Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the electrochemical windows of aprotic alkali metal (Li, Na, K) salt solutions

This work is a comparative study of the electrochemical windows and the basic processes on gold electrodes in LiClO 4 , NaClO 4 , and KClO 4 solutions in propylene carbonate (PC). The analytical tools included cyclic voltammetry, electrochemical quartz crystal microbalance, surface-sensitive Fourier transform infrared spectroscopy (ex situ, external reflectance mode), and X-ray photoelectron spectroscopy. The apparent electrochemical windows of these systems are anodically limited at potentials above 1.3 V (vs. Ag pseudoreference electrode corresponding to 4.3 vs. Li/Li 1 ) due to solvent oxidation. The apparent cathodic side is limited due to the reversible bulk active metal deposition occurring at approximately -3 and < -2.7 V vs Ag pseudoreference electrode for Li and Na, respectively. In the case of the potassium salt solution, the electrochemical window is limited by a pronounced cathodic process below -2 V (vs Ag reference electrode), which is attributed to irreversible reduction of solution species. Irreversible potassium deposition occurs at potentials below - 2.5 V. This process cannot be separated from the reduction processes of the solution starting below -2 V. The study revealed that irreversible trace O 2 , trace H 2 O. and PC reduction form passivating surface films on these electrodes, These films act as a solid electrolyte interphase, i.e., they allow transport of the alkali metal ions through them. The study also found that the major constituent in the surface films is the PC reduction product CH 3 CH(OCO 2 M)CH 2 OCO 2 M. In general, the surface films formed on the noble metal electrodes in the Li and K salt solutions are more stable than those formed in the Na salt solutions, because the sodium oxides, hydroxide, and carbonates thus formed are more soluble in PC than the corresponding Li and K compounds.
2.3% Efficiency Gains for Silicon Solar Modules Using a Durable Broadband Anti-reflection Coating

The front surface reflection losses from solar cover glass account for over 4% of incident light, limiting module efficiency. The application of a multilayer broadband antireflection coating reduces reflection losses over the wavelength range utilised by silicon solar cells. A 6-layer anti-reflection coating comprising SiO2 and ZrO2 has been deposited on glass using high rate pulsed-DC magnetron sputtering. The reflection losses are reduced by 2.4% absolute compared to uncoated glass. The increased light reaching the solar cell leads to improvements in Isc and spectral response, increasing the efficiency from 17.1 to 17.5%, a relative increase of 2.34%. The coating is environmentally robust. The sputtering process is already used for other high throughput applications by major glass manufacturers.
Decision Making Optimization for Job Offloading in Vehicular Edge Computing Networks

Vehicular Networks will play a crucial role in future Intelligent Transportation Systems (ITS). Due to the limited computing capacity of the vehicles, a certain number of data jobs could be offloaded to external servers. However, offloading to servers in remote clouds is not possible due to latency requirements of some applications or if generated jobs are too "big" (big data). For this reason, thanks to 5G technology and Multi-Access Edge Computing (MEC), it is possible to offload jobs to servers placed at the edge of the network, realizing the Vehicular Edge Computing (VEC). The aim of this paper is to define a Decision Making Scheme for computation offloading, with the objective of minimizing job offloading costs, while respecting some constraints in terms of processing delay and loss probability. Some numerical results are presented to demonstrate the performance of the proposed solution.
A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT

Medical image segmentation is one of the crucial tasks in diagnosis as well as pre-surgery. Recently, deep learning has significantly contributed to improving the efficiency of medical image extraction. The U-Net network has been a favored network model, which has been used as a platform architecture, for medical image segmentation. For the success of these studies, most of these models were primarily focused on the changing of the interconnection between the nodes in the network, and changing the structure of the convolution units. This would result in the ignorance of the output features of convolution units in the nodes. In this study, a Un-Net, an n-fold network architecture, was proposed based on the traditional U-Net. In the Un-Net model, the output features of the convolution units are taken as the skip connection. Therefore, the Un-Net network exploits the output features of the convolution units in the nodes. In this study, we investigated a U2-Net and a U3-Net for segmentation of the liver and liver tumors. Besides, dilated convolution (DC) and dense structure were also used in the nodes of our networks. The efficiency of our models was evaluated on two public datasets: LiTS and 3DIRCADb. The Dice’s Similarity Coefficient (DSC) of our proposed models achieved 96.38% for liver segmentation and 73.69% for tumor segmentation on the LiTS dataset. For the 3DIRCADb dataset, the results achieved 96.45% in DSC for the liver segmentation and 73.34% for the tumor segmentation. The experimental results show that our proposed networks achieved better results than the recent networks. And it is convinced that our network would be useful for practical deployments.
ADF2T: an Active Disk Failure Forecasting and Tolerance Software

The reliability of distributed file system is inevitably affected by hard disk failure. This paper proposes an active disk failure forecasting and tolerance software. Firstly, multiple SMART records in the time window are merged into one sample, and after sliding, tens of times of positive samples are created. Secondly, the features are selected by two-stage sorting method, so that the most conducive features are used in machine learning modeling, and the time for model training can be shortened obviously. Thirdly, through two-stage verification, parameters can be adjusted in time for unreasonable proactive reconstruction strategies. Experiments show that modeling and forecast of ZTE data set and Backblaze data set respectively, the recall rate is 95.66% and 84.28%, and the error rate is 0.23% and 2.45%. The work in this paper has been commercially used for more than one year in ZTE data center. The reliability of distributed file system software is significantly improved.
AugMapping: Accurate and Efficient Inference with Deep Double-Threshold Spiking Neural Networks

Spiking neural networks (SNNs) are regarded as one of the promising candidates to overcome the high energy costs of artificial neural networks (ANNs), but the accuracy gap between them is still large on practical tasks. A straightforward yet effective conversion scheme was developed recently to narrow this gap by mapping a trained ANN to an SNN. However, current conversion methods require a relatively large number of time steps and spikes, alleviating the advantages of spike-based computation. In this paper, we propose a new augmented spiking neuron model composed of a double-threshold firing scheme, and it is advanced with the ability to process and elicit augmented spikes whose strength is used to carry the number of typical allor-nothing spikes firing at one time step. Based on this model, a new conversion method called AugMapping is developed. We examine the performance of our methods with both MNIST and CIFAR10 datasets. Our results highlight that the as-proposed methods, as benchmarked to other baselines, are advantageous to accurate and efficient computation with SNNs. Therefore, our work contributes to improving the performance of spike-based computation, which would be of great merit to neuromorphic computing.
Predicting Stroke Risk With an Interpretable Classifier

Predicting an individual’s risk of getting a stroke has been a research subject for many authors worldwide since it is a frequent illness and there is strong evidence that early awareness of having that risk can be beneficial for prevention and treatment. Many Governments have been collecting medical data about their own population with the purpose of using artificial intelligence methods for making those predictions. The most accurate ones are based on so called black-box methods which give little or no information about why they make a certain prediction. However, in the medical field the explanations are sometimes more important than the accuracy since they allow specialists to gain insight about the factors that influence the risk level. It is also frequent to find medical information records with some missing data. In this work, we present the development of a prediction method which not only outperforms some other existing ones but it also gives information about the most probable causes of a high stroke risk and can deal with incomplete data records. It is based on the Dempster-Shafer theory of plausibility. For the testing we used data provided by the regional hospital in Okayama, Japan, a country in which people are compelled to undergo annual health checkups by law. This article presents experiments comparing the results of the Dempster-Shafer method with the ones obtained using other well-known machine learning methods like Multilayer perceptron, Support Vector Machines and Naive Bayes. Our approach performed the best in these experiments with some missing data. It also presents an analysis of the interpretation of rules produced by the method for doing the classification. The rules were validated by both medical literature and human specialists.
Semi-Global Context Network for Semantic Correspondence

