Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possible use and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering has also been minimally discussed in the literature neither. This study first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model to generate new game levels based on two well-known game settings; and to validate the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments are also utilized to evaluate the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and graphical results, several potential deficiencies, for example, difficulties in handling high-dimensional and vast datasets, as well as insufficient clarity of outputs are discussed; then measures of future enhancement, such as tokenization and the combination of VAE and GAN models, are also outlined. Hopefully, this can ultimately maximize the strengths and advantages of VAE for future game design tasks and relevant industrial missions.
Reconstructing the dietary habits and trophic positions of the Longipterygidae (Aves: Enantiornithes) using neontological and comparative morphological methods

The Longipterygidae are a unique clade among the enantiornithines in that they exhibit elongate rostra (≥60% total skull length) with dentition restricted to the distal tip of the rostrum, and pedal morphologies suited for an arboreal lifestyle (as in other enantiornithines). This suite of features has made interpretations of this group’s diet and ecology difficult to determine due to the lack of analogous taxa that exhibit similar morphologies together. Many extant bird groups exhibit rostral elongation, which is associated with several disparate ecologies and diets (e.g., aerial insectivory, piscivory, terrestrial carnivory). Thus, the presence of rostral elongation in the Longipterygidae only somewhat refines trophic predictions of this clade. Anatomical morphologies do not function singularly but as part of a whole and thus, any dietary or ecological hypothesis regarding this clade must also consider other features such as their unique dentition. The only extant group of dentulous volant tetrapods are the chiropterans, in which tooth morphology and enamel thickness vary depending upon food preference. Drawing inferences from both avian bill proportions and variations in the dental morphology of extinct and extant taxa, we provide quantitative data to support the hypothesis that the Longipterygidae were animalivorous, with greater support for insectivory.
A Systematic Review of the Soil Fertility Monitoring and Organic Farming Techniques for an Improved Crop Yield

Agriculture acquires a substantial role in the economic development and employment of every nation. Ever increasing population of the world and the depletion of agriculture provide great concern among the expert community to enhance crop production by implementing advanced techniques such as precise agriculture. Soil fertility estimation and crop recommendation are key concepts involved in increasing food production as the yield of the crops directly depends on the soil fertility and the best quality crop. Hence, in this research, a survey on the existing prediction models is performed to reveal the significance of crop yield prediction, which is the aim of this research article. The advantages and drawbacks of the existing techniques are analyzed to provide an effective organic farming experience.
Imbalanced Data Classification Method Based on LSSASMOTE

Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper. First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed. Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate. In addition, the oversampled samples were noise processed by Tomek Link. In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data. Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually. The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.
Performance Analysis and Verification of Single Auxiliary Source Suppression Array Error

Aiming at the problem that the performance of direction of arrival (DOA) estimation algorithm deteriorates when there is channel mismatch. This paper proposes the channel data pre-compensation algorithm based on a single auxiliary source. Calculate the error correction matrix based on the steering vector estimate of the auxiliary source, and error compensation for array output data. The direction of arrival of the unknown radiation source is obtained from the corrected array output matrix. The analysis of the experimental results shows that the phase correction is more important than the amplitude correction, the error compensation effect is independent of the auxiliary source angle. The algorithm can estimate the amplitude and phase errors of the linear array, and effectively improve the DOA estimation performance under the condition of channel mismatch error.
Secrecy Performance of MISO Fluctuating Two-Ray Fading Channels

In the fluctuating two-ray (FTR) fading channel, we analyze the physical layer security performance of the multiple-input single-output (MISO) millimeter wave communication system in the presence of an eavesdropper. To get physical and technical insights into the impact of channel parameters on secrecy performance, we first derive the numerical expressions of the security of the physical layer security, such as the average secrecy capacity and the secrecy outage probability. Then we derive the lower bound of secrecy outage probability and the probability of strictly positive secrecy capacity. The theoretical results are corroborated by Monte Carlo simulations.
A COMPLETE SOUTHERN SKY REDSHIFT SURVEY

We discuss a magnitude-limited redshift survey covering a region around the south Galactic pole (SSRS2). The survey includes 3592 galaxies and covers a region of 1.13 sr to a limiting m(sub B(0)) = 15.5, directly comparable to CfA2. Together, CfA2 and SSR2 cover more than a third of the sky. The northern and southern surveys are remarkably similar. Both contain voids with diameters as large as 5000 km/s. The southern survey contains the Southern Wall, similar to the northern Great Wall. The distributions of velocity dispersions for systems extracted from the SSRS2 and CfA2 surveys are also remarkably similar. For the SSRS2, an inhomogeneity-independent technique yields Schechter luminosity function parameters M(sup *) = -19.50 and alpha = -1.20 (H(sub O) = 100 h km/s/Mpc, h = 1 unless otherwise specified). The steep faint-end slope is probably attributable to bluer galaxies. By examining the normalized density fluctuations in the SSRS2 and CfA2 surveys, we suggest that the combined sample is not yet large enough to be 'fair. ' There are large fluctuations in shells at 10,000 km/s.
A Detection Method for Wood Surface Defect Based on Feature Fusion

