Search results for: autoregressive integrate moving average model selection

Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: Evan W. Krieger, Vijayan K. Asari, Saibabu Arigela

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Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image.

Keywords: dynamic range compression, multi-level Fourier features, nonlinear enhancement, super resolution

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Authors: Xia Chen, Hua-Quan Yang, Shi-Hua Zhou

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Alkali-active sandstone aggregate was ground by vertical and ball mill into particles with residue over 45 μm less than 12%, and investigations have been launched on particles distribution and characterization of ground sandstone powder, fluidity, heat of hydration, strength as well as hydration products morphology of pastes with incorporation of ground sandstone powder. Results indicated that ground alkali-active sandstone powder with residue over 45 μm less than 8% was easily obtainable, and specific surface area was more sensitive to characterize its fineness with extension of grinding length. Incorporation of sandstone powder resulted in higher water demand and lower strength, advanced hydration of C₃A and C₂S within 3days and refined pore structure. Based on its manufacturing, characteristics and influence on properties of pastes, it was concluded that sandstone powder was a good selection for use in concrete as mineral admixture.

Keywords: concrete, mineral admixture, hydration, structure

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Authors: Eduardo Wiebbelling, Marcelo Curth

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Among the sporting modalities, soccer stands out as the one that reached the world's largest spray today, moving large monetary sums. However, the modality presents potential to be explored by the agents inserted in it. New advertising campaigns have overwhelmed the media and the consumption of sports goods, especially soccer, has increased over the years by having experts increase their marketing projects linked to this specific area. However, little is studied about consumer behavior regarding the purchase of specific products linked to the club. In this sense, the research aims to understand the reasons that lead the fans of two rival clubs in southern Brazil to consume limited edition products from their respective soccer clubs. The method used was an in-depth exploratory survey with thirty memberships and non-memberships. The results showed that in the group of memberships the main motivations are emotional, of historical rescue from memories and feelings that arouse in the fan when they remember their idols and the titles conquered by the club. In the group of non-memberships, a more rational and objective view was perceived, involving aspects such as promotion, utility and extra benefits. Finally, it is realized that fans generally do not value the products to be limited edition. It is believed that this is due to the fact that the products are usually marketed at a higher price when compared to similar products offered on a regular basis.

Keywords: consumer behavior, limited edition, soccer, sports marketing

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Authors: Mehdi Brahimi, Kamal Medjaher, Noureddine Zerhouni, Mohammed Leouatni

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The Prognostics and Health Management is a system engineering discipline which provides solutions and models to the implantation of a predictive maintenance. The approach is based on extracting useful information from monitoring data to assess the “health” state of an industrial equipment or an asset. In this paper, we examine multiple extracted features from Pantograph-Catenary contact force in order to select the most relevant ones to achieve a diagnostics function. The feature extraction methodology is based on simulation data generated thanks to a Pantograph-Catenary simulation software called INPAC and measurement data. The feature extraction method is based on both statistical and signal processing analyses. The feature selection method is based on statistical criteria.

Keywords: catenary/pantograph interaction, diagnostics, Prognostics and Health Management (PHM), quality of current collection

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Dení Stincer Gómez, Zuraya Monroy Nasr

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Pedagogy of science historically has given priority to teaching strategies that mobilize the cognitive mechanisms leaving out emotional. Modern epistemology, cognitive psychology and psychoanalysis begin to argue and prove that emotions are relevant epistemological functions. They are 1) the selection function: that allows the perception and reason choose, to multiple alternative explanation of a particular fact, those are relevant and discard those that are not, 2) heuristic function: that is related to the activation cognitive processes that are effective in the process of knowing; and 3) the function that called carrier content: on the latter it arises that emotions give the material reasoning that later transformed into linguistic propositions. According to these hypotheses, scientific knowledge seems to come from emotions that meet these functions. In this paper I argue that science education should start from the presence of certain emotions in the learner if it is to form citizens with scientific or cultural future scientists.

