Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Squeezing Ground Conditions in Hard Rock Mines

Squeezing ground conditions can result in large scale deformation and instability of underground excavations in rock. In hard rock mines, squeezing ground conditions are often associated with the presence of dominant foliation and high stress with resulting deformation often exceeding the capacity of installed ground support. This can result in failure of ground support making the excavations non-operational. This thesis is a contribution to improved understanding of structurally defined squeezing mechanisms observed in underground hard rock mines. A comprehensive field campaign was undertaken in two mines where squeezing was a major ground control issue. This has allowed the development of a database comprised of 147 squeezing case studies. Reported information included drift configuration and orientation, deformation monitoring, rock mass characterization and the performance of ground support. The data were reviewed to identify the failure mechanisms and to quantify the influence of ground conditions, excavation orientation, and the performance of different support strategies. The distinct element method (DEM) was used to successfully reproduce the buckling mechanism resulting in squeezing and large deformations observed in mining drifts in the database. The developed methodology was implemented in 3D models and explicitly considered the role of foliation. Subsequently, a comprehensive strategy for explicitly modeling rock reinforcement using the DEM was developed and implemented in a series of 3D numerical models. The models were calibrated based on field testing of iii reinforcement and observations at the LaRonde Mine. They were used to investigate the impact of different reinforcement strategies and the time of installation of the support. The numerical results were in agreement with the field observations and demonstrated the practical implications of using yielding reinforcement elements. This was supported by field data where the use of yielding bolts reduced the drift convergence and rehabilitation. An analysis and interpretation of the field data and the numerical modelling investigations were used to provide tools for the assessment and mitigation of the anticipated deformation in squeezing ground. The developed strategies can be used to select optimal reinforcement options. The developed tools and procedures can be successfully used to manage large structurally controlled deformations in underground hard rock mines.
Modeling and Simulation to Material Development for Rapid Prototyping Process

Many applications in the field of rapid prototyping require materials with specific characteristics in terms of weight and mechanical resistance which are difficult to obtain using simple polymeric, ceramic and metallic materials. In such situations, rather than developing a new material, which may or may not have the desired properties for a certain application, it is interesting to use the advantages of different material to achieve the target. This can be done by incorporating another component a given pre-existing material, thus obtaining a composite material. In this work, a novel rapid prototyping process, designed thermolithography is presented which needs to use materials with very well specified characteristics. The objective is to fabricate three dimensional structures using the infrared (IR) laser beam from a carbon dioxide (CO2) laser. The material to de cured is a sample composed of epoxy resin, diethylene triamine, and silica powder. Via infra-red radiation it is demonstrated the viability of a completely thermal localized curing process. Such resins typically cure, or solidify, when heated to moderately high temperatures and the obtained results show that it was possible to confine the heating in the required material region. This means that the cure process occurs in all three dimensions. A kinetic model was applied to study the curing kinetics and describe the main features of the process, namely diffusion-controlled effects after vitrification and fractional conversion, presenting an excellent agreement with experimental and predicted values. DSC (differential scanning calorimetry) data are also presented as a tool to take decisions in how to identify the material composition and operating conditions.
Characteristics of Dust Devils in Two Pre-Selected Landing Regions of the Tianwen-1 Mission - Comparing Observations and Predictions Using Numerical Model

The spatial and temporal distribution of dust devils (DDs) in the two pre-selected landing regions (ZA and ZB) of the Tianwen-1 mission in southern Utopia Planitia have been investigated by using images from the Context Camera (CTX) of the Mars Reconnaissance Orbiter (MRO). From the images of the regions in 8 Martian years, no DD was found in ZA, while 77 DDs were found in ZB. The observed DDs are mainly distributed in the northeastern part of ZB. The temporal variation in the observed DDs shows a prominent two-peak pattern in their local early spring and late summer. The size and height of the observed DDs have also been evaluated from the images, and they show a similar temporal variation as the occurrence. To investigate the possible conditions pertinent to these observed patterns of DD distribution, some analysis based on the thermodynamic theory of heat engines was performed using the output of the Mars climate model, MarsWRF. The spatial and temporal distribution of the simulated DDs are generally consistent with the observation, with significantly more DDs in ZB. Analysis of the model results suggests that the spatial distributions of the predicted DDs are mainly related to the distribution of sensible heat flux, which, in turn, is mainly determined by the surface-to-air temperature difference. The difference in DDs between ZA and ZB (more DDs in ZB) is dominated by the difference in sensible heat flux, which, in turn, is mainly related to the spatial variation of surface albedo.
Pulse and Press Disturbances Have Different Effects on Transient Community Dynamics

