Search results for: male infertility detection

Authors: Bijad Alqahtani, Graham Arnold, Weijie Wang

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Background—Tai Chi Chuan (TCC) is a type of traditional Chinese martial art and is considered a benefiting physical fitness. Advanced techniques of motion analysis have been routinely used in the clinical assessment. However, so far, little research has been done on the biomechanical assessment of TCC players in terms of gait and balance using motion analysis. Objectives—The aim of this study was to investigate whether TCC improves the lower limb conditions and balance ability using the state of the art motion analysis technologies, i.e. motion capture system, electromyography and force platform. Methods—Twenty TCC (9 male, 11 female) with age between (42-77) years old and weight (56.2-119 Kg), and eighteen Non-TCC participants (7 male, 11 female), weight (50-110 Kg) with age (43- 78) years old at the matched age as a control group were recruited in this study. Their gait and balance were collected using Vicon Nexus® to obtain the gait parameters, and kinematic parameters of hip, knee, and ankle joints in three planes of both limbs. Participants stood on force platforms to perform a single leg balance test. Then, they were asked to walk along a 10 m walkway at their comfortable speed. Participants performed 5 trials of single-leg balance for the dominant side. Also, the participants performed 3 trials of four square step balance and 10 trials of walking. From the recorded trials, three good ones were analyzed using the Vicon Plug-in-Gait model to obtain gait parameters, e.g. walking speed, cadence, stride length, and joint parameters, e.g. joint angle, force, moments, etc. Result— The temporal-spatial variables of TCC subjects were compared with the Non-TCC subjects, it was found that there was a significant difference (p < 0.05) between the groups. Moreover, it was observed that participants of TCC have significant differences in ankle, hip, and knee joints’ kinematics in the sagittal, coronal, and transverse planes such as ankle angle (19.90±19.54 deg) for TCC while (15.34±6.50 deg) for Non-TCC, and knee angle (14.96±6.40 deg) for TCC while (17.63±5.79 deg) for Non-TCC in the transverse plane. Also, the result showed that there was a significant difference between groups in the single-leg balance test, e.g. maintaining single leg stance time in the TCC participants showed longer duration (20.85±10.53 s) in compared to Non-TCC people group (13.39±8.78 s). While the result showed that there was no significant difference between groups in the four square step balance. Conclusion—Our result showed that there are significant differences between Tai Chi Chuan and Non-Tai Chi Chuan participants in the various aspects of gait analysis and balance test, as a consequence of these findings some of biomechanical parameters such as joints kinematics, gait parameters and single leg stance balance test, the Tai Chi Chuan could improve the lower limb conditions and could reduce a risk of fall for the elderly with ageing.

Keywords: gait analysis, kinematics, single leg stance, Tai Chi Chuan

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Chua Lee Chuan

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This study was conducted to explore parents’ beliefs towards the teaching profession. This survey was conducted on 51 parents of teacher candidates in a teacher training institute. A research instrument, using questionnaires, adapted from FIT-Choice scale developed by Richardson and Watt (2006) was used to collect data from the population. The findings showed that parents, in general, have positive attitudes towards the teaching profession. They perceived teaching as a career highly valued by the society. Though the teaching job was viewed as difficult and requiring high expertise, the salary received commensurate their hard work and heavy workload. In terms of gender, male and female parents did not differ in their beliefs about the teaching profession. However, results indicated that educational attainment and income level had significant effect on parents’ beliefs on teaching as a profession. Implications and recommendations in relation to the findings are also included.

