Search results for: tree identification

Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

Procedia PDF Downloads 77

Authors: Syed Muzyan Shahzad, Liu Jianxin, Asim Shahzad, Muhammad Sharjeel Raza, Sun Ya, Fanidi Meryem

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Electrical resistivity investigation was conducted in vicinity of Tarbela Ghazi, in order to study the subsurface layer with a view of determining the depth to the aquifer and thickness of groundwater potential zones. Vertical Electrical Sounding (VES) using Schlumberger array was carried out at 16 VES stations. Well logging data at four tube wells have been used to mark the super saturated zones with great discharge rate. The present paper shows a geoelectrical identification of the lithology and an estimate of the relationship between the resistivity and Dar Zarrouk parameters (transverse unit resistance and longitudinal unit conductance). The VES results revealed both homogeneous and heterogeneous nature of the subsurface strata. Aquifer is unconfined to confine in nature, and at few locations though perched aquifer has been identified, groundwater potential zones are developed in unconsolidated deposits layers and more than seven geo-electric layers are observed at some VES locations. Saturated zones thickness ranges from 5 m to 150 m, whereas at few area aquifer is beyond 150 m thick. The average anisotropy, transvers resistance and longitudinal conductance values are 0.86 %, 35750.9821 Ω.m², 0.729 Siemens, respectively. The transverse unit resistance values fluctuate all over the aquifer system, whereas below at particular depth high values are observed, that significantly associated with the high transmissivity zones. The groundwater quality in all analyzed samples is below permissible limit according to World Health Standard (WHO).

Keywords: aquifer, Dar Zarrouk parameters, geoelectric layers, Tarbela Ghazi

Procedia PDF Downloads 190

Authors: Maulik L. Sisodia, Tirth K. Raval, Aarsh S. Mistry

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This paper summarizes the ongoing researches about the defects in both flexible and rigid pavement and the maintenance in both flexible and rigid pavements. Various defects in pavements have been identified since the existence of both flexible and rigid pavement. Flexible Pavement failure is defined in terms of decreasing serviceability caused by the development of cracks, ruts, potholes etc. Flexible Pavement structure can be destroyed in a single season due to water penetration. Defects in flexible pavements is a problem of multiple dimensions, phenomenal growth of vehicular traffic (in terms of no. of axle loading of commercial vehicles), the rapid expansion in the road network, non-availability of suitable technology, material, equipment, skilled labor and poor funds allocation have all added complexities to the problem of flexible pavements. In rigid pavements due to different type of destress the failure like joint spalling, faulting, shrinkage cracking, punch out, corner break etc. Application of correction in the existing surface will enhance the life of maintenance works as well as that of strengthening layer. Maintenance of a road network involves a variety of operations, i.e., identification of deficiencies and planning, programming and scheduling for actual implementation in the field and monitoring. The essential objective should be to keep the road surface and appurtenances in good condition and to extend the life of the road assets to its design life. The paper describes lessons learnt from pavement failures and problems experienced during the last few years on a number of projects in India. Broadly, the activities include identification of defects and the possible cause there off, determination of appropriate remedial measures; implement these in the field and monitoring of the results.

Keywords: Flexible Pavements, Rigid Pavements, Defects, Maintenance

Procedia PDF Downloads 160

Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

Procedia PDF Downloads 133

Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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The widespread adoption and increasing proliferation of smartphones that started during the first decade of the twenty-first century have enabled their users to communicate and access information in ways that were merely thought of as possibilities in the few years before the smartphone revolution. A product of the convergence of the cellular phone and portable computer, the smartphone provides an additional important function that used to be the exclusive domain of desktop-bound computers and portable computers: Web Browsing. For increasing numbers of users, the smartphone and allied devices such as tablet computers have become their first and often their only means of accessing the World Wide Web. This has led to the development of websites that cater to the needs of the new breed of smartphone-carrying web users. The smaller size of smartphones as compared with conventional computers has provided unique challenges to web interface designers. The smaller screen size and touchscreen interface have made it much more difficult to read and navigate through web pages that were in most part designed for traditional desktop and portable computers. Although increasing numbers of websites now provide an alternate website formatted for smartphones, problems with ease of use, reliability and usability still remain. This study focuses on the identification of the problems associated with smartphone web interfaces, the compliance with accepted standards of user-oriented web interface design, the strategies that could be utilized to ensure the design of user-centric web interfaces for smartphones, and the identification of the current trends and developments related to user-centric web interface design intended for the consumption of smartphone users.

