The dark matter and dark energy

The dark matter and dark energy are one of the biggest challenges facing contemporary physics and astronomy. Dark energy and dark matter play an important role the universe. The amount of dark energy and dark matter determines how the universe changes. When there’s more dark energy, the universe is accelerating. If there were more dark matter, the universe might slow down, or even stop expanding and start contracting. So in this paper, the basic definition of dark matter and dark energy are introduced. And how were dark matter and dark energy discovered and their respective detection methods and the current progress of experiments to detect dark matter and dark energy respectively

Machine learning method for 12^{12}C event classification and reconstruction in the active target time-projection chamber

Active target time projection chambers are important tools in low energy radioactive ion beams or gamma rays related researches. In this work, we present the application of machine learning methods to the analysis of data obtained from an active target time projection chamber. Specifically, we investigate the effectiveness of Visual Geometry Group (VGG) and the Residual neural Network (ResNet) models for event classification and reconstruction in decays from the excited $2^+_2$ state in $^{12}$C Hoyle rotation band. The results show that machine learning methods are effective in identifying $^{12}$C events from the background noise, with ResNet-34 achieving an impressive precision of 0.99 on simulation data, and the best performing event reconstruction model ResNet-18 providing an energy resolution of $\sigma_E<77$ keV and an angular reconstruction deviation of $\sigma_{\theta}<0.1$ rad. The promising results suggest that the ResNet model trained on Monte Carlo samples could be used for future classifying and predicting experimental data in active target time projection chambers related experiments.Comment: 9 pages, 10 figures, 9 table

Assessment of interfacial fracture of asphalt mortar-aggregate system at low temperature : a study based on four-point bending test of sandwich beams

The bonding properties of the asphalt mortar-aggregate interface at low-temperatures are investigated in this study. A novel method based on mortar-aggregate-mortar sandwich beam and four-point bending test is established. The effects of temperature, loading speed, material type, asphalt aging level and aggregate surface roughness on the bonding properties of the mortar-aggregate interface are methodically examined. Three cracking indexes, including peak bending stress, fracture energy and interface stiffness, are considered for the evaluation. It is concluded that the proposed testing method can effectively distinguish the low-temperature bonding performance of the asphalt mortar-aggregate system under different conditions. The obtained results reveal that the fracture energy can be increased by 700 % with the failure mode changing from brittle failure (-6 degrees C) to ductile failure (0 degrees C), and the positive correlation between loading speed and asphalt-mortar system fracture resistance at low temperature is verified. Additionally, the bonding properties are apparently affected by the type of asphalt and aggregate, aging level of the specimen, and aggregate surface roughness. Specimens composed of the styrene butadiene styrene (SBS) modified asphalt and basalt have the best bonding properties. The moderate aging of the specimen or increasing surface roughness of the aggregates has a positive incorporation into the bonding properties of the asphalt mortar-aggregate system, but severe aging or over-dense grooving act adversely.The bonding properties of the asphalt mortar-aggregate interface at low-temperatures are investigated in this study. A novel method based on mortar-aggregate-mortar sandwich beam and four-point bending test is established. The effects of temperature, loading speed, material type, asphalt aging level and aggregate surface roughness on the bonding properties of the mortar-aggregate interface are methodically examined. Three cracking indexes, including peak bending stress, fracture energy and interface stiffness, are considered for the evaluation. It is concluded that the proposed testing method can effectively distinguish the low-temperature bonding performance of the asphalt mortar-aggregate system under different conditions. The obtained results reveal that the fracture energy can be increased by 700 % with the failure mode changing from brittle failure (-6 degrees C) to ductile failure (0 degrees C), and the positive correlation between loading speed and asphalt-mortar system fracture resistance at low temperature is verified. Additionally, the bonding properties are apparently affected by the type of asphalt and aggregate, aging level of the specimen, and aggregate surface roughness. Specimens composed of the styrene butadiene styrene (SBS) modified asphalt and basalt have the best bonding properties. The moderate aging of the specimen or increasing surface roughness of the aggregates has a positive incorporation into the bonding properties of the asphalt mortar-aggregate system, but severe aging or over-dense grooving act adversely.A

Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China

The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters

The displacement of teeth and stress distribution on periodontal ligament under different upper incisors proclination with clear aligner in cases of extraction: a finite element study