Estimating semantic correspondence between pairs of images can be challenging as a result of intra-class variation, background clutter, and repetitive patterns. This paper proposes a convolutional neural network (CNN) that attempts to learn rich semantic representations that contain the global semantic context to enable robust semantic correspondence estimation against intra-class variation and repetitive patterns. We introduce a global context fused feature representation that efficiently employs the global semantic context in estimating semantic correspondence as well as a semi-global self-similarity feature to reduce background clutter-induced distraction in capturing the global semantic context. The proposed network is trained in an end-to-end manner using a weakly supervised loss, which requires a weak level of supervision involving annotation on image pairs. This weakly supervised loss is supplemented with a historical averaging loss to effectively train the network. Our approach decreases running time by a factor of more than four and reduces the training memory requirement by a factor of three and produces competitive or superior results relative to previous approaches on the PF-PASCAL, PF-WILLOW, and TSS benchmarks.
SMAKA: Secure Many-to-Many Authentication and Key Agreement Scheme for Vehicular Networks

With the rising popularity of the Internet and communication technology, vehicles can analyze and judge the real-time data collected by various cloud service providers (CSPs) in a vehicular network. However, in a vehicular network environment, real-time data are transmitted via wireless channels, which can lead to security and privacy issues. To avoid illegal access by adversaries, vehicle authentication and key agreement mechanism has been considered as one of the promising security measures in vehicular network environments. Besides, most of the solutions focus on authentication between one vehicle and one CSP. In such strategies, the implementation of efficient authentication for multiple vehicles and CSPs simultaneously is usually challenging. Further, they are also subjected to performance limitations due to the overhead incurred. To solve these issues, we propose a many-to-many authentication and key agreement scheme for secure authentication between multiple vehicles and CSPs. The proposed scheme can prevent unauthorized access and provide SK-security even if temporary information is leaked. To improve the service, the CSP only needs to broadcast an anonymous message periodically instead of having to generate a unique anonymous message for each of vehicles. Similarly, when a vehicle wants to request the services of $m$ CSPs, it only needs to send one request message instead of $m$ . Therefore, the proposed scheme not only implements many-to-many communication but also significantly reduces the computation and communication overhead. Moreover, a thorough security analysis shows that the proposed scheme provides better security compared to other related schemes.
Studies on biology, feeding habits and natural enemies of fall armyworm, Spodoptera frugiperda, a new invasive pest in India

Fall Armyworm, Spodoptera frugiperda (J.E. Smith), a new invasive insect pest is native to Americas and it was recorded as devastating pest of Maize in African Countries in 2016. In India, the occurrence of FAW was reported from Karnataka state during May 2018. In Southern Agroclimatic zone of Andhra Pradesh, Fall Armyworm was first noticed in Chittor District during August 2018 on Maize. Subsequently on other crops viz., Ragi, bajra, fodder maize, sugarcane. In the fields of early stages of Maize crop, egg masses that are covered with cream coloured hair and scraping symptoms on the leaves were observed. The scraping symptoms caused by first and second instars, shot holes and voracious cut feeding by later instars were recorded. The larvae were collected from maize fields of Chittor district, culture was maintained in the laboratory, studied the characters of egg, six larval instars, pupa and adult stages. The confirmation as Spodoptera frugiperda was done with the key identification character viz., “distinct inverted Y shaped suture on head region and exact square shaped pustules on 8th segment of the abdomen”. The cadavers of S. frugiperda with tree top symptom were noticed in maize fields of Chittor District, the pathogen was identified as Nuclear Polyhedrosis Virus (NPV). Like wise from the cadavers collected from Anantapur District, Cytoplasmic Polyhedrosis Virus (CPV) infection was confirmed. B.t infected caterpillars were also seen in maize fields and the pathogen was confirmed. During 2019 kharif, the cadavers infected with Metarhizium anisopliae were collected from maize fields. S. frugiperda larvae were treated with NPV, B.t, Nomuraea rileyi and Metarhizium anisopliae in the laboratory at 1X107 POBs or Spores per ml concentration and found the larvae highly susceptible to all the pathogens.
Little toys: elution test and evaluation of possible impact on disposal of wastes

