Dynamic storage and access load balancing for answering range queries in peer-to-peer networks

Recently, many applications have used Peer-to-Peer (P2P) systems to overcome the current problems with client/server systems such as non-scalability, high bandwidth requirement and single point of failure. In this paper, we propose an efficient scheme to support efficient range query processing over structured P2P systems, while balancing both the storage load and access load. The paper proposes a rotating token scheme to balance the storage load by placing joining nodes in appropriate locations in the identifier space to share loads with already overloaded nodes. Then, to support range queries, we utilize an order-preserving mapping function to map keys to nodes in order preserving way and without hashing. This may result in an access load imbalance due to non-uniform distribution of keys in the identifier space. Thus, we propose an adaptive replication scheme to relieve overloaded nodes by shedding some load on other nodes to balance the access load. We derive a formula for estimating the overhead of the proposed adaptive replication scheme. In this study, we carry simulation experiments with synthetic data to measure the performance of the proposed schemes. Our simulation experiments show significant gains in both storage load balancing and access load balancing.     

Use of a simulation game in delivering blended lifelong learning in the construction industry – Opportunities and Challenges

Continuing professional development (CPD) and life-long learning are vital to both individual and organisational success. For higher education, the intensive resource requirements requisite in the development of e-learning content and the challenges in accommodating different learning styles, developing an e-learning program can be a resource intensive exercise. A blended learning program has been developed in Ireland in an attempt to address the CPD needs of Irish construction professionals. This initiative attempts to strike a balance between the considerable resources required in the development of an e-learning initiative while addressing staff concerns in integrating technology in the delivery of programs. This pilot explores the issues encountered in integrating a simulation game, called MERIT, on a module as part of this blended learning program. The key finding from this research indicates that simulation games can play a very effective role in the delivery of lifelong learning opportunities aimed at the construction industry. However, management of the integration of a simulation game into a program requires careful planning, establishing key milestone dates and encouraging online collaboration through assigning of marks for effort and use of voice over IP communications.     

Sensitive and Reversible Detection of Methanol and Water Vapor by In Situ Electrochemically Grown CuBTC MOFs on Interdigitated Electrodes

The in situ electrochemical growth of Cu benzene‐1,3,5‐tricarboxylate (CuBTC) metal–organic frameworks, as an affinity layer, directly on custom‐fabricated Cu interdigitated electrodes (IDEs) is described, acting as a transducer. Crystalline 5–7 µm thick CuBTC layers are grown on IDEs consisting of 100 electrodes with a width and a gap of both 50 µm and a height of 6–8 µm. These capacitive sensors are exposed to methanol and water vapor at 30 °C. The affinities show to be completely reversible with higher affinity toward water compared to methanol. For exposure to 1000 ppm methanol, a fast response is observed with a capacitance change of 5.57 pF at equilibrium. The capacitance increases in time followed diffusion‐controlled kinetics (k = 2.9 mmol s^?0.5 g^?1_CuBTC). The observed capacitance change with methanol concentration follows a Langmuir adsorption isotherm, with a value for the equilibrium affinity K _e = 174.8 bar^?1. A volume fraction f _MeOH = 0.038 is occupied upon exposure to 1000 ppm of methanol. The thin CuBTC affinity layer on the Cu‐IDEs shows fast, reversible, and sensitive responses to methanol and water vapor, enabling quantitative detection in the range of 100–8000 ppm.     

Sargassum swartzii extracts ameliorate memory functions by neurochemical modulation in a rat model

Food Science and Biotechnology - Recently, considerable attention has been paid to drug exploration from natural sources for treating memory loss, a major manifestation of various neurodegenerative...     

Al₂O₃ and γAl₂O₃ Nanomaterials Based Nanofluid Models with Surface Diffusion: Applications for Thermal Performance in Multiple Engineering Systems and Industries

Thermal transport investigation in colloidal suspensions is taking a significant research direction. The applications of these fluids are found in various industries, engineering, aerodynamics, mechanical engineering and medical sciences etc. A huge amount of thermal transport is essential in the operation of various industrial production processes. It is a fact that conventional liquids have lower thermal transport characteristics as compared to colloidal suspensions. The colloidal suspensions have high thermal performance due to the thermophysical attributes of the nanoparticles and the host liquid. Therefore, researchers focused on the analysis of the heat transport in nanofluids under diverse circumstances. As such, the colloidal analysis of H₂O composed by γAl₂O₃ and Al₂O₃ is conducted over an elastic cylinder. The governing flow models of γAl₂O₃/H₂O and Al₂O₃/H₂O is reduced in the dimensionless form by adopting the described similarity transforms. The colloidal models are handled by implementing the suitable numerical technique and provided the results for the velocity, temperature and local thermal performance rate against the multiple flow parameters. From the presented results, it is shown that the velocity of Al₂O₃–H₂O increases promptly against a high Reynolds number and it decreases for high-volume fraction. The significant contribution of the volumetric fraction is examined for thermal enhancement of nanofluids. The temperature of Al₂O₃–H₂O and γAl₂O₃–H₂O significantly increases against a higher ϕ. Most importantly, the analysis shows that γAl₂O₃–H₂O has a high local thermal performance rate compared to Al₂O₃–H₂O. Therefore, it is concluded that γAl₂O₃–H₂O is a better heat transfer fluid and is suitable for industrial and technological uses.     

