Search results for: minimum spanning tree

Authors: Muhammad Mamoon-Ur-Rashid, Gul Rehman

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Entomopathogenic fungi are considered effective bio-control agents for the management of a range of insect pests including red palm weevil. The research studies were conducted under laboratory and field conditions against 5th and 6th instars larvae and adults of [Rhynchophorus ferrugineus (Olivier)] at the faculty of Agriculture, Gomal University Dera Ismail Khan (KPK) Pakistan. The 5th instar larvae were used under field conditions whereas, the 6th instar larvae and newly emerged adults were used under lab conditions. Conidial suspensions were used at five different concentrations of 1×10⁴, 1×10⁵, 1×10⁶, 1×10⁷ and 1×10⁸, conidia per ml. The data were recorded on the mortality, total larval duration, weight of larvae, pre-pupal and pupal durations, percent pupal formation, pupal weight, percent adult emergence, and adult longevity (♂ and ♀) of red palm weevil. The B. bassiana had varying degrees of pathogenicity against different developmental stages of red palm weevil. The maximum larval duration (113.40 days) was noted when 5th instar larvae were treated with the maximum concentration (1 × 10⁸) of B. bassiana, whereas; the minimum total larval duration of 87.20 days was recorded on the lowest concentration (1 × 10⁴) of B. bassiana. The maximum pre-pual and pupal durations were noted at the maximum concentration. The maximum life span of adult male and females were noted at the lowest concentration, whereas; the minimum values were noted at the maximum concentration. The earliest mortality of red palm weevil was observed 1-day after treatment at higher concentrations of 1 × 10⁷ and 1 × 10⁸, whereas; it was recorded 3 and 4 days after treatment at lower concentrations of 1 × 10⁵ and 1 × 10⁴. At 10 days after treatment, the entomopathogenic fungus caused > 80% cumulative mortality of 5th and 6th instar larvae and adult weevils at the maximum concentrations which were more than double than those recorded at the lowest concentration. Overall, the 5th instar larvae of red palm weevils were most susceptible to the fungus compared to the 6th instar larvae and adult weevils. Based on current findings, it is suggested that entomopathogenic fungi could be used for the safer management of red palm weevil.

Keywords: entomopathogenic nematodes, mortality, red palm weevil, sub-lethal effects

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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

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Authors: H. Tanyildizi, M. Abuqebitah, I. Cavdar, M. Demir, L. Kabasakal

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Aim: Whole liver radiation has the modest benefit in the treatment of unresectable hepatic metastases but the radiation doses must keep in control. Otherwise, RILD complications may arise. In this study, we aimed to calculate amount of maximum permissible activity (MPA) and critical organ absorbed doses with MIRD methodology, to evaluate tumour doses for treatment response and whole liver doses for RILD and to find optimal liver function test additionally. Materials and Methods: This study includes 29 patients who attended our nuclear medicine department suffering from Y-90 microspheres treatment. 10 mCi Tc-99m MAA was applied to the patients for dosimetry via IV. After the injection, whole body SPECT/CT images were taken in one hour. The minimum therapeutic tumour dose is on the point of being 120 Gy1, the amount of activities were calculated with MIRD methodology considering volumetric tumour/liver rate. A sub-working group was created with 11 patients randomly and liver clearance rate with Tc-99m-Mebrofenin was calculated according to Ekman formalism. Results: The volumetric tumour/liver rates were found between 33-66% (Maksimum Tolarable Dose (MTD) 48-52Gy3) for 4 patients, were found less than 33% (MTD 72Gy3) for 25 patients. According to these results the average amount of activity, mean liver dose and mean tumour dose were found 1793.9±1.46 MBq, 32.86±0.19 Gy, and 138.26±0.40 Gy. RILD was not observed in any patient. In sub-working group, the relationship between Bilirubin, Albumin, INR (which show presence of liver disease and its degree), liver clearance with Tc-99m-Mebrofenin and calculated activity amounts were found r=0.49, r=0.27, r=0.43, r=0.57, respectively. Discussions: The minimum tumour dose was found 120 Gy for positive dose-response relation. If volumetric tumour/liver rate was > 66%, dose 30 Gy; if volumetric tumour/liver rate 33-66%, dose escalation 48 Gy; if volumetric tumour/liver rate < 33%, dose 72 Gy. These dose limitations did not create RILD. Clearance measurement with Mebrofenin was concluded that the best method to determine the liver function. Therefore, liver clearance rate with Tc-99m-Mebrofenin should be considered in calculation of yttrium-90 microspheres dosimetry.

