Search results for: artificial food

Authors: Shaima Almalhan, Lubna Shukri, Miriam Talal, Safaa Teskieh

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Since artificial intelligence is a rapidly evolving topic that has had a significant impact on technical growth and innovation, this paper examines people's awareness, use, and engagement with the Google Translate application. To see how familiar aware users are with the app and its features, quantitative and qualitative research was conducted. The findings revealed that consumers have a high level of confidence in the application and how far people they benefit from this sort of innovation and how convenient it makes communication.

Keywords: artificial intelligence, google translate, speech recognition, language translation, camera translation, speech to text, text to speech

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Authors: Lesa A. Thompson, Yoshinori Ikenaka, Victor Wepener, Mayumi Ishizuka

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One method for controlling malaria in endemic regions is the killing of vector mosquitoes using pesticides such as DDT in indoor residual spraying (IRS). This study was carried out to investigate the presence of and human health risk due to DDT and its metabolites (collectively, DDTs) contaminating human food sources in areas where DDT is used for IRS. Free-range chicken products (meat and eggs) were collected from homesteads in KwaZulu-Natal Province in the northeast of South Africa, and fish meat samples from Maputo Bay in neighbouring Mozambique. Samples were analysed for DDTs (o,p’-DDT, p,p’-DDT, o,p’-DDD, p,p’-DDD, o,p’-DDE and p,p’-DDE) using a gas chromatograph with electron capture detector (GC-ECD). DDTs were detected in all food types, with the predominant congener being p,p’-DDE. The presence of p,p’-DDT confirmed recent release of DDT into the environment. By using concentration levels detected in foods and national consumption levels, the risk to human health through consumption of such food products was calculated. In order of risk level, these were: chicken eggs > chicken meat > fish meat. Human risk (carcinogenic) values greater than one suggest there is an increased health risk through consumption of these foods.

Keywords: DDT, food contamination, human health risk, Mozambique, South Africa

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Authors: Salako Rotimi, Oshungade Stephen, Ojewoye Opeyemi

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The maintenance of price stability is one of the macroeconomic challenges facing Nigeria as a nation. This paper attempts to build a co-integration multivariate time series model for inflation movement in Nigeria using data extracted from the abstract of statistics of the Central Bank of Nigeria (CBN) from 2008 to 2017. The Johansen cointegration test suggests at least one co-integration vector describing the long run relationship between Consumer Price Index (CPI), Food Price Index (FPI) and Non-Food Price Index (NFPI). All three series show increasing pattern, which indicates a sign of non-stationary in each of the series. Furthermore, model predictability was established with root-mean-square-error, mean absolute error, mean average percentage error, and Theil’s unbiased statistics for n-step forecasting. The result depicts that the long run coefficient of a consumer price index (CPI) has a positive long-run relationship with the food price index (FPI) and non-food price index (NFPI).

Keywords: economic, inflation, model, series

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Authors: Fareena Alladin

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In the Caribbean, issues of food security, health and taste are intricately linked, seen most clearly in the increasing incidence of lifestyle diseases among children coupled with a taste for high calorie and Westernized diets. In order to fully appreciate this link, the role of nutrition environments must be examined. To this end, the present study incorporates tenets of Bourdieu’s social constructivist theory with the Community Nutrition Environment Model. The aim of this study was to examine the relationships between availability of and access to healthy/unhealthy foods within nutrition environments, namely the household and school, and the development of taste preferences for healthy/unhealthy foods among primary school children in a selected educational district in Trinidad and Tobago. A cross-sectional survey of 400 children between the ages of 9 and 11 years was conducted. Data analysis was conducted using SPSS 24. Results indicated that availability of healthy food at home was positively correlated with preference for vegetables, and negatively correlated with preference for salty snacks and fast food. The availability of unhealthy food within the home was found to be negatively correlated with preference for vegetables and positively correlated with preference for salty snacks. Access to unhealthy foods at school had a positive correlation with preference for fast food. These findings highlight the role of the food environment in shaping taste preferences, and point to the need for interrogating the centrality of food security concerns in emerging health concerns of Caribbean countries. Such interrogations are a necessary part of the development of research agendas, and policy formulation and implementation.

