Search results for: artificial food

Authors: Reyhan Özdoğan, Özgür Ceylan, Mehmet Arif Kaya, Mithat Çelebi

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Food packaging is important for the food industry. Bioplastics have been used as food packaging materials. According to the European Bioplastics organization, bioplastics can be defined as plastics based on renewable resources (bio-based) or as plastics which are biodegradable and/or compostable. Poly(lactic acid) (PLA) has an industrially importance of bioplastic polymers. PLA is a family of biodegradable thermoplastic polyester made from renewable resources. It is produced by conversion of corn, or other carbohydrate sources, into dextrose, followed by fermentation into lactic acid through direct polycondensation of lactic acid monomers or through ring-opening polymerization of lactide. The processing possibilities of this transparent material are very wide, ranging from injection molding and extrusion over cast film extrusion to blow molding and thermoforming. In this study, PLA films were prepared by solution casting method. PLAs which are different molecular weights were plasticized with glycerol and the morphology of films was monitored by optical microscopy. Properties of mechanical and film of PLA were researched with the mechanical testing machine.

Keywords: biodegradable, bioplastics, morphology, solution casting, poly(lactic acid)

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Authors: Rodriguez Diego, Del Valle Martin, Hargreaves Matias, Riveros Jose Luis

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Nutrient deficiencies due to a diet low in quantitative and qualitative terms, are prevalent throughout the developing world, especially in sub-Saharan Africa. Children and women of childbearing age are especially vulnerable. Limited availability, access and intake of animal foods at home and lack of knowledge about their value in the diet and the role they play in health, contribute to poor diet quality. Poor bioavailability of micronutrients in diets based on foods high in fiber and phytates, the low content of some micronutrients in these foods are further factors to consider. Goats are deeply embedded in almost every Sub-Saharan African rural culture, generally kept for their milk, meat, hair or leather. Goats have played an important role in African social life, especially in food security. Goat meat has good properties for human wellbeing, with a special role in lower income households. It has a high-quality protein (20 protein g/100 meat g) including all essential amino acids, good unsaturated/satured fatty acids relationship, and it is an important B-vitamin source with high micronutrients bioavailability. Mozambique has major food security problems, with poor food access and utilization, undiversified diets, chronic poverty and child malnutrition. Our objective was to design a nutritional intervention based on a dietary diversification, nutritional education, cultural beliefs and local resources, aimed to strengthen food security of children at Barrio Broma village (15°43'58.78"S; 32°46'7.27"E) in Chitima, Mozambique. Two surveys were conducted first of socio-productive local databases and then to 100 rural households about livelihoods, food diversity and anthropometric measurements in children under 5 years. Our results indicate that the main economic activity is goat production, based on a native breed with two deliveries per year in the absence of any management. Adult goats weighted 27.2±10.5 kg and raised a height of 63.5±3.8 cm. Data showed high levels of poverty, with a food diversity score of 2.3 (0-12 points), where only 30% of households consume protein and 13% iron, zinc, and B12 vitamin. The main constraints to food security were poor access to water and low income to buy food. Our dietary intervention was based on improving diet quality by increasing the access to dried goat meat, fresh vegetables, and legumes, and its utilization by a nutritional education program. This proposal was based on local culture and living conditions characterized by the absence of electricity power and drinkable water. The drying process proposed would secure the food maintenance under local conditions guaranteeing food safety for a longer period. Additionally, an ancient local drying technique was rescued and used. Moreover, this kind of dietary intervention would be the most efficient way to improve the infant nutrition by delivering macro and micronutrients on time to these vulnerable populations.

Keywords: child malnutrition, dietary diversification, food security, goat meat

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Authors: Emad M.Abdullat, Hasan A. Ader-Rahman, Rayyan Al Ali, Arwa.A.Hudaib

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This retrospective study aims to determine the epidemiological features of such deaths in one of the general teaching hospitals in Jordan with a focus on weaning practices and its relation to sucking as major factors underlying the mechanism of choking in infants and young children. The study utilized a retrospective design to review the records of forensic cases due to foreign body aspiration examined at the forensic department at the Jordan University Hospital. A total of 27 cases of choking in the pediatric age group were retrieved from the reports of the autopsy cases dissected. All cases of children who died due to chocking by foreign bodies were under 11 years old. Choking by food materials constituted (44.4%) of cases under 3 years of age while choking by non-food material were less prevalent under 3 years of age and comprising 18.5% of the cases. Health care personnel and parents need to be aware that introduction of solid food, unlike exclusive breast or formula-milk feeding, can have serious consequences if occurring in inappropriate timing or consistency during early childhood physical and functional development. Parents need to be educated regarding the appropriate timing and process of weaning.