Advanced Solute Conservation Equations for Dendritic Solidification Processes Part II: Numerical Simulations and Comparisons

The mathematical model of derived solute equations in part I for equiaxed dendritic solidification with melt convection streams and interdendritic thermo‐metallurgical strain is applied numerically to predict macrosegregation distributions with different diffusing mechanisms in dendritic solid. Numerical and experimental results are present for solidification of a Al–4.5% Cu alloy inside horizontal rectangular cavity at different superheats. The numerical simulations were performed by using simpler method developed by Patanker. The experiments were conducted to measure the cooling curves via thermocouples and the metallurgical examinations to measure the grain size and macrosegregation distributions in Part I. Preliminary validity of the model is demonstrated by the qualitative and quantitative agreements between the measurements and predications of cooling curves and predicted macrosegregation distributions including mushy permeability and interdendritic strain. In addition, several important features of macrosegregation in equiaxed dendritic solidification are identified through this combined experimental and numerical study. Also, quantitative agreements between the numerical simulations and experiments reveal several areas for future research work. The differences and errors between predicted macrosegregation results under different diffusing mechanisms have been discussed.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Combustion synthesis of NbC/ZrO2 composites: Influence of Cr additives on the microstructure and mechanical properties

Composites of NbC/ZrO₂ reinforced with different weight ratios of Cr metal were prepared by dynamic compaction combustion from a blend of Nb₂O₅–Zr–Cr powders. Factors controlling the synthesis process, the microstructure, and the mechanical properties of the samples, such as Cr wt% and the compression loads were studied. The samples were characterized by XRD, SEM, porosity, and hardness measurements. The porosity value of the sample with no additives was high and reached 31.6 vol%. A sudden decrease in the sample porosity to 2.4 vol % was noticed for the sample containing 3.0 wt% of Cr. A high-density sample with less than 2.0 vol % porosity and maximum hardness of 1038 HV was produced using 5.0 wt% Cr under 300 MPa compression load. Detailed thermodynamic calculations for the effect of Cr additives on the physicochemical properties of the system were introduced.     

Compaction of fly ash–aluminum alloy composites and evaluation of their mechanical and acoustic properties

The density and elastic moduli of green compacts can be determined by ultrasonic method with the help of pre-prepared diagrams. In this way, pressing conditions can be taken under control easily. In this study, fly ash particles were used as fillers in an aluminum alloy matrix material. The weight fractions of fly ash in the composites were in the range of 5–30%. The resulting composites were compacted at pressures ranging from 63 MPa to 316 MPa. It was observed that the green density increased with increasing compacting pressure and decreased with increasing weight percent of fly ash particles resulting in lightweight composites. The green compact composites were also tested using an ultrasonic non-destructive evaluation method. Results showed that ultrasonic velocities are a strong function of the density and the fly ash fraction in this material and could be potentially used to predict the density and the fly ash fraction as well as the elastic moduli of the metal matrix composite.     

Improving PAPR performance of filtered OFDM for 5G communications using PTS

The filtered orthogonal frequency division multiplexing (F-OFDM) system has been recommended as a waveform candidate for fifth-generation (5G) communications. The suppression of out-of-band emission (OOBE) and asynchronous transmission are the distinctive features of the filtering-based waveform frameworks. Meanwhile, the high peak-to-average power ratio (PAPR) is still a challenge for the new waveform candidates. Partial transmit sequence (PTS) is an effective technique for mitigating the trend of high PAPR in multicarrier systems. In this study, the PTS technique is employed to reduce the high PAPR value of an F-OFDM system. Then, this system is compared with the OFDM system. In addition, the other related parameters such as frequency localization, bit error rate (BER), and computational complexity are evaluated and analyzed for both systems with and without PTS. The simulation results indicate that the F-OFDM based on PTS achieves higher levels of PAPR, BER, and OOBE performances compared with OFDM. Moreover, the BER performance of F-OFDM is uninfluenced by the use of the PTS technique.