Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic aperture radar detection and characteristic analysis of cyanobacterial scum in Lake Taihu

Abstract. To compensate for the limitations of optical remote sensing when restricted by cloud cover, it is worth exploring how to detect cyanobacterial blooms using synthetic aperture radar (SAR), which can penetrate clouds. A satellite–ground synchronous experiment was conducted in Lake Taihu, the third largest freshwater lake in China. A lipopeptide biosurfactant was detected in the algal scum layer, with an average content of 1.8 g/L. The viscosity (1.41 to 332 mPa.s) of the scum was significantly higher than that of scum-free water. The surface tension of the algal scum decreased by 12.5%, and the SAR microwave backscatter was reduced by 7.3 dB. This indicated that the cyanobacterial scum could effectively attenuate capillary waves and appear as dark patches in SAR images. SAR has the potential to be developed as a tool for the remote sensing of algal scum in lake waters.
Data‐Driven Experimental Evaluation Method for the Safety Assessment of Implants With Respect to RF‐Induced Heating During MRI

Small (mm size) distal electrodes of active medical implants can cause localized radio frequency (RF)‐induced power deposition with extreme spatial gradient (5–6 dB/mm). We propose a method that aims to improve the accuracy of the traditional experimental evaluation, which relies solely on hardware instrumentation. A numerically derived local deposition distribution is used to assist the estimation of power deposition. The method was successfully validated against numerical simulations, considering three different generic implants with a single electrode. The power deposition in the surrounding region of the implant electrodes estimated with the proposed method was validated with less than 1 dB (30%) of uncertainty.
Edge detection, image segmentation and their applications in microarray image analysis

We consider the problem of detecting jump location curves of regression surfaces. In the literature, most existing methods detect jumps in regression surfaces based on estimation of either the first-order or the second-order derivatives of the regression surface. Methods based on the first-order derivatives are usually better in removing the noise effect, whereas methods based on the second-order derivatives are often superior in localization of the detected jumps. In this thesis research, we suggest a new procedure for jump detection in regression surfaces, which combines the major strengths of the above two types of methods. Our method is based on estimation of both the first-order and second-order derivatives of the true regression surface. Theoretical justifications and numerical studies show that it works well in applications. Jump detection in regression surfaces has many applications. One important application is image segmentation for analyzing gene microarray images. Gene microarray data are widely used in applications, including pharmaceutical and clinical research. By comparing gene expressions in normal and abnormal cells, microarrays can be used for identifying genes involved in particular diseases, and then these genes can be targeted by therapeutic drugs. Many gene expression data are produced from spotted microarray images. A microarray image consists of thousands of spots, with individual DNA sequences first printed at each spot and then equal amount of probe samples from treatment and control cells mixed and hybridized with the printed DNA sequences. To obtain gene expression data, the image needs to be segmented first to separate foregrounds from backgrounds for individual spots, and then averages of foreground pixels are used for computing the gene expression data. So image segmentation of microarray images is related directly to the reliability of gene expression data. Several image segmentation procedures have been suggested and included in some software packages handling gene microarray data. In this thesis research, a new image segmentation methodology is proposed based on local polynomial kernel smoothing. Theoretical arguments and numerical studies show that it has some good statistical properties and would perform well in applications.
Smart Helmet for Drunk & Drive Detection and Alert System

The rise in road accidents in our country is primarily due to the negligence of not wearing helmets, reckless driving, and drunk driving, which can result in serious head injuries or even death if prompt medical attention is not given. To ensure the safety of bikers, it is crucial to have a system that mandates helmet use. This project presents the development of a smart helmet module with sensors that detect alcohol consumption and helmet use. The module also includes a GSM module that sends out an accident alert along with the GPS module for location tracking and Blink sensor is used to check the sobriety of the driver.
The Construction of a Static Source Code Scanner Focused on SQL Injection Vulnerabilties in Java