Keywords: chocking, infants, weaning practices, young children

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Authors: K. Alaka, A. Obi

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Home garden has proved to be significant to rural inhabitants by providing a wide range of useful products such as fruits, vegetables and medicine. There is need for quantitative information on its benefits and contributions to rural household. The main objective of this study is to investigate contributions of home garden to income of rural households in Raymond Mhlaba Local Municipality, formerly Nkonkobe Local Municipality of Eastern Cape Province South Africa. The stratified random sampling method was applied in order to choose a sample of 160 households.The study was conducted among 80 households engaging in home gardens and 80 non- participating households in the study area. Data analysis employed descriptive statistics with the use of frequency table and one way sample T test to show actual contributions. The overall model shows that social grant has the highest contribution to total household income for both categories while income generated from home garden has the second largest share to total household income, this shows that the majority of rural households in the study area rely on social grant as their source of income. However, since most households are net food buyers, it is essential to have policies that are formulated with an understanding that household food security is not only a function of the food that farming households produce for their own consumption but more so a function of total household income. The results produced sufficient evidence that home gardens contribute significantly to income of rural household.

Keywords: food security, home gardening, household, income

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Lea Lenhardt, Ivana Zeković, Tatjana Dramićanin, Miroslav D. Dramićanin

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Rapid and sensitive analytical technologies for food analysis are needed to respond to the growing public interest in food quality and safety. In this context, fluorescence spectroscopy offers several inherent advantages for the characterization of food products: high sensitivity, low price, objective, relatively fast and non-destructive. The objective of this work was to investigate the potential of fluorescence spectroscopy coupled with multi-way technique for characterization of cereal flours. Fluorescence landscape also known as excitation-emission matrix (EEM) spectroscopy utilizes multiple-color illumination, with the full fluorescence spectrum recorded for each excitation wavelength. EEM was measured on various types of cereal flours (wheat, oat, barley, rye, corn, buckwheat and rice). Obtained spectra were analyzed using PARAllel FACtor analysis (PARAFAC) in order to decompose the spectra and identify underlying fluorescent components. Results of the analysis indicated the presence of four fluorophores in cereal flours. It has been observed that relative concentration of fluorophores varies between different groups of flours. Based on these findings we can conclude that application of PARAFAC analysis on fluorescence data is a good foundation for further qualitative analysis of cereal flours.

Keywords: cereals, fluors, fluorescence, PARAFAC

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Authors: Temple Probyne Abali

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Port Harcourt, Rivers State in the Niger Delta region of Nigeria is bedeviled by the phenomenon of climatechange, posing threat to food security and livelihood. This study examined a 4 decadel (1980-2020) trend of climate change as well as its socio-economic impact on food security in the region. Furthermore, to achieve sustainable food security and livelihood amidst the phenomenon, the study adopted the World Bank Assisted Third National Fadama Development Programme approach. The data source for climate change involved secondary data from Nigeria Meteorological Agency (NIMET). Consequently, the results for climate change over the 4decade period were displayed in tables, charts and maps for the expected changes. Data sources on socio-economic impact of food security and livelihood were acquired through questionnairedesign. A purposive random sampling technique was used in selecting 5 coastal communities inthe region known for viable economic potentials for agricultural development and the resultswere analyzed using Analysis of Variance (ANOVA). The Participatory Rural Appraisal (PRA) technique of the World Bank for needs assessment wasadopted in selecting 5 agricultural sub-project proposals/activities based on groups’ commoneconomic interest from a total of 1,000 farmers each drawn from the 5 communities of differentage groups including men, women, youths and the vulnerable. Based on the farmers’ sub-projectinterests, the various groups’ Strength, Weakness, Opportunities and Threats (SWOT), Problem Listing Matrix, Skill Gap Analysis as well as EIAson their sub-project proposals/activities were analyzed with substantialMonitoring and Evaluation (M & E), using the Specific, Measurable, Attribute, Reliable and Time bound (SMART)approach. Based on the findings from the PRA technique, the farmers recorded considerableincreaseinincomeofover200%withinthe5yearprojectplan(2008-2013).Thestudyrecommends capacity building and advisory services on this PRA innovation. By so doing, there would be a sustainable increase in agricultural production and assured food security in an environmental friendly manner, in line with the United Nation’s Sustainable Development Goals(SDGs).

