Search results for: urea deep placement

Authors: Bal Deep Sharma, Suresh Ray Yadav

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Local soils are often chosen due to their widespread availability and low cost. However, these soils typically have poor durability, which can lead to significant limitations in their use for construction. To address this issue, various soil stabilization techniques have been developed and used over the years. This study investigates the viability of employing the mineral polymerization (MIP) technique to stabilize black soils, intending to enhance their suitability for construction applications. This technique involves the microstructural transformation of certain clay minerals into solid and stable compounds exhibiting characteristics similar to hydroxy sodalite, feldspathoid, or zeolite. This transformation occurs through the action of an alkaline reactant at atmospheric pressure and low temperature. The soil sample was characterized using grain size distribution, Atterberg limit test, organic content test, and pH-value tests. The unconfined compressive strength of the soil specimens, prepared with varying percentages of sodium hydroxide as an additive and sand as a filler by weight, was determined at the optimum moisture content. The unconfined compressive strength of the specimens was tested under three different conditions: dry, wet, and cycling. The maximum unconfined compressive strengths were 77.568 kg/cm², 38.85 kg/cm², and 56.3 kg/cm² for the dry, wet, and cycling specimens, respectively, while the unconfined compressive strength of the untreated soil was 7.38 kg/cm². The minimum unconfined compressive strength of the wet and cycling specimens was greater than that of the untreated soil. Based on these findings, it can be concluded that these soils can be effectively used as construction material after treatment with sodium hydroxide.

Keywords: soil stabilization technique, soft soil treatment, sodium hydroxide, unconfined compressive strength

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Authors: Andreas Hirseland, Lukas Kerschbaumer

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In Germany, during the past years there constantly are about one million long term unemployed who did not benefit from the prospering labor market while most short term unemployed did. Instead, they are continuously dependent on welfare and sometimes precarious short-term employment, experiencing work poverty. Long term unemployment thus turns into a main obstacle to regular employment, especially if accompanied by other impediments such as low level education (school/vocational), poor health (especially chronical illness), advanced age (older than fifty), immigrant status, motherhood or engagement in care for other relatives. Almost two thirds of all welfare recipients have multiple impediments which hinder a successful transition from welfare back to sustainable and sufficient employment. Hiring them is often considered as an investment too risky for employers. Therefore formal application schemes based on formal qualification certificates and vocational biographies might reduce employers’ risks but at the same time are not helpful for long-term unemployed and welfare recipients. The panel survey ‘Labor market and social security’ (PASS; ~15,000 respondents in ~10,000 households), carried out by the Institute of Employment Research (the research institute of the German Federal Labor Agency), shows that their chance to get back to work tends to fall to nil. Only 66 cases of such unlikely transitions could be observed. In a sequential explanatory mixed-method study, the very scarce ‘success stories’ of unlikely transitions from long term unemployment to work were explored by qualitative inquiry – in-depth interviews with a focus on biography accompanied by qualitative network techniques in order to get a more detailed insight of relevant actors involved in the processes which promote the transition from being a welfare recipient to work. There is strong evidence that sustainable transitions are influenced by biographical resources like habits of network use, a set of informal skills and particularly a resilient way of dealing with obstacles, combined with contextual factors rather than by job-placement procedures promoted by Job-Centers according to activation rules or by following formal paths of application. On the employer’s side small and medium-sized enterprises are often found to give job opportunities to a wider variety of applicants, often based on a slow but steadily increasing relationship leading to employment. According to these results it is possible to show and discuss some limitations of (German) activation policies targeting welfare dependency and long-term unemployment. Based on these findings, indications for more supportive small scale measures in the field of labor-market policies are suggested to help long-term unemployed with multiple impediments to overcome their situation.

Keywords: against-all-odds, economic sociology, long-term unemployment, mixed-methods

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Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali

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Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.

Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile

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Authors: O. B. Citil, V. Sariyel, M. Akoz

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In this study, total fatty acid composition of muscle lipids of Cyprinus carpio L. living in Suğla Dam Lake, Altinapa Dam Lake, Eğirdir Lake and Burdur Lake were determined using GC. During this study, for the summer season of July was taken from each region of the land and they were stored in deep-freeze set to -20 degrees until the analysis date. At the end of the analyses, 30 different fatty acids were found in the composition of Cyprinus carpio L. which lives in different lakes. Cyprinus carpio Suğla Dam Lake of polyunsaturated fatty acids (PUFAs), were higher than other lakes. Cyprinus carpio L. was the highest in the major SFA palmitic acid. Polyunsaturated fatty acids (PUFA) of carp, the most abundant fish species in all lakes, were found to be higher than those of saturated fatty acids (SFA) in all lakes. Palmitic acid was the major SFA in all lakes. Oleic acid was identified as the major MUFA. Docosahexaenoic acid (DHA) was the most abundant in all lakes. ω3 fatty acid composition was higher than the percentage of the percentage ω6 fatty acids in all lake. ω3/ω6 rates of Cyprinus carpio L. Suğla Dam Lake, Altinapa Dam Lake, Eğirdir Lake and Burdur Lake, 2.12, 1.19, 2.15, 2.87, and 2.82, respectively. Docosahexaenoic acid (DHA) was the major PUFA in Eğirdir and Burdur lakes, whereas linoleic acid (LA) was the major PUFA in Altinapa and Suğla Dam Lakes. It was shown that the fatty acid composition in the muscle of carp was significantly influenced by different lakes.

Keywords: Cyprinus carpio L., fatty acid, composition, gas chromatography

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Authors: Ebert Brea

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We propose a novel direct search algorithm for identifying at least a local minimum of mixed integer nonlinear unconstrained optimization problems. The Mixed Integer Randomized Pattern Search Algorithm (MIRPSA), so-called by the author, is based on a randomized pattern search, which is modified by the MIRPSA for finding at least a local minimum of our problem. The MIRPSA has two main operations over the randomized pattern search: moving operation and shrinking operation. Each operation is carried out by the algorithm when a set of conditions is held. The convergence properties of the MIRPSA is analyzed using a Markov chain approach, which is represented by an infinite countable set of state space λ, where each state d(q) is defined by a measure of the qth randomized pattern search Hq, for all q in N. According to the algorithm, when a moving operation is carried out on the qth randomized pattern search Hq, the MIRPSA holds its state. Meanwhile, if the MIRPSA carries out a shrinking operation over the qth randomized pattern search Hq, the algorithm will visit the next state, this is, a shrinking operation at the qth state causes a changing of the qth state into (q+1)th state. It is worthwhile pointing out that the MIRPSA never goes back to any visited states because the MIRPSA only visits any qth by shrinking operations. In this article, we describe the MIRPSA for mixed integer nonlinear unconstrained optimization problems for doing a deep study of its convergence properties using Markov chain viewpoint. We herein include a low dimension case for showing more details of the MIRPSA, when the algorithm is used for identifying the minimum of a mixed integer quadratic function. Besides, numerical examples are also shown in order to measure the performance of the MIRPSA.

Keywords: direct search, mixed integer optimization, random search, convergence, Markov chain

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Fadlan Atmaja Nursiwan, Ugi Kurnia Gusti

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Garba Hill, located in Tekana Village, South Sumatera Province is comprised to South Sumatra Basin and classified as back arc basin. This area is entered as an active margin of Sundaland which experiences subduction several times since Mesozoic to recent time. The traces of Mesozoic subduction in the southern part of Sumatra island are exposed in Garba Hill area. The aim of this investigation is to study the tectonic changes in the first phase in Mesozoic era at the active margin of Sundaland which causes the rocks assemblage in Garba hill consist of continental and oceanic plate rocks which the correlation between those rocks show indistinct relation. This investigation is conducted by field observation in Tekana village and Lubar Village, Muara Dua, South Sumatra along with laboratory analysis included fossil and geochemistry analysis of radiolarian chert, petrography analysis of granite and basalt, and structural modelling. Fossil and geochemistry analysis of radiolarian chert and geochemistry of granite rocks shown the relation between the two rocks and Mesozoic subduction of Woyla terrane on western margin of Sundaland. Petrography analysis from granite and basalt depict the tectonic affinity of rocks. Moreover, structural analysis showed the changes of lineation direction from N-S to WNW-ESE.

