Search results for: artificial cell

Authors: Sunita Makchuchit, Arunporn Itharat

Abstract:

Prasaprohyai, a Thai traditional medicine preparation listed in the Thai National List of Essential Medicines, is commonly used for treatment of fever and colds. Prasaprohyai preparation consists of 21 different plants, with Kaempferia galanga (50% w/w) as the main ingredient. The objective of this study was to investigate the anti-allergic activity of the crude extract from Prasaprohyai after accelerated stability test procedure. The method of extract used maceration in 95% ethanol and the crude extract was kept under accelerated condition at 40 ± 2 oC and 75 ± 5% relative humidity (RH) for six months. After six months of storage at 40 oC, the crude sample in various storage times (0, 15, 30, 45, 60, 90, 120, 150 and 180 days) were investigated for anti-allergic activity using IgE-sensitized RBL-2H3 cell lines. The results showed that the stability of crude ethanolic extract from Prasaprohyai under accelerated testing had no significant effect of anti-allergic activity when compared with day 0. The results showed that the ethanolic extract could be stored for two years at room temperature without loss of activity.

Keywords: accelerated stability, anti-allergy, prasaprohyai, RBL-2H3 cell lines

Procedia PDF Downloads 490

Authors: Miroslava Rysová, Zdena Syrová, Tomáš Zajíc, Petr Exnar

Abstract:

Currently, attention of tissue engineers has been attracted to novel nanofibrous materials having advanced properties and ability to mimic extracellular matrix (ECM) by structure which makes them interesting candidates for application in regenerative medicine as scaffolding and/or drug delivering material. Throughout the last decade, more than 200 synthetic and natural polymers have been successfully electrospun leading to the formation of nanofibres with a wide range of chemical, mechanical and degradation properties. In this family, inorganic nanofibres represent very specific group offering an opportunity to manufacture inert to body, well degradable and in properties tunable material. Aim of this work, was to reveal unique properties of silica (SiO2, CAS 7631-86-9) nanofibres and their potential in field of regenerative medicine. Silica nanofibres were prepared by sol-gel method from tetraethyl orthosilicate (TEOS, CAS 78-10-4) as a precursor and subsequently manufactured by needleless electrospinning on NanospiderTM device. Silica nanofibres thermally stabilized under 200°C were confirmed to be fully biodegradable and soluble in several simulated body fluids. In vitro cytotoxicity tests of eluate (ES ISO 10993-5:1999) and in direct contact (ES ISO 10993-5:2009) showed no toxicity - e.g. cell viabilities reached values exceeding 80%. Those results were obtained equally from two different cell lines (Vero, 3T3). Non-toxicity of silaca nanofibres´ eluate was additionally confirmed in real time by testing on xCelligence (ACEA Biosciences, Inc.) device. Both cell types also showed good adhesion to material. To conclude, all mentioned results lead to resumption that silica nanofibres have a potential as material for regenerative medicine which opens door to further research.

Keywords: cytotoxicity, electrospinning, nanofibres, silica, tissue engineering

Procedia PDF Downloads 430

Authors: K. S. Ghosh, Sagnik Bose, Kapil Tripati

Abstract:

Potentiodynamic polarization studies carried out on AA2024 and AA2014 Al-Cu-Mg alloys of various tempers in 3.5 wt. % NaCl and in 3.5 wt. % NaCl + 1.0 % H2O2 solution characteristic E-i curves. Corrosion potential (Ecorr) value has shifted towards more negative potential with the increase of artificial aging time. The Ecorr value for the alloy tempers has also shifted anodically in presence of H2O2 in 3.5 % NaCl solution. Further, passivity phenomenon has been observed in all the alloy tempers when tested in 3.5 wt. % NaCl solution at pH 12. Stress corrosion cracking (SCC) behaviour of friction stir weld (FSW) joint of AA2014 alloy has been studied bu slow strain rate test (SSRT) in 3.5 wt. % NaCl solution. Optical micrographs of the corroded surfaces of polarised samples showed general corrosion, extensive pitting and intergranular corrosion as well. Further, potentiodynamic cyclic polarization curves displayed wide hysteresis loop indicating that the alloy tempers are susceptible to pit growth damage. Attempts have been made to explain the variation of observed electrochemical and SCC behaviour of the alloy tempers and the electrolyte conditions with the help of microstructural features.