Keywords: climate change, food security, fadama, world bank, agriculture, sdgs

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Authors: Gehan Labib Abuelenain, Azza Fahmi, Salma Awad Mahmoud

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Toxoplasmosis is a global parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), with a high infection rate that affects one third of the human population and results in severe implications in pregnant women, neonates, and immunocompromised patients. Anti-parasitic treatments and schemes available against toxoplasmosis have barely evolved over the last two decades. The available T. gondii therapeutics cannot completely eradicate tissue cysts produced by the parasite and are not well-tolerated by immunocompromised patients. This work aims to highlight new trends in Toxoplasma gondii diagnosis by providing a comprehensive overview of the field, summarizing recent findings, and discussing the new technological advancements in toxoplasma diagnosis and treatment. Advancements in therapeutics utilizing trends in molecular biophysics, such as biosensors, epigenetics, and artificial intelligence (AI), might provide solutions for disease management and prevention. These insights will provide tools to identify research gaps and proffer planning options for disease control.

Keywords: toxoplamosis, diagnosis, therapeutics, biosensors, AI

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Authors: Sarah A. Alotaibi, Mohammed A. Almutairi, Abdullah A. Alsayari, Adibah M. Almutairi, Somaiah K. Almubayedh

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Concerns over the presence of acrylamide in food date back to 2002, when Swedish scientists stated that, in carbohydrate-rich foods, amounts of acrylamide were formed when cooked at high temperatures. Similar findings were reported by other researchers which, consequently, caused major international efforts to investigate dietary exposure and the subsequent health complications in order to properly manage this issue. Due to this issue, in this work, we aim to determine the acrylamide level in different foods (coffee, potato chips, biscuits, and baby food) commonly consumed by the Saudi population. In a total of forty-three samples, acrylamide was detected in twenty-three samples at levels of 12.3 to 2850 µg/kg. In reference to the food groups, the highest concentration of acrylamide was found in coffee samples (<12.3-2850 μg/kg), followed by potato chips (655-1310 μg/kg), then biscuits (23.5-449 μg/kg), whereas the lowest acrylamide level was observed in baby food (<14.75 – 126 μg/kg). Most coffee, biscuits and potato chips products contain high amount of acrylamide content and also the most commonly consumed product. Saudi adults had a mean exposure of acrylamide for coffee, potato, biscuit, and cereal (0.07439, 0.04794, 0.01125, 0.003371 µg/kg-b.w/day), respectively. On the other hand, exposure to acrylamide in Saudi infants and children to the same types of food was (0.1701, 0.1096, 0.02572, 0.00771 µg/kg-b.w/day), respectively. Most groups have a percentile that exceeds the tolerable daily intake (TDI) cancer value (2.6 µg/kg-b.w/day). Overall, the MOE results show that the Saudi population is at high risk of acrylamide-related disease in all food types, and there is a chance of cancer risk in all age groups (all values ˂10,000). Furthermore, it was found that in non-cancer risks, the acrylamide in all tested foods was within the safe limit (˃125), except for potato chips, in which there is a risk for diseases in the population. With potato and coffee as raw materials, additional studies were conducted to assess different factors, including temperature, cocking time, and additives affecting the acrylamide formation in fried potato and roasted coffee, by systematically varying processing temperatures and time values, a mitigation of acrylamide content was achieved when lowering the temperature and decreasing the cooking time. Furthermore, it was shown that the combination of the addition of chitosan and NaCl had a large impact on the formation.

