Search results for: artificial innate immune system

Authors: Nouf A. Aldarmahi, Nesrin I. Tarbiah, Nuha A. Alkhattabi, Huda F. Alshaibi

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Nigella sativa which is known as dark cumin is a well-known example for a widely applicable herbal medicine. Nigella sativa can be effective in a variety of diseases such as hypertension, diabetes, bronchitis, gastrointestinal upset, and cancer. The anticancer effect of Nigella sativa appeared to be mediated by immune-modulatory effect through stimulating human natural killer (NK) cells. This is a type of lymphocytes which is part of the innate immunity, also known as the first line of defense in the body against pathogens. This study investigated the effect of thymoquinone as a major component of Nigella sativa on the molecular cytotoxic pathway of NK cell and the role of thymoquinone therapeutic effect on NK cells. NK cells were cultured with breast tumor cells in different ways and cultured media was collected and the concentration of perforin, granzyme B and interferon-α were measured by ELISA. The cytotoxic effect of NK cells on breast tumor cells was enhanced in the presence of thymoquinone, with increased activity of perforin in NK cells. This improved anticancer effect of thymoquinone on breast cancer cells.

Keywords: breast cancer, cancer cells, natural killer cells, thymoquinone

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Hassan Naseh, Javad Roozgard

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This paper represents the methodology based on Artificial Intelligent (AI) applied to Liquid Propellant Engine (LPE) optimization. The AI methodology utilized from Adaptive neural Fuzzy Inference System (ANFIS). In this methodology, the optimum objective function means to achieve maximum performance (specific impulse). The independent design variables in ANFIS modeling are combustion chamber pressure and temperature and oxidizer to fuel ratio and output of this modeling are specific impulse that can be applied with other objective functions in LPE design optimization. To this end, the LPE’s parameter has been modeled in ANFIS methodology based on generating fuzzy inference system structure by using grid partitioning, subtractive clustering and Fuzzy C-Means (FCM) clustering for both inferences (Mamdani and Sugeno) and various types of membership functions. The final comparing optimization results shown accuracy and processing run time of the Gaussian ANFIS Methodology between all methods.

Keywords: ANFIS methodology, artificial intelligent, liquid propellant engine, optimization

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Authors: Gergely G. Karacsony

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Fundamental rights are the result of thousand years’ progress of legislation, adjudication and legal practice. They serve as the framework of peaceful cohabitation of people, protecting the individual from any abuse by the government or violation by other people. Artificial intelligence, however, is the development of the very recent past, being one of the most important prospects to the future. Artificial intelligence is now capable of communicating and performing actions the same way as humans; such acts are sometimes impossible to tell from actions performed by flesh-and-blood people. In a world, where human-robot interactions are more and more common, a new framework of peaceful cohabitation is to be found. Artificial intelligence, being able to take part in almost any kind of interaction where personal presence is not necessary without being recognized as a non-human actor, is now able to break the law, violate people’s rights, and disturb social peace in many other ways. Therefore, a code of peaceful coexistence is to be found or created. We should consider the issue, whether human rights can serve as the code of ethical and rightful conduct in the new era of artificial intelligence and human coexistence. In this paper, we will examine the applicability of fundamental rights to human-robot interactions as well as to the actions of artificial intelligence performed without human interaction whatsoever. Robot ethics has been a topic of discussion and debate of philosophy, ethics, computing, legal sciences and science fiction writing long before the first functional artificial intelligence has been introduced. Legal science and legislation have approached artificial intelligence from different angles, regulating different areas (e.g. data protection, telecommunications, copyright issues), but they are only chipping away at the mountain of legal issues concerning robotics. For a widely acceptable and permanent solution, a more general set of rules would be preferred to the detailed regulation of specific issues. We argue that human rights as recognized worldwide are able to be adapted to serve as a guideline and a common basis of coexistence of robots and humans. This solution has many virtues: people don’t need to adjust to a completely unknown set of standards, the system has proved itself to withstand the trials of time, legislation is easier, and the actions of non-human entities are more easily adjudicated within their own framework. In this paper we will examine the system of fundamental rights (as defined in the most widely accepted source, the 1966 UN Convention on Human Rights), and try to adapt each individual right to the actions of artificial intelligence actors; in each case we will examine the possible effects on the legal system and the society of such an approach, finally we also examine its effect on the IT industry.