Disturbances are important determinants of diversity, and the combination of their aspects (e.g., disturbance intensity, frequency) can result in complex diversity patterns. Here, we leverage an important approach to classifying disturbances in terms of temporal span to understand the implications for species coexistence: pulse disturbances are acute and discrete events, while press disturbances occur continuously through time. We incorporate the resultant mortality rates into a common framework involving disturbance frequency and intensity. Press disturbances can be encoded into models in two distinct ways, and we show that the appropriateness of each depends on the type of data available. Using this framework, we compare the effects of pulse versus press disturbance on both asymptotic and transient dynamics of a two-species Lotka-Volterra competition model to understand how they engage with equalizing mechanisms of coexistence. We show that press and pulse disturbances differ in transient behavior, though their asymptotic diversity patterns are similar. Our work shows that these differences depend on how the underlying disturbance aspects interact and that the two ways of characterizing press disturbances can lead to contrasting interpretations of disturbance-diversity relationships. Our work demonstrates how theoretical modeling can strategically guide and help the interpretation of empirical work.
A Multi-Attention UNet for Semantic Segmentation in Remote Sensing Images

In recent years, with the development of deep learning, semantic segmentation for remote sensing images has gradually become a hot issue in computer vision. However, segmentation for multicategory targets is still a difficult problem. To address the issues regarding poor precision and multiple scales in different categories, we propose a UNet, based on multi-attention (MA-UNet). Specifically, we propose a residual encoder, based on a simple attention module, to improve the extraction capability of the backbone for fine-grained features. By using multi-head self-attention for the lowest level feature, the semantic representation of the given feature map is reconstructed, further implementing fine-grained segmentation for different categories of pixels. Then, to address the problem of multiple scales in different categories, we increase the number of down-sampling to subdivide the feature sizes of the target at different scales, and use channel attention and spatial attention in different feature fusion stages, to better fuse the feature information of the target at different scales. We conducted experiments on the WHDLD datasets and DLRSD datasets. The results show that, with multiple visual attention feature enhancements, our method achieves 63.94% mean intersection over union (IOU) on the WHDLD datasets; this result is 4.27% higher than that of UNet, and on the DLRSD datasets, the mean IOU of our methods improves UNet’s 56.17% to 61.90%, while exceeding those of other advanced methods. The implementation code is available on the following Github Link.
Estimation of Symmetry in the Recognition System with Adaptive Application of Filters

The aim of this work is to study the influence of lighting on different types of filters in order to create adaptive systems of perception in the visible spectrum. This problem is solved by estimating symmetry operations (operations responsible for image/image transformations). Namely, the authors are interested in an objective assessment of the possibility of reproducing the image of the object (objective symmetry of filters) after the application of filters. This paper investigates and shows the results of the most common edge detection filters depending on the light level; that is, the behavior of the system in a room with indirect natural and standard (according to the requirements of the educational process in Ukraine) electric lighting was studied. The methods of Sobel, Sobel x, Sobel y, Prewitt, Prewitt x, Prewitt y, and Canny were used and compared in experiments. The conclusions provide a subjective assessment of the performance of each of the filters in certain conditions. Dependencies are defined that allow giving priority to certain filters (from those studied) depending on the lighting.
3D Boundary Element Model for Ultrasonic Wave Propagation Fractional Order Boundary Value Problems of Functionally Graded Anisotropic Fiber-Reinforced Plates

