Search results for: academic learning stress

Authors: Thomas Chong, Irene Victor

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This paper discusses the history and plausible future of assistive technology and what it means for students with disabilities in Singapore, a country known for its high quality of education in the world. Over more than a century, students with disabilities have benefitted from relatively low-tech assistive technology (like eye-glasses, Braille, magnifiers and wheelchairs) to high-tech assistive technology including electronic mobility switches, alternative keyboards, computer-screen enlargers, text-to-speech readers, electronic sign-language dictionaries and signing avatars for individuals with hearing impairments. Driven by legislation, the use of assistive technology in many countries is becoming so ubiquitous that more and more students with disabilities are able to perform as well as if not better than their counterparts. Yet in many other learning environments where assistive technology is not affordable or mandated, the learning gaps can be quite significant. Without stronger legislation, Singapore may still have a long way to go in levelling the playing field for its students with disabilities.

Keywords: assistive technology, students with disabilities, disability laws in Singapore, inclusiveness

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Authors: Ora Geiger

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Reading is a cognitive process of deciphering visual signs to produce meaning. During the reading process, written information of symbols and signs is received in the person’s eye and processed in the brain. This definition is relevant to both the reading of letters and the reading of musical notation. But while the letters of the alphabet are signs determined arbitrarily, notes are recorded systematically on a staff, with the location of each note on the staff indicating its relative pitch. In this paper, the researcher specifies the characteristics of alphabet reading in comparison to musical notation reading, and discusses the question whether a person diagnosed with dyslexia will necessarily have difficulty in reading musical notes. Dyslexia is a learning disorder that makes it difficult to acquire alphabet-reading skills due to difficulties expressed in the identification of letters, spelling, and other language deciphering skills. In order to read, one must be able to connect a symbol with a sound and to join the sounds into words. A person who has dyslexia finds it difficult to translate a graphic symbol into the sound that it represents. When teaching reading to children diagnosed with dyslexia, the multi-sensory approach, supporting the activation and involvement of most of the senses in the learning process, has been found to be particularly effective. According to this approach, when most senses participate in the reading learning process, it becomes more effective. During years of experience, the researcher, who is a music specialist, has been following the music reading learning process of elementary school age students, some of them diagnosed with Dyslexia, while studying to play soprano (descant) recorder. She argues that learning music reading while studying to play a musical instrument is a multi-sensory experience by its nature. The senses involved are: sight, hearing, touch, and the kinesthetic sense (motion), which provides the brain with information on the relative positions of the body. In this way, the learner experiences simultaneously visual, auditory, tactile, and kinesthetic impressions. The researcher concludes that there should be no contra-indication for teaching standard music reading to children with dyslexia if an appropriate process is offered. This conclusion is based on two main characteristics of music reading: (1) musical notation system is a systematic, logical, relative set of symbols written on a staff; and (2) music reading learning connected with playing a musical instrument is by its nature a multi-sensory activity since it combines sight, hearing, touch, and movement. This paper describes music reading teaching procedures and provides unique teaching methods that have been found to be effective for students who were diagnosed with Dyslexia. It provides theoretical explanations in addition to guidelines for music education practices.

Keywords: alphabet reading, dyslexia, multisensory teaching method, music reading, recorder playing

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Authors: Sri Rahayu, Supriyatin, Yuli Rahma Dini