Keywords: food security, nutrition environment, taste preference, Trinidad and Tobago

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Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

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The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: neural network computing, continuous functions generating the input-output mapping, decreasing the training time, machines with big memories

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Authors: Francesco Pinci

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This research investigates the significance of product packaging as an effective marketing tool. By using commercially available pasta as an example, the study specifically examines the visual components of packaging, including color, shape, packaging material, and logo. The insights gained from studies like this are particularly valuable to food and beverage companies as they provide marketers with a deeper understanding of the factors influencing consumer purchasing decisions. The research analyzes data collected through surveys conducted via Google Forms and visual data obtained using iMotions eye-tracker software. The results affirm the importance of packaging design elements, such as color and product information, in shaping consumer buying behavior.

Keywords: consumer behaviour, eyetracker, food marketing, neuromarketing

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Authors: Shienna Marie Esteban

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The economic growth experience of the Philippines is one of the fastest in Asia. However, the said growth has not yet trickled down to every Filipino. This is evident to agricultural-dependent population. Moreover, the contribution of the agriculture sector to GDP has been dwindling while large number of labor force is still dependent on a relatively small share of GDP. As a result, poverty incidence worsened among rural poor causing hunger and malnutrition. Therefore, the existing agricultural policies in the Philippines are pushing to achieve greater food production and productivity to alleviate poverty and food insecurity. Through a review of related literature and collection and analysis of secondary data from DA, DBM, BAS - CountrySTAT, PSA, NSCB, PIDS, IRRI, UN-FAO, IFPRI, and World Bank among others, the study revealed that Philippines is still far from its goals of poverty reduction and food security. In addition, the agricultural sector is underperforming. The productivity growth of the sector comes out mediocre. The common observation is that weakness is attributed to the failures of policy and institutional environments of the agriculture sector. The policy environment failed to create a structure appropriate for the rapid growth of the sector due to institutional and governance weaknesses. A recommendation is to go through institutional and policy reforms through legislative or executive mandates should take form to improve the implementation and enforcement of existing policies.

Keywords: agriculture, food security, policy, poverty

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Authors: Gianni Jacucci

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Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial intelligence methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while straight AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.

Keywords: artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability

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Authors: Muhammad Zubair, Aman Ullah, Jianping Wu

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During the last decades, petroleum derived synthetic polymers like polyethylene terephthalate, polyvinylchloride, polyethylene, polypropylene and polystyrene has extensively been used in the field of food packaging and mostly are non-degradable. Biopolymers are a good fit for single-use or short-lived products such as food packaging. Spent hens, a poultry by-product which is of little economic value and their disposal are environmentally harmful. Through current study, we have explored the possibility to transform proteins from spent fowl into green food packaging material. Proteins from spent fowl were extracted within 1 hour using pH shift method with recovery of about 74%. Different plasticizers were tried like glycerol, sorbitol, glutaraldehyde, 1,2 ethylene glycol and 1,2 butanediol. Glycerol was the best plasticizer among all these plasticizers. A naturally occurring and non-toxic cross-linking agent, chitosan, was used to form the chitosan/glycerol/protein blend by casting and compression molding techniques. The mechanical properties were characterized using tensile strength analyzer. The nano-reinforcements with homogeneous dispersion of nanoparticles lead to improved physical properties suggesting that these materials have great potential for food packaging applications.

Keywords: differential scanning calorimetry, dynamic mechanical analysis, scanning electron microscopy, spent hen

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Authors: Sharad Shrivastava, Arun Jalan

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In this study tensile testing was performed on randomly oriented short glass fiber/epoxy resin composite specimens which were prepared using hand lay-up method. Samples were tested over a wide range of strain rate/loading rate from 2mm/min to 40mm/min to see the effect on ultimate tensile strength of the composite. A multi layered 'back propagation artificial neural network of supervised learning type' was used to analyze and predict the tensile properties with strain rate and temperature as given input and output as UTS to predict. Various network structures were designed and investigated with varying parameters and network sizes, and an optimized network structure was proposed to predict the UTS of short glass fiber/epoxy resin composite specimens with reasonably good accuracy.

