Search results for: artificial food

Authors: Margaret S. Gumisiriza, Ernest. R. Mbega, Patrick Ndakidemi, Businge K. Edward

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The purpose of the paper was to assess existing literature regarding hydroponics in both the developing and developed countries. Furthermore, relate it to the context of African countries, how they can implement it and benefit from it in the face of climate change, high population growth rates, and reduced food production. Agriculture remains the major economic activity for a number of African countries. It is the source of income for most peasants, and still contributes to the Gross Domestic Product in most of these African countries. Unfortunately, climate change coupled with the increasing rates of population growth; rural-urban migration; and urbanization have led to food insecurity due to a reduction of available land for agriculture. This has further intensified the food security dilemma in Africa, especially in urban areas, where land is already limited. Considering the aforementioned state of affairs, there is an increasing demand for interventions that can help farmers in Africa to cope with climate change and increase food production. This review explores hydroponic farming and how it can be used as a climate-smart farming system in Africa’s rural and urban areas. Specifically, the review focuses on hydroponics, requirements for hydroponic farming and the state of hydroponic farming in LDCs and Developed countries (DCs). From the review, it was observed that African countries especially those that receive a lot of sunlight would highly benefit from the solar-powered hydroponic farming systems. Further, still, this farming system will help African countries cope with the challenges of high population pressure in urban areas and climate change as it qualifies to be an urban farming system.

Keywords: Africa, climate-smart agriculture, solar-powered-hydroponics, urban-farming

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Authors: Saruda Nitiworakarn

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This research aims to identify traditional Mon cuisines as well as gather and classify traditional cuisines of Mon communities in Bangkok. The studying of this research is used by methodology of the quantitative research. Using the questionnaire as the method in collecting information from sampling totally amount of 450 persons analyzed via frequency, percentage and mean value. The results showed that a variety of traditional Mon cuisines of Bangkok could split into 6 categories of meat diet with 54 items and 6 categories of desserts with 19 items.

Keywords: cultural identity, traditional food, Mon cuisine, Thailand

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Authors: Marisol Warthon-Medina, Jenny Plumb, Ayoub Aljawaldeh, Mark Roe, Ailsa Welch, Maria Glibetic, Paul M. Finglas

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Similar to Western Countries, the Eastern Mediterranean Region (EMR) also presents major public health issues associated with the increased consumption of sugar, fat, and salt. Therefore, one of the policies of the World Health Organization’s (WHO) EMR is to reduce the intake of salt, sugar, and fat (Saturated fatty acids, trans fatty acids) to address the risk of non-communicable diseases (i.e. diabetes, cardiovascular disease, cancer) and obesity. The project objective is to assess status and provide training and capacity development in the use of improved standardized methodologies for updated food composition data, dietary intake methods, use of suitable biomarkers of nutritional value and determine health outcomes in low and middle-income countries (LMIC). Training exchanges have been developed with clusters of countries created resulting from regional needs including Sudan, Egypt and Jordan; Tunisia, Morocco, and Mauritania; and other Middle Eastern countries. This capacity building will lead to the development and sustainability of up-to-date national and regional food composition databases in LMIC for use in dietary monitoring assessment in food and nutrient intakes. Workshops were organized to provide training and capacity development in the use of improved standardized methodologies for food composition and food intake. Training needs identified and short-term scientific missions organized for LMIC researchers including (1) training and knowledge exchange workshops, (2) short-term exchange of researchers, (3) development and application of protocols and (4) development of strategies to reduce sugar and fat intake. An initial training workshop, Morocco 2018 was attended by 25 participants from 10 EMR countries to review status and support development of regional food composition. 4 training exchanges are in progress. The use of improved standardized methodologies for food composition and dietary intake will produce robust measurements that will reinforce dietary monitoring and policy in LMIC. The capacity building from this project will lead to the development and sustainability of up-to-date national and regional food composition databases in EMR countries. Supported by the UK Medical Research Council, Global Challenges Research Fund, (MR/R019576/1), and the World Health Organization’s Eastern Mediterranean Region.

Keywords: dietary intake, food composition, low and middle-income countries, status.

