Search results for: pneumatic artificial muscles

Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: Aroob Binhimd, Lyan Sayoti, Rana Almansour

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In recent years, artificial intelligence has grown drastically. This has led to the growth of educational programs to help students in solving educational problems and assist them in understanding certain topics. The purpose of this report is to investigate the Mathway application. Mathway is a mathematics software that teaches students how to solve and handle mathematical issues. The app allows students to insert questions manually on the platform or take a picture of the question, and then they get an answer to this mathematical question. It helps students enhance their performance in mathematics. This app can also be used to verify or check if their answers are correct. The report will include a questionnaire to collect data and analyze the users of this application.

Keywords: artificial intelligence, Mathway, mathematics, mathematical problems

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: Abisha Isaac Mohanlal

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Amidst all suspicions and cynicism raised by the legal fraternity, Artificial Intelligence has found its way into the legal system and has revolutionized the conventional forms of legal services delivery. Be it legal argumentation and research or resolution of complex legal disputes; artificial intelligence has crept into all legs of modern day legal services. Its impact has been largely felt by way of big data, legal expert systems, prediction tools, e-lawyering, automated mediation, etc., and lawyers around the world are forced to upgrade themselves and their firms to stay in line with the growth of technology in law. Researchers predict that the future of legal services would belong to artificial intelligence and that the age of human lawyers will soon rust. But as far as the Judiciary is concerned, even in the developed countries, the system has not fully drifted away from the orthodoxy of preferring Natural Intelligence over Artificial Intelligence. Since Judicial decision-making involves a lot of unstructured and rather unprecedented situations which have no single correct answer, and looming questions of legal interpretation arise in most of the cases, discretion and Emotional Intelligence play an unavoidable role. Added to that, there are several ethical, moral and policy issues to be confronted before permitting the intrusion of Artificial Intelligence into the judicial system. As of today, the human judge is the unrivalled master of most of the judicial systems around the globe. Yet, scientists of Artificial Intelligence claim that robot judges can replace human judges irrespective of how daunting the complexity of issues is and how sophisticated the cognitive competence required is. They go on to contend that even if the system is too rigid to allow robot judges to substitute human judges in the recent future, Artificial Intelligence may still aid in other judicial tasks such as drafting judicial documents, intelligent document assembly, case retrieval, etc., and also promote overall flexibility, efficiency, and accuracy in the disposal of cases. By deconstructing the major challenges that Artificial Intelligence has to overcome in order to successfully invade the human- dominated judicial sphere, and critically evaluating the potential differences it would make in the system of justice delivery, the author tries to argue that penetration of Artificial Intelligence into the Judiciary could surely be enhancive and reparative, if not fully transformative.

Keywords: artificial intelligence, judicial decision making, judicial systems, legal services delivery

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Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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Authors: Mehrnoosh Abouzari, Shahrokh Shahraei

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With the development of artificial intelligence in the present age, intelligent machines and systems have proven their actual and potential capabilities and are mindful of increasing their presence in various fields of human life in the fields of industry, financial transactions, marketing, manufacturing, service affairs, politics, economics and various branches of the humanities .Therefore, despite the conservatism and prudence of law enforcement, the traces of artificial intelligence can be seen in various areas of law. Including judicial robotics capability estimation, intelligent judicial decision making system, intelligent defender and attorney strategy adjustment, dissemination and regulation of different and scattered laws in each case to achieve judicial coherence and reduce opinion, reduce prolonged hearing and discontent compared to the current legal system with designing rule-based systems, case-based, knowledge-based systems, etc. are efforts to apply AI in law. In this article, we will identify the ways in which AI is applied in its laws and regulations, identify the dominant concerns in this area and outline the relationship between these two areas in order to answer the question of how artificial intelligence can be used in different areas of law and what the implications of this application will be. The authors believe that the use of artificial intelligence in the three areas of legislative, judiciary and executive power can be very effective in governments' decisions and smart governance, and helping to reach smart communities across human and geographical boundaries that humanity's long-held dream of achieving is a global village free of violence and personalization and human error. Therefore, in this article, we are going to analyze the dimensions of how to use artificial intelligence in the three legislative, judicial and executive branches of government in order to realize its application.

