Search results for: cooperative language learning

Authors: Kothuri Sriraman, Mattupalli Komal Teja

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In this paper, a novel algorithm is proposed for the recognition of hulls in a hand written images that might be irregular or digit or character shape. Identification of objects and internal objects is quite difficult to extract, when the structure of the image is having bulk of clusters. The estimation results are easily obtained while going through identifying the sub-regional objects by using the SASK algorithm. Focusing mainly to recognize the number of internal objects exist in a given image, so as it is shadow-free and error-free. The hard clustering and density clustering process of obtained image rough set is used to recognize the differentiated internal objects, if any. In order to find out the internal hull regions it involves three steps pre-processing, Boundary Extraction and finally, apply the Hull Detection system. By detecting the sub-regional hulls it can increase the machine learning capability in detection of characters and it can also be extend in order to get the hull recognition even in irregular shape objects like wise black holes in the space exploration with their intensities. Layered hulls are those having the structured layers inside while it is useful in the Military Services and Traffic to identify the number of vehicles or persons. This proposed SASK algorithm is helpful in making of that kind of identifying the regions and can useful in undergo for the decision process (to clear the traffic, to identify the number of persons in the opponent’s in the war).

Keywords: chain code, Hull regions, Hough transform, Hull recognition, Layered Outline Extraction, SASK algorithm

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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: Bolajoko I. Malomo, Maryam O. Yusuf

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Lagos urban mini-bus drivers play a critical role in the transportation sector. The current major mode of transportation within Lagos metropolis remains road transportation and this confirms the relevance of urban mini-bus drivers in transporting the populace to their various destinations. Other modes of transportation such as the train and waterways are currently inadequate. Various threats to the well-being of urban bus drivers include congested traffic typical of modern day lifestyles, dwindling financial returns due to long hours in traffic, fewer hours of sleep, inadequate diet, time pressure, and assaults related to fare disputes. Several health-related problems have been documented to be associated with urban bus driving. For instance, greater rates of hypertension, obesity and cholesterol level has been reported. Research studies are yet to identify the influence of age and marital status on the well-being of urban mini-bus drivers in Lagos metropolis. A study of this nature is necessary as it is culturally perceived in Nigeria that older and married people are especially influenced by family affiliation and would behave in ways that would project positive outcomes. The study sample consisted of 150 urban mini-bus drivers who were conveniently sampled from six (6) different terminuses where their journey begins and terminates. The well-being questionnaire was administered to participants. The criteria for inclusion in the study included the ability to read in English language and the confirmation that interested participants were on duty and suited to be driving mini-buses. Due to the nature of the job of bus driving, the researcher administered the questionnaires on participants who were free and willing to respond to the survey. All participants were males of various age groups and of different marital statuses. Results of analyses conducted revealed no significant influence of age and marital status on the well-being of urban mini-bus drivers. This indicates that the well-being of urban mini-bus drivers is not influenced by age nor marital status. The findings of this study have cultural implications. It negates the popularly held belief that older and married people care more about their well-being than younger and single people. It brings to fore the need to also identify and consider other factors when certifying people for the job of urban bus driving.

Keywords: age, Lagos metropolis, marital status, well-being of urban mini bus drivers

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Sujeet Kumar

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Education has greater importance particularly in terms of increasing human capital and economic competitiveness. It plays a crucial role in terms of cognitive and skill development. Its plays a vital role in process of socialization, fostering social justice, and enhancing social cohesion. Policy related to education has been always a priority for developed countries, which is later adopted by developing countries also. The government of India has also brought change in education polices in line with recognizing change at national and supranational level. However, quality education is still not become an open door for every child in India and several reports are produced year to year about level of school education in India. This paper is concerned with schooling in India. Particularly, it focuses on two government and two private schools in Bihar, but reference has made to schools in Delhi especially around slum communities. The paper presents brief historical context and an overview of current school systems in India. Later, it focuses on analysis of current development in policy in reference with field observation, which is anchored around choice, diversity, market – orientation and gap between different groups of pupils. There is greater degree of difference observed at private and government school levels in terms of quality of teachers, method of teaching and overall environment of learning. The paper concludes that the recent policy development in education particularly Sarva Siksha Abhiyaan (SAA) and Right to Education Act (2009) has required renovating new approach to bridge the gap through broader consultation at grassroots and participatory approach with different stakeholders.

