Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrasses and Salt Marshes during 2010–2018

Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.
Adaptive Optimization Scheme for RIS-Aided mmWave-NOMA Systems with Hybrid Beamforming

An adaptive scheme is proposed for resource allocation in two-user millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) systems with the assistance of reconfigurable intelligent surface (RIS) that is employed to aid downlink data transmission. Through the mathematical transformation, the energy efficiency (EE) in the communication systems are derived for performance evaluation and optimization. Then, an optimization problem for EE maximization is formulated. To deal with it, an adaptive algorithm is presented, which is based on alternating optimization (AO), penalty function method, majorization minimization (MM), and Riemannian manifold optimization (RMO).
Vitamin D deficiency - a potential risk factor for sepsis development, correlation with inflammatory markers, SOFA score and higher early mortality risk in sepsis.

OBJECTIVES Sepsis is a life-threatening organ dysfunction generated due to the dysregulation of the immune response to infection. The aim of this study was to highlight the role of vitamin D in sepsis and non-infectious SIRS (systemic inflammatory response syndrome) and to find correlation of vitamin D levels with inflammatory markers, severity of the disease, and association with the 7th and 28th survival rate of patients. METHODS We investigated 32 patients (21 men, 11 women) admitted to an intensive care unit with both SIRS and sepsis. Blood was taken within 24 hours after admission. Plasma levels of 25(OH)D, sTREM-1, CRP, presepsin and procalcitonin were investigated. RESULTS Patients with sepsis had lower levels of 25(OH)D (n = 25) than SIRS patients (n = 7; p = 0.0032). Significantly lower levels of 25(OH)D were found also in patients, who did not survive the 7th (p = 0.0076) and 28th day (p = 0.0338) of hospital care compared to 7th, resp. 28th day survivors. We revealed a negative correlation between the levels of 25(OH)D and inflammatory markers CRP (p = 0.0003), presepsin (p = 0.0032) and sTREM-1 (p = 0.0065) in all SIRS/sepsis patients and clinical condition (SOFA score; p = 0.0385). CONCLUSION Our results showed that vitamin D deficiency predisposed to the development of sepsis, negatively correlated with CRP, presepsin, sTREM-1 and SOFA score and their levels associates with both 7th and 28th days survival of patients (Tab. 5, Ref. 64).
Out of Band Analysis in various 5G-NR Downlink Waveforms with Different Numerologies

The standardization of the first phase 5G system in Release 15 named New Radio (NR) will soon move on to the stand-alone version with intention to bring it commercially to the market around 2020. This paper investigates the impact of power amplifier in OFDM-based waveforms including CP-OFDM, F-OFDM and W-OFDM. We first derive a mathematical model for these OFDM-based waveforms. Then we analyze the impact of power amplifier (PA) to the out-of-band (OOB) waveform, which is a critical component in the densely spectrum with different channel widths using scalable numerologies. It is shown that W-OFDM and F-OFDM are more suitable than the conventional CP-OFDM. The provided results of this paper thus facilitate knowledge of design and practice for further NR features. As the simulation results, without PA clipping, the W-OFDM and F-OFDM present better OOB characteristic than CP-OFDM, while applying PA clipping both of them show the similar performance. Additionally, another W-OFDM design with PA clipping shows advantages to implement for 5G NR waveform.
Schizophrenia and motherhood

The primary aim of this study was to analyze the impact of schizophrenic disorders on pregnancy outcomes. The secondary aim was to briefly analyze the potential role of antipsychotic treatment on influencing pregnancy outcomes in expectant mothers with schizophrenia. We searched the MEDLINE, PsycINFO, and Science.gov databases for articles published in English from January 1980 to January 2019. We used the following search terms: ‘schizophrenia’, ‘motherhood’, ‘pregnancy/foetal/neonatal outcomes’, and ‘birth defects’. The reference lists of retrieved articles were also consulted to find additional pertinent studies missed in the electronic search and/or those published before 1980. Data were extracted from articles that provided primary data on the impact of maternal schizophrenia spectrum disorders on obstetrical and perinatal outcomes. After excluding duplicates, 35 articles were identified. Systematic reviews were searched on the same databases to briefly assess the effects of antipsychotics on pregnancy outcomes. The reviewed studies showed several limitations. They were published during a time range from the early 1970s to 2019. During this period, there were significant changes in the diagnostic criteria for schizophrenia. Moreover, such studies showed no homogeneity in the investigation of potential confounders. Most importantly, no research has differentiated the effects of maternal illness on pregnancy, fetal, and neonatal outcomes from those associated with antipsychotic treatments. Thus, it is not surprising that such studies show conflicting results. Despite such limitations, in managing pregnant women with schizophrenia clinicians should consider an integrated approach that includes: antipsychotic treatment, psychological treatment, optimal dietary approaches for prevention of excessive weight gain and gestational diabetes, meticulous gynecologic and obstetric surveillance, and social and occupational support.
Enhancing Deep Neural Networks through Pruning followed by Quantization Pipeline: A Comprehensive Review

