Search results for: deep brain stimulation

Authors: Fernando Ojedo, Alejandro Alvarez, Pedro Macizo

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In the present study, we explored electrophysiological correlates of Arabic digits naming to determine semantic processing of numbers. Participants named Arabic digits grouped by category or intermixed with exemplars of other semantic categories while the N400 event-related potential was examined. Around 350-450 ms after the presentation of Arabic digits, brain waves were more positive in anterior regions and more negative in posterior regions when stimuli were grouped by category relative to the mixed condition. Contrary to what was found in other studies, electrophysiological results suggested that the production of numerals involved semantic mediation.

Keywords: Arabic digit naming, event-related potentials, semantic processing, number production

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Chanya Inprasit, Yi-Wen Lin

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Motion sickness (MS) is an acute disorder that occurs in healthy persons without considering gender, age or ethnicity worldwide. All signs and symptoms of MS are the results of confliction and mismatch among neural signal inputs. It is known that no singular remedy works for everybody, and electroacupuncture (EA) is one of the popular alternative therapies used for MS. Our study utilized a mouse model in order to exclude any psychological factors of MS and EA. Mice lack an emetic reflex. Therefore pica behavior, which is a normal consumption of non-nutritive substances, was found to measure the response of MS in mice. In the laboratory, Kaolin was used as a non-nutrient food substance instead of natural substances lacking nutritional value such as wood, cloth, charcoal, soil or grass. It was hypothesized that EA treatment could reduce the symptoms of MS through the TRPV1 pathways. The results of pica behavior showed a significantly increased intake of kaolin in the MS group throughout the experiment period. Moreover, the Kaolin intake of the EA group decreased to the average baseline of the control group. There was no recorded difference in the food and water intake of each group. The results indicated an increase of the TRPV1, pERK, pJNK and pmTOR protein levels in the thalamus after MS stimulation, and a significant decrease in the EA group compared with that of the control group. These findings suggest that TRPV1 pathways are associated in MS mechanisms and can be reduced by EA.

Keywords: electroacupuncture, motion sickness, Thalamus, TRPV1

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Authors: Clifford Kwakwa Amoah, De-Graft Owusu-Manu, Prince Antwi-Afari

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Infrastructure is the lifeline for economic development of any country. Hence, obtaining infrastructure quality cannot be overemphasized. Nevertheless, challenges of infrastructure quality persist, and it is worse in developing countries despite the diverse study on the subject matter. Therefore, this study was formulated to identify the prevalent determinants of infrastructure quality using synthesis of extant literature (to identify key variables), and analysis of survey questionnaire of data collected by means of the inductive methodology approach, mean score ranking and descriptive statistics. The variables “partner with the private sector, growth stimulation and poverty reduction, and adherence to procurement core principles” were the most significant challenges that the government faces. Moreover, it would be of utmost concern to adopt some stringent measures to help improve and accelerate on the growth and development of the nation, thereby achieving the best quality required. This study is novel conducted to provide insight into some of the punitive measures, considered in ensuring that quality infrastructure is obtained in both developing (specifically) and developed economies. The research findings therefore provide some guidance for overcoming the accumulative challenges. Application of the stated findings will help bridge the gap of infrastructure challenges; this is because the study found strong empirical evidence that infrastructure plays a pivotal role in the productivity enhancement.

Keywords: challenges, development, economic growth, government, infrastructure quality

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Authors: Nirmal Kundu, Chandan Bhar, Visveswaran Pandurangan

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Technology is the most valued possession for a country or an organization. The economic development depends not on stock of technology but on the capabilities how the technology is being exploited. The technology transfer is the best way how the developing countries have an access to state-of- the-art technology. Traditional technology transfer is a unidirectional phenomenon where technology is transferred from developed to developing countries. But now there is a change of wind. There is a general agreement that global shift of economic power is under way from west to east. As China and India are making the transition from users to producers, and producers to innovators, this has increasing important implications on economy, technology and policy of global trade. As a result, Reverse technology transfer has become a phenomenon and field of study in technology management. The term “Reverse Technology Transfer” is not well defined. Initially the concept of Reverse technology transfer was associated with the phenomenon of “Brain drain” from developing to developed countries. In the second phase, Reverse Technology Transfer was associated with the transfer of knowledge and technology from subsidiaries to multinationals. Finally, time has come now to extend the concept of reverse technology transfer to two different organizations or countries related or unrelated by traditional technology transfer but the transfer or has essentially received the technology through traditional mode of technology transfer. The objective of this paper is to study; 1) the present status of Reverse technology transfer, 2) the factors which are the enablers and barriers of Reverse technology transfer and 3) how the reverse technology transfer strategy can be integrated in the technology policy of a country which will give the countries an economic boost. The research methodology used in this study is a combination of literature review, case studies and key informant interviews. The literature review includes both published as well as unpublished sources of literature. In case study, attempt has been made to study the records of reverse technology transfer that have been occurred in developing countries. In case of key informant interviews, informal telephonic discussions have been carried out with the key executives of the organizations (industry, university and research institutions) who are actively engaged in the process of technology transfer- traditional as well as reverse. Reverse technology transfer is possible only by creating technological capabilities. Following four important enablers coupled with government active and aggressive action can help to build technology base to reach to the goal of Reverse technology transfer 1) Imitation to innovation, 2) Reverse engineering, 3) Collaborative R & D approach, and 4) Preventing reverse brain drain. The barriers that come in the way are the mindset of over dependence, over subordination and parent–child attitude (not adult attitude). Exploitation of these enablers and overcoming the barriers of reverse technology transfer, the developing countries like India and China can prove that going “reverse” is the best way to move forward and again establish themselves as leader of the future world.