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Authors: Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević, Lidija R. Jevrić, Stela Jokić

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The subject of this paper is to correlate antibacterial behavior of benzimidazole derivatives with their molecular characteristics using chemometric QSAR (Quantitative Structure–Activity Relationships) approach. QSAR analysis has been carried out on the inhibitory activity of benzimidazole derivatives against Staphylococcus aureus. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as a calibration models for prediction of the inhibitory activity. The quality of the models was validated by leave one out (LOO) technique and by using external data set. High agreement between experimental and predicted inhibitory acivities indicated the good quality of the derived models. These results are part of the CMST COST Action No. CM1306 "Understanding Movement and Mechanism in Molecular Machines".

Keywords: Antibacterial, benzimidazoles, chemometric, QSAR.

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Authors: Ada Rocha, Beatriz Almeida, Cláudia Viegas

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Considering the growing trend of food consumption outside the home, Food Service units (FSU) achieved importance and responsibility in feeding the population. FSU have a strong environmental impact since the large-scale production of meals implies a high use of resources and produce high amounts of waste with economic and environmental consequences. At the end of 2019, with the emergence of the Covid-19 pandemic, this effort towards sustainability was affected by the contingency measures imposed to stop the spread of the virus. Preventive measures in FSU, include the provision of cutlery and paper napkins in individual bags, the use of disposable paper towels, the supply of individual portions of bread and spices, as well as bottled water. These measures are, in many cases, a setback and an obstacle to the implementation of more sustainable practices and imply greater consumption of natural resources and materials. The present study aimed to assess the impact of the implementation of the contingency measures for the Covid-19 pandemic on the environmental practices of FSU in Portugal. A questionnaire was developed to characterize the FSU and the impact of the implementation of contingency measures for the Covid-19 pandemic. A great impact of the implementation of the contingency measures in the sustainability of FSU was observed, highlighting concerns about the need to keep these measures, some of them adopted due to fear of the unknown and its consequences on an ongoing successful process. Policymakers should keep only the ones that may prove to be efficient and positive and abandon or relieve the unnecessary ones.

Keywords: COVID-19, environment, food service, sustainability, SGD

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Authors: Chen Xi, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Tong Zhiyuan

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Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature has led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using YOLOv5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network

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Authors: Lourdita Llanto

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This study described and assessed the implementation of artificial insemination (AI) for cattle and carabaos in the Bicol Region, Philippines: Albay, Sorsogon and Camarines Sur. Three hundred respondents were interviewed. Results were analyzed using frequency counts, means, percentages and chi-square test. Semen samples from different stations were analyzed for motility, viability and morphology. T-test was used in semen quality evaluation. Provincial AI coordinators (PAIC) were male, averaging 59 years old, married, had college education, served in government service for 34 years, but as PAIC for 5.7 years. All had other designations. Mean AI operation was 11.33 years with annual support from the local government unit of Php76,666.67. AI technicians were males, married, with college education, and trained on AI. Problems were on mobility; inadequate knowledge of farmers in animal raising and AI; and lack of liquid nitrogen and frozen semen supply. There was 2.95 municipalities and breedable cattle/carabaos of 3,091.25 per AI technician. Mean number of artificially inseminated animals per AI technician for 2011 was 28.57 heads for carabaos and 8.64 heads for cattle. There was very low participation rate among farmers. Carabaos were 6.52 years with parity 1.53. Cattle were 5.61 years, with parity of 1.51. Semen quality significantly (p ≤ 0.05) deteriorated in normal and live sperm with storage and handling at the provincial and field stations. Breed, AI technicians practices and AI operation significantly affected conception rate. Mean conception rate was 57.62%.

Keywords: artificial insemination, carabao, parity, mother tanks, frozen semen

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Authors: M. Riaz, Inamur Rehman, Abhishek Sharma

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The present work is the development of an experimental method for determining the thermal diffusivity variations with temperature of selected regular shaped solid fruits and vegetables subjected to forced convection cooling. Experimental investigations were carried on the sample chosen (potato and brinjal), which is approximately of spherical geometry. The variation of temperature within the food product is measured at several locations from centre to skin, under forced convection environment using a deep freezer, maintained at -10°C.This method uses one dimensional Fourier equation applied to regular shapes. For this, the experimental temperature data obtained from cylindrical and spherical shaped products during pre-cooling was utilised. Such temperature and thermal diffusivity profiles can be readily used with other information such as degradation rate, etc. to evaluate thermal treatments based on cold air cooling methods for storage of perishable food products.

