Search results for: animal artificial insemination

Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Safia Asif, Saadia Bano

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In a world with rapidly depleting resources, using artificial lighting during daytime is an act of human ignorance. Imitated light is the major source of energy consumption in public buildings. Despite, the fact that substantial working hours of these buildings usually persist in natural daylight time; there is a trend of isolated, un-fenestrated and a-contextual interiors majorly dependent on active energy sources. On the contrary, if direct and un-controlled sunlight is allowed inside the building, it will create visual and thermal discomfort. Controlled daylighting with appropriate design mechanisms is one of the important aspects of achieving thermal and visual comfort. The natural sunlight can be utilized intelligently with the help of architectural thermal controlling mechanisms to achieve a healthy and productive environment. This paper is an attempt to investigate and analyze the importance of daylighting with reference to energy efficiency and thermal comfort. For this purpose, three public buildings including two educational institutions and one general post office are selected, as case-studies in the context of Karachi, Pakistan. Various parameters of visual and thermal comfort are analyzed which includes orientation, ceiling heights, overall building profile along with daylight controlling mechanisms in terms of penetration, distribution, protection, and control. In the later part of the research, a questionnaire survey is also conducted to evaluate the user experience in terms of adequate daylighting and thermal comfort.

Keywords: daylight, public buildings, sustainable architecture, visual and thermal comfort

Procedia PDF Downloads 196

Authors: Maryam Bordbar, Yaser Mokhayeri, Sajjad Roosta, Fatemeh Ghasemi, Saeed Choobkar, Hamidreza Nikbakht, Ebrahim Falahi

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Objective: Diabetes mellitus type 2 is the most common metabolic disorder that is growing exponentially worldwide. Satureja Khuzestanica Jamzad is a native plant of Iran that grows widely in the south of Iran. Its antimicrobial, antioxidant, anti-inflammatory and pain-relieving effects have been documented in animal studies. The purpose of this study is to investigate the effect of consumption daily S. khuzestanica on inflammatory and antioxidant indicators in type 2 diabetic patients. Methods and Materials: In a double-blind, placebo-controlled clinical trial, 67 patients with type 2 diabetes were included and divided into two groups. One group received S. khuzestanica (capsule containing 500 mg) and the other group received placebo (500 mg talcum powder) once a day for 12 weeks. After the intervention, the inflammatory and antioxidant indicators of the two groups were compared. Results: In comparison to placebo groups, there was a significant difference in levels of total antioxidant capacity, superoxide dismutase, catalase, glutathione reductase, and glutathione peroxidase; these antioxidant indicators were higher in the intervention group (P<0.05). Moreover, a considerable decrease in weight, CRP and IL-6 levels were observed in patients in the S.Khuzestanica group. Conclusion: Our findings may provide novel complementary treatments without adverse effects for diabetes complications.

Keywords: Satureja khuzestanica Jamzad, diabetes mellitus, antioxidant indicators, IL-6, C-reactive protein

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Authors: Yehjune Heo

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Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

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Authors: Hyun-Kyung Lee, Jehyung Oh, Young Eun Jeong, Hyun-Shik Lee

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Perfluoroalkyl compounds (PFCs) are environmental toxicants that persistently accumulate in the human blood. Their widespread detection and accumulation in the environment raise concerns about whether these chemicals might be developmental toxicants and teratogens in the ecosystem. We evaluated and compared the toxicity of PFCs of containing various numbers of carbon atoms (C8-11 carbons) on vertebrate embryogenesis. We assessed the developmental toxicity and teratogenicity of various PFCs. The toxic effects on Xenopus embryos were evaluated using different methods. We measured teratogenic indices (TIs) and investigated the mechanisms underlying developmental toxicity and teratogenicity by measuring the expression of organ-specific biomarkers such as xPTB (liver), Nkx2.5 (heart), and Cyl18 (intestine). All PFCs that we tested were found to be developmental toxicants and teratogens. Their toxic effects were strengthened with increasing length of the fluorinated carbon chain. Furthermore, we produced evidence showing that perfluorodecanoic acid (PFDA) and perfluoroundecanoic acid (PFuDA) are more potent developmental toxicants and teratogens in an animal model compared to the other PFCs we evaluated [perfluorooctanoic acid (PFOA) and perfluorononanoic acid (PFNA)]. In particular, severe defects resulting from PFDA and PFuDA exposure were observed in the liver and heart, respectively, using the whole mount in situ hybridization, real-time PCR, pathologic analysis of the heart, and dissection of the liver. Our studies suggest that most PFCs are developmental toxicants and teratogens, however, compounds that have higher numbers of carbons (i.e., PFDA and PFuDA) exert more potent effects.

