BIOMATERIALS AND ARTIFICIAL ORGANS CONFERENCE


Biomaterials and Artificial Organs Conference is one of the leading research topics in the international research conference domain. Biomaterials and Artificial Organs is a conference track under the Biomedical and Biological Engineering Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Biomedical and Biological Engineering.

internationalscience.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Biomedical and Biological Engineering).

Biomaterials and Artificial Organs is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Biomaterials and Artificial Organs Conference Track will be held at “Biomedical and Biological Engineering Conference in Paris, France in November 2019” - “Biomedical and Biological Engineering Conference in London, United Kingdom in January 2020” - “Biomedical and Biological Engineering Conference in Tokyo, Japan in March 2020” - “Biomedical and Biological Engineering Conference in Amsterdam, Netherlands in May 2020” - “Biomedical and Biological Engineering Conference in Istanbul, Turkey in June 2020” - “Biomedical and Biological Engineering Conference in Stockholm, Sweden in July 2020” - “Biomedical and Biological Engineering Conference in Zürich, Switzerland in September 2020” - “Biomedical and Biological Engineering Conference in New York, United States in November 2020” .

Biomaterials and Artificial Organs is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

NOVEMBER 21 - 22, 2019
PARIS, FRANCE

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline October 21, 2019
  • CONFERENCE CODE: 18BBE11FR
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

JANUARY 21 - 22, 2020
LONDON, UNITED KINGDOM

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline December 19, 2019
  • CONFERENCE CODE: 20BBE01GB
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

MARCH 26 - 27, 2020
TOKYO, JAPAN

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline February 27, 2020
  • CONFERENCE CODE: 20BBE03JP
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

MAY 13 - 14, 2020
AMSTERDAM, NETHERLANDS

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline April 14, 2020
  • CONFERENCE CODE: 20BBE05NL
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

JUNE 25 - 26, 2020
ISTANBUL, TURKEY

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline May 26, 2020
  • CONFERENCE CODE: 20BBE06TR
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

JULY 14 - 15, 2020
STOCKHOLM, SWEDEN

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline June 11, 2020
  • CONFERENCE CODE: 20BBE07SE
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

SEPTEMBER 15 - 16, 2020
ZÜRICH, SWITZERLAND

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline August 13, 2020
  • CONFERENCE CODE: 20BBE09CH
  • One Time Submission Deadline Reminder

INTERNATIONAL BIOMEDICAL AND BIOLOGICAL ENGINEERING CONFERENCE

NOVEMBER 05 - 06, 2020
NEW YORK, UNITED STATES

  • Abstracts/Full-Text Paper Submission Deadline March 14, 2019
  • Notification of Acceptance/Rejection Deadline March 28, 2019
  • Final Paper and Early Bird Registration Deadline October 05, 2020
  • CONFERENCE CODE: 20BBE11US
  • One Time Submission Deadline Reminder

Biomedical and Biological Engineering Conference Call For Papers are listed below:

Previously Published Papers on "Biomaterials and Artificial Organs Conference"