Keywords: clearance, dosimetry, liver, RILD

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Authors: M. C. Schloesser

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The purpose of this paper is to explore the use and impact of business analytics in European football. Despite good evidence from other major sports leagues, research on this topic in Europe is currently very scarce. This research relies on expert interviews on the use and objective of business analytics. Along with revenue data over 16 seasons spanning from 2004/05 to 2019/20 from Manchester City FC, we conducted a time series analysis to detect a structural breakpoint on the different revenue streams, i.e., sponsorship and ticketing, after analytical tools have been implemented. We not only find that business analytics have indeed been applied at Manchester City FC and revenue increase is the main objective of their utilization but also that business analytics is indeed a good means to increase revenues if applied sufficiently. We can thereby support findings from other sports leagues. Consequently, professional sports organizations are advised to apply business analytics if they aim to increase revenues. This research has shown that analytical practices do, in fact, support revenue growth and help to work more efficiently. As the knowledge of analytical practices is very confidential and not publicly available, we had to select one club as a case study which can be considered a research limitation. Other practitioners should explore other clubs or leagues. Further, there are other factors that can lead to increased revenues that need to be considered. Additionally, sports organizations need resources to be able to apply and utilize business analytics. Consequently, findings might only apply to the top teams of the European football leagues. Nonetheless, this paper combines insights and results on usage, objectives, and impact of business analytics in European professional football and thereby fills a current research gap.

Keywords: business analytics, expert interviews, revenue management, time series analysis

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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

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Authors: Hafiza Javaria Ashraf, Asim Abbasi, Muhammad Hussnain Babar, Muhammad Sufyan

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Brevicoryne brassicae (Aphid) is not only the major biotic constraint of rapeseed crop but also transmits 20 different viral pathogens that cause diseases in crucifers. Aphids cause major losses to rapeseed by stunting growth and yield, with real damage being contamination of harvested heads. The misuse of pesticides has led to tremendous economic losses and hazards to human health and environmental pollution. Thus, newer approaches for pest control are continuously being sought. The naturally occurring, biologically active plant-based products seem to have a prominent role in the development of future commercial pesticides not only for increased productivity but their eco-friendly nature. The present experiment was carried out in Research Area of Ayub Agriculture Research Institute, Faisalabad to check the efficacy of different botanicals against rapeseed aphid. The tested botanicals were, neem seed extract, neem leaf extract, dathora seed extract, kaner leaf extract and aak leaf extract. Insecticide, advantage 20 EC served as the positive control in the experiment. Data was recorded before and after 1, 3 and 7 days of treatment application. The results of the experiment revealed that neem seed extract exhibited maximum mortality (48.42%) followed by dathora (45.54%) and kaner leaf extract (40.29%) after 7 days of treatment application. However minimum mortality i.e. 26.64% was observed in case of aak leaf extract. Advantage encountered maximum mortality i.e. 86.14%. All treatments caused maximum mortality after 7 days of treatment application. In case of pollinators maximum population reduction was observed in case of insecticide (74.29%) while minimum reduction was observed in neem leaf extract (11.57%). Hence it was concluded that unlike insecticides, plant based products can be a better option for regulating pests and conserving beneficial insect fauna.

Keywords: Aphid, mortality, plant based, pollinators

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Authors: E. S. Manuylovich, V. A. Astapenko, P. A. Golovinsky

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The dispersion relation binding the constant of propagation and frequency is calculated for silver cone. The evolution of the electric field of ultrashort pulse during its propagation in conical structure is considered. Increasing of electric field during pulse propagation to the top of the cone is observed. Reduction of the pulse duration at a certain distance is observed. The dependence of minimum pulse duration on initial chirp and cone angle is investigated.

