Miracle-dz/UniFAR-FTUs huggingface dataset:
New mass spectrometric methods for the study of noncovalent associations of biopolymers

The use of electrospray ionization–mass spectrometry(ESI–MS) for the characterization of noncovalent complexes of biomacromolecules in solution is based upon the gentle nature of the electrospray process that allows a wide range of associations to be transferred intact to the gas phase as fully desolvated complexes. Examples include multimeric proteins, oligonucleotide duplexes, DNA–drug complexes and enzyme-inhibitor complexes. Various studies have indicated that at least some qualities of the three-dimensional solution structures are retained in the gas phase. Recent investigations have also shown the relative stabilities of complexes in the gas phase can be very different than the same complexes in solution. In spite of this, the use of very gentle electrospray interface conditions can provide a direct reflection of relative solution abundances for similar complexes. Competitive binding experiments using sets of ligands have been shown to yield insights regarding relative binding affinities in solution. The potential for high throughput affinity screening of combinatorial libraries using ESI–MS is described based upon the multi-stage MS capability of Fourier transform ion cyclotron resonance instrumentation and involving the characterization of components (after dissociation) of the library constituents initially present as complexes with a target biopolymer in the ion trap. This approach combines, in one rapid experiment, both affinity selection by complex formation with a biopolymer and the identification of the ligands selected from combinatorial mixtures, thus providing information on the relative binding affinities of the library constituents. The present status, limitations and promise of these methods are discussed.
Chemistry, Catalysts, and Processes for Isoparaffin–Olefin Alkylation: Actual Situation and Future Trends

Abstract As a consequence of the Clean Air Act (CAA), beginning in 1990, USA refiners were forced to change their strategy in order to meet the new mandatory specifications on gasoline composition. The targets established by the CAA were directed to overcoming environmental problems by reducing the ground-level ozone-forming and carbon monoxide emissions from vehicles, as well as toxic hydrocarbons and SO, and NO, emissions in auto exhausts. In order to accomplish this, gasolines had to move in the following direction: Reduce volatility: that is, lower Reid vapor pressure (RVP), especially during summer months, in order to reduce ozone levels. This can be achieved by removing butanes and even C, from the gasoline. Limitations in the aromatic content, with special emphasis on benzene. This can be solved by reducing reformate severity and/or by reducing the upper cut of the fluid catalytically cracked (FCC) gas-oline. Increased amount ofoxygenates, in which MTBE and TAME are preferred, especially in the car.
Cuticular waxes of the holm (Quercus ilex l. subsp. ballota (desf.) samp.) and cork (Q. suber l.) oaks

The epicuticular leaf waxes of young leaves of the Holm oak (Quercus ilex L. ssp. ballota (Desf.) Samp. ) and the cork oak (Q suber L.) were extracted in chloroform and separated into their constituent wax classes by preparative thin layer and high pressure liquid chromatography. The wax coverage was about 125 μg/cm2 in Q. suber and about 71 μg/cm2 in Q. ilex. The main classes encountered (by weight) were odd chain n-alkanes (9% in Q. ilex, and 4–27% in Q. suber) and even chain amphiphilic compounds. The major classes of amphiphilic compounds were n-alkan-1-ols (20–27% in Q. ilex, and 18–50% in Q. suber), n-alkanals (<3% in Q. ilex, and up to 25% in Q. suber), n-alkanoic acids (11–12% in Q. ilex, and <5% in Q. suber) and n-alkyl esters (50–56% in Q. ilex, and 25–45% in Q. suber). The major components of each class, identified by gas chromatography coupled to mass spectrometry were C29 for the n-alkanes (54–58% in Q. ilex, and 47–57% in Q. suber), C24 for the n-alkan-1-ols (78–87% in Q. ilex, and 77–93% in Q. suber), C30 for the n-alkanals (up to 57% in Q. ilex, and up to 77% in Q. suber), C22 and C24 for the n-alkanoic acids in Q. ilex (39–62%) and C30 in Q. suber (49%). The n-alkyl esters were composed mainly from C22 and C24n-alkanoic acids (∼38%) and n-alkan-1-ols (43–54%) in Q. ilex, whereas in Q. suber these esters were composed mainly from C22 and C24n-alkanoic acids (44–52%) and C20 and C22n-alkan-1-ols (32–66%). In addition to the above compounds, trace amounts (<5%) of C28 and C30n-alkenes and the triterpenone, friedelin, were encountered in Q. suber and both plant waxes contained small amounts of the triterpenols α- and β-amyrin (5–9% of the total alkanol content). The variability observed in the chemical composition of the leaf waxes were attributable to seasonal causes and/or leaf age. Copyright © 1999 John Wiley & Sons, Ltd.
Design and Implementation of a Single-Phase 15-Level Inverter With Reduced Components for Solar PV Applications

