sequential pattern mining, hierarchical cluster analysis and fuzzy logic.

Keywords: association rules, sequential pattern mining, cluster analysis, fuzzy logic, evacuation route

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Authors: Kenneth Larsen, Astrid Aasland, Synnve Schjølberg, Trond Diseth

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Autism Spectrum Disorders (ASD) are neurodevelopmental disorders emerging in early development characterized by impairment in social communication skills and a restricted, repetitive and stereotyped patterns of behavior and interests. Early identification and interventions potentially improve development and quality of life of children with ASD. Symptoms of ASD are apparent through the second year of life, yet diagnostic age are still around 4 years of age. This study explored whether symptoms associated with ASD are possible to identify in typical Norwegian day-care centers in the second year of life. Results of this study clearly indicates that most described symptoms also are identifiable by day-care staff, and that a short observation list of 5 symptoms clearly identify children with ASD from a sample of normal developing peers.

Keywords: autism, early identification, day-care, screening

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Authors: Joon-Hoon Park

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In this paper the rationalized Haar transform is applied for distributed parameter system identification and estimation. A distributed parameter system is a dynamical and mathematical model described by a partial differential equation. And system identification concerns the problem of determining mathematical models from observed data. The Haar function has some disadvantages of calculation because it contains irrational numbers, for these reasons the rationalized Haar function that has only rational numbers. The algorithm adopted in this paper is based on the transform and operational matrix of the rationalized Haar function. This approach provides more convenient and efficient computational results.

Keywords: distributed parameter system, rationalized Haar transform, operational matrix, system identification

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Authors: Yen-Po Wang, Ming-Chih Huang, Ming-Lian Chang

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A scheme integrated with deterministic–stochastic subspace system identification and the method of damage localization vector is proposed in this study for damage detection of structures based on seismic response data. A series of shaking table tests using a five-storey steel frame has been conducted in National Center for Research on Earthquake Engineering (NCREE), Taiwan. Damage condition is simulated by reducing the cross-sectional area of some of the columns at the bottom. Both single and combinations of multiple damage conditions at various locations have been considered. In the system identification analysis, either full or partial observation conditions have been taken into account. It has been shown that the damaged stories can be identified from global responses of the structure to earthquakes if sufficiently observed. In addition to detecting damage(s) with respect to the intact structure, identification of new or extended damages of the as-damaged (ill-conditioned) counterpart has also been studied. The proposed scheme proves to be effective.

Keywords: damage locating vectors, deterministic-stochastic subspace system, shaking table tests, system identification

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Authors: Sophie-Bo Heinkel, Andrea Rechenburg, Thomas Kistemann

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The current status of wellbeing and life satisfaction of subsistence farmers in a wetland in Uganda and the contributing role of place attachment has been assessed. The aim of this study is to shed light on environmental factors supporting wellbeing in a wetland setting. Furthermore, it has been assessed, how the emotional bonding to the wetland as ‘place’ influences the peoples’ wellbeing and life satisfaction. The results shed light on the human-environment-relationship. A survey was carried out in three communities in urban and rural areas in a wetland basin in Uganda. A sample (n=235) provided information about the attachment to the wetland, the participants’ relation to the place of their residence and their emotional wellbeing. The Wellbeing Index (WHO-5) was assessed as well as the Perceived Stress Scale (PSS-10) and Rosenberg’s Self-Esteem scale (RSE). Furthermore, the Satisfaction With Life Scale (SWLS) was applied as well as the Place Attachment Inventory (PAI), which consists of the two intertwined dimensions of place identity and place dependence. Beside this, binary indicators as ‘feeling save’ and ‘feeling comfortable’ and ‘enjoying to live at the place of residence’ have been assessed. A bivariate correlation analysis revealed a high interconnectivity between all metric scales. Especially, the subscale ‘place identity’ showed significances with all other scales. A cluster analysis revealed three groups, which differed in the perception of place-related indicators and their attachment to the wetland as well as the status of wellbeing. First, a cluster whose majority is dissatisfied with their lives, but mainly had a good status of emotional well-being. This group does not feel attached to the wetland and lives in a town. Comparably less persons of this group feel safe and comfortable at their place of residence. In the second cluster, persons feel highly attached to the wetland and identify with it. This group was characterized by the high number of persons preferring their current place of residence and do not consider moving. All persons feel well and satisfied with their lives. The third group of persons is mainly living in rural areas and feels highly attached to the wetland. They are satisfied with their lives, but only a small minority is in a good emotional state of wellbeing. The emotional attachment to a place influences life satisfaction and, indirectly, the emotional wellbeing. In the present study it could be shown that subsistence farmers are attached to the wetland, as it is the source of their livelihood. While those living in areas with a good infrastructure are less dependent on the wetland and, therefore, less attached to. This feeling also was mirrored in the perception of a place as being safe and comfortable. The identification with a place is crucial for the feeling of being at “home”. Subsistence farmers feel attached to the ecosystem, but they also might be exposed to environmental and social stressors influencing their short-term emotional wellbeing. The provision of place identity is an ecosystem service provided by wetlands, which supports the status of wellbeing in human beings.