Keywords: human-computer interaction, user-centered design, web interface, mobile, smartphone

Procedia PDF Downloads 349

Authors: Recep Kesli, Gulsah Asik, Cengiz Demir, Onur Turkyilmaz

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Objective: Pseudomonas aeruginosa (P. aeruginosa) is an important nosocomial pathogen which causes serious hospital infections and is resistant to many commonly used antibiotics. P. aeruginosa can develop resistance during therapy and also it is very resistant to disinfectant chemicals. It may be found in respiratory support devices in hospitals. In this study, the antibiotic resistance of P. aeruginosa strains isolated from bronchial aspiration samples was evaluated retrospectively. Methods: Between October 2013 and September 2015, a total of 318 P. aeruginosa were isolated from clinical samples obtained from various intensive care units and inpatient patients hospitalized at Afyon Kocatepe University, ANS Practice and Research Hospital. Isolated bacteria identified by using both the conventional methods and automated identification system-VITEK 2 (bioMerieux, Marcy l’etoile France). Antibacterial resistance tests were performed by using Kirby-Bauer disc (Oxoid, Hampshire, England) diffusion method following the recommendations of CLSI. Results: Antibiotic resistance rates of identified 318 P. aeruginosa strains were found as follows for tested antibiotics; 32 % amikacin, 42% gentamicin, 43% imipenem, 43% meropenem, 50% ciprofloxacin, 57% levofloxacin, 38% cefepime, 63% ceftazidime, and 85% piperacillin/tazobactam. Conclusion: Resistance profiles change according to years and provinces for P. aeruginosa, so these findings should be considered empirical treatment choices. In this study, the highest and lowest resistance rates found against piperacillin/tazobactam % 85, and amikacin %32.

Keywords: Pseudomonas aeruginosa, antibiotic resistance rates, intensive care unit, Pseudomonas spp.

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Authors: Zohar Manber, Ran Elkon

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Accumulation of somatic mutations (SMs) in the genome is a major driving force of cancer development. Most SMs in the tumor's genome are functionally neutral; however, some cause damage to critical processes and provide the tumor with a selective growth advantage (termed cancer driver mutations). Current research on functional significance of SMs is mainly focused on finding alterations in protein coding sequences. However, the exome comprises only 3% of the human genome, and thus, SMs in the noncoding genome significantly outnumber those that map to protein-coding regions. Although our understanding of noncoding driver SMs is very rudimentary, it is likely that disruption of regulatory elements in the genome is an important, yet largely underexplored mechanism by which somatic mutations contribute to cancer development. The expression of most human genes is controlled by multiple enhancers, and therefore, it is conceivable that regulatory SMs are distributed across different enhancers of the same target gene. Yet, to date, most statistical searches for regulatory SMs have considered each regulatory element individually, which may reduce statistical power. The first challenge in considering the cumulative activity of all the enhancers of a gene as a single unit is to map enhancers to their target promoters. Such mapping defines for each gene its set of regulating enhancers (termed "set of regulatory elements" (SRE)). Considering multiple enhancers of each gene as one unit holds great promise for enhancing the identification of driver regulatory SMs. However, the success of this approach is greatly dependent on the availability of comprehensive and accurate enhancer-promoter (E-P) maps. To date, the discovery of driver regulatory SMs has been hindered by insufficient sample sizes and statistical analyses that often considered each regulatory element separately. In this study, we analyzed more than 2,500 whole-genome sequence (WGS) samples provided by The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) in order to identify such driver regulatory SMs. Our analyses took into account the combinatorial aspect of gene regulation by considering all the enhancers that control the same target gene as one unit, based on E-P maps from three genomics resources. The identification of candidate driver noncoding SMs is based on their recurrence. We searched for SREs of genes that are "hotspots" for SMs (that is, they accumulate SMs at a significantly elevated rate). To test the statistical significance of recurrence of SMs within a gene's SRE, we used both global and local background mutation rates. Using this approach, we detected - in seven different cancer types - numerous "hotspots" for SMs. To support the functional significance of these recurrent noncoding SMs, we further examined their association with the expression level of their target gene (using gene expression data provided by the ICGC and TCGA for samples that were also analyzed by WGS).

