Search results for: train formation

Authors: Richardson M. Abraham-A., Colombo Celso Gaeta Tassinari

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The correlation between Total Organic Carbon (TOC) and flow units have been carried out to predict and compare the carbon dioxide (CO₂) storage potential of the shale and carbonate rocks in Irati Formation of the Parana Basin. The equations for permeability (K), reservoir quality index (RQI) and flow zone indicator (FZI) are redefined and engaged to evaluate the flow units in both potential reservoir rocks. Shales show higher values of TOC compared to carbonates, as such,  porosity (Ф) is most likely to be higher in shales compared to carbonates. The increase in Ф corresponds to the increase in K (in both rocks). Nonetheless, at lower values of Ф, K is higher in carbonates compared to shales. This shows that at lower values of TOC in carbonates, Ф is low, yet, K is likely to be high compared to shale. In the same vein, at higher values of TOC in shales, Ф is high, yet, K is expected to be low compared to carbonates.  Overall, the flow unit factors (RQI and FZI) are better in the carbonates compared to the shales. Moreso, within the study location,  there are some portions where the thicknesses of the carbonate units are higher compared to the shale units. Most parts of the carbonate strata in the study location are fractured in situ, hence,  this could provide easy access for the storage of CO₂. Therefore, based on these points and the disparities between the flow units in the evaluated rock types, the carbonate units are expected to show better potentials for the storage of CO₂. The shale units may be considered as potential cap rocks or seals.

Keywords: total organic content, flow units, carbon dioxide storage, geologic structures

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Authors: Tiao Chang, Ivan Caballero, Hong Zhou

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Streams carry water and sediment naturally by maintaining channel dimensions, pattern, and profile over time. Watershed erosion as a natural process has occurred to contribute sediment to streams over time. The formation of channel dimensions is complex. This study is to relate quantifiable and consistent channel dimensions at the bankfull stage to the corresponding watershed erosion estimation by the Revised Universal Soil Loss Equation (RUSLE). Twelve sites of which drainage areas range from 7 to 100 square miles in the Hocking River Basin of Ohio were selected for the bankfull geometry determinations including width, depth, cross-section area, bed slope, and drainage area. The twelve sub-watersheds were chosen to obtain a good overall representation of the Hocking River Basin. It is of interest to determine how these bankfull channel dimensions are related to the soil erosion of corresponding sub-watersheds. Soil erosion is a natural process that has occurred in a watershed over time. The RUSLE was applied to estimate erosions of the twelve selected sub-watersheds where the bankfull geometry measurements were conducted. These quantified erosions of sub-watersheds are used to investigate correlations with bankfull channel dimensions including discharge, channel width, channel depth, cross-sectional area, and pebble distribution. It is found that drainage area, bankfull discharge and cross-sectional area correlates strongly with watershed erosion well. Furthermore, bankfull width and depth are moderately correlated with watershed erosion while the particle size, D50, of channel bed sediment is not well correlated with watershed erosion.

Keywords: watershed, stream, sediment, channel

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Tannaz Alimardani, Amirhossein Moghanian, Morteza Elsa

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Bioactive glasses (BGs) are a group of surface-reactive biomaterials used in clinical applications as implants or filler materials in the human body to repair and replace diseased or damaged bone. Sol-gel technique was employed to prepare a SiO₂-CaO-P₂O₅ glass with a nominal composition of 58S BG with the addition of Sr and Li modifiers which imparts special properties to the BG. The effect of simultaneous addition of Sr and Li on bioactivity and biocompatibility, proliferation, alkaline phosphatase (ALP) activity of osteoblast cell line MC3T3-E1 and antibacterial property against methicillin-resistant Staphylococcus aureus (MRSA) bacteria were examined. BGs were characterized by X-ray diffraction, Fourier transform infrared spectroscopy and scanning electron microscopy before and after soaking the samples in the simulated body fluid (SBF) for different time intervals to characterize the formation of hydroxyapatite (HA) formed on the surface of BGs. Structural characterization indicated that the simultaneous presence of 5% Sr and 5% Li in 58S-BG composition not only did not retard HA formation because of the opposite effect of Sr and Li of the dissolution of BG in the SBF, but also stimulated the differentiation and proliferation of MC3T3-E1s. Moreover, the presence of Sr and Li on the dissolution of the ions resulted in an increase in the mean number of DAPI-labeled nuclei which was in good agreement with the live/dead assay. The result of antibacterial tests revealed that Sr and Li-substituted 58S bioactive glass exhibited a potential antibacterial effect against MRSA bacteria. Because of optimal proliferation and ALP activity of MC3T3-E1cells, proper bioactivity and high antibacterial potential against MRSA, BG-5/5 is suggested as a multifunctional candidate for bone tissue engineering.

