Search results for: laryngeal feature variation

Authors: Basan Shrestha

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The relationship between the household size and the poverty is not well understood. Malthus followers advocate that the increasing population add pressure to the dwindling resource base due to increasing demand that would lead to poverty. Others claim that bigger households are richer due to availability of household labour for income generation activities. Facts from Nepal were analyzed to examine the relationship between the household size and poverty rate. The analysis of data from 3,968 Village Development Committee (VDC)/ municipality (MP) located in 75 districts of all five development regions revealed that the average household size had moderate positive correlation with the poverty rate (Karl Pearson's correlation coefficient=0.44). In a regression analysis, the household size determined 20% of the variation in the poverty rate. Higher positive correlation was observed in eastern Nepal (Karl Pearson's correlation coefficient=0.66). The regression analysis showed that the household size determined 43% of the variation in the poverty rate in east. The relation was poor in far-west. It could be because higher incidence of poverty was there irrespective of household size. Overall, the facts revealed that the bigger households were relatively poorer. With the increasing level of awareness and interventions for family planning, it is anticipated that the household size will decrease leading to the decreased poverty rate. In addition, the government needs to devise a mechanism to create employment opportunities for the household labour force to reduce poverty.

Keywords: household size, poverty rate, nepal, regional development

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Authors: Andrea Catorci, Federico Maria Tardella, Alessandro Brica, Muhammad Umair

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Climate change models predict for the Mediterranean region a strong increase of intensity and frequency of drought events, with an expected effect on grassland biodiversity and functioning. The research aim was to understand how the grassland species modulate their resource acquisition and conservation strategies to short-term variation of the pattern of summer water supply. The study area is mountain meadows located in the ‘‘Montagna di Torricchio’’ (1130 m a.s.l.) a Nature Reserve in central Italy. In 2017 we started a manipulative experiment for 2 year (2017-2018), and we defined two treatments, one with increasing water (watering condition) and the other with less water (drought condition). Then, we investigated how species change their resource strategies at different amount of water availability by measuring the specific leaf area (SLA) and leaf area (LA). We used ANOVAs to test the effect of treatment over time on leaf area and specific leaf area, considering all the species together and also separately according to their growth form (forb, grass, legume). Our results showed that species may respond differently depending on their growth form and that using all the species together may cover more detailed variation. Overall, resource retaining strategies (lower SLA, LA) are resulted by increase of drought condition, while increase in water amount and number of watering events fosters acquisitive strategies (higher SLA, LA). However, this pattern is not constant for all growth form. Grass species are able to maintain their strategies to variation of the pattern of water availability. Forb and legume species on the other side have shown decreasing trend of SLA, LA values with increasing drought condition, a pattern more marked for the latter growth form. These variations suggest not only an increase of slow-growing strategies for both growth form, but also a decrease of their nutrient pastoral values since their leaves are supposed to become harder. Local farmers should consider the effect of climate change on grassland and adapt their management practices to guarantee the cattle welfare.

Keywords: function strategies, grasslands, climate change, sub Mediterranean region

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Authors: Nowshin Nabila Siddique, S. M. Mustafizur Rahman

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There is evidence of cyclonic events in Bay of Bengal (BoB) throughout the year. These cyclones cause a variety of fluctuations along its track including the is the influence in Chlorophyll-a (chl-a) concentration. The main purpose of this paper is to justify this variation pattern. Six Tropical Cyclones (TC) are studied using observational method. The result suggests that there is a noticeable change in productivity after a cyclone passes, when the pre cyclonic and post cyclonic condition is observed. In case of Cyclone Amphan, it shows 1.79 mg/m3 of chlorophyll-a concentration increase after a week of cyclonic occurrence. This change is affected by several attributes such as translation speed, intensity and Ocean Pre-condition, specifically Mixed Layer Depth (MLD). Translation Speed and MLD shows a strong negative correlation with the induced chlorophyll concentration. Whereas the effect of the intensity on a cyclone is not that prominent. It is also found that the period of starting an induction is not same for all cyclone such as in case of Cyclone Amphan, the changes started to occur after one day however for Cyclone Sidr and Cyclone Mora it started after three days. Furthermore, a slightly increase in overall productivity is also observed after a cyclone. In the case of Cyclone Amphan, Hudhud, Phailin it shows a rise up to 0.12 mg/m3 in productivity which decreases gradually taking around the period of two months. On a whole this paper signifies the changes in chlorophyll concentration caused by numerous cyclones and its different characteristics that regulates these changes.