Keywords: cancer genomics, enhancers, noncoding genome, regulatory elements

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Authors: Carolina V. Viegas, Monique Gonçalves, Gisel Chenard Diaz, Yordanka Reyes Cruz, Donato Alexandre Gomes Aranda

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To develop the massive cultivation of microalgae, it is necessary to isolate and characterize the species, improving genetic tools in search of specific characteristics. Therefore, the detection, identification and quantification of the compounds that compose the Scenedesmus sp. were prerequisites to verify the potential of these microalgae. The main objective of this work was to carry out the characterization of Scenedesmus sp. as to the content of ash, carbohydrates, proteins and lipids as well as the determination of the composition of their lipid classes and main fatty acids. The biomass of Scenedesmus sp, showed 15,29 ± 0,23 % of ash and CaO (36,17 %) was the main component of this fraction, The total protein and carbohydrate content of the biomass was 40,74 ± 1,01 % and 23,37 ± 0,95 %, respectively, proving to be a potential source of proteins as well as carbohydrates for the production of ethanol via fermentation, The lipid contents extracted via Bligh & Dyer and in situ saponification were 8,18 ± 0,13 % and 4,11 ± 0,11 %, respectively. In the lipid extracts obtained via Bligh & Dyer, approximately 50 % of the composition of this fraction consists of fatty compounds, while the other half is composed of an unsaponifiable fraction composed mainly of chlorophylls, phytosterols and carotenes. From the lowest yield, it was possible to obtain a selectivity of 92,14 % for fatty components (fatty acids and fatty esters) confirmed through the infrared spectroscopy technique. The presence of polyunsaturated acids (~45 %) in the lipid extracts indicated the potential of this fraction as a source of nutraceuticals. The results indicate that the biomass of Scenedesmus sp, can become a promising potential source for obtaining polyunsaturated fatty acids, carotenoids and proteins as well as the simultaneous obtainment of different compounds of high commercial value.

Keywords: microalgae, Desmodesmus, lipid classes, fatty acid profile, proteins, carbohydrates

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

Procedia PDF Downloads 664

Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

Procedia PDF Downloads 118

Authors: D. P. Lawrence, T. M. M. D. Ariyarathna, W. N. K. De Silva, M. D. S. C. De Silva, Lasantha Abeysiri, Pradeep Abeygunawardhna

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Safari traveling is one of the most famous hobbies all over the world. In Sri Lanka, 'Yala' is the second-largest national park, which is a better place to go for a safari. Many number of local and foreign travelers are coming to go for a safari in 'Yala'. But 'Yala' does not have a mobile application that is made to facilitate the traveler with some important features that the traveler wants to achieve in the safari experience. To overcome these difficulties, the proposed mobile application by adding those identified features to make travelers, guiders, and administration's works easier. The proposed safari traveling guidance mobile application is called 'SMART SAFARI' for the 'Yala' National Park in Sri Lanka. There are four facilities in this mobile application that provide for travelers as well as the guiders. As the first facility, the guider and traveler can view the created map of the park, and the guider can add temporary locations of animals and special locations on the map. This is a Geographic Information System (GIS) to capture, analyze, and display geographical data. And as the second facility is to generate optimal paths according to the travelers' requirements through the park by using machine learning techniques. In the third part, the traveler can get information about animals using an animal identification system by capturing the animal. As in the other facility, the traveler will be facilitated to add reviews and a rate and view those comments under categorized sections and pre-defined score range. With those facilities, this user-friendly mobile application provides the user to get a better experience in safari traveling, and it will probably help to develop tourism culture in Sri Lanka.

Keywords: animal identification system, geographic information system, machine learning techniques, pre defined score range

Procedia PDF Downloads 130

Authors: Amelia Frickey, Timothy (TJ) Sheridan, Angelica Rossi, Bahar Aliakbarian

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The growth of large food supply chains demanded improved end-to-end traceability of food products, which has led to companies being increasingly interested in using smart technologies such as Radio Frequency Identification (RFID)-enabled packaging to track items. As technology is being widely used, there are several technological or economic issues that should be overcome to facilitate the adoption of this track-and-trace technology. One of the technological challenges of RFID technology is its sensitivity to different environmental form factors, including packaging materials and the content of the packaging. Although researchers have assessed the performance loss due to the proximity of water and aqueous solutions, there is still the need to further investigate the impacts of food products on the reading range of RFID tags. However, to the best of our knowledge, there are not enough studies to determine the correlation between RFID tag performance and food beverages properties. The goal of this project was to investigate the effect of the solution properties (pH and conductivity) and different packaging materials filled with food-like water-based solutions on the performance of an RFID tag. Three commercially available ultra high-frequency RFID tags were placed on three different bottles and filled with different concentrations of water-based solutions, including sodium chloride, citric acid, sucrose, and ethanol. Transparent glass, Polyethylneterephtalate (PET), and Tetrapak® were used as the packaging materials commonly used in the beverage industries. Tag readability (Theoretical Read Range, TRR) and sensitivity (Power on Tag Forward, PoF) were determined using an anechoic chamber. First, the best place to attach the tag for each packaging material was investigated using empty and water-filled bottles. Then, the bottles were filled with the food-like solutions and tested with the three different tags and the PoF and TRR at the fixed frequency of 915MHz. In parallel, the pH and conductivity of solutions were measured. The best-performing tag was then selected to test the bottles filled with wine, orange, and apple juice. Despite various solutions altering the performance of each tag, the change in tag performance had no correlation with the pH or conductivity of the solution. Additionally, packaging material played a significant role in tag performance. Each tag tested performed optimally under different conditions. This study is the first part of comprehensive research to determine the regression model for the prediction of tag performance behavior based on the packaging material and the content. More investigations, including more tags and food products, are needed to be able to develop a robust regression model. The results of this study can be used by RFID tag manufacturers to design suitable tags for specific products with similar properties.

