Search results for: artificial feeding

Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Debora B. Moretti, Wiolene M. Nordi, Thaline Maira P. Cruz, José Eurico P. Cyrino, Raul Machado-Neto

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Enzyme activity was evaluated in the intestine of juvenile dourado (Salminus brasiliensis) fed with diets containing 0, 10 or 20% of lyophilized bovine colostrum (LBC) inclusion for either 30 or 60 days. The intestinal enzymes acid and alkaline phosphatase (ACP and ALP, respectively), non-specific esterase (NSE), lipase (LIP), dipeptidyl aminopeptidase IV (DAP IV) and leucine aminopeptidase (LAP) were studied using histochemistry in four intestinal segments (S1, S2, S3 and posterior intestine). Weak proteolitic activity was observed in all intestinal segments for DAP IV and LAP. The activity of NSE and LIP was also weak in all intestines, except for the moderate activity of NSE in the S2 of 20% LBC group after 30 days and in the S1 of 0% LBC group after 60 days. The ACP was detected only in the S2 and S3 of the 10% LBC group after 30 days. Moderate and strong staining was observed in the first three intestinal segments for ALP and weak activity in the posterior intestine. The activity of DAP IV, LAP and ALP were also present in the cytoplasm of the enterocytes. In the present results, bovine colostrum feeding did not cause alterations in activity of intestinal enzymes.

Keywords: carnivorous fish, enterocyte, intestinal epithelium, teleost

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Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: Nahla Elsherbiny, Ahmed Ahmed, Hamada Mohammed, Mohamed Ali

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Background: The enterococci may be considered as opportunistic agents particularly in immunocompromised patients. It is one of the top three pathogens causing many healthcare associated infections (HAIs). Resistance to several commonly used antimicrobial agents is a remarkable characteristic of most species which may carry various genes contributing to virulence. Objectives: to determine the prevalence of enterococci species in different intensive care units (ICUs) causing health care-associated infections (HAIs), intestinal carriage and environmental contamination. Also, to study the antimicrobial susceptibility pattern of the isolates with special reference to vancomycin resistance. In addition to phenotypic and genotypic detection of gelatinase, cytolysin and biofilm formation among isolates. Patients and Methods: This study was carried out in the infection control laboratory at Assiut University Hospitals over a period of one year. Clinical samples were collected from 285 patients with various (HAIs) acquired after admission to different ICUs. Rectal swabs were taken from 14 cases for detection of enterococci carriage. In addition, 1377 environmental samples were collected from the surroundings of the patients. Identification was done by conventional bacteriological methods and confirmed by analytical profile index (API). Antimicrobial sensitivity testing was performed by Kirby Bauer disc diffusion method and detection of vancomycin resistance was done by agar screen method. For the isolates, phenotypic detection of cytolysin, gelatinase production and detection of biofilm by tube method, Congo red method and microtiter plate. We performed polymerase chain reaction (PCR) for detection of some virulence genes (gelE, cylA, vanA, vanB and esp). Results: Enterococci caused 10.5% of the HAIs. Respiratory tract infection was the predominant type (86.7%). The commonest species were E.gallinarum (36.7%), E.casseliflavus (30%), E.faecalis (30%), and E.durans (3.4 %). Vancomycin resistance was detected in a total of 40% (12/30) of those isolates. The risk factors associated with acquiring vancomycin resistant enterococci (VRE) were immune suppression (P= 0.031) and artificial feeding (P= 0.008). For the rectal swabs, enterococci species were detected in 71.4% of samples with the predominance of E. casseliflavus (50%). Most of the isolates were vancomycin resistant (70%). Out of a total 1377 environmental samples, 577 (42%) samples were contaminated with different microorganisms. Enterococci were detected in 1.7% (10/577) of total contaminated samples, 50% of which were vancomycin resistant. All isolates were resistant to penicillin, ampicillin, oxacillin, ciprofloxacin, amikacin, erythromycin, clindamycin and trimethoprim-sulfamethaxazole. For the remaining antibiotics, variable percentages of resistance were reported. Cytolysin and gelatinase were detected phenotypically in 16% and 48 % of the isolates respectively. The microtiter plate method showed the highest percentages of detection of biofilm among all isolated species (100%). The studied virulence genes gelE, esp, vanA and vanB were detected in 62%, 12%, 2% and 12% respectively, while cylA gene was not detected in any isolates. Conclusions: A significant percentage of enterococci was isolated from patients and environments in the ICUs. Many virulence factors were detected phenotypically and genotypically among isolates. The high percentage of resistance, coupled with the risk of cross transmission to other patients make enterococci infections a significant infection control issue in hospitals.