Keywords: human rights, robot ethics, artificial intelligence and law, human-robot interaction

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Authors: Arkadiusz Gut, Robert Mirski

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The nativist-constructivist debate constitutes a considerable part of current research on mindreading. Peter Carruthers and his colleagues are known for their nativist position in the debate and take issue with constructivist views proposed by other researchers, with Henry Wellman, Alison Gopnik, and Ian Apperly at the forefront. More specifically, Carruthers together with Evan Westra propose a nativistic explanation of Theory of Mind Scale study results that Wellman et al. see as supporting constructivism. While allowing for development of the innate mindreading system, Westra and Carruthers base their argumentation essentially on a competence-performance gap, claiming that cross-cultural differences in Theory of Mind Scale progression as well as discrepancies between infants’ and toddlers’ results on verbal and non-verbal false-belief tasks are fully explainable in terms of acquisition of other, pragmatic, cognitive developments, which are said to allow for an expression of the innately present Theory of Mind understanding. The goal of the present paper is to bring together arguments against the view offered by Westra and Carruthers. It will be shown that even though Carruthers et al.’s interpretation has not been directly controlled for in Wellman et al.’s experiments, there are serious reasons to dismiss such nativistic views which Carruthers et al. advance. The present paper discusses the following issues that undermine Carruthers et al.’s nativistic conception: (1) The concept of innateness is argued to be developmentally inaccurate; it has been dropped in many biological sciences altogether and many developmental psychologists advocate for doing the same in cognitive psychology. Reality of development is a complex interaction of changing elements that is belied by the simplistic notion of ‘the innate.’ (2) The purported innate mindreading conceptual system posited by Carruthers ascribes adult-like understanding to infants, ignoring the difference between first- and second-order understanding, between what can be called ‘presentation’ and ‘representation.’ (3) Advances in neurobiology speak strongly against any inborn conceptual knowledge; neocortex, where conceptual knowledge finds its correlates, is said to be largely equipotential at birth. (4) Carruthers et al.’s interpretations are excessively charitable; they extend results of studies done with 15-month-olds to conclusions about innateness, whereas in reality at that age there has been plenty of time for construction of the skill. (5) Looking-time experiment paradigm used in non-verbal false belief tasks that provide the main support for Carruthers’ argumentation has been criticized on methodological grounds. In the light of the presented arguments, nativism in theory of mind research is concluded to be an untenable position.

Keywords: development, false belief, mindreading, nativism, theory of mind

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Authors: Ahmed Emad Ahmed

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This paper aims to address the concept of human resources information systems (HRIS) and how to link it to new technologies such as artificial intelligence (AI) to be implemented in two human resources processes. A literature view has been collected to cover the main points related to HRIS, AI, and BC. A study case has been presented by generating a random HRIS to apply some AI operations to it. Then, an algorithm was applied to the database to complete some human resources processes, including training and performance appraisal, using a pre-trained AI model. After that, outputs and results have been presented and discussed briefly. Finally, a conclusion has been introduced to show the ability of new technologies such as AI and ML to be applied to the human resources management processes.

Keywords: human resources new technologies, HR artificial intelligence, HRIS AI models, construction AI HRIS

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Authors: Elabed Saud

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This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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Authors: Thomais Tsoulia, Arvind Y. M. Sundaram, Stine Braaen, Øyvind Haugland, Espen Rimstad, Øystein Wessel, Maria K. Dahle

