Miracle-dz/UniFAR-FTUs huggingface dataset:
HIGHLY EFFICIENTLY INACTIVATION OF MICROBIAL PATHOGENS USING ADVANCED OZONE GENERATOR UNIT AS AN ECO- FRIENDLY PROMISING STRATEGY

Searching for an alternative disinfection and sanitization strategy to control and prevent the contamination and diseases caused by microbial pathogens represents one of the critical challenges for all world governments. So that the antimicrobial efficiency of ozone gas as a terminal disinfectant was estimated at a relatively small level (1.2 mg/l/h) using a unit that was generated as a local unit assembled at the faculty of science against six reference strains including Staphylococcus aureus (ATCC 6538), Bacillus subtilis (ATCC 9372), Bacillus spizizenii (ATCC 6633), Escherichia coli (ATCC 8739), Pseudomonas aeroginosa (ATCC 9027), and Candida albicans (ATCC 10231) for 10, 20, 30 and 40 minutes under laboratory conditions. After 10 min of ozone treatment, the log reduction of cell viability was 97.15%, 59.25%, 24.20%, 24.09%, 14.50 %, 13.47%, and 0.46% for P. aeroginosa , strains combination, E. coli, C. albicans , B. spizizenii, B. subtilis, and S. aureus, respectively. The twenty-minute exposure to ozone resulted in a reduction in microbial viability percent 98.17%, 82.88%, 69.63%, 62.79%, 49.43%, 29.57%, and 28.08%, for P. aeroginosa , strains combination, B. subtilis, and E. coli, C. albicans , S. aureus, and B. spizizenii, respectively. The efficacy of ozone for P. aeroginosa, E. coli, and strains combination increased by more than 98% after 30 min of ozone treatment followed by 90.41%, 86.76%, 52.63%, and 36.64% for B. subtilis, C. albicans , B. spizizenii, and S. aureus, respectively. The maximum ozone efficacy reached 100% for all reference stains except B. spizizenii (62.10%) after 40 min of ozone treatment making this strategy a candidate tool recommended for the management and control of the pathogenic microorganisms.
Can Nutritional Status in Adults Be Influenced by Health Locus of Control?

The external health locus of control (HLC) is based on an assumption that obtained health results depend on the influences of external factors. As for the internal HLC—that is the effect of our own actions and capabilities. Little is known regarding how the HLC can influence body weight or the occurrence of visceral obesity. The study aimed to assess the relationship between the health locus of control and nutritional status in adults. The study included 744 adults (452 women, 292 men; 2.8% underweight, 43.8% normal weight, 29.7% overweight, and 23.7% obese). In addition to anthropometric measurement and socio-demographic data, the health locus of control, using the Multidimensional Health Locus of Control Scale (MHLC) by Wallston K, Wallston B, and DeVellis R, was assessed. The percentage of subjects with an internal HLC did not differ significantly between obese, overweight, and normal-weight groups. However, the percentage of subjects with an external HLC—dependent on the impact of others—was significantly higher in both men and women with obesity than in corresponding overweight and normal-weight groups (p < 0.01). Yet, the percentage of subjects with an external HLC subject to the impact of chance was significantly higher among overweight and obese women than in those of normal weight (p < 0.05) only. Women with overweight or obesity, with external health locus of control, experienced both the impact of others and of chance more often than women with normal weight. However, men with overweight and with obesity more often had external health locus of control influenced only by others.
A domain adaptation model based on multiscale residual networks for aeroengine bearing cross-domain fault diagnosis

As the core component of rotating machinery, the fault diagnosis of rolling bearing has important engineering practical significance. Most of the current intelligent fault diagnosis methods are based on the premise that the training data and test data have similar probability distributions. However, in practical scenarios, there will inevitably be discrepancies in the distribution of vibration signals due to internal and external factors such as changes in working conditions, which will significantly affect the diagnostic performance of the intelligent diagnostic model. Aiming at problems that the vibration signal characteristic distribution of rolling bearings is inconsistent under different working conditions and the labels of the samples to be diagnosed are difficult to obtain, a new domain-adaptive fault diagnosis method is proposed in this paper. Firstly, the multi-scale feature extraction module is used to extract the features of the input signals, and the residual network structure is used to avoid the degradation of the model performance. Then, the APReLU activation function is used to make the vibration signals perform different nonlinear transformations according to their own characteristics through adaptive learning. Finally, the Joint Maximum Mean Discrepancy (JMMD) is used to reduce the displacement of both conditional and edge distributions between different domains. Therefore, this method can extract domain-invariant feature information and align the source and target domains, which can be used for cross-domain intelligent fault diagnosis. Six transfer fault diagnosis tasks based on the rolling bearing experimental platform are designed to evaluate the performance and effectiveness of the proposed method. At the same time, four popular methods are selected for comprehensive analysis and comparison. The results show that the method has good robustness and superiority under various diagnostic tasks.
Study on the Optimal Volume Fracturing Design for Horizontal Wells in Tight Oil Reservoirs

