Search results for: artificial potential function

Authors: M. A. Ahmed, Sidra A. Shaikh, M. W. Akhtar

Abstract:

Solar radiation on horizontal surface over three southern cities of Sindh, namely Karachi, Hyderabad and Nawabshah has been investigated to asses the feasibility of solar energy application for power generation. In the present work, measured data of bright sunshine hour of the region have been used to estimate the global and diffuse solar radiation. The regression coefficient 'a' and 'b' have been calculated using first order Angstrom type co-relation. The result obtained shows that the contribution of direct solar radiation is low and diffuse radiation is high during the monsoon months July and August for Karachi and Hyderabad. The sky remains clear from September to June, whereas for Nawabshah the global radiation remains high throughout the year. The potential of grid quality solar photovoltaic power in Karachi is estimated for 10 square meter area of solar panel.

Keywords: solar potential over Sindh, global and diffuse solar radiation, radiation over three cities of Sindh, solar panels

Procedia PDF Downloads 426

Authors: M. Zemouli, K. Amara, M. Elkeurti, Y. Benallou

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We present a theoretical study of the structural, elastic and thermodynamic properties of the zinc-blende AlBi for a wide temperature range. The simulation calculation is performed in the framework of the molecular dynamics method using the three-body Tersoff potential which reproduces provide, with reasonable accuracy, the lattice constants and elastic constants. Our results for the lattice constant, the bulk modulus and cohesive energy are in good agreement with other theoretical available works. Other thermodynamic properties such as the specific heat and the lattice thermal expansion can also be predicted. In addition, this method allows us to check its ability to predict the phase transition of this compound. In particular, the transition pressure to the rock-salt phase is calculated and the results are compared with other available works.

Keywords: aluminium compounds, molecular dynamics simulations, interatomic potential, thermodynamic properties, structural phase transition

Procedia PDF Downloads 291

Authors: Zhipeng Chen, Lingfeng Wang, Shuang Qiu, Shijian Ge

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Macroalgae are larger in size compared with microalgae; hence, they imposed lower separation and drying costs. To explore the potential for enhancing cultivation conditions in macroalgae Chaetomorpha linum (C. linum)-based bioreactor for nutrient recovery from municipal wastewaters and examine the biochemical composition of the macroalgae for the potential downstream production of biofuels, screening experiments were performed. This study suggested that C. linum grew well on primary (PW), secondary (SW), and centrate wastewater (CW). A step feeding approach was shown to significantly enhance biomass productivity when grown on 10% CW; meanwhile, nitrogen and phosphorus removal efficiencies increased to 86.8 ± 1.1% and 92.6 ± 0.2%, respectively. The CO₂-supplemented SW cultures were 1.20 times more productive than the corresponding controls without CO₂ supplementation. These findings demonstrate that C. linum could represent a promising and efficient wastewater treatment alternative which could also provide a feedstock for downstream processing to biofuels.

Keywords: biofuel production, macroalgae, nutrient removal, wastewater

Procedia PDF Downloads 149

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 103

Authors: Rajendra Kumar

Abstract:

We study the Electronic spectral density of a double coupled quantum dots sandwich between superconducting leads, where one of the superconducting leads (QD1) are connected with left superconductor lead and (QD1) also connected right superconductor lead. (QD1) and (QD2) are coupling to each other. The electronic spectral density through a quantum dots between superconducting leads having s-wave symmetry of the superconducting order parameter. Such junction is called superconducting –quantum dot (S-QD-S) junction. For this purpose, we have considered a renormalized Anderson model that includes the double coupled of the superconducting leads with the quantum dots level and an attractive BCS-type effective interaction in superconducting leads. We employed the Green’s function technique to obtain superconducting order parameter with the BCS framework and Ambegaoker-Baratoff formalism to analyze the electronic spectral density through such (S-QD-S) junction. It has been pointed out that electronic spectral density through such a junction is dominated by the attractive the paring interaction in the leads, energy of the level on the dot with respect to Fermi energy and also on the coupling parameter of the two in an essential way. On the basis of numerical analysis we have compared the theoretical results of electronic spectral density with the recent transport existing theoretical analysis. QDs is the charging energy that may give rise to effects based on the interplay of Coulomb repulsion and superconducting correlations. It is, therefore, an interesting question to ask how the discrete level spectrum and the charging energy affect the DC and AC Josephson transport between two superconductors coupled via a QD. In the absence of a bias voltage, a finite DC current can be sustained in such an S-QD-S by the DC Josephson effect.