Little toys are frequently present in the packaging of some foods such as cakes, biscuits boxes, chips bags. The aim of this work was to determine the amount of mobile metals from little toys, by atomic absorption spectrophotometry (AAS). The potential impact of little toys on the composition of effluents from the urban landfills has been evaluated.
Conflicted Effects of a Solvent Additive on PTB7:PC71BM Bulk Heterojunction Solar Cells

Recently, polymer–fullerene based bulk heterojunction (BHJ) solar cells, which contain blends of poly({4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl}{3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl}) (PTB7) and [6,6]-phenyl-C71-butyric acid methyl ester (PC71BM), have been widely studied due to exhibiting high power conversion efficiency (PCE) and well-defined nanomorphology. Because of the short exciton diffusion pathway (less than 10 nm) in organic thin films, the optimization of PTB7:PC71BM BHJ with optimized morphology is very important between the donor and acceptor. In order to increase nanoscale phase separation, the chemical additives of 1,8-diiodooctane (DIO) have been used in PTB7:PC71BM blend systems. However, the mechanism studies of DIO in BHJ solar cells and its effectiveness on device stability are unclear. In this study, we fabricated polymer solar cells (PSCs) based on PTB7:PC71BM BHJ with various DIO concentrations to investigate not only correlation bet.
Growth and Characterization of Two-Dimensional FeO Nanoislands Supported on Pt(111)†

Two-dimensional (2D) FeO nanoislands with a well-controlled size, density, and surface structure have been grown on Pt(111) by a two-step preparation process, which consists of Fe deposition at low temperatures, such as 150 K, in an O2 atmosphere and subsequent annealing at elevated temperatures in ultrahigh vacuum. The atomic structure, chemical composition, and electronic state of the formed FeO nanoislands were investigated by scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy, and high-resolution electron energy loss spectroscopy. The formation of the metastable 2D FeO surface phase can be attributed to confinement effects at interfaces between nanostructured oxides and metal substrates, which originate from the strong interaction between FeO and Pt(111). Furthermore, the STM and scanning tunneling spectroscopic data indicate that the formed Pt−FeO boundaries or edges of the FeO nanoislands present distinct chemical and electronic characteristics, which could be highly active in man.
Genetic Diversity and Population Structure of a Korean Rice Germplasm Based on DNA Profiles

Information on the patterns of genetic diversity and population structure is essential for the rational use and efficient management of germplasms; accurate information aids in monitoring germplasms, and can also be used to predict potential genetic gains. In this study, we assessed genetic diversity, focusing on Korean rice accessions for theand their sustainable conserved diversity. Using DNA profiling with 12 simple sequence repeat (SSR) markers, we detected a total of 333 alleles among 2,016 accessions. The number of alleles ranged from 21 to 53, with an average of 27.8. Average polymorphism information content was 0.797, with the lowest being 0.667 and the highest 0.940. CA cluster analysis and the model-based population structure revealed two main groups that could be subdivided into five subgroups. Analysis of the molecular variance study based on the SSR profile data showed 5% variance among the profiles, whereas we recorded 93% variance among individuals and 2% variance within individuals. Specifically, the utilized diversity for of the breeding program is restricted in that cultivars were located in limited clades. These results revealed that preserving the diversity of Korean landraces could be useful sources for breeding new rice cultivars, and cwould be the basis for the sustainable conservation and utilization of a Korean rice germplasm.
Structural and Surface Modifications of LiFePO4 Olivine Particles and Their Electrochemical Properties

