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The concept of multilayers and filler content has been utilized as an effective technique to acquire excellent microwave absorption performance. MoS2@MXene nanocomposites have been successfully synthesized after exfoliation of the MXene and MoS2 particles were embedded on its surface. MoS2@MXene achieved the minimum Reflection Loss (RL) of −51 dB with 30% ratio in wax. The involved mechanisms are discussed in detail including the synergetic effect between MXene and MoS2 particles which improved the dielectric loss of the MoS2@MXene composites as a result of multiple reflections in the layered structure of MXene, abundant functional groups on the MXene surface and positive effect of the increase in surface area. Moreover, the radar cross-section reduction performance has also been confirmed by using simulation technology. The obtained MoS2@MXene composite proved as an excellent microwave absorbing material for future applications.

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: The open-access publication of this article was funded by the Qatar National Library and this work was made possible by HSREP02-1230-190019 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors. Scopus

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature.

An experimental Lead Lithium (PbLi) loop named DRAGON-V has been built at the Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences (INEST, CAS). The purpose of this loop is to test the technical issues of PbLi blankets for fusion reactors, such as thermal hydraulics, material corrosion, magnetohydrodynamics (MHD) effect and safety issues, etc. The main heat recovery system of the DRAGON-V loop is the Concentric Recuperator Heat Exchanger (CRHE) which was made of T91 steel. It has counter flow arrangements with PbLi as the working fluid across the tube and shell sides. In this paper, the system was analyzed in detail for thermal performance, pressure drop, pumping power, entropy generation, the effect of inlet temperature of shell side fluid on overall performance, and the temperature distribution and flow characteristics. It was found that the effectiveness and heat transfer rate of the CRHE was decreased with the inlet temperature of the PbLi across the shell side. The difference of effectiveness calculated by correlation and simulation was about 4.5%. The high pumping power of PbLi fluid circulation was required on the shell side due to its thermo-physical properties variation, which resulted in the high pressure drop across the shell side as compared to the tube side. The temperature variation across the tube and shell sides indicated that the simulation results agreed well with the design parameters within a deviation of less than ˜ 5%.

The use of unmanned aerial vehicles (UAVs) in future wireless networks is gaining attention due to their quick deployment without requiring the existing infrastructure. Earlier studies on UAV-aided communication consider generic scenarios, and very few studies exist on the evaluation of UAV-aided communication in practical networks. The existing studies also have several limitations, and hence, an extensive evaluation of the benefits of UAV communication in practical networks is needed. In this paper, we proposed a UAV-aided Wi-Fi Direct network architecture. In the proposed architecture, a UAV equipped with a Wi-Fi Direct group owner (GO) device, the so-called Soft-AP, is deployed in the network to serve a set of Wi-Fi stations. We propose to use a simpler yet efficient algorithm for the optimal placement of the UAV. The proposed algorithm dynamically places the UAV in the network to reduce the distance between the GO and client devices. The expected benefits of the proposed scheme are to maintain the connectivity of client devices to increase the overall network throughput and to improve energy efficiency. As a proof of concept, realistic simulations are performed in the NS-3 network simulator to validate the claimed benefits of the proposed scheme. The simulation results report major improvements of 23% in client association, 54% in network throughput, and 33% in energy consumption using single UAV relative to the case of stationary or randomly moving GO. Further improvements are achieved by increasing the number of UAVs in the network. To the best of our knowledge, no prior work exists on the evaluation of the UAV-aided Wi-Fi Direct networks. This work was supported by the NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP 8-627-2-260. Scopus

Helium gas (He-gas) / Lithium Lead (LiPb) Dual-cooled LiPb (DLL) blanket is one of the promising candidates for the DEMO fusion reactor for power generation. Brayton cycle based on He-gas has been used as the power generation system for the fusion and fission power reactors. In this work, a modified and efficient design of the power generation system with concept of two stage expansions and one intercooler named as Schematic-I has been proposed for the DLL blanket of Fusion Power Reactor (FDS-II) to increase the thermal performance (e.g. power density and efficiency) of the system and optimized the parameters of the system. The engineering equation solver tool was used to calculate the performance of the system. It is found that the use of more than one expansion stage with proper selection of intercooler is very effective as compared with either splitting the flow equally for the expansion in two turbines or a single stage expansion. The concept of two-stage expansions in the Schematic-I increased the thermal efficiency of the system up to 51%, which was 4% higher than previous design of DLL blanket power generation system. The main parameters of the system such as isentropic efficiency of turbine and compressor, pressure ratio, recuperator performance, the terminal temperature difference of the recuperator, the inlet temperature of DLL blanket and effect of different pressures on the system have all been analyzed and optimized to achieve high performance.