Keywords: smart food packaging, supply chain management, food waste, radio frequency identification

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Authors: Ashrafosadat Mousavi Laer, Elaheh Moravej

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The art of binding was simple and elementary at the beginning of Islam. This art thrived gradually and continued its development as an independent art. Identification of the binding techniques and used materials in covers and investigation of the arrays give us indexes for the better identification of different doctrines and methods of that time. The catalogers of the manuscripts usually pay attention to four items: gender, color, art elegances, injury, and exquisiteness of the cover. The criterion for classification of the covers is their art nature and gender. 15th century AD (9th century AH) was the period of the binding art development in which the most beautiful covers were produced by the so-called method of ‘burning’. At 16th century AD (10th century AH), in Safavid era, art changed completely and a fundamental evolution occurred in the technique and method of binding. The greatest change in this art was the extensive use of stamp that was made mostly of steel and copper. Theses stamps were presses against leather. These covers were called ‘beat’. In this paper, writing and bookbinding of about 32 Shahnamehs of Safavid era available in the Iranian libraries and museums are studied. An analytical-statistical study shows that four methods have been used including beat, burning, mosaic, and oily. 69 percent of the covers of these copies are cardboards with a leathery coating (goatskin) and have been produced by burning and beat methods. Its reasons are that these two methods have been common methods in Safavid era and performing them was only feasible on leather and the most desirable and commonly used leather of that time was goatskin which was the best option for cover legend durability and preserving the book and it was more durable because it had been made of goat skin. In addition, it had prepared a suitable opportunity for the binding artist’s creativity and innovation.

Keywords: Shahnameh, Safavid era, bookbinding, beat cover, burning cover

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Authors: Seyed Abolhasan Naeini, Hamidreza Rahmani

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Laboratory consolidated undrained triaxial (CU) tests were carried out to study the strength behavior of silty soil reinforced with randomly plastic waste bottle chips. Specimens mixed with plastic waste chips in triaxial compression tests with 0.25, 0.50, 0.75, 1.0, and 1.25% by dry weight of soil and tree different length including 4, 8, and 12 mm. In all of the samples, the width and thickness of plastic chips were kept constant. According to the results, the amount and size of plastic waste bottle chips played an important role in the increasing of the strength parameters of reinforced silt compared to the pure soil. Because of good results, the suggested method of soil improvement can be used in many engineering problems such as increasing the bearing capacity and settlement reduction in foundations.

Keywords: reinforcement, silt, soil improvement, triaxial test, waste bottle chips

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Authors: Thilini M. Yatanwala

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CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

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Authors: Praskovya Britskaya, Fatima Shaizadina, Alua Omarova, Nessipkul Alysheva