Keywords: glass fiber composite, mechanical properties, strain rate, artificial neural network

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Authors: Hassan Naseh, Javad Roozgard

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

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

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Authors: Giovanna Borges Bottino, Luis Felipe Freitas do Nascimento Alves Teixeira

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This article aims to present a case study of the National Civil Aviation Agency (ANAC) in Brazil, ANAC-id. ANAC-id is the artificial intelligence algorithm developed for image analysis that recognizes standard images of unobstructed and uprighted face without sunglasses, allowing to identify potential inconsistencies. It combines YOLO architecture and 3 libraries in python - face recognition, face comparison, and deep face, providing robust analysis with high level of accuracy.

Keywords: artificial intelligence, deepface, face compare, face recognition, YOLO, computer vision

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Authors: Ankit Kumar, Balram Dubey

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This study proposes a three dimensional prey-predator model with additional food, provided to predator individuals, including gestation delay in predators and delay in supplying the additional food to predators. It is assumed that the interaction between prey and predator is followed by Holling type-II functional response. We discussed the steady states and their local and global asymptotic behavior for the non-delayed system. Hopf-bifurcation phenomenon with respect to different parameters has also been studied. We obtained a range of predator’s tendency factor on provided additional food, in which the periodic solutions occur in the system. We have shown that oscillations can be controlled from the system by increasing the tendency factor. Moreover, the existence of periodic solutions via Hopf-bifurcation is shown with respect to both the delays. Our analysis shows that both delays play an important role in governing the dynamics of the system. It changes the stability behavior into instability behavior. The direction and stability of Hopf-bifurcation are also investigated through the normal form theory and the center manifold theorem. Lastly, some numerical simulations and graphical illustrations have been carried out to validate our analytical findings.

Keywords: additional food, gestation delay, Hopf-bifurcation, prey-predator

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Authors: Rafaella Nogaroli

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It is presented hypothetical cases in the context of AI algorithms to support clinical decisions, in order to discuss the importance of doctors to respect AI ethical principles. Regarding the principle of transparency and explanation, there is an impact on the new model of patient consent and on the understanding of qualified information. Besides, the human control of technology (AI as a tool) should guide the physician's activity; otherwise, he breaks the patient's legitimate expectation in a specific result, with the consequent transformation of the medical obligation nature.

Keywords: medical law, artificial intelligence, ethical principles, patient´s informed consent, medical obligations

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes a new speaker segmentation approach for two speakers. It is an online approach that does not require a prior information about speaker models. It has two phases, a conventional approach such as unsupervised BIC-based is utilized in the first phase to detect speaker changes and train a Neural Network, while in the second phase, the output trained parameters from the Neural Network are used to predict next incoming audio stream. Using this approach, a comparable accuracy to similar BIC-based approaches is achieved with a significant improvement in terms of computation time.

Keywords: artificial neural network, diarization, speaker indexing, speaker segmentation

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Authors: Ashish Payal, C. S. Rai, B. V. R. Reddy

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With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m² grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.