The wood defect detection is an important link in the process of furniture manufacturing. In this paper, a detection method for wood surface defect based on feature fusion was proposed. Firstly, the histogram of oriented gradient (HOG) was extracted from wood surface defect images, and then kernel principal component analysis (KPCA) was used to reduce the dimension of the extracted HOG features. Finally, by fusing the dimension reduced HOG-KPCA and the gray-level co-occurrence matrix (GLCM) which extracted form wood surface defect images, HOG-KPCA-GLCM features were obtained. And the support vector machine (SVM) is used as classifier. The effects of HOG dimension and weight ratio on classication performance had been experimetally investigated. The results suggested that the detection accuracy had been improved by using the proposed method on the test set, recording an accuracy of 91.26%.
Bifidobacterium longum Administration Diminishes Parasitemia and Inflammation During Plasmodium berghei Infection in Mice

Purpose During Plasmodium berghei (P. berghei) infection, infected erythrocytes are sequestered in gut tissues through microvascular circulation, leading to dysbiosis. This study aimed to investigate the effect of Lactobacillus casei (L. casei) and Bifidobacterium longum (B. longum) administration on the parasitemia level, gut microbiota composition, expression of cluster of differentiation 103 (CD103) in intestinal dendritic and T regulatory cells (T reg), plasma interferon gamma (IFN-γ) and tumor necrosis factor (TNF-α) levels in P. berghei infected mice. Methods P. berghei was inoculated intraperitoneally. Infected mice were randomly divided into 5 groups and treated with either L. casei, B. longum, or the combination of both for 5 days before up to 6 days post-infection (p.i). The control group was treated with phosphate-buffered saline (PBS), while uninfected mice were used as negative control. Levels of CD103 and forkhead box P3 (FoxP3) expression were measured by direct immunofluorescense, while plasma IFN-γ and TNF-α level were determined using enzyme-linked immunosorbent assay (ELISA). Results All treated groups showed an increase in parasitemia from day 2 to day 6 p.i, which was significant at day 2 p.i (p = 0.001), with the group receiving B. longum displaying the lowest degree of parasitemia. Significant reduction in plasma IFN-γ and TNF-α levels was observed in the group receiving B. longum (p = 0.022 and p = 0.026, respectively). The CD103 and FoxP3 expression was highest in the group receiving B. longum (p = 0.01 and p = 0.02, respectively). Conclusion B. longum showed the best protective effect against Plasmodium infection by reducing the degree of parasitemia and modulating the gut immunity. This provides a basis for further research involving probiotic supplementation in immunity modulation of infectious diseases.
A method for surface quality assessment of die-castings based on laser triangulation

This paper presents a new method for the surface quality assessment of safety-critical die-castings. We have developed a measurement system that measures the surface of a die-casting and provides quantitative surface quality assessment within a die-casting cycle of 70 s. The measurement system, based on the laser triangulation principle, has an asymmetrical measuring range and is capable of high-resolution measurements of the casting surface (0.02 mm). Geometry specific parameters (flatness of the particular surface region, average deviation of the measured points and height of the surface defects) are calculated from the acquired surface data and then checked whether/how they fit within the tolerances specified in a technical documentation. The method has been tested in the laboratory by examination of a sample of castings taken randomly from the production process. A comparison of the results obtained by this method and by a qualified operator has shown good agreement.
A Geodynamic Investigation of Plume‐Lithosphere Interactions Beneath the East African Rift