Keywords: quantum dots, S-QD-S junction, BCS superconductors, Anderson model

Procedia PDF Downloads 359

Authors: Jeena Gupta

Abstract:

In the today’s world of pharmaceutical products, one should not forget the healing properties of inexpensive food materials especially spices. They are known to possess hidden pharmaceutical ingredients, imparting them the qualities of being anti-microbial, anti-oxidant, anti-inflammatory and anti-carcinogenic. Further aberrant epigenetic regulatory mechanisms like DNA methylation, histone modifications or altered microRNA expression patterns, which regulates gene expression without changing DNA sequence, contribute significantly in the development of various diseases. Changing lifestyles and diets exert their effect by influencing these epigenetic mechanisms which are thus the target of dietary phytochemicals. Bioactive components of plants have been in use since ages but their potential to reverse epigenetic alterations and prevention against diseases is yet to be explored. Spices being rich repositories of many bioactive constituents are responsible for providing them unique aroma and taste. Some spices like curcuma and garlic have been well evaluated for their epigenetic regulatory potential, but for others, it is largely unknown. We have evaluated the biological activity of phyto-active components of Fennel, Cardamom and Fenugreek by in silico molecular modeling, in vitro and in vivo studies. Ligand-based similarity studies were conducted to identify structurally similar compounds to understand their biological phenomenon. The database searching has been done by using Fenchone from fennel, Sabinene from cardamom and protodioscin from fenugreek as a query molecule in the different small molecule databases. Moreover, the results of the database searching exhibited that these compounds are having potential binding with the different targets found in the Protein Data Bank. Further in addition to being epigenetic modifiers, in vitro study had demonstrated the antimicrobial, antifungal, antioxidant and cytotoxicity protective effects of Fenchone, Sabinene and Protodioscin. To best of our knowledge, such type of studies facilitate the target fishing as well as making the roadmap in drug design and discovery process for identification of novel therapeutics.

Keywords: epigenetics, spices, phytochemicals, fenchone

Procedia PDF Downloads 144

Authors: Modupe O. Jimoh

Abstract:

Waterfalls create an ambient environment for tourism and relaxation. They are also potential sources for water supply. Owu waterfall located at Isin Local Government, Kwara state, Nigeria is the highest waterfall in the West African region, yet none of its potential usefulness has been fully exploited. Water samples were taken from two sections of the fall and were analyzed for various water quality parameters. The results obtained include pH (6.71 ± 0.1), Biochemical oxygen demand (4.2 ± 0.5 mg/l), Chemical oxygen demand (3.07 ± 0.01 mg/l), Dissolved oxygen (6.59 ± 0.6 mg/l), Turbidity (4.43 ± 0.11 NTU), Total dissolved solids (8.2 ± 0.09 mg/l), Total suspended solids (18.25 ± 0.5 mg/l), Chloride ion (0.48 ± 0.08 mg/l), Calcium ion (0.82 ± 0.02 mg/l)), Magnesium ion (0.63 ± 0.03 mg/l) and Nitrate ion (1.25 ± 0.01 mg/l). The results were compared to the World Health Organisations standard for drinking water and the Nigerian standard for drinking water. From the comparison, it can be deduced that due to the Biochemical oxygen demand value, the water is not suitable for drinking unless it undergoes treatment. However, it is suitable for other classes of water usage.

Keywords: Owu falls, waterfall, water quality, water quality parameters, water use

Procedia PDF Downloads 161

Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

Procedia PDF Downloads 465

Authors: Donghyun Lee, Cam Nguyen

Abstract:

Multi-band systems offer a great deal of benefit in modern communication and radar systems. In particular, multi-band antenna-array radar systems with their extended frequency diversity provide numerous advantages in detection, identification, locating and tracking a wide range of targets, including enhanced detection coverage, accurate target location, reduced survey time and cost, increased resolution, improved reliability and target information. An accurate calibration is a critical issue in antenna array systems. The amplitude and phase errors in multi-band and multi-polarization antenna array transceivers result in inaccurate target detection, deteriorated resolution and reduced reliability. Furthermore, the digital beam former without the RF domain phase-shifting is less immune to unfiltered interference signals, which can lead to receiver saturation in array systems. Therefore, implementing integrated front-end architecture, which can support calibration function with low insertion and filtering function from the farthest end of an array transceiver is of great interest. We report a dual K/Ka-band T/R/Calibration switch module with quasi-elliptic dual-bandpass filtering function implementing a Q-enhanced metamaterial transmission line. A unique dual-band frequency response is incorporated in the reception and calibration path of the proposed switch module utilizing the composite right/left-handed meta material transmission line coupled with a Colpitts-style negative generation circuit. The fabricated fully integrated T/R/Calibration switch module in 0.18-μm BiCMOS technology exhibits insertion loss of 4.9-12.3 dB and isolation of more than 45 dB in the reception, transmission and calibration mode of operation. In the reception and calibration mode, the dual-band frequency response centered at 24.5 and 35 GHz exhibits out-of-band rejection of more than 30 dB compared to the pass bands below 10.5 GHz and above 59.5 GHz. The rejection between the pass bands reaches more than 50 dB. In all modes of operation, the IP1-dB is between 4 and 11 dBm. Acknowledgement: This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: microwaves, millimeter waves, T/R switch, wireless communications, wireless communications