Keywords: localization, wireless sensor networks, artificial neural network, radial basis function, multi-layer perceptron, backpropagation, RSSI, GPS

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Authors: Misganaw Chere Siferih

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This study assessed the impact of crossbreed dairy cows on household income and food security. The study area is found in Dejen Woreda, East Gojam Zone, and Amhara region of Ethiopia. Random sampling technique was used to obtain a sample of 80 crossbreed dairy cow owners and 176 indigenous dairy cow owners. The study employed food consumption score analytical framework to measure food security status of the household. No Statistical significant mean difference is found between crossbreed owners and indigenous owners. Logistic regression was employed to investigate crossbreed dairy cow adoption determinants , the result indicates that gender, education, labor number, land size cultivated, dairy cooperatives membership, net income and food security status of the household are statistically significant independent variables, which explained the binary dependent variable, crossbreed dairy cow adoption. Propensity score matching (PSM) was employed to analyze the impact of crossbreed dairy cow owners on farmers’ income and food security. The average net income of crossbreed dairy cow owners was found to be significantly higher than indigenous dairy cow owners. Estimates of average treatment effect of the treated (ATT) indicated that crossbreed dairy cow is able to impact households’ net income by 42%, 38.5%, 30.8% and 44.5% higher in kernel, radius, nearest neighborhood and stratification matching algorithms respectively as compared to indigenous dairy cow owners. However, estimates of average treatment of the treated (ATT) suggest that being an owner of crossbreed dairy cow is not able to affect food security significantly. Thus, crossbreed dairy cow enables farmers to increase income but not their food security in the study area. Finally, the study recommended establishing dairy cooperatives and advice farmers to become a member of them, attention to promoting the impact of crossbreed dairy cows and promotion of nutrition focus projects.

Keywords: crossbreed dairy cow, net income, food security, propensity score matching

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Authors: Nathaniel C. Villanueva, Ian K. H. Chun, Alyssa S. Fujiwara, Emily R. Leibovitch, Brennan E. Yamamoto, Loren G. Yamamoto

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Introduction: In previous studies of high school sports, over 15% of concussions were attributed to contact with the playing surface. While artificial turf fields are increasing in popularity due to lower maintenance costs, artificial turf has been associated with more ankle and knee injuries, with inconclusive data on concussions. In this study, natural grass and artificial football fields were compared in terms of deceleration on fall impact. Methods: Accelerometers were placed on the forehead, apex of the head, and right ear of a Century Body Opponent Bag (BOB) manikin. A Riddell HITS football helmet was secured onto the head of the manikin over the accelerometers. This manikin was dropped onto natural grass (n = 10) and artificial turf (n = 9) high school football fields. The manikin was dropped from a stationary position at a height of 60 cm onto its front, back, and left side. Each of these drops was conducted 10 times at the 40-yard line, 20-yard line, and endzone. The net deceleration on impact was calculated as a net vector from each of the three accelerometers’ x, y, and z vectors from the three different locations on the manikin’s head (9 vector measurements per drop). Results: Mean values for the multiple drops were calculated for each accelerometer and drop type for each field. All accelerometers in forward and backward falls and one accelerometer in side falls showed significantly greater impact force on synthetic turf compared to the natural grass surfaces. Conclusion: Impact force was higher on synthetic fields for all drop types for at least one of the accelerometer locations. These findings suggest that concussion risk might be higher for athletes playing on artificial turf fields.

Keywords: concussion, football, biomechanics, sports

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Authors: Gulay Oncu Ince, Sibel Karakaya