Keywords: barriers of reverse technology transfer, enablers of reverse technology transfer, knowledge transfer, reverse technology transfer, technology transfer

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Authors: Bassam Band, Simon Freeman, Rohan Munir, Hisham Band

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Objective: To determine efficacy of facial physiotherapy provided for patients with facial nerve palsy. Design: Retrospective study Subjects: 54 patients diagnosed with Facial nerve palsy were included in the study after they met the selection criteria including unilateral facial paralysis and start of therapy twelve months after the onset of facial nerve palsy. Interventions: Patients received the treatment offered at a facial physiotherapy clinic consisting of: Trophic electrical stimulation, surface electromyography with biofeedback, neuromuscular re-education and myofascial release. Main measures: The Sunnybrook facial grading scale was used to evaluate the severity of facial paralysis. Results: This study demonstrated the positive impact of physiotherapy for patient with facial nerve palsy with improvement of 24.2% on the Sunnybrook facial grading score from a mean baseline of 34.2% to 58.2%. The greatest improvement looking at different causes was seen in patient who had reconstructive surgery post Acoustic Neuroma at 31.3%. Conclusion: The therapy shows significant improvement for patients with facial nerve palsy even when started 12 months post onset of paralysis across different causes. This highlights the benefit of this non-invasive technique in managing facial nerve paralysis and possibly preventing the need for surgery.

Keywords: facial nerve palsy, treatment, physiotherapy, bells palsy, acoustic neuroma, ramsey-hunt syndrome

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Authors: Umbreen Salim, Anila Noor

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This paper analyses the influences and faces of colonialism in women’s participation in economic activity in postcolonial Pakistan, through postcolonial feminist economic lens. It is an attempt to probe the shifts in gender inequalities that have existed in three stages; pre-colonial, colonial, and postcolonial times in the Indo-Pak subcontinent. It delves into an inquiry of pre-colonial as it is imperative to understand the situation and context before colonisation in order to assess the deviations associated with its onset. Hence, in order to trace gender inequalities this paper analyses from Mughal Era (1526-1757) that existed before British colonisation, then, the gender inequalities that existed during British colonisation (1857- 1947) and the associated dynamics and changes in women’s vulnerabilities to participate in the economy are examined. Followed by, the postcolonial (1947 onwards) scenario of discriminations and oppressions faced by women. As part of the research methodology, primary and secondary data analysis was done. Analysis of secondary data including literary works and photographs was carried out, followed by primary data collection using ethnographic approaches and participatory tools to understand the presence of coloniality and gender inequalities embedded in the social structure through participant’s real-life stories. The data is analysed using feminist postcolonial analysis. Intersectionality has been a key tool of analysis as the paper delved into the gender inequalities through the class and caste lens briefly touching at religion. It is imperative to mention the significance of the study and very importantly the practical challenges as historical analysis of 18th and 19th century is involved. Most of the available work on history is produced by a) men and b) foreigners and mostly white authors. Since the historical analysis is mostly by men the gender analysis presented misses on many aspects of women’s issues and since the authors have been mostly white European gives it as Mohanty says, ‘under western eyes’ perspective. Whereas the edge of this paper is the authors’ deep attachment, belongingness as lived reality and work with women in Pakistan as postcolonial subjects, a better position to relate with the social reality and understand the phenomenon. The study brought some key results as gender inequalities existed before colonisation when women were hidden wheel of stable economy which was completely invisible. During the British colonisation, the vulnerabilities of women only increased and as compared to men their inferiority status further strengthened. Today, the postcolonial woman lives in deep-rooted effects of coloniality where she is divided in class and position within the class, and she has to face gender inequalities within household and in the market for economic participation. Gender inequalities have existed in pre-colonial, during colonisation and postcolonial times in Pakistan with varying dynamics, degrees and intensities for women whereby social class, caste and religion have been key factors defining the extent of discrimination and oppression. Colonialism may have physically ended but the coloniality remains and has its deep, broad and wide effects in increasing gender inequalities in women’s participation in the economy in Pakistan.