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Planned preventive vaccination, which is carried out in the Republic of Kazakhstan, promoted permanent decrease in the incidence of measles and viral hepatitis B. In the structure of VHB patients prevail people of young, working age. Monitoring of infectious incidence, monitoring of coverage of immunization of the population, random serological control over the immunity enable well-timed identification of distribution of the activator, effectiveness of the taken measures and forecasting. The serological blood analysis was conducted in indicator groups of the population of Central Kazakhstan for the purpose of identification of antibody titre for vaccine preventable infections (measles, viral hepatitis B). Measles antibodies were defined by method of enzyme-linked assay (ELA) with test-systems "VektoKor" – Ig G ('Vektor-Best' JSC). Antibodies for HBs-antigen of hepatitis B virus in blood serum was identified by method of enzyme-linked assay (ELA) with VektoHBsAg test systems – antibodies ('Vektor-Best' JSC). The result of the analysis is positive, the concentration of IgG to measles virus in the studied sample is equal to 0.18 IU/ml or more. Protective level of concentration of anti-HBsAg makes 10 mIU/ml. The results of the study of postvaccinal measles immunity showed that the share of seropositive people made 87.7% of total number of surveyed. The level of postvaccinal immunity to measles in age groups differs. So, among people older than 56 the percentage of seropositive made 95.2%. Among people aged 15-25 were registered 87.0% seropositive, at the age of 36-45 – 86.6%. In age groups of 25-35 and 36-45 the share of seropositive people was approximately at the same level – 88.5% and 88.8% respectively. The share of people seronegative to a measles virus made 12.3%. The biggest share of seronegative people was found among people aged 36-45 – 13.4% and 15-25 – 13.0%. The analysis of results of the examined people for the existence of postvaccinal immunity to viral hepatitis B showed that from all surveyed only 33.5% have the protective level of concentration of anti-HBsAg of 10 mIU/ml and more. The biggest share of people protected from VHB virus is observed in the age group of 36-45 and makes 60%. In the indicator group – above 56 – seropositive people made 4.8%. The high percentage of seronegative people has been observed in all studied age groups from 40.0% to 95.2%. The group of people which is least protected from getting VHB is people above 56 (95.2%). The probability to get VHB is also high among young people aged 25-35, the percentage of seronegative people made 80%. Thus, the results of the conducted research testify to the need for carrying out serological monitoring of postvaccinal immunity for the purpose of operational assessment of the epidemiological situation, early identification of its changes and prediction of the approaching danger.

Keywords: antibodies, blood serum, immunity, immunoglobulin

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Sameen Tariq, Saira Bano, Sayyada Ghufrana Nadeem

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The Ocean, which covers over 70% of the world's surface, is wealthy in biodiversity as well as a rich wellspring of microorganisms with huge potential. The oceanic climate is home to an expansive scope of plants, creatures, and microorganisms. Marine microbial networks, which incorporate microscopic organisms, infections, and different microorganisms, enjoy different benefits in biotechnological processes. Samples were collected from marine environments, including soil and water samples, to cultivate the uncultured marine organisms by using Zobell’s medium, Sabouraud’s dextrose agar, and casein media for this purpose. Following isolation, we conduct microscopy and biochemical tests, including gelatin, starch, glucose, casein, catalase, and carbohydrate hydrolysis for further identification. The results show that more gram-positive and gram-negative bacteria. The isolation process of marine organisms is essential for understanding their ecological roles, unraveling their biological secrets, and harnessing their potential for various applications. Marine organisms exhibit remarkable adaptations to thrive in the diverse and challenging marine environment, offering vast potential for scientific, medical, and industrial applications. The isolation process plays a crucial role in unlocking the secrets of marine organisms, understanding their biological functions, and harnessing their valuable properties. They offer a rich source of bioactive compounds with pharmaceutical potential, including antibiotics, anticancer agents, and novel therapeutics. This study is an attempt to explore the diversity and dynamics related to marine microflora and their role in biofilm formation.

Keywords: marine microorganisms, ecosystem, fungi, biofilm, gram-positive, gram-negative

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Authors: Catherine K. Derow

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Possibly a slime mold somehow mutated or already was mutated at progeniture and so stayed as a metazoan when it developed into the fruiting stage and so the slime mold(s) we are evolved and similar to are genetically differ from the slime molds in existence now. This may be why there are genetic links between humans and other metazoa now alive and slime molds now alive but we are now divergent branches of the evolutionary tree compared to the original slime mold, or perhaps slime mold-like organisms, that gave rise to metazoan animalia and perhaps algae and plantae as slime molds were undifferentiated enough in many ways that could allow their descendants to evolve into these three separate phylogenetic categories. Or it may be a slime mold was born or somehow progenated as multicellular, as the particular organism was mutated enough to have say divided in a a 'pseudo-embryonic' stage, and this could have happened for algae, plantae as well as animalia or all the branches may be from the same line but the missing link might be covered in 'phylogenetic sequence comparison noise'.

