Miracle-dz/UniFAR-FTUs huggingface dataset:
Second‐order scalar auxiliary variable Fourier‐spectral method for a liquid thin film coarsening model

In this paper, we propose and analyze a second‐order accurate (in time) numerical scheme for the droplet liquid film coarsening model by using the scalar auxiliary variable (SAV) method, and the Fourier‐spectral method is used in space. The scheme is linear; thus, it could be solved very efficiently. Meanwhile, due to the application of SAV approach, the unconditional energy stability of the numerical scheme is also derived, without any restriction on the time step size. In addition, we also provide a rigorous error estimate that shows that our second‐order numerical scheme with Fourier‐spectral method in space converges with order OΔt2+hm$$ O\left(\Delta {t}^2+{h}^m\right) $$ , where Δt$$ \Delta t $$ and h$$ h $$ are time and space step sizes, respectively. Finally, a few numerical experiments are performed, which confirms the efficiency and accuracy of the proposed scheme, including the tests of the convergence, the mass conservation and the decrease of the energy. The simulation of coarsening process with time is also observed.
Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders

Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained. Next, the decomposed data is then fed into the CAE that captures the underlying structure of the transformed data. Anomalies are identified when significant errors are detected between input samples and their reconstructed outputs. We demonstrate our approach on the IEEE-14 bus system considering different events such as generator faults, line-to-line faults, line-to-ground faults, load shedding, and line outages simulated on a real-time digital simulator (RTDS). The proposed implementation achieves a classification accuracy of 97.7%, precision of 98.0%, recall of 99.5%, F1 Score of 98.7%, and proves to be efficient in both time and space requirements compared to baseline approaches.
Targeting the deubiquitinase USP2 for malignant tumor therapy (Review)

The ubiquitin-proteasome system is a major degradation pathway for >80% of proteins in vivo. Deubiquitylases, which remove ubiquitinated tags to stabilize substrate proteins, are important components involved in regulating the degradation of ubiquitinated proteins. In addition, they serve multiple roles in tumor development by participating in physiological processes such as protein metabolism, cell cycle regulation, DNA damage repair and gene transcription. The present review systematically summarized the role of ubiquitin-specific protease 2 (USP2) in malignant tumors and the specific molecular mechanisms underlying the involvement of USP2 in tumor-associated pathways. USP2 reverses ubiquitin-mediated degradation of proteins and is involved in aberrant proliferation, migration, invasion, apoptosis and drug resistance of tumors. Additionally, the present review summarized studies reporting on the use of USP2 as a therapeutic target for malignancies such as breast, liver, ovarian, colorectal, bladder and prostate cancers and glioblastoma and highlights the current status of pharmacological research on USP2. The clinical significance of USP2 as a therapeutic target for malignant tumors warrants further investigation.
Post Quantum Implications on Private and Public Key Cryptography

With the increase in the computing resources power the quantum computers are penetrating into the communication networks. Therefore, assured security of classical cryptographic algorithms is highly questionable and needs inspection. Hence, this paper aims to explore the implications of quantum computing on contemporary cryptography and briefs key post-quantum algorithms. The reader will delve into various topics including current cryptographic schemes (both symmetric and asymmet-ric), the disparities between quantum and classical computing, key quantum algorithms such as Shor's and Grover's, the impact on public key encryption schemes, affected symmetric schemes, and the field of post-quantum cryptography. Specifically, the section on Post-Quantum Cryptography covers diverse quantum key distribution methods and mathematical based solutions, such as the BB84 protocol, lattice-based cryptography, multivariate-based cryptography, hash-based signatures, and code-based cryp-tography.
Behaviour and Bot Analysis on Online Social Networks: Twitter, Parler, and Reddit

The internet is home to a multitude of social networks that provide users with a sense of community and connection across the world. Among these, Twitter and Reddit are two of the most popular. While Twitter users follow and interact with other users (tweets), Reddit users follow and interact with communities known as subreddits. In addition to mainstream social networks, alternative platforms such as Parler exist for users who prefer less moderated online environments. However, there are also malicious users, such as bots and trolls, who exploit social networks for malicious purposes. Therefore, separating malicious behaviors from legitimate ones is crucial. This research aims to evaluate Botometer and RepScope systems to analyze the temporal posting behaviors of Twitter, Reddit, and Parler users and to identify bots, trolls, and malicious behaviors.
Multi-Application Hierarchical Autoscaling for Kubernetes Edge Clusters

