Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of aircraft hydrometeor spectra and differential reflectivity (ZDR) radar measurements during the Cooperative Convective Precipitation Experiment

Radar measurements of ZDR are compared with ZDR deduced from aircraft graupel spectra during the Cooperative Convective Precipitation Experiment. On August 4, 1981, the University of Wyoming's Super King Air made two penetrations (at altitudes of 3 km and 1.8 km above mean sea level) through a convective cell located 90 km NE of the CHILL radar. The aircraft was equipped with two two-dimensional PMS probes located orthogonally so as to view the falling hydrometeors along their symmetry axis (V probe) and along an axis orthogonal to it (H probe). Elliptical raindrop images were observed below cloud base (1.8 km altitude), and conical graupel images were observed at 3 km altitude. Because of beam blockage at the lowest elevation scans, radar data were not available in the rain region. Aircraft raindrop spectra were analyzed in detail, and it is shown that ZDR deduced from such spectra should be an excellent estimator of D0 provided the correct form for the drop size distribution is assumed. ZDR and ZH deduced from the graupel spectra are shown to be in the range measured by the radar.
Syzballer: Kernel Fuzzing Based on Basic Block Weight and Multi-armed Bandit

The Linux operating system is now extensively used on personal computers, cloud platforms, and enterprise servers. The security of the Linux kernel has also increased in importance. Several techniques, such as symbolic execution, data flow analysis, and reinforcement learning, have been adapted for vulnerability discovery in recent years. Among these techniques, fuzzing is the most widely used one. However, the ease of accessing each kernel code basic block has not been considered in previous research. This means many high-risk vulnerabilities cannot be detected. To solve this problem, we present Syzballer, a hybrid fuzzer that combines multi-armed bandits with basic block weight, which is calculated by traversing the control flow graph generated by the kernel source code. First, we compile the kernel source code into LLVM bitcode and use the static analysis tool SVF to compute the weight of each basic block. Then we launched the fuzzer and loaded the weight file. Finally, a multi-armed bandit machine model is used to dynamically alter the task and seed selection. To verify the effectiveness of Syzballer, we compared it with the two most popular kernel fuzzers, Syzkaller and Syzvegas. Experiments have demonstrated that our Syzballer has improved in terms of code coverage and vulnerability detection.
Accurate Prediction of Workloads and Resources With Multi-Head Attention and Hybrid LSTM for Cloud Data Centers

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers’ requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integrates Variational mode decomposition, an Adaptive Savitzky-Golay (SG) filter, a Multi-head attention mechanism, Bidirectional and Grid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces.
Persistency of Excitation in Identification Using Radial Basis Function Approximants

In this paper, identification algorithms whose convergence and rate of convergence hinge on the regressor vector being persistently exciting are discussed. It is then shown that if the regressor vector is constructed out of radial basis function approximants, it will be persistently exciting, provided a kind of "ergodic" condition is satisfied. In addition, bounds on parameters associated with the persistently exciting regressor vector are provided; these parameters are connected with both the convergence and rates of convergence of the algorithms involved.
Toward an autosetting mode-locked fiber laser cavity

We detail the implementation of an evolutionary optoelectronic feedback loop able to set ultrafast operation of a fiber laser cavity in specific dynamical regimes. In the present experimental realization, the tunable cavity parameters consist of the polarization states at two cavity locations, which are electrically controlled to shape the cavity nonlinear transfer function. After designing suitable merit functions, we show that distinct ultrafast dynamics can be obtained and discuss the challenges and potential of this advanced machine-learning strategy to target on-demand ultrafast dynamics.
Optimization of equilibrium geometries and transition structures

A modified conjugate gradient algorithm for geometry optimization is outlined for use with ab initio MO methods. Since the computation time for analytical energy gradients is approximately the same as for the energy, the optimization algorithm evaluates and utilizes the gradients each time the energy is computed. The second derivative matrix, rather than its inverse, is updated employing the gradients. At each step, a one‐dimensional minimization using a quartic polynomial is carried out, followed by an n‐dimensional search using the second derivative matrix. By suitably controlling the number of negative eigenvalues of the second derivative matrix, the algorithm can also be used to locate transition structures. Representative timing data for optimizations of equilibrium geometries and transition structures are reported for ab initio SCF–MO calculations.
Muzzle Based Identification of Cattle Using KAZE