SQL injection attacks are a significant threat to web application security, allowing attackers to execute arbitrary SQL commands and gain unauthorized access to sensitive data. Static source code analysis is a widely used technique to identify security vulnerabilities in software, including SQL injection attacks. However, existing static source code scanners often produce false positives and require a high level of expertise to use effectively. This thesis presents the design and implementation of a static source code scanner for SQL injection vulnerabilities in Java queries. The scanner uses a combination of pattern matching and data flow analysis to detect SQL injection vulnerabilities in code. The scanner identifies vulnerable code by analyzing method calls, expressions, and variable declarations to detect potential vulnerabilities. To evaluate the scanner, malicious SQL code is manually injected in queries to test the scanner's ability to detect vulnerabilities. The results showed that the scanner could identify a high percentage of SQL injection vulnerabilities. The limitations of the scanner include the inability to detect runtime user input validation and the reliance on predefined patterns and heuristics to identify vulnerabilities. Despite these limitations, the scanner provides a useful tool for junior developers to identify and address SQL injection vulnerabilities in their code. This thesis presents a static source code scanner that can effectively detect SQL injection vulnerabilities in Java web applications. The scanner's design and implementation provide a useful contribution to the field of software security, and future work could focus on improving the scanner's precision and addressing its limitations.
Mathematical simulation of process of curing auto-filler based on unsaturated polyester resin

In this article, with the help of mathematical modeling of the curing rate, the influence of factors directly affecting the curing processes of an auto-filler prepared based on unsaturated polyester resin was studied: the concentration of the accelerator, hardener, and temperature. When modeling the process, the accelerator, hardener, and temperature concentrations were taken as the initial (input) parameters, and the curing rate of the auto-filler at various temperatures was taken as the final (output) parameter.
Reinforcement Learning in Education 4.0: Open Applications and Deployment Challenges

Education 4.0 involves adopting technology in teaching and learning to drive innovation and growth across academic institutions. Artificial Intelligence and Machine Learning are frontrunners in Education 4.0, having already impacted diverse sectors globally. Since the COVID-19 pandemic, the conventional method of teaching and learning has become unpopular among institutions and is currently being replaced with intelligent educational data pattern identification and online learning. The teacher-centred pedagogical paradigm has significantly shifted to a learner-centred pedagogywith the emergence of Education 4.0. Reinforcement Learning has been deployed successfully in diverse sectors, and the educational domain should not be an exemption. This survey discusses Reinforcement Learning, a feedback-based machine learning technique, with application modules in the academicfield. Each module is analysed for the state-action-reward implementation policies with relevant features that define individual use cases. The survey primarily examined the classroom, admission, e learning, library and game development modules. In addition, the survey heightened the foreseeable challenges in the real-world deployment of Reinforcement Learning in educational institutions.
Anxiety, Achievement, and Self-Regulated Learning in CueThink

: The effects of educational anxiety have been observed across multiple disciplines; anxiety negatively influences cognition, self-regulation, performance, and educational outcomes. However, there has been limited research on anxiety within the context of interactive learning environments. In the current research, we expand this by assessing whether and how trait-level anxiety (assessed as a pre- and post-measure in a year-long study) is related to students’ self-regulated learning strategies, behaviors, belief, and achievement in the context of an open-ended math problem-solving platform, called CueThink. Results indicate that anxiety is negatively related to key constructs involving math achievement. Altogether, our findings generally imply that students with higher anxiety may avoid interacting with their stressors, in this case, math content, effectively contributing to poorer outcomes. We discuss our findings within the context of research and pedagogical and system design.
The IASI NH3 version 4 product: averaging kernels and improved consistency

Abstract. Satellite measurements play an increasingly important role in the study of atmospheric ammonia (NH3). Here, we present version 4 of the Artificial Neural Network for IASI (ANNI; IASI: Infrared Atmospheric Sounding Interferometer) retrieval of NH3. The main change is the introduction of total column averaging kernels (AVKs), which can be used to undo the effect of the vertical profile shape assumption of the retrieval. While the main equations can be matched term for term with analogous ones used in UV/Vis retrievals for other minor absorbers, we derive the formalism from the ground up, as its applicability to thermal infrared measurements is non-trivial. A large number of other smaller changes were introduced in ANNI v4, most of which improve the consistency of the measurements across time and across the series of IASI instruments. This includes a more robust way of calculating the hyperspectral range index (HRI), explicitly accounting for long-term changes in CO2 in the HRI calculation and the use of a reprocessed cloud product that was specifically developed for climate applications. The NH3 distributions derived with ANNI v4 are very similar to the ones derived with v3, although values are about 10 %–20 % larger due to the improved setup of the HRI. We exclude further large biases of the same nature by showing the consistency between ANNI v4 derived NH3 columns with columns obtained with an optimal estimation approach. Finally, with v4, we revised the uncertainty budget and now report systematic uncertainty estimates alongside random uncertainties, allowing realistic mean uncertainties to be estimated.
Leaf hydraulic maze; Differential effect of ABA on vascular bundle-sheath, palisade, and spongy mesophyll controlling hydraulic conductance