Keywords: tropical cyclone, chlorophyll-a concentration, mixed layer depth, translation speed

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Authors: Azizah M. Nahari, Peter Doerner

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Phosphate (Pi) is one of the essential macronutrients in plant growth and development, and it plays a central role in metabolic processes in plants, particularly photosynthesis and respiration. Limitation of crop productivity by Pi is widespread and is likely to increase in the future. Applications of Pi fertilizers have improved soil Pi fertility and crop production; however, they have also caused environmental damage. Therefore, in order to reduce dependence on unsustainable Pi fertilizers, a better understanding of phosphate use efficiency (PUE) is required for engineering nutrient-efficient crop plants. Enhanced Pi efficiency can be achieved by improved productivity per unit Pi taken up. We aim to identify, by using association mapping, general features of the most important loci that contribute to increased PUE to allow us to delineate the physiological pathways involved in defining this trait in the model plant Arabidopsis. As PUE is in part determined by the efficiency of uptake, we designed a hydroponic system to avoid confounding effects due to differences in root system architecture leading to differences in Pi uptake. In this system, 18 parental lines and 217 lines of the MAGIC population (a Multiparent Advanced Generation Inter-Cross) grown in high and low Pi availability conditions. The results showed revealed a large variation of PUE in the parental lines, indicating that the MAGIC population was well suited to identify PUE loci and pathways. 2 of 18 parental lines had the highest PUE in low Pi while some lines responded strongly and increased PUE with increased Pi. Having examined the 217 MAGIC population, considerable variance in PUE was found. A general feature was the trend of most lines to exhibit higher PUE when grown in low Pi conditions. Association mapping is currently in progress, but initial observations indicate that a wide variety of physiological processes are involved in influencing PUE in Arabidopsis. The combination of hydroponic growth methods and genome-wide association mapping is a powerful tool to identify the physiological pathways underpinning complex quantitative traits in plants.

Keywords: hydroponic system growth, phosphate use efficiency (PUE), Genome-wide association mapping, MAGIC population

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Authors: Mohamed Sadok Bensalem, Soulef Amamria, Khaled Lazzez, Mohamed Ghanmi

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The southern-central Tunisian Atlas presents a typical example of an external zone. It occupies a particular position in the North African chains: firstly, it is the eastern limit of atlassic structures; secondly, it is the edges between the belts structures to the north and the stable Saharan platform in the south. The evolution of deformation study is based on several methods, such as classical or numerical methods. The principals parameters controlling the genesis of folds in the southern central Tunisian Atlas are; the reactivation of pre-existing faults during the later compressive phase, the evolution of decollement level, and the relation between thin and thick-skinned. One of the more principal characters of the southern-central Tunisian Atlas is the variation of belts structures directions determined by: NE-SW direction, named the attlassic direction in Tunisia, the NW-SE direction carried along the Gafsa fault (the oriental limit of southern atlassic accident), and the E-W direction defined in the southern Tunisian Atlas. This variation of direction is the result of important variation of deformation during different tectonics phases. A classical modelling of the Jebel ElKebar anticline, based on faults throw of the pre-existing faults and its reactivation during compressive phases, shows the importance of extensional deformation, particular during Aptian-Albian period, comparing with that of later compression (Alpine phases). A numerical modelling, based on the software Rampe E.M. 1.5.0, applied on the anticline of Jebel Orbata confirms the interpretation of “fault related fold” with decollement level within the Triassic successions. The other important parameter of evolution of deformation is the vertical migration of decollement level; indeed, more than the decollement level is in the recent series, most that the deformation is accentuated. The evolution of deformation is marked the development of duplex structure in Jebel At Taghli (eastern limit of Jebel Orbata). Consequently, the evolution of deformation is proportional to the depth of the decollement level, the most important deformation is in the higher successions; thus, is associated to the thin-skinned deformation; the decollement level permit the passive transfer of deformation in the cover.

Keywords: evolution of deformation, pre-existing faults, decollement level, thin-skinned

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Authors: M. Shah Alamgir, Jun Furuya, Shintaro Kobayashi, M. Abdus Salam

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Farmers’ environmental and economic situations are very diverse. In order to develop effective policies and technologies to improve farmers’ life standard, it is important to understand which factors induce the diversity of agricultural income. Analyze both primary and secondary data, this study applied descriptive, inferential statistical tools, and econometric techniques. From the study, farmers of Sylhet Division produce potato as one of the main cash crop with other seasonal crops. The total costs of potato production per hectare varied in different districts of Sylhet division in addition seed and hired labor cost has the biggest share of the full cost. To grasp the diversity of income, the study decomposes the variance of net income into different factors of potato production. Through this decomposition, seed cost is the important factors of income variability and it is the most important sector to induce total cost disparity for potato production. The result shows that 73% of net income variation is explained by gross income. It implies that potato yield or potato price (quality) or both vary widely among farmers. This finding is important of policymaking and technology development of agricultural farming in Bangladesh.

