Miracle-dz/UniFAR-FTUs huggingface dataset:
Backward and Forward Scattering Melting Layer Composed of Hydrometeors at 5-1 00

This paper addresses the behavior of the differential reflectivity, specific attenuation, and specific phase shift due to a melting layer composed of oblate-spheroidal hydrometeors. The results are based on a melting layer model and scattering computations derived from the point-matching technique with the truncation and recurrenee adjusted. Computations at 5-100 GHz for five raindrop size distributions at rain rates below 12.5 mmh are presented. In general, the reflectivity factor and differential reflectivity features with height at centimeter wavelengths agree with available radar measurements. At millimeter wavelengths, contributions to the radar backscatter from smaller hydromete- ors become more and more important as the frequency increases and approaches 100 GHz. This should be instructive for utilizing millimeter wavelength radar techniques in radar remote sensing studies of the melting layer. Corresponding vertical profiles of the specific attenuation and phase shift are also presented at 5-100 GHz. The differential attenuation and phase shift indicate the particle shape effects. These attenuation and phase shift become more and more considerable as the frequency increases. Such forward scattering calculations should prove useful for studying propagation effects caused by the melting layer for satellite-earth communications, including depolarizations.
Applying a non-parametric bootstrap technique to assess the accuracy of biomass estimates of argentine anchovy (Engraulis anchoita) northern stock (34º S-41º S) with the Daily Egg Production Method. 1993-2008 period

SUMMARY Engraulis anchoita Northern stock reproductive biomass (SSB) during the 1993-2008 period was estimated applying the Daily Egg Production Method (DEPM) and accuracy of estimates assessed using a bootstrap technique. The basic data were collected in different research surveys carried out in late Spring, at the peak of the spawning season. The aim of this work was to build reliable confidence intervals around the estimates of the mean reproductive biomass applying a non-parametric bootstrap method. Resampling methods are becoming increasingly popular as statistical tools as
REVAMP: Rapid Exploration and Visualization through an Automated Metabarcoding Pipeline

The revolution and acceleration in DNA sequencing over the past three decades has driven the development of new biomolecular tools like environmental DNA (eDNA) metabarcoding for characterizing marine biodiversity. In order to operationalize eDNA approaches for routine NOAA observatories, new bioinformatic programs and improved organismal reference barcodes are needed to serve accurate and reliable biological data in a timely manner. To address these needs, we present Rapid Exploration and Visualization through an Automated Metabarcoding Pipeline (REVAMP), which provides streamlined end-to-end data processing from raw reads to data exploration, visualization, and hypothesis generation. One benefit of REVAMP is the ability to iteratively assess marker gene and reference database performance. Here, we used a filtered reference database that only included sequences uploaded prior to specified date cutoffs from 1995 to 2022 to analyze changes in eDNA metabarcoding taxonomic assignments, revealing patterns of uneven improvement in taxonomic assignment depth and accuracy across time, region, and marker sets. This work highlights the need for targeted reference sequencing efforts for key regional taxa and the importance of such efforts for improving eDNA biomonitoring approaches in the future.
Atherogenic Index of Plasma Predicts Obstructive Coronary Artery Disease in Patients with Stable Angina Pectoris