Keywords: PFC, xenopus, fetax, development

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: N. Boontian

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Biological conversion of biomass to methane has received increasing attention in recent years. Grasses have been explored for their potential anaerobic digestion to methane. In this review, extensive literature data have been tabulated and classified. The influences of several parameters on the potential of these feedstocks to produce methane are presented. Lignocellulosic biomass represents a mostly unused source for biogas and ethanol production. Many factors, including lignin content, crystallinity of cellulose, and particle size, limit the digestibility of the hemicellulose and cellulose present in the lignocellulosic biomass. Pretreatments have used to improve the digestibility of the lignocellulosic biomass. Each pretreatment has its own effects on cellulose, hemicellulose and lignin, the three main components of lignocellulosic biomass. Solid-state anaerobic digestion (SS-AD) generally occurs at solid concentrations higher than 15%. In contrast, liquid anaerobic digestion (AD) handles feedstocks with solid concentrations between 0.5% and 15%. Animal manure, sewage sludge, and food waste are generally treated by liquid AD, while organic fractions of municipal solid waste (OFMSW) and lignocellulosic biomass such as crop residues and energy crops can be processed through SS-AD. An increase in operating temperature can improve both the biogas yield and the production efficiency, other practices such as using AD digestate or leachate as an inoculant or decreasing the solid content may increase biogas yield but have negative impact on production efficiency. Focus is placed on substrate pretreatment in anaerobic digestion (AD) as a means of increasing biogas yields using today’s diversified substrate sources.

Keywords: anaerobic digestion, lignocellulosic biomass, methane production, optimization, pretreatment

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Authors: Nthatisi I. Molefe-Nyembe, Keisuke Suganuma, Oriel M. M. Thekisoe, Xuan Xuenan, Noboru Inoue

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African trypanosomosis is a devastating disease of animals caused by parasites of the genus Trypanosoma negatively affecting the economic status of more than 36 African countries. Few available drugs for the treatment of trypanosomosis remain inaccessible in remote areas, are associated with severe toxicity and most importantly, resistance has widely developed against their usage. Therefore, safe, effective and easily administrable drugs are urgently in need. The objective of the current study was to determine efficacy of azithromycin (AZM), on T. congolense, T. brucei brucei in vitro and in vivo. A 96 well luciferase assay was conducted to determine the trypanocidal effect of AZM on T. congolense, T. b. brucei and T. evansi as well as the cytotoxicity effect on the MDBK and NIH 3T3 cells. Additionally, TEM analysis was conducted to determine the morphological alteration on the AZM treated samples. Mice were infected with T. congolense and T. b. brucei and orally treated with AZM for 7 and 28 days referred to as the short and the long-term treatment. The in vitro IC50 values of AZM on T. congolense, T. b. brucei and T. evansi was 0.19 ± 0.17; 3.69 ± 2.26 and 1.81 ± 1.82 μg/mL, respectively, while the cytotoxicity effects values were greater than 25 μg/mL. A vacuole-like structure was observed in the TEM imaging of AZM treated T. congolense, while the presence of glycosomes and acidocalcisome-like structured were detected in T. b. brucei samples. In vivo, AZM was more effective against T. congolense infected mice than T. b. brucei. In conclusion, AZM exhibited the trypanocidal effects on T. congolense and T. b. brucei infected mice. However, further studies are necessary to determine the metabolic pathway responsible for the observed efficacy.

