Search results for: artificial food

Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

Procedia PDF Downloads 68

Authors: Clara Tramuta, Lucia Decastelli, Elisa Barcucci, Sandra Fragassi, Samantha Lupi, Enrico Arletti, Melissa Bizzarri, Daniela Manila Bianchi

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Introdution: Food allergies occurs, in the Western World, 2% of adults and up to 8% of children. The protection of allergic consumers is guaranted, in Eurrope, by Regulation (EU) No 1169/2011 of the European Parliament which governs the consumer's right to information and identifies 14 food allergens to be mandatory indicated on the label. Among these, mustard is a popular spice added to enhance the flavour and taste of foods. It is frequently present as an ingredient in spice blends, marinades, salad dressings, sausages, and other products. Hypersensitivity to mustard is a public health problem since the ingestion of even low amounts can trigger severe allergic reactions. In order to protect the allergic consumer, high performance methods are required for the detection of allergenic ingredients. Food safety laboratories rely on validated methods that detect hidden allergens in food to ensure the safety and health of allergic consumers. Here we present the test results for the validation and accreditation of a Real time PCR assay (RT-PCR: SPECIALfinder MC Mustard, Generon), for the detection of mustard traces in food. Materials and Methods. The method was tested on five classes of food matrices: bakery and pastry products (chocolate cookies), meats (ragù), ready-to-eat (mixed salad), dairy products (yogurt), grains, and milling products (rice and barley flour). Blank samples were spiked starting with the mustard samples (Sinapis Alba), lyophilized and stored at -18 °C, at a concentration of 1000 ppm. Serial dilutions were then prepared to a final concentration of 0.5 ppm, using the DNA extracted by ION Force FAST (Generon) from the blank samples. The Real Time PCR reaction was performed by RT-PCR SPECIALfinder MC Mustard (Generon), using CFX96 System (BioRad). Results. Real Time PCR showed a limit of detection (LOD) of 0.5 ppm in grains and milling products, ready-to-eat, meats, bakery, pastry products, and dairy products (range Ct 25-34). To determine the exclusivity parameter of the method, the ragù matrix was contaminated with Prunus dulcis (almonds), peanut (Arachis hypogaea), Glycine max (soy), Apium graveolens (celery), Allium cepa (onion), Pisum sativum (peas), Daucus carota (carrots), and Theobroma cacao (cocoa) and no cross-reactions were observed. Discussion. In terms of sensitivity, the Real Time PCR confirmed, even in complex matrix, a LOD of 0.5 ppm in five classes of food matrices tested; these values are compatible with the current regulatory situation that does not consider, at international level, to establish a quantitative criterion for the allergen considered in this study. The Real Time PCR SPECIALfinder kit for the detection of mustard proved to be easy to use and particularly appreciated for the rapid response times considering that the amplification and detection phase has a duration of less than 50 minutes. Method accuracy was rated satisfactory for sensitivity (100%) and specificity (100%) and was fully validated and accreditated. It was found adequate for the needs of the laboratory as it met the purpose for which it was applied. This study was funded in part within a project of the Italian Ministry of Health (IZS PLV 02/19 RC).

Keywords: allergens, food, mustard, real time PCR

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Authors: H. Bonakdari, I. Ebtehaj

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The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), bridge pier, scour depth, nonlinear regression (NLR)