Procedia PDF Downloads 149

Authors: Pranav Gunhal., Krish Jhurani

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This study tackles the significant computational challenge of predicting excited state dynamics in organic photovoltaic (OPV) materials—a pivotal factor in the performance of solar energy solutions. Time-dependent density functional theory (TDDFT), though effective, is computationally prohibitive for larger and more complex molecules. As a solution, the research explores the application of transformer neural networks, a type of artificial intelligence (AI) model known for its superior performance in natural language processing, to predict excited state dynamics in OPV materials. The methodology involves a two-fold process. First, the transformer model is trained on an extensive dataset comprising over 10,000 TDDFT calculations of excited state dynamics from a diverse set of OPV materials. Each training example includes a molecular structure and the corresponding TDDFT-calculated excited state lifetimes and key electronic transitions. Second, the trained model is tested on a separate set of molecules, and its predictions are rigorously compared to independent TDDFT calculations. The results indicate a remarkable degree of predictive accuracy. Specifically, for a test set of 1,000 OPV materials, the transformer model predicted excited state lifetimes with a mean absolute error of 0.15 picoseconds, a negligible deviation from TDDFT-calculated values. The model also correctly identified key electronic transitions contributing to the excited state dynamics in 92% of the test cases, signifying a substantial concordance with the results obtained via conventional quantum chemistry calculations. The practical integration of the transformer model with existing quantum chemistry software was also realized, demonstrating its potential as a powerful tool in the arsenal of materials scientists and chemists. The implementation of this AI model is estimated to reduce the computational cost of predicting excited state dynamics by two orders of magnitude compared to conventional TDDFT calculations. The successful utilization of transformer neural networks to accurately predict excited state dynamics provides an efficient computational pathway for the accelerated discovery and design of new OPV materials, potentially catalyzing advancements in the realm of sustainable energy solutions.

Keywords: transformer neural networks, organic photovoltaic materials, excited state dynamics, time-dependent density functional theory, predictive modeling

Procedia PDF Downloads 98

Authors: R. Cooper, M. Kurien

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Background: Coeliac Disease affects around 1 in 100 people. Untreated, it can result in serious morbidity such as malabsorption and cancers. The only treatment is to adhere to a gluten free diet (GFD). International guidelines recommend that people with the coeliac disease receive follow-up healthcare annually to detect complications early and support their adherence to a GFD. However, there is a finite amount of healthcare in the UK, and as such, not all patients receive follow-up care as recommended by the guidelines. Furthermore, there is an increasing number of patients being diagnosed with coeliac disease. Given the potential severe morbidity that non-adherence to a GFD could result in, alongside reports that the rate of non- GFD adherence could be as high as 91%, it is imperative that action is taken. One potential solution to this would be to provide follow-up care digitally through utilising technology. This abstract reports on a rapid review undertaken to explore the existing evidence in this area. Methods: In June 2020, 11 bibliographic databases were searched to find any pertinent studies. The inclusion criteria required the study to be written in the English language and report on the use of digital healthcare interventions for people with Coeliac Disease. Results: A small amount of evidence (n=8) was found which met our inclusion criteria and pertained to the provision of CD follow-up digitally. These studies focussed either on educating and supporting patients to adhere to a GFD or providing consultation remotely with a focus on detecting complications early. These studies showed that there is potential for digital healthcare interventions to positively impact people with coeliac disease. However, it is suggested that the effectiveness of these interventions may depend on local circumstances, individual knowledge of CD and general attitudes. Conclusion: The above studies suggest that providing follow-up care digitally may offer a potential solution; however, the evidence about how this should be done and in what circumstances this will work for individuals is scarce. In the light of the COVID-19 pandemic, the introduction of digital healthcare interventions appears to be highly topical, and as such, this review may benefit from being refreshed in the future.

Keywords: coeliac disease, follow-up, gluten free diet, digital healthcare interventions