Keywords: AA 2014 and AA 2024 Al-C-Mg alloy, artificial ageing, potentiodynamic polarization, TEM micrographs, stress corrosion cracking (SCC)

Procedia PDF Downloads 334

Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

Abstract:

Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

Procedia PDF Downloads 410

Authors: Wendu Pang, Yaxin Luo, Junhong Li, Yu Zhao, Danni Cheng, Yufang Rao, Minzi Mao, Ke Qiu, Yijun Dong, Fei Chen, Jun Liu, Jian Zou, Haiyang Wang, Wei Xu, Jianjun Ren

Abstract:

We aimed to compare the prognostic prediction value of positive lymph node number (PLNN) to the American Joint Committee on Cancer (AJCC) tumor, lymph node, and metastasis (TNM) staging system for patients with hypopharyngeal squamous cell carcinoma (HPSCC). A total of 826 patients with HPSCC from the Surveillance, Epidemiology, and End Results database (2004–2015) were identified and split into two independent cohorts: training (n=461) and validation (n=365). Univariate and multivariate Cox regression analyses were used to evaluate the prognostic effects of PLNN in patients with HPSCC. We further applied six Cox regression models to compare the survival predictive values of the PLNN and AJCC TNM staging system. PLNN showed a significant association with overall survival (OS) and cancer-specific survival (CSS) (P < 0.001) in both univariate and multivariable analyses, and was divided into three groups (PLNN 0, PLNN 1-5, and PLNN>5). In the training cohort, multivariate analysis revealed that the increased PLNN of HPSCC gave rise to significantly poor OS and CSS after adjusting for age, sex, tumor size, and cancer stage; this trend was also verified by the validation cohort. Additionally, the survival model incorporating a composite of PLNN and TNM classification (C-index, 0.705, 0.734) performed better than the PLNN and AJCC TNM models. PLNN can serve as a powerful survival predictor for patients with HPSCC and is a surrogate supplement for cancer staging systems.

Keywords: hypopharyngeal squamous cell carcinoma, positive lymph nodes number, prognosis, prediction models, survival predictive values

Procedia PDF Downloads 156

Authors: Zatil Athaillah, Anastasia Devi, Dian Muzdalifah, Wirasuwasti Nugrahani, Linar Udin

Abstract:

During tempe fermentation, some chemical changes occurred and they contributed to sensory, appearance, and health benefits of soybeans. Many studies on health properties of tempe have specialized on their isoflavones. In this study, other components of tempe, particularly water soluble chemicals, was investigated for their biofunctionality. The study was focused on the ability to suppress MCF-7 breast cancer cell growth and antioxidant activity, as expressed by DPPH radical scavenging activity, total phenols and total flavonoids, of the water extracts. Fermentation time of tempe was extended up to 120 hr to increase the possibility to find the functional components. Extraction yield and soluble nitrogen content were also quantified as accompanying data. Our findings suggested that yield of water extraction of tempe increased as fermentation was extended up to 120 hr, except for a slight decrease at 72 hr. Water extracts of tempe showed inhibition of MCF-7 breast cancer cell growth, as shown by lower IC50 values when compared to control (unfermented soybeans). Among the varied fermentation timescales, 60-hr period showed the highest activity (IC50 of 8.7 ± 4.95 µg/ml). The anticancer activity of extracts obtained from different fermentation time was positively correlated with total soluble nitrogens, but less relevant with antioxidant data. During 48-72 hr fermentation, at which cancer suppression activity was significant, the antioxidant properties from the three assays were not higher than control. These findings indicated that water extracts of tempe from extended fermentation could inhibit breast cancer cell growth but further study to determine the mechanism and compounds that play important role in the activity should be conducted.