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Authors: Nicola G. G. Cecca

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In Italian IPSSEOAs, high schools that give a vocational education to students that will work in the field of Enogastronomy and Hotel Management, the course of Food Science allows the students to start and see food as a mixture of substances that they will transform during their profession. These substances are characterized not only by a chemical composition but also by a molecular structure that makes them nutritionally active. But the increasing number of new products proposed by Food Industry, the modern techniques of production and transformation, the innovative preparations required by customers have made many information reported in the most wide spread Food Science textbooks not up-to-date or too poor for the people who will work in catering sector. Often Authors offer information aged to Bohr’s Atomic Model and to the ‘Octet Rule’ proposed by G.N. Lewis to describe the Chemical Bond, without giving any reference to new as Orbital Atomic Model and Molecular Orbital Theory that, in the meantime, start to be old themselves. Furthermore, this antiquated information precludes an easy understanding of a wide range of properties of nutritive substances and many reactions in which the food constituents are involved. In this paper, our attention is pointed out to use GEOMAG™ to represent the dynamics with which the chemical bond is formed during the synthesis of the molecules. GEOMAG™ is a toy, produced by the Swiss Company Geomagword S.A., pointed to stimulate in children, aged between 6-10 years, their fantasy and their handling ability and constituted by metallic spheres and metallic magnetic bars coated by coloured plastic materials. The simulation carried out with GEOMAG™ is based on the similitude existing between the Coulomb’s force and the magnetic attraction’s force and in particular between the formulae with which they are calculated. The electrostatic force (F in Newton) that allows the formation of the chemical bond can be calculated by mean Fc = kc q1 q2/d2 where: q1 e q2 are the charge of particles [in Coulomb], d is the distance between the particles [in meters] and kc is the Coulomb’s constant. It is surprising to observe that the attraction’s force (Fm) acting between the magnetic extremities of GEOMAG™ used to simulate the chemical bond can be calculated in the same way by using the formula Fm = km m1 m2/d2 where: m1 e m2 represent the strength of the poles [A•m], d is the distance between the particles [m], km = μ/4π in which μ is the magnetic permeability of medium [N•A-2]. The magnetic attraction can be tested by students by trying to keep the magnetic elements of GEOMAG™ separate by hands or trying to measure by mean an appropriate dynamometric system. Furthermore, by using a dynamometric system to measure the magnetic attraction between the GEOMAG™ elements is possible draw a graphic F=f(d) to verify that the curve obtained during the simulation is very similar to that one hypnotized, around the 1920’s by Linus Pauling to describe the formation of H2+ in according with Molecular Orbital Theory.

Keywords: chemical bond, molecular orbital theory, magnetic attraction force, GEOMAG™

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Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

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Authors: Namasivayam Navaranjan, Eric Dimla

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The world needs more quality food, shelter and transportation to meet the demands of growing population and improving living standard of those who currently live below the poverty line. Materials are essential commodities for various applications including food and pharmaceutical packaging, building and automobile. Petroleum based plastics are widely used materials amongst others for these applications and their demand is expected to increase. Use of plastics has environment related issues because considerable amount of plastic used worldwide is disposed in landfills, where its resources are wasted, the material takes up valuable space and blights communities. Some countries have been implementing regulations and/or legislations to increase reuse, recycle, renew and remanufacture materials as well as to minimise the use of non-environmentally friendly materials such as petroleum plastics. However, issue of material waste is still a concern in the countries who have low environmental regulations. Development of materials, mostly bioplastics from renewable biomass resources has become popular in the last decade. It is widely believed that the potential for up to 90% substitution of total plastics consumption by bioplastics is technically possible. The global demand for bioplastics is estimated to be approximately six times larger than in 2010. Recently, standard polymers like polyethylene (PE), polypropylene (PP), Polyvinyl Chloride (PVC) or Polyethylene terephthalate (PET), but also high-performance polymers such as polyamides or polyesters have been totally or partially substituted by their renewable equivalents. An example is Polylactide (PLA) being used as a substitute in films and injection moulded products made of petroleum plastics, e.g. PET. The starting raw materials for bio-based materials are usually sugars or starches that are mostly derived from food resources, partially also recycled materials from food or wood processing. The risk in lower food availability by increasing price of basic grains as a result of competition with biomass-based product sectors for feedstock also needs to be considered for the future bioplastic production. Manufacturing of bioplastic materials is often still reliant upon petroleum as an energy and materials source. Life Cycle Assessment (LCA) of bioplastic products has being conducted to determine the sustainability of a production route. However, the accuracy of LCA depends on several factors and needs improvement. Low oil price and high production cost may also limit the technically possible growth of these plastics in the coming years.