Keywords: antimicrobial resistance, enterococci, ICUs, virulence factors

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Authors: Byron Baron

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Typical of the Mediterranean region, the Maltese islands exhibit calcareous soils containing low organic carbon content and high salinity, in addition to being relatively shallow. This has lead to the common practice of applying copious amounts of artificial fertilisers as well as other chemical inputs, together with the use of ground water having high salinity. Such intensive agricultural activities, over a prolonged time period, on such land has lead further to the loss of any soil fertility, together with direct negative impacts on the quality of fresh water reserves and the local ecosystem. The aim of this study was to investigate whether the natural resources on the island would be sufficient to apply ecological intensification i.e. the use of natural processes to replace anthropological inputs without any significant loss in food production. This was implementing through a sustainable agricultural system based on permaculture practices. Ecological intensification following permaculture principles was implemented for two years in order to capture the seasonal changes in duplicate. The areas dedicated to wild plants were only trimmed back to avoid excessive seeding but never mowing. A number of local staple crops were grown throughout this period, also in duplicate. Concomitantly, a number of practices were implemented following permaculture principles such as reducing land tilling, applying only natural fertiliser, mulching, monitoring of soil parameters using sensors, no use of herbicides or pesticides, and precision irrigation linked to a desalination system. Numerous environmental parameters were measured at regular intervals so as to quantify any improvements in ecological conditions. Crop output was also measured as kilos of produce per area. The results clearly show that over the two year period, the variety of wild plant species increased, the number of visiting pollinators increased, there were no pest infestations (although an increase in the number of pests was observed), and a slight improvement in overall soil health was also observed. This was obviously limited by the short duration of the testing implementation. Dedicating slightly less than 15% of total land area to wild plants in the form of borders around plots of crops assisted pollination and provided a foraging area for gleaning bats (measured as an increased number of feeding buzzes) whilst not giving rise to any pest infestations and no apparent yield losses or ill effects to the crops. Observed increases in crop yields were not significant. The study concluded that with the right support for the initial establishment of a healthy ecosystem and controlled intervention, the available natural resources on the island can substantially improve the condition of the local agricultural land area, resulting is a more prolonged economical output with greater ecological sustainability. That being said, more comprehensive and long-term monitoring is required in order to fully validate these results and design a sustainable agriculture system that truly achieves the best outcome for the Maltese context.

Keywords: ecological intensification, soil health, sustainable agriculture, permaculture

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Authors: W. S. Lawal, T. A. Akande

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Four (4) different methods were employed to detoxified jatropha caucas, they are physical method (if include soaking and drying) chemical method (use of methylated spirit, hexane and methene) biological method,(use of Aspergillus niger and Sunday for 7 days and then baccillus lichifarming) and finally combined method (combination of all these methods). Phobol esther andysis was carried out after the detoxification and was found that combined method is better off (P>0.05). 100 broiler birds was used to further test the effect of detoxified Jatropha by combined method, 50 birds for Jatropha made feed at 10 birds per treatment and was replicated five times, this was also repeated for another 50 birds fed conventional feed, Jatropha made feed was compranded at 8% inclusion level. At the end of the 8th weeks, 8 birds were sacrificed each from each treatment and one bird each was fry, roast, boil and grilled from both conventional and Jatropha fed birds and panelist were served for evaluation. It was found that feeding Jatropha to poultry birds has no effect on the taste of the meat.

Keywords: phobol esther, inclusion level, tolerance level, Jatropha carcass

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Authors: Francisco Espinosa, Juan Chavarría

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Glycerol is a byproduct of biodiesel production that can be used for aqueous-phase reforming to obtain hydrogen. Iridium is a material that has high activity and hydrogen selectivity for steam phase reforming. Nevertheless, a drawback for the use of iridium in aqueous-phase reforming is the low activity in water-gas shift reaction. Therefore, in this work, it is proposed the use of nickel and copper as a second metal in the catalyst to reach a synergetic effect. Iridium, iridium-nickel and iridium-copper catalysts were prepared by incipient wetness impregnation and evaluated in the aqueous-phase reforming of glycerol using CeO₂ or La₂O₃ as support. The catalysts were characterized by XRD, XPS, and EDX. The reactions were carried out in a fixed bed reactor feeding a solution of glycerol 10 wt% in water at 270°C, and reaction products were analyzed by gas chromatography. It was found that IrNi/CeO₂ reached highest glycerol conversion and hydrogen production, slightly above 70% and 43 vol% respectively. In terms of conversion, iridium is a promising metal, and its activity for hydrogen production can be enhanced when adding a second metal.