Keywords: ultrashort pulses, surface plasmon polariton, dispersion, silver cone

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Authors: Vivek Thorat, Suhasini Desai

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The hybrid electric vehicle is widely accepted as a promising short to mid-term technical solution due to noticeably improved efficiency and low emissions at competitive costs. Retro fitment of hybrid components into a conventional vehicle for achieving better performance is the best solution so far. But retro fitment includes major modifications into a conventional vehicle with a high cost. This paper focuses on the development of a P3x hybrid prototype with rear wheel drive parallel hybrid electric Light Commercial Vehicle (LCV) with minimum and low-cost modifications. This diesel Hybrid LCV is different from another hybrid with regard to the powertrain. The additional powertrain consists of continuous contact helical gear pair followed by chain and sprocket as a coupler for traction motor. Vehicle powertrain which is designed for the intended high-speed application. This work focuses on targeting of design, development, and packaging of this unique parallel diesel-electric vehicle which is based on multimode hybrid advantages. To demonstrate the practical applicability of this transmission with P3x hybrid configuration, one concept prototype vehicle has been build integrating the transmission. The hybrid system makes it easy to retrofit existing vehicle because the changes required into the vehicle chassis are a minimum. The additional system is designed for mainly five modes of operations which are engine only mode, electric-only mode, hybrid power mode, engine charging battery mode and regenerative braking mode. Its driving performance, fuel economy and emissions are measured and results are analyzed over a given drive cycle. Finally, the output results which are achieved by the first vehicle prototype during experimental testing is carried out on a chassis dynamometer using MIDC driving cycle. The results showed that the prototype hybrid vehicle is about 27% faster than the equivalent conventional vehicle. The fuel economy is increased by 20-25% approximately compared to the conventional powertrain.

Keywords: P3x configuration, LCV, hybrid electric vehicle, ROMAX, transmission

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Authors: Vivian Khamis

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Exposure to war and forced migration have been widely linked to child subsequent adaptation. What remains sparse is research spanning multiple risk and protective factors and examining their unique and relative implications to difficulties in mental health among refugee girls. This study investigated the mechanisms through which posttraumatic stress disorder (PTSD), emotion dysregulation , neuroticism, and behavioral and emotional disorders in Syrian refugee girls is impacted by exposure to war traumas, age, and other risk and protective factors such as coping styles, family relationships, and school environment. The sample consisted of 539 Syrian refugee girls who ranged in age from 7 to 18 years attending public schools in various governorates in Lebanon and Jordan. Two school counselors carried out the interviews with children at school. Results indicated that war trauma, older age, and a combination of negative copying style associated with conflict in the family could lead to an overall state of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD in refugee girls. On the other hand, lapse of time since resettlement in host country, positive copying style, cohesion, and expressiveness in the family would lead to more positive mental health status, including lower levels of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD . Enhanced understanding of the mechanistic role of risk and protective factors in contributing to difficulties in mental health in refugee girls may contribute to the development of effective interventions to target the psychological effects of the refugee experience.

Keywords: refugee girls, PTSD, emotion dysregulation, neuroticism, behavioral and emotional disorders

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Authors: M. Nuhu, S. Bilal, A. A. Hamisu, J. A. Abbas, Y. Z. Aminu, P. O. Helen

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Exergy and energy analysis of preheating unit of FCCU of KRPC has been calculated and presented in this study. From the design, the efficiency of each heat exchanger was 86%. However, on completion of this work the efficiencies was calculated to be 39.90%, 55.66%, 56.22%, and 57.14% for 16E02, 16E03, 16E04, and 16E05 respectively. 16E04 has the minimum energy loss of 0.86%. The calculated second law and exergy efficiencies of the system were 43.01 and 56.99% respectively.

Keywords: exergy analysis, ideal work, efficiency, exergy destruction, temperature

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Igbekoyi Olusola Esther, Olurankinse Felix