Keywords: artificial intelligence, law, intelligent system, judge

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Authors: Amel Boulemtafes-Boukadoum, Ahmed Benzaoui

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Solar air heaters (SAH) are widely used in heating and drying applications using solar energy. Their efficiency needs to be improved to be competitive towards solar water heater. In this work, our goal is to study heat transfer enhancement in SAHs by the use of artificial roughness on the absorber. For this purpose, computational fluid dynamics (CFD) simulations were carried out to analyze the flow and heat transfer in the air duct of a solar air heater provided with transverse ribs. The air flows in forced convection and the absorber is heated with uniform flux. The effect of major parameters (Reynolds number, solar radiation, air inlet temperature, geometry of roughness) is examined and discussed. To highlight the effect of artificial roughness, we plotted the distribution of the important parameters: Nusselt number, friction factor, global thermohydraulic performance parameter etc. The results obtained are concordant to those found in the literature and shows clearly the heat transfer enhancement due to artifical roughness.

Keywords: solar air heater, artificial roughness, heat transfer enhancement, CFD

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Tala Toonsi, Marah Alagha, Lina Alnowaiser, Hala Rajab

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This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users.

Keywords: photomath, image recognition, app, OCR, artificial intelligence, mathematical equations.

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Authors: Chungyu Chen, Hui-Ju Chang, Pei-Shan Guo, Huei-Ling Jhan, Yi-Ping Lin

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The purpose of this study was to investigate the effect of the amount of acutely static stretching on muscular strength and power. There were 15 males, and 7 females recruited voluntarily as the participants in the study. The mean age, body height, and weight of participants were 23.4 ± 2.8 years old, 171.0 ± 7.2 cm, and 65.7 ± 8.7 kg, respectively. Participants were repeated to stretch hamstring muscles 2 or 6 30-s bouts randomly on a separate day spaced 5-7 days apart in a passive, static, sit-and-reach stretching exercise. Before and after acutely static stretching, the Biodex System 4 Pro was used to acquire the peak torque, power, total work, and range of motion for right knee under the loading of 180 deg/s. The 2 (test-retest) × 2 (number of stretches) repeated measures two-way analysis of variance were used to compare the parameters of muscular strength/power (α = .05). The results showed that the peak torque, power, and total work increased significantly after acutely passive static stretching (ps < .05) in flexor and extensor of knee. But there were no significant differences found between the 2 and 6 30-s bouts hamstring muscles stretching (ps > .05). It indicated that the performance of muscular strength and power in knee flexion and extension do not inhibit following the increase of amount of stretching.

Keywords: knee, power, flexibility, strength

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: landslide, limit analysis, artificial neural network, soil properties

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Authors: Neha Singh, Shristi Singh

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Accidents involving motor vehicles are more likely to cause serious injuries and fatalities. It also has a host of other perpetual issues, such as the regular loss of life and goods in accidents. To solve these issues, appropriate measures must be implemented, such as establishing an autonomous incident detection system that makes use of machine learning and artificial intelligence. In order to reduce traffic accidents, this article examines the overview of artificial intelligence and machine learning in autonomous event detection systems. The paper explores the major issues, prospective solutions, and use of artificial intelligence and machine learning in road transportation systems for minimising traffic accidents. There is a lot of discussion on additional, fresh, and developing approaches that less frequent accidents in the transportation industry. The study structured the following subtopics specifically: traffic management using machine learning and artificial intelligence and an incident detector with these two technologies. The internet of vehicles and vehicle ad hoc networks, as well as the use of wireless communication technologies like 5G wireless networks and the use of machine learning and artificial intelligence for the planning of road transportation systems, are elaborated. In addition, safety is the primary concern of road transportation. Route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management, according to the review's key conclusions, are essential for ensuring the safety of road transportation networks. In addition to highlighting research trends, unanswered problems, and key research conclusions, the study also discusses the difficulties in applying artificial intelligence to road transport systems. Planning and managing the road transportation system might use the work as a resource.