Keywords: mental health, positive environments, quality of life, wellbeing

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Authors: Varsha Singh, Shraddha Prajapati, M. B. Kiran

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Texture identification is useful in predicting the functionality of a component. Many of the existing texture identification methods are of contact in nature, which limits its measuring speed. These contact measurement techniques use a diamond stylus and the diamond stylus being sharp going to damage the surface under inspection and hence these techniques can be used in statistical sampling. Though these contact methods are very accurate, they do not give complete information for full characterization of surface. In this context, the presented method assumes special significance. The method uses a relatively low cost vision system for image acquisition. Software is developed based on wavelet transform, for analyzing texture images. Specimens are made using different manufacturing process (shaping, grinding, milling etc.) During experimentation, the specimens are illuminated using proper lighting and texture images a capture using CCD camera connected to the vision system. The software installed in the vision system processes these images and subsequently identify the texture of manufacturing processes.

Keywords: diamond stylus, manufacturing process, texture identification, vision system

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Authors: Kevin Kellner, Nga Lao, Orla Coleman, Paula Meleady, Niall Barron

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Chinese Hamster Ovary (CHO) cells are the prominent cell line used in biopharmaceutical production. To improve yields and find beneficial bioprocess phenotypes genetic engineering plays an essential role in recent research. The miR-23 cluster, specifically miR-24 and miR-27, was first identified as differentially expressed during hypothermic conditions suggesting a role in proliferation and productivity in CHO cells. In this study, we used sponge decoy technology to stably deplete the miRNA expression of the cluster. Furthermore, we implemented the CRISPR/Cas9 system to knockdown miRNA expression. Sponge constructs were designed for an imperfect binding of the miRNA target, protecting from RISC mediated cleavage. GuideRNAs for the CRISPR/Cas9 system were designed to target the seed region of the miRNA. The expression of mature miRNA and precursor were confirmed using RT-qPCR. For both approaches stable expressing mixed populations were generated and characterised in batch cultures. It was shown, that CRISPR/Cas9 can be implemented in CHO cells with achieving high knockdown efficacy of every single member of the cluster. Targeting of one miRNA member showed that its genomic paralog is successfully targeted as well. The stable depletion of miR-24 using CRISPR/Cas9 showed increased growth and specific productivity in a CHO-K1 mAb expressing cell line. This phenotype was further characterized using quantitative label-free LC-MS/MS showing 186 proteins differently expressed with 19 involved in proliferation and 26 involved in protein folding/translation. Targeting miR-27 in the same cell line showed increased viability in late stages of the culture compared to the control. To evaluate the phenotype in an industry relevant cell line; the miR-23 cluster, miR-24 and miR-27 were stably depleted in a Fc fusion CHO-S cell line which showed increased batch titers up to 1.5-fold. In this work, we highlighted that the stable depletion of the miR-23 cluster and its members can improve the bioprocess phenotype concerning growth and productivity in two different cell lines. Furthermore, we showed that using CRISPR/Cas9 is comparable to the traditional sponge decoy technology.