Keywords: alkaline, alkaline earth, bioglass, co-doping, ion release

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Authors: Jorge Sandoval Lezama, Arturo Ivan Sandoval Rodriguez, Jose Arturo Correa Arredondo

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This work aims to share innovative educational experiences at IPN Mexico, that involve collaborative learning at institutional and global level through course competition and global collaboration projects. Students from universities in China, USA, South Korea, Canada and Mexico collaborate to design electric vehicles to solve global urban mobility problems. The participation of IPN students in the 2015-2016 global competition (São Paolo, Brazil and Cincinnati, USA) Reconfigurable Shared-Use Mobility Systems allowed to apply pedagogical strategies of groups of collaboration and of learning based on projects where they shared activities, commitments and goals, demonstrating that students were motivated to develop / self-generate their knowledge with greater meaning and understanding. One of the most evident achievements is that the students are self-managed, so the most advanced students train the students who join the project with CAD, CAE, CAM tools. Likewise, the motivation achieved is evident since in 2014 there were 12 students involved in the project, and there are currently more than 70 students.

Keywords: collaboration projects, global competency, course competition, active learning

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Authors: Vanessa Caroline De Barros Bonato, Bruna Lima Gomes, Luciano Freschi, Eduardo Purgatto

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The plant hormone ethylene is closely related to the metabolic changes that occur during fruit ripening, including volatile biosynthesis. Although knowledge about the biochemistry pathways that produce flavor compounds and the importance of ethylene to these processes are extensively covered, little is known about the regulation mechanisms. In addition, growing body of evidences indicates that auxin is also involved in controlling ripening. However, there is scarce information about the involvement of auxin in fruit volatile production. This study aimed to assess auxin-ethylene interactions and its influence on tomato fruit volatile profile. Fruits from tomato cultivar Micro-Tom were treated with IAA and ethylene, separately and in combination. The hormonal treatment was performed by injection (IAA) or gas exposure (ethylene) and the volatiles were extracted by Solid Phase Microextraction (SPME) and analyzed by GC-MS. Ethylene levels and color were measured by gas chromatography and colorimetry, respectively. The results indicate that the treatment with IAA (even in the presence of high concentrations of exogenous ethylene), impacted the profile of volatile compounds derived from fatty acids, amino acids, carbohydrates and isoprenoids. Ethylene is a well-known regulator of the transition from green to red color and also is implicated in the biosynthesis of characteristic volatile compounds of tomato fruit. The effects observed suggest the existence of a crosstalk between IAA and ethylene in the aroma volatile formation in the fruit. A possible interference of IAA in the ethylene sensitivity in the fruit flesh is discussed. The data suggest that auxin plays an important role in the volatile synthesis in the tomato fruit and introduce a new level of complexity in the regulation of the fruit aroma formation during ripening.

Keywords: aroma compounds, fruit ripening, fruit quality, phytohormones

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Authors: Indu Chauhan, Bhupendra S. Butola, Paritosh Mohanty

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It is very well known that properties of material changes as their size approach to nanoscale level due to the high surface area to volume ratio. However, in last few decades, a tenet ‘structure dictates function’ is quickly being adopted by researchers working with nanomaterials. The design and exploitation of nanoparticles with tailored shape and size has become one of the primary goals of materials science researchers to expose the properties of nanostructures. To date, various methods, including soft/hard template/surfactant assisted route hydrothermal reaction, seed mediated growth method, capping molecule-assisted synthesis, polyol process, etc. have been adopted to synthesize the nanostructures with controlled size and shape and monodispersity. However controlling the shape and size of nanoparticles is an ultimate challenge of modern material research. In particular, many efforts have been devoted to rational and skillful control of hierarchical and complex nanostructures. Thus in our research work, role of cellulose in manipulating the nanostructures has been discussed. Nanoparticles of α-Fe₂O₃ (diameter ca. 15 to 130 nm) were immobilized on the cellulose fiber surface by a single step in situ hydrothermal method. However, nanoflakes of α-FeOOH having thickness ca. ~25 nm and length ca. ~250 nm were obtained by the same method in absence of cellulose fibers. A possible nucleation and growth mechanism of the formation of nanostructures on cellulose fibers have been proposed. The covalent bond formation between the cellulose fibers and nanostructures has been discussed with supporting evidence from the spectroscopic and other analytical studies such as Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The role of cellulose in manipulating the nanostructures has been discussed.