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This paper examined the factors that militate against Will writing among entrepreneurs in Ondo State Nigeria and the effect of intestate death on business continuity after the exit of the entrepreneurs. The paper was written with a view to providing information on the reasons why intestate death is common among entrepreneurs in Ondo State and the effects on continuity of business after death of the initial owners. Data were obtained from primary source through the administration of questionnaires to entrepreneurs drawn from 50 registered manufacturing companies. These companies have been in existence for a minimum of 10 years with minimum staff strength of 20 workers each. These companies were selected using the purposive random sampling technique in order to capture firms that meet the requirements of this paper. Data obtained were analyzed using descriptive statistics, chi-square and regression analysis. The findings of the paper revealed that administration of Will, traditional beliefs, Will execution procedures, age and non- admissibility of Wills in court are the major factors that militates against Will writing among entrepreneurs in Ondo State. It was also discovered that chaos and instability in business, reduction in sales and productivity, poor succession planning, polygamous nature of marriages, difficulty in sourcing for funds and gender preference are joint predictors of business continuity in event intestate death which is evident in the result where R2 =.954;(F 6, 26)= 89.644; (P < 0.01). The individual beta co-efficient, t- statistics and significance of each variable revealed that gender preference (.735; 7.031; .000) and poor succession plan (.402; 2.840; .009) have significant positive effect on business continuity; while reduction in sales and productivity (-.059; -.335; .740) and difficulty in sourcing for funds (-.217; -1.367; .188) have negative effect; other variables also have positive relationship but they are not significant. It is therefore concluded that business continuity after the exit of the entrepreneur is highly dependent on the rebuilding of confidence on Wills administration in ondo state Nigeria, proper succession planning and elimination of gender preferences.

Keywords: intestacy, business continuity, entrepreneurs, will, succession planning

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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: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Jun Hyung Lee, Robin B. Guevarra, Jin Ho Cho, Bo-Ra Kim, Jiwon Shin, Doo Wan Kim, Young Hwa Kim, Minho Song, Hyeun Bum Kim

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Salmonella is one of the most important swine pathogens, causing acute or chronic digestive diseases, such as enteritis. The acute form of enteritis is common in young pigs of 2-4 months of age. Salmonellosis in swine causes a huge economic burden to swine industry by reducing production. Therefore, it is necessary that swine industries should strive to decrease Salmonellosis in pigs in order to reduce economic losses. Thus, we tested three types of natural plant extracts(PEs) to evaluate antibacterial effects against Salmonella enterica Typhimurium isolated from the piglet with Salmonellosis. Three PEs including turmeric oleoresin (containing curcumin 79 to 85%), capsicum oleoresin (containing capsaicin 40%-40.1%), and garlic essential oil (100% natural garlic) were tested using the direct contact agar diffusion test, minimum inhibitory concentration test, growth curve assay, and heat stability test. The tests were conducted with PEs at each concentration of 2.5%, 5%, and 10%. For the heat stability test, PEs with 10% concentration were incubated at each 4, 20, 40, 60, 80, and 100 °C for 1 hour; then the direct contact agar diffusion test was used. For the positive and negative controls, 0.5N HCl and 1XPBS were used. All the experiments were duplicated. In the direct contact agar diffusion test, garlic essential oil with 2.5%, 5%, and 10% concentration showed inhibit zones of 1.5cm, 2.7cm, and 2.8cm diameters compared to that of 3.5cm diameter for 0.5N HCl. The minimum inhibited concentration of garlic essential oil was 2.5%. Growth curve assay showed that the garlic essential oil was able to inhibit Salmonella growth significantly after 4hours. The garlic essential oil retained the ability to inhibit Salmonella growth after heat treatment at each temperature. However, turmeric and capsicum oleoresins were not able to significantly inhibit Salmonella growth by all the tests. Even though further in-vivo tests will be needed to verify effects of garlic essential oil for the Salmonellosis prevention for piglets, our results showed that the garlic essential oil could be used as a potential natural agent to prevent Salmonellosis in swine.

Keywords: garlic essential oil, pig, salmonellosis, Salmonella enterica

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Authors: Jun Hyung Lee, Robin B. Guevarra, Jin Ho Cho, Bo-Ra Kim, Jiwon Shin, Doo Wan Kim, Young Hwa Kim, Minho Song, Hyeun Bum Kim