Keywords: risk assessment, dietary exposure, MOA, acrylamide, hazard

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: artificial intelligence, infrastructure as code, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making

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Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

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Authors: Adiyasa Muda Zannatan

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Cassava (Manihot esculenta) is a plant originating from Brazil. Cassava plants categorized as sixth major food in the world after wheat, rice, corn and potatoes. It has been cultivated on hilly land for 97 years since 1918 at Cireundeu village, West Java Province, Indonesia. Cireundeu traditional village located in the mountain valleys and has a hilly slope up to 38%. Cassava is used as the primary food in that area. Uniquely, Cassava productivity is stable and continues until now. The assessment of soil quality is taking soil samples in the area and analysis the soil in laboratory. The result of analysis that soil in the area is not degraded because it has optimum nutrient, organic matter, and high value of cation exchange capacity in soil even though it has been cultivated in scarp with high slope. Commonly, soil on scarp with high slope has a high rate erosion and poor nutrient. It proved that cassava is able to be an alternative technique of soil conservation in the areas that have a high slope. Beside that, cassava can be utilized as a plant food, feed, fertilizer, and energy. With the utilization of Cassava, the target of Sustainable Development Goals (SDG's) will be achieved with consideration three important components include economy, social, and environment. In economy, Cassava can to be the commercial product like processed food, feed, and alternative energy. In social, it will increase social welfare and will be hereditary. And for environment, Cassava prevents soil from erosion and keeps soil quality.

Keywords: Cassava, local wisdom, conservation, soil quality, sustainable

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Authors: Jesica López, Deniz Koca, Asaf Tzachor

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In 2016 the historic peace accord between the Colombian government and the Revolutionary Armed Forces of Colombia (FARC) had no strong mechanism for managing changes to land use and the environment. Since the end of a 60-year conflict in Colombia, large areas of forest in the Amazon region have been rapidly converted to agricultural uses, most recently by cattle ranching. This suggests that the peace agreement presents a threat to the conservation of the country's rainforest. We analyze the effects of cattle ranching as a driver and accelerator of deforestation from a systemic perspective, focusing on two key leverage points the legal and illegal activities involved in the cattle ranching practices. We map and understand the inherent dynamic complexity of deforestation, including factors such as land policy instruments, national strategy to tackle deforestation, land use nexus with Amazonian food systems, and loss of biodiversity. Our results show that deforestation inside Colombian Protected Areas (PAs) in the Amazon region and the surrounding buffer areas has accelerated with the onset of peace. By using a systems analysis approach, we contextualized the competition of land between cattle ranching and the need to protect tropical forests and their biodiversity loss. We elaborate on future recommendations for land use management decisions making suggest the inclusion of an Amazonian food system, interconnecting and visualizing the synergies between sustainable development goals, climate action (SDG 13) and life on land (SDG 15).

Keywords: tropical rainforest, deforestation, sustainable land use, food security, Colombian Amazon

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Samson Abayomi Olasoju, Olufemi Adekunle, Titilope Edun, Johnson Owoseni

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Science offers opportunities for revolutionizing human activities, enriched by input from scientific research and technology. Biotechnology is a major force for development in developing countries such as Nigeria. It is found to contribute to solving human problems like water and food insecurity that impede national development and threaten peace wherever it is applied. This review identified the problems of agricultural biotechnology in Nigeria. On the part of rural farmers, there is a lack of adequate knowledge or awareness of biotechnology despite the fact that they constitute the bulk of Nigerian farmers. On part of the government, the problems include: lack of adequate implementation of government policy on bio-safety and genetically modified products, inadequate funding of education as well as research and development of products related to biotechnology. Other problems include: inadequate infrastructures (including laboratory), poor funding and lack of national strategies needed for development and running of agricultural biotechnology. In spite of all the challenges associated with agricultural biotechnology, its prospects still remain great if Nigeria is to meet with the food needs of the country’s ever increasing population. The introduction of genetically engineered products will lead to the high productivity needed for commercialization and food security. Insect, virus and other related diseases resistant crops and livestock are another viable area of contribution of biotechnology to agricultural production. In conclusion, agricultural biotechnology will not only ensure food security, but, in addition, will ensure that the local farmers utilize appropriate technology needed for large production, leading to the prosperity of the farmers and national economic growth, provided government plays its role of adequate funding and good policy implementation.

