Search results for: Induction machine

Authors: Roman Kulikov, Svetlana Kolesnikova

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Last decade Cloud Computing technologies have been rapidly becoming ubiquitous. Each year more and more organizations, corporations, internet services and social networks trust their business sensitive information to Public Cloud. The data storage in Public Cloud is protected by security mechanisms such as firewalls, cryptography algorithms, backups, etc.. In this way, however, only outsider attacks can be prevented, whereas virtualization tools can be easily compromised by insider. The protection of Public Cloud’s critical elements from internal intruder remains extremely challenging. A hypervisor, also called a virtual machine manager, is a program that allows multiple operating systems (OS) to share a single hardware processor in Cloud Computing. One of the hypervisor's functions is to enforce access control policies. Furthermore, it prevents guest OS from disrupting each other and from accessing each other's memory or disk space. Hypervisor is the one of the most critical and vulnerable elements in Cloud Computing infrastructure. Nevertheless, it has been poorly protected from being compromised by insider. By exploiting certain vulnerabilities, privilege escalation can be easily achieved in insider attacks on hypervisor. In this way, an internal intruder, who has compromised one process, is able to gain control of the entire virtual machine. Thereafter, the consequences of insider attacks in Public Cloud might be more catastrophic and significant to virtual tools and sensitive data than of outsider attacks. So far, almost no preventive security countermeasures have been developed. There has been little attention paid for developing models to assist risks mitigation strategies. In this paper formal model of insider attacks on hypervisor is designed. Our analysis identifies critical hypervisor`s vulnerabilities that can be easily compromised by internal intruder. Consequently, possible conditions for successful attacks implementation are uncovered. Hence, development of preventive security countermeasures can be improved on the basis of the proposed model.

Keywords: insider attack, public cloud, cloud computing, hypervisor

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Authors: Mehran Fadaeinasab, Hamed Karimian, Najihah Mohd Hashim, Hapipah Mohd Ali

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In this study, three indole alkaloids namely; rauvolfine B, macusine B, and isoreserpiline have been isolated from the dichloromethane crude extract of Rauvolfia reflexa bark (Apocynaceae). The structural elucidation of the isolated compounds has been performed using spectral methods such as UV, IR, MS, 1D, and 2D NMR. Rauvolfine B showed anti proliferation activity on HCT-116 cancer cell line, its cytotoxicity induction was observed using MTT assay in eight different cell lines. Annexin-V is serving as a marker for apoptotic cells and the Annexin-V-FITC assay was carried out to observe the detection of cell-surface Phosphatidylserine (PS). Apoptosis was confirmed by using caspase-8 and -9 assays. Cell cycle arrest was also investigated using flowcytometric analysis. rauvolfine B had exhibited significantly higher cytotoxicity against HCT-116 cell line. The treatment significantly arrested HCT-116 cells in the S phase. Together, the results presented in this study demonstrated that rauvolfine B inhibited the proliferation of HCT-116 cells and programmed cell death followed by cell cycle arrest.

Keywords: apocynacea, indole alkaloid, apoptosis, cell cycle arrest

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Authors: Daira Radouane, Naim Boudmagh, Hamada Adel

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The but of this handling is to use the technique of the shearing with a mechanism lapping machine of image: a prism of Wollaston. We want to characterize this prism in order to be able to employ it later on in an analysis by shearing. A prism of Wollaston is a prism produced in a birefringent material i.e. having two indexes of refraction. This prism is cleaved so as to present the directions associated with these indices in its face with entry. It should be noted that these directions are perpendicular between them.

