Decay Function Research Articles

Contractor Recommendation Model Using Credit Networking and Collaborative Filtering

The credit of contractors in the construction market directly affects the cooperative intentions of owners. Although previous scholars have attempted to use credit to select appropriate contractors, they have rarely considered the trust relationship between decision-making and former owners. This work introduces and verifies a credit network recommendation model based on a collaborative filtering algorithm. The contractor’s credit established based on this model serves as a viable method for owners to select efficient contractors. The application of the model includes relevant information collection, neighbor set formation, contractor’s credit evaluation, and recommendation list formation, among which the neighbor set of the owner is used to calculate the comprehensive trust degree of the decision-making owner to the former owner. A time decay function is adopted to correct the difference in the trust relationship between an owner and a contractor introduced over time. To verify the feasibility of this model, an actual scenario was simulated, and the results obtained via simulations were compared and found to be consistent. Thus, a contractor with a high credit can be recommended to the decision-making owner. This approach is crucial for promoting contractors’ credit and conducive to the healthy development of the construction market.

Statistical Analysis of Photoluminescence Decay Kinetics in Quantum Dot Ensembles: Effects of Inorganic Shell Composition and Environment.

Discerning the kinetics of photoluminescence (PL) decay of packed quantum dots (QDs) and QD-based hybrid materials is of crucial importance for achieving their promising potential. However, the interpretation of the decay kinetics of QD-based systems, which usually are not single-exponential, remains challenging. Here, we present a method for analyzing photoluminescence (PL) decay curves of fluorophores by studying their statistical moments. A certain combination of such moments, named as the n-th order moments' ratio, R n , is studied for several theoretical decay curves and experimental PL kinetics of CdSe quantum dots (QDs) acquired by time-correlated single photon counting (TCSPC). For the latter, three different case studies using the R n ratio analysis are presented, namely, (i) the effect of the inorganic shell composition and thickness of the core-shell QDs, (ii) QD systems with Förster resonance energy transfer (FRET) decay channels, and (iii) system of QDs near a layer of plasmonic nanoparticles. The proposed method is shown to be efficient for the detection of slight changes in the PL kinetics, being time-efficient and requiring low computing power for performing the analysis. It can also be a powerful tool to identify the most appropriate physically meaningful theoretical decay function, which best describes the systems under study.

CRF-GCN: An effective syntactic dependency model for aspect-level sentiment analysis

Attention-based models have been widely used in aspect-level sentiment analysis owing to their contextual semantic-alignment capabilities. However, these models lack a mechanism that can incorporate syntactic tendencies. Thus, attention-based neural networks are less accurate when dealing with complex syntactic relationships and long-span syntactically dependent utterances. To solve this problem, a neural network model combining a conditional random field and a graph convolutional network is proposed. The model uses a conditional random field chain to extract the opinion span of an aspect-specific word, integrates the contextual information within the opinion span to the global nodes through a multilayer graph convolutional network using the improved position decay function, and predicts the aspect-specific sentiment polarity labels by computing the vector expressions of the global nodes. This study addresses the problem of fluctuations in model accuracy when multiple aspect words exist in an utterance by introducing global nodes into a graph convolutional network. The proposed model performed well in the experiments on all four datasets tested.

Accounting for the recreation benefits of the Flemish Natura 2000 network through landscape preferences and estimated spending