Procedia PDF Downloads 160

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

Procedia PDF Downloads 30

Authors: Vasudha Devi, Arul Amuthan, K. Narayanan, Praveen KS, Venkata Rao J

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Background: Siddha Medicine is one of the Indian traditional medical systems, in which the cancer disease is mentioned as 'putrunoi' which literally means the disease of growth like termite mound. There are number of formulations available for the treatment of cancer disease. Neeradi muthu vallathymezugu (NMV) and thamira kattu chendooram (TKC) are two drugs commonly prescribed by Siddha physicians. These drugs have been clinically reported to be safe and effective when given orally. Though these formulations are in practice for centuries, no efforts have been made to standardize them and explore their anti-cancer potential systematically. Objective: To compare the cytotoxic activity of NMV and TKC with doxorubicin using cancer cell lines. Materials and methods: For this study, ethanol extract of NMV was taken, whereas TKC was used as such. In vitro cytotoxic activity was evaluated by sulphorhodamine (SRB) assay against human hepatic cancer cells (HepG2), human breast cancer cells (MCF-7) and human cervical cancer cells [KeLa]. Doxorubicin was used as the standard. The SRB assay is based on the ability of cellular proteins to bind with sulphorhodamine-B. The number of live cells in drug treated cell lines directly affects the color formation in the assay, which is estimated calorimetrically by measuring the absorbance at 540 nm to calculate the cytotoxicity (inhibitory concentration - IC50 value) of the drug. Results: The IC50values of NMV, TKC and doxorubicin against HepG2 were 3.08 µg/ml, 20.21 µg/ml and 1.21µg/ml respectively. In MCF-7, it was 11.75 µg/ml, 17.67 µg/ml and 2.8µg/ml. In HeLa, the values were 24.76 µg/ml, 73.35 µg/ml and 1.12µg/ml. Conclusions: The study proves the possible anti-cancer potential of these two formulations. Compared to TKC, NMV showed good cytotoxic effect even at low dose. Human hepatic cancer cells responded well even at very low dose, when compared to other cancer cells. Though, cytotoxic potential of these compounds was found to be less compared to doxorubicin, the isolated lead compound may have the potential to be used as an anticancer drug clinically.

Keywords: Neeradi muthu vallathymezugu (Hydnocarpus laurifolia), thamira kattu chendooram, cytotoxicity, in-vitro, Siddha Medicine

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Authors: Hussnain Mukhtar, Yu-Pin Lin

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This paper deals with the concept of ecologically engineered system for sustainable agriculture production with the view of sustainable cities development. Sustainable cities offer numerous eco-services to its inhabitants, and where, among other issues, wastewater nutrients can be considered to be a valuable resource to be used for a sustainable enhancement of urban agriculture in wetlands. Existing cities can be transferred from being only consumer of food and other agriculture product into important resource conserving and sustainable generators of these products. The review provides the food production capacity through introduction of wastewater into urban wetlands, potential for nutrient recovery and ecological engineering intervention to reduce the risk of food contamination by pathogens. Finally, we discuss the potential nutrients accumulating in our cities, as an important aspect of sustainable urban development.

Keywords: ecological engineering, nutrient recovery, pathogens, urban agriculture, wetlands

Procedia PDF Downloads 242

Authors: Yulistiani, Muhammad Amin, Fasich

Abstract:

A surface charge of the nanoparticle is a very important consideration in pulmonal drug delivery system. The zeta potential (ZP) is related to the surface charge which can predict stability of nanoparticles as nebules of liposomal amikacin. Anionic lipid such as 1,2-dipalmitoyl-sn-glycero-3-phosphatidylglycerol (DPPG) is expected to contribute to the physical stability of liposomal amikacin and the optimal ZP value. Suitable ZP can improve drug release profiles at specific sites in alveoli as well as their stability in dosage form. This study aimed to analyze the effect of DPPG on ZP values and physical stability of liposomal amikacin. Liposomes were prepared by using the reserved phase evaporation method. Liposomes consisting of DPPG, 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC), cholesterol and amikacin were formulated in five different compositions 0/150/5/100, 10//150/5/100, 20/150/5/100, 30/150/5/100 and 40/150/5/100 (w/v) respectively. A chloroform/methanol mixture in the ratio of 1 : 1 (v/v) was used as solvent to dissolve lipids. These systems were adjusted in the phosphate buffer at pH 7.4. Nebules of liposomal amikacin were produced by using the vibrating nebulizer and then characterized by the X-ray diffraction, differential scanning calorimetry, particle size and zeta potential analyzer, and scanning electron microscope. Amikacin concentration from liposome leakage was determined by the immunoassay method. The study revealed that presence of DPPG could increase the ZP value. The addition of 10 mg DPPG in the composition resulted in increasing of ZP value to 3.70 mV (negatively charged). The optimum ZP value was reached at -28.780 ± 0.70 mV and particle size of nebules 461.70 ± 21.79 nm. Nebulizing process altered parameters such as particle size, conformation of lipid components and the amount of surface charges of nanoparticles which could influence the ZP value. These parameters might have profound effects on the application of nebules in the alveoli; however, negatively charge nanoparticles were unexpected to have a high ZP value in this system due to increased macrophage uptake and pulmonal clearance. Therefore, the ratio of liposome 20/150/5/100 (w/v) resulted in the most stable colloidal system and might be applicable to pulmonal drug delivery system.