Keywords: non destructive control, aluminium, interferometry, treatment of image

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Mohsina Akhter

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Cancer has become a wide spread disease around the globe and takes many lives every year. Different treatments are being practiced but all have potential side effects with somewhat less specificity towards target sites. Pseudomonas aeruginosa is known to secrete a protein azurin with special anti-cancer function. It has unique cell penetrating peptide comprising of 18 amino acids that have ability to enter cancer cells specifically. Reported function of Azurin is to stabilize p53 inside the tumor cells and induces apoptosis through Bax mediated cytochrome c release from mitochondria. At laboratory scale, we have made recombinant azurin through cloning rpTZ57R/T-azu vector into E.coli strain DH-5α and subcloning rpET28-azu vector into E.coli BL21-CodonPlus (DE3). High expression was ensured with IPTG induction at different concentrations then optimized high expression level at 1mM concentration of IPTG for 5 hours. Purification has been done by using Ni+2 affinity chromatography. We have concluded that azurin can be a remarkable improvement in cancer therapeutics if it produces on a large scale. Azurin does not enter into the normal cells so it will prove a safe and secure treatment for patients and prevent them from hazardous anomalies.

Keywords: azurin, pseudomonas aeruginosa, cancer, therapeutics

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Authors: Jong Yeop Kim, Jong Bum Choi, Hyun Jeong Kwak, Sook Young Lee

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Background: Each supraglottic airway requires different anesthetic depth because it has a specific structure and different compressive force in the oropharyngeal cavity. We designed the study to investigate remifentanil effect-site concentration (Ce) in 50% of patients (EC50) for successful insertion of i- gel, and to compare it with that for laryngeal mask airway (LMA) insertion during propofol target-controlled infusion (TCI). Methods: Forty-one female patients were randomized to the i-gel group (n=20) or the LMA group (n=21). Anesthesia induction was performed using propofol Ce of 5 μg/ml and the predetermined remifentanil Ce, and i-gel or LMA insertion was attempted 5 min later. The remifentanil Ce was estimated by modified Dixon's up-and-down method (initial concentration: 3.0 ng/ml, step size: 0.5 ng/ml). The patient’s response to device insertion was classified as either ‘success (no movement)’ or ‘failure (movement)’. Results: Using the Dixon’s up and down method, EC50 of remifentanil Ce for i-gel (1.58 ± 0.41 ng/ml) was significantly lower than that for LMA (2.25 ± 0.55 ng/ml) (p=0.038). Using isotonic regression, EC50 (83% CI) of remifentanil in the i-gel group [1.50 (1.37-1.80) ng/ml] was statistically lower than that in the LMA group [2.00 (1.82-2.34) ng/ml]. EC95 (95% CI) of remifentanil in the i-gel group [2.38 (1.48-2.50) ng/ml] was statistically lower than that in the LMA group [3.35 (2.58-3.48) ng/ml]. Conclusion: We found that EC50 of remifentanil Ce for i-gel insertion (1.58 ng/ml) was significantly lower than that for LMA insertion (2.25 ng/ml), in female patients during propofol TCI without neuromuscular blockade.

Keywords: i-gel, laryngeal mask airway, propofol, remifentanil

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Authors: M. Sorahi Nobar, V. Niknam, H. Ebrahimzadeh, H. Soltanloo

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Fusarium graminearum is one of the most destructive crop diseases in the world. Infection occurs during the flowering period in warm and humid conditions. It causes reduction in yield. Moreover, harvested grain is often contaminated with mycotoxins and its acetylated derivatives. Fusarium mycotoxines are potent inhibitor of protein synthesis, and thereby presents hazards for both human and animal health. A rapid production of reactive oxygen intermediates, primarily superoxide and hydrogen peroxide at the site of attempted infection considered as key feature underlying successful pathogen recognition. Here, we compared the time course activity of superoxide dismutase (SOD) as a first line of defenses against ROS- induced oxidative burst between FHB- resistant Sumai3 and susceptible Falat at 48, 96 and 144 hours after infection. Our results showed that Sumai3 SOD activity increased with time and reached the highest-level 4 days after infection while in susceptible cultivar Falat, SOD activity decreased during the first 96 h. after infection. Decreased was followed by an increased at 6 days after infection. According to our results rapid induction of SOD activity in resistant cultivar may play an important role in resistance against FHB in wheat.