Keywords: biosafety, biotechnology, food security, genetic engineering, genetic modification

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Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

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Authors: Palingba Aimé Marie Doilkom, Mahamadou Koïta, Jean-michel Vouillamoz, Angelbert Biaou

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Most African populations rely on groundwater in rural areas for their consumption. Indeed, in the face of climate change and strong demographic growth, groundwater, particularly in the basement, is increasingly in demand. The question of the sustainability of water resources in this type of environment is therefore becoming a major issue. Groundwater recharge can be natural or artificial. Unlike natural recharge, which often results from the natural infiltration of surface water (e.g. a share of rainfall), artificial recharge consists of causing water infiltration through appropriate developments to artificially replenish the water stock of an aquifer. Artificial recharge is, therefore, one of the measures that can be implemented to secure water supply, combat the effects of climate change, and, more generally, contribute to improving the quantitative status of groundwater bodies. It is in this context that the present research is conducted with the aim of developing artificial recharge in order to contribute to the sustainability of basement aquifers in a context of climatic variability and constantly increasing water needs of populations. In order to achieve the expected results, it is therefore important to determine the characteristics of the infiltration basins and to identify the areas suitable for their implementation. The geometry of the aquifer was reproduced, and the hydraulic properties of the aquifer were collected and characterized, including boundary conditions, hydraulic conductivity, effective porosity, recharge, Van Genuchten parameters, and saturation indices. The aquifer of the Sanon experimental site is made up of three layers, namely the saprolite, the fissured horizon, and the healthy basement. Indeed, the saprolite and the fissured medium were considered for the simulations. The first results with FEFLOW model show that the water table reacts continuously for the first 100 days before stabilizing. The hydraulic charge increases by an average of 1 m. The further away from the basin, the less the water table reacts. However, if a variable hydraulic head is imposed on the basins, it can be seen that the response of the water table is not uniform over time. The lower the basin hydraulic head, the less it affects the water table. These simulations must be continued by improving the characteristics of the basins in order to obtain the appropriate characteristics for a good recharge.

Keywords: basement area, FEFLOW, infiltration basin, MAR

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Authors: Thomas Schalow

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It is commonly recognized that our present curriculum is not preparing students to function in the 21st century. This is particularly true in regard to communication needs across cultures - both human and non-human. In this paper, a comprehensive theory of communication-based on communication with non-human cultures and intelligences is presented to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with the argument that we need to become much more serious about communicating with the non-human, intelligent life forms that already exists around us here on Earth. We need to broaden our definition of communication and reach out to other sentient life forms in order to provide humanity with a better perspective of its place within our ecosystem. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it could prove useful even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised in accordance with the communication theory being proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences. Humanity has never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other cultures can provide us with a framework for this communication. The basic concepts behind intercultural communication can be applied to the three types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will yield substantial gains. A curriculum that is truly ready for the 21st century needs to be aligned with this new theory of communication.

Keywords: artificial intelligence, CETI, communication, language

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Lamia Ismayil

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The COVID-19 pandemic has drastically changed the everyday lives of people all over the world. This pandemic affects heavily the lifestyle of people in different aspects of life, including eating habits. The objective of this paper is to study the impact of the COVID-19 pandemic on key factors affecting food habits among the Community College of Qatar (CCQ) students. Method: The research is a cross-sectional survey that deployed a questionnaire among 217 CCQ students. Results: The results showed that 47.5% of the respondents were male and 52.5% female. In our previous study about 30% percentage obesity with a BMI of 30-35 was shown. Surprisingly, during the pandemic, the obesity percentage remains almost the same, but the BMI range jumped to 30-46. The nutritional knowledge of CCQ students is at an average level. Besides, during pandemic the personal eating habits continued to be imbalanced and getting unhealthier than in the pre-pandemic period. Regarding the key factors that could affect eating habits, most of the respondents reported that during the pandemic in the absence of restaurants they had daily meals with their families, and had a lack of physical activity. Besides, most of the respondents agree that they gained weight during the pandemic. These findings indicate that the pandemic changed the food habits and the lifestyle of Qatari students. Accordingly, increasing the awareness among college students (and Qatari community at large) is recommended. College students can be given some recommendations related to healthy eating tips to overcome the effect of pandemic on their health status. The detailed comparative analysis between pre- and post-pandemic eating habits of students can help reveal the main differences and give effective guidance to improve body composition and health status of students.