Keywords: colonialism, economic participation, gender inequalities, women

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Authors: Yung-Chih Kuo, I-Hsin Wang

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Catanionic solid lipid nanoparticles (CASLNs) with surface wheat germ agglutinin (WGA) and lactoferrin (Lf) were formulated for entrapping and releasing etoposide (ETP), crossing the blood–brain barrier (BBB), and inhibiting the growth of glioblastoma multiforme (GBM). Microemulsified ETP-CASLNs were modified with WGA and Lf for permeating a cultured monolayer of human brain-microvascular endothelial cells (HBMECs) regulated by human astrocytes and for treating malignant U87MG cells. Experimental evidence revealed that an increase in the concentration of catanionic surfactant from 5 μM to 7.5 μM reduced the particle size. When the concentration of catanionic surfactant increased from 7.5 μM to 12.5 μM, the particle size increased, yielding a minimal diameter of WGA-Lf-ETP-CASLNs at 7.5 μM of catanionic surfactant. An increase in the weight percentage of BW from 25% to 75% enlarged WGA-Lf-ETP-CASLNs. In addition, an increase in the concentration of catanionic surfactant from 5 to 15 μM increased the absolute value of zeta potential of WGA-Lf-ETP-CASLNs. It was intriguing that the increment of the charge as a function of the concentration of catanionic surfactant was approximately linear. WGA-Lf-ETP-CASLNs revealed an integral structure with smooth particle contour, displayed a lighter exterior layer of catanionic surfactant, WGA, and Lf and showed a rigid interior region of solid lipids. A variation in the concentration of catanionic surfactant between 5 μM and 15 μM yielded a maximal encapsulation efficiency of ETP ata 7.5 μM of catanionic surfactant. An increase in the concentration of Lf/WGA decreased the grafting efficiency of Lf/WGA. Also, an increase in the weight percentage of ETP decreased its encapsulation efficiency. Moreover, the release rate of ETP from WGA-Lf-ETP-CASLNs reduced with increasing concentration of catanionic surfactant, and WGA-Lf-ETP-CASLNs at 12.5 μM of catanionic surfactant exhibited a feature of sustained release. The order in the viability of HBMECs was ETP-CASLNs ≅ Lf-ETP-CASLNs ≅ WGA-Lf-ETP-CASLNs > ETP. The variation in the transendothelial electrical resistance (TEER) and permeability of propidium iodide (PI) was negligible when the concentration of Lf increased. Furthermore, an increase in the concentration of WGA from 0.2 to 0.6 mg/mL insignificantly altered the TEER and permeability of PI. When the concentration of Lf increased from 2.5 to 7.5 μg/mL and the concentration of WGA increased from 2.5 to 5 μg/mL, the enhancement in the permeability of ETP was minor. However, 10 μg/mL of Lf promoted the permeability of ETP using Lf-ETP-CASLNs, and 5 and 10 μg/mL of WGA could considerably improve the permeability of ETP using WGA-Lf-ETP-CASLNs. The order in the efficacy of inhibiting U87MG cells was WGA-Lf-ETP-CASLNs > Lf-ETP-CASLNs > ETP-CASLNs > ETP. As a result, WGA-Lf-ETP-CASLNs reduced the TEER, enhanced the permeability of PI, induced a minor cytotoxicity to HBMECs, increased the permeability of ETP across the BBB, and improved the antiproliferative efficacy of U87MG cells. The grafting of WGA and Lf is crucial to control the medicinal property of ETP-CASLNs and WGA-Lf-ETP-CASLNs can be promising colloidal carriers in GBM management.

Keywords: catanionic solid lipid nanoparticle, etoposide, glioblastoma multiforme, lactoferrin, wheat germ agglutinin

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Ruxandra Aursulesei, David O’Callaghan, Cian Ryan, Diarmaid O’Cualain, Viktor Varkarakis, Alina Sultana, Joseph Lemley

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Human errors caused by drowsiness are among the leading causes of road accidents. Neurobiological research gives information about the electrical signals emitted by neurons firing within the brain. Electrical signal frequencies can be determined by attaching bio-sensors to the head surface. By observing the electrical impulses and the rhythmic interaction of neurons with each other, we can predict the mental state of a person. In this paper, we aim to better understand intersubject and intrasubject variability in terms of electrophysiological patterns that occur at the onset of drowsiness and their evolution with the decreasing of vigilance. The purpose is to lay the foundations for an algorithm that detects the onset of drowsiness before the physical signs become apparent.