The dynamic workload demands of smart city applications hosted on edge infrastructures require the development of advanced scaling mechanisms. Recent studies proposed single-application autoscaling solutions based on various technical approaches. However, for edge infrastructures with limited resource availability, it is essential to simultaneously manage heterogeneous application requirements, aiming at optimal resource allocation and minimal operational costs. This study introduces a multi-application hierarchical autoscaling framework for Kubernetes Edge Clusters. An application-based mechanism nominates the best applications’ deployments based on workload prediction and several criteria that guarantee the application’s performance while minimizing the infrastructure provider’s cost. For the joint application orchestration, an aggregation mechanism composes the candidate scaling solutions for the cluster. Then, a cluster autoscaling mechanism, based on the Analytic Hierarchy Process, undertakes the cluster’s scaling decision to optimize the resource allocation and energy consumption of the cluster. The evaluation illustrates the benefits of the proposed scaling strategy, achieving significant improvement in the average allocated resources and energy consumption compared to single-application approaches.
Automatic Detection of Lung Diseases Using CNN and SVM

Lung diseases are becoming common everywhere in the world. These contain diseases like pneumonia, emphysema, tuberculosis, etc. which come under the category of Chronic Obstructive Pulmonary Diseases (COPDs). Other than these lung related diseases, a very popular recent lung disease is COVID-19. This work proposes an approach for detecting and classifying various lung related diseases with chest X-ray images using transfer learning concept, with MobileNet architecture as a feature extractor followed by Support Vector Machine (SVM). The images of chest X-ray are used to classify healthy individuals from patients suffering with various lung diseases like COVID-19, pneumonia, tuberculosis, and emphysema. In this study, a dataset of 2500 chest X-ray images of COVID-19, pneumonia, emphysema, tuberculosis infected, and normal images are used. The proposed method was able to achieve high accuracy 98.5% and F1-score of 99.8%.
Empowering Santri to Create Sustainable Economy at Pondok Pesantren Munzalan Mubaroka Siliragung Banyuwangi through Asset-Based Community Development (ABCD)

Eco-Islamic boarding schools or green Islamic boarding schools are efforts to provide a "green" label for Islamic boarding schools that have a contribution to environmental and natural conservation. Assistance for students at the Munzalan Mubarokah Islamic Boarding School in realizing eco-Islamic boarding schools is carried out with two programs, namely the land use program and the waste and garbage program. The method used in this assistance uses the ABCD approach method. The results of the assistance of the santri to create an eco-Islamic boarding school are as follows: the first activity is to use the land to become a vegetable garden, namely leek and strawberry plants. This activity went well and was implemented by 90%. The second activity of chicken coop renovation has been carried out 100%. The third activity is sorting organic and inorganic waste and making trash bins. The activities of sorting organic and inorganic waste can already be carried out by students, although some are still not used to it. It can be said that this activity has been carried out by 80%, while the activity of making trash cans has been carried out 100%. And the fourth activity is the eco-Islamic boarding school development book, which can be said to have been implemented 80%. So that it can be said that in general the assistance of santri in realizing this eco-Islamic boarding school has been carried out by 90% with all the support and also obstacles during this assistance activity. Keywords: Santri Assistance, Eco-Islamic Boarding School, Environment, Green Islamic Boarding School
An Energy-Efficient Design Strategy for Dickson Charge Pumps with Linear Distributed Capacitance

This paper introduces a novel approach to designing energy-efficient Dickson charge pumps with linear distributed total capacitance. Theoretical analysis shows that the input energy can be minimized by opportunely choosing the optimal slope for the linear capacitance distribution and design guidelines are provided to get such value according to the given specifications. Simulation results using a $0.13-\mu \mathrm{m}$ CMOS technology confirm the validity of the proposed design strategy in various scenarios, allowing to achieve a percentage energy saving, in the worst case, of about 32%.
Human brain parcellation using time courses of instantaneous connectivity

Functional neuroimaging studies have lead to understanding the brain as a collection of spatially segregated functional networks. It is thought that each of these networks is in turn composed of a set of distinct sub-regions that together support each network's function. Considering the sub-regions to be an essential part of the brain's functional architecture, several strategies have been put forward that aim at identifying the functional sub-units of the brain by means of functional parcellations. Current parcellation strategies typically employ a bottom-up strategy, creating a parcellation by clustering smaller units. We propose a novel top-down parcellation strategy, using time courses of instantaneous connectivity to subdivide an initial region of interest into sub-regions. We use split-half reproducibility to choose the optimal number of sub-regions. We apply our Instantaneous Connectivity Parcellation (ICP) strategy on high-quality resting-state FMRI data, and demonstrate the ability to generate parcellations for thalamus, entorhinal cortex, motor cortex, and subcortex including brainstem and striatum. We evaluate the subdivisions against available cytoarchitecture maps to show that the our parcellation strategy recovers biologically valid subdivisions that adhere to known cytoarchitectural features.
Single Transistor Analog Building Blocks: Exploiting Back-Bias Reconfigurable Devices

