A Broadband Fluorographene Photodetector

High‐performance photodetectors operating over a broad wavelength range from ultraviolet, visible, to infrared are of scientific and technological importance for a wide range of applications. Here, a photodetector based on van der Waals heterostructures of graphene and its fluorine‐functionalized derivative is presented. It consistently shows broadband photoresponse from the ultraviolet (255 nm) to the mid‐infrared (4.3 µm) wavelengths, with three orders of magnitude enhanced responsivity compared to pristine graphene photodetectors. The broadband photodetection is attributed to the synergistic effects of the spatial nonuniform collective quantum confinement of sp² domains, and the trapping of photoexcited charge carriers in the localized states in sp³ domains. Tunable photoresponse is achieved by controlling the nature of sp³ sites and the size and fraction of sp³/sp² domains. In addition, the photoresponse due to the different photoexcited‐charge‐carrier trapping times in sp² and sp³ nanodomains is determined. The proposed scheme paves the way toward implementing high‐performance broadband graphene‐based photodetectors.     

Thermal and mechanical properties of polypropylene nanocomposite materials reinforced with cellulose nano whiskers

We use polypropylene (PP, an apolar polymer) and cellulose nano whiskers (CNW, a polar material) to produce nano polymer composites with enhanced mechanical and thermal properties. To improve compatibility, maleic anhydride grafted PP has also been used as a coupling agent. To enhance the uniform distribution of CNW in the composite, the matrix polymer is dissolved in toluene, and sonification and magnetic stirring are applied. Good film transparency indicates uniform CNW dispersion, but CNW domains in the composite film observed under an scanning electron microscope may indicate slight agglomeration of CNW in the composite film. The tensile strength of the composite compared with neat PP improves by 70–80% with the addition of CNW. The crystallinity has also been improved by about 50% in the CNW reinforced samples. As the content of CNW increases, the composite exhibits higher thermal degradation temperature, higher hydrophilicity, and higher thermal conductivity. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

On the peak-to-average power ratio reduction in mobile WiMAX: A discrete cosine transform matrix precoding based random-interleaved orthogonal frequency division multiple access uplink system

Mobile worldwide interoperability for microwave access (Mobile WiMAX) is a broadband wireless solution that enables the convergence of mobile and fixed broadband networks through a common wide area radio-access (RA) technology and flexible network architecture. Since January 2007, the IEEE 802.16 working group (WG) has been developing a new amendment of the IEEE 802.16 standard i.e. IEEE 802.16 m as an advanced air interface to meet the requirements of ITU-R/IMT-Advanced for 4 G systems. The mobile WiMAX air interface adopts orthogonal frequency division multiple access (OFDMA) as multiple access technique for its uplink (UL) and downlink (DL) to improve the multipath performance. All OFDMA based networks, including mobile WiMAX experiences the problem of high peak-to-average power ratio (PAPR). This paper presents: Discrete-Cosine transform matrix (DCTM) precoding based random-interleaved OFDMA uplink system and selecting mapping (SLM) based DCTM precoded random-interleaved OFDMA uplink system respectively, for PAPR reduction in mobile WiMAX systems. PAPR of the proposed systems is analyzed with the root-raised-cosine (RRC) pulse shaping to keep out of band radiation low and to meet the transmission spectrum mask requirement. Simulation results show that, the proposed systems have low PAPR than the Walsh-Hadamard transform (WHT) precoded random-interleaved OFDMA uplink systems and the conventional random-interleaved OFDMA uplink systems. Symbol-error-rate (SER) performance of the proposed system is also better than the conventional random-interleaved OFDMA uplink systems and at par with WHT based random-interleaved OFDMA uplink systems. Good improvement in PAPR and SER offered by the proposed systems can notably reduce the cost and complexity of the transmitter.     

Optimal power and subcarriers allocation in downlink OFDMA system with imperfect channel knowledge

Most of existing work on resource allocation in TDMA and OFDMA systems assumes the availability of perfect channel state information (CSI) at the transmitter, which is rarely possible due to feedback delay and channel estimation error. In this paper, we study the effect of feedback delay and channel estimation error on margin adaptive resource allocation in a downlink OFDMA system. By using convex optimization framework, we find an optimal solution to the problem. First, we study the individual effect of feedback delay and channel estimation error on resource allocation by considering them exclusively. Then, we consider the simultaneous presence of feedback delay and channel estimation error and study their combined effect on resource allocation. We derive an explicit close form expression for the users’ transmit power and propose an algorithm for power and subcarriers allocation for each of these three scenarios. The algorithms have polynomial complexities and solve the problem with zero optimality gaps. Simulation results show that the system performance is very sensitive to feedback delay and is affected significantly by imperfect channel estimation. Our proposed algorithms highly improve the system performance in the availability of only imperfect CSI at the transmitter.     