Hydrogels, biocompatible and hydrophilic polymeric networks, have been widely applied in, e.g., pharmaceutical and biomedical research. Their physico-chemical properties can be fine-tuned by changing the fraction and molecular structure of cross-linkers. Hydrogel layers with varying thickness have also been used to support biomimetic lipid bilayers on microfabricated electrodes for studies using electrochemical impedance spectroscopy (EIS). To provide deeper understanding of the impedimetric behavior of thick hydrogels that are covalently tethered on microfabricated electrodes and the influence of cross-linking, we present here a thorough EIS characterization of poly(2-hydroxyethyl methacrylate) hydrogels cross-linked with poly(ethylene glycol)-dimethacrylate in ratios 1:100, 1:200, and 1:400. We propose an equivalent circuit model comprising an open-boundary finite-length Warburg element and constant phase element in series to describe the mass transfer differences between the bulk hydrogel and covalently tethered domain at the electrode-hydrogel interface. The results indicated that an increased fraction of the hydrophilic high-molecular weight cross-linker significantly decreased the charge transfer resistance for hexacyanoferrate(III/II), which could be attributed to increased permeability and decreased electrode passivation due to the lower degree of tethering on the acrylate modified electrodes. Cryo-SEM visualization of the structural differences caused by cross-linking showed good agreement with the EIS results, whereas the degree of hydration of the hydrogels did not show any statistically significant differences.

With the prominent features of different U-Pu fuel compositions, the MOX-fueled fast spectrum metal-cooled reactors have been of increasing interest for recent years worldwide. Present study invokes a detailed investigation on the calculations for neutronic analysis and it conducts a comparative study for neutron physics parameters of a metal-cooled fast spectrum reactor – the BFS-62-3A. This work is an amalgamation of four tasks. In the first part, a detailed comparative analysis was performed to perform a code-to-code verification using the available results of three Monte Carlo codes including SuperMC, MCNP, and Serpent, and a deterministic one, the DYN3D-MG with the employment of continuous neutron energy cross-sections. The experimental results of BFS-62-3A benchmark were used to assess the potentiality of the aforementioned reactor physics codes. For most of the integral parameters, the SuperMC was found to be on the leading edge. In the second part, the effects of data libraries including ENDF/B-7.1, ENDF/B-7.0, ENDF/B-6.6, JEFF3.2, and HENDL3.0 on the simulations performed using SuperMC code for evaluating k-eff and radial fission rates were all assessed. The third part incorporates the investigation of the fission rates’ deviations in stainless steel radial reflector by changing the density of the reflector. The decrease of density by 5% was found to be in good agreement with the benchmark. In the last part, that has a special importance to the concept pertaining to safety-enhanced Sodium-cooled Fast Reactor (SFR) core, the reactivity of the critical assembly was studied by calculating the sodium void reactivity effect. The simulation results of SuperMC code agreed well with the available experimental and simulation results. The present study has enabled SuperMC code to pass another big milestone on dealing with complex and advanced nuclear systems.

Deep learning models do not perform well if they are not trained for the acoustic conditions under which they have to operate. Due to this reason, the neural network based speech separation models specifically designed for anechoic conditions do not perform well in reverberant conditions. As training a deep neural network is a lengthy and computationally expensive process, training it every time for any change in the acoustic conditions is almost always impossible. This paper presents a comparative study of few of the state-of-the-art dereverberation algorithms and suggests the best among them, which enables a U-Net based speech separation model, trained in anechoic conditions, to work under different reverberant conditions for online and offline applications. The results show that dereverberating the audio mixtures before they enter the anechoic U-Net based speech separation network by spectral subtraction (SS) dereverberation algorithm improves the signal to distortion ratio (SDR) by almost 0.84 dB for online applications over the anechoic U-Net based speech separation model if it is directly exposed to reverberations. For offline applications, there is an average improvement of 2 dB in SDR and 4% in short term objective intelligibility (STOI), when the mixtures are dereverberated by the cascaded system containing the weighted prediction error (WPE) and the spectral subtraction (SS) dereverberation models.