Keywords: metazoan evolution, unicellular bridge to metazoans, evolution, slime mold

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Authors: Igor V. Grigoriev

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Understanding of biological processes encoded in fungi is instrumental in addressing future food, feed, and energy demands of the growing human population. Genomics is a powerful and quickly evolving tool to understand these processes. The Fungal Genomics Program of the US Department of Energy Joint Genome Institute (JGI) partners with researchers around the world to explore fungi in several large scale genomics projects, changing the fungal genomics landscape. The key trends of these changes include: (i) rapidly increasing scale of sequencing and analysis, (ii) developing approaches to go beyond culturable fungi and explore fungal ‘dark matter,’ or unculturables, and (iii) functional genomics and multi-omics data integration. Power of comparative genomics has been recently demonstrated in several JGI projects targeting mycorrhizae, plant pathogens, wood decay fungi, and sugar fermenting yeasts. The largest JGI project ‘1000 Fungal Genomes’ aims at exploring the diversity across the Fungal Tree of Life in order to better understand fungal evolution and to build a catalogue of genes, enzymes, and pathways for biotechnological applications. At this point, at least 65% of over 700 known families have one or more reference genomes sequenced, enabling metagenomics studies of microbial communities and their interactions with plants. For many of the remaining families no representative species are available from culture collections. To sequence genomes of unculturable fungi two approaches have been developed: (a) sequencing DNA from fruiting bodies of ‘macro’ and (b) single cell genomics using fungal spores. The latter has been tested using zoospores from the early diverging fungi and resulted in several near-complete genomes from underexplored branches of the Fungal Tree, including the first genomes of Zoopagomycotina. Genome sequence serves as a reference for transcriptomics studies, the first step towards functional genomics. In the JGI fungal mini-ENCODE project transcriptomes of the model fungus Neurospora crassa grown on a spectrum of carbon sources have been collected to build regulatory gene networks. Epigenomics is another tool to understand gene regulation and recently introduced single molecule sequencing platforms not only provide better genome assemblies but can also detect DNA modifications. For example, 6mC methylome was surveyed across many diverse fungi and the highest among Eukaryota levels of 6mC methylation has been reported. Finally, data production at such scale requires data integration to enable efficient data analysis. Over 700 fungal genomes and other -omes have been integrated in JGI MycoCosm portal and equipped with comparative genomics tools to enable researchers addressing a broad spectrum of biological questions and applications for bioenergy and biotechnology.

Keywords: fungal genomics, single cell genomics, DNA methylation, comparative genomics

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Authors: David S. Byrne

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The purpose of this paper is to understand how phone record analysis can enable identification of subjects in communication with a target of a terrorist plot. This study also sought to understand the advantages of the implementation of simulations to develop the skills of future intelligence analysts to enhance national security. Through the examination of phone reports which in essence consist of the call traffic of incoming and outgoing numbers (and not by listening to calls or reading the content of text messages), patterns can be uncovered that point toward members of a criminal group and activities planned. Through temporal and frequency analysis, conclusions were drawn to offer insights into the identity of participants and the potential scheme being undertaken. The challenge lies in the accurate identification of the users of the phones in contact with the target. Often investigators rely on proprietary databases and open sources to accomplish this task, however it is difficult to ascertain the accuracy of the information found. Thus, this paper poses two research questions: how effective are freely available web sources of information at determining the actual identification of callers? Secondly, does the identity of the callers enable an understanding of the lifestyle and habits of the target? The methodology for this research consisted of the analysis of the call detail records of the author’s personal phone activity spanning the period of a year combined with a hypothetical theory that the owner of said phone was a leader of terrorist cell. The goal was to reveal the identity of his accomplices and understand how his personal attributes can further paint a picture of the target’s intentions. The results of the study were interesting, nearly 80% of the calls were identified with over a 75% accuracy rating via datamining of open sources. The suspected terrorist’s inner circle was recognized including relatives and potential collaborators as well as financial institutions [money laundering], restaurants [meetings], a sporting goods store [purchase of supplies], and airline and hotels [travel itinerary]. The outcome of this research showed the benefits of cellphone analysis without more intrusive and time-consuming methodologies though it may be instrumental for potential surveillance, interviews, and developing probable cause for wiretaps. Furthermore, this research highlights the importance of building upon the skills of future intelligence analysts through phone record analysis via simulations; that hands-on learning in this case study emphasizes the development of the competencies necessary to improve investigations overall.