Keywords: aqueous-phase reforming, glycerol, hydrogen production, iridium

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Authors: K. Almi, S. Lakel, A. Benchabane, A. Kriker

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Several researches are focused on natural resources for the production of biomaterials intended for technical applications. Date palm wood present one of the world’s most important natural resource. Its use as insulating materials will help to solve the severe environmental and recycling problems which other artificial insulating materials caused. This paper reports the results of an experimental investigation on the thermal proprieties of date palm wood from Algeria. A study of physical, chemical, and mechanical properties is also carried out. The goal is to use this natural material in the manufacture of thermal insulation materials for buildings. The local natural resources used in this study are the date palm fibers from Biskra oasis in Algeria. The results have shown that there is no significant difference in the morphological proprieties of the four types of residues. Their chemical composition differed slightly; with the lowest amounts of cellulose and lignin content belong to Petiole. Water absorption study proved that Rachis has a low value of sorption whereas Petiole and Fibrillium have a high value of sorption what influenced their mechanical properties. It is seen that the Rachis and leaflets exhibit high tensile strength values compared to the other residue. On the other hand, the low value of the bulk density of Petiole and Fibrillium leads to a high value of specific tensile strength and young modulus. It was found that the specific young modulus of Petiole and Fibrillium was higher than that of Rachis and Leaflets and that of other natural fibers or even artificial fibers. Compared to the other materials date palm wood provide a good thermal proprieties thus, date palm wood will be a good candidate for the manufacturing efficient and safe insulating materials.

Keywords: composite materials, date palm fiber, natural fibers, tensile tests, thermal proprieties

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Authors: Halimat M. Ajose-Adeogun, Zaynab A. Bello

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Artificial Intelligence (AI) and Natural Language Processing (NLP) have transformed computer language comprehension, allowing computers to comprehend spoken and written language with human-like cognition. NLP, a multidisciplinary area that combines rule-based linguistics, machine learning, and deep learning, enables computers to analyze and comprehend human language. NLP applications in medicine range from tackling issues in electronic health records (EHR) and psychiatry to improving diagnostic precision in orthopedic surgery and optimizing clinical procedures with novel technologies like chatbots. The technology shows promise in a variety of medical sectors, including quicker access to medical records, faster decision-making for healthcare personnel, diagnosing dysplasia in Barrett's esophagus, boosting radiology report quality, and so on. However, successful adoption requires training for healthcare workers, fostering a deep understanding of NLP components, and highlighting the significance of validation before actual application. Despite prevailing challenges, continuous multidisciplinary research and collaboration are critical for overcoming restrictions and paving the way for the revolutionary integration of NLP into medical practice. This integration has the potential to improve patient care, research outcomes, and administrative efficiency. The research methodology includes using NLP techniques for Sentiment Analysis and Emotion Recognition, such as evaluating text or audio data to determine the sentiment and emotional nuances communicated by users, which is essential for designing a responsive and sympathetic chatbot. Furthermore, the project includes the adoption of a Personalized Intervention strategy, in which chatbots are designed to personalize responses by merging NLP algorithms with specific user profiles, treatment history, and emotional states. The synergy between NLP and personalized medicine principles is critical for tailoring chatbot interactions to each user's demands and conditions, hence increasing the efficacy of mental health care. A detailed survey corroborated this synergy, revealing a remarkable 20% increase in patient satisfaction levels and a 30% reduction in workloads for healthcare practitioners. The poll, which focused on health outcomes and was administered to both patients and healthcare professionals, highlights the improved efficiency and favorable influence on the broader healthcare ecosystem.