In numerous applications, deep neural networks have proven to have outstanding skills. Complex network architectures, on the other hand, provide difficulties for effective real-time deployment and demand a substantial amount of computational resources and energy. Network compression is one example of an optimization that can be used to address these issues. Network compression can often be implemented with minimal loss of accuracy. In certain cases, accuracy may even increase. Two types of network compression are reviewed in this study: quantization and pruning. We contrast pruning methods and discuss the standards used to weed out unnecessary computations. Quantization decreases computations by lowering the datatype's precision. Although systems with a smaller bit width, like binary neural networks, are also taken into consideration, weights, biases, and activations may often be quantized to 8-bit integers. You can use quantization and trimming alone or together. We in this paper discuss contrast existing approaches of quantization, pruning and hybrid optimization. Give findings of compressed network accuracy on various frameworks using hybrid optimization technique.
Efficient High-Gain Operational Amplifier Design for Σ-Δ ADCs in VLSI

This paper presents a two-stage high gain singleended operational amplifier design for Σ-Δ analog-to-digital converters (ADCs) in very-large-scale-integration (VLSI) technology. The high-gain operational amplifier design eliminates wasted drain current at the output of the operational amplifier. The first stage of the amplifier, differential pair with active load, provides high gain. The second common source stage provides a high output voltage swing. The high-gain operational amplifiers (two-stage singleended configuration) with a gain of more than 80dB and bandwidths between 3.5MHz to 5MHz have been designed and simulated using Cadence Virtuoso Analog Design Environment in 90nm and 180nm standard CMOS processes.
Software Project Estimation Using Machine Learning

The major purpose of the project is to investigate a range of machine learning techniques in order to improve the accuracy of projecting costs and efforts for software development projects. Numerous strategies in software effort estimation have been investigated in order to develop models with the best accuracy. Our primary goal is to determine the important variables and factors that influence the costs and efforts necessary for software development. To solve the accuracy issues, we're building an Extreme Learning Machine (ELM) model that uses the Analogous Estimation technique. The performance of the model will then be compared to that of existing models such as the Constructive Cost Model (COCOMO), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). This method enables us to successfully combine Machine Learning with the Analogous Estimation methodology, thereby boosting the precision of software project estimates for more dependable outcomes.
Miniaturized Flow Sensor Based On Thermal Feedback And Digitized Power Distribution

In this paper, we have proposed a miniaturized flow sensor based on thermal feedback and digitized power distribution across two microheaters. The performance of the sensor has been verified by nitrogen (N2) gas flow from -11 m/s to 11 m/s. Our findings show a significant improvement in sensitivity, which is 10.69 %/m/s, as compared to its counterpart using a simple thermal feedback strategy with voltage redistribution. Besides, our sensor achieved an excellent long-term (15 mins) signal stability with a sensor resolution of up to 7.5 mm/s. Notably, this sensor system maintains excellent sensitivity even at low signal amplification gains, indicating that this strategy will help enable highly robust solid-state flow sensor designs in the future.
Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN

We address the challenge of creating semi-Quanta images of vertically-oriented carbon nanotubes (CNT). Our previous method, CNTNeXt, successfully predicted the mechanical properties for semi-Scanning Electron Microscopy (SEM) images of CNT forests. However, for Quanta images, a more sophisticated approach is required. We propose a novel pipeline that modifies multi-layer synthetic (MLS) CNT images, which are synthetic representations of CNT structures, to resemble Quanta images. This is achieved using CycleGAN, a deep learning model designed for image-to-image translation tasks, which can learn mappings between two different domains without paired training data. By training the CycleGAN on both Quanta and MLS images, we are able to transfer the texture of Quanta images to the MLS images. The resulting ’semi-Quanta’ images are ready to train CNTNeXt, which effectively predicts the mechanical properties, including buckling load and stiffness of synthetic MLS images. Our method represents an advancement towards our ultimate goal of creating an automated CNT growth process, and opens up possibilities for further improvements and applications.
Design of CPW Fed Quad Port MIMO Antenna for Sub-6 GHz 5G Application