Leaf hydraulic conductance (K leaf ) facilitates water for transpiration, enabling the continuation of CO 2 upsurge while maintaining water status. Nevertheless, radial water flow out-of-the-xylem and its regulation under well and stress conditions remain vague. We hypothesized that bundle-sheath and mesophyll cells play key roles in the radial flow of water out of the xylem and the regulation of that flow under optimal and stress conditions. We generated transgenic Arabidopsis plants that were insensitive to abscisic acid (ABA) exclusively in their bundle sheath (BSabi) or mesophyll (MCabi). BSabi plants showed greater K leaf and transpiration under optimal conditions, with stomatal apertures and index and vein density like those of the wild type. MCabi exhibited even higher K leaf and transpiration, as well as larger stomatal apertures, a higher stomatal index and greater vascular diameter and biomass, relative to the wild type. In response to xylem-fed ABA, both transgenic lines exhibited reduced K leaf and transpiration, while their leaf water potentials remained unchanged. The greater osmotic water permeability ( P f ) of the wild types' spongy mesophyll was reduced by ABA; whereas the lower P f of the palisade mesophyll was not affected by ABA. Genetically modified, ABA-insensitive spongy mesophyll cells had a low P f ; whereas ABA-insensitive bundle-sheath cells had a higher P f . The flow over the symplastic water pathway was increased by ABA, but its contribution to K leaf was negligible. High levels of ABA appear to regulate the P f of bundle-sheath and spongy mesophyll cells, to control K leaf . Under optimal conditions, ABA limits transpiration and K leaf via physiological, morphological, and developmental modifications of bundle-sheath and mesophyll cells. The bundle sheath–spongy mesophyll pathway controls K leaf and leaf water balance under both optimal and drought conditions.
Perfusion and Ultrasonication Produce a Decellularized Porcine Whole-Ovary Scaffold with a Preserved Microarchitecture

The application of decellularized scaffolds for artificial tissue reconstruction has been an approach with great therapeutic potential in regenerative medicine. Recently, biomimetic ovarian tissue reconstruction was proposed to reestablish ovarian endocrine functions. Despite many decellularization methods proposed, there is no established protocol for whole ovaries by detergent perfusion that is able to preserve tissue macro and microstructure with higher efficiency. This generated biomaterial may have the potential to be applied for other purposes beyond reproduction and be translated to other areas in the tissue engineering field. Therefore, this study aimed to establish and standardize a protocol for porcine ovaries’ decellularization based on detergent perfusion and ultrasonication to obtain functional whole-ovary scaffolds. For that, porcine ovaries (n = 5) were perfused with detergents (0.5% SDS and 1% Triton X-100) and submitted to an ultrasonication bath to produce acellular scaffolds. The decellularization efficiency was evaluated by DAPI staining and total genomic DNA quantification. ECM morphological evaluation was performed by histological, immunohistochemistry, and ultrastructural analyses. ECM physico-chemical composition was evaluated using FTIR and Raman spectroscopy. A cytocompatibility and cell adhesion assay using murine fibroblasts was performed. Results showed that the proposed method was able to remove cellular components efficiently. There was no significant ECM component loss in relation to native tissue, and the scaffolds were cytocompatible and allowed cell attachment. In conclusion, the proposed decellularization protocol produced whole-ovaries scaffolds with preserved ECM composition and great potential for application in tissue engineering.
Effects of Doping Into on Phase Transformation and Luminescence

The effects of doping into on the phase transformation and luminescent properties were investigated. It was found that by doping , the phase transformation in is prevented up to 1600 at least. It is helpful to dope into for sintering the cubic ceramics. Europium-doped lutetium gadolinium sesquioxide solid solutions, , with intensive luminescence intensity, could be developed into a potential ceramic scintillator candidate for X-ray detection.
The Effectiveness of Virtual Reality Interventions for Improvement of Neurocognitive Performance Post-traumatic Brain Injury: A Systematic Review.