Keywords: bioplastics, plastics, renewable resources, biomass

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Authors: Kyle Yatich Terik, Collins Oduor

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The current healthcare system has made healthcare more accessible and efficient by the use of information technology through the implementation of computer algorithms that generate menus based on the diagnosis. While many systems just like these have been created over the years, their main objective is to help healthy individuals calculate their calorie intake and assist them by providing food selections based on a pre-specified calorie. That application has been proven to be useful in some ways, and they are not suitable for monitoring, planning, and managing hospital patients, especially that critical condition their dietary needs. The system also addresses a number of objectives, such as; the main objective is to be able to design, develop and implement an efficient, user-friendly as well as and interactive dietary management system. The specific design development objectives include developing a system that will facilitate a monitoring feature for users using graphs, developing a system that will provide system-generated reports to the users, dietitians, and system admins, design a system that allows users to measure their BMI (Body Mass Index), the system will also provide food template feature that will guide the user on a balanced diet plan. In order to develop the system, further research was carried out in Kenya, Nairobi County, using online questionnaires being the preferred research design approach. From the 44 respondents, one could create discussions such as the major challenges encountered from the manual dietary system, which include no easily accessible information of the calorie intake for food products, expensive to physically visit a dietitian to create a tailored diet plan. Conclusively, the system has the potential of improving the quality of life of people as a whole by providing a standard for healthy living and allowing individuals to have readily available knowledge through food templates that will guide people and allow users to create their own diet plans that consist of a balanced diet.

Keywords: DMS, dietitian, patient, administrator

Procedia PDF Downloads 157

Authors: L. Bendifallah, S. Doumandji, K. Louadi, S. Iserbyt, F. Acheuk

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Due to their ecological key position and diversity, plant-bee relationships constitute excellent models to understand the processes of food specialisation. The purpose of this study is to define and identify the most important species of bees foraging broadbean flowers, we estimated morphological, phonological and behavioural features. We discuss the results by considering the food specialisation level of the visitor. In the studied populations (Algiers, Algeria), visiting bees belong to four different genus: Apis, Andrena, Eucera and Xylocopa. Eucera is foraging broad beans flowers during months of April, May. The genus Andrena and Xylocopa were found on weeds after the flowering period of beans. The two species have not a preferred type of vegetation compared to Eucera. The main pollinators were generalist bees such as Apis mellifera L. and Xylocopa pubescens Spinola (Apidae), and specialist bees such Eucera numida Lep. (Apidae). The results show that no one of the studied species, neither the specialist, nor the generalist ones, share adaptative morphological or behavioural features that may improve foraging on Vicia faba. However, there is a narrow synchronisation between the daily and yearly phenologies of Eucera numida and those of V. faba. This could be an adaptation of the specialist bee to its host plant. Thus, the food specialisation of Eucera numida, as for most specialist bees, would be more linked to its adapted phenology than to an adapted morphology.

Keywords: Vicia faba, bees, pollinators, Algeria

Procedia PDF Downloads 317

Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

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Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

Procedia PDF Downloads 179

Authors: Sevde Altuntas, Fatih Buyukserin

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Alzheimer’s disease is responsible for irreversible neural damage of brain parts. One of the disease markers is Amyloid-β 1-42 protein that accumulates in the brain in the form plaques. The basic problem for detection of the protein is the low amount of protein that cannot be detected properly in body liquids such as blood, saliva or urine. To solve this problem, tests like ELISA or PCR are proposed which are expensive, require specialized personnel and can contain complex protocols. Therefore, Surface-enhanced Raman Spectroscopy (SERS) a good candidate for detection of Amyloid-β 1-42 protein. Because the spectroscopic technique can potentially allow even single molecule detection from liquid and solid surfaces. Besides SERS signal can be improved by using nanopattern surface and also is specific to molecules. In this context, our study proposes to fabricate diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin - T to detect low concentrations of Amyloid-β 1-42 protein in water and artificial saliva medium by the enhancement of protein SERS signal. The nanopatterned PC surface that was used to enhance SERS signal was fabricated by using Anodic Alumina Membranes (AAM) as a template. It is possible to produce AAMs with different column structures and varying thicknesses depending on voltage and anodization time. After fabrication process, the pore diameter of AAMs can be arranged with dilute acid solution treatment. In this study, two different columns structures were prepared. After a surface modification to decrease their surface energy, AAMs were treated with PC solution. Following the solvent evaporation, nanopatterned PC films with tunable pillared structures were peeled off from the membrane surface. The PC film was then modified with Au and Thioflavin-T for the detection of Amyloid-β 1-42 protein. The protein detection studies were conducted first in water via this biosensor platform. Same measurements were conducted in artificial saliva to detect the presence of Amyloid Amyloid-β 1-42 protein. SEM, SERS and contact angle measurements were carried out for the characterization of different surfaces and further demonstration of the protein attachment. SERS enhancement factor calculations were also completed via experimental results. As a result, our research group fabricated diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin-T to detect low concentrations of Alzheimer’s Amiloid – β protein in water and artificial saliva medium. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) Grant No: 214Z167.

