106 research outputs found
Experimental investigation of the influence of wire offset and composition on complex profile WEDM of Ti6Al4V using trim-pass strategy
No full textWire electric discharge machining (WEDM) is an important non-traditional manufacturing technique for industries processing hard-to-machine materials. It can produce complex shapes with high dimensional accuracy and surface finish. Ti6Al4V is frequently used in biomedical applications such as surgical implants, dentistry, and orthopedic wires. All these applications require machining complex profiles with high accuracy in terms of dimensions and surface properties. Multi-pass machining is a proven technique for minimizing the damage on the machined surface but increasing the number of passes lowers the productivity. Hence, careful selection of wire offset value for trim cutting is crucial to maintain process efficiency and keep the number of passes minimum. The objective of this study is to investigate the effect of wire offset in multi-pass machining on surface integrity, dimensional accuracy, and cutting speed in complex WEDM of Ti6Al4V and limit the number of trim passes to one. In addition, effect of electrode composition on machining responses is studied for three different types of wires (uncoated brass, Broncocut-W, and Topas Plus X). Experimental results indicate that a single trim cut at an offset value of 0.11mmprovides better surface finish and minimum recast layer. Surface roughness of 1.31 mu m is obtained using brass wire: 16.5% and 18.6% less than for Broncocut-W and Topas plus X, respectively. Similarly, recast layer of 8.183 mu m attained by brass wire is smaller than 8.98 mu m, and 10.041 mu m produced by the other wires. The uncoated brass wire has proved to be the best electrode for surface finish, recast layer thickness, and dimensional accuracy of the machined profile. However, Bronococut-W wire has performed better in terms of cutting speed
Suicidal and homicidal tendencies after Lyme disease: an ignored problem
No full textAitzaz Munir,1 Muhammad Aadil,2 Ahmad Rehan Khan3 1Department of Psychiatry, Howard University, Washington, DC, USA; 2Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA; 3Department of Psychiatry, University of North Dakota, Grand Forks, ND, USA We would like to applaud the author for conducting such an important study by performing a comprehensive assessment of suicide and its association with Lyme-associated diseases (LADs).1 It is the first study of its kind, and it raises a need for further investigation on this subject. Suicide is a major health care issue in the USA, contributing to almost 42,773 deaths in the USA in 2014.2 There is no data available specific to suicide associated with LAD. Dr Bransfield inferred the possible prevalence of suicide associated with LAD by an indirect method which revealed that 414,540 patients with LAD have suicidal ideation, 31,100 attempt suicide and a total of 1,244 commit suicide in the USA per year from LAD.1,2 View the original paper by Bransfield. 
Probabilistic and Risk Analysis ofChannel Allocation Schemes,based on Markov Chain Model,used for Next Generation Networks
No full textA Comparative Study of Physical and Chemical Method for Separation of Benzoic Acid from Industrial Waste Stream
No full textUsing image steganography for providing enhanced medical data security
No full textRecently, image steganography is being considered as an alternative method for securing medical data to avoid medical related cybercrimes. This paper proposes a new image steganography approach for securing medical data. Swapped Huffman tree coding is used to apply lossless compression and manifold encryption to the payload before embedding into the cover image. Additionally, only edge regions of the cover image are used to embed the secret data which offers high imperceptibility. The results show that the proposed method ensures confidentiality and secrecy of patient information while maintaining imperceptibility
Estimating Passenger Car Equivalent Factors for Heterogeneous Traffic Using Occupancy-Density Linear Regression Model
No full textA variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported with funding from Exascale Open Data Analytics Lab, National Center for Big Data and Cloud Computing (NCBC) and the Higher Education Commission of Pakistan.
Acknowledgments
The authors are thankful to research students Syed Hassan Ali, Haseeb Ahmed, Zohaib Ahmed, Aqib Abbasi, Asad Rehan, Mirza Ali Haider, Syed Abbas Hasan Zaidi, and Omema for their help in this research
Waste Biorefineries: Future Energy, Green Products and Waste Treatment
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms: Nexus of field data and modelling
No full textIn this study, three novel machine learning algorithms of additive regression-random forest (AR-RF), Iterative Classifier Optimizer (ICO-AR-RF), and multi-scheme (MS-RF) were explored for carbon dioxide (CO2) flux rate prediction from three agricultural fields. To build the dataset, 401 samples were collected from two fields in Prince Edward Island (PEI) and 122 samples from the New Brunswick (NB), Canada. In addition, soil moisture (SM), temperature (ST), and electrical conductivity (EC), alongside eight climatic variables including wind speed (WS), solar radiation (SR), relative humidity (RH), precipitation (PCP), air temperature (AT), dew point (DP), vapour pressure difference (VPD) and reference evapotranspiration (ETo) were also collected. Greedy stepwise (GS) approach was implemented for feature selection. Finally, different qualitative (scatter plot, line graph, Taylor diagram, box plot, and Rug plot), and quantitative (uncertainty analysis, root mean square error (RMSE), percent of BIAS (PBIAS), Nash Sutcliff efficiency (NSE) and RMSE-observations standard deviation ratio (RSR)) techniques were used for model evaluation and comparison. Results of feature selection approaches revealed that DP, AT, SM, and ST are the four most effective variables at CO2 prediction in PEI, while AT, RH, DP, and ST are the most effective in the NB study area. For optimum input scenario, the GS algorithm was applied, and results showed that a combination of DP, AT, ST, SM, and ETo was the best for the PEI study area, while for NB, all input variables should be involved. Our analysis, for prediction of CO2 fluxes, confirmed that the ICO-AR-RF model performed the best at both PEI (RMSE=0.70, NSE=0.76, PBIAS=-5.11, RSR=0.48) and NB (RMSE=0.74, NSE=0.75, PBIAS=3.23, RSR=0.50), followed by MS-RF and AR-RF. Uncertainty analysis showed that CO2 prediction is more sensitive to input scenario selection than models in both study areas. Results revealed that climatic variables are more effective in CO2 prediction than soil characteristics and the developed hybrid model ICO-AR-RF can be a promising tool for decision-makers and beneficial for stakeholders.Atlantic Canada Opportunities AgencyGovernment of CanadaNatural Science and Engineering Council of CanadaDepartment of Energy, Environment and Climate ActionGovernment of Prince Edward Islan
Waste Biorefineries: Future Energy, Green Products and Waste Treatment
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Channel resource allocation and availability prediction in hybrid access femtocells
No full textIn this paper, three channel assignment models are proposed for channel resource allocation in femtocells. The models proposed are based on the Markov chain process. According to the access mechanisms in femtocells, the Third-Generation Partnership Project (3GPP) has described two kinds of users in the femtocell application; open users and the closed subscriber group (CSG). In hybrid access mechanism for femtocells, both CSG users and open users are usually referred as subscribers and non-subscribers respectively. So, in this work, for all the proposed models, the CSG and open users are categorized into two groups; the subscriber group (SG) and the non-subscriber group (NSG) respectively. The proposed models provide priority based channel resource allocation strategies between the SG and NSG. Furthermore, the focus of this research is to provide variable channel resource sharing among the SG and NSG to keep the wastage of channel resources minimum for better quality of service (QoS). The analysis is conducted in terms of channel resource blocking management for all the models and to validate the analysis, simulations are performed at the end of this paper. Further, channel resource blocking prediction, based on the blocking probability results for SG and NSG users, is also provided at the end. The prediction is done through risk analysis using the @ Risk tool. The simulations are provided in two parts; 1) the probability curves for SG and NSG against the total number of channels and 2) the risk analysis results for blocked channels prediction using the @ Risk tool
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