Biometric Identification for animals has been an emerging research field in computer vision. Biometric Identification plays an important role in monitoring diseases, vaccination, planning and control of the produce, and also in ownership assignment. There are several Traditional identification methods like the Ear-Tagging, Ear-Notching, Ear-Tattooing, Freeze-Branding, Hot-Branding and Electrical methods using RFID. The Traditional methods have been invasive, easily duplicable. They are also known for their low accuracies in identification as they are vulnerable to losses. A system with better performance is much needed in this field. Visual Animal Biometrics is gaining wide acceptance all over the world as it provides with better results. This paper aims to explain in detail the implementation of a feature extraction technique called KAZE and through experimental analysis show that it performs better than other feature extraction algorithms.
Association of polymorphic variants of KAP 1.3 gene with wool traits in Rambouillet sheep

The objective of the present study was to investigate the association of polymorphic variants of keratin-associatedprotein (KAP) 1.3 gene with wool traits in Rambouillet sheep. Genomic DNA was isolated from blood samples of 100 Rambouillet sheep. A 598 bp KAP 1.3 gene segment was amplified by PCR using ovine specific primers. One SNP was confirmed by sequencing, which was C264T nucleotide transition mutation. Restriction Fragment Length Polymorphism (RFLP) using Bsr I restriction enzyme revealed two alleles A and B with 0.645 and 0.355 allele frequencies, respectively. Genotypic frequencies were 0.41 for AA, 0.47 for AB and 0.12 for BB genotypes. The X2 test value for KAP 1.3 gene was non-significant revealing that the population under study was in Hardy Weinber Equilibrium (HWE). Significant differences for CWY and highly significant differences for FD, SL, WC and GFW were observed between the least squares means of sex. Males showed higher level of production for CWY (56.91±0.26%) and GFW (2.53±0.09 kg); whereas, females were found to be superior in FD (21.74±0.11 μ), SL (6.07±0.15 cm) and WC (62.80±0.29). The least squares means of various genotypes for GFW differed significantly. Highest GFW production was recorded for BB genotype (2.47±0.13 kg) followed by AB genotype (2.05±0.07 kg) and least by AA genotypes (1.62±0.08 kg). These results suggested that polymorphisms in the KAP 1.3 gene might be a potential molecular marker for genetic selection for GFW in Rambouillet sheep.
Mechanical and electronic properties of diamondlike BC5

The structural properties, mechanical behavior, and electronic structure of the newly developed diamondlike BC5 (d-BC5) was investigated using density functional theory (DFT) calculations. The results indicate that d-BC5 has great bulk modulus of 393 GPa, large shear modulus of 398 GPa, and high hardness of 62 Pa, and thus support the fact that d-BC5 is an ultra-incompressible and superhard material. Remarkably, the superhard d-BC5 exhibits metallic features. Furthermore, the trend that the mechanical behavior falls with the increase of boron content was revealed. The combination of huge stiffness, high hardness, and good metallicity makes series of diamondlike BCx (d-BCx) valid for wider applications in comparison with pure diamond.
Saturable Voltage-Behind-Reactance Models of Induction Machines Including Air-Gap Flux Harmonics

Accurate and numerically efficient models of induction machines are of critical importance for reliable analysis and simulation of large-scale power systems. Specifically, modeling magnetic saturation that causes air-gap flux harmonics, which can significantly affect machine dynamics, has been the focus of many research efforts. In this paper, two voltage-behind-reactance (VBR) models are proposed for induction machines which formulate the air-gap flux harmonics caused by the main flux saturation. Specifically, the proposed constant-parameter VBR (CPVBR) model can be conveniently interfaced with the external network using constant RL branches and controlled voltage sources. The numerical performance of the two proposed VBR models is validated and benchmarked against the state-of-the-art models of induction machines with saturation and air-gap flux harmonics. It is shown that the proposed models offer superior numerical performance (i.e., higher accuracy and simulation speed) compared to the existing models, which would be beneficial for offline and/or real-time simulations of power systems.
On Finite Soluble Groups