Reconfigurable Field Effect Transistors (RFETs) combine P-FET and N-FET properties on a single device, enabling the development of new circuit applications. In this paper, we present SPICE simulations of analog building blocks using a Verilog-A Table Model of a new RFET variant, called Back-Bias RFETs (BB-RFETs). The Verilog-A look-up model is extracted from TCAD simulations based on a previously fabricated and measured BB-RFET device on a 22 nm FDSOI platform. The model is verified to mimic the experimentally observed analog circuit behaviours such as frequency doubling and phase shifting from a single transistor setup. In addition, utilizing the reconfiguration between three operational modes, a newly conceived reconfigurable-rectifier is proposed which can be dynamically switched between half-wave rectifier and full-wave rectifier. Both the model and the newly designed circuit building blocks, therefore, aim to inspire circuit designers to utilize this emerging technology for future computing.
On Hardware Implementations of Two-Dimensional Fast Fourier Transform for Radar Signal Processing

In the automotive industry, 77/79 GHz frequency- modulated continuous-wave (FMCW) radars have been vastly used for different driver-assistance and safety applications in modern vehicles. Range- Doppler processing relying on two- dimensional fast Fourier transform (2D- FFT) has emerged as one of the most efficient algorithms in extracting target range and velocity information in such systems. Having this in mind, this paper depicts several parameterized generators of radar signal 2D- FFT processing accelerators, implemented in Chisel hardware design language. Besides the basic range-Doppler algorithm, the proposed generators can provide slightly modified modules, tailored for range-angle calculations for obtaining information about target distance and azimuth. For cases with large amounts of intermediate data, the generated processors can utilize the ex-ternal SDRAM memory through an optional memory controller. The designs have been verified using both software simulations and hardware tests. Moreover, a demonstration system consisting of both range-Doppler and range-angle processing, implemented using a commercially-available radar front-end and an FPGA board, has been created for real-time radar imaging, thus proving the reliability and time-efficiency of the aforementioned designs.
A Novel Frequency Readout Method and Circuit for Navigation-Grade Vibrating Beam Accelerometers

Vibrating beam accelerometers (VBAs) have significant application potential in navigation guidance. This article proposed a novel high-precision frequency readout method and integrated CMOS circuit implementation for VBAs, and technologies, including sigma-delta modulation, up-sample and split, data synchronization, and nonlinearity correction, are employed in the method, which is analyzed in detail in this article. The method offers the advantages of low quantization noise, enough range, fixed output rate, and high linearity. Other force-frequency sensors could also benefit from this method but note that its noise suppression is most effective when the output rate is much lower than the frequency under test. The method is implemented by a CMOS circuit and integrated with an oscillator circuit, which makes a full-function single-package VBA possible. Static, shock, random vibration, and centrifugal tests of the frequency readout circuit and VBA were performed. Test results show that the frequency quantization noise is less than <inline-formula> <tex-math notation="LaTeX">$10 \mu $ </tex-math></inline-formula>Hz/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz at 1 Hz, and can support <inline-formula> <tex-math notation="LaTeX">$0.1 \mu \text{g}$ </tex-math></inline-formula> noise output. They also show that the vibration rectification error (VRE) is lower than 0.5 mg at 8 <inline-formula> <tex-math notation="LaTeX">$\text{g}_{\text {rms}}$ </tex-math></inline-formula> and that the 20 g full range nonlinearity is 16 ppm.
Load Profile Analysis in Electrical Systems: The Impact of Electrical Signature and Monitoring Quality in the Energy Digitalization Process

In the context of energy digitization, Load Profiling can be a useful tool for making decisions about the use and the health of an electrical load and could be adopted for strategic services related to the energy efficiency, characterization, prediction, optimization, and diagnosis of monitored systems. To accomplish this task, it is important a spread of a new generation of smart meters. In this scenario, useful information for the developers of services based on Load Profiling could be the minimum metrological characteristics of smart meters, that is, the monitoring quality (MQ), and the electrical parameters to extract from the electrical signature to have a reliable Load Profiling process, that is, the electrical signature quality (ESQ). This article tries to answer these questions by investigating the impact of the MQ and the ESQ on the performance of Load Profiling. The main results of the article are: 1) generally the Load Profiling process requires metrological characteristics lower than those required for energy billing reducing submetering infrastructure costs; 2) the increasing number of energy features adopted in the Load Profiling not always improves reliability and accuracy; this happens when many of the features considered do not have great sensitivity with respect to changes in the energy states for the considered case study; 3) the use of functions that measure the sensitivity of a feature to the Load Profiling process, such as the considered kernel density estimation (KDE), and a suitable threshold process can delete the parameters with poor sensitivity and can greatly improve the Load Profiling reliability; and 4) the method considered in the article could help to analyze Load Profiling problems related to other physical quantities (i.e., thermal energy profiling or even multiphysical systems), allowing to the definition of a target MQ and selecting a minimum number of useful features to be adopted.
Automated Text Summarizer Using Google Pegasus