A new single-phase 15-level inverter with a reduced number of components for the Solar PV application is proposed in this paper. It is incorporate the proposed inverter with a boost converter to extract energy from the solar PV modules, the proposed inverter aids to generate fifteen stepped output voltage levels with lower THD. The proposed inverter can improve efficiency and reduce losses, cost, and complexity of the overall system. The conventional boost converter will boost the output voltage to a maximum voltage from Solar PV with MPPT(P&O). The proposed inverter is tested experimentally using the dSPACE RTI 1104 controller along with MATLAB/Simulink. A detailed comparison with existing MLIs with the proposed inverter. The work presents experimental results not only to show its efficiency but also to the effectiveness under different circumstances of linear and non-linear loads. The inverter is stable during the non-linear loads and well suits for grid-connected systems.
Doping Effect of Metal Cations on the Physicochemical Properties of Nanomagnetite

The influence of heavy and noble metals cations on the phase and chemical composition of nanoscale structures formed on the surface of a steel electrode in the rotational corrosion dispersion course has been investigated. Thermogravi metric analysis showed that the obtained samples are characterized by the appearance of exothermic effects, which is typical to iron-oxygen phases. Nanosized spinels are formed as spherical particles, and they compound aggregates that can be destroyed under the influence of mechanical load. The magnetometry data shows the absence of vestigial magnetization and coercivity in most of the samples, typical for superparamagnetic behavior. Depending on the nature of the doping cation, the saturation magnetization varies from 20 to 60 A $\cdot$ m2/kg.
Jakarta Flood Risk Mapping Using Index-based Approach and Spatial Analysis

Flooding occurs almost every year in Jakarta during the rainy season, including earlier this year in 2020. This is considered alarming because the scale of the flooding impact has increased rapidly in recent decades. The increase was related to a number of drivers, both physical (climate change) and socio-economic (population growth). This proposed a new approach based on the impact of both factors on flood risk, considering flood hazard and vulnerability in quantifying risk using an index-based approach. Spatial analysis is utilized to create thematic maps used to identify the geographical variation of flood risk among subdistricts. The result indicates that 16 percent of Jakarta subdistricts, mainly concentrating around Central, West, and North Jakarta, have a high risk of flooding.
Coevolutionary Deep Reinforcement Learning

The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for this, selfplay, allows reinforcement learning to develop high performing policies without large datasets or expert knowledge. Despite these benefits, self-play is known to be less sample efficient and suffer unstable learning dynamics. This is in part due to a nonstationary learning problem where an agent’s actions influence their opponents and as a consequence the training data they receive. In this paper we demonstrate that competitive pressures can be utilised to improve self-play. This paper leverages coevolution, an evolutionary inspired process in which individuals are compelled to innovate and adapt, to optimise the training of a population of reinforcement learning agents. We demonstrate that our algorithm improves the final performance of a Rainbow DQN trained in the game Connect Four, achieving a 15% higher win percentage over the next leading self-play algorithm. Furthermore, our algorithm exhibits more stable training with less variation in evaluation performance.
Diversified Semantic Attention Model for Fine-Grained Entity Typing

Fine-grained entity typing, which aims to assign specific types to entity mentions in text, is attracting increasing attention in the field of natural language processing (NLP). However, it is quite a challenging problem due to the highly ambiguous nature of many entity mentions. Most existing entity typing methods based on attention mechanism generally extract the salient features separately from the entity mention and the contextual words. However, these approaches suffer from two main limitations: (1) They ignore the rich information contained by entity mentions when applying the attention mechanisms. (2) They do not consider the diversity of attention processes which can be beneficial in finding the discriminative features. To address these issues, we propose the diversified semantic attention model (DSAM) for fine-grained entity typing, and the main novelties are: (1) It explicitly pursues the diversity of attention and is able to maximally gather discriminative information. (2) It integrates two level attentions—the mention-level attention and the context-level attention— to jointly capture the rich information from mentions and contexts to enhance their mutual promotions. (3) It combines the attention maps constraint and the attention segments constrain to exploit the subtle semantic differences for distinguishing the subtypes. Importantly, the proposed DSAM approach can be trained end-to-end without employing ad-hoc features or post-processing. Extensive experiments using three benchmark datasets demonstrated that our DSAM approach achieves competitive performance compared to the current state-of-the-art methods used for fine-grained entity typing.
First Principles assisted modeling to understand Chlorine passivation of CdTe grain boundary

A computational study on cadmium telluride (CdTe) (111)/(220) grain boundary was performed using a first-principles approach. Cadmium chloride (CdCl2) treatment of CdTe based solar cells has proven to be highly beneficial for device performance. Published literature suggests incorporation of chlorine at Te site (ClTe) in CdTe grain boundaries. Density Functional Theory was therefore used to identify the mechanisms responsible for enhancing CdTe device performance after CdCl2 treatment. The computational model explains the formation of local p-n-p junctions through field effect passivation after inclusion of ClTe, which significantly improves CdTe device performance by mitigating recombination at the interface.
A Deep CNN Approach for Plant Disease Detection