Keywords: education, public policy, participatory approach

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Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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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: Yasamin Rahimi, Ali Kamandi, Abbas Hoseini, Hesam Haddad

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Online/offline chat is a convenient approach in the electronic markets of second-hand products in which potential customers would like to have more information about the products to fill the information gap between buyers and sellers. Online peer in peer market is trying to create artificial intelligence-based systems that help customers ask more informative questions in an easier way. In this article, we introduce a method for the question/answer system that we have developed for the top-ranked electronic market in Iran called Divar. When it comes to secondhand products, incomplete product information in a purchase will result in loss to the buyer. One way to balance buyer and seller information of a product is to help the buyer ask more informative questions when purchasing. Also, the short time to start and achieve the desired result of the conversation was one of our main goals, which was achieved according to A/B tests results. In this paper, we propose and evaluate a method for suggesting questions and answers in the messaging platform of the e-commerce website Divar. Creating such systems is to help users gather knowledge about the product easier and faster, All from the Divar database. We collected a dataset of around 2 million messages in Persian colloquial language, and for each category of product, we gathered 500K messages, of which only 2K were Tagged, and semi-supervised methods were used. In order to publish the proposed model to production, it is required to be fast enough to process 10 million messages daily on CPU processors. In order to reach that speed, in many subtasks, faster and simplistic models are preferred over deep neural models. The proposed method, which requires only a small amount of labeled data, is currently used in Divar production on CPU processors, and 15% of buyers and seller’s messages in conversations is directly chosen from our model output, and more than 27% of buyers have used this model suggestions in at least one daily conversation.

Keywords: smart reply, spell checker, information retrieval, intent detection, question answering

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

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The Farmers Field School (FFS) learning approach aims to improve knowledge of the farmers through integrated crop management and provide leadership in their decision making process. The study was conducted to assess the impact of FFS on vegetables production before and after FFS intervention in four villages of district Peshawar in cropping season 2012, by interviewing 80 FFS respondents, twenty from each selected village. It was observed from the study results that all the respondents were satisfied from the impact of FFS and they informed an increased in production in vegetables. It was further observed that after the implementation of FFS the sowing seed rate of tomato and cucumber were decreased from 0.185kg/kanal to 0.100 kg/ kanal and 0.120kg/kanal to 0.010kg/kanal where as the production of tomato and cucumber were increased from 8158.75kgs/kanal to 10302. 5kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively. The cost of agriculture inputs per kanal including seed cost, crop management, Farm Yard Manure, and weedicides in case of tomato were reduced by Rs.28, Rs. 3170, Rs.658and Rs 205 whereas in cucumber reduced by Rs.35, Rs.570, Rs 80 and Rs.430 respectively. Only fertilizers cost was increased by Rs. 2200 in case of tomato and Rs 465 in case of cucumber. Overall the cost was reduced to Rs 545 in tomato and Rs 490 in cucumber production.FFS provided a healthy vegetables and also reduced input cost by adopting integrated crop management. Therefore the promotion of FFS is needed to be planned for farmers to reduce cost of production, so that the more farmers should be benefited.

Keywords: impact, farmer field schools, vegetable production, Peshawar Khyber Pakhtunkhwa