Keywords: food habits, BMI, obesity, COVID-19, nutritional knowledge, community college of Qatar, pandemic

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

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Authors: S. Susanti, V. P. Bintoro, A. Setiadi, S. I. Santoso, D. R. Febriandi

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Meat is a foodstuff of animal origin. It is perishable because a suitable medium for bacterial growth. That is why meat can be a potential hazard to humans. Several ways have been done to inhibit bacterial population in an effort to prolong the meat shelf-life. However, aberration sometimes happens in the practices of meat preservation, for example by using chemical material that possessed strong antibacterial activity like formaldehyde. For health reason, utilization of formaldehyde as a food preservative was forbidden because of DNA damage resulting cancer and birth defects. Therefore, it is important to seek a natural food preservative that is not harmful to the body. This study aims to reveal the potency of cashew apple as natural food preservative by measuring its antibacterial activity and marinating effect on the bacterial growth of beef and chicken meat. Antibacterial activity was measured by The Kirby-Bauer method while bacterial growth was determined by total plate count method. The results showed that inhibition zone of 10-30% cashew apple extract significantly wider compared to 0% extract on the medium of E. coli, S. aureus, S. typii, and Bacillus sp. Furthermore, beef marinated with 20-30% cashew apple extract and chicken meat marinated with 5-15% extract significantly less in the total number of bacteria compared to 0% extract. It can be concluded that marinating with 5-30% cashew apple extract can effectively inhibit the bacterial growth of beef and chicken meat. Moreover, the concentration of extracts to inhibit bacterial populations in chicken meat was reached at the lower level compared to beef. Thus, cashew apple is potential as a natural food preservative.

Keywords: bacterial growth, cashew apple, marinating, meat

Procedia PDF Downloads 265

Authors: Fayiz Saifurahman, Noori Fida Mohammad

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Afghanistan produces the highest rate of wheat, it is the first source of food, and food security in Afghanistan is dependent on the availability of wheat. Although Afghanistan is the main producer of wheat, on the other hand, Afghanistan is the largest importers of flour. The objective of this study is to assess the structure and dynamics of the wheat market in Afghanistan, can compute with foreign markets, and increase the level of production. To complete this, a broad series of secondary data was complied with, group discussions and interviews with farmers, agricultural and market experts. The research findings propose that; the government should adopt different policies to support the local market. The government should distribute the seed, support financially and technically to increase wheat production.

Keywords: Afghanistan, wheat, production , import

Procedia PDF Downloads 156

Authors: Magda I. Hassan

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This paper aims to investigate the health effects of aspartame on weanling male hamsters. 20 Golden Syrian hamsters drank only water (control) or water with 6, 11, and 18 mg aspartame/kg of body weight per day for 42 days. Food intake, weight gain, glucose blood level, and lipid profile were determined at the end of the experiment. The animals were sacrificed and histopathological examination of organs (liver, brain and heart) was done. Results revealed that animals in Asp.groups consumed significantly larger amount of food than the control (13.4±5.9, 8.6±2.5 and 8.8±3.0 vs 4.2±2.5 g/day, in succession). Hamsters in the control group showed higher total cholesterol and HDL levels than hamsters in aspartame 6, 11, 18 groups (160±19 vs 101±13, 130±22, 141±15 mg/dl & 144±9 vs 120±12, 118±13, 99±17 respectively (P<0•05)). The control group showed a glucose concentration below those of aspartame groups, indicating no effect of aspartame on glucose blood level. While, there were no significant differences in the triglycerides and LDL levels between control group and Asp.groups. Histopathological changes were observed, especially in brain and liver cells. Aspartame increases appetite and weight gain of young hamsters. Therefore, FDA should reconsider the acceptable daily intake (ADI) of aspartame for children.