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Food is a worldwide concern. Among the factors that have influences on human health, food, nutrition and life style have been regarded as the most important factors since they can be intervened. While some parts of the world has been faced with food shortages and hence, chronic metabolic diseases, the other part of the world have been emerged from over consumption of food. Both situations can result in shorter life expectancy and represent a major global health problem. Hunger, satiety and appetite sensation form a balance ensures the operation of feeding behavior between food intake and energy consumption. Satiety is one of the approaches that is effective in ensuring weight control and avoid eating more in the postprandial period. By manipulating the microstructure of food macro and micronutrient bioavailability may be increased or reduced. For the food industry appearance, texture, taste structural properties as well as the gastrointestinal tract behavior of the food after the consumption is becoming increasingly important. Also, this behavior has been the subject of several researches in recent years by the scientific community. Numerous studies have been published about changing the food matrix in order to increase expected impacts. In this study, yogurts were enriched with caseinomacropeptide (CMP), whey protein (WP), CMP and sodium alginate (SA), and WP + SA in order to produce goat yogurts having different food matrices. SDS Page profiles of the samples after in vitro digestion and viscosities of the stomach digesta at different share rates were determined. Energy values were 62.11kcal/100 g, 70.27 kcal/100 g, 70.61 kcal/100 g, 71.20 kcal/100 g and 71.67 kcal/100 g for control, CMP added WP added, WP + SA added, and CMP + SA added yogurts respectively. The results of viscosity analysis showed that control yogurt had the lowest viscosity value and this was followed by CMP added, WP added, CMP + SA added and WP + SA added yogurts, respectively. Protein contents of the stomach and duedonal digests of the samples after subjected to two different in vitro digestion methods were changed between 5.34-5.91 mg protein / g sample and 16.93-19.75 mg protein /g of sample, respectively. Viscosity measurements of the stomach digests showed that CMP + SA added yogurt displayed the highest viscosity value in both in vitro digestion methods. There were differences between the protein profiles of the stomach and duedonal digests obtained by two different in vitro digestion methods (p<0.05).

Keywords: caseinomacropeptide, protein profile, whey protein, yogurt

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Authors: Quang Dung Le, Kentaro Tanaka, Viet Dung Luu, Kotaro Shirai

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Mercury pollutant is great concerns in globe due to its toxicity and biomagnification through the food web. Recently increasing approaches of stable isotope analyses which have applied in food-web structure are enabled to elucidate more insight trophic transfer of pollutants in ecosystems. In this study, the integration of total mercury (Hg) and stable isotopic analyses (δ13C and δ15N) were measured from basal food sources to invertebrates and fishes in order to determine Hg transfer in Setiu lagoon food webs. The average Hg concentrations showed the increasing trend from low to high trophic levels. The result also indicated that potential Hg exposure from inside mangrove could be higher than that from the tidal flat of mangrove creek. Fish Hg concentrations are highly variable, and many factors driving this variability need further examinations. A positive correlation found between Hg concentrations and δ15N values (the trophic magnification factor was 3.02), suggesting Hg biomagnification through the lagoon food web. Almost all Hg concentrations in fishes and mud crabs did not present a risk for human consumption, however, the Hg concentrations of Caranx ignobilis exceed the permitted level could raise a concern of the potential risk for the marine system. Further investigations should be done to elucidate whether trophic relay relates to high Hg concentrations of some fish species in coastal systems.

Keywords: mercury, transfer, stable isotopes, health risk, mangrove, food web

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Authors: J. Balaraju, M. Govinda Raj, C. S. N. Murthy

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Every industry is constantly looking for enhancement of its day to day production and productivity. This can be possible only by maintaining the men and machinery at its adequate level. Prediction of performance characteristics plays an important role in performance evaluation of the equipment. Analytical and statistical approaches will take a bit more time to solve complex problems such as performance estimations as compared with software-based approaches. Keeping this in view the present study deals with an Artificial Neural Network (ANN) modelling of a Load-Haul-Dump (LHD) machine to predict the performance characteristics such as reliability, availability and preventive maintenance (PM). A feed-forward-back-propagation ANN technique has been used to model the Levenberg-Marquardt (LM) training algorithm. The performance characteristics were computed using Isograph Reliability Workbench 13.0 software. These computed values were validated using predicted output responses of ANN models. Further, recommendations are given to the industry based on the performed analysis for improvement of equipment performance.

Keywords: load-haul-dump, LHD, artificial neural network, ANN, performance, reliability, availability, preventive maintenance

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Authors: Sukhbir Singh

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This paper describes the modeling of novel solar air collector (NSAC) system by using artificial neural network (ANN) model. The objective of the study is to demonstrate the application of the ANN model to predict the performance of the NSAC with acetamide as a phase change material (PCM) storage. Input data set consist of time, solar intensity and ambient temperature wherever as outlet air temperature of NSAC was considered as output. Experiments were conducted between 9.00 and 24.00 h in June and July 2014 underneath the prevailing atmospheric condition of Kurukshetra (city of the India). After that, experimental results were utilized to train the back propagation neural network (BPNN) to predict the outlet air temperature of NSAC. The results of proposed algorithm show that the BPNN is effective tool for the prediction of responses. The BPNN predicted results are 99% in agreement with the experimental results.