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One of the consequences of excess physical activity is the oxidative stress which resulted in damage to blood cells. Oxidative stress condition can be reduced by an exogenous antioxidant. The natural exogenous antioxidant can be extracted from Manggong bamboo (Gigantochloa manggong). This research was aim to evaluate the effect of physical exercise and Manggong bamboo (Gigantochloa manggong) leaf extract on blood profile of rats. This research was conducted in July 2013 to May 2014 using experimental method with completely randomized design (CRD) with two factors, physical exercise and Manggong bamboo leaf extract. The rats blood profile to be measured were the level of erythrocyte cells, leucocyte cells and hemoglobin. Data were analyzed with parametric statistical 2-way ANOVA test (α = 0.05). Manggong bamboo leaf extract was non toxic and contained flavonoid, triterpenoid, saponin and alkaloid. There was an effect of physical exercise and manggong bamboo leaf extract on blood profile of rats. Data obtained on physical activity, giving erythrocyte cells (2.5 million/µl) and hemoglobin (12,42g/dL) declined compared to the number of leucocyte cells increases (6,500cells/L). Extract treatment was increased the erythrocytes (5,13 million/µl) and hemoglobin level (14,72 g/dL.) while the leukocytes level were decreased (1.591,67 cells/L). The extract and physical activity treatment showed an increase in erythrocytes (2,96 million/µl) and hemoglobin (14,3 g/dL) but decrease the number of leukocytes (1.291,67 cells/L). The conclusion was that physical activity and Manggong bamboo leafs extract gaves effect on the blood profile of white rat.

Keywords: antioxidant, blood profile of rats, Manggong bamboo leaf extract, leukocytes

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Ujjal Chattaraj, P. C. Dasbebartta, S. Bhuyan

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This paper aims to propose a method to detect the action of the driver’s eyes, using the concept of face detection. There are three major key contributing methods which can rapidly process the framework of the facial image and hence produce results which further can program the reactions of the vehicles as pre-programmed for the traffic safety. This paper compares and analyses the methods on the basis of their reaction time and their ability to deal with fluctuating images of the driver. The program used in this study is simple and efficient, built using the AdaBoost learning algorithm. Through this program, the system would be able to discard background regions and focus on the face-like regions. The results are analyzed on a common computer which makes it feasible for the end users. The application domain of this experiment is quite wide, such as detection of drowsiness or influence of alcohols in drivers or detection for the case of identification.

Keywords: AdaBoost learning algorithm, face detection, framework, traffic safety

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Authors: Kai Chen, Shuguang Cui, Feng Yin

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Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP.

Keywords: Gaussian process, spectral mixture, non-stationary, convolution

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Authors: Malahat Rezaee, Mahiran Basri, Raja Noor Zaliha Raja Abdul Rahman, Abu Bakar Salleh

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Sodium diclofenac is one of the most commonly used drugs of nonsteroidal anti-inflammatory drugs (NSAIDs). It is especially effective in the controlling the severe conditions of inflammation and pain, musculoskeletal disorders, arthritis, and dysmenorrhea. Formulation as nanoemulsions is one of the nanoscience approaches that have been progressively considered in pharmaceutical science for transdermal delivery of drug. Nanoemulsions are a type of emulsion with particle sizes ranging from 20 nm to 200 nm. An emulsion is formed by the dispersion of one liquid, usually the oil phase in another immiscible liquid, water phase that is stabilized using surfactant. Palm kernel oil esters (PKOEs), in comparison to other oils; contain higher amounts of shorter chain esters, which suitable to be applied in micro and nanoemulsion systems as a carrier for actives, with excellent wetting behavior without the oily feeling. This research was aimed to study the effect of O/S ratio on stability and rheological behavior of sodium diclofenac loaded and unloaded palm kernel oil esters nanoemulsion systems. The effect of different O/S ratio of 0.25, 0.50, 0.75, 1.00 and 1.25 on stability of the drug-loaded and unloaded nanoemulsion formulations was evaluated by centrifugation, freeze-thaw cycle and storage stability tests. Lecithin and cremophor EL were used as surfactant. The stability of the prepared nanoemulsion formulations was assessed based on the change in zeta potential and droplet size as a function of time. Instability mechanisms including coalescence and Ostwald ripening for the nanoemulsion system were discussed. In comparison between drug-loaded and unloaded nanoemulsion formulations, drug-loaded formulations represented smaller particle size and higher stability. In addition, the O/S ratio of 0.5 was found to be the best ratio of oil and surfactant for production of a nanoemulsion with the highest stability. The effect of O/S ratio on rheological properties of drug-loaded and unloaded nanoemulsion systems was studied by plotting the flow curves of shear stress (τ) and viscosity (η) as a function of shear rate (γ). The data were fitted to the Power Law model. The results showed that all nanoemulsion formulations exhibited non-Newtonian flow behaviour by displaying shear thinning behaviour. Viscosity and yield stress were also evaluated. The nanoemulsion formulation with the O/S ratio of 0.5 represented higher viscosity and K values. In addition, the sodium diclofenac loaded formulations had more viscosity and higher yield stress than drug-unloaded formulations.