Keywords: beliefs, teaching profession, parents, teacher candidates

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Authors: Ali Ghorbanipour

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The aim of this study is to investigate the drug resistance pattern and the value of the MIC (minimum inhibitory concentration)method to reduce the impact of infectious diseases and the slow development of resistance. Method: The study was conducted on clinical specimens collected between 2018 to 2021. identification of isolates and antibiotic susceptibility testing were performed using conventional biochemical tests. Antibiotic resistance was determined using kibry-Bauer disk diffusion and MIC by E-test methods comparative with microdilution plate elisa method. Results were interpreted according to CLSI. Results: Out of 249600 different clinical specimens, 18720 different pathogenic bacteria by overall detection ratio 7.7% were detected. Among pathogen bacterial were Gram negative bacteria (70%,n=13000) and Gram positive bacteria(30%,n=5720).Medically relevant gram-negative bacteria include a multitude of species such as E.coli , Klebsiella .spp , Pseudomonas .aeroginosa , Acinetobacter .spp , Enterobacterspp ,and gram positive bacteria Staphylococcus.spp , Enterococcus .spp , Streptococcus .spp was isolated . Conclusion: Our results highlighted that the resistance ratio among Gram Negative bacteria and Gram positive bacteria with different infection is high it suggest constant screening and follow-up programs for the detection of antibiotic resistance and the value of MIC drug susceptibility reporting that provide a new way to the usage of resistant antibiotic in combination with other antibiotics or accurate weight of antibiotics that inhibit or kill bacteria. Evaluation of wrong medication in the expansion of resistance and side effects of over usage antibiotics are goals. Ali ghorbanipour presently working as a supervision at the microbiology department of Massoud medical laboratory. Iran. Earlier, he worked as head department of pulmonary infection in firoozgarhospital, Iran. He received master degree in 2012 from Fergusson College. His research prime objective is a biologic wound dressing .to his credit, he has Published10 articles in various international congresses by presenting posters.

Keywords: antimicrobial profile, MIC & MBC Method, microplate antimicrobial assay, E-test

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Dan Geneau, Mary Claire Geneau, Seth Lenetsky, Ming -Chang Tsai, Marc Klimstra

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Introduction— Horizontal force-velocity profiling (HFVP) involves modeling an athletes linear sprint kinematics to estimate valuable maximum force and velocity metrics. This approach to performance modeling has been used in field-based team sports and has recently been introduced to ice-hockey as a forward skating performance assessment. While preliminary data has been collected on ice, distance constraints of the on-ice test restrict the ability of the athletes to reach their maximal velocity which result in limits of the model to effectively estimate athlete performance. This is especially true of more elite athletes. This report explores whether athletes on-ice are able to reach a velocity plateau similar to what has been seen in overground trials. Fourteen male Major Junior ice-hockey players (BW= 83.87 +/- 7.30 kg, height = 188 ± 3.4cm cm, age = 18 ± 1.2 years n = 14) were recruited. For on-ice sprints, participants completed a standardized warm-up consisting of skating and dynamic stretching and a progression of three skating efforts from 50% to 95%. Following the warm-up, participants completed three on ice 45m sprints, with three minutes of rest in between each trial. For overground sprints, participants completed a similar dynamic warm-up to that of on-ice trials. Following the warm-up participants completed three 40m overground sprint trials. For each trial (on-ice and overground), radar was used to collect instantaneous velocity (Stalker ATS II, Texas, USA) aimed at the participant’s waist. Sprint velocities were modelled using custom Python (version 3.2) script using a mono-exponential function, similar to previous work. To determine if on-ice tirals were achieving a maximum velocity (plateau), minimum acceleration values of the modeled data at the end of the sprint were compared (using paired t-test) between on-ice and overground trials. Significant differences (P<0.001) between overground and on-ice minimum accelerations were observed. It was found that on-ice trials consistently reported higher final acceleration values, indicating a maximum maintained velocity (plateau) had not been reached. Based on these preliminary findings, it is suggested that reliable HFVP metrics cannot yet be collected from all ice-hockey populations using current methods. Elite male populations were not able to achieve a velocity plateau similar to what has been seen in overground trials, indicating the absence of a maximum velocity measure. With current velocity and acceleration modeling techniques, including a dependency of a velocity plateau, these results indicate the potential for error in on-ice HFVP measures. Therefore, these findings suggest that a greater on-ice sprint distance may be required or the need for other velocity modeling techniques, where maximal velocity is not required for a complete profile.   

Keywords: ice-hockey, sprint, skating, power

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Authors: Dembel Bonta Gebeyehu