Keywords: tempe, anticancer, antioxidant, phenolic compounds

Procedia PDF Downloads 246

Authors: Juliana Lukša, Bazilė Ravoitytė, Elena Servienė, Saulius Serva

Abstract:

Totiviridae L-A virus is a persistent Saccharomyces cerevisiae dsRNA virus. It encodes the major structural capsid protein Gag and Gag-Pol fusion protein, responsible for virus replication and encapsulation. These features also enable the copying of satellite dsRNAs (called M dsRNAs) encoding a secreted toxin and immunity to it (known as killer toxin). Viral capsid pore presumably functions in nucleotide uptake and viral mRNA release. During cell division, sporogenesis, and cell fusion, the virions remain intracellular and are transferred to daughter cells. By employing high throughput RNA sequencing data analysis, we describe the influence of solely L-A virus on the expression of genes in three different S. cerevisiae hosts. We provide a new perception into Totiviridae L-A virus-related transcriptional regulation, encompassing multiple bioinformatics analyses. Transcriptional responses to L-A infection were similar to those induced upon stress or availability of nutrients. It also delves into the connection between the cell metabolism and L-A virus-conferred demands to the host transcriptome by uncovering host proteins that may be associated with intact virions. To better understand the virus-host interaction, we applied differential proteomic analysis of virus particle-enriched fractions of yeast strains that harboreither complete killer system (L-A-lus and M-2 virus), M-2 depleted orvirus-free. Our analysis resulted in the identification of host proteins, associated with structural proteins of the virus (Gag and Gag-Pol). This research was funded by the European Social Fund under the No.09.3.3-LMT-K-712-19-0157“Development of Competences of Scientists, other Researchers, and Students through Practical Research Activities” measure.

Keywords: totiviridae, killer virus, proteomics, transcriptomics

Procedia PDF Downloads 148

Authors: Ko-Shao Chen, Yun Tsao, Chia-Hsuan Tsen, Chien-Chung Chen, Shu-Chuan Liao

Abstract:

Electrospun microtube array membranes (MTAMs) made of PLLA (poly-L-lactic acid) have wide potential applications in tissue engineering. However, their surface hydrophobicity and poor biocompatability have limited their further usage. In this study, the surface of PLLA MTAMs were made hydrophilic by introducing extra functional groups, such as peroxide, via an acetic acid plasma (AAP). UV-graft polymerization of acrylic acid (G-AAc) was then used to produce carboxyl group on MTAMs surface, which bonded covalently with chitosan through EDC / NHS crosslinking agents. To evaluate the effects of the surface modification on PLLA MTAMs, water contact angle (WCA) measurement and cell compatibility tests were carried out. We found that AAP treated electrospun PLLA MTAMs grafted with AAc and, finally, with chitosan immobilized via crosslinking agent, exhibited improved hydrophilic and cell compatibility.

Keywords: plasma, EDC/NHS, UV grafting, Chitosan, microtube array membrane (MTAMs)

Procedia PDF Downloads 412

Authors: Marjan Golmaryami, Marzieh Behzadi

Abstract:

Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

Procedia PDF Downloads 550

Authors: Jaejeogn Kim, Jeongmin Hong, Jungin Lee

Abstract:

The Hydrogen Storage System Management Unit (HMU) is a controller that manages hydrogen charging and storage. It detects hydrogen leaks and tank pressure and temperature, calculates the charging concentration and remaining amount, and controls the opening and closing of the hydrogen tank valve. Since this role is an important part of the vehicle behavior and stability of Fuel Cell Electric Vehicles (FCEV), verifying the HMU controller is an essential part. To perform verification under various conditions, it is necessary to increase time efficiency based on an automated verification environment and increase the reliability of the controller by applying numerous test cases. To this end, we introduce the HMU controller automation verification method by applying the HILS environment and an automation test program with the ASAM XIL standard.

Keywords: HILS, ASAM, fuel cell electric vehicle, automation test, hydrogen storage system

Procedia PDF Downloads 71

Authors: M. Fadlallah, G. Ghibaudo, C. G. Theodorou

Abstract:

The dynamic variation in memory devices such as the Static Random Access Memory can give errors in read or write operations. In this paper, the effect of low-frequency and random telegraph noise on the dynamic variation of one SRAM cell is detailed. The effect on circuit noise, speed, and length of time of processing is examined, using the Supply Read Retention Voltage and the Read Static Noise Margin. New test run methods are also developed. The obtained results simulation shows the importance of noise caused by dynamic variation, and the impact of Random Telegraph noise on SRAM variability is examined by evaluating the statistical distributions of Random Telegraph noise amplitude in the pull-up, pull-down. The threshold voltage mismatch between neighboring cell transistors due to intrinsic fluctuations typically contributes to larger reductions in static noise margin. Also the contribution of each of the SRAM transistor to total dynamic variation has been identified.