Keywords: cellulose fibers, α-Fe₂O₃, α-FeOOH, hydrothermal, nanoflakes, nanoparticles

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: Rozalina Keremedchieva, Ivan Svinyarov, Milen G. Bogdanov

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In continuation of a research project on the application of ionic liquids (ILs) as an alternative to the conventional organic solvents used in the recovery of value added chemicals of industrial interest1-3 we developed a procedure for back extraction and isolation in pure form of the biologically active alkaloid glaucine from IL-based aqueous solutions. One of the approaches applied was the formation of two-phase systems (IL-ATPS) by the addition of kosmotropic salts to the plant extract. The ability of the salts (Na2CO3, MgSO4, (NH4)2SO4, NaH2PO4) to induce the formation of two-phase systems and the influence of pH value on the partition coefficients of glaucine was comprehensively studied. As a result, it was found that the target alkaloid is preferably partitioned into the IL-rich phase regardless of the pH value of the medium and thus shows the inapplicability of the approach used for the isolation of the target compound from the ionic liquid. However, the results obtained can be used as a platform for the development of an analytical method for the quantitative determination of low concentrations of glaucine in biological samples. We further examined the ability of a series of organic solvents such as diethyl ether, Tert-butylmethyl ether, ethyl acetate, butyl acetate, toluene, chloroform, dichloromethane to recover glaucine form raw IL-based aqueous extracts. Optimal conditions for quantitative extraction of glaucine into chloroform were found from which, after removal of the solvent and subsequent recrystallization from ethanol, the target compound was isolated in a high purity as a hydrobromide salt – The form in which it entrance as an active ingredient in various medicines.

Keywords: natural products, ionic liquids, solid-liquid extraction, liquid-liquid extraction

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Authors: Freja Rydahl Rasmussen, Naja Villadsen, Stig Koust

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Air purifiers have become widely implemented in a wide range of settings, including households, schools, institutions, and hospitals, as they tackle the pressing issue of indoor air pollution. With their ability to enhance indoor air quality and create healthier environments, air purifiers are particularly vital when ventilation options are limited. These devices incorporate a diverse array of technologies, including HEPA filters, active carbon filters, UV-C light, photocatalytic oxidation, and ionizers, each designed to combat specific pollutants and improve air quality within enclosed spaces. However, the safety of air purifiers has not been investigated thoroughly, and many questions still arise when applying them. Certain air purification technologies, such as UV-C light or ionization, can unintentionally generate undesirable byproducts that can negatively affect indoor air quality and health. It is well-established that these technologies can inadvertently generate nanoparticles or convert common gaseous compounds into harmful ones, thus exacerbating air pollution. However, the formation of byproducts can vary across products, necessitating further investigation. There is a particular concern about the formation of the carcinogenic substance formaldehyde from common gases like acetone. Many air purifiers use mechanical filtration to remove particles, dust, and pollen from the air. Filters need to be replaced periodically for optimal efficiency, resulting in an additional cost for end-users. Currently, there are no guidelines for filter lifespan, and replacement recommendations solely rely on manufacturers. A market screening revealed that manufacturers' recommended lifespans vary greatly (from 1 month to 10 years), and there is a need for general recommendations to guide consumers. Activated carbon filters are used to adsorb various types of chemicals that can pose health risks or cause unwanted odors. These filters have a certain capacity before becoming saturated. If not replaced in a timely manner, the adsorbed substances are likely to be released from the filter through off-gassing or losing adsorption efficiency. The goal of this study is to investigate the lifespan of filters as well as investigate the potentially harmful effects of air purifiers. Understanding the lifespan of filters used in air purifiers and the potential formation of harmful byproducts is essential for ensuring their optimal performance, guiding consumers in their purchasing decisions, and establishing industry standards for safer and more effective air purification solutions. At this time, a selection of air purifiers has been chosen, and test methods have been established. In the following 3 months, the tests will be conducted, and the results will be ready for presentation later.

Keywords: air purifiers, activated carbon filters, byproducts, clean air, indoor air quality

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Authors: B. Ashraf, A. M. Malik

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The study was accomplished at the Department of Economics and Agriculture Economics, Pir Mahar Ali Shah ARID Agriculture University, Punjab, Pakistan during 2013-16 with a purpose to discover the effect of Islamic finance/banking on employment in Punjab, Pakistan. Islamic banking system is sub-component of conventional banking system in various countries of the world; however, in Pakistan, it has been established as a separate Islamic banking system. The Islamic banking operates under the doctrine of Shariah. It is claimed that the referred banking is free of interest (Riba) and addresses the philosophy and basic values of Islam in finance that reduces the factors of uncertainty, risk and others speculative activities. Two Islamic bank’s; Meezan Bank Limited (Pakistan) and Al-Baraka Bank Limited (Pakistan) from North Punjab (Bahawalnagar) and central Punjab (Lahore) west Punjab (Gujrat), Pakistan were randomly selected for the conduct of research. A total of 206 samples were collected from the define areas and banks through questionnaire. The data was analyzed by using the Statistical Package for Social Sciences (SPSS) version 21.0. Multiple linear regressions were applied to prove the hypothesis. The results revealed that the assets formation had significant positive; whereas, the technology, length of business (experience) and bossiness size had significant negative impact with employment generation in Islamic finance/banking in Punjab, Pakistan. This concludes that the employment opportunities may be created in the country by extending the finance to business/firms to start new business and increase the Public awareness by the Islamic banks through intensive publicity. However; Islamic financial institutions may be encouraged by Government as it enhances the employment in the country.