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: R. Cooper, M. Kurien

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Background: Coeliac Disease affects around 1 in 100 people. Untreated, it can result in serious morbidity such as malabsorption and cancers. The only treatment is to adhere to a gluten free diet (GFD). International guidelines recommend that people with the coeliac disease receive follow-up healthcare annually to detect complications early and support their adherence to a GFD. However, there is a finite amount of healthcare in the UK, and as such, not all patients receive follow-up care as recommended by the guidelines. Furthermore, there is an increasing number of patients being diagnosed with coeliac disease. Given the potential severe morbidity that non-adherence to a GFD could result in, alongside reports that the rate of non- GFD adherence could be as high as 91%, it is imperative that action is taken. One potential solution to this would be to provide follow-up care digitally through utilising technology. This abstract reports on a rapid review undertaken to explore the existing evidence in this area. Methods: In June 2020, 11 bibliographic databases were searched to find any pertinent studies. The inclusion criteria required the study to be written in the English language and report on the use of digital healthcare interventions for people with Coeliac Disease. Results: A small amount of evidence (n=8) was found which met our inclusion criteria and pertained to the provision of CD follow-up digitally. These studies focussed either on educating and supporting patients to adhere to a GFD or providing consultation remotely with a focus on detecting complications early. These studies showed that there is potential for digital healthcare interventions to positively impact people with coeliac disease. However, it is suggested that the effectiveness of these interventions may depend on local circumstances, individual knowledge of CD and general attitudes. Conclusion: The above studies suggest that providing follow-up care digitally may offer a potential solution; however, the evidence about how this should be done and in what circumstances this will work for individuals is scarce. In the light of the COVID-19 pandemic, the introduction of digital healthcare interventions appears to be highly topical, and as such, this review may benefit from being refreshed in the future.

Keywords: coeliac disease, follow-up, gluten free diet, digital healthcare interventions

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Authors: Arunima Verma, Padmabati Mondal

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Serotonin-receptor binding plays a key role in several neurological and biological processes, including mood, sleep, hunger, cognition, learning, and memory. In this article, we performed molecular dynamics simulation to examine the key residues that play an essential role in the binding of serotonin to the G-protein-coupled 5-HT₁ᴮ receptor (5-HT₁ᴮ R) via electrostatic interactions. An end-point free energy calculation method (MM-PBSA) determines the stability of the 5-HT1B R due to serotonin binding. The single-point mutation of the polar or charged amino acid residues (Asp129, Thr134) on the binding sites and the calculation of binding free energy validate the importance of these residues in the stability of the serotonin-receptor complex. Principal component analysis indicates the serotonin-bound 5-HT1BR is more stabilized than the apo-receptor in terms of dynamical changes. The difference dynamic cross-correlations map shows the correlation between the transmembrane and mini-Go, which indicates signal transduction happening between mini-Go and the receptor. Allosteric communication reveals the key nodes for signal transduction in 5-HT1BR. These results provide useful insights into the signal transduction pathways and mutagenesis study to regulate the functionality of the complex. The developed protocols can be applied to study local non-covalent interactions and long-range allosteric communications in any protein-ligand system for computer-aided drug design.

Keywords: allostery, CADD, MD simulations, MM-PBSA

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Teerapong Lorchitamnuay, Thirarut Worapishet

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In the present digital age, today's customers explore various channels before finalizing a purchase, with abundant options and information at their disposal. Despite this, there is a strong digital interconnectedness. With just a few mouse clicks, customers can gather comprehensive information about a product, free from the influence of a salesperson. Salespeople must embrace cutting-edge technology to truly redefine the essence of selling if they are to thrive in this digital era. The significance of customer-salesperson communication in companies is becoming increasingly evident. It prompts the inquiry of how companies can modify or reshape their sales teams' approaches to effectively respond to evolving customer preferences and effectively manage external shifts, all in pursuit of sustaining and expanding their enterprises. Research highlights that digital and intercultural skills are the latest competencies sought by customers from salespeople in today's fast-paced world prior to making purchases of products and services. This study seeks to examine the pivotal influences of these salesperson skills in achieving customer satisfaction. The research design encompasses the analysis of descriptive statistics and quantitative data through a regression model. Data were gathered from an online convenience survey involving 260 respondents who are customers of an air express service provider in Thailand and who engage with salespeople in a traditional manner. The findings underscore that intercultural skills have a substantial impact on customer satisfaction in the digital era, particularly concerning adaptability, foreign language proficiency, active listening, and empathy skills. Organizations should focus on nurturing beneficial habits among their salespeople; since it signifies this effort, it should extend beyond just the frontline but should extend to encompass backline units and high-level management, ensuring that everyone possesses the same customer-oriented skills. The conclusions drawn from this research provide valuable insights, affirming that digital and intercultural skills can empower organizations to optimize their workforce's competencies, thereby achieving customer satisfaction in the digital age.