Keywords: animal trypanosomosis, azithromycin, oral administration, trypanosoma congolense

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Authors: Kanchan Yadav, Ai-Chuan Chou, Rajesh Kumar Ulaganathan, Hua-De Gao, Hsien-Ming Lee, Chien-Yuan Pan, Yit-Tsong Chen

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Optogenetics is an innovative technology now widely adopted by researchers in different fields of the biological sciences. However, due to the weak tissue penetration capability of the short wavelengths used to activate light-sensitive proteins, an invasive light guide has been used in animal studies for photoexcitation of target tissues. Upconverting nanoparticles (UCNPs), which transform near-infrared (NIR) light to short-wavelength emissions, can help address this issue. To improve optogenetic performance, we enhance the target selectivity for optogenetic controls by specifically conjugating the UCNPs with light-sensitive proteins at a molecular level, which shortens the distance as well as enhances the efficiency of energy transfer. We tagged V5 and Lumio epitopes to the extracellular N-terminal of channelrhodopsin-2 with an mCherry conjugated at the intracellular C-terminal (VL-ChR2m) and then bound NeutrAvidin-functionalized UCNPs (NAv-UCNPs) to the VL-ChR2m via a biotinylated antibody against V5 (bV5-Ab). We observed an apparent energy transfer from the excited UCNP (donor) to the bound VL-ChR2m (receptor) by measuring emission-intensity changes at the donor-receptor complex. The successful patch-clamp electrophysiological test and an intracellular Ca2+ elevation observed in the designed UCNP-ChR2 system under optogenetic manipulation confirmed the practical employment of UCNP-assisted NIR-optogenetic functionality. This work represents a significant step toward improving therapeutic optogenetics.

Keywords: Channelrhodopsin-2, near infrared, optogenetics, upconverting nanoparticles

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Authors: T. C. Machaba, S. M. Mahlo

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The current study investigates the antifungal properties of crude plant extracts from selected medicinal plant species. Eight plant species used by the traditional healers and local people to treat fungal infections were selected for further phytochemical analysis and biological assay. The selected plant species were extracted with solvent of various polarities such as acetone, methanol, ethanol, hexane, dichloromethane, ethyl acetate and water. Leaf, roots and bark extracts of Maerua juncea Pax, Albuca seineri (Engl & K. Krause) J.C Manning & Goldblatt, Senna italica Mill., Elephantorrhiza elephantina (Burch.) Skeels, Indigofera circinata Benth., Schinus molle L., Asparagus buchananii Bak., were screened for antifungal activity against three animal fungal pathogens (Candida albicans, Aspergillus fumigatus and Cryptococcus neoformans). All plant extracts were active against the tested microorganisms. Acetone, dichloromethane, hexane and ethanol extracts of Senna italica and Elephantorrhiza elephantine had excellent activity against Candida albicans and A. fumigatus with the lowest MIC value of 0.02 mg/ml. Bioautography assay was used to determine the number of antifungal compounds presence in the plant extracts. No active compounds were observed in plant extracts of Indigofera circinnata, Schinus molle and Pentarrhinum insipidum with good antifungal activity against C. albicans and A. fumigatus indicating possible synergism between separated metabolites.