Keywords: Evacuated tube solar air collector, Artificial neural network, Phase change material, solar air collector

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Authors: Helena Bolini, Erick Esmerino, Adriano Cruz, Juliana Paixao

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The currently concern for health has increased demand for low-calorie ingredients and functional foods as probiotics. Understand the reasons that infer on food choice, besides a challenging task, it is important step for development and/or reformulation of existing food products. The use of appropriate multivariate statistical techniques, such as External Preference Map (PrefMap), associated with regression by Partial Least Squares (PLS) can help in determining those factors. Thus, this study aimed to determine, through PLS regression analysis, the sensory attributes considered drivers of liking in probiotic petit suisse cheeses, strawberry flavor, sweetened with different sweeteners. Five samples in same equivalent sweetness: PROB1 (Sucralose 0.0243%), PROB2 (Stevia 0.1520%), PROB3 (Aspartame 0.0877%), PROB4 (Neotame 0.0025%) and PROB5 (Sucrose 15.2%) determined by just-about-right and magnitude estimation methods, and three commercial samples COM1, COM2 and COM3, were studied. Analysis was done over data coming from QDA, performed by 12 expert (highly trained assessors) on 20 descriptor terms, correlated with data from assessment of overall liking in acceptance test, carried out by 125 consumers, on all samples. Sequentially, results were submitted to PLS regression using XLSTAT software from Byossistemes. As shown in results, it was possible determine, that three sensory descriptor terms might be considered drivers of liking of probiotic petit suisse cheese samples added with sweeteners (p<0.05). The milk flavor was noticed as a sensory characteristic with positive impact on acceptance, while descriptors bitter taste and sweet aftertaste were perceived as descriptor terms with negative impact on acceptance of petit suisse probiotic cheeses. It was possible conclude that PLS regression analysis is a practical and useful tool in determining drivers of liking of probiotic petit suisse cheeses sweetened with artificial and natural sweeteners, allowing food industry to understand and improve their formulations maximizing the acceptability of their products.

Keywords: acceptance, consumer, quantitative descriptive analysis, sweetener

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Authors: Pallabi Kalita, Hui Tag, Loxmi Jamoh, H. N. Sarma, A. K. Das

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Wild edible food plants contain a number of organic phytochemical that have been linked to the promotion of good health. These plants used by the local people of Arunachal Pradesh (Northeast India) are found to have high nutritional potential to maintain general balance diet. A study was conducted to evaluate the nutritional potential of five commonly found, unexplored wild food plants namely, Piper pedicellatum C. DC (leaves), Gonostegia hirta (Blume ex Hassk.) Miq. (leaves), Mussaenda roxburghii Hook. f. (leaves), Solanum spirale Roxb. (leaves and fruits) and Cyathea spinulosa Wall. ex Hook. (pith portion and tender rachis) from East Siang District of Arunachal Pradesh Northeast (India) for ascertaining their suitability for utilization as supplementary food. Results of study revealed that P. pedicellatum, C. spinulosa, and S. spirale (leaves) are the most promising species which have high nutritional content out of the five wild food plants investigated which is required for the normal growth and development of human.

