Miracle-dz/UniFAR-FTUs huggingface dataset:
New RUL Prediction Method for Rotating Machinery via Data Feature Distribution and Spatial Attention Residual Network

The prediction of remaining useful life (RUL) is one of the important measures to ensure the safety and reliability of mechanical equipment. Aiming at the low accuracy of residual life prediction caused by difficulty in feature extraction from high data redundancy in the monitoring process of rotating machinery, an RUL prediction method based on data feature distribution and spatial attention residual network (SARN) is proposed. In the proposed method, multisensor data is segmented utilizing distribution feature difference analysis, so as to highlight the degradation characteristics in the sample sequence. Combined with the proposed SARN based on a spatial attention mechanism and residual network, more extensive and complementary features can be extracted. It not only focuses on the importance screening of feature information from all input data, but also realizes the mapping between global sample features and labels, to improve the accuracy of RUL prediction. Finally, the method is verified by the experiments on PHM2012 and C-MAPSS datasets. The results indicate that, compared with the CNNLSTM method on the C-MAPSS dataset, the proposed method has an average reduction of 14.37% in the MAE index and an average increase of 22.59% in the R2 index. This shows that the proposed method has satisfied RUL prediction performance for rotating machinery.
Investigation of Contact Electrification between 2D MXenes and MoS2 through Density Functional Theory and Triboelectric Probes

Contact electrification (triboelectrification) (CE) is a universal phenomenon in ambient environment and has been recorded for more than 2600 years. Nonetheless, the intrinsic mechanism of CE still remains controversial. Herein, based on first‐principles theory, the underlying mechanism in CE is systematically investigated between metallic MXenes and semiconductive MoS2. The results show that the work functions of contacting materials dominate the direction of electron transfer during CE process. That is, the electron will be transferred from the material with low work function to the one with high work function. The theoretical prediction is verified experimentally through investigating triboelectric probes based on MXenes and MoS2 nanomaterials. Additionally, it is noted that the interfacial potential barrier and the work function difference together modulate the amount of transferred electron. Electron transfer mainly occurs in the repulsive forces region where the interaction distance between the two materials is shorter than the normal bonding length. The quantum calculation results agree well with the Wang transition theory. Furthermore, it is also noticed that, due to the wave‐particle duality of electron, electron transfer will obviously occur at the attractive force region when the two contacting materials exhibit a larger work function difference.
Prolongement de faisceaux analytiques cohérents

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Pulmonary Infections and Outcomes in AIDS Patients with Respiratory Failure: A 10-Year Retrospective Review

Background Respiratory failure in acquired immunodeficiency syndrome (AIDS) patients was the leading cause of intensive care unit (ICU) admission in our center. We aimed to describe the pulmonary infections and outcomes for respiratory failure in AIDS patients. Methods A retrospective study was conducted on AIDS adult patients with respiratory failure who were admitted to the ICU in Beijing Ditan hospital, China, from January 2012 to December 2021. We investigated pulmonary infections complicated by respiratory failure in AIDS patients. The primary outcome was ICU mortality, and a comparison between survivors and nonsurvivors was performed. Multiple logistic regression analysis was used to identify predictors of ICU mortality. The Kaplan-Meier curve and Log rank test were used for survival analysis. Results A total of 231 AIDS patients were admitted to ICU with respiratory failure over a 10-year period with a male predominance (95.7%). Pneumocystis jirovecii pneumonia was the main etiology of pulmonary infections (80.1%). The ICU mortality was 32.9%. In multivariate analysis, ICU mortality was independently associated with invasive mechanical ventilation (IMV) [odds ratio (OR), 27.910; 95% confidence interval (CI, 8.392–92.818; p = 0.000) and the time before ICU admission (OR, 0.959; 95% CI, 0.920–0.999; p = 0.046). In the survival analysis, patients with IMV and later admission to ICU had a higher probability of mortality. Conclusion Pneumocystis jirovecii pneumonia was the primary etiology for respiratory failure in AIDS patients admitted to the ICU. Respiratory failure remains a severe illness with high mortality, and ICU mortality was negatively associated with IMV and later admission to ICU.
A lightweight convolutional neural network built on inceptio-residual and reduction modules for deep facial recognition in realistic conditions