Keywords: antifungal activity, bioautography, ethnobotanical survey, minimum inhibitory concentration

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Authors: Namfon Samsalee, Rungsinee Sothornvit

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Turmeric is a natural plant herb and generally extracted as essential oil and widely used in food, cosmetic, pharmaceutical products including insect repellent. However, turmeric oil is a volatile essential oil which is easy to be lost during storage or exposure to light. Therefore, biopolymers such as protein and polysaccharide can be used as wall materials to encapsulate the essential oil which will solve this drawback. Approximately 60% plasma from porcine blood contains 6-7% of protein content mainly albumin and globulin which can be a good source of animal protein at the low-cost biopolymer from by-product. Microencapsulation is a useful technique to entrap volatile compounds in the biopolymer matrix and protect them to degrade. The objective of this research was to investigate the different ratios of two biopolymers (PPP and maltodextrin; MD) as wall materials at 100:0, 75:25, 50:50, 25:75 and 0:100 at a fixed ratio of wall material: core material (turmeric oil) at 3:1 (oil in water) on the qualities of microencapsulated powder using freeze drying. It was found that the combination of PPP and MD showed higher solubility of microencapsules compared to the use of PPP alone (P < 0.05). Moreover, the different ratios of wall materials also affected on color (L*, a* and b*) of microencapsulated powder. Morphology of microencapsulated powder using a scanning electron microscope showed holes on the surface reflecting on free oil content and encapsulation efficiency of microencapsules. At least 50% of MD was needed to increase encapsulation efficiency of microencapsulates rather than using only PPP as the wall material (P < 0.05). Microencapsulated turmeric oil powder can be useful as food additives to improve food texture, as a biopolymer material for edible film and coating to maintain quality of food products.

Keywords: microencapsulation, turmeric oil, porcine plasma protein, maltodextrin

Procedia PDF Downloads 174

Authors: Rana Bilal, Faiz-Ul-Hassan, Moazzam Jameel

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There is a dire need to replace high-cost protein with more economical protein to overcome animal protein shortage in developing nations especially countries like Pakistan. In conjunction with these efforts, the current study was planned to evaluate the effects of various dried distillery yeast sludge (DYS) levels on the immune level, egg quality, and performance of layers by replacing soybean meal. The study was designed with two hundred layers of Hy-Line variety. Distillery yeast sludge was dried and ground for 2 mm mesh size and after this proximate and mineral analysis was determined. Five isocaloric and isonitrogeneous feeds were given containing C (control), 5, 10, 15, 20% distillery yeast sludge by replacing soybean meal. The trial was performed in the completely randomized design with five treatments, 4 replicates and 10 hen per replicate. Results demonstrated that feed intake, egg production, feed conversion ratio decreased (P < 0.05) with the increased dietary DYS. However, statistically significant decrease (P < 0.05) was found in hens having DYS20 diet than control. Layers on Diets C, DYS5 and DYS10 exerted a higher immune level than DYS15 and DYS20 diets. Egg weight, eggshell weight, eggshell thickness, egg albumen height as well as haugh unit score were affected significantly by the increased level of DYS. In general, results of this study demonstrated that inclusion of DYS up to 10% showed no adverse effects on health and performance of layers.

Keywords: egg quality, immunity, layers, performance

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Authors: Christodoulou Christos, Politis Anastasios

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The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: O. B. Oriolowo, O. J. John

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Empirical findings have shown insects to be nutritious and good source of food for man. However, human food preferences are not only determined by nutritional values of food consumed but, more importantly, by sociology and economic pressure. This study examined the interrelation between science and sociology in sustaining the acceptance of entomophagy among college students to combat food insecurity. A twenty items five Likert scale, College Students Entomophagy Questionnaire (CSEQ), was used to elucidate information from the respondents. The reliability coefficient was obtained to be 0.91 using Spearman-Brown Prophecy formula. Three research questions and three hypotheses were raised. Also, quantitative nutritional analysis of few insects and some established conventional protein sources were undertaking in order to compare their nutritional status. The data collected were analyzed using descriptive statistics of percentages and inferential statistics of correlation and Analysis of Variance (ANOVA). The results obtained showed that entomophagy has cultural heritage among different tribes in Nigeria and is an acceptable practice; it cuts across every social stratum and is practiced among both major religions. Moreover, insects compared favourably in term of nutrient contents when compared with the conventional animal protein sources analyzed. However, there is a gradual decline in the practice of entomophagy among students, which may be attributed to the influence of western civilization. This study, therefore, recommended an intensification of research and enlightenment of people on the usefulness of entomophagy so as to preserve its cultural heritage as well as boost human food security.