Keywords: natural language processing, artificial intelligence, healthcare communication, electronic health records, patient care

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Authors: Jacquelyn Burkell, Jane Bailey

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Artificial intelligence (AI) solutions deployed within the justice system face the critical task of providing acceptable explanations for decisions or actions. These explanations must satisfy the joint criteria of public and professional accountability, taking into account the perspectives and requirements of multiple stakeholders, including judges, lawyers, parties, witnesses, and the general public. This research project analyzes and integrates two existing literature on explanations in order to propose guidelines for explainable AI in the justice system. Specifically, we review three bodies of literature: (i) explanations of the purpose and function of 'explainable AI'; (ii) the relevant case law, judicial commentary and legal literature focused on the form and function of reasons for judicial decisions; and (iii) the literature focused on the psychological and sociological functions of these reasons for judicial decisions from the perspective of the public. Our research suggests that while judicial ‘reasons’ (arguably accurate descriptions of the decision-making process and factors) do serve similar explanatory functions as those identified in the literature on 'explainable AI', they also serve an important ‘justification’ function (post hoc constructions that justify the decision that was reached). Further, members of the public are also looking for both justification and explanation in reasons for judicial decisions, and that the absence of either feature is likely to contribute to diminished public confidence in the legal system. Therefore, artificially automated judicial decision-making systems that simply attempt to document the process of decision-making are unlikely in many cases to be useful to and accepted within the justice system. Instead, these systems should focus on the post-hoc articulation of principles and precedents that support the decision or action, especially in cases where legal subjects’ fundamental rights and liberties are at stake.

Keywords: explainable AI, judicial reasons, public accountability, explanation, justification

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Authors: Simone Raimondi De Souza, Glaucia Maria Moraes De Oliveira, Ronir Raggio Luiz, Glorimar Rosa

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Introduction: Obesity is among the main risk factors for cardiovascular disease (CVD). Genesis is multifactorial, including genetic, hormonal and environmental factors disorders, among which inadequate feeding pattern, for which nutritional counseling strategies have proven effective. The consumption of beta-glucans (soluble fibers that reportedly promote satiety) present in oat bran can be an effective strategy for preventing and treating obesity. Other benefits have been observed with oat bran consumption, such as reduction of hypercholesterolemia and hyperglycemia, two other risk factors for CVD. Objectives: To analyze the effect of oat bran consumption associated with nutritional counseling in reducing body mass index (BMI), blood cholesterol, glucose profile, waist and neck circumference in obese individuals, and to evaluate the change in eating pattern. Methods: clinical trial, randomized, double-blind, placebo-controlled, lasting 90 days with adults of both genders, with BMI ≥30kg/m2. The study was approved by the Ethics in Research involving human beings in a public institute of cardiology, in Rio de Janeiro, Brazil. Individuals were invited to participate and accepted formally by signing the Terms of Consent. Participants were randomized into oat bran group (gOB) or placebo group (gPCB) and received, respectively: morning prepared consisting of 40g oat bran, 30g of skimmed milk powder and 1g sweetener sucralose; refined flour 40g rice, 30g of milk powder and 1g sweetener sucralose. The Ten Steps to Healthy Eating, of Brazilian Ministry of Health were used to support the nutritional counseling. Variables analyzed: gender; age; BMI, waist circumference (WC) neck circumference (NC); systolic blood pressure (SBP); diastolic blood pressure (DBP); food consumption, total cholesterol (TC), LDL-cholesterol (LDL-c), HDL-cholesterol (HDL-c), non-HDL cholesterol (nHDLc), triglycerides (TG), fasting glucose (FG), fasting insulin (FI) and HOMA-IR. Dietary intake was assessed by 24-hour dietary recall. The Diet Quality Index revised for the Brazilian population (IQD-R) assessed quality of feeding pattern. Statistical analyzes were performed using SPSS version 21, considering statistically significant p-value less than 0.05. Results: A total of 38 participants were included, age = 50 ± 7,6years, 63% women. 19 subjects were placed in gOB and 19 in gPCB. After intervention, statistically significant reductions were observed in the following parameters: in gOB: IQD-R, TC, LDL-c, nHDL-c, FI, SBP, DBP, BMI, WC, NC; in gPCB: IQD-R, LDL-c, SBP, DBP, BMI, WC, NC. No statistically significant differences were observed in the results between groups. Conclusion: Our results reinforce nutritional counseling as important strategy for prevention and treatment of obesity and suggest that inclusion of oat bran in daily diet can bring additional benefits controlling risk factors for CVD. More studies are needed to establish all benefits of oat bran to human health as well as the ideal daily dose for consumption.