Keywords: granite, mesozoic, radiolarian, subduction traces

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Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

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The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

Procedia PDF Downloads 87

Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Beier Zhu, Rui Zhang, Qi Song

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Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

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Authors: Ogechi Nzeagwu, Assumpta Osuagwu, Charlse Nkwoala

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Malnutrition in infants due to poverty, poor feeding practices, and high cost of commercial complementary foods among others is a concern in developing countries. The study evaluated the proximate, vitamin and mineral compositions, antinutrients and functional properties, biochemical, haematological and sensory evaluation of complementary food made from sorghum, groundnut, crayfish and paw-paw flour blends using standard procedures. The blends were formulated on protein requirement of infants (18 g/day) using Nutrisurvey linear programming software in ratio of sorghum(S), groundnut(G), crayfish(C) and pawpaw(P) flours as 50:25:10:15(SGCP1), 60:20:10:10 (SGCP2), 60:15:15:10 (SGCP3) and 60:10:20:10 (SGCP4). Plain-pap (fermented maize flour)(TCF) and cerelac (commercial complementary food) served as basal and control diets. Thirty weanling male albino rats aged 28-35 days weighing 33-60 g were purchased and used for the study. The rats after acclimatization were fed with gruel produced with the experimental diets and the control with water ad libitum daily for 35days. Effect of the blends on lipid profile, blood glucose, haematological (RBC, HB, PCV, MCV), liver and kidney function and weight gain of the rats were assessed. Acceptability of the gruel was conducted at the end of rat feeding on forty mothers of infants’ ≥ 6 months who gave their informed consent to participate using a 9 point hedonic scale. Data was analyzed for means and standard deviation, analysis of variance and means were separated using Duncan multiple range test and significance judged at 0.05, all using SPSS version 22.0. The results indicated that crude protein, fibre, ash and carbohydrate of the formulated diets were either comparable or higher than values in cerelac. The formulated diets (SGCP1- SGCP4) were significantly (P>0.05) higher in vitamin A and thiamin compared to cerelac. The iron content of the formulated diets SGCP1- SGCP4 (4.23-6.36 mg/100) were within the recommended iron intake of infants (0.55 mg/day). Phytate (1.56-2.55 mg/100g) and oxalate (0.23-0.35 mg/100g) contents of the formulated diets were within the permissible limits of 0-5%. In functional properties, bulk density, swelling index, % dispersibility and water absorption capacity significantly (P<0.05) increased and compared favourably with cerelac. The essential amino acids of the formulated blends were within the amino acid profile of the FAO/WHO/UNU reference protein for children 0.5 -2 years of age. Urea concentration of rats fed with SGCP1-SGCP4 (19.48 mmol/L),(23.76 mmol/L),(24.07 mmol/L),(23.65 mmol/L) respectively was significantly higher than that of rat fed cerelac (16.98 mmol/L); however, plain pap had the least value (9.15 mmol/L). Rats fed with SGCP1-SGCP4 (116 mg/dl), (119 mg/dl), (115 mg/dl), (117 mg/dl) respectively had significantly higher glucose levels those fed with cerelac (108 mg/dl). Liver function parameters (AST, ALP and ALT), lipid profile (triglyceride, HDL, LDL, VLDL) and hematological parameters of rats fed with formulated diets were within normal range. Rats fed SGCP1 gained more weight (90.45 g) than other rats fed with SGCP2-SGCP4 (71.65 g, 79.76 g, 75.68 g), TCF (20.13 g) and cerelac (59.06 g). In all the sensory attributes, the control was preferred with respect to the formulated diets. The formulated diets were generally adequate and may likely have potentials to meet nutrient requirements of infants as complementary food.