Keywords: wild edible plants, gross energy, Gonostegia hirta, Cyathea spinulosa

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Authors: Adebola Adedugbe

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The challenge of post-harvest losses poses serious threats for food security in Nigeria and the north-eastern part with the country losing about $9billion annually due to postharvest losses in the sector. Post-harvest loss (PHL) is the quantitative and qualitative loss of food in various post-harvest operations. In Nigeria, post-harvest losses (PHL) have been a major challenge to food security and improved farmer’s income. In 2022, the Nigerian government had said over 30 percent of food produced by Nigerian farmers perish during post-harvest. For many in northeast Nigeria, agriculture is the predominant source of livelihood and income. The persistent communal conflicts, flood, decade-old attacks by boko haram and insurgency in this region have disrupted farming activities drastically, with farmlands becoming insecure and inaccessible as communities are forced to abandon ancestral homes, The impact of climate change is also affecting agricultural and fishing activities, leading to shortage of food supplies, acute hunger and loss of livelihood. This has continued to impact negatively on the region and country’s food production and availability making it loose billions of US dollars annually in income in this sector. The root cause of postharvest losses among others in crops, livestock and fisheries are lack of modern post-harvest equipment, chemical and lack of technologies used for combating losses. The 2019 Global Hunger Index showed Nigeria’s case was progressing from a ‘serious to alarming level’. As part of measures to address the problem of post-harvest losses experienced by farmers, the federal government of Nigeria concessioned 17 silos with 6000 metric tonne storage space to private sector to enable farmers to have access to storage facilities. This paper discusses the causes, effects and solutions in handling post-harvest losses and optimize returns on food security in northeast Nigeria.

Keywords: farmers, food security, northeast Nigeria, postharvest loss

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Authors: Min Shi

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State of the art research with Artificial Neural Networks for the downscaling of General Circulation Models (GCMs) mainly uses back-propagation algorithm as a training approach. This paper introduces another training approach of ANNs, Evolutionary Algorithm. The combined algorithm names neuroevolutionary (NE) algorithm. We investigate and evaluate the use of the NE algorithms in statistical downscaling by generating temperature estimates at interior points given information from a lattice of surrounding locations. The results of our experiments indicate that NE algorithms can be efficient alternative downscaling methods for daily temperatures.

Keywords: temperature, downscaling, artificial neural networks, evolutionary algorithms

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Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Xiaoyu Sun

Abstract:

In recent years, the rapid development of Artificial Intelligence (AI) has extensively promoted medicine, pharmaceutical, and other related fields. The medical research and development of artificial intelligence by scientific and commercial organizations are on the fast track. The ethics review is one of the critical procedures of registration to get the products approved and launched. However, the SOPs for ethics review is not enough to guide the healthy and rapid development of artificial intelligence in healthcare in China. Ethical Review Measures for Biomedical Research Involving Human Beings was enacted by the National Health Commission of the People's Republic of China (NHC) on December 1st, 2016. However, from a legislative design perspective, it was neither updated timely nor in line with the trends of AI international development. Therefore, it was great that NHC published a consultation paper on the updated version on March 16th, 2021. Based on the most updated laws and regulations in the States and EU, and in-depth-interviewed 11 subject matter experts in China, including lawmakers, regulators, and key members of ethics review committees, heads of Regulatory Affairs in SaMD industry, and data scientists, several suggestions were proposed on top of the updated version. Although the new version indicated that the Ethics Review Committees need to be created by National, Provincial and individual institute levels, the review authorities of different levels were not clarified. The suggestion is that the precise scope of review authorities for each level should be identified based on Risk Analysis and Management Model, such as the complicated leading technology, gene editing, should be reviewed by National Ethics Review Committees, it will be the job of individual institute Ethics Review Committees to review and approve the clinical study with less risk such as an innovative cream to treat acne. Furthermore, to standardize the research and development of artificial intelligence in healthcare in the age of AI, more clear guidance should be given to data security in the layers of data, algorithm, and application in the process of ethics review. In addition, transparency and responsibility, as two of six principles in the Rome Call for AI Ethics, could be further strengthened in the updated version. It is the shared goal among all countries to manage well and develop AI to benefit human beings. Learned from the other countries who have more learning and experience, China could be one of the most advanced countries in artificial intelligence in healthcare.