Keywords: low-frequency noise, random telegraph noise, dynamic variation, SRRV

Procedia PDF Downloads 177

Authors: Yuping Liu, Li Tao, Yin Lu

Abstract:

Accumulating evidence has been shown that diallyl trisulfide (DATS) from garlic may reduce the risk of developing several types of cancer. In view of the dynamic crosstalk interplayed by tumor cells and platelets in hematogenous metastasis, we demonstrate the effectiveness of DATS on the metastatic behaviors of MDA-MB-435s human breast cancer cell line co-incubated with activated platelets. Indeed, our data identified that DATS significantly blocked platelets fouction induced by PAF, followed by the decreased production of TXB2. DATS was found to dose-dependently suppressed MDA-MB-435s cell migration and invasion in presence of activated platelets by PAF in vitro. Furthermore, the expression, secretion and enzymatic activity of matrix metalloproteinase (MMP)-2/9, as well as the luciferase activity of upstream regulator NF-κB in MDA-MB-435s, were obviously diminished by DATS. In parallel, DATS blocked upstream NF-κB activation signaling complexes composed of extracellular signal-related kinase (ERK) as assessed by measuring the levels of the phosphorylated forms.

Keywords: DATS, ERK, metastasis, MMPs, NF-κB, platelet

Procedia PDF Downloads 389

Authors: Dequn Teng, Tianshuo Yang, Mingrui Wang, Qiuyu Chen, Xiao Wang, Katie Atkinson

Abstract:

Currently, many students are kind of at a loss in the university due to the complex environment within the campus, where every information within the campus is isolated with fewer interactions with each other. However, if the on-campus resources are gathered and combined with the artificial intelligence modelling techniques, there will be a bridge for not only students in understanding themselves, and the teachers will understand students in providing a much efficient approach in education. The objective of this paper is to provide a competency level analysis method, apparatus, electronic equipment, and storage medium. It uses a user’s target competency level analysis model from a plurality of predefined candidate competency level analysis models by obtaining a user’s promotion target parameters, promotion target parameters including at least one of the following parameters: target profession, target industry, and the target company, according to the promotion target parameters. According to the parameters, the model analyzes the user’s ability level, determines the user’s ability level, realizes the quantitative and personalized analysis of the user’s ability level, and helps the user to objectively position his ability level.

Keywords: artificial intelligence, model, university, education, recommendation system, evaluation, job hunting

Procedia PDF Downloads 145

Authors: Wu You, Burra Venkata Durga Kumar

Abstract:

This study examines the promising potential of integrating Blockchain and Artificial Intelligence (AI) technologies to scalability in Distributed Systems within the field of supply chain finance. The finance industry is continually confronted with scalability challenges in its Distributed Systems, particularly within the supply chain finance sector, impacting efficiency and security. Blockchain, with its inherent attributes of high scalability and secure distributed ledger system, coupled with AI's strengths in optimizing data processing and decision-making, holds the key to innovating the industry's approach to these issues. This study elucidates the synergistic interplay between Blockchain and AI, detailing how their fusion can drive a significant transformation in the supply chain finance sector's Distributed Systems. It offers specific use-cases within this field to illustrate the practical implications and potential benefits of this technological convergence. The study also discusses future possibilities and current challenges in implementing this groundbreaking approach within the context of supply chain finance. It concludes that the intersection of Blockchain and AI could ignite a new epoch of enhanced efficiency, security, and transparency in the Distributed Systems of supply chain finance within the financial industry.

