Biocalalyst effects of immobilized anthraquinone on the anaerobic reduction of azo dyes by the salt-tolerant bacteria

The accelerating effect of dissolved redox mediators has been studied in details in the bio-decolorization processes, but there are little literatures about the non-dissolved redox mediators. Here we describe the accelerating effect of anthraquinone as a redox mediator in the bio-decolorization. Decolorization of azo dyes was carried out experimentally using the salt-tolerant bacteria under immobilized anthraquinone and high salt conditions. Anthraquinone as a redox mediator was able to increase the decolorization rate of azo dyes, and was immobilized by entrapment in calcium alginate (CA), Polyvinyl alcohol (PVA)-H(3)BO(3) and agar, respectively. The effects of various operating conditions such as anthraquinone bead number, dissolved oxygen, temperature and pH on microbial decolorization were investigated experimentally. The reusability of the anthraquinone immobilization beads was evaluated with repeated-batch decolorization experiments. After four repeated experiments, the decolorization rate of CA immobilized anthraquinone retained over 90% of their original value. The experiments explored a great improvement of the redox mediator application and the new bio-treatment concept.     

MHD flow over an inclined radiating plate with the temperature‐dependent thermal conductivity,variable reactive index,and heat generation

We study the effects of higher‐order chemical reaction and heat generation on coupled heat and mass transfer by MHD mixed convection from a permeable radiating inclined plate with the thermal convective boundary condition. The governing boundary layer equations are formulated and transformed into a set of similarity equations using dimensionless similarity variables developed by Lie group analysis. The resulting equations are then solved numerically using Maple 13 which uses a fourth–fifth order Runge–Kutta–Fehlberg algorithm for solving nonlinear boundary value problems. A representative set of numerical results are displayed graphically and discussed to show some interesting aspects of the parameters: convective heat transfer (γ), the angle of inclination (α), generation order of chemical reaction (n), reaction rate (λ), the Prandtl number (Pr), and the Schmidt number (Sc) on the dimensionless axial velocity, the temperature, and the concentration profiles. Also effects of pertinent parameters on the skin friction factor, the rate of heat, and the rate of mass transfer are obtained and displayed in tabular form. Good agreement is found between the numerical results of the present paper with the earlier published works under some special cases. © 2012 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.20409     

Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics

The increasing demand for storage, networking and computation has driven intensification of large complex data centers that run many of today's Internet, financial, commercial and business applications. A data center comprises of many thousands of servers and can use as much energy as small city. Massive amount of computation power is required to drive and run these server farms resulting in many challenging like huge energy consumptions, emission of green house gases, backups and recovery; This paper proposes energy efficiency and low carbon enabler green IT framework for these large and complex server farms to save consumption of electricity and reduce the emission of green house gases to lower the effects of global warming. The framework uses latest energy saving techniques like virtualization, cloud computing and green metrics to achieve greener data centers. It comprises of five phase to properly implement green IT techniques to achieve green data centers. The proposed framework seamlessly divides data center components into different resource pools and then applies green metrics like Power Usage Effectiveness, Data Center Effectiveness and Carbon Emission Calculator to measure performance of individual components so that benchmarking values can be achieved and set as standard to be followed by data centers.     

Wear Measurement and Assessment of Explanted Cross-Linked PE Acetabular Cups Using a CMM

Wear has been considered the main limiting factor in the longevity of hip replacements. Wear analysis is thus essential for determining wear-related failure mechanisms and prediction of wear, which will consequently enable biomedical engineers to improve the design, material, and service life of the bearing components. This article presents wear measurement and assessment of the explanted conventional cross-linked polyethylene (XPE) and second-generation highly cross-linked polyethylene cups (X3) using a coordinate measuring machine (CMM). An expanded uncertainty analysis was performed to assess the performance of wear measurement. Wear measurement using the CMM method was validated with the gravimetric technique. The normalized error between volumetric wear measurement of the CMM method and that of the gravimetric technique was estimated to be always less than 1, suggesting that the CMM method applied to explanted hip wear measurements under the specific conditions was accurate and reliable. The approach to CMM measurement with uncertainty analysis was shown not only to locate 3D wear scar and wear direction but also to accurately quantify linear and volumetric wear with a maximum volumetric uncertainty of ±3.15 mm³ (95% confidence level). It is shown that identifying the key uncertainty components involved in the measurement process including validation, which contributes to an overall expanded uncertainty budget, is crucial to improve the confidence and the reliability of hip wear measurement results using a CMM.     