Keywords: hands-on learning, intelligence analysis, intelligence education, phone record analysis, simulations

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Authors: Juni C. Kim, Anna R. Robuck, Douglas I. Walker

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The human exposome, which includes chemical exposures over the lifetime and their effects, is now recognized as an important measure for understanding human health; however, the complexity of the data makes the identification of environmental chemicals challenging. The goal of our project was to establish a computational workflow for the improved identification of environmental pollutants containing chlorine or bromine. Using the “pattern. search” function available in the R package NonTarget, we wrote a multifunctional script that searches mass spectral clusters from untargeted gas-chromatography high-resolution mass spectrometry (GC-HRMS) for the presence of spectra consistent with chlorine and bromine-containing organic compounds. The “pattern. search” function was incorporated into a different function that allows the evaluation of clusters containing multiple analyte fragments, has multi-core support, and provides a simplified output identifying listing compounds containing chlorine and/or bromine. The new function was able to process 46,000 spectral clusters in under 8 seconds and identified over 150 potential halogenated spectra. We next applied our function to a deidentified dataset from patients diagnosed with primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and healthy controls. Twenty-two spectra corresponded to potential halogenated compounds in the PSC and PBC dataset, including six significantly different in PBC patients, while four differed in PSC patients. We have developed an improved algorithm for detecting halogenated compounds in GC-HRMS data, providing a strategy for prioritizing exposures in the study of human disease.

Keywords: exposome, metabolome, computational metabolomics, high-resolution mass spectrometry, exposure, pollutants

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Authors: Amine Alaoui Sanae, Tagjdid Reda, Loutfi Chafiqa, Melloul Merouane, Laloui Aziz, Touil Nadia, El Fahim, E. Mostafa

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Background: Rotavirus group A (RVA) strains with G8P[14] specificities are usually detected in calves and goats. However, these strains have been reported globally in humans and have often been characterized as originating from zoonotic transmissions, particularly in area where ruminants and humans live side-by-side. Whether human P[14] genotypes are two-way and can be transmitted to animal species remains to be established. Here we describe VP4 deduced amino-acid relationships of three Moroccan P[14] genotypes originating from different species and the receptiveness of an alpine goat to a human G8P[14] through an experimental infection. Material/methods: the human MA31 RVA strain was originally identified in a four years old girl presenting an acute gastroenteritis hospitalized at the pediatric care unit in Rabat Hospital in 2011. The virus was isolated and propagated in MA104 cells in the presence of trypsin. Ch_10S and 8045_S animal RVA strains were identified in fecal samples of a 2-week-old native goat and 3-week-old calf with diarrhea in 2011 in Bouaarfa and My Bousselham respectively. Genomic RNAs of all strains were subjected to a two-step RT-PCR and sequenced using the consensus primers VP4. The phylogenetic tree for MA31, Ch_10S and 8045_S VP4 and a set of published P[14] genotypes was constructed using MEGA6 software. The receptivity of MA31 strain by an eight month-old alpine goat was assayed. The animal was orally and intraperitonally inoculated with a dose of 8.5 TCID50 of virus stock at passage level 3. The shedding of the virus was tested by a real time RT-PCR assay. Results: The phylogenetic tree showed that the three Moroccan strains MA31, Ch_10S and 8045_S VP4 were highly related to each other (100% similar at the nucleotide level). They were clustered together with the B10925, Sp813, PA77 and P169 strains isolated in Belgium, Spain and Italy respectively. The Belgian strain B10925 was the most closely related to the Moroccan strains. In contrast, the 8045_S and Ch_10S strains were clustered distantly from the Tunisian calf strain B137 and the goat strain cap455 isolated in South Africa respectively. The human MA31 RVA strain was able to induce bloody diarrhea at 2 days post infection (dpi) in the alpine goat kid. RVA virus shedding started by 2 dpi (Ct value of 28) and continued until 5 dpi (Ct value of 25) with a concomitant elevation in the body temperature. Conclusions: Our study while limited to one animal, is the first study proving experimentally that a human P[14] genotype causes diarrhea and virus shedding in the goat. This result reinforce the potential role of inter- species transmission in generating novel and rare rotavirus strains such G8P[14] which infect humans.

Keywords: interspecies transmission, rotavirus, goat, human

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Jerome Cayton Barradas, Rowely Parico, Lauro Atienza, Poornima Shankar

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The effective agricultural extension is essential in facilitating improvements in various agricultural areas. One way of doing this is through Information and communication technologies (ICTs) like Rice Doctor (RD), an app-based diagnostic tool that provides accurate and timely diagnosis and management recommendations for more than 80 crop problems. This study aims to evaluate the RD usability by determining the effectiveness, efficiency, and user satisfaction of RD in making an accurate and timely diagnosis. It also aims to identify other factors that affect RD usability. This will be done by comparing RD with two other diagnostic methods: visual identification-based diagnosis and reference-guided diagnosis. The study was implemented in three rice-producing areas and has involved 96 extension workers. Respondents accomplished a self-administered survey and participated in group discussions. Data collected was then subjected to qualitative and quantitative analysis. Most of the respondents were satisfied with RD and believed that references are needed in assuring the accuracy of diagnosis. The majority found it efficient and easy to use. Some found it confusing and complicated, but this is because of their unfamiliarity with RD. Most users were also able to achieve accurate diagnosis proving effectiveness. Lastly, although users have reservations, they are satisfied and open to using RD. The study also found out the importance of visual identification skills in using RD and the need for capacity development and improvement of access to RD devices. From these results, the following are recommended to improve RD usability: review and upgrade diagnostic keys, expand further RD content, initiate capacity development for AEWs, and prepare and implement an RD communication plan.