Keywords: Fusarium graminearum, mycotoxins, resistant cultivar, superoxide dismutase

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Authors: Behrooz Nikahval, Mohammad Saeed Ahrari-Khafi, Sakineh Behroozpoor, Saeed Nazifi

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Osteoarthritis (OA) is a progressive and degenerative condition of the articular cartilage and other joints’ structures. It is essential to diagnose this condition as early as possible. The present research was performed to measure the Osteoprotegrin (OPG) and Tumor Necrosis Factor α (TNF-α) in synovial fluid and blood serum of dogs with surgically transected cruciate ligament as a model of OA, to evaluate if measuring of these parameters can be used as a way of early diagnosis of OA. In the present study, four mature, clinically healthy dogs were selected to investigate the effect of experimental OA, on OPG and TNF-α as a way of early detection of OA. OPG and TNF-α were measured in synovial fluid and blood serum on days 0, 14, 28, 90 and 180 after surgical transaction of cranial cruciate ligament in one stifle joint. Statistical analysis of the results showed that there was a significant increase in TNF-α in both synovial fluid and blood serum. OPG showed a decrease two weeks after OA induction. However, it fluctuated afterward. In conclusion, TNF-α could be used in both synovial fluid and blood serum as a way of early detection of OA; however, further research still needs to be conducted on OPG values in OA detection.

Keywords: osteoarthritis, osteoprotegrin, tumor necrosis factor α, synovial fluid, serum, dog

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Authors: Nino Grigolaia

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Introduction. The paper discusses the role of the manufacturing industry in the Georgian economy, analyzes the current trends in the development of the manufacturing industry, reveals its impact on the Georgian economy, and justifies the essential importance of industrial transformation for the future development of the Georgian economy. Objectives. The main objective of research is to study development trends of the manufacturing industry of Georgia and estimate the industrial policy in Georgia. Methodology. The paper uses methods of induction, deduction, analysis, synthesis, analogy, correlation, and statistical observation. A qualitative study was conducted based on a survey of industry experts and entrepreneurs in order to identify the factors hindering and contributing to the manufacturing industry. Conclusions. The research reveals that the development of the manufacturing industry and the formation of industrial policy are of special importance for the further growth and development of the Georgian economy. Based on the research, the factors promoting and hindering the development of the manufacturing industry are identified. The need to increase foreign direct investment in the industrial sector are highlighted. Recommendations for the development of the country's manufacturing industry are developed, taking into account the competitive advantages and international experience of Georgia.

Keywords: manufacturing, industrial policy, contributing factor, hindering factor

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Chakshu Baweja, Ishaan Prakash, Deepak Giri, Prithwish Mukherjee, Herambraj Ashok Nalawade

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The need to prevent and control rampant and indiscriminate usage of energy in present-day realm on earth has motivated active research efforts aimed at understanding of controlling mechanisms leading to sustained energy. Although much has been done but complexity of the problem has prevented a complete understanding due to nonlinear interaction between flow, heat and mass transfer in terrestrial environment. Therefore, there is need for a systematic study to clearly understand mechanisms controlling energy-spreading phenomena to increase a system’s efficiency. The present work addresses the issue of sustaining energy and proposes a devoted technique of optimizing energy in the automotive domain. The proposed method focus on utilization of the mechanical and thermal energy of an automobile IC engine by converting and storing energy due to motion of a piston in form of electrical energy. The suggested technique utilizes piston motion of the engine to generate high potential difference capable of working as a secondary power source. This is achieved by the use of a gear mechanism and a flywheel.

Keywords: internal combustion engine, energy, electromagnetic induction, efficiency, gear ratio, hybrid vehicle, engine shaft

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Tigmanshu Bhatnagar, Petra Badke-Schaub

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Facilitating Design Thinking (DT) and Creative Problem Solving (CPS) in engineering education could benefit students by aiding them to think creatively and meaningfully in their education and future profession. A study in the pseudonym of a ‘popup class’ was conducted for a week at the Indian Institute of Technology, Delhi (IITD) to have an indication for the perceived relevance, benefits and challenges of DT and CPS from the perspective of engineering students in India. 30 third year Bachelor of Technology students from various technical fields participated in the study. They were introduced to the notion of DT and CPS via a mix of theoretical lectures, case discussions and practical workshops. Their reactions were identified on the basis of silent observations made during the course and responses were recorded through a questionnaire, which was filled after the course. All the respondents felt that DT and CPS are relevant to their education. It was perceived by them that there is a subtle improvement in the quality, quantity and approach of solutions to open ended problems. 90% responded positively to the induction of such an exercise in their education and reasoned it by stating that it’s important for engineers to know, how to solve open-ended real world problems in a meaningful and innovative way.