Keywords: assets formation, borrowers, employment generation, Islamic banks, Islamic finance

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Authors: Wassim Jerbi, Abderrahmen Guermazi, Hafedh Trabelsi

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The optimum use of coverage in wireless sensor networks (WSNs) is very important. LEACH protocol called Low Energy Adaptive Clustering Hierarchy, presents a hierarchical clustering algorithm for wireless sensor networks. LEACH is a protocol that allows the formation of distributed cluster. In each cluster, LEACH randomly selects some sensor nodes called cluster heads (CHs). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node joins a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus, several sensor nodes cannot reach any CH. to solve this problem. We created an O-LEACH Orphan nodes protocol, its role is to reduce the sensor nodes which do not belong the cluster. The cluster member called Gateway receives messages from neighboring orphan nodes. The gateway informs CH having the neighboring nodes that not belong to any group. However, Gateway called (CH') attaches the orphaned nodes to the cluster and then collected the data. O-Leach enables the formation of a new method of cluster, leads to a long life and minimal energy consumption. Orphan nodes possess enough energy and seeks to be covered by the network. The principal novel contribution of the proposed work is O-LEACH protocol which provides coverage of the whole network with a minimum number of orphaned nodes and has a very high connectivity rates.As a result, the WSN application receives data from the entire network including orphan nodes. The proper functioning of the Application requires, therefore, management of intelligent resources present within each the network sensor. The simulation results show that O-LEACH performs better than LEACH in terms of coverage, connectivity rate, energy and scalability.

Keywords: WSNs; routing; LEACH; O-LEACH; Orphan nodes; sub-cluster; gateway; CH’

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: N. K. Thakur

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A major proportion of the formations drilled for the production of hydrocarbons consists of clay containing shales. The petroleum industry has hugely investigated the role of clay minerals and their subsequent effect on wellbore stability during the drilling and production of hydrocarbons. It has been found that when the shale formation comes in contact with water-based drilling fluid, the interaction of clay minerals like montmorillonite with infiltrated water leads to hydration of the clay minerals, which causes shale swelling. When shale swelling proceeds further, it may lead to major drilling complications like caving, pipe sticking, which invariably influences wellbore stability, wellbore diameter, the mechanical strength of shale, stress distribution in the wellbore, etc. These problems ultimately lead to an increase in nonproductive time and additional costs during drilling. Several additives are used to prevent shale instability. Among the popular additives used for shale inhibition in drilling muds, ionic liquids and nanoparticles are emerging to be the best additives. The efficiency of the proposed additives will be studied and compared with conventional clay inhibitors like KCl. The main objective is to develop a highly efficient water-based mud for mitigating shale instability and reducing fluid loss which is environmentally friendly and does not alter the formation permeability. The use of nanoparticles has been exploited to enhance the rheological and fluid loss properties in water-based drilling fluid ionic liquid have attracted significant research interest due to its unique thermal stability. It is referred to as ‘green chemical’. The preliminary experimental studies performed are promising. The application of more effective mud additives is always desirable to make the drilling process techno-economically proficient.

Keywords: ionic liquid, shale inhibitor, wellbore stability, unconventional

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: N. Tumenbayeva

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At the present time in Kazakhstan, the area for saxaul growing is strongly depopulateddue to anthropogenic and other factors. To prevent further reduction of natural haloxylon forest area their artificial crops are offered. Seed germination and survival of young plants in such haloxylon crops are very low. Insects, as one of the most important nutrient factors have appreciable effect on seed germination and saxaul productivity at the all stages of its formation. Insects, feeding on leaves, flowers, seeds and developing inside the trunk, branches, twigs, roots have a change in its formation and influence on the lifespan of saxaul. Representatives of Lepidoptera troop (Lepidopteraare the most harmful pests forsaxaul. As a result of our research we have identified 15 species of Lepidoptera living on haloxylon which display very different cycles and different types of food relations. It allows them to inhabit a variety of habitats, and feeding on various parts of saxaul. Some of them cause significant and sometimes very heavy damage for saxaul. There are 17identified species of Lepidoptera from the Coleophoridaefamily - 1, Gelechidae - 5, Pyralidae - 4, Noctuidae - 4, Lymantridae- 1, Cossidae - 2 species. At the same time we found 8 species for the first time, which have not been mentioned in the literature before. According to food specialization they are divided into monophages (2 types), oligophages (6 species) and polyphages (3 species). By affinity to plant parts, leaves and seeds are fed by 8 species, shoots by 1 specie, scions by 5 species, flowers, scions, seeds by 1, and 2species damage the roots and trunks. In whole installed seasonal groups of Lepidoptera - saxaul pests in the desert area, confined to the certain parts of the year, as well as certain parts of the plant for feeding. Harmfulness, depending on their activity appear during the growing season is also different.

