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Nanocarbons were successfully synthesized from benzene (BZ), nitro-benzene (BZ-NO2) and aniline (BZ-NH2) by solution plasma process (SPP). The SPP was generated by a bipolar pulsed power supply between two tungsten electrodes at room temperature. The synthesized nanocarbons were investigated. The highest synthesis rate, 40 mg min−1, was for the BZ nanocarbon. The transmission electron microscopy (TEM) morphology showed that the nanocarbon sizes were 15–25 nm. The Brunauer–Emmett–Teller (BET) analysis shows a highest surface area of 220 m2 g−1, pore size of 0.45 cm3 g−1, and average pore diameter of 20.0 nm for the BZ nanocarbon. Cyclic voltammetry (CV) in an acidic medium exhibited the oxygen reduction reaction (ORR) of the nanocarbons. The nanocarbon from BZ-NH2 obtained a high special capacity of 15 500 mA h per g of carbon at the discharge rate of 0.1 mA cm−2 with 1.0 mg carbon loading for the lithium (Li)–air battery. The ORR is an important reaction in Li–air batteries and fuel cells for the application of next-generation batteries and energy conversion devices.

Machine learning and deep learning have been perceived as a commended technique for different pattern recognition purposes among data. A chunk of consideration has been given to social and demographic research and with an amalgamation of various machine learning and deep learning algorithms. In this paper, we anticipate structuring a machine learning and deep neural network-based mechanized system that can effectively induce religion, sexual orientation utilizing just the names of the masses. Additionally, our goal stretches out by inferring valuable demographic attributes like region and religious conversion using some additional information like age and parent’s name of the individual. By and large 10 machine learning and 3 deep learning algorithms are implemented to assemble this model which can derive 4 particular religions (Hindu, Muslim, Buddhist, and Christian), Gender (Male and Female), 6 regions (Dhaka, Khulna, Rajshahi, Chittagong, Sylhet, Barisal), and religious conversions with the most raised exactness pace. We also analyzed the performance and compared among all algorithms by using different statistical methods.

Multi-response shape memory polymers (SMPs) received significant attention due to their deformation with a temporary shape, and retain their original shape when exposed to an external stimulus such as temperature, light, and electricity. In this study, a novel reduced graphene oxide (RGO)/water-borne epoxy (WEP)/RGO sandwich structure composite film was fabricated and investigated. The results showed that the composite film exhibited excellent thermo-, electro- and light-responsive shape memory effect, as a potential SMP that can be used for the sensors, switching devices, deployable structures, and medical apparatus.

Geomagnetic disturbance (GMD) creates severe effects on power system specially in auroral areas, however, its severity is less or even negligible in middle and low latitude areas. However, power systems in low latitude non-auroral areas may face difficulty due to geomagnetically induced current (GIC) as voltage level, transmission line length, substation transformers, substation grounding may significantly add to the severity of GIC effect under certain conditions. The highest voltage in Bangladesh power system network is 400 kV. Taking the planned 400 kV power grid of Bangladesh as an example, effects of GIC on transmission lines including voltage drops at the receiving ends are observed varying geo electric field magnitude and directions from 0° north to 180° south.

This paper presents a time register that uses a gated delay pipeline, to hold or propagate time information through the line by a stacked NMOS switch. The minimum supply voltage can be down to 440 mV, which is much lower than previous state-of-the-arts whose supply voltage is 1 V or higher, making this design beneficial for wearable/implantable devices in biomedical applications. The post-layout simulation performed in 65-nm CMOS technology confirms the function of the proposed time register at a conversion rate of 10 MSamples/s. The stacked-NMOS-switched architecture contributes to low leakage current, realizing low power consumption of 72 nW. In addition, the coefficient of determination, denoted R2, is 0.9956 in the linear input range of 4–11 ns, indicating good linearity.