Keywords: creative problem solving, design thinking, India, undergraduate engineering education

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

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Authors: S. Studente, S. Ellis, S. F. Garivaldis

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We report upon a study introducing a chatbot to develop learning communities at a London University, with a largely international student base. The focus of the chatbot was twofold; to ease the transition for students into their first year of university study, and to increase study engagement. Four learning communities were created using the chatbot; level 3 foundation, level 4 undergraduate, level 6 undergraduate and level 7 post-graduate. Students and programme leaders were provided with access to the chat bot via mobile app prior to their study induction and throughout the autumn term of 2019. At the end of the term, data were collected via questionnaires and focus groups with students and teaching staff to allow for identification of benefits and challenges. Findings indicated a positive correlation between study engagement and engagement with peers. Students reported that the chatbot enabled them to obtain support and connect to their programme leader. Both staff and students also made recommendation on how engagement could be further enhanced using the bot in terms of; clearly specified purpose, integration with existing university systems, leading by example and connectivity. Extending upon these recommendations, a second pilot study is planned for September 2020, for which the focus will be upon improving attendance rates, student satisfaction and module pass rates.

Keywords: chatbot, e-learning, learning communities, student engagement

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Authors: Marta Kozuń, Krystian Toczewski, Sylwester Gerus, Justyna Wolicka, Kamila Boberek, Jarosław Filipiak, Dariusz Patkowski

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Long gap esophageal atresia is a congenital defect and is a challenge for pediatric surgeons all over the world. There are different surgical techniques in use to treat atresia. One of them is esophageal elongation but the optimal suture placement technique to achieve maximum elongation with low-risk complications is still unknown. The aim of the study was to characterize the process of esophageal elongation from the biomechanical point of view. Esophagi of white Pekin Duck was used as a model based on the size of this animal which is similar to a newborn (2.5-4kg). The specimens were divided into two groups: the control group (CG) and the group with sutures (SG). The esophagi of the control group were mounted in the grips of the MTS Tytron 250 testing machine and tensile test until rupture was performed. The loading speed during the test was 10mm/min. Then the SG group was tested. Each esophagus was cut into two equal parts and that were fused together using surgical sutures. The distance between both esophagus parts was 20mm. Ten both ends were mounted on the same testing machine and the tensile test with the same parameters was conducted. For all specimens, force and elongation were recorded. The biomechanical properties, i.e., the maximal force and maximal elongation, were determined on the basis of force-elongation curves. The maximal elongation was determined at the point of maximal force. The force achieved with the suture group was 10.1N±1.9N and 50.3N±11.6N for the control group. The highest elongation was also obtained for the control group: 18mm±3mm vs. 13.5mm ±2.4mm for the suture group. The presented study expands the knowledge of elongation of esophagi. It is worth emphasizing that the duck esophagus differs from the esophagus of a newborn, i.e., its wall lacks striated muscle cells. This is why the parts of animal esophagi used in the research are may characterized by different biomechanical properties in comparison with newborn tissue.

Keywords: long gap atresia treatment, esophageal elongation, biomechanical properties, soft tissue

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Authors: Wenxing Chen, Guangying Chen, Yin Lu, Shile Huang