This paper describes the methods used to produce accounts for the recreational value of Natura 2000 areas in Flanders, Belgium. First, a biophysical account of recreation supply and demand is compiled and mapped. Demand is based on data for green visits per year per inhabitant and covers both recreation and nature-based tourism. It distinguishes local walking trips, local cycling, recreation trips with pre-transport and visits by tourists. The number of green visits is based on a combination of yearly statistics (for tourism, day trips) and irregular surveys (for local visits). The supply account is based on modelling predicted visits. The annual visits per inhabitant are attributed to ecosystems using a green visit prediction model that uses the extent and condition accounts related to availability of green-blue areas, accessibility, the attractive potential of landscapes for informal recreation (extent and condition accounts), residence and distance decay functions for different recreation types. Potential destinations include a wide range of green infrastructure, such as parks, forests, natural and agricultural areas and blue spaces (waterside and coastal natural areas). The attractiveness of landscapes is mainly based on an empirical study (choice experiment) in Flanders on people’s preferences for landscape features complemented by evidence from literature. The monetary accounts are preliminary, as there are unsufficient data available for Flanders to estimate the total value for the wide range of recreation types (from local walking and biking to tourism). Especially, data are missing to model travel and time costs for local visits (walking and biking), that account for a large share of total visits in Flanders. It should be noted that, for most visits, apart from nature-based tourism, valuation cannot be based on income fees or parking costs because, in Flanders, visits and parking are free. As unsufficient data are avaible to estimate travel and time costs in detail, we used Flemish data on average expenditure per visit per recreational type as a proxy. We discuss the limits of this preliminary approach and suggest further steps. In the results session, we discuss the implementation of the model to estimate the predicted visits to parts of the Natura2000 areas in Flanders in 2016 and 2018. As different land-uses are strongly interwoven in Flanders, these areas include a wide range of different land-uses and also areas close to residence used for local walking and biking. The differences between 2016 and 2018 illustrate how the model of predicted visits allows us to cope with land-use changes and improved quality and attractiveness of the landscapes in Natura2000 areas.

Low-temperature heat capacity and magnetism in (U1−yLny)O2 and (U1−yAmy)O2 (y = 0.01 − 0.05) solid solutions: Effects of substitution and self-irradiation

The low-temperature heat capacity of lanthanide-doped (La, Nd) and americium-doped UO2 samples was measured by calorimetry. The results showed a strong effect on the antiferromagnetic Cp anomaly as a result of crystal lattice substitution, defects resulting from self-irradiation, and electron interaction. It was found that the substitution of trivalent ions (La3+, Nd3+, Am3+) leads to a split of the λ peak, for which we hypothesize that the clustering of the trivalent ions with the charge compensator pentavalent U5+ can play a role. The overall behavior is confirmed by magnetization measurements using SQUID. The observed differences between La3+, Nd3+, and Am3+ are attributed to the f–f exchange interaction. 241Am decay causes in addition self-irradiation effects (point defects), and reduces Néel temperature, peak intensity, and magnetic entropy as a function of decay (accumulated α dose). The observed effects are similar to 238Pu-doped UO2, the slight differences being attributed to a higher degree of disorder in the Am material.

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.

The effect of spin-lattice relaxation on DEER background decay

The common approach to background removal in double electron–electron resonance (DEER) measurements on frozen solutions with a three-dimensional homogeneous distribution of doubly labeled biomolecules is to fit the background to an exponential decay function. Excluded volume effects or distribution in a dimension lower than three, such as proteins in a membrane, can lead to a stretched exponential decay. In this work, we show that in cases of spin labels with short spin–lattice relaxation time, up to an order of magnitude longer than the DEER trace length, relevant for metal-based spin labels, spin flips that take place during the DEER evolution time affect the background decay shape. This was demonstrated using a series of temperature-dependent DEER measurements on frozen solutions of a nitroxide radical, a Gd(III) complex, Cu(II) ions, and a bis-Gd(III) model complex. As expected, the background decay was exponential for the nitroxide, whereas deviations were noted for Gd(III) and Cu(II). Based on the theoretical approach of Keller et al. (Phys. Chem. Chem. Phys. 21 (2019) 8228–8245), which addresses the effect of spin–lattice relaxation-induced spin flips during the evolution time, we show that the background decay can be fitted to an exponent including a linear and quadratic term in t, which is the position of the pump pulse. Analysis of the data in terms of the probability of spontaneous spin flips induced by spin–lattice relaxation showed that this approach worked well for the high temperature range studied for Gd(III) and Cu(II). At the low temperature range, the spin flips that occured during the DEER evolution time for Gd(III) exceeded the measured spin–lattice relaxation rate and include contributions from spin flips due to another mechanisms, most likely nuclear spin diffusion.

User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function.

Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.