Keywords: anionic lipid, dipalmitoylphosphatidylglycerol, liposomal amikacin, stability, zeta potential

Procedia PDF Downloads 329

Authors: Michelle Micah Augustine

Abstract:

Soap opera narrative creates a sense of community. Uzalo is a South African local soap opera television series. It is unique because Uzalo tells the story of black people and their everyday struggle centered in KwaMashu township community, which is an excellent example of how moving image culture has contributed in portraying township community that was once marginalized by the apartheid regime in contemporary South Africa. While soap opera importance and promotion of social change and behaviours have been extensively studied throughout history, little research has examined the importance of space and place in its narrative. This study explored the conventional community space and place, the core elements that drive soap opera narrative. By means of qualitative content analysis, the study investigated the construction of public meeting places in Uzalo, using a purposive sampling technique to collect data by choosing episodes. The result indicates that characters convergence in public meeting places in soap opera creates disequilibrium which drives the narrative; reveals that construction of a public meeting place is an important way of creating a minimum of homogeneousness among disparate characters, gives a sense of unified experience drawing on the notion of the particular characteristics or attitude generated from such place. The result shows that the use of camera angles, movements, editing, music and usual tricks (mise-en-scene) applied in the narrative setting function as a guide for viewers comprehension of emotional responses of the story and to connect with the space in which the narrative is set.

Keywords: community, narrative, place, space, soap opera

Procedia PDF Downloads 131

Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

Procedia PDF Downloads 434

Authors: A.Zitouni, S.Bentata, B.Bouadjemi, T.Lantri, W. Benstaali, Z.Aziz, S.Cherid

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We investigate the structural, electronic and magnetic properties of the diluted magnetic semiconductors (DMSs) CdCoTe and CdMnTe in the zinc blende phase with 25% of Co and Mn. The calculations are performed by the recent ab initio full potential augmented plane waves (FP_L/APW) method within the spin polarized density-functional theory (DFT) and the generalized gradient approximation GGA. Structural properties are determined from the total energy calculations and we found that these compounds are stable in the ferromagnetic phase. We discuss the electronic structures, total and partial densities of states and total magnetic moments. The calculated densities of states presented in this study identify the half-metallic of CdCoTe and CdMnTe.

Keywords: electronic structure, half-metallic, magnetic moment, total and partial densities of states

Procedia PDF Downloads 479

Authors: Rahil Sahragard, Mahmoud Hatami, Rostam Bahadori Khalili

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Diabetes is one of the most important health problems in the world and a chronic disease requiring continuous care and therefore, it is necessary for patients to undergo self-care and nutrition education. This study was conducted to evaluate the effect of nutrition education on the metabolic control of diabetic patients in Tehran in 2015. An experimental study was conducted on 100 patients who had previously been approved by a specialist physician for diabetes and at least one year after their onset. At first, patients without any knowledge of the educational program were selected as sample and from them a checklist containing demographic and specific information about diabetes was filled and were taken three fasting blood glucose and three times fasting blood glucose (5 p.m.) Then, the patients received face-to-face training in the same conditions for 2 weeks in a Mehregan hospital of Tehran, and received 3 months of training, while they were fully monitored and during this time, samples that had a cold or blood pressure-related disease or were admitted to the hospital were excluded from the study. After the end of the study, the checklist was filled again and 3 fasting blood glucose and 3 fasting blood glucose samples were taken, the results were statistically analyzed by MC Nemar's statistical test. The research findings were performed on 100 patients 41.7% male and 58.3% women, the range of age was between 22 and 60 years old, with a duration of diabetes ranging from 1 to 15 years. Abnormal fasting blood glucose from 95% to 48.3% (P <0.0001) and non-fasting blood glucose decreased from 91.6% to 71.2% (P <0.001). Research has shown that training on blood glucose control has been successful, therefore, it is recommended that more research is done in the field of education to help patients with diabetes more comfortable.

Keywords: nutrition education, diabetes, function, insulin, chronic, metabolic control

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Authors: Jordan Chamarande, Lisiane Cunat, Corentine Alauzet, Catherine Cailliez-Grimal