Keywords: customer behavior, customer satisfaction, digital era, digital skill, intercultural skill

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Authors: Qaisara Parveen, M. Imran Yousuf

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The purpose of this study was to find the effectiveness of the use of advance organizer model for enhancing meta-cognition of students in the subject of science. It was hypothesized that the students of experimental group taught through advance organizer model would show the better cognition than the students of control group taught through traditional teaching. The population of the study consisted of all secondary school students studying in government high school located in Rawalpindi. The sample of the study consisted of 50 students of 9th class of humanities group. The sample was selected on the basis of their pretest scores through matching, and the groups were randomly assigned for the treatment. The experimental group was taught through advance organizer model while the control group was taught through traditional teaching. The self-developed achievement test was used for the purpose of pretest and posttest. After collecting the pre-test score and post-test score, the data was analyzed and interpreted by use of descriptive statistics as mean and standard deviation and inferential statistics t-test. The findings indicate that students taught using advance organizers had a higher level of meta-cognition as compared to control group. Further, meta cognition level of boys was found higher than that of girls students. This study also revealed the fact that though the students at different meta-cognition level approached learning situations in a different manner, Advance organizer model is far superior to Traditional method of teaching.

Keywords: descriptive, experimental, humanities, meta-cognition, statistics, science

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Jiahao Zhou, Lei Wang

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In heterogeneous supercomputing clusters, designing an efficient scheduling strategy is crucial for enhancing both energy efficiency and workflow execution performance. The dynamic allocation and reclamation of computing resources are essential for improving resource utilization. However, existing studies often allocate fixed resources to jobs prior to execution, maintaining these resources until job completion, which overlooks the importance of dynamic scheduling. This paper proposes a heterogeneous hierarchical fine-grained scheduling algorithm (HeHiFiS) based on graph convolutional networks (GCN) and proximal policy optimization (PPO) to address issues such as prolonged workflow completion times and low resource utilization in heterogeneous supercomputing clusters. Specifically, GCN is employed to extract task dependency features as part of the state information, and the PPO reinforcement learning algorithm is then used to train the scheduling policy. The trained scheduling policy dynamically adjusts scheduling actions during operation based on the continuously changing states of tasks and computing resources. Additionally, we developed a heterogeneous scheduling simulation platform to validate the effectiveness of the proposed algorithm. Experimental results indicate that HeHiFiS, by incorporating resource inheritance and intra-task parallel mechanisms, significantly improves resource utilization. Compared to existing scheduling algorithms, HeHiFiS achieves over a 50% improvement in both job completion and response performance metrics.

Keywords: heterogeneous, dynamic scheduling, GCN, PPO

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Authors: Bathmajaa Muralisankar

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As much as railway station infrastructure designs and ticket-booking norms have been changed to facilitate use by differently abled persons, the railway train compartments themselves have not been made user-friendly for differently abled persons. Owing to safety concerns, dependency on others for their travel, and fear of isolation, differently abled people do not prefer travelling by train. Rather than including a dedicated compartment open only to the differently abled, including the latter with others in the normal compartment (with the proposed modifications discussed here) will make them feel secure and make for an enhanced travel experience for them. This approach also represents the most practical way to include a particular category of people in the mainstream society. Lowering the height of the compartment doors and providing a wider entrance with a ramp will provide easy entry for those using wheelchairs. As well, removing the first two alternate rows and the first two side seats will not only widen the passage and increase seating space but also improve wheelchair turning radius. This will help them travel without having to depend on others. Seating arrangements may be done to accommodate their family members near them instead of isolating the differently abled in a separate compartment. According to present ticket-booking regulations of the Indian Railways, three to four disabled persons may travel without their family or one to two along with their family, and the numbers may be added or reduced. To help visually challenged and hearing-impaired persons, in addition to the provision of special instruments, railings, and textured footpaths and flooring, the seat numbers above the seats may be set in metal or plastic as an outward projection so the visually impaired can touch and feel the numbers. Braille boards may be included at the entrance to the compartment along with seat numbers in the aforementioned projected manner. These seat numbers may be designed as buttons, which when pressed results in an announcement of the seat number in the applicable local language as well as English. Emergency buttons, rather than emergency chains, within the easy reach of disabled passengers will also help them.