Abstract Objectives To investigate the displacement of dentition and stress distribution on periodontal ligament (PDL) during retraction and intrusion of anterior teeth under different proclination of incisors using clear aligner (CA) in cases involving extraction of the first premolars. Methods Models were constructed, consisting of the maxilla, PDLs, CA and maxillary dentition without first premolars. These models were then imported to finite element analysis (FEA) software. The incisor proclination determined the division of the models into three groups: Small torque (ST) with U1-SN = 100°, Middle torque (MT) with U1-SN = 110°, and High torque (HT) with U1-SN = 120°. Following space closure, a 200 g intrusion force was applied at angles of 60°, 70°, 80°, and 90° to the occlusal plane, respectively. Results CA therapy caused lingual tipping and extrusion of incisors, mesial tipping and intrusion of canines, and mesial tipping of posterior teeth in each group. As the proclination of incisors increased, the incisors presented more extrusion and minor retraction, and the teeth from the canine to the second molar displayed an increased tendency of intrusion. The peak Von Mises equivalent stress (VMES) value successively decreased from the central incisor to the canine and from the second premolar to the second molar, and the VMES of the second molar was the lowest among the three groups. When the angle between the intrusion force and occlusal plane got larger, the incisors exhibited greater intrusion but minor retraction. Conclusions The "roller coaster effect" usually occurred in cases involving premolar extraction with CA, especially in patients with protruded incisors. The force closer to the vertical direction were more effective in achieving incisor intrusion. The stress on PDLs mainly concentrated on the cervix and apex of incisors during the retraction process, indicating a possibility of root resorption

A global transition to flash droughts under climate change

Flash droughts have occurred frequently worldwide, with a rapid onset that challenges drought monitoring and forecasting capabilities. However, there is no consensus on whether flash droughts have become the new normal because slow droughts may also increase. In this study, we show that drought intensification rates have sped up over subseasonal time scales and that there has been a transition toward more flash droughts over 74% of the global regions identified by the Intergovernmental Panel on Climate Change Special Report on Extreme Events during the past 64 years. The transition is associated with amplified anomalies of evapotranspiration and precipitation deficit caused by anthropogenic climate change. In the future, the transition is projected to expand to most land areas, with larger increases under higher-emission scenarios. These findings underscore the urgency for adapting to faster-onset droughts in a warmer future.</p

Hot filament chemical vapor deposition temperature field optimization for diamond films deposited on silicon nitride substrates

The influence of some key parameters of hot filament chemical vapor deposition (HFCVD) on the temperature distribution during the deposition of diamond coatings on silicon nitride (Si _3 N _4 ) substrates was assessed with the help of the finite element method. Solid heat transfer, fluid heat transfer and surface radiation heat transfer mechanisms were used to calculate the substrate temperature in the steady state during the deposition process. The accuracy of the model was verified by comparing the simulation model with experimental measurements. The comparison shows that the deviation between the model and the actual substrate temperature measurements is within 3%. Furthermore, a Taguchi orthogonal experiment was designed (3 factors, 3 levels, L9). By changing the number of hot filaments, the distance between the filaments and the substrate, and the separation between two adjacent hot filaments, the influence trend of these parameters on the substrate temperature was assessed, leading to an optimal hot filament arrangement. A deposition experiment was carried out using the optimized parameters, and the results showed that the substrate surface temperature obtained by numerical simulation is highly consistent with the temperature measured by the infrared thermometer. The optimized deposition parameters contributed to a more suitable temperature range and more uniform temperature distribution on the Si _3 N _4 ceramic substrate. The deposited diamond film exhibited uniform crystal quality and grain morphology, thus verifying the validity of the simulation results

Effects of glyphosate exposure on gut-liver axis: Metabolomic and mechanistic analysis in grass carp (Ctenopharyngodon idellus)

Glyphosate, one of the most widely used herbicide worldwide, is potentially harmful to non-target aquatic organisms. However, the environmental health risks regarding impacts on metabolism homeostasis and underlying mechanisms remain unclear. Here we investigated bioaccumulation, metabolism disorders and mechanisms in grass carp after exposure to glyphosate. Higher accumulation of glyphosate and its major metabolite, aminomethylphosphonic acid, in the gut was detected. Intestinal inflammation, barrier damage and hepatic steatosis were caused by glyphosate exposure. Lipid metabolism disorder was confirmed by the decreased triglyceride, increased total cholesterol and lipoproteins in serum and decreased visceral fat. Metabolomics analysis found that glyphosate exposure significantly inhibited bile acids biosynthesis in liver with decreased total bile acids content, which was further supported by significant downregulations of cyp27a1, cyp8b1 and fxr. Moreover, the dysbiosis of gut microbiota contributed to the inflammation in liver and gut by increasing lipopolysaccharide, as well as to the declined bile acids circulation by reducing secondary bile acids. These results indicated that exposure to environmental levels of glyphosate generated higher bioaccumulation in gut, where evokedenterohepatic injury, intestinal microbiota dysbiosis and disturbed homeostasis of bile acids metabolism; then the functional dysregulation of the gut-liver axis possibly resulted in ultimate lipid metabolism disorder. These findings highlight the metabolism health risks of glyphosate exposure to fish in aquatic environment