ABSTRACT Since the last two decades, face recognition has been a significant area in the fields of biometrics and machine learning that uses the different appearances and geometric-based facial features to recognize the living substances present in a frame or video. In this article, we have proposed an inceptio-residual convolutional neural network inspired by the ResNet architecture for deep facial feature extraction and recognition. Besides that, some of the well-known lite or moderately dense and highly accurate ImageNet benchmarked convolutional neural networks, such as VGG-19 BN, ResNet 18/34/50, SE-ResNet50, ResNeXt50, Inception V2/V3/V4, Xception50, and DualPathNet68, have been utilized for face recognition. In conjunction with that, the proposed one was tested along those above-standard networks with respect to various performance metrics. Based on the results, we concluded that the proposed Incepto-residual convolutional neural network has performed better than the ImageNet benchmarked Convolutional Neural Networks.
Effect of UAV Flight Characteristics to Mobile Network Quality for UAV BVLOS Operations

Beyond Visual Line of Sight (BVLOS) operations are crucial for enabling the drone industry’s upcoming phase of UAV commercial acceleration. One of requirements to support BVLOS operations in UAV is mobile network with proper connectivity. In this study, the effect of UAV flight characteristic (altitude, attitude, and speed) to mobile network quality for UAV BVLOS operations will be analyzed. However, due to the time limitation of the publication, only preliminary results are presented. First, general technical framework including hardware, software, the data stream, and network coverage is described in the methodology. Then, a prototype UAV equips with Raspberry Pi and 4G connectivity is developed. A ground test is conducted to test functionality of the system. The preliminary result shows that the framework system functioning well in terms of transfer and receive data with the average latency 40 milliseconds. Next step, the flight test will be conducted to measure the effect of the UAV flight characteristics on the mobile network quality.
LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.
Differentiating σ-fields for Gaussian and shifted Gaussian processes

We study the notions of differentiating and non-differentiating σ-fields in the general framework of (possibly drifted) Gaussian processes and characterize their invariance properties, when changing to an equivalent probability measure. As an application, we investigate the class of stochastic derivatives associated with shifted fractional Brownian motions. We finally establish conditions for the existence of a jointly measurable version of the differentiated process and we outline a general framework for stochastic embedded equations.
The pulse shape of the Crab nebula pulsar, PSR 0531+21, in the V and I bands.

Data obtained through V and I filters indicate that the leading edge of the interpulse of the Crab pulsar is brighter in V than in I relative to the rest of the pulse shape. In terms of amplitude, the excess is approx.2 percent of the main pulse peak, and in terms of energy the excess is 4.4 +- 0.9 percent of the energy contained in the interpulse. Muncaster and Cocke found a similar result in comparing U and V pulse shapes. In addition to the change in the interpulse, the leading edge of the main pulse may be fainter in V than in I by approx.1 percent of the main pulse peak. The V and I pulses arrive simultaneously within 12 $mu$s at 95 percent confidence. (AIP)
Effects of E’Jiao on Skeletal Mineralisation, Osteocyte and WNT Signalling Inhibitors in Ovariectomised Rats

E’Jiao is a traditional Chinese medicine derived from donkey skin. E’Jiao is reported to suppress elevated bone remodelling in ovariectomised rats but its mechanism of action is not known. To bridge this research gap, the current study aims to investigate the effects of E’Jiao on skeletal mineralisation, osteocyte and WNT signalling inhibitors in ovariectomised rats. Female Sprague–Dawley rats (3 months old) were ovariectomised and supplemented with E’Jiao at 0.26 g/kg, 0.53 g/kg and 1.06 g/kg, or 1% calcium carbonate (w/v) in drinking water. The rats were euthanised after two months of supplementation and their bones were collected for Fourier-transform infrared spectroscopy, histomorphometry and protein analysis. Neither ovariectomy nor treatment affected the skeletal mineral/matrix ratio, osteocyte number, empty lacunar number, and Dickkopf-1 and sclerostin protein levels (p > 0.05). Rats treated with calcium carbonate had a higher Dickkopf-1 level than baseline (p = 0.002) and E’Jiao at 0.53 g/kg (p = 0.002). In conclusion, E’Jiao has no significant effect on skeletal mineralisation, osteocyte and WNT signalling inhibitors in ovariectomised rats. The skeletal effect of E’Jiao might not be mediated through osteocytes.
Can we recover information from concordant pairs in binary matched pairs?