Keywords: dependency, differently abled, inclusion, mainstream society

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Authors: Benyapa Thitimapong

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Teenage pregnancy and adolescent mothers have become a matter of increasing concern in Thailand. Since adolescent mothers have been a big problem for two main consequences; health outcomes and socio-economic impacts. Adolescent mothers often endure poor living conditions; limited financial resources while also experience high stress, family instability, and limited educational opportunities. These disadvantages are negative and have long-term effects on adolescent mothers, their families, and the community. The majority of pregnant students and adolescent mothers dropped out of school after becoming pregnant, and some of them return to study again after they gave birth. This research aimed to explain the meaning of adolescent mothers who had undergone with childrearing and studying simultaneously after childbirth. A phenomenological qualitative approach was undertaken to investigate this study. The participants were 20 adolescent mothers each of whom became a mother and a student concurrently within less than 2 years after giving birth to a healthy baby and had also undergone the experience of childrearing and studying in non-formal education. In-depth interview was carried out for data collection, and the data were analyzed using content analysis method. ‘Learning to move forward’ was the meaning of adolescent mothers who experienced with childrearing and studying simultaneously. Their expressions were classified into two categories 1) having more responsibility, and 2) conceding and going on. The result of this study can be used as evidence for health care providers, especially nurses to facilitate and support pregnant adolescents and adolescent mothers to continue their education. Also, it can be used to guide policy to promote in all educational system to enable these groups to remain in school for their life-long success in the future.

Keywords: adolescent mothers, childrearing, studying, teenage pregnancy

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Authors: Mateusz Jeżowski, Jadwiga Fila, Paweł Poszytek

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This abstract summarizes the combined findings of several analyses carried out by the authors on the barriers to accessing international education mobility programs by people with fewer opportunities, especially those with a low level of cultural and social capital. Two flagship educational mobility initiatives of the European Union – the Erasmus+ Program and the European Solidarity Corps are aimed at equipping young people and participants of all ages with the qualifications and skills needed for their meaningful participation in a democratic society intercultural understanding and successful transition in the labour market. The heart of these programs is to come closer to people with fewer opportunities, including people with disabilities, migrants, as well as those facing socio-economic difficulties and displaying a low level of social and cultural capital. Still, people who participate in such initiatives usually demonstrate higher than average cultural and social capital, as understood by Pierre Bourdieu. First of all, the educational attainment of their parents is higher than the average. Secondly, they mostly live in large agglomerations, with good access to education and culture, which affects their foreign language skills as well as social and cultural competencies. Finally, participation in Erasmus+ Program or European Solidarity Corps is not their first educational mobility experience. It is therefore justified to state that their social and cultural capital, already high before taking part in Erasmus+ and European Solidarity Corps, becomes even higher once their international mobility activities have been over, at the expense of those people with fewer opportunities, who, in theory, could participate in those initiatives, nonetheless did not, for the following reasons: lack of sufficient information on such programs, financial obstacles or unappreciation of the value of international mobility. In their work, the authors will discuss what are, in the light of Bourdieu’s perception of social and cultural capital, the main obstacles for young people to participate in international mobility programs of the European Union and will offer comprehensive solutions rooted in their vast experience in management and implementation of Erasmus+ Program and European Solidarity Corps.

Keywords: cultural capital, educational mobility, Erasmus+, European solidarity corps, inclusion, social capital

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Authors: Sylvanus N. Wosu

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Research shows that factors, such as low motivation, preparation, resources, emotional and social integration, and fears of risk-taking, are the most common barriers to access, matriculation, and retention into science, technology, engineering, and mathematics (STEM) disciplines for underrepresented (URM) students. These factors have been shown to impact students’ attraction and success in STEM fields. Standardized tests such as the SAT and ACT often used as predictor of success, are not always true predictors of success for African and Hispanic American students. Without an adequate academic support environment, even a high SAT score does not guarantee academic success in science and engineering. This paper proposes a model for Academic Coaching for building success and inclusive excellence in STEM education. Academic coaching is framed as a process of motivating students to be independent learners through relational mentorship, facilitating learning supports inside and outside of the classroom or school environment, and developing problem-solving skills and success attitudes that lead to higher performance in the specific subjects. The model is formulated based on best strategies and practices for enriching Academic Performance Impact skills and motivating students’ interests in STEM. A scaled model for measuring the Academic Performance Impact (API) index and STEM is discussed. The study correlates API with state standardized test and shows that the average impact of those skills can be predicted by the Academic Performance Impact (API) index or Academic Preparedness Index.