Keywords: biomedical research involving human beings, data security, ethics committees, ethical review, medical artificial intelligence

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Authors: Mansur Abdulrasheed, Ibrahim I. Hussein, Ahmed M. Mubarak, Ahmed F. Umar

Abstract:

This study was aimed at evaluating the phytochemical constituents and the antimicrobial activity of the seed oil of Moringa oleifera and garlic against some selected food-borne microorganisms (Staphylococcus aureus, Escherichia coli, Salmonella spp and Pseudomonas aeruginosa) using disc diffusion method. The results of the phytochemical screening revealed differences in the presence of the phytochemicals among the extracts. Saponins were detected in both Moringa oleifera and garlic seed oil, while alkaloid and tannins were observed in seed oil of garlic. Furthermore, the antibacterial assay results show that the seed oil of Moringa oleifera was inactive against all the tested organisms, even at 100 % concentration. In contrast, garlic oil was found to be active against all the tested organisms. The highest inhibition was observed in E. coli (12 mm) at 100 % concentration, while at 20 % concentration, Salmonella Sp and P. aeruginosa showed the least inhibiton (6 mm). The antimicrobial activity of the seed oil of garlic may be attributed to its phytochemicals components which were not detected in the seed oil of Moringa oleifera. The results of this study have shown the potentials of the seed oil of garlic as an antimicrobial agent more especially in foods, by inhibiting the growth of the test organisms, which range from food-borne pathogens to food spoilage organisms.

Keywords: antimicrobial, garlic, Moringa oleifera, food borne pathogens

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Authors: Laila A. H. F. Dashti, Tom Jackson, Andrew West, Lisa Jackson

Abstract:

Traceability systems are important in the agri-food and halal food sectors for monitoring ingredient movements, tracking sources, and ensuring food integrity. However, designing a traceability system for the halal food supply chain is challenging due to diverse stakeholder requirements and complex needs. Existing literature on stakeholder mapping and identifying requirements for halal food supply chains is limited. To address this gap, a pilot study was conducted to identify the objectives, requirements, and recommendations of stakeholders in the Kuwaiti halal food industry. The study collected data through semi-structured interviews with an international halal food manufacturer based in Kuwait. The aim was to gain a deep understanding of stakeholders' objectives, requirements, processes, and concerns related to the design of a traceability system in the country's halal food sector. Traceability systems are being developed and tested in the agri-food and halal food sectors due to their ability to monitor ingredient movements, track sources, and detect potential issues related to food integrity. Designing a traceability system for the halal food supply chain poses significant challenges due to diverse stakeholder requirements and the complexity of their needs (including varying food ingredients, different sources, destinations, supplier processes, certifications, etc.). Achieving a halal food traceability solution tailored to stakeholders' requirements within the supply chain necessitates prior knowledge of these needs. Although attempts have been made to address design-related issues in traceability systems, literature on stakeholder mapping and identification of requirements specific to halal food supply chains is scarce. Thus, this pilot study aims to identify the objectives, requirements, and recommendations of stakeholders in the halal food industry. The paper presents insights gained from the pilot study, which utilized semi-structured interviews to collect data from a Kuwait-based international halal food manufacturer. The objective was to gain an in-depth understanding of stakeholders' objectives, requirements, processes, and concerns pertaining to the design of a traceability system in Kuwait's halal food sector. The stakeholder mapping results revealed that government entities, food manufacturers, retailers, and suppliers are key stakeholders in Kuwait's halal food supply chain. Lessons learned from this pilot study regarding requirement capture for traceability systems include the need to streamline communication, focus on communication at each level of the supply chain, leverage innovative technologies to enhance process structuring and operations and reduce halal certification costs. The findings also emphasized the limitations of existing traceability solutions, such as limited cooperation and collaboration among stakeholders, high costs of implementing traceability systems without government support, lack of clarity regarding product routes, and disrupted communication channels between stakeholders. These findings contribute to a broader research program aimed at developing a stakeholder requirements framework that utilizes "business process modelling" to establish a unified model for traceable stakeholder requirements.

Keywords: supply chain, traceability system, halal food, stakeholders’ requirements

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