Keywords: nanoemulsions, palm kernel oil esters, sodium diclofenac, rheoligy, stability

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Authors: Yogesh Kumar Tiwari

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Chhattisgarh has realized the fundamental role of information and communication technology in the globalized world where knowledge is at the top for the growth and intellectual development. They are spreading so widely that one feels lagging behind if not using them. The influence of these radiating and technological tools has encompassed all aspects of the educational, business, and economic sectors of our world. Undeniably the computer has not only established itself globally in all walks of life but has acquired a fundamental role of paramount importance in the educational process also. This role is getting all pervading and more powerful as computers are being manufactured to be cheaper, smaller in size, adaptable and easy to handle. Computers are becoming indispensable to teachers because of their enormous capabilities and extensive competence. This study aims at observing the effect of using computer based software program of English language on the achievement of undergraduate level students studying in tribal area like Sarguja Division, Chhattisgarh, India. To testify the effect of an innovative teaching in the graduate classroom in tribal area 50 students were randomly selected and separated into two groups. The first group of 25 students were taught English grammar i.e., passive voice/narration, through traditional method using chalk and blackboard asking some formal questions. The second group, the experimental one, was taught English grammar i.e., passive voice/narration, using computer, projector with power point presentation of grammatical items. The statistical analysis was done on the students’ learning capacities and achievement. The result was extremely mesmerizing not only for the teacher but for taught also. The process of the recapitulation demonstrated that the students of experimental group responded the answers of the questions enthusiastically with innovative sense of learning. In light of the findings of the study, it was recommended that teachers and professors of English ought to use self-made instructional program in their teaching process particularly in tribal areas.

Keywords: achievement computer assisted language learning, use of instructional program

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Authors: V. K. Gupta, Tejeet Singh

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In many whisker reinforced composites, anisotropy may result due to material flow during processing operations such as forging, extrusion etc. The consequence of anisotropy, introduced during processing of disc material, has been investigated on the steady state creep deformations of the rotating disc. The disc material is assumed to undergo plastic deformations according to Hill’s anisotropic criterion. Steady state creep has been analyzed in a constant thickness rotating disc made of functionally graded 6061Al-SiCw (where the subscript ‘w’ stands for whisker) using Hill’s The content of reinforcement (SiCw) in the disc is assumed to decrease linearly from the inner to outer radius. The stresses and strain rates in the disc are estimated by solving the force equilibrium equation along with the constitutive equations describing multi-axial creep. The results obtained for anisotropic FGM disc have been compared with those estimated for isotropic FGM disc having the same average whisker content. The anisotropic constants, appearing in Hill’s yield criterion, have been obtained from the available experimental results. The results show that the presence of anisotropy reduces the tangential stress in the middle of the disc but near the inner and outer radii the tangential stress is higher when compared to isotropic disc. On the other hand, the steady state creep rates in the anisotropic disc are reduced significantly over the entire disc radius, with the maximum reduction observed at the inner radius. Further, in the presence of anisotropy the distribution of strain rate becomes relatively uniform over the entire disc, which may be responsible for reducing the extent of distortion in the disc.