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Cryptotanshinone (CPT), a fat-soluble tanshinone from Salvia miltiorrhiza Bunge, has been demonstrated to inhibit mTOR pathway, resulting in inhibition of cancer cell proliferation. However, the molecular mechanism how CPT acts on mTOR is unknown. Here, cancer cells expressing rapamycin-resistant mutant mTOR are also sensitive to CPT, while phosphorylation of AMPK and TSC2 was activated, suggesting that CPT inhibition of mTOR maybe due to activating upstream of mTOR, AMPK, but not directly binding to and inhibiting mTOR. Further results indicated that Compound C, inhibitor of AMPK, could partially reversed CPT inhibition effect on cancer cells, and dominant-negative AMPK in cancer cells conferred resistance to CPT inhibition of 4EBP1 and phosphorylation of S6K1, as well as sh-AMPK. Furthermore, compared with MEF cells with AMPK positive, MEF cells with AMPK knock out are less sensitive to CPT by the findings that 4E-BP1 and phosphorylation of S6K1 express comparatively much. Furthermore, downexpression of TSC2 slightly recovered expression of 4EBP1 and phosphorylation of S6K1, while co-immunoprecipitation of TSC2 did not affect expression of TSC1 by CPT. Collectively, the above-mentioned results suggest that CPT inhibited mTOR pathway mostly was due to activation of AMPK-TSC2 pathway rather than specific inhibition of mTOR and then induction of subsequent lethal cellular effect.

Keywords: cryptotanshinone, AMPK, TSC2, mTOR, cancer cells

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Authors: Amal A. Qedrah, Sofia A. Malik, Madiha Akbar

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The aim of this article is to share a rare clinical case of pregnancy and surgical delivery in a patient who has undergone augmentation cystoplasty with mitrofanoff channel in the past. Methods: This case report is about a woman who conceived naturally at the age of 27, previously underwent augmentation cystoplasty at the age of 10 years with mitrofanoff procedure using self-clean intermittent catheterization. Furthermore, this pregnancy was complicated by the presence of preeclampsia diagnosed at term and PROM. Following the failure of induction for intrapartum preeclampsia, the patient delivered a healthy baby via low transverse cesarean section at 38 weeks done at Latifa Hospital, Dubai. Conclusion: The procedure is done at a pediatric or young age, after which most patients reach reproductive age. There is no contraindication to pregnancy vaginally or surgically; however, this case was complicated by preeclampsia, due to which this patient was taken for a cesarean section. It is advisable to consult a urologist frequently along with taking regular bacteriological urine samples and blood samples with renal ultrasonography for the evaluation of the kidney. Antibacterial treatment or prophylaxis should be used during pregnancy if necessary and intermittent self-catherization is mostly performed routinely. It is also important to have a urologist on standby during the surgery in order to avoid and/or fix any complications that might come forth.

Keywords: augmentation cystoplasty, cesarean section, delivery, mitrofanoff channel

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Authors: H. Rezvani, H. Monsef, A. Hekmati

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Nowadays, the use of renewable energy sources has been increasingly great because of the cost increase and public demand for clean energy sources. One of the fastest growing sources is wind energy. In this paper, Wind Diesel Hybrid System (WDHS) comprising a Diesel Generator (DG), a Wind Turbine Generator (WTG), the Consumer Load, a Battery-based Energy Storage System (BESS), and a Dump Load (DL) is used. Voltage is controlled by Diesel Generator; the frequency is controlled by BESS and DL. The BESS elimination is an efficient way to reduce maintenance cost and increase the dynamic response. Simulation results with graphs for the frequency of Power System, active power, and the battery power are presented for load changes. The controlling parameters are optimized by using Imperialist Competitive Algorithm (ICA). The simulation results for the BESS/no BESS cases are compared. Results show that in no BESS case, the frequency control is more optimal than the BESS case by using ICA.

Keywords: renewable energy, wind diesel system, induction generator, energy storage, imperialist competitive algorithm

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Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Ghulam Mustafa Peerzada, Shagufta Rashid, Nadeem Bashir

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These the influence of initial reagent concentrations on the Belousov-Zhabotinsky (BZ) system with Mn2+/Mn3+ as redox catalyst, inorganic bromate as oxidant and pyrogallol as organic substrate was studied. The reactions were monitored by potentiometery in oxidation reduction potential (ORP) mode. The aforesaid reagents were mixed with varying concentrations to evolve the optimal concentrations at which the reaction system exhibited better oscillations. The various oscillatory parameters such as induction period (tin), time period (tp), frequency (v), amplitude (A) and number of oscillations (n) were derived and the dependence of concentration of the reacting species on these oscillatory parameters was interpreted on the basis of the Field-Koros-Noyes mechanism. Ferroin based BZ system with pyrogallol as organic substrate was optimized under CSTR condition at temperature of 30±0.1oC Effect of molecules like monomer and polymer as additives to the system was checked and their interaction with the system was also studied. It has been observed that the monomer affects the time period, while the polymer has its effect on the amplitude of oscillations because of monomer’s interaction with the bromine and polymer’s with that of the Ferroin.