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Colibacillosis is one of the major health problems in young piglets ultimately resulting in their death, and it is common especially in young piglets. For the swine industry, colibacillosis is one of the important economic burdens. Therefore, it is necessary for the swine industries to prevent Colibacillosis in piglets in order to reduce economic losses. Thus, we tested three types of natural plant extracts (PEs) to evaluate antibacterial effects against Shiga toxin-producing Escherichia coli (STEC) isolated from the piglet. Three PEs including turmeric oleoresin (containing curcumin 79 to 85%), capsicum oleoresin (containing capsaicin 40%-40.1%), and garlic essential oil (100% natural garlic) were tested using the direct contact agar diffusion test, minimum inhibitory concentration test, growth curve assay, and heat stability test. The tests were conducted with PEs at each concentration of 2.5%, 5%, and 10%. For the heat stability test, PEs with 10% concentration were incubated at each 4, 20, 40, 60, 80, and 100 °C for 1 hour, then the direct contact agar diffusion test was used. For the positive and negative controls, 0.5N HCl and 1XPBS were used. All the experiments were duplicated. In the direct contact agar diffusion test, garlic essential oil with 2.5%, 5%, and 10% concentration showed inhibit zones of 1.1cm, 3.0cm, and 3.6 cm in diameters compared to that of 3.5cm diameter for 0.5N HCl. The minimum inhibited concentration of garlic essential oil was 2.5%. Growth curve assay showed that the garlic essential oil was able to inhibit STEC growth significantly after 4 hours. The garlic essential oil retained the ability to inhibit STEC growth after heat treatment at each temperature. However, turmeric and capsicum oleoresins were not able to significantly inhibit STEC growth by all the tests. Even though further tests using the piglets will be required to evaluate effects of garlic essential oil for the Colibacillosis prevention for piglets, our results showed that the garlic essential oil could be used as a potential natural agent to prevent Colibacillosis in swine.

Keywords: garlic essential oil, pig, Colibacillosis, Escherichia coli

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Authors: Said Belaaouad

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This study aimed to explore the quantitative structure-activity relationship (QSAR) of 1,2,4-Triazine-3(2H)-one derivative as a potential anticancer agent against breast cancer. The electronic descriptors were obtained using the Density Functional Theory (DFT) method, and a multiple linear regression techniques was employed to construct the QSAR model. The model exhibited favorable statistical parameters, including R2=0.849, R2adj=0.656, MSE=0.056, R2test=0.710, and Q2cv=0.542, indicating its reliability. Among the descriptors analyzed, absolute electronegativity (χ), total energy (TE), number of hydrogen bond donors (NHD), water solubility (LogS), and shape coefficient (I) were identified as influential factors. Furthermore, leveraging the validated QSAR model, new derivatives of 1,2,4-Triazine-3(2H)-one were designed, and their activity and pharmacokinetic properties were estimated. Subsequently, molecular docking (MD) and molecular dynamics (MD) simulations were employed to assess the binding affinity of the designed molecules. The Tubulin colchicine binding site, which plays a crucial role in cancer treatment, was chosen as the target protein. Through the simulation trajectory spanning 100 ns, the binding affinity was calculated using the MMPBSA script. As a result, fourteen novel Tubulin-colchicine inhibitors with promising pharmacokinetic characteristics were identified. Overall, this study provides valuable insights into the QSAR of 1,2,4-Triazine-3(2H)-one derivative as potential anticancer agent, along with the design of new compounds and their assessment through molecular docking and dynamics simulations targeting the Tubulin-colchicine binding site.

Keywords: QSAR, molecular docking, ADMET, 1, 2, 4-triazin-3(2H)-ones, breast cancer, anticancer, molecular dynamic simulations, MMPBSA calculation

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Authors: Adebiyi Sunday Adeyanju, Kola Benson Ajeigbe, Fortune Ganda

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In the past three decades, there has been a notable increase in the number of firms delisting from the Johannesburg Stock Exchange (JSE) in South Africa. The recent increasing rate of delisting waves of corporate listed firms motivated this study. This study aims to explore the influence of institutional investor perceptions on the financial distress experienced by delisted firms within the South African market. The study further examined the impact of financial distress on the corporate performance of delisted firms. Using the data of delisted firms spanning from 2000 to 2023 and the FGLS (Feasible Generalized Least Squares) for the short run and PCSE (Panel-Corrected Standard Errors) for the long run effects of the relationship. The finding indicated that a decline in institutional investors’ perceptions was associated with the corporate financial distress of the delisted firms, particularly during the delisting year and the few years preceding the announcement of the delisting. This study addressed the importance of investor recognition in corporate financial distress and the delisting wave among listed firms- a finding supporting the stakeholder theory. This study is an insight for companies’ managements, investors, governments, policymakers, stockbrokers, lending institutions, bankers, the stock market, and other stakeholders in their various decision-making endeavours. Based on the above findings, it was recommended that corporate managements should improve their governance strategies that can help companies’ financial performances. Accountability and transparency through governance must also be improved upon with government support through the introduction of policies and strategies and enabling an easy environment that can help companies perform better.