Aims: Chronic coronary syndrome is associated with several risk factors, such as dyslipidemia and hypertension. The atherogenic index of plasma (AIP) has been demonstrated to be a biochemical risk factor for coronary artery disease (CAD). This study aimed to determine whether the AIP is an effective parameter for estimating obstructive CAD. Methods and Results: A total of 345 patients (with a mean age of 62.2 ± 10.3; 63% male) who underwent coronary angiography were included in this study. Obstructive CAD is defined as having one or more vessels with a stenosis level of ≥50%. Depending on the presence of obstructive CAD, all patients were divided into two groups. The mean AIP value was found to be 0.538 ± 0.26 in the study group. The AIP values were significantly higher in the obstructive coronary artery group (AIP; 0.49 ± 0.26 vs. 0.58 ± 0.27, p = 0.002). According to a univariable analysis, AIP values were significantly associated with obstructive coronary artery disease [OR: 3.74 (CI 95% 1.62–8.64), p = 0.020]. The AIP was further adjusted for confounding risk factors in three multivariable analysis models and, all three models showed a significant association. According to an ROC analysis, 0.49 is the cut-off value for AIP, and a value above 0.49 indicates 50% coronary artpery stenosis. Conclusions: The AIP may be used in the assessment of cardiovascular risk for patients with stable angina pectoris, and it may also be used to estimate obstructive CAD.
Glucosamine-Modified Mesoporous Silica-Coated Magnetic Nanoparticles: A “Raisin-Cake”-like Structure as an Efficient Theranostic Platform for Targeted Methotrexate Delivery

This study presents the synthesis of glucosamine-modified mesoporous silica-coated magnetic nanoparticles (MNPs) as a therapeutic platform for the delivery of an anticancer drug, methotrexate (MTX). The MNPs were coated with mesoporous silica in a templated sol–gel process to form MNP@MSN, and then chloropropyl groups were added to the structure in a post-modification reaction. Glucosamine was then reacted with the chloro-modified structure, and methotrexate was conjugated to the hydroxyl group of the glucose. The prepared structure was characterized using techniques such as Fourier transform infrared (FT-IR) spectroscopy, elemental analysis (CHN), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), a vibrating sample magnetometer (VSM), and X-ray diffraction (XRD). Good formation of nano-sized MNPs and MNP@MSN was observed via particle size monitoring. The modified glucosamine structure showed a controlled release profile of methotrexate in simulated tumor fluid. In vitro evaluation using the 4T1 breast cancer cell line showed the cytotoxicity, apoptosis, and cell cycle effects of methotrexate. The MTT assay showed comparable toxicity between MTX-loaded nanoparticles and free MTX. The structure could act as a glucose transporter-targeting agent and showed increased uptake in cancer cells. An in vivo breast cancer model was established in BALB/C mice, and the distribution of MTX-conjugated MNP@MSN particles was visualized using MRI. The MTX-conjugated particles showed significant anti-tumor potential together with MRI contrast enhancement.
A MEMETIC ALGORITHM APPLIED TO INDUCTION MACHINE PARAMETERS IDENTIFICATION BASED ON AN OUTPUT ERROR

Based on an output error, several evolutionary methods have been applied to identify the parameters of an Induction Machine (IM). The main drawback of these methods is their premature convergence in many situations. To overcome this issue and achieve a more accurate solution, this paper proposes a Memetic Algorithm (MA), which combines a Genetic Algorithm (GA) and a local search method. This approach uses the Hooke-Jeeves (HJ) method for the local search as a mutation operator.GA has proven good ability in global search. The HJ method has a good ability to refine the local search and achieve the optimal accuracy solution. The proposed MA, which maintains a tradeoff between exploration and exploitation strategies, is applied to minimize the related objective function to obtain the electrical and mechanical machine parameters. The validation of the method is confirmed by an experiment carried out on an (0.4 kW) IM with parameters estimated using the measured data. Using the estimated parameters, the computed transient and steady-state currents agree well with the measured data.
MULTI-LAYER COLOR QR CODE DYNAMIC DECODER FRAMEWORK WITH FUZZY COLOR RECOVERY

In this paper, we proposed a dynamic framework for a multi-layer color QR code decoder. The proposed decoder framework shows the general steps to decode color QR code. It contains a configuration setting standard that allows other researchers to refer in order to decode their color QR code based on the colors used in the encoder. The framework starts with color QR code detection, then search for color reference. This is followed by fuzzy sets selection based on the color QR code. Color enhancement for the QR code is implemented based on the fuzzy set decision. Next, is color de-multiplexing to get Black and White (B/W) QR code. The de-multiplexing process is based on a configuration file, for the QR code color setting. Finally, is the decoding and merging of the results for the B/W QR code to obtain the original file. We use two datasets with color reference to evaluate our framework. The first dataset used is generated by Yang et al., 2018 encoder and we obtained 83% success rate for the detection and color de-multiplexing. The second dataset is generated from our encoder and produced 90% decoding success rate. The experiment shows the framework can successfully work with different sizes of color QR code.
Guideﬁll: GPU Accelerated, Artist Guided Geometric Inpainting for 3D Conversion of Film ∗