Keywords: electroencephalography, drowsiness, ADAS, annotations, clinician

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

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Authors: R. Simões, M. Silveirinha

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Driven by the need to understand the results of previous research that clearly shows deep unbalances of the media discourses about women and men in spite of the growing numbers of female journalists, our paper aims to progress from the 'what' to the 'why' of these unbalanced representations. Furthermore, it does so at a time when journalism is undergoing a dramatic change in terms of professional practices and in how media organizations are organized and run, affecting women in particular. While some feminist research points to the fact that female and male journalists evaluate the role of the news and production methods in similar ways feminist theorizing also suggests that thought and knowledge are highly influenced by social identity, which is also inherently affected by the experiences of gender. This is particularly important at a time of deep societal and professional changes. While there are persuasive discussions of gender identities at work in newsrooms in various countries studies on the issue will benefit from cases that focus on the particularities of local contexts. In our paper, we present one such case: the case of Portugal, a country hit hard by austerity measures that have affected all cultural industries including journalism organizations, already feeling the broader impacts of the larger societal changes of the media landscape. Can we gender these changes? How are they felt and understood by female and male journalists? And how are these discourses framed by androcentric, feminist and post-feminist sensibilities? Foregrounding questions of gender, our paper seeks to explore some of the interactions of societal and professional forces, identifying their gendered character and outlining how they shape journalism work in general and the production of unbalanced gender representations in particular. We do so grounded in feminist studies of journalism as well as feminist organizational and work studies, looking at a corpus of 20 in-depth interviews of female and male Portuguese journalists. The research findings illustrate how gender in journalism practices interacts with broader experiences of the cultural and economic contexts and show the ambivalences of these interactions in news organizations.

Keywords: gender, journalism, newsroom culture, Portuguese journalists

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Authors: Liliana Villavicencio, Yohn Garcia, Pallavi Singh, Luis Fernando Cruz, Wilfrido Moreno

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Psychophysiological adaptive automation is a concept that combines human physiological data and computer algorithms to create personalized interfaces and experiences for users. This approach aims to enhance human learning by adapting to individual needs and preferences and optimizing the interaction between humans and machines. According to neurosciences, the working memory demand during the student learning process is modified when the student is learning a new subject or topic, managing and/or fulfilling a specific task goal. A sudden increase in working memory demand modifies the level of students’ attention, engagement, and cognitive load. The proposed psychophysiological adaptive automation system will adapt the task requirements to optimize cognitive load, the process output variable, by monitoring the student's brain activity. Cognitive load changes according to the student’s previous knowledge, the type of task, the difficulty level of the task, and the overall psychophysiological state of the student. Scaling the measured cognitive load as low, medium, or high; the system will assign a task difficulty level to the next task according to the ratio between the previous-task difficulty level and student stress. For instance, if a student becomes stressed or overwhelmed during a particular task, the system detects this through signal measurements such as brain waves, heart rate variability, or any other psychophysiological variables analyzed to adjust the task difficulty level. The control of engagement and stress are considered internal variables for the hypermedia system which selects between three different types of instructional material. This work assesses the feasibility of a fuzzy controller to track a student's physiological responses and adjust the learning content and pace accordingly. Using an industrial automation approach, the proposed fuzzy logic controller is based on linguistic rules that complement the instrumentation of the system to monitor and control the delivery of instructional material to the students. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the delivery of academic content based on the working memory demand without compromising students’ health. This work has a potential application in the instructional design of virtual reality environments for training and education.

Keywords: fuzzy logic controller, hypermedia control system, personalized education, psychophysiological adaptive automation

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Authors: Tery Nehemia Nugraha Joseph, J. D. P. Wisnubroto

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Thyroid cancer is a healthcare problem with high morbidity and mortality. Follicular adenoma and follicular carcinoma are thyroid tumors from the thyroid follicular cells differentiation with a microfollicular pattern that consists of follicular cuboidal cells. vascular endothelial growth factor (VEGF) is a potent and powerful mitogen for endothelial cells and increases vascular permeability. Therefore, due to an increase in thyroid-stimulating hormone (TSH), VEGF production is activated in the thyroid that leads to the end of mitogenic TSH stimulation and initiation of angiogenesis. The differences in VEGF levels in the follicular carcinoma of thyroid tissue with follicular adenoma thyroid can be used as a basis in differentiating the two types of neoplasms. This study aims to analyze VEGF in the serum so that it can be used to differentiate the types of thyroid carcinoma before surgery. This study uses a cross-sectional research design. Samples were carried out by taking serum samples, and the VEGF levels were calculated. Data were analyzed using the Mann-Whitney test. The results found a significant difference between VEGF levels in the follicular carcinoma thyroid group and VEGF levels in the follicular adenoma thyroid group with a value of p = 0.007 (p < 0.05). The results obtained are 560,427 ± 160,506 ng/mL in the type of follicular carcinoma thyroid and 320.943 ± 134.573 ng/mL in the type of follicular adenoma thyroid. VEGF levels between follicular adenoma and follicular carcinoma are different. VEGF levels are higher in follicular carcinoma thyroid than follicular adenoma thyroid.

Keywords: follicular adenoma thyroid, follicular carcinoma thyroid, thyroid, VEGF

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Authors: Kaouther Selmi, Alaeddine Sridi, Mohamed Bouallegue, Kais Bouallegue

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Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology.