Keywords: thermal diffusivity, skin temperature, precooling, forced convection, regular shaped

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Authors: Yevheniia Varyvoda, Douglas Taren

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At the aggregate level, climate variability, the rising number of active violent conflicts, globalization and industrialization of agriculture, the loss in diversity of crop species, the increase in demand for agricultural production, and the adoption of healthy and sustainable dietary patterns are exacerbating factors of food system destabilization. The importance of pseudocereals to fuel and sustain resilient food systems is recognized by leading organizations working to end hunger, particularly for their critical capability to diversify livelihood portfolios and provide plant-sourced healthy nutrition in the face of systemic shocks and stresses. Amaranth, buckwheat, and quinoa are the most promising and used pseudocereals for ensuring food system resilience in the reality of climate change due to their high nutritional profile, good digestibility, palatability, medicinal value, abiotic stress tolerance, pest and disease resistance, rapid growth rate, adaptability to marginal and degraded lands, high genetic variability, low input requirements, and income generation capacity. The study provides the rationale and examples of advancing local and regional food systems' resilience by scaling up the utilization of amaranth, buckwheat, and quinoa along all components of food systems to architect indirect nutrition interventions and climate-smart approaches. Thus, this study aims to explore the drivers for ancient pseudocereal utilization, the potential resilience benefits that can be derived from using them, and the challenges and opportunities for pseudocereal utilization within the food system components. The PSALSAR framework regarding the method for conducting systematic review and meta-analysis for environmental science research was used to answer these research questions. Nevertheless, the utilization of pseudocereals has been slow for a number of reasons, namely the increased production of commercial and major staples such as maize, rice, wheat, soybean, and potato, the displacement due to pressure from imported crops, lack of knowledge about value-adding practices in food supply chain, limited technical knowledge and awareness about nutritional and health benefits, absence of marketing channels and limited access to extension services and information about resilient crops. The success of climate-resilient pathways based on pseudocereal utilization underlines the importance of co-designed activities that use modern technologies, high-value traditional knowledge of underutilized crops, and a strong acknowledgment of cultural norms to increase community-level economic and food system resilience.

Keywords: resilience, pseudocereals, food system, climate change

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Authors: R. B. B. Samantha Ramachandra, L. D. J. Upul Senarath, S. H. Padmal De Silva

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Dietary pattern largely differs over countries and even within a country, it shows cultural differences. The dietary pattern changes the energy consumption and micronutrient intake, directly affects the pregnancy outcome. The dietary diversity was used as an indirect measure to assess micronutrient adequacy for pregnant mothers in this study. The study was conducted as a baseline survey with the objective of designing an intervention to improve the dietary diversity of pregnant mothers in Sri Lanka. The survey was conducted in Kalutara district of Sri Lanka in 2015 among 769 pregnant mothers at different gestational ages. Dietary diversity questionnaire developed by Food and Agricultural Organization’s (FAO) Food and Nutrition technical Assistance (FANTA) II project, recommended for cross-country use with adaptations was used for data collection. Trained data collectors met pregnant mothers at field ante-natal clinic and questioned on last 24hr dietary recall with portion size and coded food items to identify the diversity. Pregnant mothers were identified from randomly selected 21 clusters of public health midwife areas. 81.5% mothers (n=627) in the sample had been registered at Public Health Midwife (PHM) before 8 weeks of gestation. 24.4% of mothers were with low starting BMI and 22.7% mothers were with high starting BMI. 47.6% (n=388) mothers had abstained from at least one food item during the pregnancy. The food group with the highest consumption was rice (98.4%) followed by sugar (89.9%). 76.1% mothers had consumed milk, 73% consumed fish and sea foods. Consumption of green leaves was 52% and Vit A rich foods consumed only by 49% mothers. Animal organs, flesh meat and egg all showed low prevalence as 4.7%, 21.6% and 20% respectively. Consumption of locally grown roots, nut, legumes all showed very low prevalence. Consumption of 6 or more food groups was considered as good dietary diversity (DD), 4 to 5 food groups as moderate diversity and 3 or less food groups as poor diversity by FAO FANTA II project. 42.1% mothers demonstrated good DD while another 42.1% recorded moderate diversity. Working mothers showed better DD (51.6%, n=82/159) compared to housewives in the sample (chi = 10.656a,. df=2, p=0.005). The good DD showed gradual improvement from 43.1% to 55.5% along the poorest to richest wealth index (Chi=48.045, df=8 and p=0.000). DD showed significant association with the ethnicity and Moors showed the lowest DD. DD showed no association with the home gardening even though where better diversity expected among those who have home gardening (p=0.548). Sri Lanka is a country where many food items can be grown in the garden and semi-urban setting have adequate space for gardening. Many Sri Lankan mothers do not add homegrown items in their meal. At the same time, their consumption of animal food shows low prevalence. The DD of most of the mothers being either moderate or low (58%) may result from inadequate micro nutrient intake during pregnancy. It is recommended that adding green leaves, locally grown vegetables, roots, nuts and legumes can help increasing the DD of Sri Lankan mothers at low cost.