Keywords: saxaul, Lepidoptera, insecta, haloxylon

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Authors: Nompumelelo P. Mathebula, Roger A. Sheldon, Daniel P. Pienaar, Moira L. Bode

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The preparation of enantiopure Morita-Baylis-Hillman (MBH) adducts remains a challenge in organic chemistry. MBH adducts are highly functionalised compounds which act as key intermediates in the preparation of compounds of medicinal importance. MBH adducts are prepared in racemic form by reacting various aldehydes and activated alkenes in the presence of DABCO. Enantiopure MBH adducts can be obtained by employing Enzymatic kinetic resolution (EKR). This technique has been successfully demonstrated in our group, amongst others, using lipases in either hydrolysis or transesterification reactions. As these methods only allow 50% of each enantiomer to be obtained, our interest grew in exploring other enzymatic methods for the synthesis of enantiopure MBH adducts where, theoretically, 100% of the desired enantiomer could be obtained.Dehydrogenase enzymes can be employed on prochiral substrates to obtain optically pure compounds by reducing carbon-carbon double bonds or carbonyl groups of ketones. Ketoreductases have been used historically to obtain enantiopure secondary alcohols on an industrial scale. Ketoreductases are NAD(P)H-dependent enzymes and thus require nicotinamide as a cofactor. This project focuses on employing ketoreductase enzymes to selectively reduce ketones derived from Morita-Baylis-Hillman (MBH) adducts in order to obtain these adducts in enantiopure form.Results obtained from this study will be reported. Good enantioselectivity was observed using a range of different ketoreductases, however, reactions were complicated by the formation of an unexpected by-product, which was characterised employing single crystal x-ray crystallography techniques. Methods to minimise by-product formation are currently being investigated.

Keywords: ketoreductase, morita-baylis-hillman, selective reduction, x-ray crystallography

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Authors: Sean Paulsen, Michael Casey

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In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training

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Authors: Josselyn Mata Calidonio, Arianna I. Maddox, Kimberly Hamad-Schifferli

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Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown viruses has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross-reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.

Keywords: adaptive immunoassay, detecting unknown viruses, gold nanoparticles, paper immunoassay, repurposing antibodies

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Authors: B. Samuel Raj, Solomon R. D. Jebakumar

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Microbial fuel cells (MFCs) are an emerging and promising method for achieving sustainable energy since they can remove contaminated organic matter and simultaneously generate electricity. Our approach was driven in three different ways like Bacterial fuel cell, Soil Microbial fuel cell (Soil MFC) and Plant Microbial fuel cell (Plant MFC). Bacterial MFC: Sulphate reducing bacteria (SRB) were isolated and identified as the efficient electricigens which is able to produce ±2.5V (689mW/m2) and it has sustainable activity for 120 days. Experimental data with different MFC revealed that high electricity production harvested continuously for 90 days 1.45V (381mW/m2), 1.98V (456mW/m2) respectively. Biofilm formation was confirmed on the surface of the anode by high content screening (HCS) and scanning electron Microscopic analysis (SEM). Soil MFC: Soil MFC was constructed with low cost and standard Mudwatt soil MFC was purchased from keegotech (USA). Vermicompost soil (V1) produce high energy (± 3.5V for ± 400 days) compared to Agricultural soil (A1) (± 2V for ± 150 days). Biofilm formation was confirmed by HCS and SEM analysis. This finding provides a method for extracting energy from organic matter, but also suggests a strategy for promoting the bioremediation of organic contaminants in subsurface environments. Our Soil MFC were able to run successfully a 3.5V fan and three LED continuously for 150 days. Plant MFC: Amaranthus candatus (P1) and Triticum aestivium (P2) were used in Plant MFC to confirm the electricity production from plant associated microbes, four uniform size of Plant MFC were constructed and checked for energy production. P2 produce high energy (± 3.2V for 40 days) with harvesting interval of two times and P1 produces moderate energy without harvesting interval (±1.5V for 24 days). P2 is able run 3.5V fan continuously for 10days whereas P1 needs optimization of growth conditions to produce high energy.