Keywords: Belousov Zhabotinsky reaction, oscillatory parameters, polymer, pyrogallol

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Authors: P. Michael Preetam Raj, Souri Banerjee, Souvik Kundu

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Oxide based memristors were fabricated in order to establish its potential applications in analog/digital electronics. BaTiO₃-BiFeO₃ (BT-BFO) was employed as an active material, whereas platinum (Pt) and Nb-doped SrTiO₃ (Nb:STO) were served as a top and bottom electrodes, respectively. Piezoelectric force microscopy (PFM) was utilized to present the ferroelectricity and repeatable polarization inversion in the BT-BFO, demonstrating its effectiveness for resistive switching. The fabricated memristors exhibited excellent electrical characteristics, such as hysteresis current-voltage (I-V), high on/off ratio, high retention time, cyclic endurance, and low operating voltages. The band-alignment between the active material BT-BFO and the substrate Nb:STO was experimentally investigated using X-Ray photoelectron spectroscopy, and it attributed to staggered heterojunction alignment. An energy band diagram was proposed in order to understand the electrical transport in BT-BFO/Nb:STO heterojunction. It was identified that the I-V curves of these memristors have several discontinuities. Curve fitting technique was utilized to analyse the I-V characteristic, and the obtained I-V equations were found to be parabolic. Utilizing this analysis, a non-linear BT-BFO memristors equivalent circuit model was developed. Interestingly, the obtained equivalent circuit of the BT-BFO memristors mimics the identical electrical performance, those obtained in the fabricated devices. Based on the developed equivalent circuit, a finite state machine (FSM) design was proposed. Efforts were devoted to fabricate the same FSM, and the results were well matched with those in the simulated FSM devices. Its multilevel noise filtering and immunity to external noise characteristics were also studied. Further, the feature of variable negative resistance was established by controlling the current through the memristor.

Keywords: band alignment, finite state machine, polarization inversion, resistive switching

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Parastoo Motallebi, Vahid Niknam, Hassan Ebrahimzadeh, Majid Hashemi

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Wheat, the most strategically important worldwide crop, is widely grown in various countries. Based on international wheat production statistics (FAOSTAT database), the total production of wheat in 2012 was 13.8 in Iran. Fusarium culmorum is one of the principal causative agents of Fusarium crown rot (FCR), an overwhelming disease of wheat and barley which is in the early stages causing yield losses, stand reductions and rotting of root and lower stem tissues. In this study inoculation of two wheat seedlings of the susceptible cultivar Falat and the partially field-resistant cultivar Pishtaz were carried out in greenhouse conditions and root samples were taken for 6 days. The activity of peroxidase (POX) and polyphenoloxidase (PPO) enzymes were analyzed to identify possible relations between resistance and enzymatic activities. Although the POX and PPO activities in both geno types increased, this significant increase was more dominant in Pishtaz. The results showed an earlier elevation in the activity of these defense related enzymes in semi-resistant cv. Pishtaz after inoculation, suggested that the activities of POX and PPO in wheat geno types play an important role in the induction of resistance to this disease.