The application of horizontal well volume fracturing technology is an important method for enhancing oil recovery in tight oil reservoirs. However, the influence mechanism of the fracture placement scheme (FPC) on postfracturing productivity is still unclear. Based on the theory of the black oil model, combined with the reservoir stimulation characteristics of horizontal well volume fracturing in tight oil reservoirs, this paper established a postfracturing reservoir production simulation model. History fitting was used to verify the accuracy of the production model simulations. A series of numerical simulations was carried out to study the influence mechanisms of the fracture parameters and FPC on productivity. The simulation results show that compared with the fracture conductivity, the fracture length and number are the main parameters affecting tight oil reservoir productivity. Selecting a reasonable fracture length and number can realize the economical and efficient production of tight oil reservoir volume fracturing. Compared with the traditional fracture equal-length scheme, an FPC with an uneven fracture length can increase the cumulative oil production of oil wells. Under the condition of the same total fracture length, the scheme with a staggered distribution of long fractures and short fractures has the largest cumulative oil production over five years. A reasonable well spacing can greatly reduce the impact of interwell interference on postfracturing dual branch horizontal well productivity. When dual branch horizontal well fractures are alternately distributed, the postfracturing productivity is higher. The production simulation model established in this paper provides a method to accurately evaluate the productivity of horizontal wells after volume fracturing, which can provide guidance for the optimization of hydraulic fracturing operation parameters.
Single-Cell Sequencing Reveals the Regulatory Role of Maresin1 on Neutrophils during Septic Lung Injury

Acute lung injury (ALI) is the most common type of organ injury in sepsis, with high morbidity and mortality. Sepsis is characterized by an inappropriate inflammatory response while neutrophils exert an important role in the excessive inflammatory response. The discovery of specialized pro-resolving mediators (SPMs) provides a new direction for the treatment of a series of inflammatory-related diseases including sepsis. Among them, the regulation of Maresin1 on immune cells was widely demonstrated. However, current research on the regulatory effects of Maresin1 on immune cells has remained at the level of certain cell types. Under inflammatory conditions, the immune environment is complex and immune cells exhibit obvious heterogeneity. Neutrophils play a key role in the occurrence and development of septic lung injury. Whether there is a subpopulation bias in the regulation of neutrophils by Maresin1 has not been elucidated. Therefore, with the well-established cecal ligation and puncture (CLP) model and single-cell sequencing technology, our study reveals for the first time the regulatory mechanism of Maresin1 on neutrophils at the single-cell level. Our study suggested that Maresin1 can significantly reduce neutrophil infiltration in septic lung injury and that this regulatory effect is more concentrated in the Neutrophil-Cxcl3 subpopulation. Maresin1 can significantly reduce the infiltration of the Neutrophil-Cxcl3 subpopulation and inhibit the expression of related inflammatory genes and key transcription factors in the Neutrophil-Cxcl3 subpopulation. Our study provided new possibilities for specific modulation of neutrophil function in septic lung injury.
Charlson Comorbidity Index as a Predictor of Difficult Cholecystectomy in Patients With Acute Cholecystitis

Background The Charlson Comorbidity Index (CCI) has been validated as a predictor of overall survival and post-surgical mortality. CCI is adopted by Tokyo Guidelines as one of the main criteria in the management of acute cholecystitis. Our study evaluates the role of CCI in predicting difficult cholecystectomy. Methods All patients who underwent cholecystectomy for acute cholecystitis between January 2017 and September 2019 were included. CCI, Emergency Surgery Score (ESS), and American Society of Anesthesiologists (ASA) score were calculated and analyzed to assess their predictive value for difficult cholecystectomy. Results A total of 96 patients were included and allocated to difficult and non-difficult cholecystectomy groups. CCI was found to be a significant predictor of difficult cholecystectomy (OR 1.59; 59% CI, 1.04. 2.42; p= 0.031). Similarly, ESS was found to be a predictor tool of difficult cholecystectomy (OR 1.42; 59% CI, 1.05. 1.93; p= 0.024). There was no significant difference in adverse outcomes between the two groups. Conclusion CCI was able to predict a difficult cholecystectomy in our study population. However further studies are required to evaluate if it can be used as a predictor of adverse outcomes in the context of acute cholecystitis.
Charge states of energetic (≈0.5 MeV/n) ions in corotating interaction regions at 1 AU and implications on source populations