The force balance that drives and maintains continental rifting to breakup is poorly understood. The East African Rift (EAR) provides an ideal natural laboratory to elucidate the relative role of plate driving forces as only lithospheric buoyancy forces and horizontal mantle tractions act on the system. Here, we employ high‐resolution 3D thermomechanical models to test whether: (a) the anomalous, rift‐parallel surface deformation observed by Global Navigation Satellite System (GNSS) data in the EAR are driven by viscous coupling to northward mantle flow associated with the African Superplume, and (b) the African Superplume is the dominant source mechanism of anomalous rift‐parallel seismic anisotropy beneath the EAR. We calculate Lattice Preferred Orientations (LPO) and surface deformation from two types of mantle flow: (a) a scenario with multiple plumes constrained by shear wave tomography and (b) a single superplume model with northward boundary condition to simulate large‐scale flow. Comparison of calculated LPO with observed seismic anisotropy, and surface velocities with GNSS and plate kinematics reveal that there is a better fit with the superplume mantle flow model, rather than the tomography‐based (multiple plumes) model. We also find a relatively better fit spatially between observed seismic anisotropy and calculated LPO with the superplume model beneath northern and central EAR, where the superplume is proposed to be shallowest. Our results suggest that the viscous coupling of the lithosphere to northward mantle flow associated with the African Superplume drives most of the rift‐parallel deformation and is the dominant source of the first‐order pattern of the observed seismic anisotropy in the EAR.
Testbed for a Novel Approach to Minimizing the PAPR of 5G Systems

The generalized frequency division multiplexing (GFDM) system has attracted the interest of the research community due to its unique characteristics such as high spectrum efficiency, low latency, and high transmission rate. However, like every multicarrier technique superimposition of a number of subsymbols in the time domain results in a high peak-to-average power ratio (PAPR). In general, the PAPR reduction system in the literature increases the average power while decreasing the PAPR which is not a plausible solution for practical 5G applications. In order to address this issue, we propose an efficient PAPR reduction strategy that maintains the PAPR without increasing the average power. In this method, an optimal orthogonal precoding matrix based on singular value decomposition (SVD) is designed to reduce the system’s average power. Because this optimal precoding matrix cannot successfully reduce the PAPR, we introduce a second technique called peak samples affixing to minimize both the peak and average power. For the proposed method’s assessment, using LabVIEW software and the universal software radio peripheral 2953R (USRP) as hardware, we developed an experimental setup to enable real-time transmission. The received spectral response from USRP authenticated the proposed method by showing a good agreement with simulations.
Shallow Low‐Velocity Layer in the Hyuga‐Nada Accretionary Prism and Its Hydrological Implications: Insights From a Passive Seismic Array

Shear wave velocity (Vs) estimations of accretionary prisms can pose unique constraints to the physical properties of rocks, which are hard to obtain from compressional velocities (Vp) alone. Thus, it would help better understand the fluid processes of the accretion system. This study investigates the Vs structure of the Hyuga‐nada accretionary prism using an array of ocean‐bottom seismometers (OBSs) with a 2 km radius. Teleseismic Green's functions and a surface wave dispersion curve are inverted to one‐dimensional Vs structures using transdimensional inversion. The results indicate the presence of a low‐velocity zone 3–4 km below the seafloor. The reduced Vs corresponds to a reduced Vp feature obtained in a previous seismic refraction survey, and the high Vp/Vs ratio suggests the presence of high pore fluid pressure. In addition, the resolved lithological boundary exhibits a sharp offset that extends laterally across the OBS array. We attribute this offset to a blind fault below while acknowledging other possibilities, such as due to mud diapirism or intense fracturing. The predicted fault is located at the Kyushu–Palau Ridge flank, oriented roughly parallel to the ridge axis, and thus likely caused by ridge subduction. This fault may act as a fluid conduit, contributing to the formation of a fluid reservoir beneath the compacted sediment layers.
On the asymptotic variance of higher order crossings with special reference to a fast white noise test

SUMMARY The rate of decrease in the variance of higher order crossings is obtained for stationary m-dependent Gaussian sequences. This result provides a quick approximation to the variance of the number of axis-crossings of m-dependent processes, and is applied in determining approximate probability limits for the higher order crossings of white noise. The limits are used in testing for white noise in simulated and also real data examples.
Medium and Short-Term Power Load Forecasting Based on Parallel Temporal Convolutional Neural Network

The medium and short-term forecast of power load can reasonably determine the operation mode of unit and the overhaul plan of equipment, arrange the daily dispatching plan and the weekly dispatching plan, improve the measurement accuracy, which is of great significance to realize the power balance and ensure the safe and economic operation of the system. When using neural network to predict load in medium and short-term, the learning ability of the model will be greatly reduced if the training data is insufficient. At the same time, due to the characteristics of hourly cycle, daily cycle, weekly cycle and seasonal cycle of power load data, the conventional neural network training model cannot reflect the different cycle characteristics of load, which will also have a certain impact on the accuracy of prediction results. Therefore, this paper first proposes a data enhancement method to effectively solve the problem of insufficient training data in power load forecasting. Secondly, according to the periodic characteristics of load, a fusion scheme of parallel temporal convolutional neural network model with different periodic features is further proposed, which effectively reflects the multi-periodic characteristics of load data, and thus improves the accuracy of medium and short-term prediction of power load. Through the modeling and training of load data in a certain city, the effectiveness and superiority of the proposed method in medium and short-term forecasting are verified.
Vessel segmentation and quantification based on U-Net