Keywords: anisotropy, creep, functionally graded composite, rotating disc

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Authors: Ram Mohan, Mahendran Samykano, Shyam Aravamudhan

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Metallic nanowires with sub-micron and hundreds of nanometer diameter have a diversity of applications in nano/micro-electromechanical systems (NEMS/MEMS). Characterizing the mechanical properties of such sub-micron and nano-scale metallic nanowires are tedious; require sophisticated and careful experimentation to be performed within high-powered microscopy systems (scanning electron microscope (SEM), atomic force microscope (AFM)). Also, needed are nanoscale devices for placing the nanowires; loading them with the intended conditions; obtaining the data for load–deflection during the deformation within the high-powered microscopy environment poses significant challenges. Even picking the grown nanowires and placing them correctly within a nanoscale loading device is not an easy task. Mechanical characterizations through experimental methods for such nanowires are still very limited. Various techniques at different levels of fidelity, resolution, and induced errors have been attempted by material science and nanomaterial researchers. The methods for determining the load, deflection within the nanoscale devices also pose a significant problem. The state of the art is thus still at its infancy. All these factors result and is seen in the wide differences in the characterization curves and the reported properties in the current literature. In this paper, we discuss and present our experimental method, results, and discussions of uniaxial tensile loading and the development of subsequent stress–strain characteristics curves for Nickel nanowires. Nickel nanowires in the diameter range of 220–270 nm were obtained in our laboratory via an electrodeposition method, which is a solution based, template method followed in our present work for growing 1-D Nickel nanowires. Process variables such as the presence of magnetic field, its intensity; and varying electrical current density during the electrodeposition process were found to influence the morphological and physical characteristics including crystal orientation, size of the grown nanowires1. To further understand the correlation and influence of electrodeposition process variables, associated formed structural features of our grown Nickel nanowires to their mechanical properties, careful experiments within scanning electron microscope (SEM) were conducted. Details of the uniaxial tensile characterization, testing methodology, nanoscale testing device, load–deflection characteristics, microscopy images of failure progression, and the subsequent stress–strain curves are discussed and presented.

Keywords: uniaxial tensile characterization, nanowires, electrodeposition, stress-strain, nickel

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Authors: Jonathan Nash, Allen Hartt, Catherine Plante

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In many nations, community colleges, or similar institutions, play a crucial role in higher education. For example, in the United States more than half of all undergraduate students enroll in a community college at some point during their academic career. Similar statistics have been reported for Australia and Canada. Recognizing the important role these institutions play in educating future accountants, the American Accounting Association has called for research that contributes to a better understanding of these members of the academic community. Although previous literature has shown that community colleges and 4-year institutions differ on many levels, the extant literature has provided data on the characteristics of introductory accounting courses for four-year institutions but not for community colleges. We fill a void in the literature by providing data on the characteristics of introductory accounting courses offered by community colleges in the United States. Data are collected on several dimensions including: course size and staffing, pedagogical orientation, standardization of course elements, textbook selection, and use of technology-based course management tools. Many of these dimensions have been used in previous research examining four-year institutions thereby facilitating comparisons. The resulting data should be of interest to instructors, regulators and administrators, researchers, and the accounting profession. The data provide information on the introductory accounting courses completed by the average community college student which can help instructors identify areas where transfer students’ experiences might differ from their contemporaries at four-year colleges. Regulators and administrators may be interested in the differences between accounting courses offered by two- and four-year institutions when implementing standardized transfer programs. Researchers might use the data to motivate future research into whether differences between two- and four-year institutions affect outcomes like the probability of students choosing to major in accounting and their performance within the major. Accounting professionals may use our findings as a springboard for facilitating discussions related to the accounting labor supply.

Keywords: Accounting curricula, Community college, Descriptive study, Introductory accounting