Investigation of Magnetic,Magnetocaloric, and Critical Properties of La<Subscript>0.5</Subscript>Ba<Subscript>0.5</Subscript>MnO<Subscript>3</Subscript> Manganite

We present an extensive study of the magnetic properties of a novel La_0.5Ba_0.5MnO₃ perovskite material prepared by the hydrothermal method. The explored sample was structurally studied by the x-ray diffraction (XRD) method which confirms the formation of a pure cubic phase of a perovskite structure with Pm3m space group. The magnetic properties were probed by employing temperature M (T) and external magnetic field M (μ_oH) dependence of magnetization measurements. A magnetic phase transition from ferromagnetic to paramagnetic phase occurs at 339 K in this sample. The maximum magnetic entropy change (\(\left | {{\Delta } S}_{M}^{\max } \right |\)) took a value of 1.4 J kg^??1 K^??1 at the applied magnetic field of 4.0 T for the explored sample and has also been found to occur at Curie temperature (T_C). This large entropy change might be instigated from the abrupt reduction of magnetization at T_C. The magnetocaloric effect (MCE) is maximum at T_C as represented by M (μ_oH) isotherms. The relative cooling power (RCP) is 243.2 J kg^??1 at μ_oH =?4.0 T. Moreover, the critical properties near T_C have been probed from magnetic data. The critical exponents δ, β, and γ with values 3.82, 0.42, and 1.2 are close to the values predicted by the 3D Ising model. Additionally, the authenticity of the critical exponents has been confirmed by the scaling equation of state and all data fall on two separate branches, one for T < T_C and the other for T > T_C, signifying that the critical exponents obtained in this work are accurate.     

Integration of Internet of Things and blockchain technology in healthcare domain: A systematic literature review

Healthcare is a vitally important field in the industry and evolving day by day in the aspect of technology, services, computing, and management. Its potential significance can be increased by incorporating Internet of Things (IoT) technology to make it smart in the aspect of automating activities, which is then further reformed in the healthcare domain with the help of blockchain technology. Blockchain technology provides many features to IoT-based healthcare domain applications such as restructuring by securing traditional practices, data management, data sharing, patient remote monitoring, and drug analysis. In this study, a systematic literature review has been carried out in which a total of 52 studies were selected to conduct systematic literature review from databases PubMed, IEEE Access, and Scopus; the study includes IoT technology and blockchain integration in healthcare domain-related application areas. This study also includes taxonomy that mentions the aspects and areas in healthcare domain incorporating the traditional system with the integration of IoT and blockchain to provide transparency, security, privacy, and immutability. This study also includes the incorporation of related sensors, platforms of blockchain, the objective focus of selected studies, and future directions by incorporating IoT and blockchain in healthcare domain. This study will help researchers who want to work with IoT and blockchain technology integration in healthcare domain.     

QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm

One of the requirements of QoS-aware service composition in cloud computing environment is that it should be executed on-the-fly. It requires a trade-off between optimality and the execution speed of service composition. In line with this purpose, many researchers used combinatorial methods in previous works to achieve optimality within the shortest possible time. However, due to the ever-increasing number of services which leads to the enlargement of the search space of the problem, previous methods do not have adequate efficiency in composing the required services within reasonable time. In this paper, genetic algorithm was used to achieve global optimization with regard to service level agreement. Moreover, service clustering was used for reducing the search space of the problem, and association rules were used for a composite service based on their histories to enhance service composition efficiency. The conducted experiments acknowledged the higher efficiency of the proposed method in comparison with similar related works.     

Predicting scientific impact based on <Emphasis Type="Italic">h</Emphasis>-index

Predicting the future impact of a scientist/researcher is a critical task. The objective of this work is to evaluate different h-index prediction models for the field of Computer Science. Different combinations of parameters have been identified to build the model and applied on a large data set taken from Arnetminer comprised of almost 1.8 million authors and 2.1 million publications’ record of Computer Science. Machine learning prediction technique, regression, is used to find the best set of parameters suitable for h-index prediction for the scientists from all career ages, without enforcing any constraint on their current h-index values with R ² as a metric to measure the accuracy. Further, these parameters are evaluated for different career ages and different thresholds for h-index values. Prediction results for 1 year are really good, having R ² 0.93 but for 5 years R ² declines to 0.82 on average. Hence inferred that prediction of h-index is difficult for longer periods. Predictions for the researchers having 1 year experience are not precise, having R ² 0.60 for 1 year and 0.33 for 5 years. Considering scientists of different career ages, average R ² values for researchers having 20–36 years of experience were 0.99. For the researches having different h-index values, researchers having low h-index were difficult to predict. Parameters set comprising of current h-index, average citations per paper, number of coauthors, years since publishing first article, number of publications, number of impact factor publications, and number of publications in distinct journals performed better than all other combinations.