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Fish red blood cells (RBC) are nucleated, and in addition to their function in gas exchange, they have been characterized as mediators of immune responses. Salmonid RBC are the major target cells of Piscineorthoreovirus (PRV), a virus associated with heart and skeletal muscle inflammation (HSMI) in farmed Atlantic salmon. The activation of antiviral response genesin RBChas previously been described in ex vivo and in vivo PRV-infection models, but not explored in the initial virus encounter phase. In the present study, mRNA transcriptome responses were explored in erythrocytes from individual fish, kept ex vivo, and exposed to purified PRV for 24 hours. The responses were compared to responses in macrophage-like salmon head kidney (SHK-1) and endothelial-like Atlantic salmon kidney (ASK) cells, none of which support PRV replication. The comparative analysis showed that the antiviral response to PRV was strongest in the SHK-1 cells, with a set of 80 significantly induced genes (≥ 2-fold upregulation). In RBC, 46 genes were significantly upregulated, while ASK cells were not significantly responsive. In particular, the transcriptome analysis of RBC revealed that PRV significantly induced interferon regulatory factor 1 (IRF1) and interferon-induced protein with tetratricopeptide repeats 5-like (IFIT9). However, several interferon-regulated antiviral genes which have previously been reported upregulated in PRV infected RBC in vivo (myxovirus resistance (Mx), interferon-stimulated gene 15 (ISG15), toll-like receptor 3 (TLR3)), were not significantly induced after 24h of virus stimulation. In contrast to RBC, these antiviral response genes were significantly upregulated in SHK-1. These results confirm that RBC are involved in the innate immune response to viruses, but with a delayed antiviral response compared to SHK-1. A notable difference is that interferon regulatory factor 1 (IRF-1) is the most strongly induced gene in RBC, but not among the significantly induced genes in SHK-1. Putative differences in the binding, recognition, and response to PRV, and any link to effects on the ability of PRV to replicate remains to be explored.

Keywords: antiviral responses, atlantic salmon, piscine orthoreovirus-1, red blood cells, RNA-seq

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Authors: Abdul Qadir Qadis, Satoru Goya, Minoru Yatsu, Yu-uki Yoshida, Toshihiro Ichijo, Shigeru Sato

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Bacterial probiotics are known to modulate the gut-associated lymphoid and epithelial tissue response to enhance the activities of intestinal and systemic immune system in human and animals. In cattle, the immune-stimulatory effects of probiotics have been evaluated during intestinal disorders. To investigate the effects of probiotic on the function of peripheral blood mononuclear cells, eight healthy Holstein calves (10 ± 3 weeks) were assigned to a 4 × 2 experimental design. The probiotic, consisting of Lactobacillus plantarum, Enterococcus faecium and Clostridium butyricum, was administered orally at 3.0 g/100 kg body weight to calves once daily for 5 consecutive days. Calves given no probiotic served as the control. In the treatment group, increases in numbers of CD282+ monocytes, CD3+ T-cells and CD4+, CD8+ and WC1+ γδ T- cell subsets were noted on day 7 post-placement compared to pre-dose day and the control group. Expression of interleukin-6, interferon-gamma and tumor necrosis factor-alpha was elevated in peripheral leukocytes on days 7 and 14. These results suggest that peripheral blood leukocytes in healthy calves may be stimulated via the gastrointestinal microbiota, which was increased by the oral probiotic treatment. The 5-day repeated administration of a bacterial probiotic may enhance cellular immune function in weaned calves.

Keywords: bacterial-probiotic, calf, interleukin, leukocyte

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Authors: Paula C. Latorre Arroyo, Andrea C. Latorre Arroyo

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In the present society, children are exposed to and impacted by technology from very early on in various ways. Artificial intelligence (AI), in particular, directly affects them, be it positively or negatively. The concept of artificial intelligence is commonly defined as the technological programming of computers or robotic mechanisms with humanlike capabilities and characteristics. These technologies are often designed as helpful machines or disguised as handy tools that could ultimately steal private information for illicit purposes. Children, being one of the most vulnerable groups due to their lack of experience and knowledge, do not have the ability to recognize or have the malice to distinguish if an apparatus with artificial intelligence is good or bad for them. For this reason, as a society, there must be a sense of responsibility to regulate and monitor different types of uses for artificial intelligence to protect children from potential risks that might arise. This article aims to investigate the many implications that artificial intelligence has in the lives of children, starting from a home setting, within the classroom, and, ultimately, in online spaces. Irrefutably, there are numerous beneficial aspects to the use of artificial intelligence. However, due to its limitless potential and lack of specific and substantial regulations to prevent the illicit use of this technology, safety and privacy concerns surface, specifically regarding the youth. This written work aims to provide an in-depth analysis of how artificial intelligence can both help children and jeopardize their safety. Concluding with resources and data supporting the aforementioned statement.