Keywords: entomophagy, food security, malnutrition, poverty alleviation, sociology

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Authors: Nathaniel Carpenter

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Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Daudi S. Simbeye, Mbazingwa E. Mkiramweni

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Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand.

Keywords: early identification, guava plants, fruit diseases, deep learning

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Authors: Juhi Faridi, Mohd. Ajmal Kafeel

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The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal circuits using memristors to achieve a Memristor based NCS.  Here we throw a light on the research conducted in the field of memristors for building analog and digital circuits in order to motivate the research in the field of NCS by building memristor based neural circuits for advanced AI applications. This literature is a step in the direction where we describe the various Key findings about memristors and its analog and digital circuits implemented over the years which can be further utilized in implementing the neuronal circuits in the NCS. This work aims to help the electronic circuit designers to understand how the research progressed in memristors and how these findings can be used in implementing the neuronal circuits meant for the recent progress in the NCS.

Keywords: analog circuits, digital circuits, memristors, neuromorphic computing systems

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: M. R. Akshaya, Veena Rao

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Pet population in India is increasing phenomenally owing to the changes in urban lifestyle with increasing number of single professionals, single parents, delayed parenthood etc. The animal companionship as a means of reducing stress levels, deriving emotional support, and unconditional love provided by dogs are a few reasons attributed for increasing pet ownership. The consequence is the booming of the pet care products and dog care centers catering to the different requirements of rearing the pets. Dog care centers quite popular in tier 1 metros of India cater to the requirement of the dog owners providing space for the dogs in absence of the owner. However, it is often reported that the absence of the owner leads to destructive and exploratory behavior issues; the main being the anxiety disorders. In the above context, it becomes imperative for a designer to design dog boarding centers that help in reducing the separation anxiety in dogs keeping in mind the different interior design parameters. An exploratory research with focus group discussion is employed involving a group of dog owners, behaviorists, proprietors of day care as well as boarding centers, and veterinarians to understand their perception on the significance of different interior parameters of color, texture, ventilation, aroma therapy and acoustics as a means of reducing the stress levels in dogs sent to the boarding centers. The data collected is organized as thematic networks thus enabling the listing of the interior design parameters that needs to be considered in designing dog boarding centers. 

Keywords: behavioral response, design parameters, dog boarding centers, interior environment

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Authors: Silvia Boschetti, Jakub Binter, Robin Kopecký, Lenka PříPlatová, Jaroslav Flegr

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A strict dichotomy was present between religion and science, but recently, cognitive science focusses on the impact of supernatural beliefs on cognitive processes such as pattern recognition. It has been hypothesized that cognitive and perceptual processes have been under evolutionary pressures that ensured amplified perception of patterns, especially when in stressful and harsh conditions. The pattern detection in religious and non-religious individuals after induction of negative, anxious mood shall constitute a cornerstone of the general role of anxiety, cognitive bias, leading towards or against the by-product hypothesis, one of the main theories on the evolutionary studies of religion. The apophenia (tendencies to perceive connection and meaning on unrelated events) and perception of visual patterns (or pateidolia) are of utmost interest. To capture the impact of culture and upbringing, a comparative study of two European countries, the Czech Republic (low organized religion participation, high esoteric belief) and Italy (high organized religion participation, low esoteric belief), are currently in the data collection phase. Outcomes will be presented at the conference. A battery of standardized questionnaires followed by pattern recognition tasks (the patterns involve color, shape, and are of artificial and natural origin) using an experimental method involving the conditioning of (controlled, laboratory-induced) stress is taking place. We hypothesize to find a difference between organized religious belief and personal (esoteric) belief that will be alike in both of the cultural environments.