Keywords: blockchain, artificial intelligence (AI), scaled distributed systems, supply chain finance, efficiency and security

Procedia PDF Downloads 94

Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

Abstract:

Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

Procedia PDF Downloads 363

Authors: Miguel A. Figueroa, José A. Lara-Ramos, Miguel A. Mueses

Abstract:

Pharmaceutical residues are a section of emerging contaminants of anthropogenic origin that are present in a myriad of waters with which human beings interact daily and are starting to affect the ecosystem directly. Conventional waste-water treatment systems are not capable of degrading these pharmaceutical effluents because their designs cannot handle the intermediate products and biological effects occurring during its treatment. That is why it is necessary to hybridize conventional waste-water systems with non-conventional processes. In the specific case of an ozonation process, its efficiency highly depends on a perfect dispersion of ozone, long times of interaction of the gas-liquid phases and the size of the ozone bubbles formed through-out the reaction system. In order to increase the efficiency of these parameters, the use of a modified flotation cell has been proposed recently as a reactive system, which is used at an industrial level to facilitate the suspension of particles and spreading gas bubbles through the reactor volume at a high rate. The objective of the present work is the development of a mathematical model that can closely predict the kinetic rates of reactions taking place in the flotation cell at an experimental scale by means of identifying proper reaction mechanisms that take into account the modified chemical and hydrodynamic factors in the FeO-catalyzed Ozonation of Diclofenac aqueous solutions in a flotation cell. The methodology is comprised of three steps: an experimental phase where a modified flotation cell reactor is used to analyze the effects of ozone concentration and loading catalyst over the degradation of Diclofenac aqueous solutions. The performance is evaluated through an index of utilized ozone, which relates the amount of ozone supplied to the system per milligram of degraded pollutant. Next, a theoretical phase where the reaction mechanisms taking place during the experiments must be identified and proposed that details the multiple direct and indirect reactions the system goes through. Finally, a kinetic model is obtained that can mathematically represent the reaction mechanisms with adjustable parameters that can be fitted to the experimental results and give the model a proper physical meaning. The expected results are a robust reaction rate law that can simulate the improved results of Diclofenac mineralization on water using the modified flotation cell reactor. By means of this methodology, the following results were obtained: A robust reaction pathways mechanism showcasing the intermediates, free-radicals and products of the reaction, Optimal values of reaction rate constants that simulated Hatta numbers lower than 3 for the system modeled, degradation percentages of 100%, TOC (Total organic carbon) removal percentage of 69.9 only requiring an optimal value of FeO catalyst of 0.3 g/L. These results showed that a flotation cell could be used as a reactor in ozonation, catalytic ozonation and photocatalytic ozonation processes, since it produces high reaction rate constants and reduces mass transfer limitations (Ha > 3) by producing microbubbles and maintaining a good catalyst distribution.

Keywords: advanced oxidation technologies, iron oxide, emergent contaminants, AOTS intensification

Procedia PDF Downloads 114

Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

Abstract:

This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

Procedia PDF Downloads 449

Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

Abstract:

This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

Procedia PDF Downloads 124

Authors: Monique Du Plessis, Anina Coetzee, Colleen L. Seymour, Claire N. Spottiswoode

Abstract:

Artificial nectar feeders are used to attract nectarivorous birds to gardens and are increasing in popularity. The costs and benefits of these feeders remain controversial, however. Nectar feeders may have positive effects by attracting nectarivorous birds towards suburbia, facilitating their urban adaptation, and supplementing bird diets when floral resources are scarce. However, this may come at the cost of luring them away from the plants they pollinate in neighboring indigenous vegetation. This study investigated the effect of nectar feeders on an African pollinator-plant mutualism. Given that birds are important pollinators to many fynbos plant species, this study was conducted in gardens and natural vegetation along the urban edge of the Cape Peninsula. Feeding experiments were carried out to compare relative bird abundance and local distribution patterns for nectarivorous birds (i.e., sunbirds and sugarbirds) between feeder and control treatments. Resultant changes in their visitation rates to Erica flowers in the natural vegetation were tested by inspection of their anther ring status. Nectar feeders attracted higher densities of nectarivores to gardens relative to natural vegetation and decreased their densities in the neighboring fynbos, even when floral abundance in the neighboring vegetation was high. The consequent changes to their distribution patterns and foraging behavior decreased their visitation to at least Erica plukenetii flowers (but not to Erica abietina). This study provides evidence that nectar feeders may have positive effects for birds themselves by reducing their urban sensitivity but also highlights the unintended negative effects feeders may have on the surrounding fynbos ecosystem. Given that nectar feeders appear to compete with the flowers of Erica plukenetii, and perhaps those of other Erica species, artificial feeding may inadvertently threaten bird-plant pollination networks.