Keywords: Defense responses, Fusarium culmorum, Wheat

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Authors: Shiva Emami, Jenny Persson, Bengt Johansson Lindbom

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Group A streptococcus (GAS) is a prevalent human pathogen, causing a wide range of infections and diseases. One of the most well-known virulence factors in GAS is M protein, a surface protein that facilitates bacterial invasion. In this study, we used a recombinant GAS strain (GAS-2W) expressing M protein containing a hyper immunogenic peptide (2W). Mice were immunized three times with heat-killed-GAS subcutaneously at three weeks intervals. Three weeks post last immunization, mice were challenged intraperitoneally with a lethal dose of live GAS. In order to investigate the impact of IFN-ƴ and antibodies in protection against GAS infection, we used a mouse model knock-out for IFN-ƴ (IFN-ƴ KO). We observed immunization with GAS-2W strain can increase protection against GAS infection in mice compared with the original GAS strain. Higher levels of antibodies against M1 protein were measured in GAS-2W-immunized mice. There was also a significant increase in IgG2c response in mice immunized with GAS2W. By using IFN-ƴ KO mice, we showed that not a high level of total IgG, but IgG2c was correlated with protection through the i.p challenge. It also emphasizes the importance of IFN-ƴ cytokine to combat GAS by isotype switching to IgG2c (which is opsonic for phagocytosis). Our data indicate the crucial role of IFN-ƴ in the protective immune response that, together with IgG2c, can induce protection against GAS.

Keywords: Group A streptococcus, IgG2c, IFN-γ, protection

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Authors: Sameer N. Goyal, Chandragauda R. Patil

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Oleanolic acid (3/3-hydroxy-olea-12-en-28-oic acid) and its isomer, Ursolic acid (38-hydroxy-urs-12-en-28-oic acid) are triterpenoids compounds which exist widely in plant kingdom in the free acid form or as glycosidic triterpenoids saponins. The aim of the study is to evaluate intravenously administered oleanolic acid and ursolic acid in doxorubicin induced cardiotoxicity. Cardiotoxicity was induced in albino wistar rat with single intravenous injection of doxorubicin at dose of 67.75mg/kg i.v for 48 hrs at 12 hrs interval following doxorubicin administration in the same model cardioprotective effect of amifostine (90 mg/kg i.v, single dose prior 30 min before doxorubicin administration) was evaluated as standard treatment. Induction of cardiotoxicity was confirmed by rise in cardiac markers in serum such as CK–MB, LDH and also by electrocardiographically. The doxorubicin treated group significantly increased in QT interval, serum CK-MB, serum LDH, SGOT, SGPT and antioxidant parameter. Both the treatment group showed significant protective effect on Hemodynamic, electrocardiographic, biochemical, and antioxidant parameters. The oleanolic acid showed slight protective effect in histological lesions in doxorubicin induced cardiotoxicity. Hence, the results indicate that Oleanolic acid has more cardioprotective potential than ursolic acid against doxorubicin induced cardiotoxicity in rats.

Keywords: cardioprotection, doxorubicin, oleanolic acid, ursolic acid

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Authors: Melkamu Tadesse Getachew

Abstract:

Intelligent control systems have revolutionized biomedical engineering, advancing research and enhancing medical practice. This review paper examines the impact of intelligent control on various aspects of biomedical engineering. It analyzes how these systems enhance precision and accuracy in biomedical instrumentation, improving diagnostics, monitoring, and treatment. Integration challenges are addressed, and potential solutions are proposed. The paper also investigates the optimization of drug delivery systems through intelligent control. It explores how intelligent systems contribute to precise dosing, targeted drug release, and personalized medicine. Challenges related to controlled drug release and patient variability are discussed, along with potential avenues for overcoming them. The comparison of algorithms used in intelligent control systems in biomedical control is also reviewed. The implications of intelligent control in computational and systems biology are explored, showcasing how these systems enable enhanced analysis and prediction of complex biological processes. Challenges such as interpretability, human-machine interaction, and machine reliability are examined, along with potential solutions. Intelligent control in biomedical engineering also plays a crucial role in risk management during surgical operations. This section demonstrates how intelligent systems improve patient safety and surgical outcomes when integrated into surgical robots, augmented reality, and preoperative planning. The challenges associated with these implementations and potential solutions are discussed in detail. In summary, this review paper comprehensively explores the widespread impact of intelligent control on biomedical engineering, showing the future of human health issues promising. It discusses application areas, challenges, and potential solutions, highlighting the transformative potential of these systems in advancing research and improving medical practice.

Keywords: Intelligent control systems, biomedical instrumentation, drug delivery systems, robotic surgical instruments, Computational monitoring and modeling

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