Keywords: microbial fuel cell, biofilm, soil microbial fuel cell, plant microbial fuel cell

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Authors: Y. Hajjam, I. T. Alami, S. M. Udupa, S. Cherkaoui

Abstract:

Faba bean (Vicia faba L.) is an important food legume crop in Morocco. It is mainly used as human food and feed for animals. Faba bean also plays an important role in cereal-based cropping systems, when rotated with cereals it improves soil fertility by fixing N2 in root nodules mediated by Rhizobium. Both faba bean and its biological nitrogen fixation symbiotic bacterium Rhizobium are affected by different stresses such as: salinity, drought, pH, heavy metal, and the uptake of inorganic phosphate compounds. Therefore, the aim of the present study was to evaluate the phenotypic diversity among the faba bean rhizobial isolates and to select the tolerant strains that can fix N2 under environmental constraints for inoculation particularly for affected soils, in order to enhance the productivity of faba bean and to improve soil fertility. Result have shown that 62% of isolates were fast growing with the ability of producing acids compounds , while 38% of isolates are slow growing with production of alkalins. Moreover, 42.5% of these isolates were able to solubilize inorganic phosphate Ca3(PO4)2 and the index of solubilization was ranged from 2.1 to 3.0. The resistance to extreme pH, temperature, water stress heavy metals and antibiotics lead us to classify rhizobial isolates into different clusters. Finally, the authentication test under greenhouse conditions showed that 55% of the rhizobial isolates could induce nodule formation on faba bean (Vicia faba L.) under greenhouse experiment. This phenotypic characterization may contribute to improve legumes and non legumes crops especially in affected soils and also to increase agronomic yield in the dry areas.

Keywords: rhizobia, vicia faba, phenotypic characterization, nodule formation, environmental constraints

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Authors: Constant Gillot, Pauline Michaux, Julien Favresse, Jean-Michel Dogné, Jonathan Douxfils

Abstract:

Introduction: NETosis has emerged as a crucial yet paradoxical factor in severe COVID-19 cases. While neutrophil extracellular traps (NETs) help contain and eliminate viral particles, excessive NET formation can lead to hyperinflammation, exacerbating tissue damage and acute respiratory distress syndrome (ARDS). Aims: This study evaluates the relationship between COVID-19-infected sera and NETosis using an ex-vivo model. Methods: Sera from 8 post-admission COVID-19 patients, after receiving corticoid therapy, were used to induce NETosis in neutrophils from a healthy donor. NET formation was tracked using fluorescent markers for DNA and neutrophil elastase (NE) every 2 minutes for 8 hours. The results were expressed as a percentage of DNA/NE released over time. Key metrics, including T50 (time to 50% release) and AUC (area under the curve), representing total NETosis potential), were calculated. A 27-cytokine screening kit was used to assess the cytokine composition of the sera. Results: COVID-19 sera induced NETosis based on their cytokine profile. The AUC of NE and DNA release decreased with time following corticoid therapy, showing a significant reduction in 6 of the 8 patients (p<0.05). T50 also decreased in parallel with AUC for both markers. Cytokines concentration decrease with time after therapy administration. There is correlation between 14 cytokines concentration and NE release. Conclusion: This ex-vivo model successfully demonstrated the induction of NETosis by COVID-19 sera using two markers. A clear decrease in NETosis potential was observed over time with glucocorticoid therapy. This model can be a valuable tool for monitoring NETosis and investigating potential NETosis inducers and inhibitors.

Keywords: NETosis, COVID-19, cytokine storm, biomarkers

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Sanat Ramesh, Tarun Dutt, Abhilasha Aswal, Anushka Chandrababu, G. N. Srinivasa Prasanna

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Optimal Schedule Generation for a large national railway network operating thousands of passenger trains with tens of thousands of kilometers of track is a grand computational challenge in itself. We present heuristics based on a Mixed Integer Program (MIP) formulation for local optimization. These methods provide flexibility in scheduling new trains with varying speed and delays and improve utilization of infrastructure. We propose methods that provide a robust solution with hundreds of trains being scheduled over a portion of the railway network without significant increases in delay. We also provide techniques to validate the nominal schedules thus generated over global correlated variations in travel times thereby enabling us to detect conflicts arising due to delays. Our validation results which assume only the support of the arrival and departure time distributions takes an order of few minutes for a portion of the network and is computationally efficient to handle the entire network.

Keywords: mixed integer programming, optimization, railway network, train scheduling

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Authors: Abdolamir Allameh, Shahnaz Esmaeili, Mina Allameh, Safoura Khajeniazi