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Authors: Anne Giles

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Humans naturally wish they could forget traumatic experiences. To help prevent future harm, however, the human brain has evolved to retain data about experiences of threat, alarm, or violation. When given compassionate support and assistance with thinking helpfully and realistically about traumatic events, most people can adjust to experiencing hardships, albeit with residual sad, unfortunate memories. Persistent, recurrent, intrusive memories, difficulty sleeping, emotion dysregulation, and avoidance of reminders, however, may be symptoms of Post-traumatic Stress Disorder (PTSD). Brain scans show that PTSD affects brain functioning. We currently have no physical means of restoring the system of brain structures and functions involved with PTSD. Medications may ease some symptoms but not others. However, forms of "talk therapy" with cognitive components have been found by researchers to reduce, even resolve, a broad spectrum of trauma symptoms. Many cultures have taboos against talking about hardships. Individuals may present themselves to mental health care professionals with severe, disabling trauma symptoms but, because of cultural norms, be unable to speak about them. In China, for example, relationship expectations may include the belief, "Bad things happening in the family should stay in the family (jiāchǒu bùkě wàiyán 家丑不可外扬)." The concept of "family (jiā 家)" may include partnerships, close and extended families, communities, companies, and the nation itself. In contrast to many trauma therapies, Cognitive Processing Therapy (CPT) for Post-traumatic Stress Disorder asks its participants to focus not on "what" happened but on "why" they think the trauma(s) occurred. The question "why" activates and exercises cognitive functioning. Brain scans of individuals with PTSD reveal executive functioning portions of the brain inadequately active, with emotion centers overly active. CPT conceptualizes PTSD as a network of cognitive distortions that keep an individual "stuck" in this under-functioning and over-functioning dynamic. Through asking participants forms of the question "why," plus offering a protocol for examining answers and relinquishing unhelpful beliefs, CPT assists individuals in consciously reactivating the cognitive, executive functions of their brains, thus restoring normal functioning and reducing distressing trauma symptoms. The culturally sensitive components of CPT that allow people to "talk about it without talking about it" may offer the possibility for worldwide relief from symptoms of trauma.

Keywords: cognitive processing therapy (CPT), cultural norms, post-traumatic stress disorder (PTSD), trauma recovery

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Authors: Nguyen Van Toan, Suguru Sangu, Tetsuro Saito, Naoki Inomata, Takahito Ono

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This paper presents the design and fabrication of an optical window for an optical modulator toward image sensing applications. An optical window consists of micrometer-order SiO2 capillaries (porous solid) that can modulate transmission light intensity by moving the liquid in and out of porous solid. A high optical transmittance of the optical window can be achieved due to refractive index matching when the liquid is penetrated into the porous solid. Otherwise, its light transmittance is lower because of light reflection and scattering by air holes and capillary walls. Silicon capillaries fabricated by deep reactive ion etching (DRIE) process are completely oxidized to form the SiO2 capillaries. Therefore, high aspect ratio SiO2 capillaries can be achieved based on silicon capillaries formed by DRIE technique. Large compressive stress of the oxide causes bending of the capillary structure, which is reduced by optimizing the design of device structure. The large stress of the optical window can be released via thin supporting beams. A 7.2 mm x 9.6 mm optical window area toward a fully integrated with the image sensor format is successfully fabricated and its optical transmittance is evaluated with and without inserting liquids (ethanol and matching oil). The achieved modulation range is approximately 20% to 35% with and without liquid penetration in visible region (wavelength range from 450 nm to 650 nm).

Keywords: thermal oxidation process, SiO2 capillaries, optical window, light transmittance, image sensor, liquid penetration

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Elvira Hörandl

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The term “Geographical parthenogenesis” describes the phenomenon that asexual organisms usually occupy larger and more northern distribution areas than their sexual relatives and tend to colonize previously glaciated areas. Several case studies in flowering plants confirm the geographical pattern, but the causal factors behind the phenomenon are still unclear. Previous authors regarded predominant polyploidy in asexual (apomictic) plants as the main factor. However, the geographical pattern is not the rule for sexual polyploids. Recent research confirmed a previous hypothesis of the author that a combination of factors is acting: Although uniparental reproduction provides better colonization abilities, it is most efficient in combination with polyploidy. I will present results on case studies in the genus Ranunculus of both autopolyploid and allopolyploid species and species complexes reproducing via facultative apomixis. Polyploidy seems to contribute mainly to a better tolerance of colder climates and temperate extremes, whereby epigenetic flexibility, changes in gene expression, and phenotypic plasticity play an important role in occupying ecological niches under harsh conditions. Phylogenomic studies entangle complex hybrid origins of asexual taxa, which increases intragenomic heterozygosity of asexual plants. Interestingly, our results suggest an association of sexuality with abiotic stresses, specifically with light stress, which might explain that still, most plants in high altitudes and in southern areas retain sexual reproduction despite other climatic conditions that would favor apomictic plants. We conclude that geographical parthenogenesis results from the complex interplay of the genomic constitution, mode of reproduction and environmental factors.