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In the study area, the impacts of deforestation for environmental degradation and livelihood of farmers manifest in different faces. They are more vulnerable as they depend on rain-fed agriculture and immediate natural forests. On the other hand, after planting seedling, waste disposal and management system of the plastic cover is poorly practiced and administered in the country in general and in the study area in particular. If this situation continues, the plastic waste would also accentuate land degradation. Besides, there is the absence of empirical studies conducted comprehensively on the research under study the case. The results of the study could suffice to inform any intervention schemes or to contribute to the existing knowledge on these issues. The study employed a qualitative approach based on intensive fieldwork data collected via various tools namely open-ended interviews, focus group discussion, key-informant interview and non-participant observation. The collected data was duly transcribed and latter categorized into different labels based on pre-determined themes to make further analysis. The major causes of deforestation were the expansion of agricultural land, poor administration, population growth, and the absence of conservation methods. The farmers are vulnerable to soil erosion and soil infertility culminating in low agricultural production; loss of grazing land and decline of livestock production; climate change; and deterioration of social capital. Their adaptation and coping strategies include natural conservation measures, diversification of income sources, safety-net program, and migration. Due to participatory natural resource conservation measures, soil erosion has been decreased and protected, indigenous woodlands started to regenerate. These brought farmers’ attitudinal change. The existing forestation program has many flaws. Especially, after planting seedlings, there is no mechanism for the plastic waste disposal and management. It was also found out organizational challenges among the mandated offices In the studied area, deforestation is aggravated by a number of factors, which made the farmers vulnerable. The current forestation programs are not well-planned, implemented, and coordinated. Sustainable and efficient seedling plastic cover collection and reuse methods should be devised. This is possible through creating awareness, organizing micro and small enterprises to reuse, and generate income from the collected plastic etc.

Keywords: land-cover and land-dynamics, vulnerability, adaptation strategy, mitigation strategies, sustainable plastic waste management

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Authors: S. Paengkoum, P. Paengkoum, W. Kaewwongsa

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Twenty-four male growing goats were randomly assigned to a Randomized Complete Block Design. Dietary treatments were different level of feeding concentrate diet at 1.0, 1.5, 2.0, and 2.5% of body weight (BW). The results showed that average daily gain, microbial N supply, N retention of meat goats in the group of feeding level at 2.0% BW and 2.5% BW were significantly higher (P<0.05) than those goats fed with feeding levels of 1.0% BW and 1.5% BW. Based on this result the conclusion can be made that using 75% fermented cassava pulp by Saccharomyces cerevisiae as the main source of protein to completely replace soybean meal was beneficial to meat goats in terms of feed intake. The feeding concentrate at levels between 2.0-2.5% BW gives highest in the growth of meat goat in this experiment.

Keywords: cassava pulp, yeast, goat, nitrogen retention

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Authors: M. Hakami, A. Jammaly, I. Attafi, M. Oraiby, M. Jeraiby

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We reviewed an unusual case of a 65-year-old male taking an herbal mixture containing compounds with anticholinesterase activity for a long period of time, presented with acute my myocardial infarction and multiple organ dysfunction syndrome followed by death. Clinically, there are findings correlated with anticholinesterase activity, such as bilateral miosis, diaphoresis, vomiting and fasciculation without a history of any toxic ingestion or exposure. Gas chromatography–mass spectrometry screening studies identified the presence of thymol, anethole in the herbal extract and butylated hydroxytoluene in the blood sample. Hence, with this case report, we intend to highlight the necessity of evaluating the long-term use of the herbal mixture.

Keywords: cholinesterase inhibitors, thymole, anethole, butylatedhydroxytoluene, cardiac toxicity, myocardial infarction

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Authors: Bitewulign Mekonnen

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

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

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Authors: Ali Bahkali, Rayan Yousef Booq, Mohammad Khiyami

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Soil samples were collected from five different regions in the Kingdom of Saudi Arabia. Microbiological methods included dilution methods and pour plates to isolate and purify bacteria soil. The ability of isolates to develop biopolymer was investigated on petri dishes containing elements and substance concentrations stimulating developing biopolymer. Fluorescent stains, Nile red and Nile blue were used to stain the bacterial cells developing biopolymers. In addition, Sudan black was used to detect biopolymers in bacterial cells. The isolates which developed biopolymers were identified based on their gene sequence of 1 6sRNA and their ability to grow and synthesize PHAs on mineral medium supplemented with 1% dates molasses as the only carbon source under nitrogen limitation. During the study 293 bacterial isolates were isolated and detected. Through the initial survey on the petri dishes, 84 isolates showed the ability to develop biopolymers. These bacterial colonies developed a pink color due to accumulation of the biopolymers in the cells. Twenty-three isolates were able to grow on dates molasses, three strains of which showed the ability to accumulate biopolymers. These strains included Bacillus sp., Ralstonia sp. and Microbacterium sp. They were detected by Nile blue A stain with fluorescence microscopy (OLYMPUS IX 51). Among the isolated strains Ralstonia sp. was selected after its ability to grow on molasses dates in the presence of a limited nitrogen source was detected. The optimum conditions for formation of biopolymers by isolated strains were investigated. Conditions studied included, best incubation duration (2 days), temperature (30°C) and pH (7-8). The maximum PHB production was raised by 1% (v1v) when using concentrations of dates molasses 1, 2, 3, 4 and 5% in MSM. The best inoculated with 1% old inoculum (1= OD). The ideal extraction method of PHA and PHB proved to be 0.4% sodium hypochlorite solution, producing a quantity of polymer 98.79% of the cell's dry weight. The maximum PHB production was 1.79 g/L recorded by Ralstonia sp. after 48 h, while it was 1.40 g/L produced by R.eutropha ATCC 17697 after 48 h.