Keywords: agricultural income, seed, hired labor, technology development

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

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Authors: Kafuko M. Maria, Namisango Fatuma, Byomire Gorretti

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Mobile learning (m-learning) is steadily growing and has undoubtedly derived benefits to learners and tutors in different learning environments. This paper investigates the variation in benefits derived from enhanced classroom learning through use of m-learning platforms in the context of a developing country owing to the fact that it is still in its initial stages. The study focused on how basic technology-enhanced pedagogic innovation like cell phone-based learning is enhancing classroom learning from the learners’ perspective. The paper explicitly indicates the opportunities presented by enhanced learning to a conventional learning environment like a physical classroom. The findings were obtained through a survey of two universities in Uganda in which data was quantitatively collected, analyzed and presented in a three banded diagram depicting the variation in the obtainable benefits. Learners indicated that a smartphone is the most commonly used device. Learners also indicate that straight lectures, student to student plus student to lecturer communication, accessing learning material and assignments are core activities. In a TEL environment support by smartphones, learners indicated that they conveniently achieve the prior activities plus discussions and group work. Learners seemed not attracted to the possibility of using TEL environment to take lectures, as well as make class presentations. The less attractiveness of these two factors may be due to the teacher centered approach commonly applied in the country’s education system.

Keywords: technology enhanced learning, m-learning, classroom learning, perceived benefits

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

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Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

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Authors: Hamed Yousefi, Farnaz Mohammadi, Niloufar Mirian, Navid Amini

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Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed.

Keywords: visual evoked potential, time-frequency feature extraction, short-time Fourier transform, event-related spectrum potential classification, linear discriminant analysis

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Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

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Current flooding risk modeling focuses on resilience, defined as the probability of recovery from a severe flooding event. However, the long-term damage to property and well-being by nuisance flooding and its long-term effects on communities are not typically included in risk assessments. An approach was developed to address the probability of recovering from a severe flooding event combined with the probability of community performance during a nuisance event. A consolidated model, namely the conflation flooding recovery (&FR) model, evaluates risk-coping mitigation strategies for communities based on the recovery time from catastrophic events, such as hurricanes or extreme surges, and from everyday nuisance flooding events. The &FR model assesses the variation contribution of each independent input and generates a weighted output that favors the distribution with minimum variation. This approach is especially useful if the input distributions have dissimilar variances. The &FR is defined as a single distribution resulting from the product of the individual probability density functions. The resulting conflated distribution resides between the parent distributions, and it infers the recovery time required by a community to return to basic functions, such as power, utilities, transportation, and civil order, after a flooding event. The &FR model is more accurate than averaging individual observations before calculating the mean and variance or averaging the probabilities evaluated at the input values, which assigns the same weighted variation to each input distribution. The main disadvantage of these traditional methods is that the resulting measure of central tendency is exactly equal to the average of the input distribution’s means without the additional information provided by each individual distribution variance. When dealing with exponential distributions, such as resilience from severe flooding events and from nuisance flooding events, conflation results are equivalent to the weighted least squares method or best linear unbiased estimation. The combination of severe flooding risk with nuisance flooding improves flood risk management for highly populated coastal communities, such as in South Florida, USA, and provides a method to estimate community flood recovery time more accurately from two different sources, severe flooding events and nuisance flooding events.

Keywords: community resilience, conflation, flood risk, nuisance flooding

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Authors: Safitri W. Nur, Prathisto Panuntun L. Unggul, M. Ivan Adi Perdana, R. Dary Wira Mahadika

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On soft soil areas, high embankment as a preloading needed to improve the bearing capacity of the soil. For sustainable development, the construction of embankment must not disturb the area around of them. So, the influence area must be known before the contractor applied their embankment design. For several cases in Indonesia, the area around of embankment construction is housing resident and other building. So that, the influence area must be identified to avoid the differential settlement occurs on the buildings around of them. Differential settlement causes the building crack. Each building has a limited tolerance for the differential settlement. For concrete buildings, the tolerance is 0,002 – 0,003 m and for steel buildings, the tolerance is 0,006 – 0,008 m. If the differential settlement stands on the range of that value, building crack can be avoided. In fact, the settlement around of embankment is assumed as zero. Because of that, so many problems happen when high embankment applied on soft soil area. This research used the superposition method combined with plaxis analysis to know the influences area around of embankment in some location with the differential characteristic of the soft soil. The undisturbed soil samples take on 55 locations with undisturbed soil samples at some soft soils location in Indonesia. Based on this research, it was concluded that the effects of embankment variation are if more gentle the slope, the influence area will be greater and vice versa. The largest of the influence area with h initial embankment equal to 2 - 6 m with slopes 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8 is 32 m from the edge of the embankment.