When possible values of a response variable are limited, distributional assumptions about random effects may not be checkable. This may cause a distribution-robust estimator, such as the conditional maximum likelihood estimator to be recommended; however, it does not utilize all the information in the data. We show how, with binary matched pairs, the hierarchical likelihood can be used to recover information from concordant pairs, giving an improvement over the conditional maximum likelihood estimator without losing distribution-robustness.
Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks

During neurogenesis, the generation and differentiation of neuronal progenitors into inhibitory gamma-aminobutyric acid-containing interneurons is dependent on the combinatorial activity of transcription factors (TFs) and their corresponding regulatory elements (REs). However, the roles of neuronal TFs and their target REs in inhibitory interneuron progenitors are not fully elucidated. Here, we developed a deep-learning-based framework to identify enriched TF motifs in gene REs (eMotif-RE), such as poised/repressed enhancers and putative silencers. Using epigenetic datasets (e.g., ATAC-seq and H3K27ac/me3 ChIP-seq) from cultured interneuron-like progenitors, we distinguished between active enhancer sequences (open chromatin with H3K27ac) and non-active enhancer sequences (open chromatin without H3K27ac). Using our eMotif-RE framework, we discovered enriched motifs of TFs such as ASCL1, SOX4, and SOX11 in the active enhancer set suggesting a cooperativity function for ASCL1 and SOX4/11 in active enhancers of neuronal progenitors. In addition, we found enriched ZEB1 and CTCF motifs in the non-active set. Using an in vivo enhancer assay, we showed that most of the tested putative REs from the non-active enhancer set have no enhancer activity. Two of the eight REs (25%) showed function as poised enhancers in the neuronal system. Moreover, mutated REs for ZEB1 and CTCF motifs increased their in vivo activity as enhancers indicating a repressive effect of ZEB1 and CTCF on these REs that likely function as repressed enhancers or silencers. Overall, our work integrates a novel framework based on deep learning together with a functional assay that elucidated novel functions of TFs and their corresponding REs. Our approach can be applied to better understand gene regulation not only in inhibitory interneuron differentiation but in other tissue and cell types.
Matching Algorithm for Data Similarity of Public Dataset of Network Intrusion Detection

In order to provide the basis for the improvement and optimization of the public network intrusion detection data set, a matching algorithm for data similarity of the KDD CUP ’ 99 data set is proposed in this paper. According to the characteristics of the feature record of the network flow of the data set, the structure is defined to save a complete network flow feature record. The sub-algorithm of reading flow feature record, constructing flow feature record queue, calculating flow feature record similarity, and matching data set similarity are designed for the process of data set similarity matching including reading, parsing, similarity calculation, and mutual matching. With the program based on the above matching algorithm, repeated experiments are carried out. The experiment result shows that the matching algorithm can calculate data similarity accurately.
Unraveling the significance of exosomal circRNAs in cancer therapeutic resistance

Exosomes are nanoscale extracellular vesicles secreted by a variety of cells, affecting the physiological and pathological homeostasis. They carry various cargoes including proteins, lipids, DNA, and RNA and have emerged as critical mediators of intercellular communication. During cell–cell communication, they can internalize either by autologous or heterologous recipient cells, which activate different signaling pathways, facilitating malignant progression of cancer. Among different types of cargoes in exosomes, the endogenous non-coding RNAs, such as circular RNAs (or circRNAs), have gained tremendous attention for their high stability and concentration, playing promising functional roles in cancer chemotherapeutic response by regulating the targeted gene expression. In this review, we primarily described the emerging evidence demonstrating the important roles of circular RNAs derived from exosomes in the regulation of cancer-associated signaling pathways that were involved in cancer research and therapeutic interventions. Additionally, the relevant profiles of exosomal circRNAs and their biological implications have been discussed, which is under investigation for their potential effect on the control of cancer therapeutic resistance.
Regimentation of geochemical indicator elements employing convolutional deep learning algorithm

Recently, deep learning algorithms have been popularly developed for identifying multi-element geochemical patterns related to various mineralization occurrences. Effective recognition of multi-element geochemical anomalies is essential for mineral exploration, and effective recognition is extremely dependent on integral clustering. Deep learning algorithms can achieve impressive results in comparison to the prior methods of clustering indicator elements correlated to mineralization for a region of interest due to their superb capability of extracting features from complex data. Although numerous supervised and unsupervised deep learning algorithms have been executed for the recognition of geochemical anomalies, employing them for clustering geochemical indicator elements is rarely observed. In this research, a convolutional deep learning (CDL) algorithm was architected to recognize and regiment geochemical indicator elements in Takht-e Soleyman District, Iran. Various opinions and experiments were considered to reach optimum parameters of this architecture. Fortunately, the achieved root mean square error (RMSE) values were in the appropriate range (<20%) which display the predicted values of the dependent variables (Pb as a pioneer of the first group and Ag as a pioneer of the second group) through their independent variables that are so close to their actual values. Also, the great R2adj calculated (more than 90%) for the last stage of regimentation confirms impressive accuracy and performance of the convolutional deep learning algorithm for clustering geochemical indicator elements of the study area.
Binary Nonlinear Joint Transform Correlator with Sinusoidal Iterative Filter in Spectrum Domain