Keywords: bacteria forming polyhydroxyalkanoate, detection, molecular, Saudi Arabia

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Authors: Duchduen Bhanthumnavin

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One of the most important characteristics to increase competitive ability in undergraduate students after post COVID-19 era is goal setting and self-control. This correlational study aimes at investigating the influence of psycho-social antecedents on goal setting and self-control in 550 Thai university students. Results from multiple regression analysis revealed that the important predictors of this characteristic were reasoning ability, psychological immunity, attitudes toward competition, core self-evaluation, and family nurture, which yielded 54.28 predictive percentage in the total sample. Moreover, the analysis identified three at-risk groups, namely, male students, low GPA students, and students with siblings. Discussion and implications in general and for specific purposes for the at-risk groups were offered.

Keywords: antecedents, plan and self-control, predictors, university students

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Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

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The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

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Authors: Khajeerat Phumpurk

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This research aims to study the knowledge, attitude toward household accounting of people in Bangkeaw Samutsongkhram. The sample use in this study was the people in tambol bangkeao Mueang Samut songkhram province. The Sample size for 100 household by using the accidental sampling and data collection was a questionnaire. Statistical analysis for frequency, percentage, mean, minimum, maximum value and standard deviation. It was found that the most of the respondent are farmers for 63.4. Most of them are male, with average of age 49.54 years. The education is vocational. The average household income is 60,873.74 per year. The respondent has the debt with the bank for 64.4 percent. The greatest influence to do the household accounting of farmers is the bank expertise. All the advice about doing household accounts get it from the staff of the bank for agriculture and agricultural cooperatives. The farmers for 57.3 do household accounting during the night time.

Keywords: Bangkeaw Samutsongkhram, household accounting, knowledge, sufficiency economy

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Authors: Proud Arunrangsiwed

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This study analyzes the crisis management and image repair strategies during the crisis of Mahidol Wittayanusorn School (MWIT) library burning. The library of this school was burned by a 16-year-old-male student on June 6th, 2010. This student blamed the school that the lesson was difficult, and other students were selfish. Although no one was in the building during the fire, it had caused damage to the building, books and electronic supplies around 130 million bahts (4.4 million USD). This event aroused many discourses arguing about the education system and morality. The strategies which were used during crisis were denial, shift the blame, bolstering, minimization, and uncertainty reduction. The results of using these strategies appeared after the crisis. That was the numbers of new students, who registered for the examination to get into this school in the later years, have remained the same.

Keywords: school, crisis management, violence, image repair strategies, uncertainty, burn

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Authors: Mahmoud M. S. Farrag, Alaa A. K. Elhaweet, El-Sayed Kh. A. Akel, Mohsen A. Moustafa