To provide doctors with more accurate angiograms and quantitative data, angiography and its image processing play a vital role in the medical field. Angiograms often contain background noise and artifacts, so it is necessary to segment and extract blood vessels. In this paper, we propose a modified U-Net structure, which improves the accuracy of vessel segmentation by introducing an attention mechanism. Compared with traditional segmentation algorithms, such as threshold segmentation, modified U-Net is a pixel-level semantic segmentation method, which significantly improves segmentation accuracy. At the same time, this paper realizes the extraction of vascular centerline based on vascular segmentation, which can accurately obtain vascular morphology and carry out quantitative analysis of blood vessels. This study can assist clinical medicine in better completing the analysis of cardiovascular disease, preoperative planning, and improving the success rate of cardiovascular disease treatment.
Cis-Nerolidol Inhibits MAP Kinase and NF-κB Signaling Pathways and Prevents Epithelial Tight Junction Dysfunction in Colon Inflammation: In Vivo and In Vitro Studies

Inflammation of the GI tract leads to compromised epithelial barrier integrity, which increases intestine permeability. A compromised intestinal barrier is a critical event that leads to microbe entry and promotes inflammatory responses. Inflammatory bowel diseases that comprise Crohn’s disease (CD) and ulcerative colitis (UC) show an increase in intestinal permeability. Nerolidol (NED), a naturally occurring sesquiterpene alcohol, has potent anti-inflammatory properties in preclinical models of colon inflammation. In this study, we investigated the effect of NED on MAPKs, NF-κB signaling pathways, and intestine epithelial tight junction physiology using in vivo and in vitro models. The effect of NED on proinflammatory cytokine release and MAPK and NF-κB signaling pathways were evaluated using lipopolysaccharides (LPS)-stimulated RAW 264.7 macrophages. Subsequently, the role of NED on MAPKs, NF-κB signaling, and the intestine tight junction integrity were assessed using DSS-induced colitis and LPS-stimulated Caco-2 cell culture models. Our result indicates that NED pre-treatment significantly inhibited proinflammatory cytokine release, expression of proteins involved in MAP kinase, and NF-κB signaling pathways in LPS-stimulated RAW macrophages and DSS-induced colitis. Furthermore, NED treatment significantly decreased FITC-dextran permeability in DSS-induced colitis. NED treatment enhanced tight junction protein expression (claudin-1, 3, 7, and occludin). Time-dependent increases in transepithelial electrical resistance (TEER) measurements reflect the formation of healthy tight junctions in the Caco-2 monolayer. LPS-stimulated Caco-2 showed a significant decrease in TEER. However, NED pre-treatment significantly prevented the fall in TEER measurements, indicating its protective role. In conclusion, NED significantly decreased MAPK and NF-κB signaling pathways and decreased tight junction permeability by enhancing epithelial tight junction protein expression.
Estimating the POLDER sensitivity to aerosol size using PARASOL observations

The POLDER sensitivity to aerosol size is investigated based on PARASOL and AERONET observations made during 2005–2013. In this study, three AERONET sites: Xianghe, Beijing (China) and Banizoumbou (Niger) where aerosols are dominated by the rural emission, urban pollution and desert dust, respectively, are selected, and the data are screened and matched with PARASOL observations. Then, the AOD870 (aerosol optical depth at the central wavelength of 870 nm) within different size ranges is calculated using AERONET data and compared with POLDER AOD. The POLDER sensitivity is determined by looking for the best fit between the calculated AOD and the POLDER AOD. The results show: (1) The POLDER sensitivity is around 0.27 μm. (2) The AOD contributed by particles with radius less than 0.27 μm can be retrieved with high quality by using POLDER measurements. (3) Even using POLDER measurements, the retrievals in rural areas are still better than in urban. This study will help us properly understand the POLDER remote-sensing products and use them in climate studies, therefore improve our understanding of the role of human activities in climate change.
Metric-Based Semi-Supervised Fuzzy C-Means Clustering

This paper presents a metric-based semi-supervised fuzzy c-means algorithm called MSFCM. Through using side information and unlabeled data together, MSFCM can be applied to both clustering and classification tasks. The resulting algorithm has the following advantages compared with semi-supervised clustering: firstly, membership degree as side information is used to guide the clustering of the data; secondly, through the metric learned, clustering accuracy can be greatly improved. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.