Keywords: differential settlement, embankment, influence area, slope, soft soil

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Authors: Dian Mayasari, Yosi Bayu Murti, Sylvia Utami Tunjung Pratiwi, Sudarsono

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Melastoma malabathricum L. is an Indo-Pacific herb that has been traditionally used to treat several ailments such as wounds, dysentery, diarrhea, toothache, and diabetes. This plant is common across tropical Indo-Pacific archipelagos and is tolerant of a range of soils, from low-lying areas subject to saltwater inundation to the salt-free conditions of mountain slopes. How the soil and environmental variation influences secondary metabolite production in the herb, and an understanding of the plant’s utility as traditional medicine, remain largely unknown and unexplored. The objective of this study is to evaluate the variability of the metabolic profiles of M. malabathricum L. across its geographic distribution. By employing high-performance liquid chromatography-diode array detector (HPLC-DAD), a highly established, simple, sensitive, and reliable method was employed for establishing the chemical fingerprints of 72 samples of M. malabathricum L. leaves from various geographical locations in Indonesia. Specimens collected from six terrestrial and archipelago regions of Indonesia were analyzed by HPLC to generate chromatogram peak profiles that could be compared across each region. Data corresponding to the common peak areas of HPLC chromatographic fingerprint were analyzed by hierarchical component analysis (HCA) and principal component analysis (PCA) to extract information on the most significant variables contributing to characterization and classification of analyzed samples data. Principal component values were identified as PC1 and PC2 with 41.14% and 19.32%, respectively. Based on variety and origin, the high-performance liquid chromatography method validated the chemical fingerprint results used to screen the in vitro antioxidant activity of M. malabathricum L. The result shows that the developed method has potential values for the quality of similar M. malabathrium L. samples. These findings provide a pathway for the development and utilization of references for the identification of M. malabathricum L. Our results indicate the importance of considering geographic distribution during field-collection efforts as they demonstrate regional metabolic variation in secondary metabolites of M. malabathricum L., as illustrated by HPLC chromatogram peaks and their antioxidant activities. The results also confirm the utility of this simple approach to a rapid evaluation of metabolic variation between plants and their potential ethnobotanical properties, potentially due to the environments from whence they were collected. This information will facilitate the optimization of growth conditions to suit particular medicinal qualities.

Keywords: fingerprint, high performance liquid chromatography, Melastoma malabathricum l., metabolic profiles, principal component analysis

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Prittesh Patel, Ramar Krishnamurthy

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

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

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Authors: S. F. Halim, H. F. Naguib, S. N. Lawandy, R. S. Hegazy, M. N. Baheg

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The main drawbacks of the traditional carbon fabric reinforced epoxy resin (CFRP) are low strain failure, delamination between composites layers, and low impact resistance due to the brittleness of epoxy resin. The aim of this study is to enhance the fracture properties of the CFRP composites laminates via the variation of composite's designs. A series of composites were fabricated in which bidirectional (00/900) carbon fabric (CF) layers were laid inside the resin matrix with orientation codes as F1 [(00, 900)/ (00, 900)], F2 [(900, 00)/ (00, 900)] and F3 [(00,900)/ (900, 00). The mechanical and dynamic properties of the composites were estimated. In addition, the morphology of samples surface was examined by scanning electron microscope (SEM) after impact fracture. The results revealed that the CFRP properties could be tailored fitting specific applications by controlling the fabric orientation inside the CFRP composite design. F2 orientation [(900, 00)/ (00.900)] showed the highest tensile and flexural strength values. On the other hand, the impact strength values of composites were in the order F1 > F2 > F3. The storage modulus, loss modulus, and glass transition temperature Tg values obtained from the dynamic mechanical analysis (DMA) examination was in the order F1 > F2 > F3. The variation in the properties of the composite was clearly explained by the SEM micrographs as the failure of F3 orientation properties was referred to as the complete breakage of the CF layers upon fracture.

Keywords: carbon fiber, CFRP, composites, epoxy resins, flexural strength

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Authors: Juliet Udoudom

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Complement ordering principles of natural language phrases (XPs) stipulate that Head terms be consistently placed phrase initially or phrase-finally, yielding two basic theoretical orders – Head – Complement order or Complement – Head order. This paper examines the principles which determine complement ordering in English V- and N-bar structures. The aim is to determine the extent to which complement linearisations in the two phrase types are consistent with the two theoretical orders outlined above given the flexible and varied nature of natural language structures. The objective is to see whether there are variation(s) in the complement linearisations of the XPs studied and the implications which such variations hold for the inter-language syntax of English and Ibibio. A corpus-based approach was employed in obtaining the English data. V- and -N – bar structures containing complement structures were isolated for analysis. Data were examined from the perspective of the X-bar and Government – theories of Chomsky’s (1981) Government-Binding format. Findings from the analysis show that in V – bar structures in English, heads are consistently placed phrase – initially yielding a Head – Complement order; however, complement linearisation in the N – bar structures studied exhibited parametric variations. Thus, in some N – bar structures in English the nominal head is ordered to the left whereas in others, the head term occurs to the right. It may therefore be concluded that the principles which determine complement ordering are both Language – Particular and Phrase – specific following insights provided within Phrasal Syntax.

