Miracle-dz/UniFAR-FTUs huggingface dataset:
Rate of Humic Substance Uptake During Activated Carbon Adsorption

The adsorption‐diffusion rate of a humic acid and three molecular size fractions thereof by two particle size fractions of activated carbon was investigated over an extended time period. Long‐term removal of humic substances was found, which deviated significantly from the equilibrium state predicted by single‐domain diffusion models. During the initial stages, i.e. up to 50% uptake, both pore and surface diffusion models adequately simulate the experimental data. Use of the pore diffusion model results in apparent tortuosities on the order of 10–20, indicating a hindered diffusion mechanism (DP=0.85×10-12to3.5×10-12m2/s). Both rate and equilibrium experiments, as well as analysis of the adsorbent pore structure, support a two‐domain diffusion mechanism. No desorption of humic acid was observed in response to reducing the solution concentration or the ionic strength.
Gas Permeability of Thermoplastic Polyurethane Elastomers

We have studied the influence of the chemical structures of thermoplastic polyurethane elastomers (TPUs) on their permeability to carbon dioxide and oxygen. The gas permeability of a TPU was dependent on the chemical structures not only of the soft segments but also of the hard segments. A TPU whose soft segment consists of poly(oxytetramethylene) exhibited a higher carbon dioxide permeability than does a TPU with ester bonds in the soft segment. The introduction of poly(dimethylsiloxane) chains into the TPU markedly enhanced the carbon dioxide permeability. In a series of TPUs containing poly(oxytetramethylene)glycol (PTMG), the gas permeability increased with the chain length of the soft segments. In the dissolution–diffusion process of gas permeation, the diffusion process predominated.
Performance Analysis for DOA Estimation Using Compressive Measurements With Coprime Frequencies

Exploiting compressive measurements of two signals with coprime carrier frequencies to estimate direction-of-arrivals (DOAs) can obtain a high estimation performance with low system complexity. Coprime carrier frequencies can dramatically reduce the number of sensors, while compressive measurements effectively control the number of front-end circuit chains. However, the corresponding theoretical performance analysis has not been conducted. Therefore, we aim at revealing the estimation performance mathematically in this paper. First, the Cramér-Rao bound (CRB) expression with respect to the DOAs is derived to illustrate the performance limit. Then, the existence condition of the derived CRB expression is obtained, and the number of degrees of freedom (DOFs) is analyzed based on the condition. Finally, the CRB asymptotic properties are explicitly discussed, and it is found that, in the under-determined case, the CRB approaches a constant, which is similar as the asymptotic properties of sparse arrays. Through theoretical analysis in this paper, the superiorities of the proposed scheme are further verified. Specifically, the proposed scheme can obtain a higher estimation performance, including the number of DOFs and estimation accuracy, with the same number of front-end circuit chains than classical structure using coprime frequencies. From the simulation results, 24 targets can be resolved by the proposed scheme with 5 channels, while the classical structure can resolve only 20 targets. Other properties are also examined by numerical simulations.
An Image Reconstruction Method of Capacitively Coupled Electrical Impedance Tomography (CCEIT) Based on DBSCAN and Image Fusion

Based on linear back projection (LBP) algorithm, density-based spatial clustering of applications with noise (DBSCAN) algorithm and image fusion technique, a new image reconstruction method of capacitively coupled electrical impedance tomography (CCEIT) is proposed in this work. LBP is used to obtain the original image of the real part and the original image of the imaginary part. The gray level thresholds of the two original images are determined by DBSCAN clustering algorithm and the initial image of the real part and the initial image of the imaginary part are obtained by gray level threshold filtering, respectively. Finally, the two initial images are fused and the final image is obtained. Image reconstruction experiments are carried out with a 12-electrode CCEIT system prototype. The experimental results verify the effectiveness of the proposed image reconstruction method. Experimental results show that the quality of the final image is satisfactory. Compared with the conventional electrical tomography (ET) image reconstruction methods, the new proposed image reconstruction method needs less prior knowledge or manual intervention and realizes more effective usage of the impedance information. The research results also indicate that the DBSCAN clustering algorithm is an effective way to determine the gray level thresholds of the original images and the mean square error (MSE) is a reasonable criterion for image fusion. Meanwhile, LBP + DBSCAN is an effective algorithm for image reconstruction and image fusion technique is an effective way to utilize the whole impedance information of the conductive gas–liquid two-phase flow.
Auto-Diagnosis of COVID-19 Using Lung CT Images With Semi-Supervised Shallow Learning Network

In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an $\mathcal {N}$ -connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening.
Improving HW/SW Adaptability for Accelerating CNNs on FPGAs Through A Dynamic/Static Co-Reconfiguration Approach