In this paper, we formulate a blind source separation (BSS) framework, which allows integrating U-Net based deep learning source separation network with probabilistic spatial machine learning expectation maximization (EM) algorithm for separating speech in reverberant conditions. Our proposed model uses a pre-trained deep learning convolutional neural network, U-Net, for clustering the interaural level difference (ILD) cues and machine learning expectation maximization (EM) algorithm for clustering the interaural phase difference (IPD) cues. The integrated model exploits the complementary strengths of the two approaches to BSS: the strong modeling power of supervised neural networks and the ease of unsupervised machine learning algorithms, whose few parameters can be estimated on as little as a single segment of an audio mixture. The results show an average improvement of 4.3 dB in signal to distortion ratio (SDR) and 4.3% in short time speech intelligibility (STOI) over the EM based source separation algorithm MESSL-GS (model-based expectation–maximization source separation and localization with garbage source) and 4.5 dB in SDR and 8% in STOI over deep learning convolutional neural network (U-Net) based speech separation algorithm SONET under the reverberant conditions ranging from anechoic to those mostly encountered in the real world.

OpenPET has recently been developed as a modular, flexible data acquisition (DAQ) platform for nuclear imaging applications. We present a description of the system architecture and DAQ implementation using OpenPET for a small animal positron emission tomography (PET) insert designed for hybrid PET/magnetic resonance imaging (MRI) at 7T. The PET insert consists of 16 silicon photomultiplier-based detector modules, creating a total of 64 analog channels. The OpenPET system used consisted of four 16 channel detector boards connected to one support board (SB), in turn connected to a Host PC via a USB2.0 interface. Both detector and SBs contain field-programmable gate arrays, allowing customized firmware for both. The initial OpenPET firmware release allowed data capture in oscilloscope mode only, limiting data collection to a system single events rate of 18.8 kcps. Customized firmware changes were implemented in four revisions, resulting in a final version using an 8 byte data packet and supporting a system single events rate of > 4.5 Mcps. Noise equivalent count rate (NECR) results are presented for each firmware revision, showing improvement from peak NECR of 1.0 kcps @ 0.6 MBq to 19.5 kcps @ 15.3 MBq. The final version of the firmware enables its routine use in small animal PET/MRI.

Exoplanets, short for extra solar planets, are planets outside our solar system. They are objects with masses fewer than around 15 Jupiter-masses that orbit stars other than the sun. They are small enough so they cannot burn deuterium in their cores, yet large enough that they are not so-called dwarf planets like Pluto.

In recent years nanotechnology has become increasingly important in almost every field. The new and improved physical, chemical and biological properties of material at nanoscale have far reaching implications in the fields of science and technology. Nanoparticles’ effect on various plant species must be investigated to develop a comprehensive toxicity profile for nanoparticles. The current study strives to evaluate the effects of nine types of metal nanoparticles including monometallic and bimetallic alloy nanoparticles [Ag, Au, Cu, AgCu (1:3), AgCu (3:1), AuCu (1:3), AuCu (3:1), AgAu (1:3), AgAu (3:1)] on seed germination, root and shoot growth and biochemical profile of Silybum marianum plant. Seed germination was greatly affected and increased significantly upon treatment with nanoparticles’ suspensions and was recorded highest for Ag nanoparticle suspension. Metal nanoparticles also had a significant effect on the biochemical profile of S. marianum. For the first week, the effect on DPPH, total phenolics content, total flavonoids content, total protein content, peroxidase activity and superoxide dismutase activity was enhanced, but declined as the time progressed. Among the nanoparticles being used, the effect of Ag nanoparticle was mostly enhancing. The results obtained are significant in mapping the effects of different monometallic and bimetallic nanoparticles on medicinal plant species.

Density functional theoretical (DFT) calculations were carried out to explore the electronic and optical properties of double ordered Ba2NaReO6, Ba2LiReO6, and Sr2LiReO6 perovskites by employing the state-of-the-art exchange-correlation potential, i.e., Tran-Blaha modified Becke-Johnson for the electronic system. The calculated electronic band structures show an indirect band gap along with a semiconductor nature. Total and partial densities of state peaks were analyzed in light of effective contributions of various electronic states. The significant optical parameters, including the components of dielectric constant, the energy loss function, the absorption coefficient, the reflectivity spectra, the refractive index, and the extinction coefficient, were computed and discussed in details for radiation up to 14 eV. Finally, we studied the inter-band contributions from the optical characteristics. Our present study might be considered as first theoretical quantitative calculations of the optical and electronic behavior in the cubic phase of double perovskite materials based on rhenium.