Keywords: epistemic emotions, science education, formation of citizens and scientists., philosophy of emotions

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Bekhemmas Youcef

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The category of the disabled, like other social groups, is considered to have been affected by fate with a disability that led to a reduction in the fulfillment of its social roles to the fullest extent or led to its complete abandonment. Psychological, and until we understand its behavioral methods that express a lot of this complexity and intertwining, and despite all that, this category has not yet received the appropriate great interest from specialized researchers, and even officials, and it is natural that the category of people with disabilities has psychological and social requirements in order to regains their capabilities or some From her, it also needs to prepare the environment in which she lives in order to integrate into society As the motor disability is one of the most common types of disability in the world, and it is constantly increasing, considering the increase in the causes leading to it, such as the traffic accident, and the motor disability often affects individuals from a psychological point of view, but it also affects their social surroundings, whether close or extended, and thus it draws limits and quality For their way of life, as well as determining roles for them as actors of a special kind within their societies. The methodology is similar to the organizational framework for the production of any scientific knowledge and based on the fact that sociology is a project that aims to understand and interpret the social reality scientifically and through the nature of the subject studied in the framework of the reality of the disabled in the city and in order to get closer to the daily life of the physically disabled within the urban center, we adopted the qualitative approach A choice that complies with the spirit of Viberian sociology, especially since Max Weber insists on the need to search for the meaning that the social actor gives to his behavior. Through the results reached in this study, it was found that the city still suffers from several deficiencies at the level of equipment and urban planning in a way that keeps pace with the number of people with disabilities in the city.

Keywords: physically, handicapped, in, the city

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Authors: Jayesh Mahitkar, V. K. Singh, Surendra Singh Kachhwaha

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Wire Mesh Heat Pipe (WMHP) as an effective component of thermal control system in the payload of spacecraft, utilizing ammonia to transfer efficient amount of heat. One dimensional generic and robust mathematical model with partial-analytical hydraulic approach (PAHA) is developed to study inside behaviour of WMHP. In this model, inside performance during operation is investigated like mass flow rate, and velocity along the wire mesh as well as vapour core is modeled respectively. This numerical model investigate heat flow along length, pressure drop along wire mesh as well as vapour line in axial direction. Furthermore, WMHP is modeled into equivalent resistance network such that total thermal resistance of heat pipe, temperature drop across evaporator end and condenser end is evaluated. This numerical investigation should be carried out for single layer and double layer wire mesh each with heat input at evaporator section is 10W, 20 W and 30 W at condenser temperature maintained at 20˚C.

Keywords: ammonia, heat transfer, modeling, wire mesh

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Bouchemha Amel, Chachoui Takieddine, H. Maalem

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Automatic detection of targets in a modern communication system RADAR is based primarily on the concept of adaptive CFAR detector. To have an effective detection, we must minimize the influence of disturbances due to the clutter. The detection algorithm adapts the CFAR detection threshold which is proportional to the average power of the clutter, maintaining a constant probability of false alarm. In this article, we analyze the performance of two variants of adaptive algorithms CA-CFAR and OS-CFAR and we compare the thresholds of these detectors in the marine environment (no-Gaussian) with a Weibull distribution.

Keywords: CFAR, threshold, clutter, distribution, Weibull, detection

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Authors: Bahadir Batar, Elif Serdal, Berna Erdal, Hasan Ogul