Let p be a class of finite soluble groups which is closed under epimorphic images and let g be a saturated formation. Then if G is a group of minimal order belonging to p but not to g, F(G), the Fitting subgroup of G, is the unique minimal normal subgroup of G. It is to groups with this property that the following proposition is applicable.
Preliminary Laboratory Results on the Coalescence of Small Precipitation-Size Drops Falling Freely in a Refrigerated Environment

Abstract Results from laboratory observations of isolated collisions between small precipitation-size drops falling freely at terminal velocity in a refrigerated collision chamber are presented. The average radii of the size pair studied were 353 and 306 μm. Air temperatures ranged from 20° to −15°C. Drop temperatures ranged from 20° to approximately 2°C. Experimentation revealed that the coalescence efficiency increased from approximately 42% for mean drop temperatures between 20° and 10°C to about 81% for mean drop temperatures between 10° and 2°C. A particularly interesting finding was an abrupt, rather than gradual, increase in coalescence efficiency at a mean drop temperature of about 10°C. A reduction in drop deformation during impact due to a substantial increase in viscosity with decreasing temperature is considered as a mechanism that can act to promote coalescence. The apparent abrupt increase in coalescence efficiency requires further investigation. The extent to which these results may be exte.
Empirical Analysis of Income Prediction Using Deep Learning Techniques

One of the main problems with determining income for employees is that there is no single formula that can be applied to all employees. Each employee's income is based on a variety of factors such as job position, experience, and special skills. Another problem is that the cost of living in different parts of the country can vary significantly, making it difficult to accurately assign a salary to different employees. The third issue is that there are variations in the types of benefits and perks that employers offer, which can make it difficult to accurately compare total compensation between different positions. Finally, there are often external factors that can affect the income of employees, such as the economic conditions of the area they are located in or the cost of health insurance. It is important to know what will be the income of employees to ensure that they are paid fair wages for their work and to ensure that total compensation is competitive with the market. Knowing the income of employees is also important to ensure that the employer can budget for salaries, benefits, and other costs associated with hiring and employing staff. To overcome the problems, we have made a model using the different models of LSTM. This paper presents a comparative study of various deep learning techniques for income prediction. Specifically, Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM, and Convolutional Long Short-Term Memory (Conv-LSTM) architectures are used to predict income. Simulations are processed on the Socio-Economic dataset having twenty-six years of monthly income data. This study provides insights into the effectiveness of deep learning techniques for predicting income.
Occupational Exposure to Inhalational Anesthetics and Teratogenic Effects: A Systematic Review

(1) Background: In the current healthcare environment, there is a large proportion of female staff of childbearing age, so, according to existing conflicting studies, the teratogenic effects that inhalational anesthetics may have on exposed pregnant workers should be assessed. This investigation aims to analyze the teratogenic effects of inhalational anesthetics in conditions of actual use, determining any association with spontaneous abortion or congenital malformations. (2) Methods: A systematic review was carried out according to the PRISMA statement based on PICO (problem of interest–intervention to be considered–intervention compared–outcome) (Do inhalational anesthetics have teratogenic effects in current clinical practice? ). The level of evidence of the selected articles was evaluated using the SIGN scale. The databases used were PubMed, Embase, Scopus, Web of Science, Google academic and Opengrey. Primary studies conducted in professionals exposed to inhalational anesthetics that evaluate spontaneous abortions or congenital malformations, conducted in any country and language and published within the last ten years were selected. (3) Results: Of the 541 studies identified, 6 met all inclusion criteria in answering the research question. Since many methodological differences were found in estimating exposure to inhalational anesthetics, a qualitative systematic review was performed. The selected studies have a retrospective cohort design and mostly present a low level of evidence and a low grade of recommendation. Studies with the highest level of evidence do not find an association between the use of inhalational anesthetics and the occurrence of miscarriage or congenital malformations. (4) Conclusions: The administration of inhalational anesthetics, especially with gas extraction systems (scavenging systems) and the adequate ventilation of operating rooms, is not associated with the occurrence of spontaneous abortions or congenital malformations.
A Shooting and Bouncing Ray Method for Scattering from Anisotropic Material