Keywords: culture, esoteric belief, pattern perception, religiosity

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Authors: Karthika Durairaj, Buvaneswari G., Gowdham M., Gilles M., Velmurugan G.

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Background: Hyperglycaemia is the primary cause of metabolic illness. Recently, researchers focused on the possibility that chemical exposure could promote metabolic disease. Hyperglycaemia causes a variety of metabolic diseases dependent on its etiologic conditions. According to animal and population-based research, individual chemical exposure causes health problems through alteration of endocrine function with the influence of microbial influence. We were intrigued by the function of gut microbiota variation in high fat and chemically induced hyperglycaemia. Methodology: C57/Bl6 mice were subjected to two different treatments to generate the etiologic-based diabetes model: I – a high-fat diet with a 45 kcal diet, and II - endocrine disrupting chemicals (EDCs) cocktail. The mice were monitored periodically for changes in body weight and fasting glucose. After 120 days of the experiment, blood anthropometry, faecal metagenomics and metabolomics were performed and analyzed through statistical analysis using one-way ANOVA and student’s t-test. Results: After 120 days of exposure, we found hyperglycaemic changes in both experimental models. The treatment groups also differed in terms of plasma lipid levels, creatinine, and hepatic markers. To determine the influence on glucose metabolism, microbial profiling and metabolite levels were significantly different between groups. The gene expression studies associated with glucose metabolism vary between hosts and their treatments. Conclusion: This research will result in the identification of biomarkers and molecular targets for better diabetes control and treatment.

Keywords: hyperglycaemia, endocrine-disrupting chemicals, gut microbiota, host metabolism

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Authors: Salah Hadjout, Mohamed Zouidi

Abstract:

In Algeria, several works carried out in recent years have shown the importance of fusarium head blight in durum wheat. Indeed, this disease is caused by a complex of Fusarium genus pathogens. The research carried out reports that F. culmorum is the main species infecting cereals. These informations motivated our interest in the field evaluation of the behavior of some durum wheat genotypes (parental varieties and lines) with regard to fusarium head blight, mainly caused by four F. culmorum isolates. Our research work focused on following the evolution of symptom development throughout the grain filling, after artificial inoculation of ears by Fusarium isolates in order to establish a first image on the differences in genotype behavior to fusarium haed blight. Field disease assessment criteria are: disease assessment using a grading scale, thousand grain weight measurement and AUDPC. The results obtained revealed that the varieties and lines resulting from crosses had a quite different level of sensitivity to F. culmorum species and no genotype showed complete resistance in our culture conditions. Among the material tested, some lines showed higher resistance than their parents. The results also show a slight behavioral variability also linked to the aggressiveness of the Fusarium species studied in this work. Our results open very important research perspectives on fusarium head blight, in particular the search for toxins produced by Fusarium species.

Keywords: fusarium head blight, durum wheat, Fusarium culmorum, field disease assessment criteria, Algeria

Procedia PDF Downloads 87

Authors: Seung Ju Yeon, Seul Ki Min, Jun Sang Park, Yeo Seon Kwon, Hoo Cheol Lee, Hyun Jung Shim, Il-Doo Kim, Ja Kyeong Lee, Hwa Sung Shin

Abstract:

In brain injury, depleting oxidative stress is the most effective way to reduce the brain infarct size. C-phycocyanin (C-Pc) is a well-known antioxidant protein that has neuroprotective effects obtained from green microalgae. Astrocyte is glial cell that supports the nerve cell such as neuron, which account for a large portion of the brain. In brain injury, such as ischemia and reperfusion, astrocyte has an important rule that overcomes the oxidative stress and protect from brain reactive oxygen species (ROS) injury. However little is known about how C-Pc regulates the anti-oxidants effects of astrocyte. In this study, when the C-Pc was treated in oxidized astrocyte, we confirmed that inflammatory factors Interleukin-6 and Interleukin-3 were increased and antioxidants enzyme, Superoxide dismutase (SOD) and catalase was upregulated, and neurotrophic factors, brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) was alleviated. Also, it was confirmed to reduce infarct size of the brain in ischemia and reperfusion because C-Pc has anti-oxidant effects in middle cerebral artery occlusion (MCAO) animal model. These results show that C-Pc can help astrocytes lead neuroprotective activities in the oxidative stressed environment of the brain. In summary, the C-PC protects astrocytes from oxidative stress and has anti-oxidative, anti-inflammatory, neurotrophic effects under ischemic situations.