With the continuous evolution of Convolutional Neural Networks (CNNs) and the improvement of the computing capability of FPGAs, the deployment of CNN accelerator based on FPGA has become more and more popular in various computing scenarios. The key element of implementing these accelerators is to take full advantage of underlying hardware characteristics to adapt to the computational features of the software-level CNN model. To achieve this goal, however, previous designs mainly focus on the static hardware reconfiguration pattern, which is not flexible enough and can hardly make the accelerator architecture and the CNN features fully fit, resulting in inefficient computations and data communications. By leveraging the dynamic partial reconfiguration technology equipped in the modern FPGA devices, in this article, we propose a new accelerator architecture for implementing CNNs on FPGAs in which static and dynamic reconfigurabilities of the hardware are cooperatively utilized to maximize the acceleration efficiency. Based on this architecture, we further present a systematic design and optimization methodology for implementing the specific CNN model in the particular computing scenario, in which a static design space exploration method and a reinforcement learning-based decision method are proposed to obtain the optimal static hardware configuration and run-time reconfiguration strategy respectively. We evaluate our proposal by implementing three widely used CNN models, AlexNet, VGG16C, and ResNet34, on the Xilinx ZCU102 FPGA platform. Experimental results show that our implementations on average can achieve 683 GOPS under 16-bit fixed data type and 1.37 TOPS under 8-bit fixed data type for three targeted CNN models, and improve the computational density from 1.1× to 1.91× compared with previous implementations on the same type of FPGA platform.
Joint Algorithm for Multi-Hop Localization and Time Synchronization in Underwater Sensors Networks Using Single Anchor

In this study, an effort to synchronize and localize the underwater sensors simultaneously in a multi-hop environment is considered. Underwater Sensor Networks (UWSNs) has limited range and connectivity due to propagation delay and limited bandwidth. By using a multi-hop environment, we can extend the range and enhancing the network connectivity for providing more accurate localization and synchronization technique. We have established a connection between sensors point to point directed links and then analytically construct the model for the synchronization as a function of range, delay, and timestamps. Secondly, we have formulated the unconstrained optimization problem for localization by using a gradient technique. The proposed research scheme first synchronizes and then localizes the unknown nodes with known depth by using the single anchor node in a multi-hop scenario. According to the literature survey, this joint effort has never been addressed before; both localization and time synchronization for multi-hop scenarios are addressed separately. The proposed algorithm will also calculate the propagation delay that will be generated during message propagation for both localization and time synchronization. The numerical results are compared with some well-known techniques in terms of the number of nodes, localization error, synchronization error, total processing time up to four hops. Experimental results show that the proposed model outperforms in terms of localization and synchronization accuracy, but since this work has never been done before therefore, we compare our results with the techniques that had separately address this problem having some constraints.
Image Processing Framework for Performance Enhancement of Low-Light Image Sensors

Night vision devices are used for capturing images under low light conditions ( $ < \,\,10^{-1}$ lux), which are significantly different from images under standard illumination. The night vision devices based on image intensifier tube have power and cost advantages over other night vision devices, such as thermal imaging for short-range surveillance. However, these vision devices suffer from scintillation noise, which makes the resulting images noisy under very low light conditions. This paper presents a comprehensive methodology that uses a consolidated image processing module to improve the performance of low-light images acquired using image-intensifier-based night vision devices. The proposed module is a combination of a two-stage image processing framework. First, a spatio-temporal filter (based on wavelet and recursive frame averaging) is used to minimize inter-frame and intra-frame noise. Then, image enhancement is performed followed by residual noise reduction using a local spatial filter before applying image tone adjustment for display mapping. The results of the proposed module demonstrate performance enhancement of night vision devices under controlled simulated light conditions as well as field conditions. Objective and subjective analysis also reveals that the proposed module can effectively reduce noise and significantly improve visual image quality by 20–30%.
Design and Evaluation of an Electrodermal Activity Sensor (EDA) With Adaptive Gain

Electrodermal activity (EDA) is a physiological signal that can be measured non-intrusively and associated with the sympathetic nervous system, hence being widely used nowadays. Although multiple sensor designs have been proposed over the years, practical hardware limitations still persist, due to differences in the electrode materials used to interface with the body, sweat gland density at the recording sites, and variability in the overall electrical properties of the skin in-between users. This is particularly challenging when low resolution Analog-to-Digital Converters (ADC) are used, which, due to the proliferation of open source and low-cost hardware platforms, are currently widespread. In this article we present a design and experimental evaluation of a new circuit for EDA measurement with adaptive gain control, allowing the dynamic adaptation of the measurement range. The proposed circuit has shown comparable results to those obtained with a reference sensor, obtaining a mean correlation coefficient of 0.967, and proved to have superior performance in cases where the reference sensor would saturate.
Determination of Plutonium in Soil by ICP‐MS After CCC Preseparation