Exploring the Use of Tobacco Waste as a Metal Ion Adsorbent and Substrate for Sulphate-Reducing Bacteria during the Treatment of Acid Mine Drainage

Treatment of acid mine drainage (AMD) was successfully demonstrated using tobacco waste (dust and stem) as a metal cation adsorbent, pH modifier and carbon source for sulphate-reducing bacteria (SRB). Synthetic and industrial AMD wastewaters were used in batch adsorption and SRB facilitated bioremediation experiments. Up to absorbent loading of 80 g/L, metal removal increased. However, increases above 160 g/L did not offer a proportional increase. At an adsorbent loading of 80 g/L, the highest metal removals of 38, 41, 31 and 43% for iron, nickel, copper and zinc respectively were achieved. The iron data fitted well to the Langmuir adsorption isotherm while the Sips adsorption isotherm better-described nickel, copper and zinc adsorption to tobacco waste. SRBs used were able to use tobacco waste as a carbon source while reducing sulphates to metal sulphides in acid mine drainage. In the presence of SRBs, metal removals by both adsorption and sulphide precipitation were 95, 97, 70 and 93% for iron, nickel, copper and zinc, respectively. Copper, however, demonstrated lower removal yields in both adsorption and bioremediation. Bioremediation improved acid mine drainage pH by 2.05 units. The exponential decay function could model both the metal and sulphate removal perfectly. It was concluded that tobacco waste can be confidently used as an adsorbent and carbon source for sulphate-reducing bacteria while facilitating AMD biological treatment.

Time-Aware Multi-Type Data Fusion Representation Learning Framework for Risk Prediction of Cardiovascular Diseases.

Predicting the future risk of cardiovascular diseases from the historical Electronic Health Records (EHRs) is a significant research task in personalized healthcare fields. In recent years, many deep neural network-based methods have emerged, which model patient disease progression by capturing the temporal patterns in sequential visit data. However, existing methods usually cannot effectively integrate the features of heterogeneous clinical data, and do not fully consider the impact of patients age and irregular time interval between consecutive medical records on the patients disease development. To address these challenges, we propose a Time-Aware Multi-type Data fUsion Representation learning framework (TAMDUR) for CVDs risk prediction. In this framework, we design a time-aware decay function, which is based on the patients age and the elapsed time between visits, to model the disease progression pattern. A parallel combination of Bi LSTM and CNN is constructed to respectively learn the temporal and non-temporal features from various types of clinical data. Finally, a multi-type data fusion representation layer based on self-attention is utilized to integrate various features and their correlations to obtain the final patient representation. We evaluate our model on a real medical dataset, and the experimental results demonstrate that TAMDUR outperforms the state-of-the-art approaches.

Experimental and Simulation Studies of the Effects of Nanosecond Pulse Frequency on the Dielectric Motion of BNNSs

Application of nanosecond pulsed electric fields to insulating nanoparticles can be used to prepare oriented high-performance composites, but the mechanism and related laws are still unclear. Therefore, in this article, highly insulating boron nitride nanosheet (BNNS) nanoparticles are selected, their alignment motions under different nanosecond intraburst frequencies (100 Hz–1 MHz) are studied for the first time in both experiments and simulations, and the two sets of results are compared and analyzed. The results show that the simulation results based on COMSOL are in good agreement with the experimental results, the variations in the local orientation angle with time and the local orientation time with intraburst frequency can be expressed by an allometric function, and the variation in the center-to-center distance with time can be described by an exponential decay function. Further experiments show that with the continuous application of the pulsed electric field, short chains of BNNSs continue to move to form longer chains, and long chains of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$342~\mu \text{m}$ </tex-math></inline-formula> form under the external application of the pulsed electric field for 3 min. The good agreement between experiments and simulations verifies the strong reliability of the proposed theory and the established mathematical model for predicting the orientation alignment motion of BNNS particles. The theory and simulation method can be used as effective tools to predict the trajectories of nanoparticles under a nanosecond pulsed electric field and their final alignment into chains.

High-Gain Nonlinear Active Disturbance Rejection Control Strategy for Traction Permanent Magnet Motor Drives

In order to improve the performance of the traction permanent magnet motor drives without weight transducer for direct-drive elevator applications during starting operation, this article proposes a novel control method based on the high-gain nonlinear active disturbance rejection control (HNLADRC) strategy. First, the limitation of linear and nonlinear error decay function of active disturbance rejection control (ADRC) method is analyzed, which is not suitable for the complex situation at starting operation of the traction motor. A high-gain nonlinear error decay function based ADRC method is proposed, which can achieve better performance in the starting operation of the traction motor. Both the high-gain extended state observer (HNLESO) and the high-gain nonlinear error feedback law (HNLEFL) are constructed by the high-gain error decay function to improve the compensation accuracy of the starting torque and the dynamics of the speed loop. By analyzing the error decay performance of the HNLESO and the HNLEFL, it is proved that the HNLADRC method has a faster error decay rate. Experimental results show that the proposed method can obtain a shorter rollback distance and faster response capability.