The recent growth of the Internet due to an increase in users has led to an explosion of all types of data. Text data is one of them and it is usually voluminous and contains a lot of information. Extraction of the important information would save a lot of time for all involved. An automatic text summarizer does exactly what everyone is looking for, a tool that distills the source into an abridged version while maintaining the semantics and various ideas of the source. In this paper, discuss the previous methods of summarizing text data and the approach to developing an application which does the same.
Classification of Skin Cancer using Machine Learning Techniques

Skin cancer is the abnormal proliferation of skin cells. A tumor can be cancerous or non-cancerous. Non-cancerous skin lesions are called benign. Common skin tumors that are malignant include squamous cell carcinoma, basal cell carcinoma and melanoma. Melanoma is one of the harmful forms of malignant skin tumor, which accounts for the majority of deaths caused by skin cancer. In this paper, we have proposed a fast, efficient and easy way to determine if a rash could be cancerous or determine the stages of melanoma using convolutional neural networks and obtained an accuracy of about 85%.
Colorimetric Chemosensor for Cu2+ and Fe3+ Based on a meso-Triphenylamine-BODIPY Derivative

Optical chemosensors are a practical tool for the detection and quantification of important analytes in biological and environmental fields, such as Cu2+ and Fe3+. To the best of our knowledge, a BODIPY derivative capable of detecting Cu2+ and Fe3+ simultaneously through a colorimetric response has not yet been described in the literature. In this work, a meso-triphenylamine-BODIPY derivative is reported for the highly selective detection of Cu2+ and Fe3+. In the preliminary chemosensing study, this compound showed a significant color change from yellow to blue–green in the presence of Cu2+ and Fe3+. With only one equivalent of cation, a change in the absorption band of the compound and the appearance of a new band around 700 nm were observed. Furthermore, only 10 equivalents of Cu2+/Fe3+ were needed to reach the absorption plateau in the UV-visible titrations. Compound 1 showed excellent sensitivity toward Cu2+ and Fe3+ detection, with LODs of 0.63 µM and 1.06 µM, respectively. The binding constant calculation indicated a strong complexation between compound 1 and Cu2+/Fe3+ ions. The 1H and 19F NMR titrations showed that an increasing concentration of cations induced a broadening and shifting of the aromatic region peaks, as well as the disappearance of the original fluorine peaks of the BODIPY core, which suggests that the ligand–metal (1:2) interaction may occur through the triphenylamino group and the BODIPY core.
Influence of Tropical Western Pacific Warm Pool Thermal State on the Interdecadal Change of the Onset of the South China Sea Summer Monsoon in the Late-1990s

Abstract An interdecadal shift in the onset date of the South China Sea summer monsoon (SCSSM) is identified during the late 1990s by using the European Centre for Medium-Range Weather Forecasts Interim Reanalysis dataset. The mean onset date was brought forward by two pentads during 1999–2013 compared to that during 1979–1998. The large-scale atmospheric and oceanic change associated with this shift exhibits a significant interdecadal variation signal around 1998/1999, indicating that the shift during the late 1990s is robust. Different from the well-known mid-1990s shift, this shift carried more important systematical significance. Diagnostic analysis suggests that the earlier outbreak of the SCSSM was due to the interdecadal warming of the warm pool, which brought stronger convection anomalies and led to a weak western Pacific subtropical high (WPSH) during boreal spring (March–May). The earlier retreat of the WPSH was a direct cause of this shift.
Podarkeopsischinensis sp. nov. (Annelida, Hesionidae) from southeastern China

﻿Abstract Podarkeopsischinensissp. nov. (Annelida, Hesionidae) is described based on specimens collected from the coast of southeast China. It is the first Podarkeopsis species described from the Indo-Pacific, although there are already nine valid Podarkeopsis species known from other parts of the world. This new species can be distinguished from the other Podarkeopsis species in having a palpostyle as long as the palpophore and double aciculae in both notopodia and neuropodia, and in bearing bifid furcate chaetae which have a smooth base on the shorter tine. A phylogenetic analysis based on the concatenated sequences of five gene fragments (COI, 16S rRNA, 18S rRNA, 28S rRNA, and histone H3) from 18 specimens of P.chinensissp. nov. showed that they formed a monophyletic clade that is sister to P.levifuscina. K2P genetic distances indicated that the four gene fragments (COI, 16S rRNA, 18S rRNA, and 28S rRNA) of P.chinensissp. nov. diverged from the corresponding sequences of the closest related species of Podarkeopsis in GenBank and BOLD Systems by 21.1–27.5%, 20.3–23.1%, 0.1–0.2%, and 2.1–3.2%. An identification key is provided for species in the genus Podarkeopsis.