The conversion of traditional ﬁlm into stereo 3D has become an important problem in the past decade. One of the main bottlenecks is a disocclusion step, which in commercial 3D conversion is usually done by teams of artists armed with a toolbox of inpainting algorithms. A current diﬃculty in this is that most available algorithms either are too slow for interactive use or provide no intuitive means for users to tweak the output. In this paper we present a new fast inpainting algorithm based on transporting along automatically detected splines, which the user may edit. Our algorithm is implemented on the GPU and ﬁlls the inpainting domain in successive shells that adapt their shape on the ﬂy. In order to allocate GPU resources as eﬃciently as possible, we propose a parallel algorithm to track the inpainting interface as it evolves, ensuring that no resources are wasted on pixels that are not currently being worked on. Theoretical analyses of the time and processor complexity of our algorithm without and with tracking (as well as numerous numerical experiments) demonstrate the merits of the latter. Our transport mechanism is similar to the one used in coherence transport [F. Bornemann and T. M¨arz, J. Math. Imaging Vision , 28 (2007), pp. 259–278; T. M¨arz, SIAM J. Imaging Sci. , 4 (2011), pp. 981–1000] but improves upon it by correcting a “kinking” phenomenon whereby extrapolated isophotes may bend at the boundary of the inpainting domain. Theoretical results explaining this phenomenon and its resolution are presented. Although our method ignores texture, in many cases this is not a problem due to the thin inpainting domains in 3D conversion. Experimental results show that our method can achieve a visual quality that is competitive with the state of the art while maintaining interactive speeds and providing the user with an intuitive interface to tweak the results
Deep Learning-Based Visual Recognition for Inline Defects in Production of Semiconductors

Semiconductor manufacturing is a complex process that involves different kind of mechanical, chemical, and photo lithography treatments, such as deposition, etching, and impurity implantation. Quality inspection is vital during such the process, since defects and anomalies introduced during the production process can lead to a variety of failures in later stages. The task of manually or automatically detecting and classifying defects is challenging due to the large variation in design, material, layout, colors, and visual pattern. Existing automated optical inspection methods do not offer flexibility and scalability toward large numbers of defects or new designs. Therefore, visual inspection based on deep learning is considered as a long-term alternative for industry level quality assessment. In this article, automated visual defect identification in patterned wafers during the production process is proposed. By employing transfer learning, we trained and evaluated a number of convolutional neural network architectures including VGG, ResNet, and dense convolutional network (DenseNet), over a range of parameters for a binary and multiclass classification and determined optimal architecture. The experimental evaluation is carried out on real industrial data of patterned wafers comprising of nine different defect types and from 14 production steps. The results show that the network based on DenseNet201 achieves the highest performance up to 98.17% on binary classification and 96.04% on multiclass classification.
Simulation of distribution system for low temperature district heating in future urban areas – Case study of a planned city district in Gävle