Artificial Thermal Quenching and Salt Crystallization Weathering Processes for the Assessment of Long-Term Degradation Characteristics of Some Sedimentary Rocks, Egypt

This research aims at investigating the deterioration of limestone rocks due to the influences of thermal quenching and salt crystallization weathering tests and predicting their long-term durability. Therefore, six types of limestones were quarried from different provinces of Egypt and subjected to 50 cycles of thermal quenching and 25 cycles of salt crystallization weathering processes. The porosity, Schmidt hammer rebound hardness, ultrasound pulse velocity, Brazilian tensile strength, and uniaxial compression strength were determined before and after weathering processes. In addition, the mathematical decay function model was developed to evaluate the degradation rate of samples against weathering processes. Results proved that the cyclic salt crystallization deteriorates the physico-mechanical characteristics of the studied limestone more strongly than the thermal quenching cycles do. The decay constant and half-life indexes obtained here indicate that the degradation rate differs for various limestone specimens under thermal and salt weathering processes. This model also showed that the deterioration rate of the studied rocks was higher during cyclic salt crystallization in comparison with thermal quenching. Therefore, the rock degradation rate and or long-term durability under cyclic thermal and salt processes can be estimated accurately. These results show that the studied limestones can be used as building stones in regions exposed to frequent cyclic thermal and salty weathering conditions for long periods without degradation. However, partial attention should be given to LSG limestone rocks characterized by increased porosity and water absorption characteristics.

Eliminating brightness induction effects when measuring motion centre-surround suppression of contrast

The perceived contrast of a central stimulus is supressed when it is embedded in a higher contrast surround, centre-surround suppression of contrast. Local brightness induction effects between the two stimulus regions have been proposed to account for conflicting results when relative grating phases were different. Here, suppression and brightness induction effects are dissociated using a centre-surround arrangement with moving gratings. Four experienced observers were involved in experiments, utilising two-interval forced-choice contrast matching tasks. The stimuli were drifting sinusoidal grating patterns with surrounds (95% contrast) differing in direction of motion and orientation relative to the 40% contrast centre grating. First a 90°-phase-offset same direction surround condition was compared to both same direction (phase aligned) and opposing direction conditions. The reduction in the suppression for the phase-offset condition suggested a reduction in brightness induction influences. Then suppression was examined when surround directions varied and where phase was either fixed or randomised. For small changes in the motion direction between centre and surround (0° to 26.6°) the amount of brightness induction varied sinusoidally with the difference in phase introduced by the direction difference. Finally, the spatial separation between the centre and surround was varied to determine the reduction of suppression and brightness induction with increasing spatial distance. We found both fit an exponential decay function, with surround suppression producing the larger range of influence. Our findings quantify both brightness induction and suppression effects and validate the use of phase randomisation to remove effects of brightness induction when evaluating surround suppression.

A Dynamic Heterogeneous Information Network Embedding Method Based on Meta-Path and Improved Rotate Model

Aiming at the current situation of network embedding research focusing on dynamic homogeneous network embedding and static heterogeneous information network embedding but lack of dynamic information utilization, this paper proposes a dynamic heterogeneous information network embedding method based on the meta-path and improved Rotate model; this method first uses meta-paths to model the semantic relationships involved in the heterogeneous information network, then uses GCNs to get local node embedding, and finally uses meta-path-level aggression mechanisms to aggregate local representations of nodes, which can solve the heterogeneous information utilization issues. In addition, a temporal processing component based on a time decay function is designed, which can effectively handle temporal information. The experimental results on two real datasets show that the method has good performance in networks with different characteristics. Compared to current mainstream methods, the accuracy of downstream clustering and node classification tasks can be improved by 0.5~41.8%, which significantly improves the quality of embedding, and it also has a shorter running time than most comparison algorithms.