Keywords: apomixis, polyploidy, hybridization, abiotic stress, epigenetics, phylogenomics

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Authors: Christian Luarca

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The study aims to define the efficiency of e-Trainings by the use of cloud platform as part of the onboarding process for IT support engineers. Traditional lecture based trainings involves human resource to guide and assist new hires as part of onboarding which takes time and effort. The use of electronic medium as a platform for training provides a two-way basic communication that can be done in a repetitive manner. The study focuses on determining the most efficient manner of learning the basic knowledge on IT support in the shortest time possible. This was determined by conducting the same set of knowledge transfer categories in two different approaches, one being the e-Training and the other using the traditional method. Performance assessment will be done by the use of Service Tracker Assessment (STA) Tool and Service Manager. Data gathered from this ongoing study will promote the utilization of e-Trainings in the IT onboarding process.

Keywords: cloud platform, e-Training, efficiency, onboarding

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Authors: Roderick Melnik, Sanjay Prabhakar

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The relaxed state of graphene nanostructures due to externally applied tensile stress along both the armchair and zigzag directions are analyzed in detail. The results, obtained with the Finite Element Method (FEM), demonstrate that the amplitude of ripple waves in such nanostructures increases with temperature. Details of the multi-scale multi-physics computational procedure developed for this analysis are also provided.

Keywords: nanostructures, modeling, coupled processes, computer-aided design, nanotechnological applications

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Authors: Carla Silva

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The aim of this research is to study the relationship between the performance of humanities students in different statistics classes and their performance in their specific courses. Several factors are been studied, such as gender and final grades in statistics and math. Participants of this study comprised a sample of students at a Lisbon University during their academic year. A significant relationship tends to appear between these factors and the performance of these students. However this relationship tends to be stronger with students who had previous studied calculus and math.

Keywords: education, performance, statistic, humanities

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Authors: Napasri Suwanajote

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The purposes of this research were to analyse and evaluate successful factors in OTOP production process for the developing of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The research has been designed as a qualitative study to gather information from 30 OTOP producers in Bangkontee District, Samudsongkram Province. They were all interviewed on 3 main parts. Part 1 was about the production process including 1) production 2) product development 3) the community strength 4) marketing possibility and 5) product quality. Part 2 evaluated appropriate successful factors including 1) the analysis of the successful factors 2) evaluate the strategy based on Sufficiency Economic Philosophy and 3) the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality. The results showed that the production did not affect the environment with potential in continuing standard quality production. They used the raw materials in the country. On the aspect of product and community strength in the past 1 year, it was found that there was no appropriate packaging showing product identity according to global market standard. They needed the training on packaging especially for food and drink products. On the aspect of product quality and product specification, it was found that the products were certified by the local OTOP standard. There should be a responsible organization to help the uncertified producers pass the standard. However, there was a problem on food contamination which was hazardous to the consumers. The producers should cooperate with the government sector or educational institutes involving with food processing to reach FDA standard. The results from small group discussion showed that the community expected high education and better standard living. Some problems reported by the community included informal debt and drugs in the community. There were 8 steps in developing the model of learning centre on OTOP production process based on Sufficiency Economic Philosophy for sustainable life quality.