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Micro-ribonucleic acids (miRNAs) are small short non-coding ribonucleic acid molecules about 22 nucleotides long. miRNAs play a key role in response to chemotherapeutic agents. WW domain-containing oxidoreductase (WWOX) gene encodes a tumor suppressor protein. Loss or reduction of Wwox protein is observed in many breast cancer cases. WWOX protein deficiency is increased in triple-negative breast cancer (TNBC). TNBC is a heterogeneous, highly aggressive, and difficult to treat tumor type. WWOX loss contributes to resistance to cisplatin therapy in patients with TNBC. Here, the aim of the study was to investigate the potential role of miRNAs in cisplatin therapy resistance of WWOX-deficient TNBC cells. This was a cell culture study. miRNA expression profiling was analyzed by LightCycler 480 system. miRNA Set Enrichment Analysis tool was used to integrate experimental data with literature-based biological knowledge to infer a new hypothesis. Increased miR-182 and decreased miR-214 were significantly correlated with cisplatin resistance in WWOX-deficient TNBC cells. miR-182 and miR-214 may involve in cisplatin resistance of WWOX-deficient TNBC cells by deregulating the DNA repair, apoptosis, or protein kinase B signaling pathways. These data highlight the mechanism by which WWOX regulates cisplatin resistance of TNBC and the potential use of WWOX as a predictor biomarker for cisplatin resistance.

Keywords: cisplatin, microRNA, triple-negative breast cancer, WWOX

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Authors: Shatha Ahmad Alharthi

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Prior research suggests that reduced social interaction can negatively affect well-being and impair mental health (e.g., depression and anxiety), resulting in lower academic performance. The COVID-19 pandemic has significantly limited social interaction among Saudi Arabian school children since the government closed schools and implemented lockdown restrictions to reduce the spread of the disease. These restrictions have resulted in prolonged remote learning for middle school students with unknown consequences for perceived academic performance, mental health, and well-being. This research project explores how middle school Saudi students’ current remote learning practices affect their mental health (e.g., depression and anxiety) and well-being during the lockdown. Furthermore, the study will examine the association between social interaction, mental health, and well-being pertaining to students’ perceptions of their academic achievement. Research findings could lead to a better understanding of the role of lockdown on depression, anxiety, well-being and perceived academic performance. Research findings may also inform policy-makers or practitioners (e.g., teachers and school leaders) about the importance of facilitating increased social interactions in remote learning situations and help to identify important factors to consider when seeking to re-integrate students into a face-to-face classroom setting. Potential implications for future educational research include exploring remote learning interventions targeted at bolstering students’ mental health and academic achievement during periods of remote learning.

Keywords: depression, anxiety, academic performance, social interaction

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Authors: Sun Zhe, Ruggero Micheletto

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The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Haga Abdelrahman Elimam

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This paper investigates the connotation, and some of the realistic implications, of the economic reform of health sector in under developed countries. The paper investigates the issues that economic reforms have to address, and the policy targets they are considered to accomplish. The work argues that the development of economic reform is not connected only with understanding the priorities and refining them, furthermore with reformation and restructuring the organizations through which health policies are employed. Considering various organizational values, that are likely to be regular to all economic reform programs, a regulatory approach to institutional reform is unsuitable. The paper further investigates the selection of economic reform that may as well influence via technical suggestions and analysis, but the verdict to continue, and the consequent success of execution, eventually depends on the progressive political sustainability. The paper concludes by giving examples of institutional reforms from various underdeveloped countries and includes recommendation of the responsibility and control of donor organizations.

Keywords: economic reform, health sector, underdeveloped countries, technical suggestions

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Authors: Chantouch Wannathanom

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Research The study of elders’ needs in Bangkok metropolis for the options of health tourism. The objective is to study of elders’ needs in Bangkok Metropolis for the options of health tourism. The research her collected data using a questionnaire. The samples used in this research is elderly people living in the Dusit area. Of 400 people found the majority were female than male. Accounted for18 percent aged between 50-55 years, mostly undergraduate degree. Moreover, most seniors do not have underlying disease. The study found that 1. Elders’ needs in Bangkok Metropolis for the options of health tourism; 2. Tourism activity patterns that fit elderly was divided into 5 categories, including massage, massage, and herbal sauna. Practicing meditation and ascetic. The results showed that selection of elderly tourist activities by choosing healthy eating are the first. The hypothesis testing period: Elders’ needs in Bangkok Metropolis for the options of health tourism is different. The level of statistical significance .05 level.