The ionic charge states of He, C, O, Ne, Mg and Fe at ≈0.5 MeV/n have been obtained in several corotating interaction regions (CIRs) in 1999 and 2000 with the Solar Energetic Particle Ionic Charge Analyzer (SEPICA) on the Advanced Composition Explorer (ACE). A large fraction (on average 25%) of He+ is found relative to He2+, indicating a substantial contribution of interstellar pickup ions. The mean charge states of the heavy ions are consistent with those in CME related energetic particle events and in solar wind. A rather low upper limit of <1% is found for singly charged ions for C, O and Mg, while Ne shows a small, but noticeable, fraction (4.7%) of Ne+. These observations are consistent with a contribution from interstellar pickup ions, but seem to eliminate inner source pickup ions as a substantial source for CIRs at and near 1 AU.
Cosmic crystallography: the euclidean isometries

Exact expressions for probability densities of conjugate pair separation in euclidean isometries are obtained, for the cosmic crystallography.These are the theoretical counterparts of the mean histograms arising from computer simulation of the isometries. For completeness, also the isometries with fixed points are examined, as well as the orientation reversing isometries.
Reconstruction stack shape detection for robot palletizing

As the robot enters the carriage or container for stacking, it is inevitable to develop a reasonable stack shape detection method to timely find the wrong situations such as stacking boxes tipping and non-conforming to packing constraints, and timely correct them to ensure the smooth stacking. Therefore, a high precision stack detection method based on three-dimensional reconstruction of solid state lidar (Livox Mid-70) is designed. The actual palletizing type point cloud was obtained by laser SLAM reconstruction. Combined with the theoretical palletizing type point cloud result, the overall root mean square error of the actual reconstructed palletizing type and the theoretical planned palletizing type was calculated by point cloud registration technology, and the palletizing effect was tested. The point cloud registration experiment is carried out by using the point cloud obtained by simulation and the point cloud extracted by planning stack shape. The results show that the stack shape detection method can meet the actual requirements in detection accuracy and detection time, and verify the effectiveness of the method.
Deepwater Horizon oil spill monitoring using airborne multispectral infrared imagery

On April 28, 2010, the Environmental Protection Agency's (EPA) Airborne Spectral Photometric Environmental Collection Technology (ASPECT) aircraft was deployed to Gulfport, Mississippi to provide airborne remotely sensed air monitoring and situational awareness data and products in response to the Deepwater Horizon oil spill disaster. The ASPECT aircraft was released from service on August 9, 2010 after having flown over 85 missions that included over 325 hours of flight operation. This paper describes several advanced analysis capabilities specifically developed for the Deepwater Horizon mission to correctly locate, identify, characterize, and quantify surface oil using ASPECT's multispectral infrared data. The data products produced using these advanced analysis capabilities provided the Deepwater Horizon Incident Command with a capability that significantly increased the effectiveness of skimmer vessel oil recovery efforts directed by the U.S. Coast Guard, and were considered by the Incident Command as key situational awareness information.
The geometric and arithmetic volume of Shimura varieties of orthogonal type

We apply the theory of Borcherds products to calculate arithmetic volumes (heights) of Shimura varieties of orthogonal type up to contributions from very bad primes. The approach is analogous to the well-known computation of their geometric volume by induction, using special cycles. A functorial theory of integral models of toroidal compactifications of those varieties and a theory of arithmetic Chern classes of integral automorphic vector bundles with singular metrics are used. We obtain some evidence in the direction of Kudla’s conjectures on relations of heights of special cycles on these varieties to special derivatives of Eisenstein series.
An effective fake news detection using genetic algorithm decision tree

With the rapid development of the internet, the spread of fake news in the media is vast and fast, becomes a social phenomenon. The proliferation of fake news has a devastating impact on the country and society. Many scholars are looking for automated methods to detect fake news. For saving computing resources, fake news detection is often solved by using machine learning algorithms. However, there are many hyperparameters in machine learning algorithms. And different hyperparameters significantly impact the model, which leads to significant differences in the results of the same model for specific problems. Finding a way to obtain stable and accurate hyperparameters has become a research direction of many scholars. This paper first analyzes the characteristics of fake news and then uses genetic algorithm to assist the decision tree model in finding hyperparameters. Compared with random search and traversal hyperparameter search within a specific range, this combined method is faster and more accurate. The results verify the high accuracy of the proposed method. The proposed model can improve the classification performance and is a compelling new method to solve fake news detection.
An accurate fake news detection approach based on a Levy flight honey badger optimized convolutional neural network model