Keywords: complement order, complement–head order, head–complement order, language–particular principles

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Authors: Surinder Deswal, Vineet Verma

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Air Quality Index (AQI) is used as a tool to indicate the level of severity and disseminate the information on air pollution to enable the public to understand the health and environmental impacts of air pollutant concentration levels. The annual and seasonal variation of criteria air pollutants concentration based on the National Ambient Air Quality Monitoring Programme has been conducted for a period of nine years (2006-2014) using the AQI system. AQI was calculated using IND-AQI methodology and Maximum Operator Concept is applied. An attempt has been made to quantify the variations in AQI on an annual and seasonal basis over a period of nine years. Further, year-wise frequency of occurrence of AQI in each category for all the five stations is analysed, which presents in depth analysis of trends over the period of study. The best air quality was observed in the Noida residential area, followed by Noida industrial area during the study period; whereas, Bulandshahar industrial area and Faridabad residential area were observed to have the worst air quality. A shift in the worst air quality from winter to summer season has also been observed during the study period. Further, the level of Respirable Suspended Particulate Matter was found to be above permissible limit at all the stations. The present study helps in enhancing public awareness and calls for the need of immediate measures to be taken to counter-effect the cause of the increasing level of air pollution.

Keywords: air quality index, annual trends, criteria pollutants, seasonal variation

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Authors: Mustapha Sadouk

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This study investigates the sensitivity of high-frequency acoustic parameters in a rigid air-saturated porous bilayer material within the framework of the equivalent fluid theory, a specific case of the Biot model. The study specifically focuses on the sensitivity analysis in the frequency domain. The interaction between the fluid and solid phases of the porous medium incorporates visco-inertial and thermal exchange, characterized by two functions: the dynamic tortuosity α(ω) proposed by Johnson et al. and the dynamic compressibility β(ω) proposed by Allard, refined by Sadouki for the low-frequency domain of ultrasound. The parameters under investigation encompass porosity, tortuosity, viscous characteristic length, thermal characteristic length, as well as viscous and thermal shape factors. A +30% variation in these parameters is considered to assess their impact on the transmitted wave amplitudes. By employing this larger variation, a more comprehensive understanding of the sensitivity of these parameters is obtained. The outcomes of this study contribute to a better comprehension of the high-frequency wave behavior in porous bilayer materials, providing valuable insights for the design and optimization of such materials across various applications.

Keywords: bilayer materials, ultrasound, sensitivity analysis, equivalent fluid theory, dynamic tortuosity., porous material

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Authors: Mala Mukherjee

Abstract:

Background: India is urbanizing very fast and urbanisation in India is treated as one of the most crucial components of economic growth. Though the pace of urbanisation (31.6 per cent in 2011) is however slower and lower than the average for Asia but the absolute number of people residing in cities and towns has increased substantially. Rapid urbanization leads to urban poverty and it is well represented in slums. Currently India has four metropolises and 53 million plus cities. All of them have significant slum population but the standard of living and success of slum development programmes varies across regions. Objectives: Objectives of the paper are to show how urbanisation and slum development varies across space; to show spatial variation in the standard of living in Indian slums; to analyse how the implementation of slum development policies like JNNURM, Rajiv Awas Yojana varies across cities and bring different results in different regions and what are the factors responsible for such variation. Data Sources and Methodology: Census 2011 data on urban population and slum households and amenities have been used for analysing the regional variation of urbanisation in 53 million plus cities of India. Special focus has been put on Kolkata Metropolitan Area. Statistical techniques like z-score and PCA have been employed to work out Standard of Living Deprivation score for all the slums of 53 metropolises. ARC-GIS software is used for making maps. Standard of living has been measured in terms of access to basic amenities, infrastructure and assets like drinking water, sanitation, housing condition, bank account, and so on. Findings: 1. The first finding reveals that migration and urbanization is very high in Greater Mumbai, Delhi, Bangaluru, Chennai, Hyderabad and Kolkata; but slum population is high in Greater Mumbai (50% population live in slums), Meerut, Faridabad, Ludhiana, Nagpur, Kolkata etc. Though the rate of urbanization is high in southern and western states but the percentage of slum population is high in northern states (except Greater Mumbai). 2. Standard of Living also varies widely. Slums of Greater Mumbai and North Indian Cities score fairly high in the index indicating the fact that standard of living is high in those slums compare to the slums in eastern India (Dhanbad, Jamshedpur, Kolkata). Therefore, though Kolkata have relatively lesser percentage of slum population compare to north and south Indian cities but the standard of living in Kolkata’s slums is deplorable. 3. It is interesting to note that even within Kolkata Metropolitan Area slums located in the southern and eastern municipal towns like Rajpur-Sonarpur, Pujali, Diamond Harbour, Baduria and Dankuni have lower standard of living compare to the slums located in the Hooghly Industrial belt like Titagarh, Rishrah, Srerampore etc. Slums of the Hooghly Industrial Belt are older than the slums located in eastern and southern part of the urban agglomeration. 4. Therefore, urban development and emergence of slums should not be the only issue of urban governance but standard of living should be the main focus. Slums located in the main cities like Delhi, Mumbai, Kolkata get more attention from the urban planners and similarly, older slums in a city receives greater political attention compare to the slums of smaller cities and newly emerged slums of the peripheral parts.