The recent introduction of neural networks to speech separation has dramatically boosted the separation performance. This paper presents a novel psychoacoustic approach for speech source separation in anechoic conditions, using semantic segmentation of the interaural spectrograms of the audio mixtures. We have trained two separate U-Nets (a specialized neural network for semantic segmentation) on the interaural level difference (ILD) spectrogram, and the interaural phase difference (IPD) spectrogram of a single source. After training, these U-Nets are used to predict the class of each time frequency (TF) unit of the interaural spectrogram of the audio mixture. The ILD and IPD soft masks obtained from these U-Nets are combined by a novel scheme which utilizes the strength of the interaural cues in different frequency bands. The results show improved separation over two state of the art machine learning source separation systems utilizing the same interaural cues. There is average improvement of 7.32 dB in signal to distortion ratio (SDR) and 0.3 points improvement in short term objective intelligibility (STOI) over degenerate un-mixing estimation technique (DUET) algorithm and 2.51 dB improvement in SDR with comparable intelligibility over model-based expectation–maximization source separation and localization (MESSL) algorithm.

Here we reported the first principle-based calculations for the structural and optoelectronic properties of AInSe2 (A ​= ​K and Li) chalcopyrite type single crystals with-in the framework of density functional theory (DFT). The calculated structural parameters using the local density approximation (LDA) and general gradient approximation (GGA) are in a best agreement with existing experimentals and others theoretical results. The band structures and density of states of these materials are investigated and discussed in detail. Our calculations reveal a direct band-gap semiconductor type nature for both materials. The calculated band gaps follow that Eg (LiInSe2) ​> ​Eg (KInSe2) showing a good agreement for a decrease in bond energy for the corresponding In-s and Se-p anti-bonding orbital states. Furthermore, the frequency dependent linear optical properties, such as the complex dielectric functions, energy loss function, absorption coefficient, reflectivity, refractive index, the extinction coefficient and the real part of optical conductivity are also computed and discussed in detail. Our calculated optical results were found to be in a good agreement with the results from the literature There exists a significant variation in optical characteristics of both these materials. Our predicted calculated results would guide to understand the experimental and theoretical work on the basic material properties of these materials including crystal structures, optoelectronic properties, as well as their device applications.

A full-ring PET insert consisting of 16 PET detector modules was designed and constructed to fit within the 114 mm diameter gradient bore of a Bruker 7 T MRI. The individual detector modules contain two silicon photomultiplier (SiPM) arrays, dual-layer offset LYSO crystal arrays, and high-definition multimedia interface (HDMI) cables for both signal and power transmission. Several different RF shielding configurations were assessed prior to construction of a fully assembled PET insert using a combination of carbon fibre and copper foil for RF shielding. MR-compatibility measurements included field mapping of the static magnetic field (B 0) and the time-varying excitation field (B 1) as well as acquisitions with multiple pulse sequences: spin echo (SE), rapid imaging with refocused echoes (RARE), fast low angle shot (FLASH) gradient echo, and echo planar imaging (EPI). B 0 field maps revealed a small degradation in the mean homogeneity (+0.1 ppm) when the PET insert was installed and operating. No significant change was observed in the B 1 field maps or the image homogeneity of various MR images, with a 9% decrease in the signal-to-noise ratio (SNR) observed only in EPI images acquired with the PET insert installed and operating. PET detector flood histograms, photopeak amplitudes, and energy resolutions were unchanged in individual PET detector modules when acquired during MRI operation. There was a small baseline shift on the PET detector signals due to the switching amplifiers used to power MRI gradient pulses. This baseline shift was observable when measured with an oscilloscope and varied as a function of the gradient duty cycle, but had no noticeable effect on the performance of the PET detector modules. Compact front-end electronics and effective RF shielding led to minimal cross-interference between the PET and MRI systems. Both PET detector and MRI performance was excellent, whether operating as a standalone system or a hybrid PET/MRI.