Keywords: oat bran, cardiovascular disease, nutritional counseling, obesity

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: A. Djahbar, M. Aillerie, E. Bounadja

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In this paper we treat a new structure of a high-power actuator which is used to either industry or electric traction. Indeed, the actuator is constituted by two induction motors, the first is a six-phase motor connected in series with another three-phase motor via the stators. The whole is supplied by a single static converter. Our contribution in this paper is the optimization of the system supply source. This is feeding the multimotor group by a direct converter frequency without using the DC-link capacitor. The modelling of the components of multimotor system is presented first. Only the first component of stator currents is used to produce the torque/flux of the first machine in the group. The second component of stator currents is considered as additional degrees of freedom and which can be used for power conversion for the other connected motors. The decoupling of each motor from the group is obtained using the direct vector control scheme. Simulation results demonstrate the effectiveness of the proposed structure.

Keywords: induction machine, motor drives, scalar modulation technique, three-to-six phase matrix converter

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Vanja Skoric

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The paper examines whether the impacts of artificial intelligence (AI) can be meaningfully addressed through the rights-based approach to AI design, investigating in particular how the inclusive, participatory process of assessing the AI impact would make this viable. There is a significant gap between envisioning rights-based AI systems and their practical application. Plausibly, internalizing human rights approach within AI design process might be achieved through identifying and assessing implications of AI features human rights, especially considering the case of vulnerable individuals and communities. However, there is no clarity or consensus on how such an instrument should be operationalised to usefully identify the impact, mitigate harms and meaningfully ensure relevant stakeholders’ participation. In practice, ensuring the meaningful inclusion of those individuals, groups, or entire communities who are affected by the use of the AI system is a prerequisite for a process seeking to assess human rights impacts and risks. Engagement in the entire process of the impact assessment should enable those affected and interested to access information and better understand the technology, product, or service and resulting impacts, but also to learn about their rights and the respective obligations and responsibilities of developers and deployers to protect and/or respect these rights. This paper will provide an overview of the study and practice of the participatory design process for AI, including inclusive impact assessment, its main elements, propose a framework, and discuss the lessons learned from the existing theory. In addition, it will explore pathways for enhancing and promoting individual and group rights through such engagement by discussing when, how, and whom to include, at which stage of the process, and what are the pre-requisites for meaningful and engaging. The overall aim is to ensure using the technology that works for the benefit of society, individuals, and particular (historically marginalised) groups.

Keywords: rights-based design, AI impact assessment, inclusion, harm mitigation

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Maryam Hajrezaie, Mahmood Ameen Abdulla, Nazia Abdul Majid, Hapipa Mohd Ali, Pouya Hassandarvish, Maryam Zahedi Fard

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Background: Colon cancer is one of the most prevalent cancers in the world and is the third leading cause of death among cancers in both males and females. The incidence of colon cancer is ranked fourth among all cancers but varies in different parts of the world. Cancer chemoprevention is defined as the use of natural or synthetic compounds capable of inducing biological mechanisms necessary to preserve genomic fidelity. Andrographolide is the major labdane diterpenoidal constituent of the plant Andrographis paniculata (family Acanthaceae), used extensively in the traditional medicine. Extracts of the plant and their constituents are reported to exhibit a wide spectrum of biological activities of therapeutic importance. Laboratory animal model studies have provided evidence that Andrographolide play a role in inhibiting the risk of certain cancers. Objective: Our aim was to evaluate the chemopreventive efficacy of the Andrographolide in the AOM induced rat model. Methods: To evaluate inhibitory properties of andrographolide on colonic aberrant crypt foci (ACF), five groups of 7-week-old male rats were used. Group 1 (control group) were fed with 10% Tween 20 once a day, Group 2 (cancer control) rats were intra-peritoneally injected with 15 mg/kg Azoxymethan, Gropu 3 (drug control) rats were injected with 15 mg/kg azoxymethan and 5-Flourouracil, Group 4 and 5 (experimental groups) were fed with 10 and 20 mg/kg andrographolide each once a day. After 1 week, the treatment group rats received subcutaneous injections of azoxymethane, 15 mg/kg body weight, once weekly for 2 weeks. Control rats were continued on Tween 20 feeding once a day and experimental groups 10 and 20 mg/kg andrographolide feeding once a day for 8 weeks. All rats were sacrificed 8 weeks after the azoxymethane treatment. Colons were evaluated grossly and histopathologically for ACF. Results: Administration of 10 mg/kg and 20 mg/kg andrographolide were found to be effectively chemoprotective, as evidenced microscopily and biochemically. Andrographolide suppressed total colonic ACF formation up to 40% to 60%, respectively, when compared with control group. Pre-treatment with andrographolide, significantly reduced the impact of AOM toxicity on plasma protein and urea levels as well as on plasma aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and gamma-glutamyl transpeptidase (GGT) activities. Grossly, colorectal specimens revealed that andrographolide treatments decreased the mean score of number of crypts in AOM-treated rats. Importantly, rats fed andrographolide showed 75% inhibition of foci containing four or more aberrant crypts. The results also showed a significant increase in glutathione (GSH), superoxide dismutase (SOD), nitric oxide (NO), and Prostaglandin E2 (PGE2) activities and a decrease in malondialdehyde (MDA) level. Histologically all treatment groups showed a significant decrease of dysplasia as compared to control group. Immunohistochemical staining showed up-regulation of Hsp70 and down-regulation of Bax proteins. Conclusion: The current study demonstrated that Andrographolide reduce the number of ACF. According to these data, Andrographolide might be a promising chemoprotective activity, in a model of AOM-induced in ACF.