This paper proposes a three-dimensional (3D) local boundary element model based on meshless moving least squares (MLS) method for ultrasonic wave propagation fractional order boundary value problems of functionally graded anisotropic (FGA) fiber-reinforced plates. The problem domain is split into several circular sub-domains. The nodal points are randomly distributed across the examined region. Each node is the focal point of a circular sub-domain that encircles it. The Laplace-transform approach is used to solve dynamic issues. In the local weak form of the governing equations for the converted quantities, a unit test function is utilized. The Gauss divergence theorem to the weak-form is used to produce local boundary-domain integral equations. A meshless approximation is achieved using the MLS method. To find time-dependent solutions, an inverse Laplace-transform approach is used. The effects of the fractional order parameter, functionally graded material, anisotropy, and the time characteristic of the laser pulse are investigated. The proposed method's validity and performance are demonstrated for a two-dimensional problem with excellent agreement with the finite element method.
Investigation of Avian Influenza H5N6 Virus-like Particles as a Broad-Spectrum Vaccine Candidate against H5Nx Viruses

Highly pathogenic avian influenza (HPAI) clade 2.3. 4.4 viruses have been reported to be the source of infections in several outbreaks in the past decades. In a previous study, we screened out a broad-spectrum virus strain, H5N6-Sichuan subtype, by using a lentiviral pseudovirus system. In this project, we aimed to investigate the potential of H5N6 virus-like particles (VLPs) serving as a broad-spectrum vaccine candidate against H5Nx viruses. We cloned the full-length M1 gene and H5, N6 genes derived from the H5N6-Sichuan into pFASTBac vector and generated the VLPs using the baculovirus-insect cell system. H5N6 VLPs were purified by sucrose gradient centrifugation, and the presence of H5, N6 and M1 proteins was verified by Western blot and SDS-PAGE. The hemagglutination titer of H5N6 VLPs after purification reached 5120 and the particle structure remained as viewed by electron microscopy. The H5N6 VLPs and 293T mammalian cell-expressed H5+N6 proteins were sent for mice immunization. Antisera against the H5+N6 protein showed 80 to 320 neutralizing antibody titers to various H5Nx pseudoviruses. In contrast, H5N6 VLPs not only elicited higher neutralizing antibody titers, ranging from 640 to 1280, but also induced higher IL-2, IL-4, IL-5, IFN-γ and TNF production, thus indicating that H5N6 VLPs may be a potential vaccine candidate for broad-spectrum H5Nx avian influenza vaccines.
Global stability and control strategies of a SIQRS epidemic model with time delay

The existing research models of SIQRS are often defined by linear incidence, ignoring the impact of lagging public awareness during outbreaks of infectious diseases. Their research results often deviate from the actual situation. For decision‐makers, the delay of quarantine measures according to its formulation is liable to bring losses. Given this, we establish a new class of SIQRS models based on the nonlinear incidence and focus on the effect of time delay on the propagation characteristics of the system. Firstly, we calculate the basic reproduction number of the transmission dynamics model to obtain the expression of the transmission threshold. Secondly, by constructing suitable Lyapunov functions, we prove the stability of the disease‐free equilibrium point and endemic equilibrium point of the system. Thirdly, we add model immune parameters to study three different immunization strategies, namely, uniform immunization, targeted immunization, and acquaintance immunization. Studies show that compared with uniform immunization, targeted and acquaintance immunization have better control effects. When the immunization ratio increases, the number of infected people decreases, and immune factors can control the spread of the disease. Meanwhile, we calculate the optimal quarantine level value u*≈0.56 to solve the problem of financial costs in quarantine measures to control infectious diseases. Finally, we verify conclusions of the theoretical research by numerical simulation and further discover that when an infection is in the high‐density outbreak, the time delay will lead to a slower convergence rate of the disease. And with the increase of time delay, the control cost Ju* will also increase gradually.
Network Transmission Flags Data Affinity-based Classification by K-Nearest Neighbor

Abstract—This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used becauseit is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags withtheir effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577. 296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs.
The Gravitational Wave Universe Toolbox. II. Constraining the binary black hole population with second and third generation detectors