Keywords: urbanisation, slum, spatial variation, India

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Authors: Siwapech Silapaprayoon, Januluk Khanobdee, Sompid Samipak

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Chili peppers are the fruits of Capsicum pepper plants well known for their fiery burning sensation on the tongue after consumption. They are members of the Solanaceae or common nightshade family along with potato, tomato and eggplant. Thai cuisine has gained popularity for its distinct flavors due to usages of various spices and its heat from the addition of chili pepper. Though being used in little quantity for each dish, chili pepper holds a special place in Thai cuisine. There are many varieties of chili peppers in Thailand, and thirty accessions were collected at Rajamangala University of Technology Lanna, Lampang, Thailand. To effectively manage any germplasm it is essential to know the diversity and relationships among members. Thirty-six Inter Simple Sequence Repeat (ISSRs) DNA markers were used to analyze the germplasm. Total of 335 polymorphic bands was obtained giving the average of 9.3 alleles per marker. Unweighted pair-group mean arithmetic method (UPGMA) clustering of data using NTSYS-pc software indicated that the accessions showed varied levels of genetic similarity ranging from 0.57-1.00 similarity coefficient index indicating significant levels of variation. At SM coefficient of 0.81, the germplasm was separated into four groups. Phenotypic variation was discussed in context of phylogenetic tree clustering.

Keywords: diversity, germplasm, Chili pepper, ISSR

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Naso Isaiah Thanavisuth

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Medical cannabis can be used for treatment including pain, multiple sclerosis, Parkinson's disease, and cancer. However, medical cannabis leads to adverse effects (AEs), which is delta-9-tetrahydrocannabinol (THC). In previous studies, the major of THC metabolism enzymes are CYP2C9. Especially, the variation of CYP2C9 gene consist of CYP2C9*2 on exon 3 and CYP2C9*3 on exon 7 to decrease enzyme activity. Notwithstanding, there is no data describing whether the variant of CYP2C9 genes are apharmacogenetics marker for the prediction of THC-induced AEs in Thai patients. We want to investigate the association between CYP2C9 gene and THC-induced AEs in Thai patients. We enrolled 39 Thai patients with medical cannabis treatment who were classified by clinical data. The CYP2C9*2 and *3 genotyping were conducted using the TaqMan real time PCR assay. All Thai patients who received the medical cannabis consist of twenty-four (61.54%) patients were female, and fifteen (38.46%) were male, with age range 27- 87 years. Moreover, the most AEs in Thai patients who were treated with medical cannabis between cases and controls were tachycardia, arrhythmia, dry mouth, and nausea. Particularly, thirteen (72.22%) medical cannabis-induced AEs were female and age range 33 – 69 years. In this study, none of the medical cannabis groups carried CYP2C9*2 variants in Thai patients. The CYP2C9*3 variants (*1/*3, intermediate metabolizer, IM) and (*3/*3, poor metabolizer, PM) were found, three of thirty-nine (7.69%) and one of thirty-nine (2.56%), respectively. Although, our results indicate that there is no found the CYP2C9*2. However, the variation of CYP2C9 allele might serve as a pharmacogenetics marker for screening before initiating the therapy with medical cannabis for the prevention of medical cannabis-induced AEs.