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The present work aimed to study the reproductive strategy of the common lessepsian puffer fish Lagocephalus sceleratus (Gmelin, 1879) from the Egyptian Mediterranean Waters. It is a famous migratory species plays an important role in the field of fisheries and ecology of aquatic ecosystem. The specimens were collected monthly from the landing centers along the Egyptian Mediterranean coast during 2012. Six maturity stages were recorded: (I) Thread like stage, (II) Immature stage (Virgin stage), (III) Maturing stage (Developing Virgin and recovering spent), (IV) Nearly ripe stage, (V) Fully ripe, (VI) Spawning stage, (VII) Spent stage. According to sex ratio, males exhibited higher number than females representing 52.44 % of the total fishes with sex ratio 1: 0.91. Fish length corresponding to 50% maturation was 38.5 cm for males and 41 cm for females. The corresponding ages (age at first maturity) are equal to 2.14 and 2.27 years for male and female respectively. The ova diameter ranged from 0.02mm to 0.85mm, the mature ova ranged from 0.16mm to 0.85mm and showed progressive increase from April towards September. Also, the presences of ova diameter in one peak of mature and ripe eggs in the ovaries were observed during spawning period. The relationship between gutted weight and absolute fecundity indicated that that fecundity increased as the fish grew in weight. The absolute fecundity ranged from 260288 to 2372931 for fish weight ranged from 698 to 3285 cm with an average of 1449522±720975. The relative fecundity ranged from 373 to 722 for fish weight ranged from 698 to 3285 cm with an average of 776±231. The spawning season of L. sceleratus was investigated from the data of gonado-somatic index and monthly distribution of maturity stages along the year as well as sequence of ova diameter for mature stages and exhibited a relatively prolong spawning season extending from April for both sexes and ends in August for male while ends in September for female. Fish releases its ripe ova in one batch during the spawning season. Histologically, the ovarian cycle of L. sceleratus was classified into six stages and the testicular cycle into five stages. Histological characters of gonads of L. sceleratus during the year of study had confirmed the previous results of distribution of maturity stages, gonado-somatic index and ova diameter, indicating this fish species has prolonged spawning season from April to September. This species is considered totally or uni spawner with synchronous group as it contained one to two developmental stages at the same gonad.

Keywords: Lagocephalus sceleratus, reproductive biology, oogenesis, histology

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Authors: Linjie Zhang, Baifen Ren, Xi Zhang, Genwang Liu

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Ship detection is of great significance for both military and civilian applications. Synthetic aperture radar (SAR) with all-day, all-weather, ultra-long-range characteristics, has been used widely. In view of the low time resolution of low orbit SAR and the needs for high time resolution SAR data, GEO (Geosynchronous orbit) SAR is getting more and more attention. Since GEO SAR has short revisiting period and large coverage area, it is expected to be well utilized in marine ship targets monitoring. However, the height of the orbit increases the time of integration by almost two orders of magnitude. For moving marine vessels, the utility and efficacy of GEO SAR are still not sure. This paper attempts to find the feasibility of GEO SAR by giving a GEO SAR simulator of moving ships. This presented GEO SAR simulator is a kind of geometrical-based radar imaging simulator, which focus on geometrical quality rather than high radiometric. Inputs of this simulator are 3D ship model (.obj format, produced by most 3D design software, such as 3D Max), ship's velocity, and the parameters of satellite orbit and SAR platform. Its outputs are simulated GEO SAR raw signal data and SAR image. This simulating process is accomplished by the following four steps. (1) Reading 3D model, including the ship rotations (pitch, yaw, and roll) and velocity (speed and direction) parameters, extract information of those little primitives (triangles) which is visible from the SAR platform. (2) Computing the radar scattering from the ship with physical optics (PO) method. In this step, the vessel is sliced into many little rectangles primitives along the azimuth. The radiometric calculation of each primitive is carried out separately. Since this simulator only focuses on the complex structure of ships, only single-bounce reflection and double-bounce reflection are considered. (3) Generating the raw data with GEO SAR signal modeling. Since the normal ‘stop and go’ model is not available for GEO SAR, the range model should be reconsidered. (4) At last, generating GEO SAR image with improved Range Doppler method. Numerical simulation of fishing boat and cargo ship will be given. GEO SAR images of different posture, velocity, satellite orbit, and SAR platform will be simulated. By analyzing these simulated results, the effectiveness of GEO SAR for the detection of marine moving vessels is evaluated.

Keywords: GEO SAR, radar, simulation, ship

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Authors: Waheed Akhtar

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In the current study, we aim to access the different month-wise reproductive biology of S. esocinus. Samples were collected from Rive Swat in the period of March 2022 to March 2023. Samples were collected using different gills nets of different sizes. Gonado Somatic Index and fecundity were studied using gravimetric to identify the breeding season and reproductive potential. The highest GSI was recorded in the month of April and November. Male to female ratio was in balance. The weight of the fish, size of the fish and ovary were parallel to the fecundity. This is the baseline study for the breeding biology of S. esocinus and further molecular study is required to identify the internal and external factors associated with the breeding biology of S. esocinus.