  • Risk Assessment of Lead in Meat from Different Environments of Egypt
    Authors: A. A. K. Abou-Arab, M. A. Abou Donia, A. K. Enab, Keywords: Heavy metals, lead, meats, organs, liver, kidney, spleen, heart, environments. DOI:10.5281/zenodo.1127492 Abstract: Lead is among the heavy metals and it is one of the highly toxic metals, recognized in most countries. This metal accumulates in animal organs as liver and kidney. The present investigation provides the concentrations of lead in cow's meat and different animal organs collected from three Egyptian environments. The results revealed that lead levels in muscle, liver, kidney, spleen and heart in industrial areas were higher than those detected in the same organs of other two areas (heavy traffic and rural), which recorded mean values of 3.0091, 1.7070, 1.8609, 0.6401 and 0.5332 mg/kg, respectively, followed by traffic areas, 2.9166, 1.4443, 1.6967, 0.4042 and 0.4103 mg/kg, respectively. The corresponding values of rural areas were 1.8895, 0.9550, 0.9117, 0.3215 and 0.2856 mg/kg, in the same order. It could be recommended that monitoring and evaluation of lead levels in meat at regular intervals are very important.
  • Inductions of CaC2 on Sperm Morphology and Viability of the Albino Mice (Mus musculus)
    Authors: Dike H. Ogbuagu, Etsede J. Oritsematosan, Keywords: Artificial ripening, Calcium carbide, fruit vendors, sperm morphology, sperm viability. DOI:10.5281/zenodo.1123709 Abstract: This work investigated possible inductions of CaC2, often misused by fruit vendors to stimulate artificial ripening, on mammalian sperm morphology and viability. Thirty isogenic strains of male albino mice, Mus musculus (age≈ 8weeks; weight= 32.52.0g) were acclimatized (ambient temperature 28.0±1.0°C) for 2 weeks and fed standard growers mash and water ad libutum. They were later exposed to graded toxicant concentrations (w/w) of 2.5000, 1.2500, 0.6250, and 0.3125% in 4 cages. A control cage was also established. After 5 weeks, 3 animals from each cage were sacrificed by cervical dislocation and the cauda epididymis excised. Sperm morphology and viability were determined by microscopic procedures. The ANOVA, means plots, Student’s t-test and variation plots were used to analyze data. The common abnormalities observed included Double Head, Pin Head, Knobbed Head, No Tail and With Hook. The higher toxicant concentrations induced significantly lower body weights [F(829.899) ˃ Fcrit(4.19)] and more abnormalities [F(26.52) ˃ Fcrit(4.00)] at P˂0.05. Sperm cells in the control setup were significantly more viable than those in the 0.625% (t=0.005) and 2.500% toxicant doses (t=0.018) at the 95% confidence limit. CaC2 appeared to induced morphological abnormalities and reduced viability in sperm cells of M. musculus.
  • Logic Programming and Artificial Neural Networks in Pharmacological Screening of Schinus Essential Oils
    Authors: José Neves, M. Rosário Martins, Fátima Candeias, Diana Ferreira, Sílvia Arantes, Júlio Cruz-Morais, Guida Gomes, Joaquim Macedo, António Abelha, Henrique Vicente, Keywords: Artificial neuronal networks, essential oils, knowledge representation and reasoning, logic programming, Schinus molle L, Schinus terebinthifolius raddi. DOI:10.5281/zenodo.1107037 Abstract: Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.
  • The Efficiency of Mechanization in Weed Control in Artificial Regeneration of Oriental Beech (Fagus orientalis Lipsky.)
    Authors: Tuğrul Varol, Halil Barış Özel, Keywords: Artificial regeneration, weed control, oriental beech, productivity, mechanization, man power, cost analysis. DOI:10.5281/zenodo.1326842 Abstract: In this study which has been conducted in Akçasu Forest Range District of Devrek Forest Directorate; 3 methods (weed control with labourer power, cover removal with Hitachi F20 Excavator, and weed control with agricultural equipment mounted on a Ferguson 240S agriculture tractor) were utilized in weed control efforts in regeneration of degraded oriental beech forests have been compared. In this respect, 3 methods have been compared by determining certain work hours and standard durations of unit areas (1 hectare). For this purpose, evaluating the tasks made with human and machine force from the aspects of duration, productivity and costs, it has been aimed to determine the most productive method in accordance with the actual ecological conditions of research field. Within the scope of the study, the time studies have been conducted for 3 methods used in weed control efforts. While carrying out those studies, the performed implementations have been evaluated by dividing them into business stages. Also, the actual data have been used while calculating the cost accounts. In those calculations, the latest formulas and equations which are also used in developed countries have been utilized. The variance of analysis (ANOVA) was used in order to determine whether there is any statistically significant difference among obtained results, and the Duncan test was used for grouping if there is significant difference. According to the measurements and findings carried out within the scope of this study, it has been found during living cover removal efforts in regeneration efforts in demolished oriental beech forests that the removal of weed layer in 1 hectare of field has taken 920 hours with labourer force, 15.1 hours with excavator and 60 hours with an equipment mounted on a tractor. On the other hand, it has been determined that the cost of removal of living cover in unit area (1 hectare) was 3220.00 TL for labourer power, 1250 TL for excavator and 1825 TL for equipment mounted on a tractor. According to the obtained results, it has been found that the utilization of excavator in weed control effort in regeneration of degraded oriental beech regions under actual ecological conditions of research field has been found to be more productive from both of aspects of duration and costs. These determinations carried out should be repeated in weed control efforts in degraded forest fields with different ecological conditions, it is compulsory for finding the most efficient weed control method. These findings will light the way of technical staff of forestry directorate in determination of the most effective and economic weed control method. Thus, the more actual data will be used while preparing the weed control budgets, and there will be significant contributions to national economy. Also the results of this and similar studies are very important for developing the policies for our forestry in short and long term.