Keywords: production process, OTOP, sufficiency economic philosophy, marketing management

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Lei Zhu, Nan Li

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Cold stamping has been widely applied in the automotive industry for the mass production of a great range of automotive panels. Predicting the springback to ensure the dimensional accuracy of the cold-stamped components is a critical step. The main approaches for the prediction and compensation of springback in cold stamping include running Finite Element (FE) simulations and conducting experiments, which require forming process expertise and can be time-consuming and expensive for the design of cold stamping tools. Machine learning technologies have been proven and successfully applied in learning complex system behaviours using presentative samples. These technologies exhibit the promising potential to be used as supporting design tools for metal forming technologies. This study, for the first time, presents a novel application of a Convolutional Neural Network (CNN) based surrogate model to predict the springback fields for variable U-shape cold bending geometries. A dataset is created based on the U-shape cold bending geometries and the corresponding FE simulations results. The dataset is then applied to train the CNN surrogate model. The result shows that the surrogate model can achieve near indistinguishable full-field predictions in real-time when compared with the FE simulation results. The application of CNN in efficient springback prediction can be adopted in industrial settings to aid both conceptual and final component designs for designers without having manufacturing knowledge.

Keywords: springback, cold stamping, convolutional neural networks, machine learning

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Authors: Aboagye Boye Gilbert

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Teaching and learning (Education), not only in Ghana but the whole world is regarded as the (Stepping stone) vehicle to accelerate the country’s economy, development and social growth. Basically the ingredients for human development and the country in general is Vocational and Technical education and this has been stressed in Ghana’s education system since Pre-independence. To this effect, this research seeks to determine using demonstration method of Teachings sewing to improve the skills of form 3 Fashion Designing students of Baworo Integrated Community Centre for Employable Skills. In this research, reviewed literature on opinions of other researchers and what other people have done and said on related articles or topics, analyzed the research design used, translate the data gathered in the study. The study was design to gather information from the school on how they use Teaching methods to teach sewing. The targeted respondent contacted to give assistance Consist of students from BICCES, fashion teachers and tailored garment makers. The sample size consisted of 5 teachers, 20 students and 5 tailors were selected to answer questionnaire items that were used to gather the data for the study. The study revealed that most teachers and students agreed to the fact that demonstration, teaching and learning materials had a positive attitude towards the students in learning sewing. The study recommends that there should be more mechanisms in place to serve as a guide.

Keywords: VOTEC, BECE, BICCES, SHS

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Authors: Buğra Sinmez

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In some countries, such as China, Turkey, andseveralEuropeanUnionnations, the therailwaypavementstructuralsystem has recently undergonerapid growth as a vital element of the transportation infrastructure, particularlyfortheuse of high-speed trains. It is vitaltoconsiderthe High-SpeedInfrastructureDemandwhendevelopingandconstructingtherailwaypavementstructure. HSRL can create more substantial ldifficultiestotheballastorbaselayer of regularlyusedballastedrailwaypavementsthanstandardrailwaytrains. The deterioration of the theballastorbaselayermayleadtosubstructuredegradation, which might lead to safety concerns and catastrophicincidents. As a result, the efficiency of railways will be impactedbylargecargoesorhigh-speed trains. A railwaypavement construction can be strengthened using geosyntheticmaterials in theballastorfoundationlayer as a countermeasure. However, there is still a need in the literature to quantifytheinfluence of geosynthetic materials, particularlygeogrid, on the mechanical responses of railwaypavementstructuresto HSRL loads which is essential knowledge in supporting the selection of appropriate material and geogridinstallationposition. As a result, the purpose of this research is to see how a geogridreinforcementlayermayaffectthekeyfeatures of a ballastedrailwaypavementstructure, with a particular focus on the materialtypeandgeogridplacementpositionthatmayassistreducethe rate of degradation of the therailwaypavementstructuresystem. Thisstudyusesnumericalmodeling in a genuinerailwaycontexttovalidatethebenefit of geogrid reinforcement. The usage of geogrids in the railway system has been thoroughly researched in the technical literature. Three distinct types of geogrid installed at two distinct positions (i.e.,withintheballastlayer, betweentheballastandthesub-ballast layer) within a railwaypavementconstructionwereevaluatedunder a variety of verticalwheelloadsusing a three-dimensional (3D) finite element model. As a result, fouralternativegeogridreinforcementsystemsweremodeledtoreflectdifferentconditions in the ballastedrailwaysystems (G0: no reinforcement; G1: reinforcedwithgeogridhavingthelowestdensityandYoung'smodulus; G2: reinforcedwithgeogridhavingtheintermediateYoung'smodulusanddensity; G3: reinforcedwithgeogridhavingthegreatestdensityandYoung'smodulus). Themechanicalreactions of the railway, such as verticalsurfacedeflection, maximumprimarystressandstrain, andmaximumshearstress, werestudiedandcomparedbetweenthefourgeogridreinforcementscenariosandfourverticalwheelloadlevels (i.e., 75, 100, 150, and 200 kN). Differences in the mechanical reactions of railwaypavementconstructionsowingtotheuse of differentgeogridmaterialsdemonstratethebenefits of suchgeosynthetics in ballast. In comparison to a non-reinforcedrailwaypavementconstruction, thereinforcedconstructionsfeaturedecreasedverticalsurfacedeflection, maximum shear stress at the sleeper-ballast contact, and maximum main stress at the bottom of the ballast layer. As a result, addinggeogridtotheballastlayerandbetweentheballastandsub-ballast layer in a ballastedrailwaypavementconstruction has beenfoundtolowercriticalshearand main stresses as well as verticalsurfacedeflection.