Keywords: snow trout, length and weight relationship, fecundity, river Swat

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Authors: A. Sathiya Susuman, N. Wilson

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Background: The sexual behaviour of youths is believed to play an important role in the spread of sexually transmitted infections (STIs). Method: The data from the Malawi Demographic and Health Survey 2010 and a sample of 16,217 youth’s age 15 to 24 years (with each household 27.2% female and 72.8% male) was the basis for analysis. Bivariate and logistic regression analysis was performed. Results: The result shows married youth were not interested in condom use (94.2%, p<0.05). Those who were living together were 69 times (OR=1.69, 95% CI, 1.26–2.26) more likely to be involved in early sexual activity compared to those who were not living together. Conclusion: This scientific paper will help other researchers, policy makers, and planners to create strategies to encourage these youths to make use of contraception.

Keywords: sexually transmitted infections (STIs), reproductive tract infections (RTIs), condom use, sexual partners, early sexual debut, youths

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Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

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Authors: Mohammed Hassan Alshaikhi

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Teaching vocabulary is a fundamental step in helping students to develop a good grasp of language. Exploring new strategies is an essential part of improving the teaching of vocabulary. The study aimed to explore the teaching vocabulary strategies in Saudi primary classrooms (aged 11 and 12 years old) in Jeddah, Saudi Arabia. The study was based on qualitative data collected from a large-scale case study, which utilised observations at eight male state and private primary schools during the academic year 2016-2017. The observations were transcribed, coded and entered into Nvivo software to be organised and analysed. Varying teaching vocabulary strategies were explored, and then they were circulated to many English teachers to be used in their classes.

Keywords: case study, English language, Saudi teachers, teaching vocabulary strategies

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Authors: Tian Luo, Wing Mui Winnie So

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Gaining career awareness in STEM is an important educational outcome in STEM education. While many studies focused on students’ understanding of scientists and engineers, very few studies explore students’ perceptions of technologists as a group of STEM professionals. In this study, 300 valid surveys which include drawing task and follow-up questions about technologist were collected from 4th to 6th grade students. The results showed that 75.1% of the students draw a technologist as a male and 19.3% draw a technologist as a female. Most students believe that technologists use math, science or engineering in their work and can name a few categories of technologists. The drawings also showed that students tend to present technologists as people who work with a computer.

Keywords: STEM (Science, Technology, Engineering, and Mathematics) education, elementary students, technologist, STEM careers

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Authors: Preetinder Kaur Randhawa

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The rural areas can be differentiated from urban areas in terms of their economic activities as rural areas are primarily involved in agricultural sector and provide natural resources whereas, urban areas are primarily involved in infrastructure sector and provide manufacturing services. Census of India defines a Census Town as an area which satisfies the following three criteria i.e. population exceeds 5000, at least 75 percent of male population engaged in non-agricultural sector and minimum population density of 400 persons per square kilometers. Urban areas can be attributed to the improvement of transport facilities, the massive decline in agricultural, especially male workers and workers shift to non-agricultural activities. This study examines the pattern, process of rural areas transformed into urban areas/ census town. The study has analyzed the various factors which are responsible for land transformation as well as the socio-economic transformation of the village population. Nilpur (CT) which belongs to Rajpura Tehsil in Patiala district, Punjab has been selected for the present study. The methodology adopted includes qualitative and quantitative research design, methods based on secondary data. Secondary data has been collected from unpublished revenue record office of Rajpura Tehsil and Primary Census Abstract of Patiala district, Census of India 2011. The results have showed that rate of transformation of a village to census town in Rajpura Tehsil has been one of highest among other villages. The census town has evolved through the evolutionary process of human settlement which grows in size, population and physical development. There must be a complete economic transformation and attainment of high level of technological development. Urban design and construction of buildings and infrastructure can be carried out better and faster and can be used to aid human habitation with the enhancement of quality of life. The study has concluded that in the selected area i.e Nilpur (CT) literacy rate has increased to 72.1 percent in year 2011 from 67.6 percent in year 2001. Similarly non-agricultural work force has increased to 95.2 percent in year 2011 from 81.1 percent in year 2001. It is very much clear that the increased literacy rate has put a positive impact on the involvement of non-agricultural workers have enhanced. The study has concluded that rural-urban linkages are important tools for understanding complexities of people livelihood and their strategies which involve mobility migration and the diversification of income sources and occupations.