The joint transform correlator (JTC) has been the best known technique for pattern recognition and identification. This paper proposes a new technique of fringe adjustment by adopting a sinusoidal amplitude-modulated iterative filter convolved with an interference fringe pattern in the joint power spectrum (JPS) domain. The comparison of our new technique and other techniques is presented to show that the newly proposed technique can successfully improve both the correlation peaks and the peak signal-to-noise ratio (PSNR). Simulated results of enhanced interference fringes are also presented.
A recursive traffic flow predictor based on dynamic generalized linear model framework

Previous approaches used to perform online flow predictions included time-series models, nonparametric regression, simple moving average, and adaptive filtering. This paper explores the statistical nature of traffic flows aggregated at short time interval and investigate the potential of using the dynamic generalized linear model (DGLM) for traffic flow predictions. Specifically, recursive algorithms for weighted least square (WLS) estimators resulted from the generalized linear model family, including Poisson, binomial, and negative binomial distributions were derived based on the quasi-likelihood and prediction error minimization principles. The Kalman filter interpretation of the recursive WLS is also described. The resulted formulations are distinct from the existing DGLM and suitable for multivariate case implementation.
Fabrication of mesoporous silica nanoparticles for targeted delivery of sunitinib to ovarian cancer cells

Introduction: Mesoporous silica nanoparticles (MSNPs) are considered innovative multifunctional structures for targeted drug delivery owing to their outstanding physicochemical characteristics. Methods: MSNPs were fabricated using the sol-gel method, and polyethylene glycol-600 (PEG600) was used for MSNPs modification. Subsequently, sunitinib (SUN) was loaded into the MSNPs, MSNP-PEG and MSNP-PEG/SUN were grafted with mucin 16 (MUC16) aptamers. The nanosystems (NSs) were characterized using FT-IR, TEM, SEM, DLS, XRD, BJH, and BET. Furthermore, the biological impacts of MSNPs were evaluated on the ovarian cancer cells by MTT assay and flow cytometry analysis. Results: The results revealed that the MSNPs have a spherical shape with an average dimension, pore size, and surface area of 56.10 nm, 2.488 nm, and 148.08 m2g-1, respectively. The cell viability results showed higher toxicity of targeted MSNPs in MUC16 overexpressing OVCAR-3 cells as compared to the SK-OV-3 cells; that was further confirmed by the cellular uptake results. The cell cycle analysis exhibited that the induction of sub-G1 phase arrest mostly occurred in MSNP-PEG/SUN-MUC16 treated OVCAR-3 cells and MSNP-PEG/SUN treated SK-OV-3 cells. DAPI staining showed apoptosis induction upon exposure to targeted MSNP in MUC16 positive OVCAR-3 cells. Conclusion: According to our results, the engineered NSs could be considered an effective multifunctional targeted drug delivery platform for the mucin 16 overexpressing cells.
Remote Probing of the Moon by Infrared and Microwave Emissions and by Radar

The results of the remote probing of the moon by means of infrared and microwave emissions and by radar are reviewed. Also, we discuss how the various observational results can help to explain physical parameters of the lunar surface, such as thermal and electrical conductivities, dielectric constant, density, particle sizes in the lunar regolith, depth of the surface layer, roughness of the surface, variation of these parameters from point to point on the surface, and amount of heat generated in the lunar interior.
Cloud Particle Swarm Optimization for Vehicle Routing Problem

Vehicle routing problem (VRP) is a kind of NP combination problem. In order to overcome PSOs premature convergence, a Cloud Particle Swarm Optimization (CPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In the paper, the algorithm is used to solve VRP, a kind of new particles coding method is established and the solution algorithm is developed. The simulation results prove that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.
Non-Conservative Evolution of Binary Stars

Abstract Various processes can lead to non-conservative evolution in binary stars. Under conservative mass transfer, both the total mass and the orbital angular momentum of the system are conserved. Thus, the transfer of angular momentum between the orbit and the spins of the stars can represent one such effect. Stars generally lose mass and angular momentum in a stellar wind so, even with no interaction, evolution is non-conservative. Indeed, a strong wind can actually drive mass transfer. During Roche lobe overflow itself, mass transfer becomes non-conservative when the companion cannot accrete all the material transferred by the donor. In some cases, material is simply temporarily stored in an accretion disc. In others, the companion may swell up and initiate common envelope evolution. Often the transferred material carries enough angular momentum to spin the companion up to break-up, at which point it could not accrete more. We investigate how this is alleviated by non-conservative evolution.