Keywords: biochemical, chemical evaluation, complementary food, quadrimix

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Authors: Mohammad Hossein Badamchi

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As Leo Strauss demonstrates in his works, Political Philosophy in the western tradition is an epistemic-naturalistic tradition insofar Hanna Arendt mentioning the deep conflict between philosophy and politics, opposed to be named “political philosopher” prefer the title “political thinker” for herself. In fact, the Western political philosophy’s tendency to derive politics from natural law and epistemic argumentations makes a paradox between the actual “the political” and the theoretical “natural politics” in the western tradition. In this paper, we want to show that Hanna Arendt, in her exploration to find a new realm of the non-naturalistic way of thinking about the political is walking on a completely different tradition of political philosophy which was first established by Al-Farabi, the founder of Islamic political philosophy around thousand years after Greek Philosophy. Despite Aristotelian Polis which is a Natural community based on true natural rationality to reach the natural purposes of mankind, Al-Farabi’s Madine (his reconstructed concept of Aristotelian Polis) is completely constructed against natural cities, which are formulated by necessity logic of natural arguments and natural deception of humanity. In fact, Farabi considers the natural understanding of politics as Ignorant ideologies used by governments to suppress people. Madine in Farabi’s work is not a natural institution but is a collaborative constitution founded by citizens. So despite Aristotelian thinking, here we don’t have just A Polis that is the one true polis, but we have various multiple Madines among one, is virtuous not by definition but by real action of citizens and civil relations. Al-Farabi’s political philosophy is not a Naturalistic-epistemic Political Philosophy but is a Phronetic Political Philosophy which Hanna Arendt wants to establish outside of western contemplative anti-active political philosophy tradition.

Keywords: al-farabi, hanna arendt, natural politics, the political, political philosophy

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Authors: Mohd Iqbal Malik

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Ethical philosophy of Quran pinnacled virtue and economics as the part and parcel of human life. Human beings are to be imagined by the sign of morals. Soul and morality are both among the essences of human personality. Islam lays the foundation of ethics by installation of making a momentous variance between virtue and vice. It suggests for the distribution of wealth in-order to terminate accumulation of economic resources. Quran claims for the ambiguous pavement to attain virtue by saying, ‘Never will you attain the good (reward) until you spend (in the way of Allah) from that which you love. And whatever you spend indeed, Allah knows of it.’ The essence of Quran is to eliminate all the deep-seated approaches through which the wealth of nations is being accumulated within few hands. The paper will study the Quranic Philosophy Of Islamic Economic System. In recent times, to get out of the human resource development mystery of Muslims, Ismail Al-Raji Faruqi led the way in the so-called ‘Islamization’ of knowledge. Rahman and Faruqi formed opposite opinions on this project. Al-Faruqi thought of the Islamization of knowledge in terms of introducing Western learning into received Islamic values and vice versa. This proved to be a mere peripheral treatment of Islamic values in relation to Western knowledge. It is true that out of the programme of Islamization of knowledge arose Islamic universities in many Muslim countries. Yet the academic programmes of these universities were not founded upon a substantive understanding and application of the tawhidi epistemology.

Keywords: ethical philosophy, modern Islamic finance, knowledge of finance, Islamic banking

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Authors: Shohreh Ghasemi

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Introduction: Melanoma is a complex disease with various clinical and histopathological features that impact prognosis and treatment decisions. Traditional methods of melanoma prognosis involve manual examination and interpretation of clinical and histopathological data by dermatologists and pathologists. However, the subjective nature of these assessments can lead to inter-observer variability and suboptimal prognostic accuracy. AI, with its ability to analyze vast amounts of data and identify patterns, has emerged as a promising tool for improving melanoma prognosis. Methods: A comprehensive literature search was conducted to identify studies that employed AI techniques for melanoma prognosis. The search included databases such as PubMed and Google Scholar, using keywords such as "artificial intelligence," "melanoma," and "prognosis." Studies published between 2010 and 2022 were considered. The selected articles were critically reviewed, and relevant information was extracted. Results: The review identified various AI methodologies utilized in melanoma prognosis, including machine learning algorithms, deep learning techniques, and computer vision. These techniques have been applied to diverse data sources, such as clinical images, dermoscopy images, histopathological slides, and genetic data. Studies have demonstrated the potential of AI in accurately predicting melanoma prognosis, including survival outcomes, recurrence risk, and response to therapy. AI-based prognostic models have shown comparable or even superior performance compared to traditional methods.