Keywords: dietary diversity, pregnant mothers, micro-nutrient, food groups

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Authors: Pil Kyoo Jo, Jee Young Kim, Yu Jin Oh, Sohyun Park, Young Ha Joo, Hye Suk Kim, Semi Kang

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When convenience drives daily food choices, the increased consumption of processed foods may be associated with the increased intakes of sodium and fat and further with the onset of chronic diseases. The purpose of this study was to investigate the levels of sodium, saturated fat, and calories intakes through processed foods and the dietary patterns among adult populations in South Korea. We used the nationally representative data from the 5th Korea National Health and Nutrition Examination Survey (KNHANES, 2010-2012) and a cross-sectional survey on the eating behaviors among university students(N=893, 380 men, 513 women) aged from 20 to 24 years. Results showed that South Koreans consumed 43.5% of their total food consumption from processed foods. The 24-hour recalls data showed that 77% of sodium, 60% of fats, 59% of saturated fat, and 44% of calories were consumed from processed food. The intake of processed foods increased by 1.7% in average since 2008 annually. Only 33% of processed food that respondents consumed had nutrition labeling. The data from university students showed that students selected processed foods in convenience store when eating alone compared to eating with someone else. Given the convenience and lack of time, more people will consume processed foods and it may impact their overall dietary intake and further their health. In order to help people to make healthier food choices, regulations and policies to reduce the potentially unhealthy nutrients of processed foods should be strengthened. This research was supported by the National Research Foundation of Korea for 2011 Korea-Japan Basic Scientific Cooperation Program. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6037369).

Keywords: sodium, fat, processed foods, diet trends

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Authors: Debjani Dasgupta, Aman Zalawadia, Anuj Thapa, Pranjali Sing, Ashish Dabade

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Organophosphate group pesticides are common pesticide group, which gain entry into food product due to incomplete removal of pesticide residues. The current food industry raw material handling process is not sufficient to eliminate pesticide residues. A neutralizer was used to neutralize the residues of pesticide on Vitis vinifera (Grapes). The water based dilution of neutralizer was demonstrated on fruits like grapes. Analysis for pesticides in water wash and neutralizer wash was carried out using GCMS. Fruits washed with neutralizer exhibited 72.95% removal of pesticides compared with normal water wash method. An economical chemical neutralizer can be used to remove such residues in raw material handling at industrial scale with minor modification in process to achieve minimum pesticide entry into final food products.

Keywords: GCMS, organophosphate, raw material handling, Vitis vinifera, pesticide neutralizer

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Authors: Sung Hyun Yoo, In Hwan Choi, Jun Ho Jung, Choon Ki Ahn, Myo Taeg Lim

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Home energy management system (HEMS) has been issued to reduce the power consumption. The HEMS performs electric power control for the indoor electric device. However, HEMS commonly treats the smart devices. In this paper, we suggest the output estimation of home legacy device using the artificial neural fuzzy inference system (ANFIS). This paper discusses the overview and the architecture of the system. In addition, accurate performance of the output estimation using the ANFIS inference system is shown via a numerical example.