Keywords: alzheimer, anodic aluminum oxide, nanotopography, surface enhanced Raman spectroscopy

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Authors: A. Soni, D. R. Mishra, D. K. Koul

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α-Al2O3:C has been reported to have deeper traps at 600°C and 900°C respectively. These traps have been reported to accessed at relatively earlier temperatures (122 and 322 °C respectively) using thermally assisted OSL (TA-OSL). In this work, the dose response α-Al2O3:C was studied in the dose range of 10Gy to 10kGy for its application in food irradiation in low ( upto 1kGy) and medium(1 to 10kGy) dose range. The TOL (Thermo-optically stimulated luminescence) measurements were carried out on RisØ TL/OSL, TL-DA-15 system having a blue light-emitting diodes (λ=470 ±30nm) stimulation source with power level set at the 90% of the maximum stimulation intensity for the blue LEDs (40 mW/cm2). The observations were carried on commercial α-Al2O3:C phosphor. The TOL experiments were carried out with number of active channel (300) and inactive channel (1). Using these settings, the sample is subjected to linear thermal heating and constant optical stimulation. The detection filter used in all observations was a Hoya U-340 (Ip ~ 340 nm, FWHM ~ 80 nm). Irradiation of the samples was carried out using a 90Sr/90Y β-source housed in the system. A heating rate of 2 °C/s was preferred in TL measurements so as to reduce the temperature lag between the heater plate and the samples. To study the dose response of deep traps of α-Al2O3:C, samples were irradiated with various dose ranging from 10 Gy to 10 kGy. For each set of dose, three samples were irradiated. In order to record the TA-OSL, initially TL was recorded up to a temperature of 400°C, to deplete the signal due to 185°C main dosimetry TL peak in α-Al2O3:C, which is also associated with the basic OSL traps. After taking TL readout, the sample was subsequently subjected to TOL measurement. As a result, two well-defined TA-OSL peaks at 121°C and at 232°C occur in time as well as temperature domain which are different from the main dosimetric TL peak which occurs at ~ 185°C. The linearity of the integrated TOL signal has been measured as a function of absorbed dose and found to be linear upto 10kGy. Thus, it can be used for low and intermediate dose range of for its application in food irradiation. The deep energy level defects of α-Al2O3:C phosphor can be accessed using TOL section of RisØ reader system.

Keywords: α-Al2O3:C, deep traps, food irradiation, TA-OSL

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: Seong Jun Yoon, Jasurbek Doliev, Sang Min Oh, Rodi Hartono, Kyoojae Shin

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Electric power steering (EPS), which is commonly used in electric vehicles recently, is an electric-driven steering device for vehicles. Compared to hydraulic systems, EPS offers advantages such as simple system components, easy maintenance, and improved steering performance. However, because the EPS system is a nonlinear model, difficult problems arise in controller design. To address these, various machine learning and artificial intelligence approaches, notably artificial neural networks (ANN), have been applied. ANN can effectively determine relationships between inputs and outputs in a data-driven manner. This research explores two main areas: designing an EPS identifier using an ANN-based backpropagation (BP) algorithm and enhancing the EPS system controller with an ANN-based Levenberg-Marquardt (LM) algorithm. The proposed ANN-based BP algorithm shows superior performance and accuracy compared to linear transfer function estimators, while the LM algorithm offers better input angle reference tracking and faster response times than traditional PID controllers. Overall, the proposed ANN methods demonstrate significant promise in improving EPS system performance.