Keywords: artificial intelligence, machine learning, incident detector, road transport systems, traffic management, automatic incident detection, deep learning

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Authors: Amir A. Beltagi, Ahmed R. Abdelbaki

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Background: Core stability training has recently attracted attention for optimizing performance and improving muscle balance for healthy and unhealthy individuals. The purpose of this study was to investigate the effect of beginner’s core stability exercises on the trunk flexors’/extensors’ peak torque ratio and trunk flexors’ and extensors’ peak torques. Methods: Thirty five healthy individuals, randomly assigned into two groups; experimental (group I) and control (group II), participated in the study. Group I involved 20 participants (10 male & 10 female) with mean ±SD age, weight, and height of 20.7±2.4 years, 66.5±12.1 kg and 166.7±7.8 cm respectively. Group II involved 15 participants (6 male & 9 female) with mean ±SD age, weight, and height of 20.3±0.61 years, 68.57±12.2 kg and 164.28 ±7.59 cm respectively. Data were collected using the Biodex Isokinetic system. The participants were tested twice; before and after a 6-week period during which the experimental group performed a core stability training program. Findings: Statistical analysis using the 2x2 Mixed Design ANOVA revealed that there were no significant differences in the trunk flexors’/extensors’ peak torque ratio between the ‘pre’ and ‘post’ tests for either group (p > 0.025). Moreover, there were no significant differences in the trunk flexors’/extensors’ ratios between both groups at either test (p > 0.025). Meanwhile, the 2x2 Mixed Design MANOVA revealed that there were significant differences in the trunk flexors’ and extensors’ peak torques between the ‘pre’ and ‘post’ tests for group I (p < 0.025), while there were no significant differences inbetween for group II (p > 0.025). Moreover, there were no significant differences between both groups for the tested muscles’ peak torques at either test except for that of the trunk flexors at the ‘post’ test only (p < 0.025). Interpretation: The improvement in muscle performance indicated by the increase in the trunk flexors’ and extensors’ peak torques in the experimental group recommends including core stability training in the exercise programs that aim to improve muscle performance.

Keywords: core stability, isokinetic, trunk muscles, muscle balance

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Authors: Vitoldas Vidikas, Algirdas Augonis

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Different ecological binders have been used to minimize the negative effects of cement production and use on the environment. Wood ash is one of these alternative binders, and there has been increasing research related to this topic recently. The incineration process in power plants produces numerous amounts of residues, the potential applications of which remain incompletely understood. However, it is established that wood ash improves concrete properties, serves as a fertilizer, and substitutes natural aggregates in artificial aggregate production. This study presents the production and properties of wood ash artificial aggregate, their integration into concrete, and the assessment of their strength. Due to the aforementioned large amount of incineration waste accumulating in landfills, the recovery of this waste is important, and reuse and recycling of this waste is necessary. Artificial aggregates stand out as a significant innovation in this effort. In this study, the artificial aggregate was carbonized using wood waste incineration ash and alkali activators, with the alkaline activator consisting of Ca(OH)2. Various mixtures were formulated, incorporating different materials and compositions of activators. Initially, fillers were created using wood ash, followed by formulations subsequently supplemented with wood ash. A series of tests, including XRD, SEM, and compression tests, were conducted. The artificial aggregate exhibits minimal water absorption and holds potential as a substitute for natural materials. Its prospective applications extend to agriculture, where it could function as a fertilizer, and construction, where it could serve as an artificial aggregate. Concrete incorporating the artificial aggregate demonstrates stability, stiffness, and relatively low density. In our research, a test was developed and applied to determine the compressive strength of a manufactured artificial aggregate, not by direct loading, but by subjecting a cementitious test specimen containing the aggregate under test to a load. In this way, the test not only determines the effect of the aggregate on the compressive behavior of such a specimen but also the characteristics of the fracture, which shows how these artificial aggregates adhere to the cement matrix. This testing methodology holds promise for evaluating the suitability of artificial aggregates in construction materials, not only in terms of their load-bearing capacity but also of their adhesion to the mineral binder. The results showed that the mechanical properties of granular artificial aggregates vary significantly with the amount of binder (lime), i.e. an increase of ~15% in the amount of binder resulted in an increase in the crushing strength of the carbonized aggregate by ~15-20%, while the compressive strength of the cementitious specimen with this aggregate increased by ~18%.