Keywords: diversity, equity, graduate education, inclusion, inclusive excellence, model

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Authors: Sasha Mullally

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This paper investigates the transnational circulation of European Nordic ideas about and programs for manual education and training over the decades spanning the late 19th and early 20th centuries. Based on the unexamined but voluminous correspondence (English-language) of Otto Salomon, an internationally famous education reformer who popularized a form of manual training called "slöjd" (anglicized as "sloyd"), this paper examines it's circulation and translation across global cultures. Salomon, a multilingual promoter of new standardized program for manual training, based his curricula on traditional handcrqafts, particularly Swedish woodworking. He and his followers claimed that the integration of manual training and craft work provided primary and secondary educators with an opportunity to cultivate the mental, but also the physical, and tangentially, the spiritual, health of children. While historians have examined the networks who came together in person to train at his slöjd school for educators in western Sweden, no one has mapped the international community he cultivated over decades of letter writing. Additionally, while the circulation of his ideas in Britain and Germany, as well as the northeastern United States has been placed in a broader narrative of "western" education reform in the Progressive or late Victorian era, no one has examined the correspondence for evidence of the program's wider international appeal beyond Europe and North America. This paper fills this gap by examining the breadth of his reach through active correspondence with educators in Asia (Japan), South America (Brazil), and Africa (South Africa and Zimbabwe). As such, this research presents an opportunity to map the international communities of education reformers active at the turn of the last century, compare and contrast their understandings of and interpretations of "holistic" education, and reveal the ways manual formation was understood to be foundational to the healthy development of children.

Keywords: history of education, history of medicine and psychiatry, child health, child formation, internationalism

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Authors: A. Degale Desta, Cheng Jian

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Background: Vehicle detection accuracy is critical to intelligent transportation systems and autonomous driving. The state-of-the-art object identification technology YOLOv8 has shown significant gains in efficiency and detection accuracy. This study uses the BDD100K dataset, which is renowned for its extensive and varied annotations, to assess how well YOLOv8 performs in vehicle detection. Objectives: The primary objective of this research is to assess YOLOv8's performance in intelligent transportation system vehicle identification and its ability to accurately identify cars in urban environments for safety prioritization. Methods: The primary objective of this research is to assess YOLOv8's performance in intelligent transportation system vehicle identification and its ability to accurately identify cars in urban environments for safety prioritization. Results: The results show that YOLOv8 achieves high mAP, recall, precision, and F1-score values, indicating state-of-the-art performance. This suggests that YOLOv8 can identify cars in complex urban environments with a high degree of accuracy and reliable results in a variety of traffic scenarios. Conclusion: The results indicate that YOLOv8 is a useful tool for enhancing vehicle detection accuracy in intelligent transportation systems, hence advancing urban public safety and security. The model's demonstrated performance shows how well it may be incorporated into autonomous driving applications to improve situational awareness and responsiveness.

Keywords: vehicle detection, YOLOv8, BDD100K, object detection, deep learning

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Authors: Tyler T. Procko, Steve Collins

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New features in .NET (6 and above) permit streamlined access to information residing in JSON-capable relational databases, such as SQL Server (2016 and above). Traditional methods of data access now comparatively involve unnecessary steps which compromise system performance. This work posits that the established ORM (Object Relational Mapping) based methods of data access in applications and APIs result in common issues, e.g., object-relational impedance mismatch. Recent developments in C# and .NET Core combined with a framework of modern SQL Server coding conventions have allowed better technical solutions to the problem. As an amelioration, this work details the language features and coding conventions which enable this streamlined approach, resulting in an open-source .NET library implementation called Codeless Data Access (CODA). Canonical approaches rely on ad-hoc mapping code to perform type conversions between the client and back-end database; with CODA, no mapping code is needed, as JSON is freely mapped to SQL and vice versa. CODA streamlines API data access by improving on three aspects of immediate concern to web developers, database engineers and cybersecurity professionals: Simplicity, Speed and Security. Simplicity is engendered by cutting out the “middleman” steps, effectively making API data access a whitebox, whereas traditional methods are blackbox. Speed is improved because of the fewer translational steps taken, and security is improved as attack surfaces are minimized. An empirical evaluation of the speed of the CODA approach in comparison to ORM approaches ] is provided and demonstrates that the CODA approach is significantly faster. CODA presents substantial benefits for API developer workflows by simplifying data access, resulting in better speed and security and allowing developers to focus on productive development rather than being mired in data access code. Future considerations include a generalization of the CODA method and extension outside of the .NET ecosystem to other programming languages.