Keywords: Census Town, India, Nilpur, Punjab

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Authors: Arpit Sachan, Nilanshu Kumar

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This paper tries to understand the nuances of feminisation of agriculture in the Indian context and how that is associated with better intrahousehold bargaining power for women. The economic survey of India indicates a constant increase in the share of the female workforce in Indian agriculture in the past few decades. This can be accounted for by many factors like the migration of male workers to urban areas and, therefore, the complete burden of agriculture shifting on the female counterparts. Therefore this study is an attempt to study that how this increase in the female workforce corresponds to a better decision-making ability for women in rural farm households. This paper is an attempt to carefully evaluate this aspect of the feminisation of Indian agriculture. The paper tries to study how various factors that improve the status of women in agriculture change with things like resource ownership. This paper uses both the macro-level and micro-level data to study the dynamics of the proportion of the workforce in agriculture across different states in India and how that has translated into better indicators for women in rural areas. The fall in India’s rank in the global gender wage gap index is alarming in such a context, and this creates a puzzle with increasing female workforce participation. The paper will consider if the condition of women improved over time with the increased share of employment or not? Using field survey data, this paper tries to understand if there exists any digression for some of the indicators both at the macro and micro level. The paper also tries to integrate the economic understanding of gender aspects of the workforce and the sociological stance prevailing in the existing literature. Therefore, this paper takes a mixed-method approach to better understand the role that social structure plays in the improved status of women within and across various households. Therefore, this paper will finally help us understanding if at all there is a feminisation of Indian agriculture or it's just exploitation of a different kind. This study intends to create a distinction between the gendered labour force in Indian agriculture and the complete democratization of Indian agriculture. The study is primarily focused on areas where the exodus of male migrants pushes women to work on agricultural farms. The question posits is whether it is the willingness of women to work in agriculture or is it urbanisation and development-induced conditions that make women work in agriculture as farm labourers? The motive is to understand if factors like resource ownership and the ability to autonomous decision-making are interlinked with an increased proportion of the female workforce or not? Based on this framework, we finally provide a brief comment on policy implications of government intervention in improving Indian agriculture and the gender aspects associated with it.

Keywords: feminisation, intrahousehold bargaining, farm households, migration, agriculture, decision-making

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Authors: Ifigeneia V. Mavragani, Zacharenia Nikitaki, George Kalantzis, George Iliakis, Alexandros G. Georgakilas

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Complex DNA damage consisting of a combination of DNA lesions, such as Double Strand Breaks (DSBs) and non-DSB base lesions occurring in a small volume is considered as one of the most important biological endpoints regarding ionizing radiation (IR) exposure. Strong theoretical (Monte Carlo simulations) and experimental evidence suggests an increment of the complexity of DNA damage and therefore repair resistance with increasing linear energy transfer (LET). Experimental detection of complex (clustered) DNA damage is often associated with technical deficiencies limiting its measurement, especially in cellular or tissue systems. Our groups have recently made significant improvements towards the identification of key parameters relating to the efficient detection of complex DSBs and non-DSBs in human cellular systems exposed to IR of varying quality (γ-, X-rays 0.3-1 keV/μm, α-particles 116 keV/μm and 36Ar ions 270 keV/μm). The induction and processing of DSB and non-DSB-oxidative clusters were measured using adaptations of immunofluorescence (γH2AX or 53PB1 foci staining as DSB probes and human repair enzymes OGG1 or APE1 as probes for oxidized purines and abasic sites respectively). In the current study, Relative Biological Effectiveness (RBE) values for DSB and non-DSB induction have been measured in different human normal (FEP18-11-T1) and cancerous cell lines (MCF7, HepG2, A549, MO59K/J). The experimental results are compared to simulation data obtained using a validated microdosimetric fast Monte Carlo DNA Damage Simulation code (MCDS). Moreover, this simulation approach is implemented in two realistic clinical cases, i.e. prostate cancer treatment using X-rays generated by a linear accelerator and a pediatric osteosarcoma case using a 200.6 MeV proton pencil beam. RBE values for complex DNA damage induction are calculated for the tumor areas. These results reveal a disparity between theory and experiment and underline the necessity for implementing highly precise and more efficient experimental and simulation approaches.