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Gut microbiota (GM) is now considered a new organ mainly due to the microorganism’s specific biochemical interaction with its host. Although mechanisms underlying host-microbiota interactions are not fully described, it is now well-defined that cell surface molecules and structures of the GM play a key role in such relation. The study of surface structures of GM members is also fundamental for their role in the establishment of species in the versatile and competitive environment of the digestive tract and as a potential virulence factor. Among these structures are capsular polysaccharides (CPS), fimbriae, pili and lipopolysaccharides (LPS), all well-described for their central role in microorganism colonization and communication with host epithelium. The health-promoting Parabacteroides distasonis, which is part of the core microbiome, has recently received a lot of attention, showing beneficial properties for its host and as a new potential biotherapeutic product. However, to the best of the authors’ knowledge, the cell surface molecules and structures of P. distasonis that allow its maintain within the GM are not identified. Moreover, although P. distasonis is strongly recognized as intestinal commensal species with benefits for its host, it has also been recognized as an opportunistic pathogen. In this study, we reported gene clusters potentially involved in the synthesis of the capsule, fimbriae-like and pili-like cell surface structures in 26 P. distasonis genomes and applied the new RfbA-Typing classification in order to better understand and characterize the beneficial/pathogenic behaviour related to P. distasonis strains. In context, 2 different types of fimbriae, 3 of pilus and up to 14 capsular polysaccharide loci, have been identified over the 26 genomes studied. Moreover, the addition of data to the rfbA-Type classification modified the outcome by rearranging rfbA genes and adding a fifth group to the classification. In conclusion, the strain variability in terms of external proteinaceous structure could explain the inter-strain differences previously observed in P. distasonis adhesion capacities and its potential pathogenicity.

Keywords: gut microbiota, Parabacteroides distasonis, capsular polysaccharide, fimbriae, pilus, O-antigen, pathogenicity, probiotic, comparative genomics

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Authors: Julian David Hunt, Andreas Nascimento

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Green hydrogen is the most environmental, renewable alternative to produce hydrogen. However, an important challenge to make hydrogen a competitive energy carrier is a constant supply of renewable energy, such as solar, wind and hydropower. Given that the electricity generation potential of these sources vary seasonally and interannually, this paper proposes installing an electrolysis hydrogen production plant in a ship and move the ship to the locations where electricity is cheap, or where the seasonal potential for renewable generation is high. An example of electrolysis ship application is to produce green hydrogen with hydropower from the North region of Brazil and then sail to the Northeast region of Brazil and generate hydrogen using excess electricity from offshore wind power. The electrolysis ship concept is interesting because it has the flexibility to produce green hydrogen using the cheapest renewable electricity available in the market.

Keywords: green hydrogen, electrolysis ship, renewable energies, seasonal variations

Procedia PDF Downloads 145

Authors: A. Errasti, F. Doffagne, O. Foucrier, S. Kao, A. Meigne, H. Pellae, T. Rouland

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Renewable energy recovery is an important domain of research in past few years in view of protection of our ecosystem. Several industrial companies are setting up widespread recovery systems to exploit wave energy. Most of them have a large size, are implanted near the shores and exploit current flows. However, as oceans represent 70% of Earth surface, a huge space is still unexploited to produce energy. Present analysis focuses on surface small scale wave energy recovery. The principle is exactly the opposite of wheel damper for a car on a road. Instead of maintaining the car body as non-oscillatory as possible by adapted control, a system is designed so that its oscillation amplitude under wave action will be maximized with respect to a boat carrying it in view of differential potential energy recuperation. From parametric analysis of system equations, interesting domains have been selected and expected energy output has been evaluated.

Keywords: small scale wave, potential energy, optimized energy recovery, auto-adaptive system

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Authors: Sahar El Shair, Laura Greco, William Reay, Murray Cairns

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Background: Rheumatoid arthritis (RA) is a chronic, systematic, inflammatory, autoimmune disease that involves damages to joints and erosions to the associated bones and cartilage, resulting in reduced physical function and disability. RA is a multifactorial disorder influenced by heterogenous genetic and environmental factors. Whilst different medications have proven successful in reducing inflammation associated with RA, they often come with significant side effects and limited efficacy. To address this, the novel pharmagenic enrichment score (PES) algorithm was tested in self-reported RA patients from the UK Biobank (UKBB), which is a cohort of predominantly European ancestry, and identified individuals with a high genetic risk in clinically actionable biological pathways to identify novel opportunities for precision interventions and drug repurposing to treat RA. Methods and materials: Genetic association data for rheumatoid arthritis was derived from publicly available genome-wide association studies (GWAS) summary statistics (N=97173). The PES framework exploits competitive gene set enrichment to identify pathways that are associated with RA to explore novel treatment opportunities. This data is then integrated into WebGestalt, Drug Interaction database (DGIdb) and DrugBank databases to identify existing compounds with existing use or potential for repurposed use. The PES for each of these candidates was then profiled in individuals with RA in the UKBB (Ncases = 3,719, Ncontrols = 333,160). Results A total of 209 pathways with known drug targets after multiple testing correction were identified. Several pathways, including interferon gamma signaling and TID pathway (which relates to a chaperone that modulates interferon signaling), were significantly associated with self-reported RA in the UKBB when adjusting for age, sex, assessment centre month and location, RA polygenic risk and 10 principal components. These pathways have a major role in RA pathogenesis, including autoimmune attacks against certain citrullinated proteins, synovial inflammation, and bone loss. Encouragingly, many also relate to the mechanism of action of existing RA medications. The analyses also revealed statistically significant association between RA polygenic scores and self-reported RA with individual PES scorings, highlighting the potential utility of the PES algorithm in uncovering additional genetic insights that could aid in the identification of individuals at risk for RA and provide opportunities for more targeted interventions. Conclusions In this study, pharmacologically annotated genetic risk was explored through the PES framework to overcome inter-individual heterogeneity and enable precision drug repurposing in RA. The results showed a statistically significant association between RA polygenic scores and self-reported RA and individual PES scorings for 3,719 RA patients. Interestingly, several enriched PES pathways were targeted by already approved RA drugs. In addition, the analysis revealed genetically supported drug repurposing opportunities for future treatment of RA with a relatively safe profile.