Keywords: API data access, database, JSON, .NET core, SQL server

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Authors: Pansak Vandee

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The research aims to study the background and format of Tableau Vivant suite song and the singing style of Kru Luen SunTharawatin. The data is gained by content analysis and the in-depth interview from experts on Tableau Vivant suite song, as well as an analysis on singing style of Kru Luen SunTharawatin. The research results are as follows: (1) The Tableau Vivant suite song first appeared in the reign of King Rama V, composing by Prince Naris, for paralleling with the still picture presentation offered to Crown Prince Maha Vajirunahit, which was adapted from the French performance Tableau Vivant. The performance is assigned to perform by the Crown Prince’s younger brother and other young royal siblings. The 8 suite songs were played by archeological Thai orchestra (Piphat duek dam ban) and were restored once again in the reign of King Rama VII for the pleasure of the king, being sung by the Royal Entertainment Department Officers. (2) The format of Tableau Vivant suite song is a short series song, composing a double strand of 4 to 6 songs with 6 suite songs in 8 suites, in the form of a combination of suite lyric – Unnarut (the play narrated by King Rama I) and Phra Pen Chao (the homage to Brahman gods in Na Phat song); and suite song – Khom Dam Din with Khmer tone; the Three Kingdoms with Chinese tone; Rajadhirat with Burmese and Mon tone; Nitra Chakrit (the Arabian Night) with Arabian tone; Cinderella with Western tone; Phra Lor in Laotian tone. (3) The Tableau Vivant suite singing style of Kru Luen SunTharawatin is based on Phra-ya Sanor Duriyang (Cham SunTharawatin) style, which was newly created for Kru Luen, his daughter. The five suite songs Khom Dam Din, the Three Kingdoms, Rajadhirat, Nitra Chakrit (the Arabian Night), Cinderella, Phra Lor have their distinguish tones according to their language accents. The songs were recorded in the album of the Royal Institute of Thailand in 1931; but, unfortunately, the entire album was completely destroyed during the World War II in Germany.

Keywords: Krue Luen Sun Tharawatin, tableau vivant, singing style, suite song

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Authors: Peter R. Reuter

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Health professionals play important roles in helping patients use Complementary and Alternative Medicine (CAM) practices safely and accurately. Consequently, it is important for future health professionals to learn about CAM practices during their time in undergraduate and graduate programs. To satisfy this need for education, teaching CAM in nursing and medical schools and other health professions programs is becoming more prevalent. Our study was the first to look specifically at the knowledge of, and attitude toward CAM of undergraduate health professions students at a university in the U.S. Students were invited to participate in one of two anonymous online surveys depending on whether they were pre-health professions students or graduating health professions seniors. Of the 763 responses analyzed, 71.7% were from pre-health professions students, and 28.3% came from graduating seniors. The overall attitude of participants toward and interest in learning about CAM practices was generally fairly positive with graduating seniors being more positive than pre-health professions students. Yoga, meditation, massage therapy, aromatherapy, and chiropractic care were the practices most respondents had personal experience with. Massage therapy, yoga, chiropractic care, meditation, music therapy, and diet-based therapy received the highest ratings from respondents. Three-quarters of respondents planned on including aspects of holistic medicine in their future career as a health professional. The top five practices named were yoga, meditation, massage therapy, diet-based therapy, and music therapy. The study confirms the need to educate health professions students about CAM practices to give them the background information they need to select or recommend the best practices for their patients' needs.