Keywords: artificial intelligence, melanoma, accuracy, prognosis prediction, image analysis, personalized medicine

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: Chris Schoborg, Morgan C. Wang

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In the realm of the National Football League (NFL), substantial dedication of time and effort is invested by both players and coaches in meticulously analyzing the game footage of their opponents. The primary aim is to anticipate the actions of the opposing team. Defensive players and coaches are especially focused on deciphering their adversaries' intentions to effectively counter their strategies. Acquiring insights into the specific play type and its intended direction on the field would confer a significant competitive advantage. This study establishes pre-snap information as the cornerstone for predicting both the play type (e.g., deep pass, short pass, or run) and its spatial trajectory (right, left, or center). The dataset for this research spans the regular NFL season data for all 32 teams from 2013 to 2022. This dataset is acquired using the nflreadr package, which conveniently extracts play-by-play data from NFL games and imports it into the R environment as structured datasets. In this study, we employ a recently developed machine learning algorithm, XGBoost. The final predictive model achieves an impressive lift of 2.61. This signifies that the presented model is 2.61 times more effective than random guessing—a significant improvement. Such a model has the potential to markedly enhance defensive coaches' ability to formulate game plans and adequately prepare their players, thus mitigating the opposing offense's yardage and point gains.

Keywords: lift, NFL, sports analytics, XGBoost

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Authors: Mohamed Lotfy, Ghada Soliman

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Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis.

Keywords: computer vision, pattern recognition, optical character recognition, deep learning

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Authors: Andrei Nosov

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This paper discusses the development directions of dialogue systems within the digitalisation of the railway industry, where technologies based on conversational AI are already potentially applied or will be applied. Conversational AI is one of the popular natural language processing (NLP) tasks, as it has great prospects for real-world applications today. At the same time, it is a challenging task as it involves many areas of NLP based on complex computations and deep insights from linguistics and psychology. In this review, we focus on dialogue systems and their implementation in the railway domain. We comprehensively review the state-of-the-art research results on dialogue systems and analyse them from three perspectives: type of problem to be solved, type of model, and type of system. In particular, from the perspective of the type of tasks to be solved, we discuss characteristics and applications. This will help to understand how to prioritise tasks. In terms of the type of models, we give an overview that will allow researchers to become familiar with how to apply them in dialogue systems. By analysing the types of dialogue systems, we propose an unconventional approach in contrast to colleagues who traditionally contrast goal-oriented dialogue systems with open-domain systems. Our view focuses on considering retrieval and generative approaches. Furthermore, the work comprehensively presents evaluation methods and datasets for dialogue systems in the railway domain to pave the way for future research. Finally, some possible directions for future research are identified based on recent research results.

Keywords: digitalisation, railway, dialogue systems, conversational AI, natural language processing, natural language understanding, natural language generation

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Authors: R. Ziaie Moayed, E. Ghanbari Alamouty

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Deep soil mixing is one of the improvement methods in geotechnical engineering which is widely used in soft soils. This article investigates the consolidation behavior of a soft clay soil which is improved by soil-cement column (SCC) by numerical modeling using Plaxis2D program. This behavior is simulated under vertical static and cyclic load which is applied on the soil surface. The static load problem is the simulation of a physical model test in an axisymmetric condition which uses a single SCC in the model center. The results of numerical modeling consist of settlement of soft soil composite, stress on soft soil and column, and excessive pore water pressure in the soil show a good correspondence with the test results. The response of soft soil composite to the cyclic load in vertical direction also compared with the static results. Also the effects of two variables namely the cement content used in a SCC and the area ratio (the ratio of the diameter of SCC to the diameter of composite soil model, a) is investigated. The results show that the stress on the column with the higher value of a, is lesser compared with the stress on other columns. Different rate of consolidation and excessive pore pressure distribution is observed in cyclic load problem. Also comparing the results of settlement of soil shows higher compressibility in the cyclic load problem.