Keywords: wood ash, artificial aggregate, carbonization, compressive strength

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Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

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Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

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Authors: Himanshu Payal, Sachin Maheshwari, Pushpendra S. Bharti

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Electrical discharge machining (EDM), a non-conventional machining process, finds wide applications for shaping difficult-to-cut alloys. Process modeling of EDM is required to exploit the process to the fullest. Process modeling of EDM is a challenging task owing to involvement of so many electrical and non-electrical parameters. This work is an attempt to model the EDM process using artificial neural network (ANN). Experiments were carried out on die-sinking EDM taking Inconel 825 as work material. ANN modeling has been performed using experimental data. The prediction ability of trained network has been verified experimentally. Results indicate that ANN can predict the values of performance measures of EDM satisfactorily.

Keywords: artificial neural network, EDM, metal removal rate, modeling, surface roughness

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Authors: Serena Gomez, Raeesa Tanseen, Netra Shaligram, Nithin Francis, Sandesh B. J.

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The human gut consists of a community of microbes which has a lot of effects on human health disease. Metabolic modeling can help to predict relative populations of stable microbes and their effect on health disease. In order to study and visualize microbes in the human gut, we developed a tool that offers the following modules: Build a tool that can be used to perform Flux Balance Analysis for microbes in the human gut using the Artificial Bee Colony optimization algorithm. Run simulations for an individual microbe in different conditions, such as aerobic and anaerobic and visualize the results of these simulations.

Keywords: microbes, metabolic modeling, flux balance analysis, artificial bee colony

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Authors: O. O. Olawusi-Peters, K. I. Adejugbagbe

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The concentration of heavy metal (Cd, Pb, Fe, Zn, Cu) in Clarias gariepinus collected from fish markets; Fanibi (Station I) and Fiwasaye (Station II) in Akure metropolis, Ondo state, Nigeria were investigated to ascertain the safety for the consumers. 60 samples were collected from the two markets in three batches (I, II, III) for a period of six months and analyzed for heavy metals in the gills and muscles of the fish. Also, the Health Risk Index (HRI) was used to determine the health risk of these metals to the consumer. The results showed that the investigated metal concentration was higher in station I than station II, except Pb having higher concentration in station II than station I. In both stations, the highest concentration of Fe was recorded in the gills (12.60 ± 1.51; 6.94 ± 1.38) and muscles (3.72 ± 0.09; 3.86 ± 0.33) of samples in batch I. Also, the HRI revealed that consumption of Clarias gariepinus from these study areas did not pose any health risk (HRI < 1). In addition, concentrations of the heavy metals were all below the permissible limits recommended by FAO/WHO.

Keywords: health risk index, heavy metals, clarias gariepinus, akure metropolis, fish monger

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Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

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Authors: F. Allag, S. Bouharati, M. Belmahdi, R. Zegadi

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The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.

Keywords: desert soil, climatic changes, bacteria, vegetation, artificial neural networks

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Authors: Sulaiman Rabiu, Muazu Gusau Abubakar, Jafar Usman Zakari

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The consumption of meat is of great importance as it provides a good source of proteins and significant amount of essential trace element to the body. However, contamination of meat and meat products with heavy metals is becoming a serious threat to food safety and public health. Therefore, the present study is aimed to evaluate the concentration of some heavy metals in muscles and entrails of free-ranged cattle, sheep and goats. A total of sixty (60) fresh samples of muscles, liver, kidney, small intestines and stomach of free ranged cattle, sheep and goats were collected from abattoirs of different goldmine communities of Anka, Bukkuyum, Maru andTalata-Mafara Local Government Areas of Zamfara State, Nigeria. The samples were digested using 10 mL of a mixed 70% high grade concentration of HNO₃ and 65% HCl (4:1 v/v); the mixture was heated until dense fumes disappeared forming a clear transparent solution and diluted to 50 mL with deionized water. Actual concentrations of Cd, Cr, Cu, Co, As, Ni, Mn, Pb and Zn were determined using Microwave Plasma Atomic Emission Spectrophotometer (MP-AES). From the results obtained, goat liver had the highest mean concentration of lead, arsenic, cobalt and manganese (12.43± 0.31, 14.25±0.32, 3.47± 0.86 and 12.68± 0.92 mg/kg respectively) while goat kidney had the highest concentration of copper and zinc (10.08±0.61 and 24.16±1.30 mg/kg respectively). The highest concentrations of cadmium and nickel were recorded in sheep kidney (7.75± 0.65 and 2.08±0.10 mg/kg respectively). Cattle muscles had the highest chromium concentration than all the organs analysed. The target hazard quotients (THQs) for all the metals were below 1.0, but TR which is a risk indices for carcinogenicity indicates an alarming result that requires stringent control to protect public health.Therefore, intensive public health awareness on the risk associated with contamination of heavy metals in meat should be advocated.