Abstract Environmental plutonium was determined by ICP‐MS after countercurrent chromatography (CCC) preseparation with 20% tri‐n‐octylamine in toluene as stationary phase. The matrix elements, thorium and uranium, were eluted by using 7.5 mol/L nitric acid, 10 mol/L hydrochloric acid, and 3 mol/L nitric acid, respectively, and the plutonium was quantitatively stripped by using 0.025 mol/L oxalic acid–0.15 mol/L nitric acid. The spectral interferences due to the tailing of adjacent actinoids, polyatomic ion, and the matrix effect were depleted after the sample was subjected to the CCC preseparation because uranium and thorium were well separated with decontamination factors of 104 and 105, respectively; the memory effect could be eliminated effectively with a solution with the same composition of plutonium eluant. Isotopic dilution was used to determine ultra‐trace 239Pu using 242Pu as the spike. The limit of detection of 239Pu is 6 fg (27 attomoles), and the limit of determination is 25.5 fg for soil. The depth profile of 239Pu was revealed with maximum 239Pu concentrations of ∼0.08 × 10−12 g/g (0.3 femtomole per g) in Xi'an and ∼1.6 × 10−12 g/g (6.7 femtomole per g) in Gansu, China. The ratio of 240Pu/239Pu in the soil sampled from Gansu differs from the mean value of the global fallout, indicating a different origin.
Dual-Band Store-and-Use System for RF Energy Harvesting With Off-the-Shelf DC/DC Converters

RF energy harvesting (RFEH) has recently become part of the alternatives for powering wireless sensor networks. Our focus in this article was to introduce a design, MSwitch, able to use off-the-shelf dc/dc converters for running a real and self-powered Internet of Things (IoT) application. MSwitch is an RF dual-band energy harvesting stage employing the store-and-use principle that mixes efficiently and in a dynamic manner both inputs. We proved an increase on the performance with respect to the single-band case and to the most successful approaches found in the literature, achieving about 10 % better efficiency and around 35 % lower minimum power levels. Besides, we carried out an analysis of the commercial dc/dc converters, characterizing them for ultralow-power conditions. Likewise, we analyzed the state-of-the-art rectification circuits and commercial Schottky diodes, searching the best performance under our RFEH conditions. Finally, we manufactured our designs as a complete system and demonstrated its operation with an IoT concrete application.
Wear‐Resistant Nanoscale Silicon Carbide Tips for Scanning Probe Applications

The search for hard materials to extend the working life of sharp tools is an age‐old problem. In recent history, sharp tools must also often withstand high temperatures and harsh chemical environments. Nanotechnology extends this quest to tools such as scanning probe tips that must be sharp on the nanoscale, but still very physically robust. Unfortunately, this combination is inherently contradictory, as mechanically strong, chemically inert materials tend to be difficult to fabricate with nanoscale fidelity. Here a novel process is described, whereby the surfaces of pre‐existing, nanoscale Si tips are exposed to carbon ions and then annealed, to form a strong silicon carbide (SiC) layer. The nanoscale sharpness is largely preserved and the tips exhibit a wear resistance that is orders of magnitude greater than that of conventional silicon tips and at least 100‐fold higher than that of monolithic, SiO‐doped diamond‐like‐carbon (DLC) tips. The wear is well‐described by an atom‐by‐atom wear model, from which kinetic parameters are extracted that enable the prediction of the long‐time scale reliability of the tips.
A Multi-layer Stack Ensemble Approach to Improve Intrusion Detection System's Prediction Accuracy

Intrusion is a series of activities that violate an organisation's existing security goals and procedures. Hence, an Intrusion Detection System (IDS) should be capable of analysing incoming network traffic (packet) and determining if it is an attack or otherwise. Lack of recent and up to date data sets for the training of IDS is a critical issue in the development of effective IDS. This paper focuses on creating a more realistic data set in our case UMaT-OD-20 using ONDaSCA and also the building a Multi-layer Stack Ensemble (MLS) IDS Model for Intrusion Detection Systems. Multi-layer Stacked Ensemble exploits the strengths of various base-level model predictions to build a more robust meta-classifier that meliorate classification accuracy and reduces False Alarm Rate (FAR). Five (5) Supervised Machine Learning (ML) based algorithms videlicet K Nearest Neighbor (KNN), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF) and Naive Bayes' (NB) were employed to generate predictive models for all features. The Python programming language was used for the entire research and all programming and evaluation of data was done with an Inter Core i7, 16GB RAM and 1TB HDD Windows 10 Pro Laptop computer. The predictions of the Multi-layer stacked ensemble showed an improvement of 0.97% over the best base model. This improvement reduced the FAR during the classification of network connections types. Again, the evaluation of our work shows a significant improvement over similar works in literature.
A Self-Tuning Congestion Tracking Control for TCP/AQM Network for Single and Multiple Bottleneck Topology