Keywords: area ratio, consolidation behavior, cyclic load, numerical modeling, soil-cement column

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Authors: Rodolfo Alfonso, David Alonso, Albin Garcia, Jose Luis Alonso

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Recently a new kilovoltage radiotherapy unit model Xstrahl 200 - donated to the INOR´s Department of Radiotherapy (DR-INOR) in the framework of a IAEA's technical cooperation project- has been commissioned. This unit is able to treat shallow and low deep laying lesions, as it provides 8 discrete beam qualities, from 40 to 200 kV. As part of the patient-specific quality assurance program established at DR-INOR for external beam radiotherapy, it has been recommended to implement in vivo dose measurements (IVD), as they allow effectively discovering eventual errors or failures in the radiotherapy process. For that purpose a radio-photoluminescence (RPL) dosimetry system, model XXX, -also donated to DR-INOR by the same IAEA project- has been studied and commissioned. Main dosimetric parameters of the RPL system, such as reproducibility, linearity, and filed size influence were assessed. In a similar way, the response of radiochromic EBT3 type film was investigated for purposes of IVD. Both systems were calibrated in terms of entrance surface dose. Results of the dosimetric commissioning of RPL and EBT3 for IVD, and their pre-clinical implementation through end-to-end test cases are presented. The RPL dosimetry seems more recommendable for hyper-fractionated schemes with larger fields and curved patient contours, as those in chest wall irradiations, where the use of more than one dosimeter could be required. The radiochromic system involves smaller corrections with field size, but it sensibility is lower; hence it is more adequate for hypo-fractionated treatments with smaller fields.

Keywords: glass dosimetry, in vivo dosimetry, kilovotage radiotherapy, radiochromic dosimetry

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Authors: Mi-Hyang Lee, Seung-Hoon Han

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Korean traditional residences have been built with deep design issues for various values such as social, cultural, and environmental influences to be started from a few thousand years ago, but its meaning is being vanished due to the different lifestyles these days. It is necessary, therefore, to grasp the meaning of the Korea traditional building called Hanok and to get Korean people understand its real advantages. The purpose of this study is to propose a standardization methodology for evaluating comfort features towards Korean traditional houses. This paper is also trying to build an official standard evaluation system and to integrate aesthetic and psychological values induced from Hanok. Its comfort performance values could be divided into two large categories that are physical and psychological, and fourteen methods have been defined as the Korean Standards (KS). For this research, field survey data from representative Hanok types were collected for each method. This study also contains a qualitative in-depth analysis of the Hanok comfort index by the professions using AHP (Analytical Hierarchy Process) and has examined the effect of the methods. As a result, this paper could define what methods can provide trustful outcomes and how to evaluate the own strengths in aspects of spatial comfort of Hanok using suggested procedures towards the spatial configuration of the traditional dwellings. This study has finally proposed an integrated development of a standardization methodology assessing the comfort performance for Korean traditional residences, and it is expected that they could evaluate inhabitants of the residents and interior environmental conditions especially structured by wood materials like Hanok.

Keywords: Hanok, comfort performance, human condition, analytical hierarchy process

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Authors: Mariem Saadi, Sabri Kanzari, Adel Zghibi

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In semi-arid areas like the Kairouan region, the constant irrigation with saline water and the overuse of groundwater resources, soils and aquifers salinization has become an increasing concern. In this study, a methodology has been developed to evaluate the groundwater contamination risk based on the unsaturated zone hydraulic properties. Two soil profiles with different ranges of salinity, one located in the north of the plain and another one in the south of plain (each 30 m deep) and both characterized by direct recharge of the aquifer were chosen. Simulations were conducted with Hydrus-1D code using measured precipitation data for the period 1998-2003 and calculated evapotranspiration for both chosen profiles. Four combinations of initial conditions of water content and salt concentration were used for the simulation process in order to find the best match between simulated and measured values. The success of the calibration of Hydrus-1D allowed the investigation of some scenarios in order to assess the contamination risk under different natural conditions. The aquifer risk contamination is related to the natural conditions where it increased while facing climate change and temperature increase and decreased in the presence of a clay layer in the unsaturated zone. Hydrus-1D was a useful tool to predict the groundwater level and quality in the case of a direct recharge and in the absence of any information related to the soil layers except for the texture.