Keywords: geosynthetics, geogrid, railway, transportation

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Hammad Riaz, Abubakr Saeed

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Environmental Management System (EMS), i.e., ISO 14001 helps to build corporate reputation, legitimacy and can also be considered as firms’ strategic response to institutional pressure to reduce the impact of business activity on natural environment. The financial outcomes of certifying with ISO 14001 are still unclear and equivocal. Drawing on institutional and market learning theories, the impact of ISO 14001 on firms’ market performance is examined for Chinese firms. By employing rigorous event study approach, this paper compared ISO 14001 certified firms with non-certified counterpart firms based on different matching criteria that include size, return on assets and industry. The results indicate that the ISO 14001 has been negatively signed by the investors both in the short and long-run. This paper suggested implications for policy makers, managers, and other nonprofit organizations.

Keywords: ISO 14001, legitimacy, institutional forces, event study approach, emerging markets

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Authors: Marcel Duh, Jadranka Stricevic, David Halozan, Dusan Celan

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Back pain is not only the result of structural or biomechanical abnormalities of the spine but is also associated with cognitive and behavioral aspects of pain and thus represents biopsychosocial problem. Stressors are not only interpersonal conflicts, negative life events, and dangerous situations but also noise. Effects of noise on human beings are psychological (excitement, stress), sensory, and physiological. The harmful effects of noise can be seen in the 40-65 dB range and are manifested as fatigue, irritability, poor sleep and psychological discomfort of the worker. Within 65-90 dB range, body metabolism increases, oxygen consumption is higher, tachycardia and hypertension appear, and the tone of skeletal muscles increases. The purpose of the study was to determine whether the stress caused by noise at the work place increases the incidence of back pain. Measurements of noise levels were carried out in three different wards of social care institution. The measurement on each ward was repeated 3 times (total of 9 measurements) for 8 hours during the morning shift. The device was set up in the room where clients spent most of the day. The staff on the ward replied to the questionnaire consisting of closed type questions about basic demographic information and information about back pain. We find that noise levels as measured in our study had no statistically significant effect on the incidence of back pain (p = 0.90). We also find that health care workers who perceive their work as stressful, have more back pain than those who perceive their job as unstressful, but correlation is statistically insignificant (p = 0.682). With our study, we have proven findings of other authors, that noise level below 65 dB does not have a significant influence on the incidence of back pain.

Keywords: health care workers, musculoskeletal disorder, noise, sick leave

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Authors: Roxanne J. Kovacs

Abstract:

This paper shows that political corruption in Ghana and Kenya does not, as is assumed by a considerable part of the academic literature, depend on the level of party competition as such, but rather on the kinds of issues that parties compete about. Party competition in Ghana revolves around party performance, which gives political leaders a strong incentive to control corruption. In contrast, party competition in Kenya revolves around ethnic identities, which directly reduces competition based on candidate quality and therefore fosters corruption.

Keywords: corruption, electoral competition, Kenya, Ghana

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Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

Abstract:

This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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