Keywords: artificial neural fuzzy inference system (ANFIS), home energy management system (HEMS), smart device, legacy device

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

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Authors: Aya Idriss, Esraa Othman, Lujain Malak

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Artificial intelligence is the emulation of human intelligence processes by machines, particularly computer systems that act logically. Some of the specific applications of AI include natural language processing, speech recognition, and machine vision. Cortana is a virtual assistant and she’s an example of an AI Application. Microsoft made it possible for this app to be accessed not only on laptops and PCs but can be downloaded on mobile phones and used as a virtual assistant which was a huge success. Cortana can offer a lot apart from the basic orders such as setting alarms and marking the calendar. Its capabilities spread past that, for example, it provides us with listening to music and podcasts on the go, managing my to-do list and emails, connecting with my contacts hands-free by simply just telling the virtual assistant to call somebody, gives me instant answers and so on. A questionnaire was sent online to numerous friends and family members to perform the study, which is critical in evaluating Cortana's recognition capacity and the majority of the answers were in favor of Cortana’s capabilities. The results of the questionnaire assisted us in determining the level of Cortana's skills.

Keywords: artificial intelligence, Cortana, AI, abstract

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Authors: Solomon Ngoako Mabapa, Selekane Ananias Motadi, Nteseng Mailula, Hlekani Vanessa Mbhatsani, Lindelani Fhumudzani Mushaphi

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Background: South Africa has indeed made good progress towards IDD elimination, as far as implementation of salt iodization and coverage of iodized salt are concerned, the education and promotion aspects of the iodized salt intervention are seriously lacking. Objective: To determine the iodine nutritional knowledge of food handlers at primary schools under the National School Nutrition Programme in Capricorn and Waterberg district. Design: This study included 300 food handlers recruited from 95 primary schools in Capricorn district and 105 primary schools in Waterberg district, Limpopo Province, South Africa. Primary schools and study participants where conveniently selected. The data was collected by means of a structured questionnaire. Information obtained was on the socio-demographic characteristics of the participants, general knowledge on salt fortification and knowledge test. Results: The iodine knowledge for the food handlers in two districts was poor with the entire population’s iodine nutritional knowledge of 12% on the Lickert scale. The mean score on the Lickert scale for Capricorn and Waterberg districts was 17% and 8.6% respectively indicated poor iodine nutritional knowledge. Conclusion: The two districts had poor iodine nutritional knowledge. Giving nutrition education to the public on the importance of iodine and the consequences of iodine deficiency disorder (IDD) and continue advocacy on mass media on the iodine fortification as an intervention strategy to combat the escalating problem of micronutrient malnutrition control.

Keywords: food handlers, nutritional knowledge, iodine, National School Nutrition Programme

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Authors: Nir Wittenberg, Moshe Farhi

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This mixed-methods study examines the integration of artificial intelligence (AI) tools in a first-year social work course to assess their potential for enhancing professional knowledge and skills. The incorporation of digital technologies, such as AI, in social work interventions, training, and research has increased, with the expectation that AI will become as commonplace as email and mobile phones. However, policies and ethical guidelines regarding AI, as well as empirical evaluations of its usefulness, are lacking. As AI is gradually being adopted in the field, it is prudent to explore AI thoughtfully in alignment with pedagogical goals. The outcomes assessed include professional identity, course satisfaction, and motivation. AI offers unique reflective learning opportunities through personalized simulations, feedback, and queries to complement face-to-face lessons. For instance, AI simulations provide low-risk practices for situations such as client interactions, enabling students to build skills with less stress. However, it is essential to recognize that AI alone cannot ensure real-world competence or cultural sensitivity. Outcomes related to student learning, experience, and perceptions will help to elucidate the best practices for AI integration, guiding faculty, and advancing pedagogical innovation. This strategic integration of selected AI technologies is expected to diversify course methodology, improve learning outcomes, and generate new evidence on AI’s educational utility. The findings will inform faculty seeking to thoughtfully incorporate AI into teaching and learning.