Keywords: c-phycocyanin, astrocyte, reactive oxygen species, ischemia and reperfusion, neuroprotective effect

Procedia PDF Downloads 305

Authors: Sintayehu W. Dejene, Ignas M. A. Heitkönig, Herbert H. T. Prins, Zewdu K. Tessema, Willem F. de Boer

Abstract:

Current theories on diversity-disease relationships describe host species diversity and species identity as important factors influencing disease risk, either diluting or amplifying disease prevalence in a community. Whereas the simple term ‘diversity’ embodies a set of animal community characteristics, it is not clear how different measures of species diversity are correlated with disease risk. We, therefore, tested the effects of species richness, Pielou’s evenness and Shannon’s diversity on bTB risk in cattle in the Afar Region and Awash National Park between November 2013 and April 2015. We also analysed the identity effect of a particular species and the effect of host habitat use overlap on bTB risk. We used the comparative intradermal tuberculin test to assess the number of bTB infected cattle. Our results suggested a dilution effect through species evenness. We found that the identity effect of greater kudu - a maintenance host – confounded the dilution effect of species diversity on bTB risk. bTB infection was positively correlated with habitat use overlap between greater kudu and cattle. Different diversity indices have to be considered together for assessing diversity-disease relationships, for understanding the underlying causal mechanisms. We posit that unpacking diversity metrics is also relevant for formulating control strategies to manage cattle in ecosystems characterized by seasonally limited resources and intense wildlife-livestock interactions.

Keywords: evenness, diversity, greater kudu, identity effect, maintenance hosts, multi-host disease ecology, habitat use overlap

Procedia PDF Downloads 316

Authors: Lindiwe Ngcobo

Abstract:

The aim of the study is to describe and understand the benefits and costs of having biogas digesters at both household and society level. On a household level, the purpose is to understand how rural households benefit from the biogas digesters, for example, by converting animal and human waste through biogas digesters, and at what costs the benefits are realized. At a societal level, the purpose is to understand the costs and benefits of biogas digesters relative to the situation of rural communities who do not have flush toilets and have no appropriate waste disposal services while they incur electricity costs. Multiple regression analysis was used to determine the effect of biogas digesters on electricity availability and waste management. The results showed that beneficiaries spent less on electricity using household waste, and also waste disposal costs were eliminated from household expenses. A move to biogas energy production can be beneficial to rural households. It is economically and environmentally friendly. Small-scale farmers need to be introduced to agricultural innovations that can assist them in producing nutritious crops at a low cost. This can be a good opportunity to start an agribusiness that focuses on organic crops. Extensions and training institutions have to play a part in supporting households to develop entrepreneurial skills. Cost-benefit analysis showed that the benefits of biogas exceed the costs of the biogas projects. This implies that this technology should be promoted in rural households. Government financial incentives must be put in place to motivate a generation of organic Agri-prenuers.