In this paper, we propose a widely tunable CMOS biosensor with an active-inductor-based voltage-controlled oscillator (VCO) for detecting circulating tumor cells (CTCs) and exosomes. The active-inductor-based VCO is employed to achieve broadband operation in a relatively small area. To validate the operation of the proposed biosensor, electromagnetic (EM) and SPICE simulations are performed. The results of the EM simulation indicate that an electric field extends to cells on the biosensor, whereas the SPICE simulation verifies that the oscillation frequencies of the biosensor vary with the presence of cells. These simulation results demonstrate the biosensor's potential in detecting biomarkers such as CTCs and exosomes.

This work proposes a solar-cell-assisted wireless biosensing system that operates using a biofuel cell (BFC). To facilitate the reduction of the BFC area and apply this system to area-constrained continuous glucose monitoring (CGM) contact lenses, an energy harvester combined with an on-chip solar cell is introduced as a dedicated power source for the transmitter. A dual-oscillator-based supply voltage monitor is employed to convert the BFC output into digital codes. From measurements of the test chip fabricated with the 65-nm CMOS technology, the proposed system can achieve BFC area reduction of 99.66%.

This paper presents a 40-GHz fully-integrated CMOS biosensing system with an on-chip vector network analyzer (VNA) and a detection area for circulating tumor cells (CTCs) analysis. A first-stage mixer is configurated to enable the operation at 40 GHz, which has the capability of analyzing CTCs highly sensitively owing to γ dispersion. To confirm the effectiveness of the proposed system, a test chip was fabricated with 65-nm CMOS technology, and measured by adding magnetic beads at different concentrations on the detection area. The measurement result demonstrated that the proposed circuit could characterize beads even at a low concentration of 10 beads/μL. Additionally, it was observed it could improve the sensitivity and limit of detection (LOD) compared with the previous 1.4-GHz-system: from 0.0385 dB/(beads/μL) to 0.0745 dB/(beads/μL) in sensitivity and from 8.3 to 3.3 beads/μL in LOD. It can be estimated that this biosensing circuit can detect a lower concentration of a CTCs sample named H1976 cells than 50 cells/μL. Therefore, the proposed circuit has the possibilities to analyze CTCs practically.

In this study, we developed an AI-based edge-intelligent hypoglycemia prediction system for the environment with low-periodic blood glucose level. By using long-short-term memory (LSTM), a specialized network for handling time series data among neural networks along with introducing alternate learning and inference, it was possible to predict the BG level with high accuracy. In order to achieve, the system for predicting the blood glucose level was created using LSTM, and the performance of the system was evaluated using the method of the classification problem. The system was successfully predicted the probability of occurrence of hypoglycemia after 30 min at approximately 80% times. Furthermore, it was demonstrated that accuracy is improved by alternately performing learning and prediction.

Smartphones have become the ultimate ‘personal’ computers with sufficient processing power and storage to perform machine learning tasks. Building on our previous research, this paper investigates an example practical application of this capability, combining it with a smartphone’s on-board sensors to develop a personalised, self-contained machine-learning framework for monitoring mental health. We present a mobile application for Android devices called ‘Mindful’ that incorporates data collection from the phone’s sensors and data sources, pre-processes the data locally and executes data mining on that data to provide pre-emptive feedback to the phone user about their mind state. Rather than as a finished product, this application is presented as a first step to show that from a technological perspective, smartphones are well equipped to perform this type of role. We invite colleagues from the mental health sciences to join us in furthering this work into a smart monitor for mental health.

A combination method is an integral part of an ensemble classifier. Existing combination methods determine the combined prediction of a new instance by relying on the predictions made by the majority of base classifiers. This can result in incorrect combined predictions when the majority predict the incorrect class. It has been noted that in group decision-making, the decision by the majority, if lacking consistency in the reasons for the decision provided by its members, could be less reliable than the minority’s decision with higher consistency in the reasons of its members. Based on this observation, in this paper, we propose a new combination method, EBCM, which considers the consistency of the features, i.e. explanations of individual predictions for generating ensemble classifiers. EBCM firstly identifies the features accountable for each base classifier’s prediction, and then uses the features to measure the consistency among the predictions. Finally, EBCM combines the predictions based on both the majority and the consistency of features. We evaluated the performance of EBCM with 16 real-world datasets and observed substantial improvement over existing techniques Refereed/Peer-reviewed