In Europe, the prices of natural gas and electricity reached an all-time high in 2022. A way to mitigate high electricity costs is to expand district heating systems in urban areas, this will reduce electric load as well as increase the power generation possibilities in combined heat and power plants. District heating has been the dominant heat supply technology in urban areas in Sweden since the 1980s. However, as the energy efficiency in buildings increase, district heating distribution losses must be reduced to ensure a cost-efficient heat supply. This has led to the idea of the 4th generation district heating which is characterized by low distribution temperatures. In this study, low-temperature district heating distribution in a planned future city district is simulated using a Python-based tool. Two different low-temperature distribution systems are investigated: 1) 2-pipe low-temperature system, and 2) a cascading 3-pipe low-temperature system. The focus is on simulating the distribution losses, temperature drop, and mass flow in the pipe network. The scope of the analysis also includes an investigation of the effect of lower return temperatures on the central district heating network. The results indicate that the low-temperature distribution system with the 2-pipe system performs better than the cascading system when considering distribution losses and temperature drop. The mass flow depends on the temperature demand in the heating systems in the buildings and is considerably high for both low-temperature distribution systems investigated.
Obtaining soliton solutions of the perturbed Radhakrishnan-Kundu-Lakshmanan model with M-truncated derivative

This study explores the soliton solutions for the perturbed Radhakrishnan-Kundu-Lakshmanan (RKL) equation which includes M-truncated derivative. Initially, a wave transformation technique is employed for the RKL equation, yielding a nonlinear ordinary differential equation (NODE). Then, the NODE is solved using the new Kudryashov method. A candidate solution and its related derivatives are incorporated into the NODE, resulting in a polynomial expression. A system of algebraic equations emerges by equating coefficients of terms with similar degrees to zero. Solving this system provides the identification of unknown variables in the candidate solution, thereby yielding the solutions to the RKL equation. Diverse illustrations of the obtained solutions are presented via contour, two-dimensional, and three-dimensional plots. The findings of this research may present potential implications for future studies in nonlinear optics.
A Residual Multihead Self-Attention Network Using Multimodal Shallow Feature Fusion for Motor Fault Diagnosis

Fault diagnosis (FD) is a crucial task in motor-based industrial systems, but traditional signal analysis-based methods face various challenges that make it difficult to achieve high-level performance. This article proposes a novel approach called residual multihead self-attention network using multimodal shallow feature fusion (ResMHA-MMSFF) for motor FD. The method first applies an improved feature fusion technique based on a gating mechanism (GM) to combine the datasets provided by both vibration and current signals. The fused features are then introduced into the residual multihead self-attention network to obtain more detailed information. Finally, a SoftMax function is adopted to classify faults and diagnose motor system faults. Comparative experiments under different loads and speed conditions are conducted to demonstrate the effectiveness of the proposed method, which outperforms existing single-signal or other deep learning-based methods.
A Comparative Study of Image Classification Method to Detect Water Body Based on UAS

Recently, there has been a growing interest in UAS(Unmanned Aerial System), and it is required to develop techniques to effectively detect water body from the recorded images in order to implement flood monitoring using UAS. This study used a UAS with RGB and NIR+RG bands to achieve images, and applied supervised classification method to evaluate the accuracy of water body detection. Firstly, the result for accuracy in water body image classification by RGB images showed high Kappa coefficients of 0.791 and 0.783 for the artificial neural network and minimum distance method respectively, and the maximum likelihood method showed the lowest, 0.561. Moreover, in the evaluation of accuracy in water body image classification by NIR+RG images, the magalanobis and minimum distance method showed high values of 0.869 and 0.830 respectively, and in the artificial neural network method, it was very low as 0.779. Especially, RGB band revealed errors to classify trees or grasslands of Songsan amusement park as water body, but NIR+RG presented noticeable improvement in this matter. Therefore, it was concluded that images with NIR+RG band, compared those with RGB band, are more effective for detection of water body when the mahalanobis and minimum distance method were applied.
The Combinatorics of Tandem Duplication Trees

