Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable, Compact, and Tunable MEMS Bandpass Filter Using Arrays of Series and Shunt Bridges for 28-GHz 5G Applications

This work presents radio frequency (RF) microelectromechanical system (MEMS)-based compact, high power, and reliable tunable bandpass filter for millimeter-wave RF front end. The design is fabricated on 635- $\mu \text{m}$ alumina substrate using surface micromachining process. All functional building blocks of the filter are fabricated and tested independently to ensure the optimum filter performances. Total area of the filter is only 2.3 mm2 including bias lines and pads. The proposed filter can be tuned to any frequency between 27 and 29 GHz and it demonstrates insertion loss of 1.92 dB and matching of 23.4 dB at 28 GHz with fractional bandwidth (FBW) of 6.7%. Filter also gives bandwidth tuning of 4.4 GHz with acceptable loss. Filter works satisfactorily up to 1 billion cycles with 1 W of power. Device reliability tested within a low-cost package. To the best of our knowledge, this is the first reported tunable MEMS bandpass filter in the literature which has undergone and presented with extensive reliability results.
Extending Kubernetes Clusters to Low-Resource Edge Devices Using Virtual Kubelets

In recent years, containers have gained popularity as a lightweight virtualization technology. This rise in popularity has gone hand in hand with the adoption of microservice architectures, mostly thanks to the scalable, ethereal, and isolated nature of containers. More recently, edge devices have become powerful enough to be able to run containerized microservices, while remaining flexible enough in terms of size and power to be deployed almost anywhere. This has triggered research into several container placement strategies involving edge networks, leading to concepts such as osmotic computing. While these container placement strategies are optimal in terms of workload placement, current container orchestrators are often not suitable for running on edge devices due to their high resource requirements. In this article, FLEDGE is presented as a Kubernetes-compatible container orchestrator based on Virtual Kubelets, aimed primarily at container orchestration on low-resource edge devices. Several aspects of low-resource container orchestration are examined, such as the choice of container runtime and how to realize container networking. A number of evaluations are performed to determine how FLEDGE compares to Kubernetes and K3S in terms of resource requirements, showing that it needs around 60MiB memory and 78MiB storage to run on a Raspberry Pi 3, including all dependencies, which is significantly less than both studied alternatives.
Evaluation of the Landsat-Based Canadian Wetland Inventory Map Using Multiple Sources: Challenges of Large-Scale Wetland Classification Using Remote Sensing

The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.
Heterogeneous photocatalytic reactions of organic acids and water. New reaction paths besides the photo-Kolbe reaction

The heterogeneous photocatalytic reactions of acetic, propionic, and butyric acids were studied on several kinds of powdered semiconductors, mainly TiO/sub 2/. The influence of Ag/sup +/ as an electron acceptor, and the dependence of the reactions on the kind of semiconductor and pH, were investigated. The ratio of yields of hydrogen to hydrocarbon depends strongly on pH. At neutral or alkaline pH, hydrocarbon stops being evolved and only hydrogen is evolved. It is concluded that the pH dependence of the valence band position of the semiconductor and the concentration of OH/sup -/ ion control the reaction paths. From the pH dependence of methane production from acetic acid the valence band of anatase is estimated to be located more deeply by 0.1 eV than that of rutile. The results of product analysis and the complete decomposition of acetic acid to CO/sub 2/ show that there exist new reaction paths in which water is an oxidizing agent. Reaction schemes of these organic acids are proposed.
Atomistic mechanisms of ionic transport in fast-ion conductors

We review the types of atomic migration mechanism operative in ionic solids with exceptionally high ionic mobilities. We classify mechanisms as simple-hopping, highly correlated or liquid-like. We discuss current knowledge of transport mechanisms in fluorite structured ionic conductors, β-Al2O3, Agl and proton conductors.
Silicon nanowire-transistor biosensor for study of molecule-molecule interactions

Abstract Monitoring the molecular recognition, binding, and disassociation between probe and target is important in medical diagnostics and drug screening, because such a wealth of information can be used to identify the pathogenic species and new therapeutic candidates. Nanoelectronic biosensors based on silicon nanowire field-effect transistors (SiNW-FETs) have recently attracted tremendous attention as a promising tool in the investigation of biomolecular interactions due to their capability of ultrasensitive, selective, real-time, and label-free detection. Herein, we summarize the recent advances in label-free analysis of molecule-molecule interactions using SiNW-FETs, with a discussion and emphasis on small molecule-biomolecule interaction, biomolecule-biomolecule interactions (including carbohydrate-protein interaction, protein-protein or antigen-antibody binding, and nucleic acid-nucleic acid hybridization), and protein-virus interaction. Such molecular recognitions offer a basis of biosensing and the dynamics assay of biomolecular association or dissociation. Compared to the conventional technologies, SiNW-FETs hold great promise to monitor molecule-molecule interactions with higher sensitivity and selectivity. Finally, several prospects concerning the future development of SiNW-FET biosensor are discussed.
Deterministic Channel Aggregation for LTE in Unlicensed Spectrum