The diagnosis of the plants is carried out with a visual inspection by experts and a biological examination is the second choice if necessary. They are usually expensive and time consuming. This inspired several computer methodologies to detect plant blights based on their leaf images. We apply a computer methodology on Deep Learning systems based on artificial neural networks, this branch also allows for the early detection of plant diseases, by applying convolutional neural networks (CNNs) familiar with some of the famous architectures, notably the “ResNet” architecture, using an augmented dataset containing images of healthy and diseased leaves (each leaf is manually cut and placed on a uniform background) with acceptable accuracy rates in the research environment. This Deep Learning technique has shown very good performance for various object detection problems. The model fulfills its role by classifying images into two categories (disease-free) and (diseased). According to the results obtained, the developed system achieves better detection performances than those proposed in the state of the art. Finally, to compare their performances, we use the implementation under Anaconda 2019.10.
A Multimode 5–6 GHz FD/HD/FDD Quadrature Balanced CMOS Front End With Transmit–Receive Cross-Modulation Distortion Correction

In this work, we introduce a multimode and wideband quadrature balanced CMOS RF front end (RFFE) supporting full-duplex (FD), half-duplex (HD), and frequency-division duplex (FDD) operation at the 5–6 GHz band. The proposed RFFE is enhanced by nonlinear digital algorithms that maintain optimal performance at high transmit (Tx) power levels. A theoretical model of the transmit–receive (TR) cross-modulation (CM) distortion mechanism stemming from instantaneous variations in the power amplifiers (PAs) output impedance is laid out and verified in simulation and measurement. A 2-D digital postdistortion (2-D DPoD) algorithm that utilizes the known baseband Tx signal and the downconverted distorted Rx signal is proposed to linearize the CM-distorted Rx signal, in conjunction with wideband digital RF and baseband cancellation of the Tx self-interference at the receiver input and 1-D digital predistortion to linearize the Tx signal. The system was characterized in simultaneous TR operation using 802.11ac VHT Wi-Fi signals at 10 dBm average Tx power. In the FD mode using 20 MHz signals, TR isolation of 66 dB was achieved, and the 2-D DPoD corrected Rx error vector magnitude (EVM) by 11 dB to a value of −33 dB. In the FDD mode with 800 MHz frequency separation, 14 dB improvement was demonstrated in Rx EVM with 2-D DPoD to the levels of −35 and −31 dB for 20 and 160 MHz signals, respectively. Similar EVM values were measured in the HD Rx mode at −40 dBm average Rx power, along with Rx insertion loss lower than 1 dB without using a TR switch.
Changes in the Si−O Vibrations of Smectite Layers Accompanying the Sorption of Interlayer Water

The frequencies of Si−O stretching, νSi-O, in layers of montmorillonite and of H−O−H bending, ν2, in the interlayer water were investigated by infrared spectroscopy. Oriented gels of Li- and Na-montmorillonite equilibrated at different water contents, mw/mc, were obtained by using a miniature pressure membrane apparatus. One part of each gel was used for the gravimetric determination of mw/mc. The other part was transferred to an FTIR spectrometer where the spectrum of the gel was measured by attenuated total reflectance. Thus, the dependence of both νSi-O and ν2 on mw/mc was determined in the same samples. The absorption bands for Si−O stretching were resolved by using a curve-fitting technique to determine the frequency, area, and height of the component peaks. Two prominent peaks, labeled peak III and peak IV, were selected for analysis. It was found that νSi-O for each peak and the ratio of the areas (or heights) of the two peaks were all exponential functions of mc/mw. Moreover, a linear relation was.
PMC Packaged Single-Substrate 4 × 4 Butler Matrix and Double-Ridge Gap Waveguide Horn Antenna Array for Multibeam Applications

A perfect magnetic conductor (PMC) packaging concept is utilized at 30 GHz, which suppresses the higher order cavity modes, improves insertion losses, and helps in developing packaged microstrip lines (PMSLs) and double-ridge gap waveguide (DRGW) transmission lines. The Quasi-TEM PMSLs are used to design quadratic hybrid, crossover, and phase shifters as components of a wideband <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> Butler matrix (BM) on a single substrate. Two types of electromagnetic bandgap (EBG) unit cells—full height and half height—are investigated to realize artificial magnetic conductors (AMCs) required for the present design. A parametric study is performed for all components. BM is numerically assembled from these components and analyzed. It achieves a 5-GHz (28–33 GHz) bandwidth with return loss and isolation, both better than 15 dB. At 30 GHz, the insertion loss is 0.8 ± 0.3 dB, and antenna-ports’ phase distributions are ±° and ±°. <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane-flared horn antennas terminate the BM antenna ports as a linear array. The DRGW horn antenna is designed to reduce the scan loss within an array environment. The <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane fan-beam switching covers ±° with a maximum gain of 11.7 and 11.2 dBi for <inline-formula> <tex-math notation="LaTeX">$1R$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2R$ </tex-math></inline-formula> beams, respectively. The <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane element pattern and the half-height EBG AMC at the array aperture help to maintain 0.5 dB of gain loss for the outer beams (<inline-formula> <tex-math notation="LaTeX">$2R$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2L$ </tex-math></inline-formula>) compared to the inner beams (<inline-formula> <tex-math notation="LaTeX">$1R$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1L$ </tex-math></inline-formula>). The multibeam prototype is the right candidate for millimeter-wave 5G base station and can easily be scaled to higher frequency bands.
BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications

EEG based brain-computer interface (BCI) allows people to communicate and control external devices using brain signals. The application of BCI ranges from assisting in disabilities to interaction in a virtual reality environment by detecting user intent from EEG signals. The major problem lies in correctly classifying the EEG signals to issue a command with minimal requirement of pre-processing and resources. To overcome these problems, we have proposed, BCINet, a novel optimized convolution neural network model. We have evaluated the BCINet over two EEG based BCI datasets collected in mobile brain/body imaging (MoBI) settings. BCINet significantly outperforms the classification for two datasets with up to 20% increase in accuracy while fewer than 75% trainable parameters. Such a model with improved performance while less requirement of computation resources opens the possibilities for the development of several real-world BCI applications with high performance.
A Data-driven MPC Algorithm for Bridge Cranes

A crane system often works in a complex environment. Traditional system identification methods are difficult to accurately model crane system identification's real dynamic performance, whether online or offline. Therefore, a Data-driven model predictive control (D-MPC) algorithm is proposed in this paper. Based on Bayesian optimization, the crane's local linear dynamic model is learned to improve the rapidly anti-swing and precise positioning. By collecting data through closed-loop experiments and using Bayes to construct the Gaussian model for guiding learning, the controller parameters and prediction model with the best closed-loop performance can be found. Simulation results show that we explicitly find the dynamics model that produces the best control performance for the actual system, and the method can quickly suppress the swing and realize the accurate trolley positioning. The results verify the proposed control algorithm's effectiveness, feasibility and superior performance of the proposed method by comparing it with double-closed-loop proportional–integral– derivative (PID).
Edge Intelligence and Internet of Things in Healthcare: A Survey

With the advent of new technologies and the fast pace of human life, patients today require a sophisticated and advanced smart healthcare framework that is tailored to suit their individual health requirements. Along with 5G and state-of-the-art smart Internet of Things (IoT) sensors, edge computing provides intelligent, real-time healthcare solutions that satisfy energy consumption and latency criteria. Earlier surveys on smart healthcare systems were centered on cloud and fog computing architectures, security, and authentication, and the types of sensors and devices used in edge computing frameworks. They did not focus on the healthcare IoT applications deployed within edge computing architectures. The first purpose of this study is to analyze the existing and evolving edge computing architectures and techniques for smart healthcare and recognize the demands and challenges of different application scenarios. We examine edge intelligence that targets health data classification with the tracking and identification of vital signs using state-of-the-art deep learning techniques. This study also presents a comprehensive analysis of the use of cutting-edge artificial intelligence-based classification and prediction techniques employed for edge intelligence. Even with its many advantages, edge intelligence poses challenges related to computational complexity and security. To offer a higher quality of life to patients, potential research recommendations for improving edge computing services for healthcare are identified in this study. This study also offers a brief overview of the general usage of IoT solutions in edge platforms for medical treatment and healthcare.
Temperature dependence of polysilicon passivating contact and its device performance

The temperature coefficient (TC) of solar cells is an important figure of merit for any photovoltaic technology. Recently, tunnel oxide passivated contact (TOPCon) solar cells have attracted significant attention due to their potential to obtain high efficiency. Quantification of their TC is therefore of significant interest. In this study, we investigate (a) TCs of the TOPCon solar cells, and (b) the temperature dependence of polysilicon passivating contacts. For TOPCon cells using Czochralski-wafers, the relative TC of the cell efficiency is −0.285±0.005 %/°C. To our best knowledge, this is the first time that the efficiency TC of TOPCon cells is reported. This TC value is superior to those of devices without passivating contacts and is comparable to the one of silicon heterojunction cells. Using test samples, we observe an increase of the effective lifetime over the entire measured injection level range and a small improvement of the surface passivation quality when increasing the temperature. However, it seems these effects do not have a strong impact on the TC of the open-circuit voltage. We find that the improvement of the contact resistivity at elevated temperatures offsets some of the fill factor reduction and hence, improves the TC of the fill factor.