Keywords: needs, elder, health tourism, Bangkok

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Jatinderkumar R. Saini

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E-mail has become a key mechanism of electronic communication. This is truer for professional organizations that like to communicate with their subjects online and are slowly shifting to paper-less office. The current paper focuses specifically on academic institutions offering Engineering course in Gujarat state and attempts for textual analysis of the usernames of the institutional e-mail addresses. We found that the institutions tend to design the username segment of their e-mail addresses by choosing words or combination of words from specific categories. The paper also highlights the use of special characters, digits and random words in designing the usernames. On the sidelines, the paper lists the style of employing department names and designations for the design process. To the best of our knowledge, this is the first formal attempt to analyze the selection of words employed for designing username segment of e-mail addresses of Engineering institutions.

Keywords: e-mail address, institute, engineering, username

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Authors: Sneha Thakur, Sanjeev Karmakar

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This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.

Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level

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Authors: Xi Gu, Guan Heng Yeoh, Victoria Timchenko

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In the current work, a three-dimensional geometry of a 75% stenosed blood vessel is analysed. Large eddy simulation (LES) with the help of a dynamic subgrid scale Smagorinsky model is applied to model the turbulent pulsatile flow. The geometry, the transmural pressure and the properties of the blood and the elastic boundary were based on clinical measurement data. For the flexible wall model, a thin solid region is constructed around the 75% stenosed blood vessel. The deformation of this solid region was modelled as a deforming boundary to reduce the computational cost of the solid model. Fluid-structure interaction is realised via a two-way coupling between the blood flow modelled via LES and the deforming vessel. The information of the flow pressure and the wall motion was exchanged continually during the cycle by an arbitrary lagrangian-eulerian method. The boundary condition of current time step depended on previous solutions. The fluctuation of the velocity in the post-stenotic region was analysed in the study. The axial velocity at normalised position Z=0.5 shows a negative value near the vessel wall. The displacement of the elastic boundary was concerned in this study. In particular, the wall displacement at the systole and the diastole were compared. The negative displacement at the stenosis indicates a collapse at the maximum velocity and the deceleration phase.

Keywords: Large Eddy Simulation, Fluid Structural Interaction, constricted artery, Computational Fluid Dynamics

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Authors: Fatemeh Jannesarvatan, Ghazaal Parastooei, Jimmy frerejan, Saedeh Mokhtari, Peter Van Rosmalen

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Medical and dental education increasingly emphasizes the acquisition, integration, and coordination of complex knowledge, skills, and attitudes that can be applied in practical situations. Instructional design approaches have focused on using real-life tasks in order to facilitate complex learning in both real and simulated environments. The Four component instructional design (4C/ID) model has become a useful guideline for designing instructional materials that improve learning transfer, especially in health profession education. The objective of this study was to apply the 4C/ID model in the creation of virtual patients (VPs) that dental students can use to practice their clinical management and clinical reasoning skills. The study first explored the context and concept of complication factors and common errors for novices and how they can affect the design of a virtual patient program. The study then selected key dental information and considered the content needs of dental students. The design of virtual patients was based on the 4C/ID model's fundamental principles, which included: Designing learning tasks that reflect real patient scenarios and applying different levels of task complexity to challenge students to apply their knowledge and skills in different contexts. Creating varied learning materials that support students during the VP program and are closely integrated with the learning tasks and students' curricula. Cognitive feedback was provided at different levels of the program. Providing procedural information where students followed a step-by-step process from history taking to writing a comprehensive treatment plan. Four virtual patients were designed using the 4C/ID model's principles, and an experimental design was used to test the effectiveness of the principles in achieving the intended educational outcomes. The 4C/ID model provides an effective framework for designing engaging and successful virtual patients that support the transfer of knowledge and skills for dental students. However, there are some challenges and pitfalls that instructional designers should take into account when developing these educational tools.