— We developed a recurrence relation that counts the number of tandem duplication trees (either rooted or unrooted) that are consistent with a set of n tandemly repeated sequences generated under the standard unequal recombination (or crossover) model of tandem duplications. The number of rooted duplication trees is exactly twice the number of unrooted trees, which means that on average only two positions for a root on a duplication tree are possible. Using the recurrence, we tabulated these numbers for small values of n. We also developed an asymptotic formula that for large n provides estimates for these numbers. These numbers give a priori probabilities for phylogenies of the repeated sequences to be duplication trees. This work extends earlier studies where exhaustive counts of the numbers for small n were obtained. One application showed the significance of finding that most maximum-parsimony trees constructed from repeat sequences from human immunoglobins and T-cell receptors were tandem duplication trees. Those findings provided strong support to the proposed mechanisms of tandem gene duplication. The recurrence relation also suggests efficient algorithms to recognize duplication trees and to generate random duplication trees for simulation. We present a linear-time recognition algorithm. Duplicated sequences in DNA are very common. Baltimore (2001) stated that over half of the (human) DNA consists of repeated sequences. Among these repeated sequences, we can distinguish so-called tandem repeats, in which the copies are adjacent along the genome. Tandem repeats can be made of mini-or micro-satellites that are relatively short and are usually considered parasitic and potentially deleterious. But these repeats can also be larger and can contain genes. The duplication (in tandem or not) of genes is one of the most important evolutionary mechanisms for producing genes with novel functions. The process of unequal recombination (or crossover) is widely viewed as responsible for the production of large tandemly repeated sequences, possibly containing genes. At each step, a segment (containing one or several copies) is duplicated into two adjacent and identical segments that are then free to diverge through mutations. As the process is repeated, a linearly ordered set of paralogous segments results. The evolutionary history of these segments shares many common points with that of orthologous sequences, as used in phylogenetic studies. However, because of the linear order of the segments and the nature of the process, tandem duplication histories are much more constrained than speciation histories , as was first pointed out …
Attitude Control of Robotic System According to Fuzzy Logic

— At Present world, the working intention, capability, and priority of a robot have been changed on demand. Robotics requires a working skill of electronics, mechanics, and software. When Software and machines come together there is must need of logic. Without logical conception, the robotic system couldn’t be work properly. When we think about the controlling system of robot then it’s a matter of complexity that which method or logic should establish. If errorless attitude needed then Fuzzy Logic is essential. It’s like an umbrella term for any robotic system that does something smart. This solution will help to solve the problem on decision making that why fuzzy logic is an essential technique for controlling the attitude of robotic system.
Development of a Machine Learning-Based Model for Accurate Detection and Classification of Cervical Spine Fractures Using CT Imaging

Cervical spine fractures represent a significant healthcare challenge, necessitating accurate detection for appropriate management and improved patient outcomes. This study aims to develop a machine learning-based model utilizing a computed tomography (CT) image dataset to detect and classify cervical spine fractures. Leveraging a large dataset of 4,050 CT images obtained from the Radiological Society of North America (RSNA) Cervical Spine Fracture dataset, we evaluate the potential of machine learning and deep learning algorithms in achieving accurate and reliable cervical spine fracture detection. The model demonstrates outstanding performance, achieving an average precision of 1 and 100% precision, recall, sensitivity, specificity, and accuracy values. These exceptional results highlight the potential of machine learning algorithms to enhance clinical decision-making and facilitate prompt treatment initiation for cervical spine fractures. However, further research and validation efforts are warranted to assess the model's generalizability across diverse populations and real-world clinical settings, ultimately contributing to improved patient outcomes in cervical spine fracture cases.
A 15-Gb / s Preampliﬁer with 10-dB Gain Control and 8-mV Sensitivity in 65-nm CMOS

—A broadband preampliﬁer with gain control and automated common-mode level regulation is presented. It is implemented in 65-nm CMOS as part of an analog front-end (AFE) that is especially suitable for wireline receivers incorporating digital signal processing or multilevel modulation. S-parameter measurements of the receiver show that the preampliﬁer remains well matched to the channel impedance across all gain settings with an S 11 of less than -8 dB to frequencies beyond 25 GHz. The measured S 21 of the receiver shows that the preampliﬁer is capable of achieving 10 dB of gain control. The preampliﬁer alone consumes from 16.7 mA to 56.7 mA from a 1.2 V supply, depending on its gain setting.