Keywords: delisting wave, institutional investors, financial distress, corporate performance, investors’ perceptions

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Authors: Arun K. Yadav, Adam J. Carroll, Gonzalo M. Estavillo, Greg J. Rebetzke, Barry J. Pogson

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Water limits crop productivity, so selecting for minimal yield-gap in drier environments is critical to mitigate against climate change and land-use pressures. To date, no markers measured in glasshouses have been reported to predict field-based drought tolerance. In the field, the best measure of drought tolerance is yield-gap; but this requires multisite trials that are an order of magnitude more resource intensive and can be impacted by weather variation. We investigated the responses of relative water content (RWC), stomatal conductance (gs), chlorophyll content and metabolites in flag leaves of commercial wheat (Triticum aestivum L.) cultivars to three drought treatments in the glasshouse and field environments. We observed strong genetic associations between glasshouse-based RWC, metabolites and Yield gap-based Drought Tolerance (YDT): the ratio of yield in water-limited versus well-watered conditions across 24 field environments spanning sites and seasons. Critically, RWC response to glasshouse drought was strongly associated with both YDT (r2 = 0.85, p < 8E-6) and RWC under field drought (r2 = 0.77, p < 0.05). Multiple regression analyses revealed that 98% of genetic YDT variance was explained by drought responses of four metabolites: serine, asparagine, methionine and lysine (R2 = 0.98; p < 0.01). Fitted coefficients suggested that, for given levels of serine and asparagine, stronger methionine and lysine accumulation was associated with higher YDT. Collectively, our results demonstrate that high-throughput, targeted metabolic phenotyping of glasshouse-grown plants may be an effective tool for the selection of wheat cultivars with high YDT in the field.

Keywords: drought stress, grain yield, metabolomics, stomatal conductance, wheat

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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: 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

Abstract:

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: Marzieh Zarei

Abstract:

Well instability is one of the most fundamental challenges faced by the oil and gas industry. Well wall stability analysis is a gap to be filled in the oil industry. The collection of static data such as well logging leads to the construction of a geomechanical numerical model, which will help in assessing the probable risks in future drilling. In this paper, geomechanical model was designed, and mechanical properties of the rock was determined at all points of the model. It was found the safe mud window was determined and the minimum and maximum mud pressures were determined in the ranges of 70-60 MPa and 110-100 MPa, respectively.

Keywords: geomechanics, numerical model, well stability, in-situ stress, underbalanced drilling

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Authors: Nissar Mohammad Karim, Norhayati Soin

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To ensure fast switching in high-K incorporated Complementary Metal Oxide Semiconductor (CMOS) transistors, the results on the basis of d (NBTI) by incorporating SiO2 dielectric with aged samples of CuO sol-gels have been reported. Precursor ageing has been carried out for 4 days. The minimum obtained refractive index is 1.0099 which was found after 3 hours of adhesive UV curing. Obtaining a low refractive index exhibits a low dielectric constant and hence a faster system.

Keywords: refractive index, Sol-Gel, precursor aging, aging

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Authors: Paul Saah, Charles Mbohwa, Nelson Sizwe Madonsela

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The capacity to deliver projects on schedule, within budget, and to the pleasure of the client depends on effective communication, which is the lifeblood of project-based businesses. In order to pinpoint areas for development and shed light on the crucial role that communication plays in project success, the aim of this study is to evaluate the efficacy of communication practises and processes inside project-based organisations. In order to analyse concepts and get a greater grasp of their theoretical basis, this study's methodology combines a careful review of the relevant literature with a conceptual analysis of the subject. Data from a varied sample of project-based businesses spanning all industries and sizes were collected via document analysis. The relationship between communication practises, and processes were investigated in connection to key performance measures such as project outcomes, client satisfaction, and team dynamics. According to the study's findings, project-based businesses that adopt effective communication practises, and procedures experience a reduction in unfavourable experiences, stronger integration, and coordination, clarity of purpose, and practises that can hasten problem resolution. However, failing to adopt effective communication practises and procedures in project-based company result in counter issues, including project derailment from the schedule, failure to meet goals, inefficient use of existing resources, and failure to meet organisational goals. Therefore, optimising their communication practises, and procedures are crucial for sustainable growth and competitive advantage as project-based enterprises continue to play a crucial part in today's dynamic business scene.