Keywords: 4C/ID model, virtual patients, education, dental, instructional design

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Authors: Kim Jin Sup

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This paper presents a vertical hall effect device using current-mode. Among different geometries that have been studied and simulated using COMSOL Multiphysics, optimized cross-shaped model displayed the best sensitivity. The cross-shaped model emerged as the optimum plate to fit the lowest noise and residual offset and the best sensitivity. The symmetrical cross-shaped hall plate is widely used because of its high sensitivity and immunity to alignment tolerances resulting from the fabrication process. The hall effect device has been designed using a 0.18-μm CMOS technology. The simulation uses the nominal bias current of 12μA. The applied magnetic field is from 0 mT to 20 mT. Simulation results achieved in COMSOL and validated with respect to the electrical behavior of equivalent circuit for Cadence. Simulation results of the one structure over the 13 available samples shows for the best geometry a current-mode sensitivity of 6.6 %/T at 20mT. Acknowledgment: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R7117-16-0165, Development of Hall Effect Semiconductor for Smart Car and Device).

Keywords: vertical hall device, current-mode, crossed-shaped model, CMOS technology

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Authors: Reza Zakerinejad

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Gully erosion is a serious problem that threading the sustainability of agricultural area and rangeland and water in a large part of Iran. This type of water erosion is the main source of sedimentation in many catchment areas in the north of Iran. Since in many national assessment approaches just qualitative models were applied the aim of this study is to predict the spatial distribution of gully erosion processes by means of detail terrain analysis and GIS -based logistic regression in the loess deposition in a case study in the Golestan Province. This study the DEM with 25 meter result ion from ASTER data has been used. The Landsat ETM data have been used to mapping of land use. The TreeNet model as a stochastic modeling was applied to prediction the susceptible area for gully erosion. In this model ROC we have set 20 % of data as learning and 20 % as learning data. Therefore, applying the GIS and satellite image analysis techniques has been used to derive the input information for these stochastic models. The result of this study showed a high accurate map of potential for gully erosion.

Keywords: TreeNet model, terrain analysis, Golestan Province, Iran

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Authors: Sven Gehrke, Johannes Ruhland

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Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

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Authors: Adebayo J. Julius, Oluremi N. Iluyomade

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Livelihood in Nigeria is a paradox of poverty amidst plenty. The Country is endowed with a good climate for agriculture, naturally growing fruit trees and vegetables, and undomesticated water resources. In spite of all its endowment, Nigeria continues to live in poverty year in year out. This thus raises a very important question as to how can there be so much poverty in Nigeria with all its natural endowments. This study focused comparative analysis of the utilization of community informatics for sustainable livelihoods through agriculture. The idea projected in this study is that small strategic changes in the modus operandi of social informatics can have a significant impact on sustainability of livelihoods. This paper carefully explored the theories of community informatics and its efficacies in dealing with sustainability issues. This study identified, described and evaluates the roles of community informatics in some sectors of the economy, different analytical tools to benchmark the influence of social informatics in agriculture against what is obtainable in agricultural sectors of the economy were used. It further employed comparative analysis to build a case model for sustainable livelihood in agriculture through community informatics.

Keywords: informatics , model, rural community, livelihoods sustainability, Nigeria

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Authors: Victoria Brennan, Andrea Mallaburn, Linda Seton