Compatibility and efficiency are the two important factors that need to be considered in current wireless technology. Maintaining good MIMO parameters, such as ECC and DG, can help achieve these goals. A compact dimension of 45 x 45 mm2 is utilized to build the quad-port MIMO structure. A compact CPW-fed single element of dimension 20 mm x 20 mm with a -10 dB bandwidth between 5.1 GHz and 5.85 GHz is used to design a compact MIMO structure. The distance between each single antenna element is 5 mm, and the elements are placed in an orthogonal orientation, which enables good isolation. The MIMO design has good isolation over the entire operational band, with a value of less than -30 dB. The proposed antenna design has an ECC value of 0.0003 and a DG value of around 10 dB. Also, the design shows good radiation characteristics and positive gain. Ansys HFSS software is used to carry out all necessary simulations.
A Safer Web Experience: Deep Learning-Enhanced Obscene Content Filtering Plugin

Our lives have benefited greatly from the internet's rapid development, but it has also presented new challenges. Obscene content and advertising on websites is one such issue that users find highly offensive and upsetting. The language or imagery used in the content, as well as any graphic or personal values, may be explicit. When obscene content is readily available, it can pose a major risk to youngsters who are unaware of it, negatively affecting their development. Pictures, videos, or words that are inappropriate for minors' emotional development may be found in this type of content. Users are distressed when obscene content is present on web pages, which has a detrimental effect on their online experience. Users may mistakenly click on obscene adverts or other content that directs them to dangerous websites, endangering their safety and online security. Our study report suggests a novel solution—installing a web plugin or extension in the web browser—to address this crucial issue. This plugin will examine or scan the webpage's elements and advertisements. It will be able to tell the difference between NSFW (Not Safe For Work) and non-NSFW content and use a blocking mechanism to obfuscate the NSFW or offensive material.
All-Inkjet-Printed, Color-Selective Photodetector Array based on Organic Macrocycles-Carbon Nanotube Heterostructures

We introduce a fully inkjet-printed flexible photodetector (PD) array. The PDs leverage a unique heterostructure combining carbon nanotubes (CNTs) with photoactive organic macrocycles (OMs), enabling color selectivity. The CNT-OM integration facilitates the transfer of photo-excited carriers into the CNT channel, resulting in photocurrent generation. We adopted a multi-cartridge printing approach to simultaneously incorporate different OMS into CNTs, utilizing an inkjet home printer for the entire fabrication process. As proof of concept, a color detection system was demonstrated by reading and comparing photosignals from PDs using a microcontroller unit (MCU). Experimental results show that the PD array presents distinct signal patterns for each wavelength tested and thus can effectively differentiate the colors of light. This highlights the potential of our mass-producible PD array for various applications, such as wearable optoelectronic sensing and imaging systems.
Toward Flotation Process Operation-State Identification via Statistical Modeling of Biologically Inspired Gabor Filtering Responses

This paper presents a froth image statistical modeling-based online flotation process operation-state identification method by introducing a biologically inspired Gabor wavelet transform in accordance with the physiological findings in the biological vision system. It derived the latent probabilistic density models of these biologically inspired Gabor filtering responses (GFRs) based on a versatile intermediate probability modeling frame, Gaussian scale mixture model. It has demonstrated that both the real and the imaginary representation of GFR obey a Laplace distribution. Accordingly, the amplitude representation of GFR obeys a Gamma distribution. Whereas the phase representation of GFR is an important yet frequently ignored aspect in Gabor-based signal analysis; it is demonstrated to be a periodic distribution and can be expressed by a von Mises-like distribution model. Successively, a local spline regression (LSR)-based classifier that the maps scattered statistical feature points of froth images directly to the operation-state labels smoothly is introduced for the operation-state recognition. Extensive confirmatory and comparative experiments on an industrial-scale bauxite flotation process demonstrate the effectiveness and superiority of the proposed method. Performance effects on different parameter settings, e.g., parameters of Gabor kernel and dimensionalities of multivariate statistical models, are further discussed.
The Effectiveness of Depth Data in Liveness Face Authentication Using 3D Sensor Cameras †