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Stem cells with therapeutic applications can be isolated from human placenta/umblical cord blood (UCB) as well as the cord tissue (UC). Stem cells in culture are vulnerable to oxidative stress, particularly when subjected to differentiation process. The aim of this study was to examine the chnages in the rate of oxidation that occurs to cellular macromolecules during hepatic differentiation of mononuclear cells (MSCs). In addition, the impact of the hepatic differentiation process of MSC on cellular and biological activity of the cells will be undertaken. For this purpose, first mononuclear cells (MNCs) were isolated from human UCB which was obtained from a healthy full-term infant. The cells were cultured at a density of 3×10⁵ cells/cm² in DMEM- low-glucose culture media supplemented with 20% FBS, 2 mM L-glutamine, 100 μg/ml streptomycin and 100 U/ml penicillin. Cell cultures were then incubated at 37°C in a humidified 5% CO₂ incubator. After removing non-adherent cells by replacing culture medium, fibroblast-like adherent cells were resuspended in 0.25% trypsin-EDTA and plated in 25 cm² flasks (1×10⁴/ml). Characterization of the MSCs was routinely done by observing their morphology and growth curve. MSCs were subjected to a 2-step hepatocyte differentiation protocol in presence of hepatocyte growth factor (HGF), dexamethazone (DEX) and oncostatin M (OSM). The hepatocyte-like cells derived from MSCs were checked every week for 3 weeks for changes in lipid peroxidation, protein carbonyl formation and DNA oxidation i.e., 8-hydroxy-2'-deoxyguanosine (8-OH-dG) assay. During the 3-week differentiation process of MSCs to hepatocyte-like cells we found that expression liver-specific markers such as albumin, was associated with increased levels of lipid peroxidation and protein carbonyl formation. Whereas, undifferentiated MSCs has relatively low levels of lipid peroxidation products. There was a significant increase ( p < 0.05) in lipid peroxidation products in hepatocytes on days 7, 14, and 21 of differentiation. Likewise, the level of protein carbonyls in the cells was elevated during the differentiation. The level of protein carbonyls measured in hepatocyte-like cells obtained 3 weeks after differentiation induction was estimated to be ~6 fold higher compared to cells recovered on day 7 of differentiation. On the contrary, there was a small but significant decrease in DNA damage marker (8-OH-dG) in hepatocytes recovered 3 weeks after differentiation onset. The level of 8-OHdG which was in consistent with formation of reactive oxygen species (ROS). In conclusion, this data suggest that despite the elevation in oxidation of lipid and protein molecules during hepatocyte development, the cells were normal in terms of DNA integrity, morphology, and biologically activity.

Keywords: adult stem cells, DNA integrity, free radicals, hepatic differentiation

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Authors: T. D. Ibragimov, A. R. Imamaliyev, G. M. Bayramov

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Influence of barium titanate particles on electro-optic properties of liquid crystal 4-cyano-4′-pentylbiphenyl (5CB) with positive dielectric anisotropy and the liquid crystalline (LC) mixture Н-37 consisting of 4-methoxybezylidene-4'–butylaniline and 4-ethoxybezylidene-4'–butylaniline with negative dielectric anisotropy was investigated. It was shown that a presence of particles inside 5СВ and H-37 decreased the clearing temperature from 35.2 °С to 32.5°С and from 61.2 oC to 60.1oC, correspondingly. The threshold voltage of the Fredericksz effect became 0.3 V for the BaTiO3-5CB colloid while the beginning of this effect of the pure 5СВ was observed at 2.1 V. Threshold voltage of the Fredericksz effect increased from 2.8 V to up 3.1 V at additive of particles into H-37. A rise time of the BaTiO3-5CB colloid improved while a decay time worsened in comparison with the pure 5CB at all applied voltages. The inverse trends were observed for the H-37 matrix, namely, a rise time worsened and a decay time improved. Among other things, the effect of fast light modulation was studied at application of the rectangular impulse with direct bias to an electro-optical cell with the BaTiO3 particles+5CB and the pure 5CB. At this case, a rise time of the composite worsened, a decay time improved in comparison with the pure 5CB. The pecularities of electrohydrodynamic instability (EHDI) formation was also investigated into the composite with the H-37 matrix. It was found that the voltage of the EHDI formation decreased, a rise time increased and a decay time decreased in comparison with the pure H-37. First of all, experimental results are explained by appearance of local electric fields near the polarized ferroelectric particles at application of external electric field and an existence of the additional obstacles (particles) for movement of ions.

Keywords: liquid crystal, ferroelectric particles, composite, electro-optics

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Authors: Shyam Mehraaj, Samanthi E. R. Siriwardana, Shehara A. K. G. H., Wanigasinghe N. T., Wandana R. A. K., Wedage C. V.

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A presentation is a critical tool for conveying not just spoken information but also a wide spectrum of human emotions. The single most effective thing to make the presentation successful is to practice it beforehand. Preparing for a presentation has been shown to be essential for improving emotional control, intonation and prosody, pronunciation, and vocabulary, as well as the quality of the presentation slides. As a result, practicing has become one of the most critical parts of giving a good presentation. In this research, the main focus is to analyze the audio, video, and slides of the presentation uploaded by the presenters. This proposed solution is based on the Natural Language Processing and Computer Vision techniques to cater to the requirement for the presenter to do a presentation beforehand using a mobile responsive web application. The proposed system will assist in practicing the presentation beforehand by identifying the presenters’ emotions, body language, tonality, prosody, pronunciations and vocabulary, and presentation slides quality. Overall, the system will give a rating and feedback to the presenter about the performance so that the presenters’ can improve their presentation skills.