Keywords: artificial intelligence, children, privacy, rights, safety

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Carlos Tarmeño Noriega, Igor Aguilar Alonso

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Fungal diseases are common in vineyards; they cause a decrease in the quality of the products that can be sold, generating distrust of the customer towards the seller when buying fruit. Currently, technology allows the classification of fruits according to their characteristics thanks to artificial intelligence. This study proposes the implementation of a control system that allows the identification of the main fungal diseases present in the Italia grape, making use of a convolutional neural network (CNN), OpenCV, and TensorFlow. The methodology used was based on a collection of 20 articles referring to the proposed research on quality control, classification, and recognition of fruits through artificial vision techniques.

Keywords: computer vision, convolutional neural networks, quality control, fruit market, OpenCV, TensorFlow

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Authors: K. Raghuwaiya, B. Sharma, J. Vanualailai

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This paper presents a set of artificial potential field functions that improves upon; in general, the motion planning and posture control, with theoretically guaranteed point and posture stabilities, convergence and collision avoidance properties of the general 3-trailer system in a priori known environment. We basically design and inject two new concepts; ghost walls and the distance optimization technique (DOT) to strengthen point and posture stabilities, in the sense of Lyapunov, of our dynamical model. This new combination of techniques emerges as a convenient mechanism for obtaining feasible orientations at the target positions with an overall reduction in the complexity of the navigation laws. Simulations are provided to demonstrate the effectiveness of the controls laws.

Keywords: artificial potential fields, 3-trailer systems, motion planning, posture

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Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

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Authors: Javed Mohammed

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Robotics is the fastest growing industry in the world, poised to become the largest in the next decade. The use of robots requires design, application and implementation of the appropriate safety controls in order to avoid creating hazards to production personnel, programmers, maintenance specialists and systems engineers. The increasing use of artificial intelligence (AI) and related technologies in the workplace are dramatically changing the employment landscape. The impact of robotics technology on workplace policy is dramatic and complex. The robotics revolution calls for a comprehensive approach to job training, and retraining, to mitigate worker displacement and enable workers to benefit from the new jobs that the technology will generate. It calls for a thoughtful, forward-thinking approach by lawmakers, regulators and employers to prepare for the oncoming transformation of the workplace and workforce.

Keywords: design, artificial intelligence, programmers, system engineers, robotics, transformation

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Authors: Dehini Rachid, Ferdi Brahim

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The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

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Authors: Javad Abdi, Azam Famil Khalili

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Modeling emotions have attracted much attention in recent years, both in cognitive psychology and design of artificial systems. However, it is a negative factor in decision-making; emotions have shown to be a strong faculty for making fast satisfying decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. Learning in this model based on Temporal Difference (TD) Learning, we applied the proposed controller (termed BELBIC) for a simple model of a submarine. The model was supposed to reach the desired depth underwater. Our results demonstrate excellent control action, disturbance handling, and system parameter robustness for TDBELBIC. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the least amount of time and the best way.

Keywords: artificial neural networks, temporal difference, brain emotional learning based intelligent controller, heating- ventilating and air conditioning

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Authors: Salah Ghamari

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In this study, the ability of self organizing map artificial (SOM) neural networks in clustering the wheat seeds varieties according to morphological properties of them was considered. The SOM is one type of unsupervised competitive learning. Experimentally, five morphological features of 300 seeds (including three varieties: gaskozhen, Md and sardari) were obtained using image processing technique. The results show that the artificial neural network has a good performance (90.33% accuracy) in classification of the wheat varieties despite of high similarity in them. The highest classification accuracy (100%) was achieved for sardari.