Computation of a compact state space model for an adaptive spindle head configuration with piezo actuators using balanced truncation

Finite element models of machine tools or their building blocks are usually very large and thus do not allow for fast simulation or application in controller design. Especially when algebraic constraints come into play the models become differential algebraic equations and therefore are even more difficult to handle in the application. In this contribution we propose a method based on modern system theoretic model order reduction algorithms that allows to generate a first order standard state space reduced order model for a structural model of an adaptive spindle support that is of second order index 1 differential algebraic form due to the piezo actuation applied. The accuracy of the method is demonstrated by a numerical frequency domain error analysis.     

Adaptive computer-based spatial-filtering method for more accurate estimation of the surface velocity of debris flow

An adaptive computer-based spatial-filtering velocimeter to measure the surface velocity of a natural debris flow with high accuracy is described that can adjust the filter parameters, specifically, the slit width of the filter, based on the surface-pattern characteristics of the flow. A computer simulation confirms the effectiveness of this technique. The surface velocity of a natural debris flow at the Mt. Yakedake Volcano, Japan, was estimated by this adaptive method, and the results were compared with those obtained by two other methods: hardware-based spatial filtering and normal computer-based spatial filtering.     

Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank

One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth, established by the expert ophthalmologist’s tests. The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%. The proposed method potentially offers a second opinion to ophthalmologists, and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.     

A Cost-Effective Approach for NDN-Based Internet of Medical Things Deployment

Nowadays, healthcare has become an important area for the Internet of Things (IoT) to automate healthcare facilities to share and use patient data anytime and anywhere with Internet services. At present, the host-based Internet paradigm is used for sharing and accessing healthcare-related data. However, due to the location-dependent nature, it suffers from latency, mobility, and security. For this purpose, Named Data Networking (NDN) has been recommended as the future Internet paradigm to cover the shortcomings of the traditional host-based Internet paradigm. Unfortunately, the novel breed lacks a secure framework for healthcare. This article constructs an NDN-Based Internet of Medical Things (NDN-IoMT) framework using a lightweight certificateless (CLC) signature. We adopt the Hyperelliptic Curve Cryptosystem (HCC) to reduce cost, which provides strong security using a smaller key size compared to Elliptic Curve Cryptosystem (ECC). Furthermore, we validate the safety of the proposed scheme through AVISPA. For cost-efficiency, we compare the designed scheme with relevant certificateless signature schemes. The final result shows that our proposed scheme uses minimal network resources. Lastly, we deploy the given framework on NDN-IoMT.     

A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything

Internet of Everything (IoE) indicates a fantastic vision of the future, where everything is connected to the internet, providing intelligent services and facilitating decision making. IoE is the collection of static and moving objects able to coordinate and communicate with each other. The moving objects may consist of ground segments and flying segments. The speed of flying segment e.g., Unmanned Ariel Vehicles (UAVs) may high as compared to ground segment objects. The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE (Ue-IoE). The routing maintenance overhead may increase when scaling the Ue-IoE (number of objects increases). A single change in topology can force all the objects of the Ue-IoE to update their routing tables. Similarly, the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease. The objects consume more energy on routing computations. To prevent the frequent updation of routing tables associated with each object, the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE. In this article, we propose a routing scheme in which the responsibility of route computation (from neighbor objects to destination) is assigned to some IoE-objects in the Ue-IoE. The route computation objects (RCO) are selected on the basis of certain parameters like remaining energy and mobility. The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects. The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption, messages overhead, throughput, delay etc.     

Liver-Tumor Detection Using CNN ResUNet

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest (ROI). This study uses ResUNet, an updated version of U-Net and ResNet Models that utilize the service of Residential blocks. We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients. The results showed the True Value Accuracy around 99% and F1 score performance around 95%. This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.