Keywords: neural networks, neuron, dendrites, heart behavior, ECG

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Authors: Kotaro Sasai, Daijiro Mizutani, Kiyoyuki Kaito

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Expressways in Japan have been built in an accelerating manner since the 1960s with the aid of rapid economic growth. About 40 percent in length of expressways in Japan is now 30 years and older and has become superannuated. Time-related deterioration has therefore reached to a degree that administrators, from a standpoint of operation and maintenance, are forced to take prompt measures on a large scale aiming at repairing inner damage deep in pavements. These measures have already been performed for bridge management in Japan and are also expected to be embodied for pavement management. Thus, planning methods for the measures are increasingly demanded. Deterioration of layers around road surface such as surface course and binder course is brought about at the early stages of whole pavement deterioration process, around 10 to 30 years after construction. These layers have been repaired primarily because inner damage usually becomes significant after outer damage, and because surveys for measuring inner damage such as Falling Weight Deflectometer (FWD) survey and open-cut survey are costly and time-consuming process, which has made it difficult for administrators to focus on inner damage as much as they have been supposed to. As expressways today have serious time-related deterioration within them deriving from the long time span since they started to be used, it is obvious the idea of repairing layers deep in pavements such as base course and subgrade must be taken into consideration when planning maintenance on a large scale. This sort of maintenance requires precisely predicting degrees of deterioration as well as grasping the present situations of pavements. Methods for predicting deterioration are determined to be either mechanical or statistical. While few mechanical models have been presented, as far as the authors know of, previous studies have presented statistical methods for predicting deterioration in pavements. One describes deterioration process by estimating Markov deterioration hazard model, while another study illustrates it by estimating Proportional deterioration hazard model. Both of the studies analyze deflection data obtained from FWD surveys and present statistical methods for predicting deterioration process of layers around road surface. However, layers of base course and subgrade remain unanalyzed. In this study, data collected from FWD surveys are analyzed to predict deterioration process of layers deep in pavements in addition to surface layers by a means of estimating a deterioration hazard model using continuous indexes. This model can prevent the loss of information of data when setting rating categories in Markov deterioration hazard model when evaluating degrees of deterioration in roadbeds and subgrades. As a result of portraying continuous indexes, the model can predict deterioration in each layer of pavements and evaluate it quantitatively. Additionally, as the model can also depict probability distribution of the indexes at an arbitrary point and establish a risk control level arbitrarily, it is expected that this study will provide knowledge like life cycle cost and informative content during decision making process referring to where to do maintenance on as well as when.

Keywords: deterioration hazard model, falling weight deflectometer, inner damage, load bearing capacity, pavement

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Authors: Benjamin J. Kuo, Silvia D. Vaca, Joao Ricardo Nickenig Vissoci, Catherine A. Staton, Linda Xu, Michael Muhumuza, Hussein Ssenyonjo, John Mukasa, Joel Kiryabwire, Lydia Nanjula, Christine Muhumuza, Henry E. Rice, Gerald A. Grant, Michael M. Haglund

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Background: Traumatic Brain Injury (TBI) is disproportionally concentrated in low- and middle-income countries (LMICs), with the odds of dying from TBI in Uganda more than 4 times higher than in high income countries (HICs). The disparities in the injury incidence and outcome between LMICs and resource-rich settings have led to increased health outcomes research for TBIs and their associated risk factors in LMICs. While there have been increasing TBI studies in LMICs over the last decade, there is still a need for more robust prospective registries. In Uganda, a trauma registry implemented in 2004 at the Mulago National Referral Hospital (MNRH) showed that RTI is the major contributor (60%) of overall mortality in the casualty department. While the prior registry provides information on injury incidence and burden, it’s limited in scope and doesn’t follow patients longitudinally throughout their hospital stay nor does it focus specifically on TBIs. And although these retrospective analyses are helpful for benchmarking TBI outcomes, they make it hard to identify specific quality improvement initiatives. The relationship among epidemiology, patient risk factors, clinical care, and TBI outcomes are still relatively unknown at MNRH. Objective: The objectives of this study are to describe the processes of care and determine risk factors predictive of poor outcomes for TBI patients presenting to a single tertiary hospital in Uganda. Methods: Prospective data were collected for 563 TBI patients presenting to a tertiary hospital in Kampala from 1 June – 30 November 2016. Research Electronic Data Capture (REDCap) was used to systematically collect variables spanning 8 categories. Univariate and multivariate analysis were conducted to determine significant predictors of mortality. Results: 563 TBI patients were enrolled from 1 June – 30 November 2016. 102 patients (18%) received surgery, 29 patients (5.1%) intended for surgery failed to receive it, and 251 patients (45%) received non-operative management. Overall mortality was 9.6%, which ranged from 4.7% for mild and moderate TBI to 55% for severe TBI patients with GCS 3-5. Within each TBI severity category, mortality differed by management pathway. Variables predictive of mortality were TBI severity, more than one intracranial bleed, failure to receive surgery, high dependency unit admission, ventilator support outside of surgery, and hospital arrival delayed by more than 4 hours. Conclusions: The overall mortality rate of 9.6% in Uganda for TBI is high, and likely underestimates the true TBI mortality. Furthermore, the wide-ranging mortality (3-82%), high ICU fatality, and negative impact of care delays suggest shortcomings with the current triaging practices. Lack of surgical intervention when needed was highly predictive of mortality in TBI patients. Further research into the determinants of surgical interventions, quality of step-up care, and prolonged care delays are needed to better understand the complex interplay of variables that affect patient outcome. These insights guide the development of future interventions and resource allocation to improve patient outcomes.