We present the initial results from a small animal PET insert designed to be operated inside a 7T MRI. The insert fits within the 114 mm inner diameter of the Bruker BGA-12S gradient coil while accommodating the Bruker 35 mm volume RF coil (outer diameter 60 mm), both used in the Bruker 70/20 MRI systems. The PET insert is a ring comprising 16 detectors. Each detector has a dual-layer offset (DLO) lutetium-yttrium oxyorthsilicate (LYSO) scintillator array read out by two SensL SPMArray4B SiPM arrays. The DLO scintillator has bottom (top) layers of: $22\times 10$ ( $21 \times 9$ ) crystals of size $1.2 \times 1.2 \times 6$ (4) mm3 for a total of 409 crystals per block, providing an axial extent of 28.17 mm. The detector outputs are multiplexed to four signals using a custom readout board and digitized using the OpenPET data acquisition platform. The detector flood images successfully resolve over 99% of the crystals, with average energy resolution of 12.5 ± 2.0% at 511 keV. Testing of the PET system inside the MRI showed that the PET insert had no effect on MRI image homogeneity and only a small effect on echo planar images (EPI) signal to noise ratio (SNR) (-9%), with neither PET nor MRI images showing obvious artefacts. These acquisitions used the OpenPET operating in “oscilloscope mode” with USB2.0 interface, allowing a maximum total singles event rate of 280 kcps, strongly limiting the count rate capabilities of the system. The PET radial spatial resolution (as measured with a 22Na point source and FBP-3DRP reconstruction) is 1.17 mm at the centre, degrading to 1.86 mm at a 15 mm radial offset. Simultaneous phantom and mouse PET/MR imaging produced good quality images that were free of any obvious artefacts.

In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration models within the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.

Accurate characterization of two phase bubbly flows is crucial in many industrial processes such as fluidized reactors, ore froth flotation, etc. The bubble size determines the rate at which components present in the gas phase are transferred to the surroundings and vice versa while bubble rate defines the appropriate bubbly flow regime occurring in the heterogeneous system. This research work employs deep neural networks (DNNs) to predict bubble size and bubble rate using data obtained from validated computational fluid dynamics (CFD) computations. Pure water and slurry (in conditions similar to those employed in mineral froth flotation) case studies are evaluated. It is found that the DNN can predict the CFD results accurately when using four hidden layers, describing discontinuities in the bubbly flow regime. The relative errors computed between the CFD data and the prediction obtained by the DNN is as low as 8.8% and 1.8% for bubble size and bubble rate, respectively. These results confirm that the DNN can be applied to sophisticated fluid dynamics systems and allow developing better control process strategies since once the DNN is trained critical variables can be computed very efficiently. The slurry case study, although restricted to the application of mineral froth flotation, can also be generalized to other industrial operations keeping the exact same procedure.

Here, we thoroughly studied the ground state structural, electronic and optical properties of high crystalline type cubic ACu3Se4 (A = Ta, Nb and V) ternary chalcogenide materials. The the FP-LAPW method within the framework of density functional theory is employed. The electronic structure calculations are treated with highly accurate modified-Becke-Johnson potential to yield the exact band gap values along with the use of Perdew-Burke-Ernzerhof and Wu-Cohen generalized gradient approximation approach for the corresponding optical properties. The band structures and density of states for the three materials are compared and discussed in detail. The complex optical dielectric function’s and the related optical parameters like the absorption coefficient, reflectivity, energy loss function refractive indices and extension coefficient were calculated and discussed in detail for radiations up to energy value of 14 eV. The calculated optical properties display a significant anisotropy between the two components. We further studied the inter-band contributions for the optical characteristics in these ternary chalcogenides. Our described results could pave the way towards further fundamental investigations of the optoelectronic properties of these direct band gap semiconductors in nanoscale regime for solar energy harvesting.

We reported and discussed results for the first time for the electronic and optical properties of Fe doped MgH2 and Ga doped MgH2Fe hydride materials by first-principles calculations using generalized gradient approximation (GGA) within the density functional theory (DFT). The effective contributions of various electronic bands and density of states calculations confirms a quite weak bonding nature between Fe, Mg and H atoms in Fe doped MgH2 system. Also, the addition of Ga, the interaction between Mg and H atoms show weak bonding along with extensive reduction in the PDOS of both Mg and H atoms closer to Fermi level. The optical properties such as dielectric function, refractive index, extinction coefficient, absorption, reflectivity spectra, electron energy loss function and the real part of optical conductivity were calculated and discussed in detail. The observed sharp energy loss peak values for both the studied systems match up with the edge of plasma energy and corresponds to plasma frequency which indicates transition between semiconductor and dielectric characters. The base for the absorption was discussed in detail in addition to observed spectral peaks on the basis of their electronic band structures and crystal field nature.