Objective : The aim of this study was to evaluate current evidence for the effectiveness of virtual reality (VR) interventions in improving neurocognitive performance in individuals who have sustained a traumatic brain injury (TBI). Methods : A systematic literature search was performed across multiple databases (PubMed, EMBASE, Web of Science) for articles of relevance. Studies were evaluated according to study design, patient cohort, VR intervention, neurocognitive parameters assessed, and outcome. VR interventions were evaluated qualitatively, with respect to methodology and extent of immersion, and quantitatively with respect to intervention duration. Outcomes : Our search yielded 324 articles, of which only 13 studies including 132 patients with TBI met inclusion criteria. A wide range of VR interventions and cognitive outcome measures were reported. Cognitive measures included learning and memory, attention, executive function, community skills, problem solving, route learning, and driving attitude. Several studies (n=10) reported statistically significant improvements in outcome, and two studies demonstrated successful translation into real-life performance. Conclusions : VR interventions hold significant potential for improving neuro-cognitive performance in patients with TBI. Whilst there is some evidence for translation into activities of daily living, further studies are required to confirm the validity of cognitive measures and reliable translation into real-life performance.
Measurement report: Ion clusters as indicator for local new particle formation

Atmospheric aerosol particles have considerable impact on climate, both directly by scattering radiation and indirectly by acting as cloud condensation nuclei. A major fraction of global aerosol number is formed in atmospheric new particle formation (NPF). These atmospheric particles consist of both neutral particles and charged atmospheric ions, and atmospheric ion number concentrations have been observed to indicate NPF. In this work, atmospheric ion concentrations were studied with the aim of finding the best suited size range of ions to characterize local NPF. Both negative and positive ion number size distributions measured by Neutral cluster and Air Ion Spectrometer (NAIS) at the SMEAR II measurement station in Hyytiälä, Finland were used. Ion sizes between 1.6 and 3 nm were considered. We found that the negative ions between 2.0 and 2.3 nm are well suited for representing NPF. In addition, the influence of transport on the observed 2.0-2.3 nm ion concentrations is considerably smaller than for larger ions. Therefore, we recommend the negative ions with diameters 2.0-2.3 nm as the best choice for characterization of local NPF.
REMOVING OF MULTIPLE VOTES BY USING DE-DUPLICATION ANALYSIS

Data de duplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To protect the confidentiality of sensitive data while supporting deduplication, the convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this project makes the first attempt to formally address the problem of authorized data de duplication. Different from traditional de duplication systems, the differential privileges of users are further considered induplicate check besides the data itself. We also present several new de duplication constructions supporting authorized duplicate check in hybrid cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. As a proof of concept, the proposed work implements a prototype of our proposed authorized duplicate check scheme and conduct test bed experiments using our prototype. The proposed work shows that our proposed authorized duplicate check scheme incurs minimal overhead compared to normal operations
VIPER: Spotting Syscall-Guard Variables for Data-Only Attacks

As control-flow protection techniques are widely deployed, it is difficult for attackers to modify control data, like function pointers, to hijack program control flow. Instead, data-only attacks corrupt security-critical non-control data (critical data), and can bypass all control-flow protections to revive severe attacks. Previous works have explored various methods to help construct or prevent data-only attacks. However, no solution can automatically detect program-specific critical data. In this paper, we identify an important category of critical data, syscall-guard variables , and propose a set of solutions to automatically detect such variables in a scalable manner. Syscall-guard variables determine to invoke security-related system calls (syscalls), and altering them will allow attackers to request extra privileges from the operating system. We propose branch force , which intentionally flips every conditional branch during the execution and checks whether new security-related syscalls are invoked. If so, we conduct data-flow analysis to estimate the feasibility to flip such branches through common memory errors. We build a tool, V IPER , to implement our ideas. V IPER successfully detects 34 previously unknown syscall-guard variables from 13 programs. We build four new data-only attacks on sqlite and v8, which execute arbitrary command or delete arbitrary file. V IPER completes its analysis within five minutes for most programs, showing its practicality for spotting syscall-guard variables.
Machine learning applications in central banking

On 18–22 October 2021, the Irving Fisher Committee on Central Bank Statistics (IFC) and the Bank of Italy co-organised, with the support of the European Central Bank (ECB) and the South African Reserve Bank (SARB), a workshop on “Data science in central banking” that focused on machine learning (ML) applications. This event was an opportunity to take stock of how central banks are deploying ML across a variety of use cases. It also illustrated the importance of these new techniques in improving the efficiency and effectiveness of their related operations, including by increasing their availability to deal with larger and new sources of information in a more automatised way.
Bitcoin Heist Ransomware Attack Prediction Using Data Science Process

In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science methodology. Understanding the data and identifying the variables are essential elements of a successful model. A variety of machine learning algorithms are applied to the pre-processed data, and the accuracy of each technique is compared to determine which approach performed better. Additional performance indicators including recall, accuracy, and f1-score are also taken into account while evaluating the model. It uses machine learning to predict how the ransomware attack would pan out.