We propose Deterministic Channel Aggregation (DCA) for LTE-Unlicensed, where the LTE eNodeB aggregates a predetermined number of channels in the unlicensed spectrum to achieve high data-rate communications. We introduce the MAC layer design, and analyze the collision probability and channel occupation ratio for DCA. Simulation results validate the effectiveness of DCA and our analytical results, when the eNB coexists with multiple WiFi systems under a range of traffic load conditions. DCA is particularly useful for applications that require high bandwidth and enables efficient access control of mobile broadband applications in the LTE-Unlicensed.
ADAPTATION OF SINGAPORE DAISY (WEDELIA TRILOBATA) TO DIFFERENT ENVIRONMENTAL CONDITIONS; WATER STRESS, SOIL TYPE AND TEMPERATURE

Invasion success of the invasive plant species (Singapore daisy) mostly depends on environmental factors, therefore, this study was conducted to investigate the effect of temperature, 25/30 °C (outside), 30/35 °C (inside) and water fluctuation, volumetric water content at 100% field capacity (normal water), volumetric water content at 33% field capacity (water stress) in three different soil types (nutrient soil, normal soil and sandy soil) on the growth and photosynthetic traits of Wedelia trilobata (Wt) in Jiangsu University, Zhenjiang China. In comparison, Wt significantly performed better in outside temperature at all water levels in every soil type. Wt can tolerate high temperature at normal water level in normal and nutrient soil. At outside temperature along with water stress in sandy soil, Wt exhibited reduction in growth and photosynthetic trails, but the adverse effect was more severe at inside temperature with water stress in normal and sandy soil. After 4 week, leaf chlorophyll content of Wt decreased in water stress at outside and inside temperatures in normal and sandy soil. Furthermore, Wt maintained its water status under water stress, after the 4 week to the 8 week in every soil type, which indicated that Wt have the ability to grow in every harsh environmental condition. In conclusion Wt successfully invaded different soil types at high temperature, under water stress conditions.
The Precision of Symbolic Numerical Representation in Verbal Format has an Indirect Effect on Math Performance in First Grade

Numerical information can be represented in three formats: two symbolic (visual (digits) and verbal (number words)) and one nonsymbolic (analog) format. Studies have shown that the precision of symbolic numerical representation is associated with math performance. The precision of symbolic representation is mostly discussed as the precision of representation in a visual format, whereas the precision of representation in verbal format and its relation with math performance is less studied. The current study examines the precision of symbolic numerical representation in visual and verbal formats and the relationship between such precision and math performance when controlling for prior math performance, nonsymbolic numerical representation, phonological processing, reading skills and working memory. We used data from 367 Russian first graders (mean age, 7.6 years; 53% girls). To assess the precision of symbolic numerical representation, magnitude comparison tasks with digits and number words were used. It was found that the precision of symbolic representation in verbal format did not have a direct effect on math performance, but has an indirect effect via visual format of symbolic representation, even when controlling for prior math performance and other cognitive abilities.
Using Classic Networks for Classifying Remote Sensing Images: Comparative Study

A R T I C L E I N F O A B S T R A C T Article history: Received: 12 August, 2020 Accepted: 25 September, 2020 Online: 12 October, 2020 This paper presents a comparative study for using the deep classic convolution networks in remote sensing images classification. There are four deep convolution models that used in this comparative study; the DenseNet 196, the NASNet Mobile, the VGG 16, and the ResNet 50 models. These learning convolution models are based on the use of the ImageNet pretrained weights, transfer learning, and then adding a full connected layer that compatible with the used dataset classes. There are two datasets are used in this comparison; the UC Merced land use dataset and the SIRI-WHU dataset. This comparison is based on the inspection of the learning curves to determine how well the training model is and calculating the overall accuracy that determines the model performance. This comparison illustrates that the use of the ResNet 50 model has the highest overall accuracy and the use of the NASNet Mobile model has the lowest overall accuracy in this study. The DenseNet 169 model has little higher overall accuracy than the VGG 16 model.
A Methodological Approach to Assess the Potential of Non-Recycled Waste for Refuse Derived Fuels Material