Keywords: care continuum, global neurosurgery, Kampala Uganda, LMIC, Mulago, prospective registry, traumatic brain injury

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Authors: Annu Panghal, Bimla Dhanda

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This research paper aims to determine the genetic-environment influences on the cognitive abilities of twins. Using the 100 pairs of twins from two districts, namely: Bhiwani (N = 90) and Hisar (N = 110) of Haryana State, genetic and environmental influences were assessed in twin study design. The cognitive abilities of twins were measured using the Wechsler Intelligence Scale for Children (WISC-R). Home Observation for Measurement of the Environment (HOME) Inventory was taken to examine the home environment of twins. Heritability estimate was used to analyze the genes contributing to shape the cognitive abilities of twins. The heritability estimates for cognitive abilities of 6-7 years old twins in Hisar district were 74% and in Bhiwani District 76%. Further the heritability estimates were 64% in the twins of Hisar district and 60 in Bhiwani district % in the age group of 7-8 years. The remaining variations in the cognitive abilities of twins were due to environmental factors namely: provision for Active Stimulation, paternal involvement, safe physical environment. The findings provide robust evidence that the cognitive abilities were more influenced by genes than the environmental factors and also revealed that the influence of genetic was more in the age group 6-7 years than the age group 7-8 years. The conclusion of the heritability estimates indicates that the genetic influence was more in the age group of 6-7 years than the age group of 7-8 years. As the age increases the genetic influence decreases and environment influence increases. Mother education was strongly associated with the cognitive abilities of twins.

Keywords: genetics, heritability, twins, environment, cognitive abilities

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Authors: Auj-E Taqaddas

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The use of proton beam therapy is increasing globally. It seems to offer dosimetric advantages, especially in paediatric central nervous system (CNS) and brain tumours. A short E-survey was conducted to assess the clinical, technical, and educational resources and strategies employed in the state of the art proton beam therapy (PBT) centres in the USA to determine the current status of proton beam therapy. The study also aimed at finding out which PBT skills are in demand as well as what improvements are needed to ensure efficient treatment planning, delivery, and dosimetry. The study resulted in identifying areas for future research and development and in identifying cancers for which PBT is most suitable compared to other modalities to facilitate the implementation and use of PBT in clinical settings for cancer treatment.

Keywords: cancer, intensity modulated proton therapy, proton beam therapy, single field uniform scanning

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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MicroRNAs (miRNA) are a class of endogenous, single-stranded, small, and non-protein coding RNA molecules typically 20-25 nucleotides long. They are thought to regulate the expression of other genes in a broad range by binding to 3’- untranslated regions (3’-UTRs) of specific mRNAs. The detection of miRNAs is very important for understanding of the function of these molecules and in the diagnosis of variety of human diseases. However, detection of miRNAs is very challenging because of their short length and high sequence similarities within miRNA families. So, a simple-to-use, low-cost, and highly sensitive method for the detection of miRNAs is desirable. In this study, we demonstrate a novel bi-directional extension (BDE) assay. In the first step, a specific linear RT primer is hybridized to 6-10 base pairs from the 3’-end of a target miRNA molecule and then reverse transcribed to generate a cDNA strand. After reverse transcription, the cDNA was hybridized to the 3’-end which is BDE sequence; it played role as the PCR template. The PCR template was amplified in an SYBR green-based quantitative real-time PCR. To prove the concept, we used human brain total RNA. It could be detected quantitatively in the range of seven orders of magnitude with excellent linearity and reproducibility. To evaluate the performance of BDE assay, we contrasted sensitivity and specificity of the BDE assay against a commercially available poly (A) tailing method using miRNAs for let-7e extracted from A549 human epithelial lung cancer cells. The BDE assay displayed good performance compared with a poly (A) tailing method in terms of specificity and sensitivity; the CT values differed by 2.5 and the melting curve showed a sharper than poly (A) tailing methods. We have demonstrated an innovative, cost-effective BDE assay that allows improved sensitivity and specificity in detection of miRNAs. Dynamic range of the SYBR green-based RT-qPCR for miR-145 could be represented quantitatively over a range of 7 orders of magnitude from 0.1 pg to 1.0 μg of human brain total RNA. Finally, the BDE assay for detection of miRNA species such as let-7e shows good performance compared with a poly (A) tailing method in terms of specificity and sensitivity. Thus BDE proves a simple, low cost, and highly sensitive assay for various miRNAs and should provide significant contributions in research on miRNA biology and application of disease diagnostics with miRNAs as targets.