Keywords: agricultural extension, crop protection, information and communication technologies, rice doctor

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Authors: Raymond Ndhlovu, Jen Snowball

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It has long been known that the Cultural and Creative Industries (CCIs) have the potential to aid in physical, social and economic renewal and regeneration of towns and cities, hence their importance when dealing with regional development. The CCIs can act as a catalyst for activity and investment in an area because the ‘consumption’ of cultural activities will lead to the activities and use of other non-cultural activities, for example, hospitality development including restaurants and bars, as well as public transport. ‘Consumption’ of cultural activities also leads to employment creation, and diversification. However, CCIs tend to be clustered, especially around large cities. There is, moreover, a case for development of CCIs around smaller towns and cities, because they do not rely on high technology inputs, and long supply chains, and, their direct link to rural and isolated places makes them vital in regional development. However, there is currently little research on how to craft cultural policy for regions with smaller towns and cities. Using the Sarah Baartman District (SBDM) in South Africa as an example, this paper describes the process of developing cultural policy for a region that has potential, and existing, cultural clusters, but currently no one, coherent policy relating to CCI development. The SBDM was chosen as a case study because it has no large cities, but has some CCI clusters, and has identified them as potential drivers of local economic development. The process of developing cultural policy is discussed in stages: Identification of what resources are present; including human resources, soft and hard infrastructure; Identification of clusters; Analysis of CCI labour markets and ownership patterns; Opportunities and challenges from the point of view of CCIs and other key stakeholders; Alignment of regional policy aims with provincial and national policy objectives; and finally, design and implementation of a regional cultural policy.

Keywords: cultural and creative industries, economic impact, intrinsic value, regional development

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: J. K. Visser, H. T. Malan

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A need exists to identify the sources of risks associated with the process automation systems within petrochemical companies or similar energy related industries. These companies use many different process automation technologies in its value chain. A crucial part of the process automation system is the information technology component featuring in the supervisory control layer. The ever-changing technology within the process automation layers and the rate at which it advances pose a risk to safe and predictable automation system performance. The age of the automation equipment also provides challenges to the operations and maintenance managers of the plant due to obsolescence and unavailability of spare parts. The main objective of this research was to determine the risk sources associated with the equipment that is part of the process automation systems. A secondary objective was to establish whether technology managers and technicians were aware of the risks and share the same viewpoint on the importance of the risks associated with automation systems. A conceptual model for risk sources of automation systems was formulated from models and frameworks in literature. This model comprised six categories of risk which forms the basis for identifying specific risks. This model was used to develop a questionnaire that was sent to 172 instrument technicians and technology managers in the company to obtain primary data. 75 completed and useful responses were received. These responses were analyzed statistically to determine the highest risk sources and to determine whether there was difference in opinion between technology managers and technicians. The most important risks that were revealed in this study are: 1) the lack of skilled technicians, 2) integration capability of third-party system software, 3) reliability of the process automation hardware, 4) excessive costs pertaining to performing maintenance and migrations on process automation systems, and 5) requirements of having third-party communication interfacing compatibility as well as real-time communication networks.

Keywords: distributed control system, identification of risks, information technology, process automation system

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Authors: C. Hamamura, V. Gialluca

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Much of the Eclectricity Distributors has about 70% of its electricity interruptions arising from cause "trees", alone or associated with wind and rain and with or without falling branch and / or trees. This contributes inexorably and significantly to outages, resulting in high costs as compensation in addition to the operation and maintenance costs. On the other hand, there is little data structure and solutions to better organize the trees pruning plan effectively, minimizing costs and environmentally friendly. This work describes the development of an algorithm to provide data of trees associated to power grid. The method is accomplished on several steps using satellite imagery and geographically vectorized grid. A sliding window like approach is performed to seek the area around the grid. The proposed method counted 764 trees on a patch of the grid, which was very close to the 738 trees counted manually. The trees data was used as a part of a larger project that implements a system to optimize tree pruning plan.

Keywords: image pattern recognition, trees pruning, trees recognition, neural network

Procedia PDF Downloads 496