Keywords: rheumatoid arthritis, precision medicine, drug repurposing, system biology, bioinformatics

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

Procedia PDF Downloads 453

Authors: Gregory Chown, Benjamin Infantolino, Christopher Wise, Rachel Holmes, Samantha O'Donnell, Katelyn Pfeiffer, Victoria Ward

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Background: There is a lack of understanding of how Kinesio Tape® physiologically works. Furthermore, few studies compare Kinesio Tape® to other forms of taping. The research question is: Does Kinesio Tape® cause a difference in muscle facilitation, inhibition, and pain, between Kinesio Tape® and no tape for collegiate athletes with self-reported shoulder pain? Method: This quantitative non-randomized design used a convenience sampling method. There were eleven participants with self-reported shoulder pain who were athletes on the men’s and women’s lacrosse and tennis teams. Participants attended one 30-45 minute session for data collection. Each participant received all three taping conditions and performed four repetitions of 120 degrees of active shoulder flexion for the three separate trials (no tape, Kinesio Tape® inhibition, and Kinesio Tape® facilitation). Surface electromyography (sEMG) electrodes were placed on the anterior deltoid, supraspinatus, and lower trapezius to measure muscle facilitation and inhibition. Each participant completed the visual analogue scale (VAS) before and after each trial to measure pain. Results: No statistical significance was found for pain scores on the VAS between the taping methods of facilitation, inhibition, and no tape (p = .118). No statistical significance was found for the percentage of change in muscle function for each taping method; Anterior deltoid (p = .993), supraspinatus (p = .997) and lower trapezius (p = .922). Conclusion: Based on the results, Kinesio Tape® appears to not have an effect on muscle function or pain when utilizing the facilitation or inhibition taping method when compared to no tape.

Keywords: Kinesio tape, muscle facilitation, muscle inhibition, pain

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Authors: Argyris Karapetsas, Maria Bampou, Andriani Mitropoulou

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The aim of the present work is to study the role of brain function in social awareness processing. Mind controls all the psychosomatic functions. Mind’s functioning enables individual not only to recognize one's own self and propositional attitudes, but also to assign such attitudes to other individuals, and to consider such observed mental states in the elucidation of behavior. Participants and Methods: Twenty (n=20) undergraduate students (mean age 18 years old) were involved in this study. Students participated in a clinical assessment, being conducted in Laboratory of Neuropsychology, at University of Thessaly, in Volos, Greece. Assessment included both electrophysiological (i.e.Event Related Potentials (ERPs) esp.P300 waveform) and neuropsychological tests (Raven's Progressive Matrices (RPM) and Sally-Anne test). Results: Initial assessment’s results confirmed statistically significant differences between the males and females, as well as in score performance to the tests applied. Strong correlations emerged between prefrontal lobe functioning, RPM, Sally-Anne test and P300 latencies. Also, significant dysfunction of mind has been found, regarding its three dimensions (straight, circular and helical). At the end of the assessment, students received consultation and appropriate guidelines in order to improve their intrapersonal and interpersonal skills. Conclusions: Mind and social awareness phenomena play a vital role in human development and may act as determinants of the quality of one’s own life. Meanwhile, brain function is highly correlated with social awareness and it seems that different set of brain structures are involved in social behavior.

Keywords: brain activity, emotions, ERP's, social awareness

Procedia PDF Downloads 177

Authors: Amani Waheed, Mostafa Kofi, Shaymaa Attia, Soha Younis, Basma Abdel Hadi