Keywords: presentation, self-evaluation, natural learning processing, computer vision

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Authors: Yihao Kuang, Bowen Ding

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With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology.

Keywords: reinforcement learning, PPO, knowledge inference, supervised learning

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Authors: Emmanuel Nikiema

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This paper examines article incorporation in Mauritian Creole, a French Lexifier Creole which exhibits three forms of article incorporation as illustrated in (1-3). While various analyses of article incorporation have been proposed in the literature, fewer studies have explored the motivation of this widespread phenomenon in Mauritian Creole (MC) as opposed to other French Lexifier Creoles spoken in the Caribbean. For example, Mauritian Creole exhibits 4 times more CV incorporation than Haitian Creole, and 40 times more than Reunion Creole. (1) Consonantal type (C): loraz ‘thunder storm’, lete ‘summer’, zwazo ‘bird’, nide ‘idea’. (2) Syllabic type (CV): lapo ‘skin’, liku ‘neck’, ledo ‘back’, leker ‘heart’, diber ‘butter’. (3) Bi-consonantal (CVC): delo ‘water’, dizef ‘egg’, lizye ‘eye’, dilwil ‘oil’. The goal of this study is twofold: 1) uncover the rules governing the three types of article incorporation in MC, and 2) account for its remarkable occurrence in MC as opposed to its quasi-absence in Reunion Creole. We have collected a corpus of over 700 cases and organized it into three categories (C; CV and CVC). For example, there are 471 examples of CV incorporation in MC against 112 in Haitian Creole and only 12 in Reunion Creole. Two questions can be raised: 1) what is the motivation and distribution of the three types of incorporation in MC, and 2) how can one account for the high volume of incorporation in MC as opposed to its quasi-absence in Reunion Creole? We suggest that article incorporation in MC is related to the structure of nouns in Bantou languages. While previous authors have largely used population settlement data in the colonies during the Creole formation period to justify their analyses, we propose an account based on the syntactic structure of Bantou nouns. This analysis will shed light on the contribution of African languages to the formation of MC, and on to why MC has exhibited more article incorporation cases than any other French Lexifier Creole.

Keywords: article incorporation, creole languages, description, phonology

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Authors: Nabil Al-Zaqri

Abstract:

Semiconductor photocatalysts with surface defects display incredible light absorption bandwidth, and these defects function as highly active sites for oxidation processes by interacting with the surface band structure. Accordingly, engineering the photocatalyst with surface oxygen vacancies will enhance the semiconductor nanostructure's photocatalytic efficiency. Herein, a CeO2₋ₓ nanostructure is designed under the influence of low-frequency ultrasonic waves to create surface oxygen vacancies. This approach enhances the photocatalytic efficiency compared to many heterostructures while keeping the intrinsiccrystal structure intact. Ultrasonic waves induce the acoustic cavitation effect leading to the dissemination of active elements on the surface, which results in vacancy formation in conjunction with larger surface area and smaller particle size. The structural analysis of CeO₂₋ₓ revealed higher crystallinity, as well as morphological optimization, and the presence of oxygen vacancies is verified through Raman, X-rayphotoelectron spectroscopy, temperature-programmed reduction, photoluminescence, and electron spinresonance analyses. Oxygen vacancies accelerate the redox cycle between Ce₄+ and Ce₃+ by prolongingphotogenerated charge recombination. The ultrasound-treated pristine CeO₂ sample achieved excellenthydrogen production showing a quantum efficiency of 1.125% and efficient organic degradation. Ourpromising findings demonstrated that ultrasonic treatment causes the formation of surface oxygenvacancies and improves photocatalytic hydrogen evolution and pollution degradation. Conclusion: Defect engineering of the ceria nanoparticles with oxygen vacancies was achieved for the first time using low-frequency ultrasound treatment. The U-CeO₂₋ₓsample showed high crystallinity, and morphological changes were observed. Due to the acoustic cavitation effect, a larger surface area and small particle size were observed. The ultrasound treatment causes particle aggregation and surface defects leading to oxygen vacancy formation. The XPS, Raman spectroscopy, PL spectroscopy, and ESR results confirm the presence of oxygen vacancies. The ultrasound-treated sample was also examined for pollutant degradation, where 1O₂was found to be the major active species. Hence, the ultrasound treatment influences efficient photocatalysts for superior hydrogen evolution and an excellent photocatalytic degradation of contaminants. The prepared nanostructure showed excellent stability and recyclability. This work could pave the way for a unique post-synthesis strategy intended for efficient photocatalytic nanostructures.

Keywords: surface defect, CeO₂₋ₓ, photocatalytic, water treatment, H₂ production

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