Keywords: CAM education, health professions, health professions students, pre-health professions students

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Authors: Hilal Al Shamsi, Abdullah Almutairi

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Introduction: As primary healthcare centres play an important role in implementing Saudi Arabia’s health strategy, this paper offers a review of publications on the quality of the country’s primary health care. With the aim of deciding on solutions for improvement, it provides an overview of healthcare quality in this context and indicates barriers to quality. Method: Using two databases, ProQuest and Scopus, data extracted from published articles were systematically analysed for determining the care quality in Saudi primary health centres and obstacles to achieving higher quality. Results: Twenty-six articles met the criteria for inclusion in this review. The components of healthcare quality were examined in terms of the access to and effectiveness of interpersonal and clinical care. Good access and effective care were identified in such areas as maternal health care and the control of epidemic diseases, whereas poor access and effectiveness of care were shown for chronic disease management programmes, referral patterns (in terms of referral letters and feedback reports), health education and interpersonal care (in terms of language barriers). Several factors were identified as barriers to high-quality care. These included problems with evidence-based practice implementation, professional development, the use of referrals to secondary care and organisational culture. Successful improvements have been implemented by other systems, such as mobile medical units, electronic referrals, online translation tools and mobile devices and their applications; these can be implemented in Saudi Arabia for improving the quality of the primary healthcare system in this country. Conclusion: The quality of primary health care in Saudi Arabia varies among the different services. To improve quality, management programmes and organisational culture must be promoted in primary health care. Professional development strategies are also needed for improving the skills and knowledge of healthcare professionals. Potential improvements can be implemented to improve the quality of the primary health system.

Keywords: quality, primary health care, Saudi Arabia, health centres, general medical

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Authors: Lal Hussain, Wajid Aziz, Imtiaz Ahmed Awan, Sharjeel Saeed

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Multiscale entropy analysis (MSE) is a useful technique recently developed to quantify the dynamics of physiological signals at different time scales. This study is aimed at investigating the electroencephalogram (EEG) signals to analyze the background activity of alcoholic and control subjects by inspecting various coarse-grained sequences formed at different time scales. EEG recordings of alcoholic and control subjects were taken from the publically available machine learning repository of University of California (UCI) acquired using 64 electrodes. The MSE analysis was performed on the EEG data acquired from all the electrodes of alcoholic and control subjects. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. The area under receiver operator curve was computed to find the degree separation between the groups. The mean ranks of MSE values at all the times scales for all electrodes were higher control subject as compared to alcoholic subjects. Higher mean ranks represent higher complexity and vice versa. The finding indicated that EEG signals acquired through electrodes C3, C4, F3, F7, F8, O1, O2, P3, T7 showed significant differences between alcoholic and control subjects at time scales 1 to 5. Moreover, all electrodes exhibit significance level at different time scales. Likewise, the highest accuracy and separation was obtained at the central region (C3 and C4), front polar regions (P3, O1, F3, F7, F8 and T8) while other electrodes such asFp1, Fp2, P4 and F4 shows no significant results.

Keywords: electroencephalogram (EEG), multiscale sample entropy (MSE), Mann-Whitney test (MMT), Receiver Operator Curve (ROC), complexity analysis

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Authors: Fujia Yang

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Alongside technological innovation, the current “double reduction” policy and English Curriculum Standards for Compulsory Education in China both emphasise and encourage appropriately integrating educational technologies into the classroom. Therefore, schools are increasingly using digital means to engage students in English reading, but the impact of such technologies on Chinese pupils’ reading achievement remains unclear. To serve as a reference for reforming English reading education in primary schools under the double reduction policy, this study investigates the effects and primary influencing factors of a specific reading programme, Accelerated Reader (AR), on Chinese primary school students’ reading achievement. A quantitative online survey was used to collect 37 valid questionnaires from teachers, and the results demonstrate that, from teachers’ perspectives, the AR program seemed to positively affect students’ reading achievement by recommending material at the appropriate reading levels and developing students’ reading habits. Although the reading enjoyment derived from the AR program does not directly influence students’ reading achievement, these factors are strongly correlated. This can be explained by the self-paced, independent learning AR format, its high accuracy in predicting reading level, the quiz format and external motivation, and the importance of examinations and resource limitations in China. The results of this study may support reforming English reading education in Chinese primary schools.

Keywords: educational technology, reading programme, primary students, accelerated reader, reading effects

Procedia PDF Downloads 88