A conceptual design of compact and highly efficient heat exchanger has been developed and proposed for the 10 MWth lead-based reactor using mathematical and numerical methods. Lead Bismuth Eutectic (LBE) and Pressurized Liquid Water (PLW) are selected as working fluid across the hot and cold sides of the Compact Heat Excahnger (CHE). The mathematical method based on energy balance equations has been used to develop the design of CHE with complete specifications of structural parameters. While, in the numerical method, FLUENT has been used to analyze and evaluate the detailed observation of thermal performance, Stanton number, and development of the irreversible heat transfer by the Bejan number. The thermal performance of CHE can be controlled with the flow rate of the PLW. The maximum overall heat transfer coefficient and effectiveness were 7528 W⋅m−2K−1 and 0.79, respectively. The thermal-hydraulic analysis involves the assessment of Nusselt number (Nu), temperature contour, and velocity vector and stream line. The numerical simulation results are agreed well with the correlation and the error is within ranges of ~ 2- 8%. This study can be used as a reference for the development and design of a compact and efficient heat exchanger for lead-based reactors.

The Cu-based oxychalcogenides among p-type transparent conducting materials (TCMs) demonstrate significant results in term of their optoelectronic properties. So, they are being focused on our current study. In this paper, we reported a detailed analysis relating to the spin-dependent electronic and optical properties of Cu-based Lanthanide oxychalcogenides materials, ACuOS (A ​= ​La, Ce and Pr) by means of Density Functional Theory (DFT). We observe a decrease in the energy band-gap values for the substitution of lanthanide La→Pr which was in a fine agreement to the experimental results. The band-gap energy values are concluded to be smaller for the case of spin up in comparison to spin down case. The majority and minority spin cases of the band structures and density of states for the three materials are compared and discussed in detail. We also computed spin-dependent optical parameters like the real and imaginary parts of dielectric function, refractive index, reflectivity, absorption coefficient and the electron energy loss function for radiations up to energy value of 14 ​eV. Our investigated optical parameters for these Cu-based oxychalcogenides reveals dissimilarities for both the spin up and down, which are the outcomes of the energy band-gap variations and also the existence of additional structures closer to the Fermi levels which are mostly due to the lanthanides 4f electrons. Their basic material properties including their crystal structures, optical and electronic properties will be covered, as well as their device applications. Also, the development of performance enhancement strategies including doping and other innovative ways to improve performance is still not satisfactory. This has impeded the development of many devices such as photovoltaics, sensors, and transparent electronics properties of materials.

Contrastive Divergence (CD) has shown success in estimating the parameters of Markov Random Fields. Restricted Boltzmann Machine (RBM) updates weights of RBM architecture using CD to minimize the reconstruction error. Minimization of CD is fundamentally equivalent of finding the self-similarity among the training data and the estimated data. However, this self-similarity is measured at single measurement scale. Using fractal dimension, self-similarity at multiple scales is obtained and is denoted by a noninteger value. Fractal dimension provides a way to extract complexity features of an object using multiscale probabilistic measures and represents self-similar relationship in terms of a loglog scale. Also, CD algorithm is based on temperature based Boltzmann distribution but it does not incorporate the effects of temperature in the calculations. In this paper, the authors propose an information fractal dimension based temperature factor in RBM that is theoretically analogous to minimization of temperature based CD and provides better error optimization due to the inclusion of temperature dynamics between the visible and hidden states of RBM. Experimental results on standard data sets of MNIST and SVHN demonstrate promising performance showing better convergence of error and better generalization on validation data.

One key issue of a fusion power plant is the requirement of highly efficient heat extraction from the primary loop to ensure the safety and reliability. The Dual-coolant Lithium Lead (DLL) breeder blanket of the FDS-II with a compact heat exchanger (CHE) is one design option for the power extraction system, and the Lithium Lead eutectic (LiPb) and helium gas are used as the thermal fluids. In this work, the flat and corrugated plate structures with SiCf/SiC composites and ODS steel were designed for the comparison of thermal performance. The heat transfer rate of the SiCf/SiC was 11.4% higher as compared to the ODS steel and was chosen as a candidate structural material. According to the design parameters of the DLL, effectiveness and the overall heat transfer coefficient for the corrugated structure increased up to 19.8% and 25.06% at the LiPb velocity of 0.099 m/s and increased up to 26.2% and 33.03%, when the velocity of the LiPb decreased to 0.012 m/s. The Nusselt number for the LiPb and helium gas was calculated for the convective heat transfer. The numerical results agreed well with the correlations within a deviation of 2%. In addition, pressure drop of the corrugation structure increased but it can be neglected as compared to the increase in the thermal performance. The concept of CHE with the SiCf/SiC structure could be applied to the design of intermediate heat exchanger working in a fusion power plant.