  • Phytopathology Prediction in Dry Soil Using Artificial Neural Networks Modeling
    Authors: F. Allag, S. Bouharati, M. Belmahdi, R. Zegadi, Keywords: Desert soil, Climatic changes, Bacteria, Vegetation, Artificial neural networks. DOI:10.5281/zenodo.1093868 Abstract: The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.
  • Modelling of Energy Consumption in Wheat Production Using Neural Networks “Case Study in Canterbury Province, New Zealand“
    Authors: M. Safa, S. Samarasinghe, Keywords: Artificial neural network, Canterbury, energy consumption, modelling, New Zealand, wheat. DOI:10.5281/zenodo.1084418 Abstract: An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).
  • Artificial Neural Network Models of the Ruminal pH in Holstein Steers
    Authors: Alireza Vakili, Mohsen Danesh Mesgaran, Majid Abdollazade, Keywords: Ruminal pH, Artificial Neural Network (ANN), Non Fiber Carbohydrate, Neutral Detergent Fiber. DOI:10.5281/zenodo.1078301 Abstract: In this study four Holstein steers with rumen fistula fed 7 kg of dry matter (DM) of diets differing in concentrate to alfalfa hay ratios as 60:40, 70:30, 80:20, and 90:10 in a 4 × 4 latin square design. The pH of the ruminal fluid was measured before the morning feeding (0.0 h) to 8 h post feeding. In this study, a two-layered feed-forward neural network trained by the Levenberg-Marquardt algorithm was used for modelling of ruminal pH. The input variables of the network were time, concentrate to alfalfa hay ratios (C/F), non fiber carbohydrate (NFC) and neutral detergent fiber (NDF). The output variable was the ruminal pH. The modeling results showed that there was excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 >0.96). Therefore, we suggest using these model-derived biological values to summarize continuously recorded pH data.
  • Effect of Different Treatments on the Periphyton Quantity and Quality in Experimental Fishponds
    Authors: T. Kosáros, D. Gál, F. Pekár, Gy. Lakatos, Keywords: Artificial substrate, fishpond, periphyton, waterquality DOI:10.5281/zenodo.1078044 Abstract: Periphyton development and composition were studied in three different treatments: (i) two fishpond units of wetland-type wastewater treatment pond systems, (ii) two fishponds in combined intensive-extensive fish farming systems and (iii) three traditional polyculture fishponds. Results showed that amounts of periphyton developed in traditional polyculture fishponds (iii) were different compared to the other treatments (i and ii), where the main function of ponds was stated wastewater treatment. Negative correlation was also observable between water quality parameters and periphyton production. The lower trophity, halobity and saprobity level of ponds indicated higher amount of periphyton. The dry matter content of periphyton was significantly higher in the samples, which were developed in traditional polyculture fishponds (2.84±3.02 g m-2 day-1, whereby the ash content in dry matter 74%), than samples taken from (i) (1.60±2.32 g m-2 day-1, 61%) and (ii) fishponds (0.65±0.45 g m-2 day-1, 81%).
  • Investigating Feed Mix Problem Approaches: An Overview and Potential Solution
    Authors: Rosshairy Abd Rahman, Chooi-Leng Ang, Razamin Ramli, Keywords: Artificial bee algorithm, feed mix problem, hybrid genetic algorithm. DOI:10.5281/zenodo.1075012 Abstract: Feed is one of the factors which play an important role in determining a successful development of an aquaculture industry. It is always critical to produce the best aquaculture diet at a minimum cost in order to trim down the operational cost and gain more profit. However, the feed mix problem becomes increasingly difficult since many issues need to be considered simultaneously. Thus, the purpose of this paper is to review the current techniques used by nutritionist and researchers to tackle the issues. Additionally, this paper introduce an enhance algorithm which is deemed suitable to deal with all the issues arise. The proposed technique refers to Hybrid Genetic Algorithm which is expected to obtain the minimum cost diet for farmed animal, while satisfying nutritional requirements. Hybrid GA technique with artificial bee algorithm is expected to reduce the penalty function and provide a better solution for the feed mix problem.
  • Computational Model for Predicting Effective siRNA Sequences Using Whole Stacking Energy (% G) for Gene Silencing
    Authors: Reena Murali, David Peter S., Keywords: Artificial Neural Network, Double Stranded RNA, RNA Interference, Short Interfering RNA. DOI:10.5281/zenodo.1326828 Abstract: The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies show that upregulation of mRNA because serious diseases like cancer. So designing effective siRNA with good knockdown effects plays an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (%G), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.