Keywords: contamination, goldmine, heavy metals, meat

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Authors: Erica Cochran Hameen, Bobuchi Ken-Opurum, Shalini Priyadarshini, Berangere Lartigue, Sadhana Anath-Pisipati

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School districts throughout the United States are constantly seeking measures to improve test scores, reduce school absenteeism and improve indoor environmental quality. It is imperative to identify key building investments which will provide the largest benefits to schools in terms of improving the aforementioned factors. This study uses Analysis of Variance (ANOVA) tests to statistically evaluate the impact of a school building’s mechanical and plumbing characteristics on a child’s educational performance. The educational performance is measured via three indicators, i.e. test scores, suspensions, and absenteeism. The study investigated 125 New York City school facilities to determine the potential correlations between 50 mechanical and plumbing variables and the performance indicators. Key findings from the tests revealed that elementary schools with pneumatic systems in “good” condition have 48.8% lower percentages of students scoring at the minimum English Language Arts (ELA) competency level compared with those with no pneumatic system. Additionally, elementary schools with “unit heaters/cabinet heaters” in “good to fair” conditions have 1.1% higher attendance rates compared to schools with no “unit heaters/cabinet heaters” or those in inferior condition. Furthermore, elementary schools with air conditioning have 0.6% higher attendance rates compared to schools with no air conditioning, and those with interior floor drains in “good” condition have 1.8% higher attendance rates compared to schools with interior drains in inferior condition.

Keywords: academic attendance and performance, mechanical and plumbing systems, schools, student health

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Authors: Sandra So, Mohd Roslan Ismail, Safurah Jaafar

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With the rapid proliferation of data in healthcare has provided an opportune platform creation of Artificial Intelligence (AI). AI has brought a paradigm shift for healthcare professionals, promising improvement in delivery and quality. This study aims to determine the perception of healthcare personnel on perceived ease of use, perceived usefulness, and subjective norm toward attitude for artificial intelligence acceptance. A cross-sectional single institutional study of employees’ perception of adopting AI in the hospital was conducted. The survey was conducted using a questionnaire adapted from Technology Acceptance Model and a four-point Likert scale was used. There were 96 or 75.5% of the total population responded. This study has shown the significant relationship and the importance of ease of use, perceived usefulness, and subjective norm to the acceptance of AI. In the study results, it concluded that the determining factor to the strong acceptance of AI in their practices is mostly those respondents with the most interaction with the patients and clinical management.

Keywords: artificial intelligence, machine learning, perceived ease of use, perceived usefulness, subjective norm

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Authors: Danah S. Alabdulmohsin

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Due to the fast growth of organizational data as well as the emergence of new technologies such as artificial intelligence (AI), organizations tend to utilize these new technologies in their enterprise information systems (EIS) either to overcome the issues they struggle with or to enhance their functions. The aim of this paper is to review the potential role of AI technologies in EIS, namely: enterprise resource planning systems (ERP), customer relation management systems (CRM), supply chain management systems (SCM), knowledge systems (KM), and human resources management systems (HRM). The paper provided the definitions of these systems as well as the definitions of AI technologies that have been used in EIS. In addition, the paper discussed the challenges that organizations might face while integrating AI with their information systems and explained why some organizations fail in achieving successful implementations of the integration.

Keywords: artificial intelligence, AI, enterprise information system, EIS, integration

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Authors: Jasmin S. Nurkovic, Ljubisa Dj. Jovasevic, Zana C. Dolicanin, Zoran S. Bajin