Keywords: avian nectarivores, bird feeders, bird pollination, indirect effects in human-wildlife interactions, sugar water feeders, supplementary feeding

Procedia PDF Downloads 159

Authors: Dheyaa W. Abbood, Rafa H. AL-Suhaili, May S. Saleh

Abstract:

A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs length sand their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy.The optimization carried out subjected to constraints that ensure a safe structure against the uplift pressure force and sufficient protection length at the downstream side of the structure to overcome an excessive exit gradient. The Geo-studios oft ware, was used to analyze 1200 different cases. For each case the length of protection and volume of structure required to satisfy the safety factors mentioned previously were estimated. An ANN model was developed and verified using these cases input-output sets as its data base. A MatLAB code was written to perform a genetic algorithm optimization modeling coupled with this ANN model using a formulated optimization model. A sensitivity analysis was done for selecting the cross-over probability, the mutation probability and level ,the number of population, the position of the crossover and the weights distribution for all the terms of the objective function. Results indicate that the most factor that affects the optimum solution is the number of population required. The minimum value that gives stable global optimum solution of this parameters is (30000) while other variables have little effect on the optimum solution.

Keywords: inclined cutoff, optimization, genetic algorithm, artificial neural networks, geo-studio, uplift pressure, exit gradient, factor of safety

Procedia PDF Downloads 325

Authors: Aishi Kundu, Elhan Raza

Abstract:

"Necessity is the mother of invention" - Rapid increase in the global human population has directed the agricultural domain toward machine learning. The basic need of human beings is considered to be food which can be satisfied through farming. Farming is one of the major revenue generators for the Indian economy. Agriculture is not only considered a source of employment but also fulfils humans’ basic needs. So, agriculture is considered to be the source of employment and a pillar of the economy in developing countries like India. This paper provides a brief review of the progress made in implementing Machine Learning in the agricultural sector. Accurate predictions are necessary at the right time to boost production and to aid the timely and systematic distribution of agricultural commodities to make their availability in the market faster and more effective. This paper includes a thorough analysis of various machine learning algorithms applied in different aspects of agriculture (crop management, soil management, water management, yield tracking, livestock management, etc.).Due to climate changes, crop production is affected. Machine learning can analyse the changing patterns and come up with a suitable approach to minimize loss and maximize yield. Machine Learning algorithms/ models (regression, support vector machines, bayesian models, artificial neural networks, decision trees, etc.) are used in smart agriculture to analyze and predict specific outcomes which can be vital in increasing the productivity of the Agricultural Food Industry. It is to demonstrate vividly agricultural works under machine learning to sensor data. Machine Learning is the ongoing technology benefitting farmers to improve gains in agriculture and minimize losses. This paper discusses how the irrigation and farming management systems evolve in real-time efficiently. Artificial Intelligence (AI) enabled programs to emerge with rich apprehension for the support of farmers with an immense examination of data.

Keywords: machine Learning, artificial intelligence, crop management, precision farming, smart farming, pre-harvesting, harvesting, post-harvesting

Procedia PDF Downloads 107

Authors: Tanveer Beg, Yasir H. Siddique, Gulshan Ara, Asfar S. Azmi, Mohammad Afzal

Abstract:

Doxorubicin is a well-known DNA intercalating chemotherapy drug that is widely used for treatment of different cancers. Its clinical utility is limited due to the observed genotoxic side effects on healthy cells suggesting that newer combination and genoprotective regimens are urgently needed for the management of doxorubicin chemotherapy. Some dietary phytochemicals are well known for their protective mechanism of action and genistein from soy is recognized as an anti-oxidant with similar properties. Therefore, the present study investigates the effect of genistein against the genotoxic doses of doxorubicin by assessing chromosomal aberrations, sister chromatid exchanges, cell cycle kinetics, cell viability, apoptosis, and DNA damage markers in cultured human lymphocytes. Our results reveal that genistein treatment significantly suppresses genotoxic damage induced by doxorubicin. It is concluded that genistein has the potential to reduce the genotoxicity induced by anti-cancer drugs, thereby reducing the chances of developing secondary tumors during the therapy.