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Background: Exposure to pesticides is one of the most important occupational risks among farmers in developing countries. Along with the wide use of pesticides in the world, the concerns over their health impacts are rapidly growing. Objective: To investigate thyroid and reproductive hormones and fasting blood glucose levels among farmers chronically exposed to pesticide from East Almnaif district, Ismailia governorate. Methods: An analytical cross-sectional study was conducted on 43 farmers with active involvement pesticides handling and 43 participants not occupationally exposed to pesticides as the control group. A structured interview questionnaire measuring the sociodemographic characteristics, pesticides exposure characteristics, and safety measures was used. General examination including measurements of height, weight, and blood pressure was done. Moreover, levels of plasma cholinesterase enzyme (PChE), glucose, as well as reproductive and thyroid hormones (TSH, T4, and testosterone) were determined. Results: There were no statistically significant differences between both groups regarding their age, educational level, smoking status, and body mass index. The mean duration of exposure was 20.60 11.06 years. Majority of farmers (76.7%) did not use any personal protective equipment (PPE) during pesticides handling. The mean systolic blood pressure among exposed farmers was greater (134.88 17.18 mm Hg) compared to control group (125 14.69 mm Hg) with statistically significant difference (p = 0.003). The mean diastolic blood pressure was higher (84.02 8.69 mm Hg) compared to control group (78.79 8.98 mm Hg) with statistically significant difference (p = 0.006). The pesticide exposed farmers had statistically significant lower level of PChE (3969.93 1841U/L) than control group (4879.29 1950.08 U/L). Additionally, TSH level was significantly higher in exposed farmers (median =1.39µIU/ml) compared to controls (median = 0.91 µIU/ml) (p=0.032). While, the exposed group had a lower T4 level (6.91 1.91 µg/dl) compared to the control group (7.79 2.10µg/dl), with the statistically significant difference between the two groups (p = 0.045). The exposed group had significantly lower level of testosterone hormone (median=3.37 ng/ml) compared to the control group (median= 6.22 ng/ml) (p=0.003). While, the exposed farmers had statistically insignificant higher level of fasting blood glucose (median =89 mg/dl) than the controls (median=88 mg/dl). Furthermore, farmers who did not use PPE had statistically significant lower level of T4 (6.57 1.81µg/dl) than farmers who used PPE during handling of pesticides (8.01 1.89 µg/dl). Conclusion: Chronic exposure to pesticides exerts disturbing action on reproductive function and thyroid function of the male farmers.

Keywords: chronic occupational pesticide exposure, Diabetes mellitus, male reproductive hormones, thyroid function

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Authors: Kgomotso Matobole, Pascal Mwenge, Tumisang Seodigeng

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The global urbanisation and worldwide economic growth have caused a high rate of food waste generation, resulting in environmental pollution. Food waste disposed on landfills decomposes to produce methane (CH₄), a greenhouse gas. Inadequate waste management practices contribute to food waste polluting the environment. Thus effective organic fraction of municipal solid waste (OFMSW) management and treatment are attracting widespread attention in many countries. This problem can be minimised by the employment of anaerobic digestion process, since food waste is rich in organic matter and highly biodegradable, resulting in energy generation and waste volume reduction. The current study investigated the Biomethane Potential (BMP) of the Vaal University of Technology canteen food waste using anaerobic digestion. Tests were performed on canteen food waste, as a substrate, with total solids (TS) of 22%, volatile solids (VS) of 21% and moisture content of 78%. The tests were performed in batch reactors, at a mesophilic temperature of 37 °C, with two different types of inoculum, primary and digested sludge. The resulting CH₄ yields for both food waste with digested sludge and primary sludge were equal, being 357 Nml/g VS. This indicated that food waste form this canteen is rich in organic and highly biodegradable. Hence it can be used as a substrate for the anaerobic digestion process. The food waste with digested sludge and primary sludge both fitted the first order kinetic model with k for primary sludge inoculated food waste being 0.278 day^-1 with R² of 0.98, whereas k for digested sludge inoculated food waste being 0.034 day^-1, with R² of 0.847.

Keywords: anaerobic digestion, biogas, bio-methane potential, food waste

Procedia PDF Downloads 216

Authors: Mehdi Ghommem, Daniel Garcia, Nathan Collier, Victor Calo

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We develop a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM). The computational framework uses the Python programming language which has easy integration with the scripts requiring computationally-expensive operations written in Fortran. The mixed-language approach enables high performance in terms of solution time and high flexibility in terms of easiness of code adaptation to different system configurations and applications. This computational tool is intended to predict the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges...) subject to an incoming air. We simulate different aerodynamic problems to validate and illustrate the usefulness and effectiveness of the developed computational tool.

Keywords: unsteady aerodynamics, numerical simulations, mixed-language approach, potential flow

Procedia PDF Downloads 276

Authors: Kerols Seif Said Botros

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The relationship between development and human rights has been debated for a long time. Various principles, from the right to development to development-based human rights, are applied to understand the dynamics between these two concepts. Despite the measures calculated, the connection between enhancement and human rights remains vague. Despite, the connection between these two opinions and the need to strengthen human rights have increased in recent years. It will then be examined whether the right to sustainable development is acceptable or not. In various human rights instruments and this is a good vibe to the request cited above. The book then cites domestic and international human rights treaties, as well as jurisprudence and regulations defining human rights institutions, to support this view.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security.

Procedia PDF Downloads 33