Keywords: chemopreventive, andrographolide, colon cancer, aberrant crypt foci (ACF)

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Authors: Magdi Elmessiry, Adel Elmessiry

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The fashion industry represents a significant portion of the global gross domestic product, however, it is plagued by cheap imitators that infringe on the trademarks which destroys the fashion industry's hard work and investment. While eventually the copycats would be found and stopped, the damage has already been done, sales are missed and direct and indirect jobs are lost. The infringer thrives on two main facts: the time it takes to discover them and the lack of tracking technologies that can help the consumer distinguish them. Blockchain technology is a new emerging technology that provides a distributed encrypted immutable and fault resistant ledger. Blockchain presents a ripe technology to resolve the infringement epidemic facing the fashion industry. The significance of the study is that a new approach leveraging the state of the art blockchain technology coupled with artificial intelligence is used to create a framework addressing the fashion infringement problem. It transforms the current focus on legal enforcement, which is difficult at best, to consumer awareness that is far more effective. The framework, Crypto CopyCat, creates an immutable digital asset representing the actual product to empower the customer with a near real time query system. This combination emphasizes the consumer's awareness and appreciation of the product's authenticity, while provides real time feedback to the producer regarding the fake replicas. The main findings of this study are that implementing this approach can delay the fake product penetration of the original product market, thus allowing the original product the time to take advantage of the market. The shift in the fake adoption results in reduced returns, which impedes the copycat market and moves the emphasis to the original product innovation.

Keywords: fashion, infringement, blockchain, artificial intelligence, textiles supply chain

Procedia PDF Downloads 245

Authors: Tae-ho Chung, Chul Park, Gi-wook Shin, Joo-min Kim, Seong-hyun Kim, Namjung Kim

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Mealworm (Tenebrio molitor) was evaluated to investigate the effect of partial or total replacement of fish meal in diets for white shrimp, Litopenaeus vannamei. Experimental groups of shrimp with average initial body weight (2.43 ± 0.54 g) were fed each with 4 isonitrogeneous (38% crude protein) diets formulated to include 0, 25, 50 and 100% (diets 1 to 4, respectively) of fish meal substituted with mealworm. After eight weeks of feeding trials, shrimp fed with diet 3 and 4 revealed the highest values for live weight gain(8.01 ± 2.51 and 7.93 ± 1.12), specific growth rates (2.70 ± 1.12 and 2.59 ± 0.51) as well as better feed conversion ratio (2.69 ± 0.09 and 2.72 ± 0.19) compared to the control group with statistically significant manner (p<0.05). Survival range was 98% in all the treatments. An increase in weight gain and other growth associated parameters was observed with higher replacement. These results clearly indicate that 50% and 100% of fish meal protein in shrimp diet can be replaced by mealworm not only without any adverse effect but also the effect of promoting growth performance.