Keywords: CYP2C9, medical cannabis, adverse effects, THC, P450

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Authors: Avadhoot Bhosale, Robin Davis P., Pradip Sarkar

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The seismic performance of buildings with irregular distribution of mass, stiffness and strength along the height may be significantly different from that of regular buildings with masonry infill. Masonry infilled reinforced concrete (RC) frames are very common structural forms used for multi-storey building construction. These structures are found to perform better in past earthquakes owing to additional strength, stiffness and energy dissipation in the infill walls. The seismic performance of a building depends on the variation of material, structural and geometrical properties. The sensitivity of these properties affects the seismic response of the building. The main objective of the sensitivity analysis is to found out the most sensitive parameter that affects the response of the building. This paper presents a sensitivity analysis by considering 5% and 95% probability value of random variable in the infills characteristics, trying to obtain a reasonable range of results representing a wide number of possible situations that can be met in practice by using pushover analysis. The results show that the strength-related variation values of concrete and masonry, with the exception of tensile strength of the concrete, have shown a significant effect on the structural performance and that this effect increases with the progress of damage condition for the concrete. The seismic risk assessments of the selected frames are expressed in terms of reliability index.

Keywords: fragility curve, sensitivity analysis, reliability index, RC frames

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Authors: Azene Tesfaye, Yohannes Petros, Habtamu Zeleke

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To evaluate genetic variation among Ethiopian lentil, laboratory experiment were conducted to screen 12 accessions of lentil (Lens culinaris M.) for salt tolerance. Seeds of 12 Lentil accessions were grown at laboratory (Petri dish) condition with different levels of salinity (0, 2, 4, and 8 dSm-1 NaCl) for 4 weeks. The experimental design was completely randomized design (CRD) in factorial combination with three replications. Data analysis was carried out using SAS software. Average germination time, germination percentage, seedling shoot and root traits, seedling shoot and root weight were evaluated. The two way ANOVA for varieties revealed statistically significant variation among lentil accession, NaCl level and their interactions (p<0.001) with respect to the entire parameters. It was found that salt stress significantly delays germination rate and decreases germination percentage, shoot and root length, seedling shoot and root weight of lentil accessions. The degree of decrement varied with accessions and salinity levels. Accessions 36120, 9235 and 36004 were better salt tolerant than the other accessions. As the result, it is recommended to be used as a genetic resource for the development of lentil accession and other very salt sensitive crop with improved germination under salt stress condition.

Keywords: accession, germination, lentil, NaCl, screening, seedling stage

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Authors: John G. Skelly, Peter K. Dearden, Thomas W. R. Harrop, Sarah N. Inwood, Joseph Guhlin

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The Argentine stem weevil (ASW; L. bonariensis) is an economically important pasture pest in New Zealand, which causes about $200 million of damage per annum. Microctonus hyperodae (Mh), a parasite of the ASW in its natural range in South America, was introduced into New Zealand to curb the pasture damage caused by the ASW. Mh is an endoparasitic wasp that lays its eggs in the ASW halting its reproduction. Mh was initially successful at preventing ASW proliferation and reducing pasture damage. The effectiveness of Mh has since declined due to decreased parasitism rates and has resulted in increased pasture damage. Although the mechanism through which ASW has developed resistance to Mh has not been discovered, it has been proposed to be due to the different reproductive modes used by Mh and the ASW in New Zealand. The ASW reproduces sexually, whereas Mh reproduces asexually, which has been hypothesised to have allowed the ASW to ‘out evolve’ Mh. Other species within the Microctonus genus reproduce both sexually and asexually. Strains of Microctonus aethiopoides (Ma), a species closely related to Mh, reproduce either by sexual or asexual reproduction. Comparing the genomes of sexual and asexual Microctonus may allow for the identification of the mechanism of asexual reproduction and other characteristics that may improve Mh as a biocontrol agent. The genomes of Mh and three strains of Ma, two of which reproduce sexually and one reproduces asexually, have been sequenced and annotated. The French (MaFR) and Moroccan (MaMO) reproduce sexually, whereas the Irish strain (MaIR) reproduces asexually. Like Mh, The Ma strains are also used as biocontrol agents, but for different weevil species. The genomes of Mh and MaIR were subsequently upgraded using Hi-C, resulting in a set of high quality, highly contiguous genomes. A subset of the genes involved in mitosis and meiosis, which have been identified though the use of Hidden Markov Models generated from genes involved in these processes in other Hymenoptera, have been catalogued in Mh and the strains of Ma. Meiosis and mitosis genes were broadly conserved in both sexual and asexual Microctonus species. This implies that either the asexual species have retained a subset of the molecular components required for sexual reproduction or that the molecular mechanisms of mitosis and meiosis are different or differently regulated in Microctonus to other insect species in which these mechanisms are more broadly characterised. Bioinformatic analysis of the chemoreceptor compliment in Microctonus has revealed some variation in the number of olfactory receptors, which may be related to host preference. Phylogenetic analysis of olfactory receptors highlights variation, which may be able to explain different host range preferences in the Microctonus. Hi-C clustering implies that Mh has 12 chromosomes, and MaIR has 8. Hence there may be variation in gene regulation between species. Genome alignment of Mh and MaIR implies that there may be large scale genome structural variation. Greater insight into the genetics of these agriculturally important group of parasitic wasps may be beneficial in restoring or maintaining their biocontrol efficacy.