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Ruptured muscles and tendons very often must be repatriated by open operation in young persons. In young, muscles are ruptured more often than tendons, at the sane time in older persons are more exposed to rupture than muscles. Ruptured of the calcaneus are the most present of all ruptures. Sometime the rupture is complete, but very often the incomplete rupture can be noticed. During six years, from 2006 to 2012, we treated nineteen male patients and three female patients with the rupture of tendon Achilles. The youngest patient was aged thirty two, and the oldest was also managed sixty four. The youngest female patient was forty one and the oldest was forty six. One of our patients who was under corticosteroid treatment did not take any part in sport activities but she was, as she told us, going for a long walk, the same was with other two patients one man and one woman. We had nineteen male patients age 32 to 64 and three female patients age 41, 44 and 46. Conservative treatment by cast was applied in five patients and very good results were in three of them. In two patients surgical treatment failed in patient’s age 53 and 64. Only one of all patients treated by surgery had healing problems because of necrotic changes of the skin where incision was made. One of our female patients age 45 was under steroid treatment for almost 20 years because of asthmatic problems. We suggested her wearing boots with 8cm long heels by day and by night eight weeks. The final results were satisfactory and all the time she was able to work and to walk. It was the only case we had with bilateral tendon rupture. After eight weeks the cast is removed and psychiatric treatment started, patient is using crutches with partial weight bearing over a period of two weeks. Quite the same treatment conservative treatment, only the cast is not removed after two but after four weeks. Everyday activities after the surgical treatment started ten weeks and sport activities can start after fourteen to sixteen weeks. An increased activity of our patient without previous preparing for forces activity can result, as we already see, with tendon rupture. Treatment is very long and very often surgical. We find that surgical treatment resulted as safer and better solution for patients. We also had a patient with spontaneous rupture of tendon during longer walking but this patient was under prolonged corticosteroid treatment.

Keywords: tendon, Achilles, rupture, sport

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Authors: M. Hassani, Y. Hassani, N. Ajudanioskooei, N. N. Benvid

Abstract:

Laser Forming process as a non-contact thermal forming process is widely used to forming and bending of metallic and non-metallic sheets. In this process, according to laser irradiation along a specific path, sheet is bent. One of the most important output parameters in laser forming is bending angle that depends on process parameters such as physical and mechanical properties of materials, laser power, laser travel speed and the number of scan passes. In this paper, Artificial Neural Network and Fuzzy Logic System were used to predict of bending angle in laser forming process. Inputs to these models were laser travel speed and laser power. The comparison between artificial neural network and fuzzy logic models with experimental results has been shown both of these models have high ability to prediction of bending angles with minimum errors.

Keywords: artificial neural network, bending angle, fuzzy logic, laser forming

Procedia PDF Downloads 558

Authors: Aydin Azizi, Aburrahman Tanira

Abstract:

The main source of energy used in this modern age is fossil fuels. There is a myriad of problems that come with the use of fossil fuels, out of which the issues with the greatest impact are its scarcity and the cost it imposes on the planet. Fossil fuels are the only plausible option for many vital functions and processes; the most important of these is transportation. Thus, using this source of energy wisely and as efficiently as possible is a must. The aim of this work was to explore utilising mathematical modelling and artificial intelligence techniques to enhance fuel consumption in passenger cars by focusing on the speed at which cars are driven. An artificial neural network with an error less than 0.05 was developed to be applied practically as to predict the rate of fuel consumption in vehicles.

Keywords: mathematical modeling, neural networks, fuel consumption, fossil fuel

Procedia PDF Downloads 372

Authors: Shib Shankar Banerjeea, Andreas Ferya, Gert Heinricha, Amit Das

Abstract:

Recently, the growing need for the development of soft robots consisting of highly deformable and compliance materials emerge from the serious limitations of conventional service robots. However, one of the main challenges of soft robotics is to develop such compliance materials, which facilitates the design of soft robotic structures and, simultaneously, controls the soft-body systems, like soft artificial muscles. Generally, silicone or acrylic-based elastomer composites are used for soft robotics. However, mechanical performance and long-term reliabilities of the functional parts (sensors, actuators, main body) of the robot made from these composite materials are inferior. This work will present the development and characterization of robust super-soft programmable elastomeric materials from crosslinked natural rubber that can serve as touch and strain sensors for soft robotic arms with very high elastic properties and strain, while the modulus is altered in the kilopascal range. Our results suggest that such soft natural programmable elastomers can be promising materials and can replace conventional silicone-based elastomer for soft robotics applications.

Keywords: elastomers, soft materials, natural rubber, sensors

Procedia PDF Downloads 126

Authors: Dehini Rachid, Ferdi Brahim

Abstract:

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

Procedia PDF Downloads 354