Keywords: mealworm, Litopenaeus vannamei, Tenebrio molitor, white shrimp

Procedia PDF Downloads 450

Authors: Isaac Kayode Ogunlade

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Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments.

Keywords: data acquisition system, design device, weather development, predict precipitation and (FUTA) standard device

Procedia PDF Downloads 75

Authors: Redhouane Benfares, Kamel Boudjemaa, Affaf Kord, Sonia Messis, Linda Farai, Belkacem Guenachi, Kherarba Maha, Jaroslava ŠVarc-Gajić

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In recent years valorization of biowaste has attracted a lot of attention worldwide owing to its high nutritional value and low price. In this work, biowaste of animal (sardines) and plant (tomato) biowaste was used to formulate a new feed for red tilapia that showed to be competitive in its price, and zootechnical performance in comparison to commercially available tilapia feeds. Mathematical modelling was used to formulate optimal feed composition with favorable chemical composition and the lowest price. Formulated feed had high protein content (40.76%) and an energy value of 279.6 Kcal/100 g. Optimised feed was manufactured and compared to commercially available reference feed with respect to feeding intake, feed efficiency, the specific growth rate of fingerlings of Tilapia sp, and, most important, zootechnical parameters. With a fish survival rate of 100% calculated feed conversion index for the formulated feed was 2.7.

Keywords: conversion index, fish waste, formulated feed, tomato waste

Procedia PDF Downloads 134

Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

Procedia PDF Downloads 49

Authors: Gülseren Kırbaş Doğan, Emin Karakurt, Mushap Kuru, Hilmi Nuhoğlu

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Developmental anomalies can be caused by defects in bone tissue, cartilage tissue, or primitive mesenchymal tissue. Genetic-, environmental-, teratogenic-, faulty breeding selection–, or feeding-related anomalies can be observed either locally or systemically. This study aimed to evaluate in detail the various anomalies in six calves according to pathological and anatomical investigations. Six calves were delivered to the Department of Pathology at the Kafkas University Faculty of Veterinary Medicine between 2017 and 2019. These calves comprised one with anencephaly, one with the diencephalic syndrome, one with Schistosoma reflexum, two with anasarca, and one with nasal and calvarium openings. After necropsy, samples were taken from the organs, foreseen, and routine pathological examinations were performed. Following these procedures, the calves were brought to the anatomy laboratory and anatomically examined. As a result, various anomalies in 6 calves were evaluated according to pathological and anatomical investigations. These findings are believed to contribute to the literature.

Keywords: anatomy, anomaly, calf, pathology

Procedia PDF Downloads 160

Authors: A. Szabó Nagy, J. Szabó, I. Vass

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Water and sediment samples from the Danube River and Moson Danube Arm (Hungary) have been collected and analyzed for contamination by 18 polycyclic aromatic hydrocarbons (PAHs) and eight trace metal(loid)s (As, Cu, Pb, Ni, Cr, Cd, Hg and Zn) in the period of 2014-2015. Moreover, the trace metal(loid) concentrations were measured in the Rába and Marcal rivers (parts of the tributary system feeding the Danube). Total PAH contents in water were found to vary from 0.016 to 0.133 µg/L and concentrations in sediments varied in the range of 0.118 mg/kg and 0.283 mg/kg. Source analysis of PAHs using diagnostic concentration ratios indicated that PAHs found in sediments were of pyrolytic origins. The dissolved trace metal and arsenic concentrations were relatively low in the surface waters. However, higher concentrations were detected in the water samples of Rába (Zn, Cu, Ni, Pb) and Marcal (As, Cu, Ni, Pb) compared to the Danube and Moson Danube. The concentrations of trace metals in sediments were higher than those found in water samples.

Keywords: surface water, sediment, PAH, trace metal

Procedia PDF Downloads 291

Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

Procedia PDF Downloads 96

Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: artificial neural networks, ANN, discrete wavelet transform, DWT, gray-level co-occurrence matrix, GLCM, k-nearest neighbor, KNN, region of interest, ROI

Procedia PDF Downloads 131

Authors: Meimei Shi

Abstract:

Objectives: The identification of endangered species, especially simultaneous detection of multiple species in complex samples, plays a critical role in alleged wildlife crime incidents and prevents illegal trade. This study was to develop a multi-locus DNA metabarcoding method for endangered animal species identification. Methods: Several pairs of universal primers were designed according to the mitochondria conserved gene regions. Experimental mixtures were artificially prepared by mixing well-defined species, including endangered species, e.g., forest musk, bear, tiger, pangolin, and sika deer. The artificial samples were prepared with 1-16 well-characterized species at 1% to 100% DNA concentrations. After multiplex-PCR amplification and parameter modification, the amplified products were analyzed by capillary electrophoresis and used for NGS library preparation. The DNA metabarcoding was carried out based on Illumina MiSeq amplicon sequencing. The data was processed with quality trimming, reads filtering, and OTU clustering; representative sequences were blasted using BLASTn. Results: According to the parameter modification and multiplex-PCR amplification results, five primer sets targeting COI, Cytb, 12S, and 16S, respectively, were selected as the NGS library amplification primer panel. High-throughput sequencing data analysis showed that the established multi-locus DNA metabarcoding method was sensitive and could accurately identify all species in artificial mixtures, including endangered animal species Moschus berezovskii, Ursus thibetanus, Panthera tigris, Manis pentadactyla, Cervus nippon at 1% (DNA concentration). In conclusion, the established species identification method provides technical support for customs and forensic scientists to prevent the illegal trade of endangered animals and their products.

Keywords: DNA metabarcoding, endangered animal species, mitochondria nucleic acid, multi-locus

Procedia PDF Downloads 120

Authors: Hamid Aslani, Ahmed Radwan

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In this paper, a single element microstrip antenna is presented for UWB applications by using techniques as partial ground plane and modified the shape of the patch. The antenna is properly designed to have a compact size and constant gain against frequency. The simulated results have done using two EM software and show good agreement with the measured results for the fabricated antenna. Then a designing of two elements patch antenna array for UWB in the frequency band of 3.1-10 GHz is presented in this paper. The array is constructed by means of feeding two omni-directional modified circular patch elements with a modified power divider. Experimental results show that the array has a stable radiation pattern and low return loss over a broad bandwidth of 64% (3.1–10 GHz). Due to its planar profile, physically compact size, wide impedance bandwidth, directive performance over a wide bandwidth proposed antenna is a good candidate for portable UWB applications and other UWB integrated circuits.

Keywords: ultra wide band, radiation performance, microstrip antenna, size miniaturized antenna

Procedia PDF Downloads 246

Authors: Laatra Zemmouri, Said Boukhechem, Samia Haffaf, Mohamed Lafri

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A study was conducted in order to determine the prevalence and risk factors associated with abortion in ewes in the region of M’sila, located in central-eastern Algeria. A questionnaire was carried out to obtain information about the occurrence of abortion, sheep housing conditions, vaccination, feeding and management practices, and whether the farmers kept other livestock. This cross-sectional study was conducted for 36 months (between 2016 and 2019). A total of 71 sheep flocks were visited. Among 8168 ewes, we recorded 734 (8.99%) abortions and 3861 lambings. The risk factor analysis using multivariable logistic regression showed an association between abortion and vaccination against brucellosis (CI 95%= 2,76-1,35; p<0,001). Abortion decreased when dogs are owned (CI 95%= 0,36-0,84; p= 0.006), however, abortion increased with the presence of cats in farms (CI 95%= 1,24-2,8; p=0.003). There was a significant association between abortion and keeping goats (CI 95%= 1,18-2,40; p= 0.004), bovins (CI 95%= 0,3-0,68; p<0,001) and poultry CI 95%= 0,39-0,77; p= 0.001) in farms. Through this study, it is noticed that a strong association between the occurrence of abortion and estrus synchronization, stillbirth occurrence, and feed supplementation (p<0.05). Identification of the causes of abortion is an important task to reduce foetal losses and to improve livestock productivity.

Keywords: abortion, ewes, questionnaire, risk factors

Procedia PDF Downloads 207

Authors: Zouari Ezzeddine, Ghraieb Ikram

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From the results of the academic literature which evokes the limitations of previous studies, this article shows the reasons for multidimensionality Prediction of financial bubbles. A new framework for modeling study predicting financial bubbles by linking a set of variable presented on several dimensions dictating its multidimensional character. It takes into account the preferences of financial actors. A multicriteria anticipation of the appearance of bubbles in international financial markets helps to fight against a possible crisis.

Keywords: classical measures, predictions, financial bubbles, multidimensional, artificial neural networks

Procedia PDF Downloads 555