Keywords: artificial intelligence (AI), social work education, students, developing a professional identity, ethical considerations

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Authors: Matar Celine, Gaucel Sebastien, Gontard Nathalie, Guilbert Stephane, Guillard Valerie

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To increase the very short shelf life of fresh fruits and vegetable, Modified Atmosphere Packaging (MAP) allows an optimal atmosphere composition to be maintained around the product and thus prevent its decay. This technology relies on the modification of internal packaging atmosphere due to equilibrium between production/consumption of gases by the respiring product and gas permeation through the packaging material. While, to the best of our knowledge, benefit of MAP for fresh fruits and vegetable has been widely demonstrated in the literature, its effect on shelf life increase has never been quantified and formalized in a clear and simple manner leading difficult to anticipate its economic and environmental benefit, notably through the decrease of food losses. Mathematical modelling of mass transfers in the food/packaging system is the basis for a better design and dimensioning of the food packaging system. But up to now, existing models did not permit to estimate food quality nor shelf life gain reached by using MAP. However, shelf life prediction is an indispensable prerequisite for quantifying the effect of MAP on food losses reduction. The objective of this work is to propose an innovative approach to predict shelf life of MAP food product and then to link it to a reduction of food losses and wastes. In this purpose, a ‘Virtual MAP modeling tool’ was developed by coupling a new predictive deterioration model (based on visual surface prediction of deterioration encompassing colour, texture and spoilage development) with models of the literature for respiration and permeation. A major input of this modelling tool is the maximal percentage of deterioration (MAD) which was assessed from dedicated consumers’ studies. Strawberries of the variety Charlotte were selected as the model food for its high perishability, high respiration rate; 50-100 ml CO₂/h/kg produced at 20°C, allowing it to be a good representative of challenging post-harvest storage. A value of 13% was determined as a limit of acceptability for the consumers, permitting to define products’ shelf life. The ‘Virtual MAP modeling tool’ was validated in isothermal conditions (5, 10 and 20°C) and in dynamic temperature conditions mimicking commercial post-harvest storage of strawberries. RMSE values were systematically lower than 3% for respectively, O₂, CO₂ and deterioration profiles as a function of time confirming the goodness of model fitting. For the investigated temperature profile, a shelf life gain of 0.33 days was obtained in MAP compared to the conventional storage situation (no MAP condition). Shelf life gain of more than 1 day could be obtained for optimized post-harvest conditions as numerically investigated. Such shelf life gain permitted to anticipate a significant reduction of food losses at the distribution and consumer steps. This food losses' reduction as a function of shelf life gain has been quantified using a dedicated mathematical equation that has been developed for this purpose.

Keywords: food losses and wastes, modified atmosphere packaging, mathematical modeling, shelf life prediction

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Authors: Samir Hessas

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Modern medicine is witnessing a profound transformation as advanced technology reshapes surgical environments. Hybrid operating rooms, where state-of-the-art medical equipment, advanced imaging solutions, and Artificial Intelligence (AI) converge, are at the forefront of this revolution. In this comprehensive exploration, we scrutinize the multifaceted facets of AI and delve into an array of groundbreaking technologies. We also discuss visionary concepts that hold the potential to revolutionize hybrid operating rooms, making them more efficient and patient-centered. These innovations encompass real-time imaging, surgical simulation, IoT and remote monitoring, 3D printing, telemedicine, quantum computing, and nanotechnology. The outcome of this fusion of technology and imagination is a promising future of surgical precision, individualized patient care, and unprecedented medical advances in hybrid operating rooms.

Keywords: artificial intelligence, hybrid operating rooms, telemedicine, monitoring

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Authors: Hussien Ali, Mesfin Menza, Fitsum Hagos, Amare Haileslassie