Keywords: Chinese Hamster ovary cells, CRISPR/Cas9, microRNAs, sponge decoy technology

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Authors: Isari Pairoa, Proud Arunrangsiwed

Abstract:

Brand mascots are the cartoon characters, which are mainly designed for advertising or other related marketing purposes. Many brand mascots are extremely popular, since they were presented in commercial advertisements and Line Stickers. Brand Line Stickers could lead the users to identify with the brand and brand mascots, where might influence users to become loyal customers, and share the identity with the brand. The objective of the current study is to examine the effect of brand mascots on consumers’ decision and consumers’ intention to purchase the product. This study involved 400 participants, using cluster sampling from 50 districts in Bangkok metropolitan area. The descriptive analysis shows that using brand mascot causes consumers' positive attitude toward the products, and also heightens the possibility to purchasing the products. The current study suggests the new type of marketing strategy, which is brand fandom. This study has also contributed the knowledge to the area of integrated marketing communication and identification theory.

Keywords: brand mascot, consumers’ behavior, marketing communication, purchasing

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

Abstract:

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

Procedia PDF Downloads 479

Authors: Emilie Zimet

Abstract:

During networking and NCCD moderation meetings, best practices for identifying students who require Literacy Intervention are often discussed. Once these students are identified, consideration is given to the most effective process for prioritising those who have the greatest need for Literacy Support and the allocation of resources, tracking of intervention effectiveness and communicating with teachers/external providers/parents. Through a workshop, the group will investigate best practices to identify students who require literacy support and strategies to communicate and track their progress. In groups, participants will examine what they do in their settings and then compare with other models, including the researcher’s model, to decide the most effective path to identification and communication. Participants will complete a worksheet at the beginning of the session to deeply consider their current approaches. The participants will be asked to critically analyse their own identification processes for Literacy Intervention, ensuring students are not overlooked if they fall into the borderline category. A cut-off for students to access intervention will be considered so as not to place strain on already stretched resources along with the most effective allocation of resources. Furthermore, communicating learning needs and differentiation strategies to staff is paramount to the success of an intervention, and participants will look at the frequency of communication to share such strategies and updates. At the end of the session, the group will look at creating or evolving models that allow for best practices for the identification and communication of Literacy Interventions. The proposed outcome for this research is to develop a model of identification of students requiring Literacy Intervention that incorporates the allocation of resources and communication to key stakeholders. This will be done by pooling information and discussing a variety of models used in the participant's school settings.

Keywords: identification, student selection, communication, special education, school policy, planning for intervention

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Authors: Chang-Fa Liu, Yi-Ting Wang, Yuan Liu, Hai-Feng Wei, Lei Fang, Jin Li

Abstract:

Heavy metals in sediments can cause adverse ecological effects while it exceeds a given criteria. The present study investigated sediment environmental quality, pollutant enrichment, ecological risk, and source identification for copper, cadmium, lead, zinc, mercury, and arsenic in the sediments collected from tidal flat marsh of Shuangtai estuary, China. The arithmetic mean integrated pollution index, geometric mean integrated pollution index, fuzzy integrated pollution index, and principal component score were used to characterize sediment environmental quality; fuzzy similarity and geo-accumulation Index were used to evaluate pollutant enrichment; correlation matrix, principal component analysis, and cluster analysis were used to identify source of pollution; environmental risk index and potential ecological risk index were used to assess ecological risk. The environmental qualities of sediment are classified to very low degree of contamination or low contamination. The similar order to element background of soil in the Liaohe plain is region of Sanjiaozhou, Honghaitan, Sandaogou, Xiaohe by pollutant enrichment analysis. The source identification indicates that correlations are significantly among metals except between copper and cadmium. Cadmium, lead, zinc, mercury, and arsenic will be clustered in the same clustering as the first principal component. Copper will be clustered as second principal component. The environmental risk assessment level will be scaled to no risk in the studied area. The order of potential ecological risk is As > Cd > Hg > Cu > Pb > Zn.

Keywords: ecological risk assessment, heavy metals, sediment, marsh, Shuangtai estuary

Procedia PDF Downloads 320