Smartphones are increasingly being used to capture data and perform complex tasks, however, this rarely extends to the local training of data models. This study investigates the implementation of data mining classification algorithms on smartphones, using 20 classifiers and nine mixed-design datasets on three devices. Their accuracy and processing speed are further compared against a laptop computer using our cross-platform ‘DataBench’ software. Results show that smartphones not only deliver classification accuracy equal to that of more powerful computers when using the same algorithms, as expected, but also, as many as 75% of the 180 algorithm/dataset learning tasks tested were completed on smartphone hardware within three seconds. However, tests further show that the increased complexity of newer algorithms searching for ever greater classification accuracy is resulting in model-build times growing at an exponential rate. Additional testing identified that while a single algorithm execution can have negligible effect on battery life, power efficiency is affected by algorithm complexity, data size and attribute type. The increased processing demand of local model-learning on smartphones also results in increased power dissipation. Yet, even on a continuous-loop execution basis, mobile temperature gains over a 15-min period did not exceed 7 °C. Our conclusion is that smartphones are ready to form self-reliant mobile data-mining solutions able to efficiently execute a wide range of classification algorithms. This offers numerous advantages, including data security and privacy improvements, removal of reliance on network connectivity and delivery of personalised learning.

Octadecane (OD), a heat storage material, was encapsulated into poly(divinylbenzene-co-methyl methacrylate) or P(DVB-co-MMA) matrix via the microsuspension polymerization. The oil droplets were first generated by using the phase inversion emulsification, i.e., adding a sodium dodecyl sulphate (SDS) aqueous solution into an oil phase (monomers:OD = 1:1) containing various concentrations of poly(vinyl alcohol) (PVA). Results showed that 0.1 wt% of SDS in aqueous medium with the additional rate of 2 mL/min and 3 wt% of PVA in oil phase were the optimal conditions for the formation of nonspherical microcapsules. The smallest number- (dn) and weight- (dw) average diameters of P(DVB-co-MMA)/OD were, respectively, 3.1 and 3.2 µm with a narrow particle size distribution (dw/dn) of 1.02. The latent heats of the encapsulated OD increased with increasing MMA content of microcapsules. The heat of melting (ΔHm; 223 J/g-OD) and crystallization (ΔHc; 230 J/g-OD) of the OD encapsulated in microcapsules using DVB:...

Decision trees are one of the most popular classifiers used in a wide range of real-world problems. Thus, it is very important to achieve higher prediction accuracy for decision trees. Most of the well-known decision tree induction algorithms used in practice are based on greedy approaches and hence do not consider conditional dependencies among the attributes. As a result, they may generate suboptimal solutions. In literature, often genetic programming-based (a complex variant of genetic algorithm) decision tree induction algorithms have been proposed to eliminate some of the problems of greedy approaches. However, none of the algorithms proposed so far can effectively address conditional dependencies among the attributes. In this paper, we propose a new, easy-to-implement genetic algorithm-based decision tree induction technique which is more likely to ascertain conditional dependencies among the attributes. An elaborate experimentation is conducted on thirty well known data sets from the UCI Machine Learning Repository in order to validate the effectiveness of the proposed technique.

Electrostatic interactions (EIs) play important roles in the structure and stability of inverse bicontinuous cubic (QII) phases of lipid membranes. We examined the effect of pH on the phase of dioleoylphosphatidylserine (DOPS)/monoolein (MO) membranes at low ionic strengths using small-angle X-ray scattering (SAXS). We found that the phase transitions from lamellar liquid-crystalline (Lα) to primitive cubic (QIIP) phases in DOPS/MO (2/8 molar ratio) membranes occurred in buffers containing 50 mM NaCl at and below the final pH of 2.75 as the pH of the membrane suspension was decreased from a neutral value. The kinetic pathway of this transition was revealed using time-resolved SAXS with a stopped-flow apparatus. The first step is a rapid transition from the Lα phase to the hexagonal II (HII) phase, and the second step is a slow transition from the HII phase to the QIIP phase. We determined the rate constants of the first step, k1, and of the second step, k2, by analyzing the time course of SAXS intensities...