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Climate change has perverse effects on agricultural productivity and natural resource base, negatively affecting the well-being of the households and communities. The government and NGOs promote climate smart agricultural (CSA) practices to help farmers adapt to and mitigate the negative effects of climate change. This study aims to identify widely available CSA practices and examine their impacts on food security and multi-dimensional poverty of rural farm households in the Central Rift Valley, Ethiopia. Using three-stage proportional to size sampling procedure, the study randomly selected 278 households from two kebeles from four districts each. A cross-sectional data of 2020/21 cropping season was collected using structured and pretested survey questionnaire. Food consumption score, dietary diversity score, food insecurity experience scale, and multidimensional poverty index were calculated to measure households’ welfare indicators. Multinomial endogenous switching regression model was used to assess average treatment effects of CSA on these outcome indicators on adopter and non-adopter households. The results indicate that the widely adopted CSA practices in the area are conservation agriculture, soil fertility management, crop diversification, and small-scale irrigation. Adopter households have, on average, statistically higher food consumption score, dietary diversity score and lower food insecurity access scale than non-adopters. Moreover, adopter households, on average, have lower deprivation score in multidimensional poverty compared to non-adopter households. Up scaling the adoption of CSA practices through the improvement of households’ implementation capacity and better information, technical advice, and innovative financing mechanisms is advised. Up scaling CSA practices can further promote achieving global goals such as SDG 1, SDG 2, and SDG 13 targets, aimed to end poverty and hunger and mitigate the adverse impacts of climate change, respectively.

Keywords: climate-smart agriculture, food security, multidimensional poverty, upscaling CSA, Ethiopia

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

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This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

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Authors: Chen Xi, LIU Xuebin, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Lao Xuerui

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Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature have led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using Yolov5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network, machine vision

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

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Authors: Rara Wulan Anggareni, Narita Putri, Riski Septianing Astuti

Abstract:

Breastfeeding is a key to prevent mortality and morbidity in children. It is also the second highest risk responsible for Disability Adjusted Life Years (DALYs) among children below five years old. UNICEF estimates that during 1995 – 2003, there are only about 38% infants in developing countries who get to be exclusively breastfed during the first six months of their lives. According to Demography and Health Survey in Indonesia 2007, breastfeed practice rate still considered as low which is about 41%. One of the factors causing the low breastfeed practice rate in Indonesia is the anxiety and postpartum depression, and also the weanling dilemma in which mother feels that her breastmilk cannot suffice infant needs. Those factors finally resulting into low or even stopped production of breastmilk. Breastmilk production can be enhanced by consuming food containing phytosterol and lactogoga effect. Food with the highest phytosterol level is Sauropus androgynus (L.) Merr leaf (merr leaf). In this study, we made alternative food which named Chocomerr for breastfeeding mothers. Chocomerr consists of merr leaves which have lactogoga effect and chocolate for relaxation. Based on organoleptic tests conducted towards 2 age groups, which are 18 – 21 and 25 – 40 years old, this product gets good acceptance in taste, texture, and colour categories. Chocomerr can be used as an alternative way for increasing breastmilk production to aim for the decreasing number of DALYs among children aged under 5 years old.

Keywords: breastfeeding, increasing, chocolate, merr leaves

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Authors: A. Rezazadeh, N. Omidvar, H. Eini-Zinab

Abstract:

Food insecurity (FI) influences may result in poor physical and mental health outcomes. Minor ethnic group may experience higher level of FI, and this situation may be related with higher depression prevalence. The aim of this study was to determine the association of depression with food security status in major (Azeri) and minor (Kurdish) ethnicity living in Urmia, West Azerbaijan, north of Iran. In this cross-sectional study, 723 participants (427 women and 296 men) aged 20–64 years old, from two ethnic groups (445 Azeri and 278 Kurdish), were selected through a multi stage cluster systematic sampling. Depression rate was assessed by “Beck” short form questionnaire (validated in Iranians) through interviews. Household FI status (HFIS) was measured using adapted HFI access scale through face-to-face interviews at homes. Multinomial logistic regression was used to estimate odds ratios (OR) of depression across HFIS. Higher percent of Kurds had moderate and severe depression in comparison with Azeri group (73 [17.3%] vs. 86 [27.9%]). There were not any significant differences between the two ethnicities in mild depression. Also, of all the subjects, moderate-to-sever FI was more prevalent in Kurds (28.5%), compared to Azeri group (17.3%) [P < 0.01]. Kurdish ethnic group living in food security or mild FI households had lower chance to have symptom of severe depression in comparison to those with sever FI (OR=0.097; 95% CI: 0.02-0.47). However, there was no significant association between depression and HFI in Azeri group. Findings revealed that the severity of HFI was related with severity depression in minor studied ethnic groups. However, in Azeri ethnicity as a major group, other confounders may have influence on the relation with depression and FI, that were not studied in the present study.