We employ the method used by the GW-Universe Toolbox to generate a synthetic catalogue of detection of stellar mass binary black hole (BBH) mergers. We study advanced LIGO (aLIGO) and Einstein Telescope (ET) as two representatives for the 2nd and 3rd generation GW observatories, and study how GW observations of BBHs can be used to constrain the merger rate as function of redshift and masses. We also simulate the observations from a detector that is half as sensitive as the ET at design which represents an early phase of ET. Two methods are used to obtain the constraints on the source population properties from the catalogues: 1. parametric differential merger rate model and applies a Bayesian inference on the parameters; and 2. non-parametric and uses weighted Kernel density estimators. The results show the overwhelming advantages of the 3rd generation detector over the 2nd generation for the study of BBH population properties, especially at a redshifts higher than ~2, where the merger rate is believed to peak. With the simulated aLIGO catalogue, the parametric Bayesian method can still give some constraints on the merger rate density and mass function beyond its detecting horizon, while the non-parametric method lose the constraining ability completely there. We also find that, despite the numbers of detection of the half-ET can be easily compatible with full ET after a longer observation duration, the catalogue from the full ET can still give much better constraints on the population properties, due to its smaller uncertainties on the physical parameters of the GW events.
A new swarm intelligent optimization algorithm: Pigeon Colony Algorithm (PCA)

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.
CNTFET-based design of multi-bit adder circuits

ABSTRACT This paper presents a novel design of a high performance full adder cell based on carbon nanotube field-effect transistors (CNTFET). This full adder cell has been designed to be used in ripple carry adder (RCA) optimally so that carry-propagation delay decreases without increase in hardware costs. High speed in RCA structure, the low number of transistors and simplicity in design are the main advantages of the proposed design in comparison with the previous works. Moreover, to increase the proposed RCA speed, carry-propagation chain has been reduced to six cells using only 14 transistors per 4 bits in the worst case, regardless of the adder width. The simulation results using HSPICE demonstrate that a significant improvement in delay, power and power-delay product compared with the state-of-the-art works can be achieved. The results in different temperatures, supply voltages, frequencies and load capacitors have also been obtained.
Maize and Wheat Response to Drought Stress under Varied Sulphur Fertilisation

This study aimed to examine the influence of long-lasting moderate (45% field water capacity—FWC) and severe (30% FWC) water stress and application of sulphur (elemental sulphur or sulphate) on the growth, yield and mineral composition of wheat and maize. Concentrations of macro- and micronutrients were determined in the aboveground parts of the plants. Drought stress caused a marked decrease in the growth parameters of both plants. Under both optimal water conditions (60% FWC) and moderate water stress (45% FWC), grain yields of wheat grown without sulphur application were not significantly different. Applying elemental sulphur caused an increase in grain yield under moderate stress, whereas sulphate was more effective in wheat grown under adequate water supply. Severe water stress significantly lowered wheat yield, regardless of sulphur fertilisation. Increasing water stress resulted in a greater reduction in maize growth, with an average 50% decrease in dry mass under severe water stress. Both crops maintained relatively high levels of macro- (N, P, K, Mg, Ca, S) and microelements (Mn, Fe, Cu, Zn) and did not suffer noticeably from deficiencies in such. Sulphur application did not modify these relationships. In conclusion, sulphur fertilisation may be recommended in wheat cultivation when plants are exposed to moderate water stress.
Assessing the Impacts of Final Demand on CO 2 -eq Emissions in the Mexican Economy: An Input-Output Analysis

The aim of this paper is to analyze the Mexican energy system and its greenhouse gas (GHG) emissions for the year 2012 and to estimate a baseline scenario for 2026 using an input-output analysis. The elasticity of emissions with respect to national demand is calculated in order to identify the total and distributed effects of CO2 equivalent (CO2-eq) emissions. In this framework, the analysis evaluates the effects in the economy related to changes in individual sector demands, and, vice versa, the effect on individual sectors due to global changes in national demands. Results show that passenger and freight transport, power generation, iron and steel industry, chemical industry, air transportation and agriculture concentrate the largest potential for mitigation strategies, and also have important distributive effects on the Mexican economy. Results are evaluated under the mitigation strategies of industrial sector proposed by the Fifth Assessment Report of the IPCC.