Keywords: complex DNA damage, DNA damage simulation, protons, radiotherapy

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Authors: Iván Castelló, Georgiana Stoica, Emilio Palomares, Fernando Bravo

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We present herein two colour encoded silica nanospheres (2nanoSi) for the fluorescence quantitative ratiometric determination of trypsin in humans. The system proved to be a faster (minutes) method, with two times higher sensitivity than the state-of-the-art biomarkers based sensors for cystic fibrosis (CF), allowing the quantification of trypsin concentrations in a wide range (0-350 mg/L). Furthermore, as trypsin is directly related to the development of cystic fibrosis, different human genotypes, i.e. healthy homozygotic (> 80 mg/L), CF homozygotic (< 50 mg/L), and heterozygotic (> 50 mg/L), respectively, can be determined using our 2nanoSi nanospheres.

Keywords: cystic fibrosis, trypsin, quantum dots, biomarker, homozygote, heterozygote

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Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: Miguel A. Borrella, Ana P. Fanjul, Suca Munoz, Liliana Herrera

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This paper evaluates the impact of LEADER, a remarkable Community-Led Local Development (CLLD) approach of the European Program for Rural Development applied to rural municipalities of Spain in 2018 and 2019. Using a difference-in-difference estimation strategy and a newly-constructed database, results show that aided municipalities have significantly lower unemployment levels than non-aided municipalities. Results are significant for the decrease in unemployment for both women and people younger than 25 years old, two of the target groups of the policy. Nevertheless, they are larger for male and older workers. Therefore, findings suggest that LEADER 2017-2018 was successful in reducing unemployment in rural areas.

Keywords: community-led local development, ex-post evaluation, LEADER, rural development

Procedia PDF Downloads 321

Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: D. Heinemann, S. Schramm, S. Knabner, D. Baumgarten

Abstract:

Purpose: Applying solder paste to printed circuit boards (PCB) with stencils has been the method of choice over the past years. A new method uses a jet printer to deposit tiny droplets of solder paste through an ejector mechanism onto the board. This allows for more flexible PCB layouts with smaller components. Due to the viscosity of the solder paste, air blisters can be trapped in the cartridge. This can lead to missing solder joints or deviations in the applied solder volume. Therefore, a built-in and real-time inspection of the printing process is needed to minimize uncertainties and increase the efficiency of the process by immediate correction. The objective of the current study is the design of an optimal imaging system and the development of an automatic algorithm for the detection of applied solder joints from optical from the captured images. Methods: In a first approach, a camera module connected to a microcomputer and LED strips are employed to capture images of the printed circuit board under four different illuminations (white, red, green and blue). Subsequently, an improved system including a ring light, an objective lens, and a monochromatic camera was set up to acquire higher quality images. The obtained images can be divided into three main components: the PCB itself (i.e., the background), the reflections induced by unsoldered positions or screw holes and the solder joints. Non-uniform illumination is corrected by estimating the background using a morphological opening and subtraction from the input image. Image sharpening is applied in order to prevent error pixels in the subsequent segmentation. The intensity thresholds which divide the main components are obtained from the multimodal histogram using three probability density functions. Determining the intersections delivers proper thresholds for the segmentation. Remaining edge gradients produces small error areas which are removed by another morphological opening. For quantitative analysis of the segmentation results, the dice coefficient is used. Results: The obtained PCB images show a significant gradient in all RGB channels, resulting from ambient light. Using different lightings and color channels 12 images of a single PCB are available. A visual inspection and the investigation of 27 specific points show the best differentiation between those points using a red lighting and a green color channel. Estimating two thresholds from analyzing the multimodal histogram of the corrected images and using them for segmentation precisely extracts the solder joints. The comparison of the results to manually segmented images yield high sensitivity and specificity values. Analyzing the overall result delivers a Dice coefficient of 0.89 which varies for single object segmentations between 0.96 for a good segmented solder joints and 0.25 for single negative outliers. Conclusion: Our results demonstrate that the presented optical imaging system and the developed algorithm can robustly detect solder joints on printed circuit boards. Future work will comprise a modified lighting system which allows for more precise segmentation results using structure analysis.

Keywords: printed circuit board jet-printing, inspection, segmentation, solder paste detection

Procedia PDF Downloads 322