Keywords: Agri-prenuers, biogas digester, biogas energy, disposal costs

Procedia PDF Downloads 121

Authors: Sumra Siddique Abbasi, Cheng Shikun

Abstract:

Biogas technology has rapidly developed in agriculture sector to upgrade and improve the life of farmers by providing them alternative and cost-effective energy source. Main purpose of this study is to understand the advantages of biogas plants by livestock owners either they are household-based livestock owners or may own farms for livestock. Similarly, a pertinent and major purpose of this research is to examine the factors affecting the decision to adopt biogas technologies at the household level. Based on the result, both public and private sector organization can make decisions related to the installation of biogas projects. Biogas is major energy source which can be used as an alternative and renewable energy source. This energy production technology can contribute in uplifting the lifestyle of farmers and can contribute into sustainable development of rural communities in Pakistan. People with livestock in any community in Pakistan can get benefit from biogas plants and it will contribute in sustainable development program which generates socio economic benefits, heath upgradation, cost effective energy source and positive impact on climate change or environmental issues. This study was conductive using survey method and descriptive analysis. One hundred fifty (150) farmers were the respondents who participated in survey. These farmers were from Layyah district of Punjab and were selected using snowball sampling technique. To generate the results, SPSS tool was used for data analysis.

Keywords: biogas plant, animal dunk, renewable energy, pakistan

Procedia PDF Downloads 49

Authors: Santosh Thapa, Sankar Gurung

Abstract:

Chaupadi is a tradition widely practiced in the far and mid-western region of Nepal. It is a practice where girls and women are not allowed to inter the house and touch the food, water and milk during their menstruation period of 4-7 days. They have to spend all the nights during the period in a specific hut (Chhaupadi Goth) which is a bit far from their residence where they faces various kinds of risk and violence like bullying, snakes and insect bite, wild animal attack etc. Sometimes the girls even do not go to school during their menstruation periods. After childbirth, the woman must stay in a cow shed for 11 days in such Chhaupadi practiced areas. This study limits the Achham district of the far western region which is the most vulnerable Chhaupadi practicing district. Several governmental and non-governmental organizations have been involving and spending huge amount of money for capacity building and awareness raising campaign for last 2 decades but still 9 out of 75 Village Development Committees (VDCs) have been partially practicing Chaupadi in the district. This study shows that the school attendance rate of the girls during the period have visibly increased which helps to increase the number of the girl graduation as well. Similarly, the practice of Chhaupadi is one of the reasons for increasing the number of cases of uterus prolapsus and poor reproductive health of women and girls. Triggering tools are the one of the best ways to accelerate the awareness campaign in the VDCs. This study recommends that the local bodies should coordinate and lead the overall awareness campaign program to sustain the Chaupadi free VDCs.

Keywords: awareness campaign, chaupadi practice, gender discrimination, violence

Procedia PDF Downloads 295

Authors: Kyou Hee Shim, Hwa Sung Shin

Abstract:

When a thrombolysis agent is administered to treat ischemic stroke, excessive reactive oxygen species are generated due to a sudden provision of oxygen and occurs secondary damage cell necrosis. Thus, it is necessary to administrate adjuvant as well as thrombolysis agent to protect and reduce damaged tissue. As cerebral blood vessels have specific structure called blood-brain barrier (BBB), it is not easy to transfer substances from blood to tissue. Therefore, development of a drug carrier is required to increase drug delivery efficiency to brain tissue. In this study, cyanobacterial pigment from the blue-green algae known for having neuroprotective effect as well as antioxidant effect was nasally administrated for bypassing BBB. In order to deliver cyanobacterial pigment efficiently, the nano-sized liposome was used as a carrier. Liposomes were coated with a positive charge of chitosan since negative residues are present at the nasal mucosa the first gateway of nasal administration. Characteristics of liposome including morphology, size and zeta potential were analyzed by transmission electron microscope (TEM) and zeta analyzer. As a result of cytotoxic test, the liposomes were not harmful. Also, being administered a drug to the ischemic stroke animal model, we could confirm that the pharmacological effect of the pigment delivered by chitosan coated liposome was enhanced compared to that of non-coated liposome. Consequently, chitosan coated liposome could be considered as an optimized drug delivery system for the treatment of acute ischemic stroke.

Keywords: ischemic stroke, cyanobacterial pigment, liposome, chitosan, nasal administration

Procedia PDF Downloads 216