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Science outreach programmes connect school pupils with external agencies to provide activities and experiences that enhance their exposure to science. It can be argued that these programmes not only aim to support teachers with curriculum engagement and promote scientific literacy but also provide pivotal opportunities to spark scientific interest in students. In turn, a further objective of these programmes is to increase awareness of career opportunities within this field. Although outreach work is also often described as a fun and satisfying venture, a plethora of researchers express caution to how successful the processes are to increases engagement post-16 in science. When researching the impact of outreach programmes, it is often student feedback regarding the activities or enrolment numbers to particular science courses post-16, which are generated and analysed. Although this is informative, the longevity of the programme’s impact could be better informed by the teacher’s perceptions; the evidence of which is far more limited in the literature. In addition, there are strong suggestions that teachers can have an indirect impact on a student’s own self-concept. These themes shape the focus and importance of this ongoing research project as it presents the rationale that teachers are under-used resources when it comes to considering the design of science outreach programmes. Therefore, the end result of the research will consist of a presentation of an ‘optimum’ model of outreach. The result of which should be of interest to the wider stakeholders such as universities or private or government organisations who design science outreach programmes in the hope to recruit future scientists. During phase one, questionnaires (n=52) and interviews (n=8) have generated both quantitative and qualitative data. These have been analysed using the Wilcoxon non-parametric test to compare teachers’ perceptions of science outreach interventions and thematic analysis for open-ended questions. Both of these research activities provide an opportunity for a cross-section of teacher opinions of science outreach to be obtained across all educational levels. Therefore, an early draft of the ‘optimum’ model of science outreach delivery was generated using both the wealth of literature and primary data. This final (ongoing) phase aims to refine this model using teacher focus groups to provide constructive feedback about the proposed model. The analysis uses principles of modified Grounded Theory to ensure that focus group data is used to further strengthen the model. Therefore, this research uses a pragmatist approach as it aims to focus on the strengths of the different paradigms encountered to ensure the data collected will provide the most suitable information to create an improved model of sustainable outreach. The results discussed will focus on this ‘optimum’ model and teachers’ perceptions of benefits and drawbacks when it comes to engaging with science outreach work. Although the model is still a ‘work in progress’, it provides both insight into how teachers feel outreach delivery can be a sustainable intervention tool within the classroom and what providers of such programmes should consider when designing science outreach activities.

Keywords: educational partnerships, science education, science outreach, teachers

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Authors: Ayesha Sanjana Kawser Parsha

Abstract:

S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

Procedia PDF Downloads 59

Authors: Mohammadamin Hashemi, Mohammadreza Kashizadeh

Abstract:

Droughts pose a significant threat to sustainable water resource management, agriculture, and socioeconomic sectors, particularly in the field of climate change. This study investigates drought simulation using rainfall-runoff modelling in the Cuneo district, Italy, over the past 60-year period. The study leverages the TUW model, a lumped conceptual rainfall-runoff model with a semi-distributed operation capability. Similar in structure to the widely used Hydrologiska Byråns Vattenbalansavdelning (HBV) model, the TUW model operates on daily timesteps for input and output data specific to each catchment. It incorporates essential routines for snow accumulation and melting, soil moisture storage, and streamflow generation. Multiple catchments' discharge data within the Cuneo district form the basis for thorough model calibration employing the Kling-Gupta Efficiency (KGE) metric. A crucial metric for reliable drought analysis is one that can accurately represent low-flow events during drought periods. This ensures that the model provides a realistic picture of water availability during these critical times. Subsequent validation of monthly discharge simulations thoroughly evaluates overall model performance. Beyond model development, the investigation delves into drought analysis using the robust Standardized Runoff Index (SRI). This index allows for precise characterization of drought occurrences within the study area. A meticulous comparison of observed and simulated discharge data is conducted, with particular focus on low-flow events that characterize droughts. Additionally, the study explores the complex interplay between land characteristics (e.g., soil type, vegetation cover) and climate variables (e.g., precipitation, temperature) that influence the severity and duration of hydrological droughts. The study's findings demonstrate successful calibration of the TUW model across most catchments, achieving commendable model efficiency. Comparative analysis between simulated and observed discharge data reveals significant agreement, especially during critical low-flow periods. This agreement is further supported by the Pareto coefficient, a statistical measure of goodness-of-fit. The drought analysis provides critical insights into the duration, intensity, and severity of drought events within the Cuneo district. This newfound understanding of spatial and temporal drought dynamics offers valuable information for water resource management strategies and drought mitigation efforts. This research deepens our understanding of drought dynamics in the Cuneo region. Future research directions include refining hydrological modelling techniques and exploring future drought projections under various climate change scenarios.

Keywords: hydrologic extremes, hydrological drought, hydrological modelling, machine learning, rainfall-runoff modelling

Procedia PDF Downloads 26