Keywords: ANN backpropagation modelling, electric power steering, transfer function estimator, electrical vehicle driving system

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Authors: Md Iqbal Hossain, Tahmeed Ahmed, Kenneth H. Brown

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Objective: To assess the effect of community-based follow up, with or without food-supplementation and/or psychosocial stimulation, as an alternative to current hospital-based follow-up of children with moderate-acute-malnutrition (WHZ < -2 to -3) (MAM). Design/methods: The study was conducted at the ICDDR,B Dhaka Hospital and in four urban primary health care centers of Dhaka, Bangladesh during 2005-2007. The efficacy of five different randomly assigned interventions was compared with respect to the rate of completion of follow-up, growth and morbidity in 227 MAM children aged 6-24 months who were initially treated at ICDDR,B for diarrhea and/or other morbidities. The interventions were: 1) Fortnightly follow-up care (FFC) at the ICDDR,B’s outpatient-unit, including growth monitoring, health education, and micro-nutrient supplementation (H-C, n=49). 2) FFC at community follow-up unit (CNFU) [established in the existing urban primary health-care centers close to the residence of the child] but received the same regimen as H-C (C-C, n=53). 3) As per C-C plus cereal-based supplementary food (SF) (C-SF, n=49). The SF packets were distributed on recruitment and at every visit in CNFU [@1 packet/day for 6–11 and 2 packets/day for 12-24 month old children. Each packet contained 20g toasted rice-powder, 10g toasted lentil-powder, 5g molasses, and 3g soy bean oil, to provide a total of ~ 150kcal with 11% energy from protein]. 4) As per C-C plus psychosocial stimulation (PS) (C-PS, n=43). PS consisted of child-stimulation and parental-counseling conducted by trained health workers. 5) As per C-C plus both SF+PS (C-SF+PS, n=33). Results: A total of 227children (48.5% female), with a mean ± SD age of 12.6 ±3.8 months, and WHZ of - 2.53±0.28 enrolled. Baseline characteristics did not differ by treatment group. The rate of spontaneous attendance at scheduled follow-up visits gradually decreased in all groups. Follow-up attendance and gain in weight and length were greater in groups C-SF, C-SF+PS, and C-PS than C-C, and these indicators were observed least in H-C. Children in the H-C group more often suffered from diarrhea (25 % vs. 4-9%) and fever (28% vs. 8-11%) than other groups (p < 0.05). Children who attended at least five of the total six scheduled follow-up visits gained more in weight (median: 0.86 vs. 0.62 kg, p=0.002), length (median: 2.4 vs. 2.0 cm, p=0.009) than those who attended fewer. Conclusions: Community-based service delivery, especially including supplementary food with or without psychosocial stimulation, permits better rehabilitation of children with MAM compared to current hospital outpatients-based care. By scaling the community-based follow-up including food supplementation with or without psychosocial stimulation, it will be possible to rehabilitate a greater number of MAM children in a better way.

Keywords: community-based management, moderate acute malnutrition, psychosocial stimulation, supplementary food

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Authors: Pardis Moslemzadeh Tehrani

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Due to the emergence of blockchain technology and the benefits of cryptocurrencies, intelligent or smart contracts are gaining traction. Artificial intelligence (AI) is transforming our lives, and it is being embraced by a wide range of sectors. Smart contracts, which are at the heart of blockchains, incorporate AI characteristics. Such contracts are referred to as "smart" contracts because of the underlying technology that allows contracting parties to agree on terms expressed in computer code that defines machine-readable instructions for computers to follow under specific situations. The transmission happens automatically if the conditions are met. Initially utilised for financial transactions, blockchain applications have since expanded to include the financial, insurance, and medical sectors, as well as supply networks. Raw material acquisition by suppliers, design, and fabrication by manufacturers, delivery of final products to consumers, and even post-sales logistics assistance are all part of supply chains. Many issues are linked with managing supply chains from the planning and coordination stages, which can be implemented in a smart contract in a blockchain due to their complexity. Manufacturing delays and limited third-party amounts of product components have raised concerns about the integrity and accountability of supply chains for food and pharmaceutical items. Other concerns include regulatory compliance in multiple jurisdictions and transportation circumstances (for instance, many products must be kept in temperature-controlled environments to ensure their effectiveness). Products are handled by several providers before reaching customers in modern economic systems. Information is sent between suppliers, shippers, distributors, and retailers at every stage of the production and distribution process. Information travels more effectively when individuals are eliminated from the equation. The usage of blockchain technology could be a viable solution to these coordination issues. In blockchains, smart contracts allow for the rapid transmission of production data, logistical data, inventory levels, and sales data. This research investigates the legal and technical advantages and disadvantages of AI-blockchain technology in the supply chain business. It aims to uncover the applicable legal problems and barriers to the use of AI-blockchain technology to supply chains, particularly in the food industry. It also discusses the essential legal and technological issues and impediments to supply chain implementation for stakeholders, as well as methods for overcoming them before releasing the technology to clients. Because there has been little research done on this topic, it is difficult for industrial stakeholders to grasp how blockchain technology could be used in their respective operations. As a result, the focus of this research will be on building advanced and complex contractual terms in supply chain smart contracts on blockchains to cover all unforeseen supply chain challenges.