Modernization of waste processing through Waste-to-Energy has been a new trend to solve waste management and energy scarcity. This model however it may raise a conflict with recycling activities. This article establishes a methodological approach to assess Non-Recycled Waste for Refuse Derived Fuels materials. The approach is simulated by using calorific value of waste from Semarang Landfill which has calorific value 5,500 to 6,070 kcal/kg and waste composition of Banjarmasin Landfill which has been classified according to recycling perspective. The simulation shows that a low recycling rate (25% plastic and paper taken for recycling) will produce 3,882 kcal/Kg while a high recycling rate (50%) will produce 3,793 kcal/Kg. This simulation successfully calculates that non-recycled waste materials are still potential for RDFs materials.
A Separate Floating Heatsink Based Suppression Method for Conducted Common-Mode EMI

In order to effectively suppress the intractable high-frequency conducted common-mode (CM) electromagnetic interference (EMI) and reduce the dependence on the high-frequency performance of the EMI filter, this article proposes a separate floating heatsink based suppression method (SFHBSM). SFHBSM suppresses high-frequency CM EMI by improving both the CM EMI source and the propagation path. On the one hand, SFHBSM achieves the nonringing CM EMI source without introducing additional resistive loss, by properly designing the parameters of the SFHBSM, the ringing effect in the CM EMI source can be pushed into a narrow bandwidth in the very high-frequency domain, where the ringing effect can be naturally killed by the skin effect. On the other hand, the CM EMI propagation path of the SFHBSM-based power converter is mathematically deduced, verifying that the SFHBSM can provide effective blocking for the CM EMI propagation in the high-frequency domain. Experiments show that under the combined effect of the ringing suppression and propagation blocking, SFHBSM achieves EMI reduction in almost the whole high-frequency domain, and provides up to 35 dB attenuation in a two-level three-phase power converter. Besides, ablation experiments have been conducted to evaluate the individual and combined effectiveness of each part of the SFHBSM.
GLOBAL STABILITY OF AN EPIDEMIC MODEL WITH TWO INFECTED STAGES AND MASS-ACTION INCIDENCE

In this research work, we study the global stability of the SIR model which describes the dynamics of infectious disease with two classes of infected stages and varying total population size. The incidence used in the mathematical modeling was the mass-action incidence. The basic reproduction number R0 is computed. If the basic reproduction number is less than one, then the disease-free equilibrium point is locally and globally asymptotically stable. Existence and uniqueness of the endemic equilibrium is established when the basic reproduction number is greater than one and locally stable. We prove that global stability of the disease free equilibrium point using Lyapunov function. Numerical simulations have been carried out applying mat lab. Our result show that if the basic reproduction number R0 is below one the disease free equilibrium point is locally and globally stable in the feasible region, so that the disease dies out. If the basic reproduction number R0 is greater than one a unique endemic equilibrium point is locally asymptotically stable and the disease free equilibrium point is unstable in the interior of the feasible region and the disease will persist at the endemic equilibrium point if it is initially present. Keywords: Equilibrium Stability, SIR, Basic Reproduction Number, Local and Global Stability
A Review on Big Data Utilization in Metaverse World

We are currently in the big data era. The Metaverse is an upcoming technology that combines big data, virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and other technologies to reduce the gap between online and offline contact. It wants to be a place where we may work, travel, communicate, shop, play, and mingling. When a person joins the Metaverse, they all engage with the virtual environment in some way using data. Data will continue to increase as the Metaverse is developed and used, creating a big data network that will put a tremendous amount of pressure on the digital world's process data ability. Therefore, one of the crucial technologies to construct the Metaverse is large data processing technology. In this study, we thoroughly analyze how Metaverse is transforming large data. Furthermore, we go into great length about the important security and privacy of Metaverse large data. Finally, we provide a summary of the Metaverse's current challenges and potential as well as its prospects for using big data. With this survey, we hope to give researchers ideas for future research and the potential of using big data in the Metaverse.
Highly ordered mesostructured Ni particles prepared from lyotropic liquid crystals by electroless deposition: the effect of reducing agents on the ordering of mesostructure