Keywords: aspartame, brain, food intake, hamsters

Procedia PDF Downloads 282

Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

Procedia PDF Downloads 280

Authors: P. Raajeswari, S. M. Devatha, R. Pragatheeswari

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The edible packaging is a new trendsetter in the era of modern packaging. The researchers and food scientist recognise edible packaging as a useful alternative or addition to conventional packaging to reduce waste and to create novel applications for improving product stability. Starch was extracted from different sources that contains abundantly like potato, tapioca, rice, wheat, and corn. The starch based edible films and cutleries are developed as an alternative for conventional packages providing the nutritional benefit when consumed along with the food. The development of starch based edible films by the extraction of starch from various raw ingredients at lab scale level. The films are developed by the employment of plasticiser at different concentrations of 1.5ml and 2ml. The films developed using glycerol as a plasticiser in filmogenic solution to increase the flexibility and plasticity of film. It reduces intra and intermolecular forces in starch, and it increases the mobility of starch based edible films. The films developed are tested for its functional properties such as thickness, tensile strength, elongation at break, moisture permeability, moisture content, and puncture strength. The cutleries like spoons and cups are prepared by making dough and rolling the starch along with water. The overall results showed that starch based edible films absorbed less moisture, and they also contributed to the low moisture permeability with high tensile strength. Food colorants extracted from red onion peel, pumpkin, and red amaranth adds on the nutritive value, colour, and attraction when incorporated in edible cutleries, and it doesn’t influence the functional properties. Addition of a low quantity of glycerol in edible films and colour extraction from onion peel, pumpkin, and red amaranth enhances biodegradability and provides a good quantity of nutrients when consumed. Therefore, due to its multiple advantages, food starch can serve as the best response for eco-friendly industrial products aimed to replace single use plastics at low cost.

Keywords: edible films, edible cutleries, plasticizer, glycerol, starch, functional property

Procedia PDF Downloads 178

Authors: Laura Brennan, Ram Balakumar

Abstract:

ChatGPT has been developed by Open AI (Nov 2022) as a powerful artificial intelligence (AI) language model which has been designed to produce human-like text from user written prompts. To gain the most from the system, user written prompts must give context specific information. This article aims to give guidance on how to optimise the ChatGPT system in the context of education for otolaryngology. Otolaryngology is a specialist field which sees little time dedicated to providing education to both medical students and doctors. Additionally, otolaryngology trainees have seen a reduction in learning opportunities since the COVID-19 pandemic. In this article we look at these various barriers to medical education in Otolaryngology training and suggest ways that ChatGPT can overcome them and assist in simulation-based training. Examples provide how this can be achieved using the Authors’ experience to further highlight the practicalities. What this article has found is that while ChatGPT cannot replace traditional mentorship and practical surgical experience, it can serve as an invaluable supplementary resource to simulation based medical education in Otolaryngology.

Keywords: artificial intelligence, otolaryngology, surgical training, medical education

Procedia PDF Downloads 147

Authors: Hussaini Bala

Abstract:

There are different opinions in the literature on the relationship between Audit Committee characteristics and earnings management. The mix of opinions makes the direction of their relationship ambiguous. This study investigated the relationship between Audit Committee characteristics and earnings management of listed food and beverages Firms in Nigeria. The study covered the period of six years from 2007 to 2012. Data for the study were extracted from the Firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences. The dependent variable was generated using two steps regression in order to determine the discretionary accrual of the sample Firms. Multiple regression was employed to run the data of the study using Random Model. The results from the analysis revealed a significant association between audit committee characteristics and earnings management of the Firms. While audit committee size and committees’ financial expertise showed an inverse relationship with earnings management, committee’s independence, and frequency of meetings are positively and significantly related to earnings management. In line with the findings, the study recommended among others that listed food and beverages Firms in Nigeria should strictly comply with the provision of Companies and Allied Matters Act (CAMA) and SEC Code of Corporate Governance on the issues regarding Audit Committees. Regulators such as SEC should increase the minimum number of Audit Committee members with financial expertise and also have a statutory position on the maximum number of Audit Committees meetings, which should not be greater than four meetings in a year as SEC code of corporate governance is silent on this.

Keywords: audit committee, earnings management, listed Food and beverages size, leverage, Nigeria

Procedia PDF Downloads 262