Keywords: blockchain, supply chain, IoT, smart contract

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Authors: L. K. Davis

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The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time-based DNA codes, writing DNA code from scratch

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Authors: Kaarunya Sampathkumar, Say Chye Joachim Loo

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Oral delivery is regarded as the facile method for the administration of active pharmaceutical ingredients (API) and drug carriers. In an initiative towards sustainable nanotechnology, an oral nano-delivery system has been developed that is made entirely of food-based materials and can also act as a site-specific delivery device depending on the stimulus encountered in different parts of the gastrointestinal tract (GIT). The delivery system has been fabricated from food grade polysaccharide materials like chitosan and starch through electrospraying technique without the use of any organic solvents. A nutraceutical extracted from an Indian medicinal plant, has been loaded into the nano carrier to test its efficacy in encapsulation and stimuli based release of the active ingredient. The release kinetics of the nutraceutical from the carrier was evaluated in simulated gastric, intestinal and colonic fluid and was found to be triggered both by the enzymes and the pH in each part of the intestinal tract depending on the polysaccharide being used. The toxicity of the nanoparticles on the intestinal epithelial cells was tested and found to be relatively safe for up to 24 hours at a concentration of 0.2 mg/mL with cellular uptake also being observed. The developed nano carrier thus serves as a promising delivery vehicle for targeted delivery to different parts of the GIT with the inherent conditions of the GIT itself acting as the stimulus. In addition, being fabricated from food grade materials, the carrier could be potentially used for the targeted delivery of nutrients through functional foods.

Keywords: bioavailability, chitosan, delivery systems, encapsulation

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Authors: Steffon Campbell

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This research explores the potential of using artificial intelligence (AI) autoethnographies to study, document, explore, and understand aspects of Caribbean culture. As a digital research methodology, AI autoethnography merges computer science and technology with ethnography, providing a fresh approach to collecting and analyzing data to generate novel insights. This research investigates how AI autoethnography can best be applied to understanding the various complexities and nuances of Caribbean culture, as well as examining how technology can be a valuable tool for enriching study of the region. By applying AI autoethnography to Caribbean studies, the research aims to produce new and innovative ways of discovering, understanding, and appreciating the Caribbean. The study found that AI autoethnographies can offer a valuable method for exploring Caribbean culture. Specifically, AI autoethnographies can facilitate experiences of self-reflection, facilitate reconciliation with the past, and provide a platform to explore and understand the cultural, social, political, and economic concerns of Caribbean people. Findings also reveal that these autoethnographies can create a space for people to reimagine and reframe the conversation around Caribbean culture by enabling them to actively participate in the process of knowledge creation. The study also finds that AI autoethnography offers the potential for cross-cultural dialogue, allowing participants to connect with one another over cultural considerations and engage in meaningful discourse.

Keywords: artificial intelligence, autoethnography, caribbean, culture

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Authors: Neha Sharma, Kanthi K. Kondepudi, Naveen Gupta

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Phytases are phytate specific phosphatases catalyzing the step-wise dephosphorylation of phytate, which acts as an anti-nutritional factor in food due to its strong binding capacity to minerals. In recent years microbial phytases have been explored for improving nutritional quality of food. But the major limitation is acceptability of phytases from these microorganisms. Therefore, efforts are being made to isolate organisms which are generally regarded as safe for human consumption such as Lactic Acid Bacteria (LAB). Phytases from these organisms will have an edge over other phytase sources due to its probiotic attributes. Only few LAB have been reported to give phytase activity that too is generally seen as intracellular. LAB producing extracellular phytase will be more useful as it can degrade phytate more effectively. Moreover, enzyme from such isolate will have application in food processing also. Only few species of Lactobacillus producing extracellular phytase have been reported so far. This study reports the isolation of a probiotic strain of Lactobacillus fermentum spp KA1 which produces extracellular phytase. Conditions for the optimal production of phytase have been optimized and the enzyme production resulted in an approximately 13-fold increase in yield. The phytate degradation potential of extracellular phytase in rice bran has been explored and conditions for optimal degradation were optimized. Under optimal conditions, there was 43.26% release of inorganic phosphate and 6.45% decrease of phytate content.