Mesostructured Ni and Co particles are formed by metal deposition in the aqueous domains of lyotropic liquid crystals (LLC) using different reducing agents. We have investigated the role of reducing agents in metal deposition for the synthesis of highly-ordered mesoporous metals by electroless deposition. Slow metal deposition accompanied by the autocatalytic reaction of dimethylamineborane (DMAB), caused by the combination of Ni and DMAB, led to the formation of highly-ordered mesostructured Ni.
Different entities of liver failure need clear definitions

patients with AOCi must present with certain clinical features such as typical skin signs (e.g., spider naevi) and splenomegaly with thrombocytopenia or esophageal varices. Conversely, these signs of advanced liver disease are lacking in patients with AOCLF (Fig. 2). Per definition, preexisting liver damage with or without signs of cirrhosis excludes an ALF. Why do we see the necessity to distinguish AOCLF from AOCi? The prevalence of patients with metabolic liver damage such as NAFLD and non-alcoholic steatohepatitis is rising worldwide. Estimates assume a 25% prevalence of NAFLD, which would make NAFLD the The definition of acute liver failure (ALF) is universally accepted and usually implies acute damage to the liver with no previous liver injury, which is one of the most dangerous complications in hepatologic patients. ALF goes along with a coagulopathy due to hepatic dysfunction and an altered level of consciousness because of hepatic encephalopathy. Moreover, altered liver function due to ALF can completely regenerate and return to its original capacity. However, at least two complementary entities need to be distinguished in liver failure. In contrast to ALF, acutely decompensated cirrhosis and acute-on-chronic liver failure (AOCLF) without signs of cirrhosis are clinical conditions that are associated with a much lower capacity of liver regeneration. The term acutely decompensated cirrhosis is widely accepted and refers to the development of ascites, encephalopathy, gastrointestinal hemorrhage, or any combination of these disorders in patients with prediagnosed cirrhosis (Fig. 1). [1] A lot of controversies exist in recent years concerning the term acuteon-chronic liver failure, a term originally suggested by Jalan and Williams. The European Association for the Study of Liver well as the Asian Pacific Association for the Study of Liver (APASL) proposed definitions for AOCLF, which unfortunately are not in full accordance. This unclear terminology complicates the clinical definition of this entity and discussions in research on this topic. [2–4] Therefore, this study proposed new and adapted definitions regarding acute damage of the liver. Like the APASL, we propose to define AOCLF as a clinical syndrome consisting of severely impaired liver function in patients with a known chronic hepatic injury where signs of cirrhosis are absent. Preexisting chronic liver damage, e.g., in the form of non-alcoholic fatty liver disease (NAFLD) explicit without signs of cirrhosis, predisposes to the development of AOCLF. Thus, we propose to define patients with liver failure, with preexisting liver damage (independent of the cause, e.g., fatty liver disease, chronic viral hepatitis, and so on), with no cirrhosis, and no prior history of compensation as AOCLF. Patients with cirrhosis, independent of decompensation events, should be grouped as acute-on-cirrhosis liver failure (AOCi). Per definition,
Influence of Loading Rate on the Material Deformation Behaviour under Bi-axial Loading

In recent years, great effort has been made in all branches of industries to put into practise new methods and procedures using numerical support for technological processes, digitization and robotization of the production operations. The complex of these measures is known as Industry 4.0, which aims to reduce production costs, while increasing the flexibility of companies. The concept of the so-called virtual factory makes possible to predict possible problems and problematic production points already in the design phase by means of the numerical modelling. A prerequisite for the proposed virtual concept functioning is necessity to master the individual partial solutions with the sufficient accuracy of the whole computation with the subsequent data sharing in case of their need in other stages of the virtual solution. With the development of the computer technology (both in hardware and software), there is obvious effort to use still more and more complex mathematical computation models that allow taking into account a higher number of input process parameters and thus to achieve a higher computation accuracy. The paper is focused on the issue of technological processes numerical modelling at thin sheets drawing. As a needed prerequisite for obtaining the result having required accuracy, there is the correct choice of proper deformation model and definition of boundary conditions, which take into account the monitored technological process. In order to define advanced mathematical models of thin steel sheets deformation behaviour, it is necessary to perform (beside standard tests as e.g. static tensile test) also tests under biaxial loading. One of the most common tests for determining mechanical properties under biaxial loading is the hydrostatic bulge test (HBT). In the experimental part, the paper evaluates the influence of loading rates on the resulting mechanical properties of tested material. The required strain rate is affected by the time change of pressure increase. There were used three loading rates with linear pressure increase and two loading rates with holding time to enable stress relaxation of tested material. The contact-less optical system Mercury RT was used for data acquisition and deformation analysis. For the selected test parameters, stress-strain curves were determined from the measured values. Based upon these stress-strain curves, there was subsequently evaluated influence of the strain rate on the deformation behaviour of tested material and stress relaxation during the tests. As a testing material was used deep-drawing material specified for drawing stampings in the automotive industry.