Keywords: Hydrus-1D, Kairouan, salinization, semi-arid region, solute transport, unsaturated zone

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Authors: Sai Amulya Gollapalli, Dilip A. Patel, Parth Patel K., Lukman E. Mansuri

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Accumulation of moisture is one of the most worrisome aspects of a building. Architects and engineers tend to ignore its vitality during the designing and construction stage. Major fatalities in buildings can be caused by it. People avoid spending a lot of money on waterproofing. If the same mistake is repeated, no deep thinking is done. The quality of workmanship and construction is depleting due to negligence. It is important to do an analysis of the water maintenance issues happening in the current buildings and give a database for all the factors that are causing the defect. In this research, surveys are done with two waterproofing consultants, two client engineers, and two project managers. The survey was based on a matrix that was based on the causes of water maintenance issues. There were around 100 causes that were identified. The causes were categorized into six, namely, manpower, finance, method, management, environment, and material. In the matrices, the causes on the x-direction matched with the causes on the y-direction. 3 Likert scale was used to make a pairwise comparison between causes on each cell. Matrices were evaluated for the main categories and for each category separately. A final ranking was done by the weights achieved, and ‘cracks arriving from various construction joints’ was the highest with 0.57 relative significance, and ‘usage of the material’ was the lowest with 0.03 relative significance. Twelve defects due to water leakage were identified, and interviewees were asked to make a pairwise comparison of them, too, to understand the priorities. When the list of causes is achieved, the prioritization as per the stratification analysis is done. This will be beneficial to the consultants and contractors as they will get a primary idea of which causes to focus on.

Keywords: water leakage, survey, causes, matrices, prioritization

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Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

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Authors: Mohamed Ibrahim, Suzan Kholeif

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The Cenomanian-Turonian boundary was a ‘greenhouse’ period. The atmosphere at that time was characterized by high CO₂; in addition, there was the widespread deposition of organic-rich sediments anomalously rich in organic carbon. The sediments, palynological, total organic carbon (TOC), stable carbon and oxygen isotopes (δ¹³C, δ¹⁸O, organic) of the Cenomanian-Turonian Bahariya and basal Abu Roash formations at the southern Tethys margin were studied in two deep wells (AG5 and AG-13), Abu Gharadig Oil Field, North Western Desert, Egypt. Some of the marine (dinoflagellate cysts), as well as the terrestrial palynoflora (spores and pollen grains), reveal extinction and origination patterns that are known elsewhere, although other species may be survived across the Cenomanian-Turonian boundary. This implies control of global changes on the palynoflora, i.e., impact of Oceanic Anoxic Event OAE2 (Bonarelli Event), rather than changes in the local environmental conditions. The basal part of the Abu Roach Formation ('G' and 'F' members, late Cenomanian) shows a positive δ ¹³C excursion of the organic fraction. The TOC is generally high between 2.20 and 3.04 % in the basal Abu Roash Formation: shale of 'G' and carbonate of 'F' members, which indicates that these two members are the main Cretaceous source rocks in the Abu Gharadig Basin and have a type I-II kerogen composition. They are distinguished by an abundance of amorphous organic matter AOM and Chlorococcalean algae, mainly Pediastrum and Scenedesmus, along with subordinate dinoflagellate cysts.