Keywords: effective communication, project-based firms, communication practices, project success, communication strategies

Procedia PDF Downloads 49

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: Kaleeswari R. K., Seevagan L .

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Soil quality in an agroecosystem is influenced by the cropping system, water and soil fertility management. A valid soil quality index would help to assess the soil and crop management practices for desired productivity and soil health. The soil quality indices also provide an early indication of soil degradation and needy remedial and rehabilitation measures. Imbalanced fertilization and inadequate organic carbon dynamics deteriorate soil quality in an intensive cropping system. The rice soil ecosystem is different from other arable systems since rice is grown under submergence, which requires a different set of key soil attributes for enhancing soil quality and productivity. Assessment of the soil quality index involves indicator selection, indicator scoring and comprehensive score into one index. The most appropriate indicator to evaluate soil quality can be selected by establishing the minimum data set, which can be screened by linear and multiple regression factor analysis and score function. This investigation was carried out in intensive rice cultivating regions (having >1.0 lakh hectares) of Tamil Nadu viz., Thanjavur, Thiruvarur, Nagapattinam, Villupuram, Thiruvannamalai, Cuddalore and Ramanathapuram districts. In each district, intensive rice growing block was identified. In each block, two sampling grids (10 x 10 sq.km) were used with a sampling depth of 10 – 15 cm. Using GIS coordinates, and soil sampling was carried out at various locations in the study area. The number of soil sampling points were 41, 28, 28, 32, 37, 29 and 29 in Thanjavur, Thiruvarur, Nagapattinam, Cuddalore, Villupuram, Thiruvannamalai and Ramanathapuram districts, respectively. Principal Component Analysis is a data reduction tool to select some of the potential indicators. Principal Component is a linear combination of different variables that represents the maximum variance of the dataset. Principal Component that has eigenvalues equal or higher than 1.0 was taken as the minimum data set. Principal Component Analysis was used to select the representative soil quality indicators in rice soils based on factor loading values and contribution percent values. Variables having significant differences within the production system were used for the preparation of the minimum data set. Each Principal Component explained a certain amount of variation (%) in the total dataset. This percentage provided the weight for variables. The final Principal Component Analysis based soil quality equation is SQI = ∑ i=1 (W ᵢ x S ᵢ); where S- score for the subscripted variable; W-weighing factor derived from PCA. Higher index scores meant better soil quality. Soil respiration, Soil available Nitrogen and Potentially Mineralizable Nitrogen were assessed as soil quality indicators in rice soil of the Cauvery Delta zone covering Thanjavur, Thiruvavur and Nagapattinam districts. Soil available phosphorus could be used as a soil quality indicator of rice soils in the Cuddalore district. In rain-fed rice ecosystems of coastal sandy soil, DTPA – Zn could be used as an effective soil quality indicator. Among the soil parameters selected from Principal Component Analysis, Microbial Biomass Nitrogen could be used quality indicator for rice soils of the Villupuram district. Cauvery Delta zone has better SQI as compared with other intensive rice growing zone of Tamil Nadu.

Keywords: soil quality index, soil attributes, soil mapping, and rice soil

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Authors: Shweta Dhindhwal

Abstract:

Dopamine is an essential neurotransmitter in the central nervous system of all vertebrates and plays an important role in many processes such as motor function, learning and behavior, and sensory activity. One of the important functions of dopamine is release of pituitary hormones. It is synthesized from the amino acid tyrosine. Two types of dopamine receptors, D1-like and D2-like, have been reported in fish. The dopamine containing neurons are located in the olfactory bulbs, the ventral regions of the pre-optic area and tuberal hypothalamus. Distribution of the dopaminergic system has not been studied in the murrel, Channa punctatus. The present study deals with identification of D2 receptors in the brain of murrel. A phylogenetic tree has been constructed using partial sequence of D2 receptor. Distribution of putative dopaminergic neurons in the brain has been investigated. Also, formalin induced hypertrophy of neurosecretory cells in murrel has been studied.

Keywords: dopamine, fish, pre-optic area, murrel

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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

Procedia PDF Downloads 107