In this work a self-tuning rate and queue based proportional and integral controller called SRQ-PI is proposed to efficiently control the queue length with small overshoot and faster settling time. SRQ-PI proposes a new control tracking function that maps level of congestion to the packet drop probability dynamically. In SRQ-PI, the incoming traffic rate is estimated and used with the proportional and integral controller. The SRQ-PI tunes itself and stabilizes the system with internal feedback without requiring any external feedback. Furthermore, the stability of the SRQ-PI is analyzed using control theory and presents systematic guidelines to select the control gain parameters. NS2 is used to carry out the simulation work. The simulation result demonstrates that SRQ-PI is stable and gets faster transient response due to lower average delay jitter and robust against dynamic network parameters. The SRQ-PI outperforms proportional integral (PI), Intelligent adaptive PI (IAPI) and Random exponential marking (REM) algorithm.
Thermodynamic tabulations for selected phases in the system CaO‐Al2O3‐ SiO2‐H2 at 101.325 kPa (1 atm) between 273.15 and 1800 K

The standard thermodynamic properties of phases in the lime‐alumina‐silica‐ water system between 273.15 and 1800 K at 101.325 kPa (1 atm) were evalated from published experimental data. Phases included in the compilation are boehmite, diaspore, gibbsite, kaolinite, dickite, halloysite, andalusite, kyanite, sillimanite, Ca‐Al cliniopyroxene, anorthite, gehlenite, grossular, prehnite, zoisite, margarite, wollastonite, cyclowollastonite ( = pseudowollastonite), larnite, Ca olivine, hatrurite, and rankinite. The properties include heat capacity, entropy, relative enthalpy, and the Gibbs energy function of the phases and the enthalpies, Gibbs energies, and equilibrium constants for formation both from the elements and the oxides. Tabulated values are given at 50 K intervals with the 2‐sigma confidence limit at 250 K intervals. Summaries for each phase give the temperature‐ dependent functions for heat capacity, entropy, and relative enthalpy and the experimental data used in the final evaluation.
Comparison of Pressure Gradient Correlations for the Spherical Capsule Train Flow

Design parameters (i.e., power of pumps, number of pumps, diameter of capsule, diameter of pipe, and geometry of pipe) of pipelines in which capsule flow can be developed if pressure drops (ΔP)m are caused by the two-phase flow are known. Therefore, designers are in need of a general model to calculate pressure drops. In the experimental part of this study, pressure drops that occur in the flow of low density (specific gravity of capsule: 0.87) spherical capsule (d = 0.08 m) trains with water inside horizontal pipes (D = 0.1 m) were measured at 5–30% capsule transport concentrations, and the capsule flow mechanism was observed in Plexiglas pipes installed in the experimental equipment. A mathematical correlation developed to calculate the pressure drops of the flow of a low-density spherical capsule train is presented. Experimental results have been compared with general correlations regarding spherical capsule flows, and it is pointed out that available models are incompatible (average deviation exceeding 70–90%) with experimental results. However, comparison of the empirical correlation developed for pressure drops of spherical capsule train flow (2.5 × 104 < Re < 1.5 × 105) with the experimental findings revealed an average deviation of 3.37% between the pressure drop values.
SUPERCRITICAL WATER TREATMENT OF BIOMASS FOR ENERGY AND MATERIAL RECOVERY

ABSTRACT Supercritical water liquefaction and gasification is reviewed with the introduction of some recent findings by the authors. Supercritical water gasification is suitable for recovery of energy from wet biomass while supercritical water liquefaction opens the door to effective treatment of biomass species in terms of material recovery. Cellulose, one of the main components of biomass, is completely dissolved in supercritical water. Once dissolved, reaction of cellulose can take place swiftly by hydrolysis and pyrolysis. The hydrolysis reaction, otherwise slower than pyrolysis due to the mass transfer limitation, is faster than decomposition in supercritical water, and a possibility of efficient glucose recovery has been shown. Once dissolved, super saturation is kept when the solution is cooled down, and swift hydrolysis by enzyme is also possible. Lignin can be also converted into specialty chemicals by using supercritical cresol/water mixture as a solvent. Dissolution of cellulose also enables efficient gasification of biomass. Complete gasification of biomass has been realized with production of combustible gas including hydrogen, carbon monoxide, and methane.