Keywords: bi-directional extension (BDE), microRNA (miRNA), poly (A) tailing assay, reverse transcription, RT-qPCR

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Authors: Faizan Tariq, Moayid Ali Zaidi

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Facial emotion recognition algorithms are expanding rapidly now a day. People are using different algorithms with different combinations to generate best results. There are six basic emotions which are being studied in this area. Author tried to recognize the facial expressions using object detector algorithms instead of traditional algorithms. Two object detection algorithms were chosen which are Faster R-CNN and YOLO. For pre-processing we used image rotation and batch normalization. The dataset I have chosen for the experiments is Static Facial Expression in Wild (SFEW). Our approach worked well but there is still a lot of room to improve it, which will be a future direction.

Keywords: face recognition, emotion recognition, deep learning, CNN

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Authors: Ketevan Goletiani, Parmen Khvedelidze

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discusses the importance of the topic, which is reflected in the advanced and developed countries in the formation of a new innovative stage of the distinctive mark of the modern world development. This phase includes the construction of the economy, which generates stockpiling and use is based. Intensifying the production and use of the results of new scientific and technical innovation has led to a sharp reduction in the cycle and accelerate the pace of product and technology updates. The world's leading countries in the development of innovative management systems for the formation of long-term and stable development of the socio-economic order conditions. The last years of the 20th century, the social and economic relations, modification, accelerating economic reforms, and profound changes in the system of the time. At the same time, the country should own place in the world geopolitical and economic space. Accelerated economic development tasks, the World Trade Organization, the European Union deep and comprehensive trade agreement, the new system of economic management, technical and technological renewal of production potential, and scientific fields in the share of the total volume of GDP growth requires new approaches. XX - XXI centuries Georgia's socio-economic changes is one of the urgent tasks in the form of a rise to the need for change, involving the use of natural resource-based economy to the latest scientific and technical achievements of an innovative and dynamic economy based on an accelerated pace. But Georgia still remains unresolved in many methodological, theoretical, and practical nature of the problem relating to the management of the economy in various fields for the development of innovative systems for optimal implementation. Therefore, the development of an innovative system for the formation of a complex and multi-problem, which is reflected in the following: countries should have higher growth rates than the geopolitical space of the neighboring countries that its competitors are. Formation of such a system is possible only in a deep theoretical research and innovative processes in the multi-level (micro, meso- and macro-levels) management on the basis of creation.

Keywords: georgia, innovative, socio-economic, innovative manage

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Authors: Pranjal Srivastava, Piyali Sengupta

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The performance of marine drilling risers is crucial in the offshore oil and gas industry to ensure safe drilling operation with minimum downtime. Experimental investigations on marine drilling risers are limited in the literature owing to the expensive and exhaustive test setup required to replicate the realistic riser model and ocean environment in the laboratory. Therefore, this study presents an analytical model of marine drilling riser for determining its fragility under ocean environmental loading. In this study, the marine drilling riser is idealized as a continuous beam having a concentric circular cross-section. Hydrodynamic loading acting on the marine drilling riser is determined by Morison’s equations. By considering the equilibrium of forces on the marine drilling riser for the connected and normal drilling conditions, the governing partial differential equations in terms of independent variables z (depth) and t (time) are derived. Subsequently, the Runge Kutta method and Finite Difference Method are employed for solving the partial differential equations arising from the analytical model. The proposed analytical approach is successfully validated with respect to the experimental results from the literature. From the dynamic analysis results of the proposed analytical approach, the critical design parameters peak displacements, upper and lower flex joint rotations and von Mises stresses of marine drilling risers are determined. An extensive parametric study is conducted to explore the effects of top tension, drilling depth, ocean current speed and platform drift on the critical design parameters of the marine drilling riser. Thereafter, incremental dynamic analysis is performed to derive the fragility functions of shallow water and deep-water marine drilling risers under ocean environmental loading. The proposed methodology can also be adopted for downtime estimation of marine drilling risers incorporating the ranges of uncertainties associated with the ocean environment, especially at deep and ultra-deepwater.