Keywords: apoptosis, DNA damage markers, doxorubicin, genistein, genotoxicity, human lymphocyte culture

Procedia PDF Downloads 361

Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

Abstract:

High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch

Procedia PDF Downloads 190

Authors: Sh. Heidari, A. J. Andrews, A. Oberoi

Abstract:

Electrochemical storage of hydrogen in activated carbon electrodes as part of a reversible fuel cell offers a potentially attractive option for storing surplus electrical energy from inherently variable solar and wind energy resources. Such a system – which we have called a proton flow battery – promises to have a roundtrip energy efficiency comparable to lithium ion batteries, while having higher gravimetric and volumetric energy densities. In this paper, a theoretical model is presented of the process of H+ ion (proton) conduction through an acid electrolyte into a highly porous activated carbon electrode where it is neutralised and absorbed on the inner surfaces of pores. A Butler-Volmer type equation relates the rate of adsorption to the potential difference between the activated carbon surface and the electrolyte. This model for the hydrogen storage electrode is then incorporated into a more general computer model based on MATLAB software of the entire electrochemical cell including the oxygen electrode. Hence a theoretical voltage-current curve is generated for given input parameters for a particular activated carbon electrode. It is shown that theoretical VI curves produced by the model can be fitted accurately to experimental data from an actual electrochemical cell with the same characteristics. By obtaining the best-fit values of input parameters, such as the exchange current density and charge transfer coefficient for the hydrogen adsorption reaction, an improved understanding of the adsorption reaction is obtained. This new model will assist in designing improved proton flow batteries for storing solar and wind energy.

Keywords: electrochemical hydrogen storage, proton flow battery, butler-volmer equation, activated carbon

Procedia PDF Downloads 501

Authors: Shi-Yu Xu

Abstract:

The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

Procedia PDF Downloads 91

Authors: Lie-Sian Yap, Ming-Chien Yang

Abstract:

This study demonstrated a novel injectable hydrogel scaffold composing of Pluronic F127, carboxymethyl hexanoyl chitosan (CA) and glutaraldehyde (GA) for encapsulating human fetal osteoblastic cells (hFOB) 1.19. The hydrogel was prepared by mixing F127 and GA in CA solution at 4°C. The mechanical properties and cytotoxicity of this hydrogel were determined through rheological measurements and MTT assay, respectively. After encapsulation process, the hFOB 1.19 cells morphology was examined using fluorescent and confocal imaging. The results indicated that the Tgel of this system was around 30°C, where sol-gel transformation occurred within 90s and F127/CA/GA gel was able to remain intact in the medium for more than 1 month. In vitro cell culture assay revealed that F127/CA/GA hydrogels were non-cytotoxic. Encapsulated hFOB 1.19 cells not only showed the spherical shape and formed colonies, but also reduced their size. Moreover, the hFOB 1.19 cells showed that cells remain alive after the encapsulation process. Based on these results, these F127/CA/GA hydrogels can be used to encapsulate cells for tissue engineering applications.

Keywords: carboxymethyl hexanoyl chitosan, cell encapsulation, hFOB 1.19, Pluronic F127

Procedia PDF Downloads 245

Authors: Furqan M. Kadhum, Asmaa A. Hussein, Maysaa Ch. Hatem

Abstract:

Hyaluronidase enzyme was purified from the clinical isolate Staphyloccus aureus in three purification steps, first by precipitation with 90% saturated ammonium sulfate, ion exchange chromatography on DEAE-Cellulose, and gel filtration chromatography throughout Sephacryl S-300. Specific activity of the purified enzyme was reached 930 U/mg protein with 7.4 folds of purification and 46.5% recovery. The enzyme has an average molecular weight of about 69 kDa, with an optimum pH of enzyme activity and stability at pH 7, also the optimum temperature for activity was 37oC. The enzyme was stable with full activity at a temperature ranged between 30-40 oC. Metal ions showed variable inhibitory degree with the strongest effect for Fe+3, however, the chelating and reducing agents had no or little effects. Cytotoxic studies for purified and crude hyaluronidase against cancer cell Hep G2 type at different enzyme concentrations and exposure times showed that the inhibition effect of both crude and purified enzyme increased by increasing the enzyme concentration with no change was observed at 24hr, while at 48 and 72 hrs the same inhibition rate were observed for purified enzyme and differ for the crude filtrate.