Even though biometric technology increases the security of systems that use it, they are prone to spoof attacks where attempts of fraudulent biometrics are used. To overcome these risks, techniques on detecting liveness of the biometric measure are employed. For example, in systems that utilise face authentication as biometrics, a liveness is assured using an estimation of blood flow, or analysis of quality of the face image. Liveness assurance of the face using real depth technique is rarely used in biometric devices and in the literature, even with the availability of depth datasets. Therefore, this technique of employing 3D cameras for liveness of face authentication is underexplored for its vulnerabilities to spoofing attacks. This research reviews the literature on this aspect and then evaluates the liveness detection to suggest solutions that account for the weaknesses found in detecting spoofing attacks. We conduct a proof-of-concept study to assess the liveness detection of 3D cameras in three devices, where the results show that having more flexibility resulted in achieving a higher rate in detecting spoofing attacks. Nonetheless, it was found that selecting a wide depth range of the 3D camera is important for anti-spoofing security recognition systems such as surveillance cameras used in airports. Therefore, to utilise the depth information and implement techniques that detect faces regardless of the distance, a 3D camera with long maximum depth range (e.g., 20 m) and high resolution stereo cameras could be selected, which can have a positive impact on accuracy.
Radiation Beam Steering Using a Binary-Coded Reflective Metasurface

A programmable binary-coded reflective metasurface (BCRM) containing $16\times 16$ elements for dual-polarization radiation beam steering is presented in this paper. The radiating elements are designed at the frequency of 5.8 GHz with a Jerusalem cross-shaped structure printed on the top layer and a ground plane on the bottom layer. Each element is loaded with two PIN diodes, with each PIN diode for one polarization, which can be independently and electronically controlled to produce two different states of the reflection phase. These two states are selected by the digital coding of “0” and “1”. A standard open-ended waveguide is used as the feeding antenna and placed at the focal point of the metasurface. The BCRM is simulated and measured for different combinations of PIN diode states of radiating elements, and radiation beam scanning can be achieved through the programmable control of diode states. The results demonstrate that the proposed metasurface can efficiently achieve ±60° of the scanning angle.
A Piezoelectric MEMS Resonant Temperature Sensor with 10-μk Resolution and 0.06-PJK2 Resolution FOM

This paper reports a new MEMS temperature sensor based on piezoelectric membrane resonator for temperature measurement and compensation applications. For the first time, it theoretically analyzed and experimentally validated that the main factor of temperature coefficient of frequency (TCF) is the difference in coefficient of thermal expansion (CTE) between the resonating structure and silicon substrate. The engineered temperature sensor achieved a TCF, i.e. sensor sensitivity, as high as -660 ppm/℃. Because of the high sensitivity and low noise oscillating circuit, the sensor reached an extremely high temperature resolution of 10 μK which is the best among state-of-the-art MEMS resonant temperature sensors; and the sensor also reached a superior resolution FoM of 0.06 pJK2. In addition, the MEMS resonator operates at atmosphere pressure without vacuum packaging, and the cost-effective solution should have better reliability and long-time drift.
A Systematic Literature Review on Host-Based Intrusion Detection Systems

With the advancements in computer networks and systems, the number of security vulnerabilities and cyber attacks targeting/using these vulnerabilities continues to increase. Consequently, various intrusion detection systems (IDS) have been developed to detect cyber attacks and ensure information security. IDSs are categorized into two classes based on the data sources: Network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS). In this systematic literature review (SLR), studies are examined that focus on HIDS or propose methods applicable to HIDS, as well as those related to IDSs that can be converted into HIDSs. The studies published between 2020 and 2023 are collected from widely used academic databases through various query statements. Filtering based on specific selection and elimination criteria is undergone by the collected studies, resulting in 21 studies for examination. Subsequently, these studies and their advantages and disadvantages are discussed. In addition, while examining the studies, five research questions are addressed. Finally, the defects, potential areas for improvement, and future research directions related to HIDSs are discussed.