Keywords: depression, ethnicity, food security status, Iran

Procedia PDF Downloads 198

Authors: Gadanya, A.M., Ponfa, S., Jibril, M.M., Abubakar, S. M.

Abstract:

Background: Edible insects such as grasshopper are important sources of food for humans, and have been consumed as traditional foods by many indigenous communities especially in Africa, Asia, and Latin America. These communities have developed their skills and techniques in harvesting, preparing, consuming, and preserving edible insects, widely contributing to the role played by the use of insects in human nutrition. Aim/ objective: This study was aimed at determining the effect of insect enriched sprouted soyabean /millet based food on some gut health markers in albino rats. Methods. Four different formulations of Complementary foods (i.e Complementary Food B (CFB): sprouted millet (SM), Complementary Food C (CFC): sprouted soyabean (SSB), Complementary Food D (CFD): sprouted soybean and millet (SSBM) in a ratio of (50:50) and Complementary Food E (CFE): insect (grasshopper) enriched sprouted soybean and millet (SSBMI) in a ratio of (50:25:25)) were prepared. Proximate composition and short chain fatty acid contents were determined. Thirty albino rats were divided into5 groups of six rats each. Group 1(CDA) were fed with basal diet and served as a control group, while groups 2,3,4 and 5 were fed with the corresponding complimentary foods CFB, CFC, CFD and CFE respectively daily for four weeks. Concentrations of fecal protein, serum total carotenoids and nitric oxide were determined. DNA extraction for molecular isolation and characterization were carried out followed by PCR, the use of mega 11 software and NCBI blast for construction of the phylogenetic tree and organism identification respectively. Results: Significant increase (P<0.05) in percentage ash, fat, protein and moisture contents, as well as short chain fatty acid (acetate, butyrate and propionate) concentrations were recorded in the insect enriched sprouted composite food (CFE) when compared with the CFA, CFB, CFC and CFD composite food. Faecal protein, carotenoid and nitric oxide concentrations were significantly lower (P>0.05) in group 5 in comparison to groups 1to 4. Ruminococcus bromii and Bacteroidetes were molecularly isolated and characterized by 16s rRNA from the sprouted millet/sprouted soybean and the insect enriched sprouted soybean/sprouted millet based food respectively. The presence of these bacterial strains in the feaces of the treated rats is an indication that the gut of the treated rats is colonized by good gut bacteria, hence, an improved gut health. Conclusion: Insect enriched sprouted soya bean/sprouted millet based complementary diet showed a high composition of ash, fat, protein and fiber. Thus, could increase the availability of short chain fatty acids whose role to the host organism cannot be overemphasized. It was also found to have decrease the level of faecal protein, carotenoid and nitric oxide in the serum which is an indication of an improvement in the immune system function.

Keywords: gut-health, insect, millet, soybean, sprouted

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Authors: Franklin Nantui Mabe, Gideon Danso-Abbeam, Dennis Sedem Ehiakpor

Abstract:

In order for the government of Ghana through the Ministry of Food and Agriculture to motivate farmers to adopt improved agricultural technologies, expand their farms and encourage youth to enter into agricultural production so as to increase crop productivity, “Planting for Food and Jobs” (PFJ) programme was launched in April 2017. The PFJ programme covers five pillars, namely, provision of subsidized and improved seeds; subsidized fertilizer; agricultural extension services; establishment of markets; and e-agriculture. This study assesses the awareness of farmers about the packages of these pillars using the Likert scale, paired t-test and Spearman’s rank correlation coefficient. The study adopted a mixed research design. A semi-structured questionnaire and checklist were used to collect data. The data collection was done using interviews and focus group discussions. The PFJ pillar farmers are much aware is a subsidy on fertilizer followed by a subsidy on improved seeds. Electronic agriculture is a pillar with the lowest level of awareness. There is a strong positive correlation between awareness of fertilizer and seed packages suggestion their complementarities. Lack of information/awareness of the packages of the programme can affect farmers’ participation in all the pillars. Farmers, in particular, should be educated for them to know what they are entitled to in each of the pillars. The programme implementation plan should also be made available to farmers as a guide.

Keywords: awareness, planting for food and jobs, programme, farmers, likert scale

Procedia PDF Downloads 218

Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

Abstract:

Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

Procedia PDF Downloads 523