Keywords: argentine stem weevil, asexual, genomics, Microctonus hyperodae

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Authors: Peifei Zhu, Zisheng Li, Yasuki Kakishita, Mayumi Suzuki, Tomoaki Chono

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Segmentation of left ventricle (LV) from cardiac ultrasound images provides a quantitative functional analysis of the heart to diagnose disease. Active Shape Model (ASM) is a widely used approach for LV segmentation but suffers from the drawback that initialization of the shape model is not sufficiently close to the target, especially when dealing with abnormal shapes in disease. In this work, a two-step framework is proposed to improve the accuracy and speed of the model-based segmentation. Firstly, a robust and efficient detector based on Hough forest is proposed to localize cardiac feature points, and such points are used to predict the initial fitting of the LV shape model. Secondly, to achieve more accurate and detailed segmentation, ASM is applied to further fit the LV shape model to the cardiac ultrasound image. The performance of the proposed method is evaluated on a dataset of 800 cardiac ultrasound images that are mostly of abnormal shapes. The proposed method is compared to several combinations of ASM and existing initialization methods. The experiment results demonstrate that the accuracy of feature point detection for initialization was improved by 40% compared to the existing methods. Moreover, the proposed method significantly reduces the number of necessary ASM fitting loops, thus speeding up the whole segmentation process. Therefore, the proposed method is able to achieve more accurate and efficient segmentation results and is applicable to unusual shapes of heart with cardiac diseases, such as left atrial enlargement.

Keywords: hough forest, active shape model, segmentation, cardiac left ventricle

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Authors: A. C. Blaga, A. Cârlescu, M. Turnea, A. I. Galaction, D. Caşcaval

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The performance of an aerobic stirred bioreactor for fungal fermentation was analyzed on the basis of mixing time and oxygen mass transfer coefficient, by quantifying the influence of some specific geometrical and operational parameters of the bioreactor, as well as the rheological behavior of Penicillium chrysogenum broth (free mycelia and mycelia aggregates). The rheological properties of the fungus broth, controlled by the biomass concentration, its growth rate, and morphology strongly affect the performance of the bioreactor. Experimental data showed that for both morphological structures the accumulation of fungus biomass induces a significant increase of broths viscosity and modifies the rheological behavior. For lower P. chrysogenum concentrations (both morphological conformations), the mixing time initially increases with aeration rate, reaches a maximum value and decreases. This variation can be explained by the formation of small bubbles, due to the presence of solid phase which hinders the bubbles coalescence, the rising velocity of bubbles being reduced by the high apparent viscosity of fungus broths. By biomass accumulation, the variation of mixing time with aeration rate is gradually changed, the continuous reduction of mixing time with air input flow increase being obtained for 33.5 g/l d.w. P. chrysogenum. Owing to the superior apparent viscosity, which reduces considerably the relative contribution of mechanical agitation to the broths mixing, these phenomena are more pronounced for P. chrysogenum free mycelia. Due to the increase of broth apparent viscosity, the biomass accumulation induces two significant effects on oxygen transfer rate: the diminution of turbulence and perturbation of bubbles dispersion - coalescence equilibrium. The increase of P. chrysogenum free mycelia concentration leads to the decrease of kla values. Thus, for the considered variation domain of the main parameters taken into account, namely air superficial velocity from 8.36 10-4 to 5.02 10-3 m/s and specific power input from 100 to 500 W/m3, kla was reduced for 3.7 times for biomass concentration increase from 4 to 36.5 g/l d.w. The broth containing P. crysogenum mycelia aggregates exhibits a particular behavior from the point of view of oxygen transfer. Regardless of bioreactor operating conditions, the increase of biomass concentration leads initially to the increase of oxygen mass transfer rate, the phenomenon that can be explained by the interaction of pellets with bubbles. The results are in relation with the increase of apparent viscosity of broths corresponding to the variation of biomass concentration between the mentioned limits. Thus, the apparent viscosity of the suspension of fungus mycelia aggregates increased for 44.2 times and fungus free mycelia for 63.9 times for CX increase from 4 to 36.5 g/l d.w. By means of the experimental data, some mathematical correlations describing the influences of the considered factors on mixing time and kla have been proposed. The proposed correlations can be used in bioreactor performance evaluation, optimization, and scaling-up.

Keywords: biomass concentration, mixing time, oxygen mass transfer, P. chrysogenum broth, stirred bioreactor

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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