People can quickly acquire the news through a variety of sources, including websites, blogs, and social media, among others. The spread of fake news has become easier as a result of the availability of these platforms. Anybody with access to these networks generates and distributes fake news for professional or personal gain. Numerous studies relying on supervised and unsupervised learning techniques are available to address the issue of recognizing fake news. All of those studies, though, have one flaw: they all deliver mostly inaccurate or unmatched results. Poor accuracy is attributed to a variety of factors, including imbalanced datasets, inefficient parameter tuning, poor feature selection, and so on. To tackle these issues, we proposed a novel approach for fake news detection. Initially, the data were obtained from the ISOT dataset and data cleaning is performed. After that, preprocessing is done which includes three major steps such as stemming, stop word removal, and tokenization are carried out. Next to preprocessing, various features that involve name entity recognition‐based features are selected during feature extraction. From this, the short dimensionality features are selected with the help of the ensemble modified independent component analysis model. Finally, the hybrid convolutional neural network‐based Levy flight‐based honey badger algorithm detects fake news. The experiments are simulated using python software with various performance metrics such as accuracy, specificity, sensitivity, precision, and F‐scores to validate the performance of the proposed method. The proposed model offers a precision, recall, and accuracy value of 95%, 97%, and 98% when evaluated with the ISOT dataset. When compared to the existing state‐of‐art methods, the proposed method yielded superior detection results and higher accuracy rates.
Phase transition for the vacant set left by random walk on the giant component of a random graph

We study the simple random walk on the giant component of a supercritical Erd\H{o}s-R\'enyi random graph on $n$ vertices, in particular the so-called vacant set at level $u$, the complement of the trajectory of the random walk run up to a time proportional to $u$ and $n$. We show that the component structure of the vacant set exhibits a phase transition at a critical parameter $u_{\star}$: For $u u_{\star}$ it has with high probability all components small. Moreover, we show that $u_{\star}$ coincides with the critical parameter of random interlacements on a Poisson-Galton-Watson tree, which was identified in [Tas10].
Laminar, Gravitationally Driven Flow of a Thin Film on a Curved Wall

Gravitationally driven flow of a thin film down an arbitrarily curved wall is analyzed for moderate Reynolds number by generalizing equations previously developed for flow on a planar wall. In the analysis, the ratio of the characteristic film thickness to the characteristic dimension of the wall is presumed small, and terms estimated to be first order in this parameter are retained. Partial differential equations are reduced to ordinary differential equations by the method of von Karman and Pohlhausen; namely, an expression for the velocity profile is assumed, and the equation for conservation of linear momentum is averaged across the film. The assumed velocity profile changes shape in the flow direction because a self-similar profile, one of fixed shape but variable magnitude, leads to an equation that typically fails under critical conditions. The resulting equations for film thickness routinely accommodate subcritical-to-supercritical transitions and supercritical-to-subcritical transitions as classified by the underlying wave propagation. The more severe supercritical-to-subcritical transition is manifested by a standing wave where the film noticeably thickens; this standing wave is a simple analogue of a hydraulic jump
Parametric modeling of CNC machine tool geometric errors based on low-order fourier polynomials

To further improve the forecast accuracy of the geometric error of CNC machine tool, a low order Fourier polynomials fitting model (LFPFM) parametric modeling method based on Improved Gray Wolf Optimization(IGWO) algorithm is proposed. Straight-axis geometric error term is analyzed, and the introduction of piecewise attenuation factor can be adjusted to the GWO algorithm is Improved, to improve the scouting speed of the Optimization algorithm and precision, the error data in all parameters based on the IGWO algorithm to find the best fitting values, and get the error about low-order Fourier polynomial fitting polynomial, Use laser interferometer to measure the location errors of three straight line axes in the CNC machine tool, and parametric modeling was carried out. The experimental results show that the machine tool error parameterized model based on the LFPFM the IGWO algorithm can fit the error data well, and the residual error is smaller, the accuracy is higher, the fitting effect is better, so it is more suitable for the prediction and compensation model of CNC machine tool geometric error.
Proposal of a multi-layer network architecture for OBS/GMPLS network interworking

In order to enable the existing optical circuit switching (OCS) network to support both wavelength and subwavelength granularities, this paper proposes overlay-based multi-layer network architecture for interworking the generalized multi-protocol label switching (GMPLS) controlled OCS network with optical burst switching (OBS) networks. A dedicated GMPLS border controller with necessary GMPLS extensions, including group label switching path (LSP) provisioning, node capability advertisement, and standard wavelength label as well as wavelength availability advertisement, is introduced in this multi-layer network to enable a simple but flexible interworking operation. The feasibility of this proposal is experimentally confirmed by demonstrating an OBS/GMPLS testbed, in which the extended node capability advertisement and group LSP functions successfully enabled the burst header packet (BHP) and data burst (DB) to transmit over a GMPLS-controlled transparent OCS network.