LiFePO 4 -based olivine compounds were prepared by the usual ceramic method, where some modifications were carried out in order to improve the electrical conductivity of the cathode compound. Considering that the cathode is made up of electrochemical active particles and a nonactive polymer binder, two different methods were applied. The first one is related to the improvement of the interparticle conduction, and the other is the enhancement of the conductivity in the active particle itself. The former was attained with thermal decomposition of an organic additive (pyrene) during the sample preparation process, and the precipitated carbon was investigated with Raman spectroscopy. The bulk conductivity of the olivine was improved with a partial substitution of Fe 2+ with Mn 2+ , where the holes were incorporated into the valence band. These modifications significantly altered the electrochemical properties of the olivine cathode: large specific capacity (> 140 mAh/g) and small capacity fading. Although the importance of carbon deposition was confirmed as reported elsewhere, the structural modification effect was also more important than the surface one. The result will contribute to reduce carbon content of LiFePO 4 -based positive electrodes, which is important for their practical use.
Effects of hydrophobic and hydrophilic organic matter on the water repellency of model sandy soils

Abstract Soil water repellency affects the hydrological functions of soil systems. Water repellency is associated with the content and the composition of soil organic matter. In the present study, we examined the effects of hydrophobic and hydrophilic organic matter contents, the hydrophobic/hydrophilic organic matter ratio and the total organic matter content on water repellency using model sandy soils. Stearic acid and guar gum were used as the hydrophobic and hydrophilic organic compounds, respectively. Water repellency was estimated using the sessile drop method. Hydrophobic organic matter content was found to be the dominant factor affecting soil water repellency. Hydrophilic organic matter was found to increase the contact angle to some extent without the presence of hydrophobic organic matter. With the presence of both hydrophobic and hydrophilic organic matter, the effects of the hydrophilic organic matter content on contact angle were found to be dependent on the hydrophobic organic matter content of the soil. This relationship was explained by the differences in the surface free energies of different organic matter and mineral surfaces. The contact angle increased with increasing hydrophobic/hydrophilic organic matter ratio when the hydrophilic organic matter content was constant. When the hydrophobic organic matter content was constant, contact angles were roughly comparable, irrespective of the hydrophobic/hydrophilic organic matter ratio. The contact angles were not comparable at each total organic matter content. Accordingly, the hydrophobic/hydrophilic organic matter ratio and the total organic matter content in soil may not provide satisfactory information about soil water repellency.
Decomposition Rate Constants of Size and Density Fractions of Soil Organic Matter

One of the main drawbacks of models describing soil organic matter turnover is that most pools cannot be experimentally identified. The aim of this study was to define meaningful soil organic matter fractions that can be physically isolated and to determine their decomposition rate constants so that they can be incorporated into models. This would be a major step toward verification of models. In this study, soil organic matter was separated into five fractions : the light (density 1.37 g cm -3 ) fractions of macroorganic matter (>150 μm) and the microaggregate fractions with particle sizes between 20 and 150 μm and smaller than 20 μm. The decomposition rate constants of the fractions decreased in the order light, intermediate, and heavy macroorganic matter (23.9, 9.8, and 3.9 x 10 -4 d -1 , respectively) and were lowest for C in the microaggregate fractions <20 and 20 to 50 μm (0.5-0.7 x 10 -4 d -1 ). Since the rate constants of the fractions seemed not to depend on soil texture, they can probably be applied widely. We propose to use these fractions as the principal pools in future soil organic matter models.
The Flood in the Odra River 1997 – Impact of Suspended Solids on Water Quality

The flood in the Odra river in 1997 has led to considerable additional pollution of the Stettin Lagoon and the Baltic Sea with contaminated suspended solids. For some priority substances, the pollutant entries via suspended solids during the flood period are estimated to be approximately 1/3 3 of the usual annual load. Among these priority pollutants there are total organic carbon (TOC), nitrogen, and the heavy metals Cu, Pb and Zn. For the concentrations of the priority pollutants in suspended solids accumulation factors from 2 to 4 in the comparison with normal conditions were observed. On the basis of the analysis of sediments sampled after the flood, main sources of the pollutants should be evaluated. As reference area with an industrial background as well as a typical pollutant pattern the region around Glogow/Legnica is proposed.