This case study illustrates the effects of organizational contexts (e.g., structure and climate) on knowledge conversion, along with the mediating effect of socialization and the moderating impact of technology. Using the hierarchical regression methodology in the Malaysian service sector, this article argues that socialization has a significant effect on knowledge conversion. The case analysis underlines the issue that arise when socialization serves as both a dependent variable (to organizational context variables) and an independent (mediating) variable (to knowledge conversion). However, technology does not act as a moderator between organizational context and knowledge conversion. This paper brings them together to explain their linkage through mediation by socialization and moderation by technology, and to quantify the relationship.

In this research, we've tried to develop a method with background subtraction, distance measurement and color histogram with particle filter, to track any single moving object. In visual moving object tracking, the appearance of both objects and the surrounding scenes may experience enormous variations due to changes in the scale and viewing angles, or partial occlusions. Also the objects and the backgrounds may have confusing color. These challenges may weaken the effectiveness of a dedicated target observation model when based on color feature. Background subtraction helps, to eliminate unnecessary regions, to track even when the target object and the background has similar color and thereby reduces the number of particles as well as the execution time and cost. Moreover we use distance measurement information, to make the tracker successful, when there are several objects with similar color. Experimental results have been presented to show the effectiveness of our proposed system.

DOI: 10.19072/ijet.280499, An aerofoil is stream line body. Symmetric aerofoil (NACA 0015) is used in many applications such as in aircraft submarine fins, rotary and some fixed wings. The ultimate objective of an aerofoil is to obtain the lift necessary to keep an airplane in the air. But construction of the blade with proper angle of attack and implementation has significant effect on lift force. Insufficient lift force might cause fail of airplane flying, especially at high speed. Modern technologist use different simulation techniques to avoid costly model testing. But simulation is based on some assumption. Thus practically results are not fully authentic and have a deviation. In this work numerical and experimental investigation of NACA 0015 is studied at different angle of attack (degree) at different velocity of air by determining the forces at every two degrees from 00 to 180. The experimental is conveyed in a low speed wind tunnel. The numerical analysis is conducted using ANSYS (combined with CFD and FLUENT FLOW). The use of the CFD technology greatly reduces the overall investment and efforts for aerofoil design. CFD method contributes to visualize the flow pattern inside aerofoil and takes less time and faster according to experimental methods. After completing the experimental and numerical data is compared. Therefore objective of this paper is to find the deviation and validation of aerodynamics characteristics of aerofoil NACA 0015 for experimental and numerical method.

Data mining information about people is becoming increasingly important in the data-driven society of the 21st century. Unfortunately, sometimes there are real-world considerations that conflict with the goals of data mining; sometimes the privacy of the people being data mined needs to be considered. This necessitates that the output of data mining algorithms be modified to preserve privacy while simultaneously not ruining the predictive power of the outputted model. Differential privacy is a strong, enforceable definition of privacy that can be used in data mining algorithms, guaranteeing that nothing will be learned about the people in the data that could not already be discovered without their participation. In this survey, we focus on one particular data mining algorithm -- decision trees -- and how differential privacy interacts with each of the components that constitute decision tree algorithms. We analyze both greedy and random decision trees, and the conflicts that arise when trying to balance privacy requirements with the accuracy of the model., Pre-print of paper accepted in ACM Computing Surveys, 35 pages

It is already discovered that miRNA works as a regulator for the functions and production of mRNA. Malfunctions in this regulatory relationship can lead to different types of genetic life threatening diseases. Identifying miRNA-mRNA interaction is very important to biological research for understanding complex process of genes, and to medical research for discovering new genetic medicines. Standard methods for identifying miRNA-mRNA interactions requires chemical experiment which is very time consuming and costly. Therefore, researchers have been trying to find alternatives. Several computational methods are being developed to replace chemical experiments. Many computational methods show promising results. IDA is a computational method used to discover miRNA-mRNA regulatory relationships. Although IDA shows good predictions for gene-gene interactions, it's running time is very high. We propose an improvement to the IDA reducing its running time without affecting prediction performance.