Keywords: hyaluronidase, S. aureus, metal ions, cytotoxicity

Procedia PDF Downloads 448

Authors: Shivendra Pratap Singh, Rishabh Sharma

Abstract:

The widespread adoption of mobile technology in recent years has revolutionized the way we communicate and access information. The evolution of mobile systems has been rapid and impactful, shaping our lives and changing the way we live and work. However, despite its significant influence, the history and development of mobile technology are not well understood by the general public. This research paper aims to examine the history, technology and future of mobile systems, exploring their evolution from early mobile phones to the latest smartphones and beyond. The study will analyze the technological advancements and innovations that have shaped the mobile industry, from the introduction of mobile internet and multimedia capabilities to the integration of artificial intelligence and 5G networks. Additionally, the paper will also address the challenges and opportunities facing the future of mobile technology, such as privacy concerns, battery life, and the increasing demand for high-speed internet. Finally, the paper will also provide insights into potential future developments and innovations in the mobile sector, such as foldable phones, wearable technology, and the Internet of Things (IoT). The purpose of this research paper is to provide a comprehensive overview of the history, technology, and future of mobile systems, shedding light on their impact on society and the challenges and opportunities that lie ahead.

Keywords: mobile technology, artificial intelligence, networking, iot, technological advancements, smartphones

Procedia PDF Downloads 94

Authors: Hülya Kuru Mutlu, Mustafa Kulakcı, Uğur Serincan

Abstract:

The sun is one of the latest developments in renewable energy sources, which has a variety of application. Solar energy is the most preferred renewable energy sources because it can be used directly, it protects the environment and it is economic. In this work, we investigated that important parameter of GaAs-based solar cells with respect to the growth temperature. The samples were grown on (100) oriented p-GaAs substrates by solid source Veeco GEN20MC MBE system equipped with Ga, In, Al, Si, Be effusion cells and an Arsenic cracker cell. The structures of the grown samples are presented. After initial oxide desorption, Sample 1 and Sample 2 were grown at about 585°C and 535°C, respectively. From the grown structures, devices were fabricated by using the standard photolithography procedure. Current-voltage measurements were performed at room temperature (RT). It is observed that Sample 1 which was grown at 585°C has higher efficiency and fill factor compared to Sample 2. Hence, it is concluded that the growth temperature of 585°C is more suitable to grow GaAs-based solar cells considering our samples used in this study.

Keywords: molecular beam epitaxy, solar cell, current-voltage measurement, Sun

Procedia PDF Downloads 475

Authors: Doufoungognon Carine Kone

Abstract:

Heavy metals present in large quantities in ecosystems can alter biological and cellular functions and disrupt trophic functions. However, their toxicity can change according to thermal conditions, as toxicity depends on their bioavailability and thermal optimum of organisms. Organisms can develop different tolerance strategies to maintain themselves in a stressful environment, but these strategies are often studied in a single-stressor context. This study evaluates the responses of the ciliate Tetrahymena thermophila to copper, high temperature, and their interaction. Six genotypes were exposed to a gradient of copper concentrations ranging from 0 to 350mg/L in synthetic media at three temperatures: 15°C, 23°C, and 31°C. Cell density, cell shape and size (and their variance), swimming speed and trajectory, and copper uptake rate were measured. Depending on the genotype, swimming speed, trajectory, and cell size were highly affected by stress gradients. One gets bigger, while two genotypes get smaller and the other remain unchanged. Some genotypes swam less rapidly, while others speed up as copper and temperature increased. Concerning copper uptake, the two genotypes accumulating the best and the worst, whatever the copper concentration or temperature, were also those that had the highest densities. Finally, very few temperature x copper interactions were observed on phenotypic parameters. The diversity of phenotypic responses revealed in this study reflects the existence of divergent strategies adopted by Tetrahymena thermophila to resist to copper and thermal stress, which suggests an important role of intraspecific variability in biodiversity response to environmental stress. One general and the surprising pattern was a global absence of interactive effects between copper and high temperature exposure on the observed phenotypic responses.

Keywords: ciliate, copper, intraspecific variability, phenotype, temperature, tolerance, multiple stressors

Procedia PDF Downloads 77