Keywords: Lactobacillus, phytase, phytate reduction, rice bran

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Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: Seyong Oh, Jin-Hong Park

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Recently, neuromorphic computing based on brain-inspired artificial neural networks (ANNs) has attracted huge amount of research interests due to the technological abilities to facilitate massively parallel, low-energy consuming, and event-driven computing. In particular, research on artificial synapse that imitate biological synapses responsible for human information processing and memory is in the spotlight. Here, we demonstrate a photosynaptic device, wherein a synaptic weight is governed by a mixed spike consisting of voltage and light spikes. Compared to the device operated only by the voltage spike, ∆G in the proposed photosynaptic device significantly increased from -2.32nS to 5.95nS with no degradation of nonlinearity (NL) (potentiation/depression values were changed from 4.24/8 to 5/8). Furthermore, the Modified National Institute of Standards and Technology (MNIST) digit pattern recognition rates improved from 36% and 49% to 50% and 62% in ANNs consisting of the synaptic devices with 20 and 100 weight states, respectively. We expect that the photosynaptic device technology processed by optoelectronic spike will play an important role in implementing the neuromorphic computing systems in the future.

Keywords: optoelectronic synapse, IGZO (Indium-Gallium-Zinc Oxide) photosynaptic device, optoelectronic spiking process, neuromorphic computing

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Authors: Anshuman Srivastava, Shiv Singh, Sheelendra Pratap Singh

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A simple, sensitive and effective gas chromatography tandem mass spectrometry (GC-MS/MS) method was developed for simultaneous analysis of multi pesticide residues (organophosphate, organochlorines, synthetic pyrethroids and herbicides) in food commodities using phenolic resin based activated carbon fibers (ACFs) as reversed-dispersive solid phase extraction (r-DSPE) sorbent in modified QuEChERS (Quick Easy Cheap Effective Rugged Safe) method. The acetonitrile-based QuEChERS technique was used for the extraction of the analytes from food matrices followed by sample cleanup with ACFs instead of traditionally used primary secondary amine (PSA). Different physico-chemical characterization techniques such as Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction and Brunauer-Emmet-Teller surface area analysis were employed to investigate the engineering and structural properties of ACFs. The recovery of pesticides and herbicides was tested at concentration levels of 0.02 and 0.2 mg/kg in different commodities such as cauliflower, cucumber, banana, apple, wheat and black gram. The recoveries of all twenty-six pesticides and herbicides were found in acceptable limit (70-120%) according to SANCO guideline with relative standard deviation value < 15%. The limit of detection and limit of quantification of the method was in the range of 0.38-3.69 ng/mL and 1.26 -12.19 ng/mL, respectively. In traditional QuEChERS method, PSA used as r-DSPE sorbent plays a vital role in sample clean-up process and demonstrates good recoveries for multiclass pesticides. This study reports that ACFs are better in terms of removal of co-extractives in comparison of PSA without compromising the recoveries of multi pesticides from food matrices. Further, ACF replaces the need of charcoal in addition to the PSA from traditional QuEChERS method which is used to remove pigments. The developed method will be cost effective because the ACFs are significantly cheaper than the PSA. So the proposed modified QuEChERS method is more robust, effective and has better sample cleanup efficiency for multiclass multi pesticide residues analysis in different food matrices such as vegetables, grains and fruits.