Keywords: drilling riser, marine, analytical model, fragility

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Cheba Ben Amar

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Bacillus and Paenibacillus are Gram-positive aerobic endospore-forming bacteria (AEFB) and most abundant in the rhizosphere, they mediated plant growth promotion and disease protection by several complex and interrelated processes involving direct and indirect mechanisms that include nitrogen fixation, phosphate solubilization, siderophores production, phytohormones production and plant diseases control. In addition to their multiple PGPR properties, high secretory capacity, spore forming ability and spore resistance to unfavorable conditions enabling their extended commercial applications for long shelf-life. Due to these unique advantages, Bacillus species were the most an ideal candidate for developing efficient PGPR products such as biopesticides, fungicides and fertilizers. This review list all studied and reported plant growth promoting Bacillus species and strains, discuss their capacities to enhance plant growth and protection with special focusing on the most frequent species Bacillus subtilis, B. pumilus ,B. megaterium, B. amyloliquefaciens , B. licheniformis and B. sphaericus, furthermore we recapitulate the beneficial activities and mechanisms of several species and strains of the genus Paenibacillus involved in plant growth stimulation and plant disease control.

Keywords: bacillus, paenibacillus, PGPR, beneficial activities, mechanisms, growth promotion, disease control, agrobiotechnology

Procedia PDF Downloads 392

Authors: Javier Muro, Anja Linstadter, Florian Manner, Lisa Schwarz, Stephan Wollauer, Paul Magdon, Gohar Ghazaryan, Olena Dubovyk

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Monitoring the trade-off between biomass production and biodiversity in grasslands is critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can model grasslands’ characteristics with varying accuracies. However, studies often fail to cover a sufficiently broad range of environmental conditions, and evidence suggests that prediction models might be case specific. In this study, biomass production and biodiversity indices (species richness and Fishers’ α) are modeled in 150 grassland plots for three sites across Germany. These sites represent a North-South gradient and are characterized by distinct soil types, topographic properties, climatic conditions, and management intensities. Predictors used are derived from Sentinel-1 & 2 and a set of topoedaphic variables. The transferability of the models is tested by training and validating at different sites. The performance of feed-forward deep neural networks (DNN) is compared to a random forest algorithm. While biomass predictions across gradients and sites were acceptable (r2 0.5), predictions of biodiversity indices were poor (r2 0.14). DNN showed higher generalization capacity than random forest when predicting biomass across gradients and sites (relative root mean squared error of 0.5 for DNN vs. 0.85 for random forest). DNN also achieved high performance when using the Sentinel-2 surface reflectance data rather than different combinations of spectral indices, Sentinel-1 data, or topoedaphic variables, simplifying dimensionality. This study demonstrates the necessity of training biomass and biodiversity models using a broad range of environmental conditions and ensuring spatial independence to have realistic and transferable models where plot level information can be upscaled to landscape scale.

Keywords: ecosystem services, grassland management, machine learning, remote sensing

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Authors: Sukru Merey, Caglar Sinayuc

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Gas hydrate deposits which are found in deep ocean sediments and in permafrost regions are supposed to be a fossil fuel reserve for the future. The Black Sea is also considered rich in terms of gas hydrates. It abundantly contains gas hydrates as methane (CH₄~80 to 99.9%) source. In this study, by using the literature, seismic and other data of the Black Sea such as salinity, porosity of the sediments, common gas type, temperature distribution and pressure gradient, the optimum gas production method for the Black Sea gas hydrates was selected as mainly depressurization method. Numerical simulations were run to analyze gas production from gas hydrate deposited in turbidites in the Black Sea by depressurization.

Keywords: CH4 hydrate, Black Sea hydrates, gas hydrate experiments, HydrateResSim

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Betty Kibirige

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Students entering the University of Zululand (UNIZULU) Science Faculty mostly come with skills that allow them to prepare for exams and pass them in order to satisfy requirements for entry into a tertiary Institution. Some students hail from deep rural schools with limited facilities, while others come from well-resourced schools. Personal experience has shown that it may take a student the whole time at a tertiary institution following the same skills as those acquired in high school as a sure means of entering the next level in their development, namely a postgraduate program. While it is apparent that at this point in human history, it is totally impossible to teach all the possible content in any one subject, many academics approach teaching and learning from the traditional point of view. It therefore became apparent to explore ways of developing a graduate that will be able to approach life with skills that allows them to navigate knowledge by applying conceptual and critical thinking skills. Recently, the Science Faculty at the University of Zululand introduced two Engineering programs. In an endeavour to approach the development of the Engineering graduate in this institution to be able to tackle problem-solving in the present-day excessive information availability, it became necessary to study and review approaches used by various academics in order to settle for a possible best approach to the challenge at hand. This paper focuses on the development of a deep rural child in a graduate with conceptual and critical thinking skills as major attributes possessed upon graduation. For this purpose, various approaches were studied. A combination of these approaches was repackaged to form an approach that may appear novel to UNIZULU and the rural child, especially for the Engineering discipline. The approach was checked by offering quiz questions to students participating in an engineering module, observing test scores in the targeted module and make comparative studies. Test results are discussed in the article. It was concluded that students’ graduate attributes could be tailored subconsciously to indeed include conceptual and critical thinking skills, but through more than one approach depending mainly on the student's high school background.

Keywords: graduate attributes, conceptual skills, critical thinking skills, traditional approach

Procedia PDF Downloads 235