Keywords: artificial neural networks, clustering, self organizing map, wheat variety

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Authors: Ilker Ali Ozkan

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In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Vahab Behmanesh

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One of the topics that has always attracted the attention of sports medicine and sports science experts is the positive or negative effect of sports activities on the functioning of the body's immune system. In the present research, a course of aerobic running with spirulina consumption has been studied on the maximum oxygen consumption and the performance of some indicators of the immune system of men who have trained after one session of physical activity. In this research, 50 trained students were studied randomly in four groups, spirulina- aerobic, spirulina, placebo- aerobic, and control. In order to test the research hypotheses, one-way statistical method of variance (ANOVA) was used considering the significance level of a=0.005 and post hoc test (LSD). A blood sample was taken from the participants in the first stage test in fasting and resting state immediately after Bruce's maximal test on the treadmill until complete relaxation was reached, and their Vo2max value was determined through the aforementioned test. The subjects of the spirulina-aerobic running and placebo-aerobic running groups took three 500 mg spirulina and 500 mg placebo pills a day for six weeks and ran three times a week for 30 minutes at the threshold of aerobic stimulation. The spirulina and placebo groups also consumed spirulina and placebo tablets in the above method for six weeks. Then they did the same first stage test as the second stage test. Blood samples were taken to measure the number of CD4+, CD8+, NK, and the ratio of CD4+ to CD8+ on four occasions before and after the first and second stage tests. The analysis of the findings showed that: aerobic running and spirulina supplement alone increase Vo2max. Aerobic running and consumption of spirulina increases Vo2max more than other groups (P<0.05), +CD4 and hemoglobin of the spirulina-aerobic running group was significantly different from other groups (P=0.002), +CD4 of the groups together There was no significant difference, NK increased in all groups, the ratio of CD4+ to CD8+ between the groups had a significant difference (P=0.002), the ratio of CD4+ to CD8+ in the spirulina- aerobic group was lower than the spirulina and placebo groups. All in all, it can be concluded that the supplement of spirulina and aerobic exercise may increase Vo2max and improve safety indicators.

Keywords: spirulina (Q2), hemoglobin (Q3), aerobic exercise (Q3), residual activity (Q2), CD4+ to CD8+ ratio (Q3)

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Authors: Wan Liu, Haibo Wang, Cathy Huang, Zhiwu Tan, Zhiwei Chen

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The tremendous diversity of HIV-1 has been a major challenge for an effective AIDS vaccine development. Mosaic approach presents the potential for vaccine design aiming for global protection. The mosaic antigen of HIV-1 Gag allows antigenic breadth for vaccine-elicited immune response against a wider spectrum of viral strains. However, the enhancement of immune response using vaccines is dependent on the strategy used. Heterologous prime/boost regimen has been shown to elicit high levels of immune responses. Here, we investigated whether priming using plasmid DNA with electroporation followed by boosting with the live replication-competent modified vaccinia virus vector TianTan (MVTT) combined with the mosaic antigenic sequence could elicit a greater and broader antigen-specific response against HIV-1 Gag in mice. When compared to DNA or MVTT alone, or MVTT/MVTT group, DNA/MVTT group resulted in coincidentally high frequencies of broadly reactive, Gag-specific, polyfunctional, long-lived, and cytotoxic CD8+ T cells and increased anti-Gag antibody titer. Meanwhile, the vaccination could upregulate PD-1+, and Tim-3+ CD8+ T cell, myeloid-derived suppressive cells and Treg cells to balance the stronger immune response induced. Importantly, the prime/boost vaccination could help control the EcoHIV and mesothelioma AB1-gag challenge. The stronger protective Gag-specific immunity induced by a Mosaic DNA/MVTT vaccine corroborate the promise of the mosaic approach, and the potential of two acceptably safe vectors to enhance anti-HIV immunity and cancer prevention.

Keywords: DNA/MVTT vaccine, EcoHIV, mosaic antigen, mesothelioma AB1-gag

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

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Authors: Rohit Rutka

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The aim of the present paper is to reveal the importance of music as an indispensable aspect in education of art, with regard to every single culture which serves as indisputable support to sustainable development in India. Indian system of education is one of the oldest systems of the world. Both secular and sacred education was handed over systematically by formalizing the system of education. We have found significant growth in the system of education in our country since ancient times. It is a veritable avenue which enables societies to transmit music and musical skills from one generation to the upcoming ones. The research is based on a comprehensive literature review on the impact of music to sustainable development. This paper contextualized that music education is imperative to Sustainable Development, to the adult. It is a vital force of self-expression, communication and empowerment economically, in growing children, involvement in music education will promote their creative ability, thereby contribute to the full development of intellectual capacities, apt emotional development that gives the right values and feelings to various events and happenings, music helps to develop skills, innate and instinctive talent in human being and recommend that the informal music teaching should be incorporated into school system so as to transmit and preserve the cultural music and that the study of music should be made compulsory at all levels of the Indian educational system.