A shooting and bouncing ray (SBR) method for anisotropic material is presented in this paper. The SBR technique describes the multi-reflection of electromagnetic rays on the target. The reflection coefficients of the anisotropic material are calculated by the generalized transmitting matrix (GTM) method. The present algorithm is able to calculating the radar cross section (RCS) of arbitrary target coated with anisotropic material. Numerical results show the good accuracy of the proposed method.
Abnormal grain growth of 68Cu–16Al–16Zn alloys for elastocaloric cooling via cyclical heat treatments

Cu-based superelastic shape memory alloys are promising for low-stress elastocaloric cooling. We have synthesized bulk alloys of 68Cu–16Al–16Zn under different conditions in order to promote its grain growth and enhance its elastocaloric properties. High-temperature x-ray diffraction of untreated 68Cu–16Al–16Zn alloy showed that the phase boundary between the α + β mixed phases and the high temperature phase (β phase) was between 973 K and 1023 K. Based on this result, the 68Cu–16Al–16Zn alloy was heated and cooled in a furnace repeatedly between 773 K and 1173 K. The maximum grain size after heat treatment of the ingot rolled to 67% reached 11.1 mm. The latent heat of the martensitic transformation after grain growth was 6.3 J g−1, which is higher than the previously reported value for the compound. The stress–strain curve of 68Cu–16Al–16Zn rolled to 67% rolling with cyclical heat treatments showed a maximum stress of 106 MPa at 4.5% strain, with adiabatic temperature change of 5.9 K in heating during stress loading and 5.6 K in cooling in stress removal. Furthermore, no fatigue in the stress–strain behavior was observed up to at least 60 000 mechanical cycles at 2% strain.
A GPU-based tensor decomposition method for large-scale tensors

Recently, as the sizes of real tensors have become overwhelmingly large including billions of nonzeros, fast and scalable Tucker decomposition methods have become increasingly important. Tucker decomposition has been widely used to analyze multidimensional data modeled as tensors. Several GPU-based Tucker decomposition methods have been proposed to enhance the decomposition speed. However, they easily fail to process large-scale tensors owing to the high memory requirements, which are larger than the GPU memory. This paper presents a scalable GPU-based Tucker decomposition method called GTucker, which carefully partitions large-scale tensors into subtensors and processes them with reduced overhead on a single machine. The results of the experiments indicate that GTucker outperforms state-of-the-art methods in terms of scalability and decomposition speed.
Laminar flow of non-Newtonian fluids in annuli

Laminar flow of non-Newtonian fluids in annuli has been investigated experimentally. A simple theoretical derivation is made by matching the generalised Mooney-Rabinowitsch equation with the limiting cases of flow in cylindrical pipes and between parallel plates. The resulting correlation represents adequately both the present and previous literature data
Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

Simple Summary Distinguishing between benign vs. malignant bone lesions is often difficult on imaging. Many bone lesions are infrequent or rarely seen, and often only specialist radiologists have sufficient expertise to provide an accurate diagnosis. In addition, some benign bone tumours may exhibit potentially aggressive features that mimic malignant bone tumours, making the diagnosis even more difficult. The rapid development of artificial intelligence (AI) techniques has led to remarkable progress in image-recognition tasks, including the classification and characterization of various tumours. This study will review the most recent evidence for AI techniques in differentiating bone lesions on various imaging modalities using a systematic approach. Potential clinical applications of AI techniques for clinical diagnosis and management of bone lesions will also be discussed. Abstract An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.
Physiological modeling for hearing aid design

Physiological data from hearing‐impaired cats suggest that conventional hearing aid signal‐processing schemes do not restore normal auditory‐nerve responses to a vowel [Miller et al., J. Acoust. Soc. Am. 101, 3602 (1997)] and can even produce anomalous and potentially confounding patterns of activity [Schilling et al., Hear. Res. 117, 57 (1998)]. These deficits in the neural representation may account at least partially for poor speech perception in some hearing aid users. An amplification scheme has been developed that produces neural responses to a vowel more like those seen in normal cats and that reduces confounding responses [Miller et al., J. Acoust. Soc. Am. 106, 2693 (1999)]. A physiologically accurate model of the normal and impaired auditory periphery would provide simpler and quicker testing of such potential hearing aid designs. Details of such a model, based on that of Zhang et al. [J. Acoust. Soc. Am. 109, 648 (2001)], will be presented. Model predictions suggest that impairment of both oute.