Existing systems for temporal relation extraction is spread over many domains from statistical approaches to hand crafted inference rules. Systems using hand crafted rules show good results in spite of the fact that they require human expertise that can be costly sometimes. Therefore, there is a need for fully automated systems independent of human interactions. Most of the existing temporal relation extraction systems suffer from sparseness of the available dataset. We aim to minimize this problem using an Apriori based algorithm which is proved to efficiently deal market basket datasets. Market basket datasets are sparse by nature; it is therefore hoped that Apriori will perform better in temporal relation extraction. Our proposed method first extract features from the dataset, then vectorize the features so that Apriori algorithm can be applied on the data. Then frequent itemsets are generated by the Apriori algorithm from which the temporal relations are extracted.

Persistent efforts are going on to propose more accurate decision forest building techniques. In this paper, we propose a new decision forest building technique called “Forest by Continuously Excluding Root Node (Forest CERN)”. The key feature of the proposed technique is that it strives to exclude attributes that participated in the root nodes of previous trees by imposing penalties on them to obstruct them appear in some subsequent trees. Penalties are gradually lifted in such a manner that those attributes can reappear after a while. Other than that, our technique uses bootstrap samples to generate predefined number of trees. The target of the proposed algorithm is to maximize tree diversity without impeding individual tree accuracy. We present an elaborate experimental results involving fifteen widely used data sets from the UCI Machine Learning Repository. The experimental results indicate the effectiveness of the proposed technique in most of the cases.

In this study, the mechanical properties of the composites made of areca fibers as reinforcing agent with polyester matrix were evaluated. Both untreated and treated (mercerized) fibers were used to fabricate composites and the mechanical properties such as tensile strength (TS), tensile modulus (TM), flexural strength (FS), flexural modulus (FM) and impact strength (IS) of the composites were examined. Composites were manufactured by means of hand lay-up technique with varying fiber content (5 and 10%). Mechanical properties of plywood were also tested and compared with the composites. It was found that areca fiber composites showed significant improved mechanical properties than plywood. Among the composite materials 10% fiber loading presented higher mechanical properties than 5% fiber content and the treated fiber composites exhibited enhanced mechanical properties than the untreated fiber composites due to strong interfacial bonding between fiber and matrix. In addition, water uptake test was performed and specifies that areca composites absorbed very much lower amount of water than that of plywood. The morphology was investigated by scanning electron microscopy (SEM).

Managing access control to data in an authorized and authenticated way is still one of the key challenges in cloud security. In a complex environment like cloud, data owners and cloud service providers need to continuously monitor all data access activities (who is accessing which data) in order to prevent unauthorized access. In this research, we have developed a dynamic access control system for cloud computing environment along with policy conflict resolution algorithm and several authorization validation processes. Our proposed system includes four models, namely access right model, policy model, access control management model and authorization model. The proposed system introduces a more efficient security scheme using an enhanced authorization scheme. Our experimental analysis depicts that the proposed model can efficiently deal with the access control management in the cloud environment. Refereed/Peer-reviewed

The thermal diffusivity of two bulk thermoelectric elements and a thermoelectric module was measured by an infrared camera using a thermographic method without any contact in air at room temperature. The estimated values for the elements (3.45 × 10−6 m2/s for a BiSb sample and 1.60 × 10−6 m2/s for a BiTe sample) were slightly larger than those measured in vacuum. The difference was explained as the effect of heat convection on the surface of the samples by solving the one-dimensional heat conduction equation numerically. The thermal diffusivity of thermoelectric elements in a thermoelectric module was also estimated using the thermographic method, and values of (1.1–1.7) × 10−6 m2/s in air were obtained, depending on the element. On the basis of the measurement results, the performance of the module was estimated using impedance spectroscopy, which can estimate not only the dimensionless figure of merit but also the thermal loss and response. The thermal response and thermal loss in air were similar to those in vacuum; however, the dimensionless figure of merit was 0.82 in vacuum and 0.70 in air.