Keywords: sustainable development, music education, culture, music as a pillar to sustainable development

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Authors: Hager Elsheikh, Matthias Sendler, Juliana Glaubnitz

Abstract:

In pancreatitis, an inflammatory reaction occurs in the pancreatic secretory cells due to premature activation of proteases, leading to pancreatic self-digestion and necrotic cell death of acinar cells. Acute pancreatitis in patients is characterized by a severe immune reaction that could lead to serious complications, such as organ failure or septic shock, if left untreated. Chronic pancreatitis is a recurrence of episodes of acute pancreatitis resulting in a fibro-inflammatory immune response, in which the type 2 immune response is primarily driven by AAMs in the pancreas. One of the most important signaling pathways for M2 macrophage activation is the IL-4/STAT6 pathway. Pancreatic fibrosis is induced by the hyperactivation of pancreatic stellate cells by dysregulation in the inflammatory response, leading to further damage, autodigestion and possibly necrosis of pancreatic acinar cells. The aim of this research is to investigate the effect of STAT6 knockout in disease severity and development of fibrosis wound healing in the presence of different macrophage populations, regulated by the type 2 immune response, after inducing chronic and/or acute pancreatitis in mice models via cerulean injection. We further investigate the influence of the JAK/STAT6 signaling pathway on the balance of fibrosis and regeneration in STAT6 deficient and wild-type mice. The characterization of resident and recruited macrophages will provide insight into the influence of the JAK/STAT6 signaling pathway on infiltrating cells and, ultimately, tissue fibrosis and disease severity.

Keywords: acute and chronic pancreatitis, tissue regeneration, macrophage polarization, Gastroenterology

Procedia PDF Downloads 33

Authors: S. M. Anowarul Haque, Md. Asiful Islam

Abstract:

Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: load forecasting, artificial neural network, particle swarm optimization

Procedia PDF Downloads 145

Authors: Supriyadi, Andi Intan Purnamasari, Aminuddin Kasim, Sulbadana, Mohammad Reza

Abstract:

The development of the Industrial Revolution Era 4.0 brought a significant influence in the administration of countries in all parts of the world, including Indonesia, not only in the administration and economic sectors but the ways and methods of forming laws should also be adjusted. Until now, the process of making laws carried out by the Parliament with the Government still uses the classical method. The law-making process still uses manual methods, such as typing harmonization of regulations, so that it is not uncommon for errors to occur, such as writing errors, copying articles and so on, things that require a high level of accuracy and relying on inventory and harmonization carried out manually by humans. However, this method often creates several problems due to errors and inaccuracies on the part of officers who harmonize laws after discussion and approval; this has a very serious impact on the system of law formation in Indonesia. The use of artificial intelligence in the process of forming laws seems to be justified and becomes the answer in order to minimize the disharmony of various laws and regulations. This research is normative research using the Legislative Approach and the Conceptual Approach. This research focuses on the question of how to use Artificial Intelligence for Harmonization in the Lawmaking Process.

Keywords: artificial intelligence, harmonization, laws, intelligence

Procedia PDF Downloads 117

Authors: Ali Isapour, Ramin Nateghi

Abstract:

— Markov parameters are unique parameters of the system and remain unchanged under similarity transformations. Markov parameters from a power series that is convergent only if the system matrix’s eigenvalues are inside the unity circle. Therefore, Markov parameters of a stable discrete-time system are convergent. In this study, we aim to realize the system based on Markov parameters by using Artificial Neural Networks (ANN), and this end, we use Flexible Neural Networks. Realization means determining the elements of matrices A, B, C, and D.

Keywords: Markov parameters, realization, activation function, flexible neural network

Procedia PDF Downloads 160