Keywords: QuEChERS, activated carbon fibers, primary secondary amine, pesticides, sample preparation, carbon nanomaterials

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Authors: Alexander Chinyere Nwankpa, Nneka Ngozi Nwankpa

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In Nigeria, the Federal government is trying to make sure that everyone has access to enough food that is nutritiously adequate and safe. But in the southwest of Nigeria, notably in Ondo State, the most valuable minerals such as oil and gas, bitumen, kaolin, limestone talc, columbite, tin, gold, coal, and phosphate are abundant. Therefore, some regions of Ondo State are now linked to large quantities of natural radioactivity as a result of the mineral presence. In this work, the baseline radioactivity levels in some of the most important food crops in Ondo State were analyzed, allowing for the prediction of probable radiological health impacts. To this effect, maize (Zea mays), yam (Dioscorea alata) and cassava (Manihot esculenta) tubers were collected from the farmlands in the State because they make up the majority of food's nutritional needs. Ondo State was divided into eight zones in order to provide comprehensive coverage of the research region. At room temperature, the maize (Zea mays), yam (Dioscorea alata), and cassava (Manihot esculenta) samples were dried until they reached a consistent weight. They were pulverized, homogenized, and 250 g packed in a 1-liter Marinelli beaker and kept for 28 days to achieve secular equilibrium. The activity concentrations of Radium-226 (Ra-226), Thorium-232 (Th-232), and Potassium-40 (K-40) were determined in the food samples using Gamma-ray spectrometry. Firstly, the Hyper Pure Germanium detector was calibrated using standard radioactive sources. The gamma counting, which lasted for 36000s for each sample, was carried out in the Centre for Energy Research and Development, Obafemi Awolowo University, Ile-Ife, Nigeria. The mean activity concentration of Ra-226, Th-232 and K-40 for yam were 1.91 ± 0.10 Bq/kg, 2.34 ± 0.21 Bq/kg and 48.84 ± 3.14 Bq/kg, respectively. The content of the radionuclides in maize gave a mean value of 2.83 ± 0.21 Bq/kg for Ra-226, 2.19 ± 0.07 Bq/kg for Th-232 and 41.11 ± 2.16 Bq/kg for K-40. The mean activity concentrations in cassava were 2.52 ± 0.31 Bq/kg for Ra-226, 1.94 ± 0.21 Bq/kg for Th-232 and 45.12 ± 3.31 Bq/kg for K-40. The average committed effective doses in zones 6-8 were 0.55 µSv/y for the consumption of yam, 0.39 µSv/y for maize, and 0.49 µSv/y for cassava. These values are higher than the annual dose guideline of 0.35 µSv/y for the general public. Therefore, the values obtained in this work show that there is radiological contamination of some foodstuffs consumed in some parts of Ondo State. However, we recommend that systematic and appropriate methods also need to be established for the measurement of gamma-emitting radionuclides since these constitute important contributors to the internal exposure of man through ingestion, inhalation, or wound on the body.

Keywords: contamination, environment, radioactivity, radionuclides

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Authors: Jayant Kumar, Aarcha Jayachandran Sasikala, Barry Adrian Shepherd

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Public Distribution System is a flagship welfare programme of the Government of India with both historical and political significance. Targeted at lower sections of society,it is one of the largest supply chain networks in the world. There has been several studies by academics and planning commission about the effectiveness of the system. Our study focuses on applying predictive analytics to aid the central body to keep track of the problem of breach of service level agreement between the two echelons of food supply chain. Each shop breach is leading to a potential additional inventory carrying cost. Thus, through this study, we aim to show that aided with such analytics, the network can be made more cost effective. The methods we illustrate in this study are applicable to other commercial supply chains as well.

Keywords: PDS, analytics, cost effectiveness, Karnataka, inventory cost, service level JEL classification: C53

Procedia PDF Downloads 528

Authors: Ching-Ju Hsiao, Mao-Chen Huang, Pei-Fan Chen, Ruo-Yun Chung, Jiun-Hua Chou, Chih-Hui Yang, Keng-Shiang Huang, Jei-Fu Shaw

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Chlorophyll has many benefits for human body. It is known to improve the health of the circulatory, digestive, immune and detoxification systems of the body. However, Chl can’t be preserved at the environment of high temperature and light exposure for a long time due to it is chemical structure is easily degradable. This characteristic causes that human body is difficult to absorb Chl effective components. In order to solve this problem, we utilize polycaprolactone (PCL) polymer encapsulation technology to increase the stability of Chl. In particular, we also established a microfluidic platform provide the control of composite beads diameter. The new composite beads is potential to be a health food. Result show that Chl effective components via the microfludic platform can be encapsulated effectively and still preserve its effective components.

Keywords: chlorophyll, PCL, PVA, microfluidic

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Authors: Emine Akar

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The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

Procedia PDF Downloads 122