Keywords: oceanic anoxic event, cenomanian-turonian, palynofacies, western desert, Egypt

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Authors: Sanjeev Mukherjee, Satyendra Mittal

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This paper presents the experimental investigations on the behaviour of a group of single, double and triple helical screw anchors embedded vertically at the same level in sand. The tests were carried out on one, two, three and four numbers of anchors in sand for different depths of embedment keeping shallow and deep mode of behaviour in mind. The testing program included 48 tests conducted on three model anchors installed in sand whose density kept constant throughout the tests. It was observed that the ultimate pullout load varied significantly with the installation depth of the anchor and the number of anchors. The apparent coefficient of friction (f*) between anchor and soil was also calculated based on the test results. It was found that the apparent coefficient of friction varies between 1.02 and 4.76 for 1, 2, 3, and 4 numbers of single, double and triple helical screw anchors. Plate load tests conducted on model soil showed that the value of ф increases from 35o for virgin soil to 48o for soil with four double screw helical anchors. The graphs of ultimate pullout capacity of a group of two, three and four no. of anchors with respect to one anchor were plotted and design equations have been proposed correlating them. Based on these findings, it has been concluded that the load-displacement relationships for all groups can be reduced to a common curve. A 3-D finite element model, PLAXIS, was used to confirm the results obtained from laboratory tests and the agreement is excellent.

Keywords: apparent coefficient of friction, helical screw anchor, installation depth, plate load test

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Ali Essam El Shazly

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The slum survey of 'Sequina' area in Alexandria details the building rooms of twenty-building samples according to the integral measure of space syntax. The essence of room organization sets the most integrative 'visitor' domain between the 'inhabitant' wings of less integrated 'parent' than the 'children' structure with visual ring of 'balcony' space. Despite the collective real relative asymmetry of 'pheno-type' aggregation, the relative asymmetry of individual layouts reveals 'geno-type' structure of spatial diversity. The multifunction of rooms optimizes the integral structure of graph and visibility merge, which contrasts with the deep tailing structure of distinctive social domains. The most integrative layout inverts the geno-type into freed rooms of shallow 'inhabitant' domain against the off-centered 'visitor' space, while the most segregated layout further restricts the pheno-type through isolated 'visitor' from 'inhabitant' domains across the 'staircase' public domain. The catalyst 'kitchen & living' spaces demonstrate multi-structural dimensions among the various social domains. The former ranges from most exposed central integrity to the most hidden 'motherhood' territories. The latter, however, mostly integrates at centrality or at the further ringy 'childern' domain. The study concludes social structure of spatial integrity for redevelopment, which is determined through the micro-level survey of rooms with integral dimensions.

Keywords: Alexandria, Sequina slum, spatial integration, space syntax

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: S. M. Moustafa, A. Ezzat, Y. S. Taha, G. H. Hassib, S. Hamada

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After the occurrence of 14, Nov, 1981 earthquake (M = 5.3), on Kalabska fault, south of Egypt, seismic stations distributed in and around the Kalabsha area, in order to monitoring, recording and studying the seismic activity in the area. In addition of that, from 1989 a number of piezometer wells drilled in the same area, distribed on at the both side of the active faults area and in different water bearing formations, in order to measuring the groundwater parameters (level, temperature, ph, and conductivity) to monitoring the relationship between those parameters and the seismic activity at Kalabsha area. The behavior of groundwater due to seismic activity over the world studied by several scientists i.e. H. Wakita (1979) on Izu-Oshima earthquake (M= 7.0) at Japan, M. E. Contadakis & G.asteriadis (1972), and Evans (1966), they found an anomalies on groundwater measurements prior, co, and post the occurrence of bigger earthquakes, referring to the probability of precursory evidence of impending earthquakes. In Kalabsha area south of Egypt, this study has been done using recorded seismic data, and the measurements of underground water parameters. same phenomena of anomalies founded on groundwater measurements pre, co. and post the occurrence of earthquakes with magnitude bigger than 3, and no systematic regularity exists for epicenter distance, duration of anomalies or time lag between anomalies appear and occurrence of events. Also the results found present strong relation between the groundwater in the upper unconfined aquifer Nubian Sandstone formation, and Kalabsha seismic activity, otherwise no relation between the seismic activities in the area with the deep groundwater in the lower confined aquifer Sandstone.

Keywords: seismicity, groundwater, Aswan, Egypt

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