A Shortcut Method to Solve for a 1D Heat Conduction Model under Complicated Boundary Conditions

The function of boundary temperature variation with time, f(t) is generally defined according to measured data. For f(t), which has a complicated expression, a corresponding one-dimensional heat conduction model was constructed under the first type of boundary conditions (Dirichlet conditions) in a semi-infinite domain. By taking advantage of the Fourier transform properties, a theoretical solution was given for the model, under the condition that f(t) does not directly participate in the transformation process. The solution consists of the product of erfc(t) and f(0) and the convolution of erfc(t) and the derivative of f(t). The piecewise linear interpolation equation of f(t), based on the measured data of temperature, was substituted into the theoretical solution, thus quickly solving the model and deriving a corresponding analytical solution. Based on the analytical solution under the linear decay function boundary condition, the inflection point method and curve fitting method for calculating the thermal diffusivity were introduced and exemplified, and the variation laws of the appearance moment of the inflection point were discussed. The obtained results show that the values of thermal diffusivity calculated by the two methods are basically consistent, and that the inflection point values rise with the increasing values of the initial temperature variation of the boundary, the decrease in boundary temperature velocity, and the distance from the boundary, respectively.

High-Temperature 2D Optical Relaxation Visualizes Enhanced Oxygen Exchange Kinetics at Metal-Mixed Conducting Oxide Interfaces.

Solid-state heterointerfaces are of interest for emergent local behavior that is distinct from either bulk parent compound. One technologically relevant example is the case of mixed ionic/electronic conductor (MIEC)-metal interfaces, which play an important role in electrochemistry. Metal-MIEC composite electrodes can demonstrate improved catalytic activity vs single-phase MIECs, improving fuel cell efficiency. Similarly, MIEC surface reaction kinetics are often evaluated using techniques that place metal current collectors in contact with the surface under evaluation, potentially altering the response vs the native surface. Techniques enabling direct and local in situ observation of the behavior at and around such heterointerfaces are needed. Here, we develop a spatially resolved optical transmission relaxation (2D-OTR) method providing continuous evaluation of local, high-temperature, controlled atmosphere defect kinetics across a ∼1 cm2 sample area simultaneously in a contact-free manner. We apply it to observe the spatial variance of oxygen incorporation and evolution rates at ∼525-620 °C, in response to step changes in oxygen partial pressure, on MIEC SrTi0.65Fe0.35O3-x films as a function of distance from porous Pt and Au layers. Using this model geometry, we find significant enhancements in kinetics adjacent to the metals that decay over a few millimeter distance. To extract kinetic parameters, we fit the short-term optical data (initial portion of relaxations) with an exponential decay function appropriate for surface-exchange-limited kinetics, yielding apparent surface exchange coefficients (kchem) with spatial resolution, decreasing with distance from the metal. To understand the kinetic processes governing the complete (long-term) optical relaxations, we performed COMSOL simulations, which demonstrated that a combination of laterally varying kchem and in-plane diffusion controls the observed kinetics over the full time range. Further support for spatially varying kchem comes from demonstrations of changing surface and bulk chemistry vs distance from the metal-MIEC interface, by X-ray photoelectron and optical absorption spectroscopies, respectively. Although microporous Pt and Au are not excellent electrodes in isolation, both metals exert a synergistic effect on the oxygen surface exchange rate in the presence of the mixed conducting film.

Direct observation of stepping rotation of V-ATPase reveals rigid component in coupling between Vo and V1 motors

V-ATPases are rotary motor proteins that convert the chemical energy of ATP into the electrochemical potential of ions across cell membranes. V-ATPases consist of two rotary motors, Vo and V1, and Enterococcus hirae V-ATPase (EhVoV1) actively transports Na+ in Vo (EhVo) by using torque generated by ATP hydrolysis in V1 (EhV1). Here, we observed ATP-driven stepping rotation of detergent-solubilized EhVoV1 wild-type, aE634A, and BR350K mutants under various Na+ and ATP concentrations ([Na+] and [ATP], respectively) by using a 40-nm gold nanoparticle as a low-load probe. When [Na+] was low and [ATP] was high, under the condition that only Na+ binding to EhVo is rate limiting, wild-type and aE634A exhibited 10 pausing positions reflecting 10-fold symmetry of the EhVo rotor and almost no backward steps. Duration time before the forward steps was inversely proportional to [Na+], confirming that Na+ binding triggers the steps. When both [ATP] and [Na+] were low, under the condition that both Na+ and ATP bindings are rate limiting, aE634A exhibited 13 pausing positions reflecting 10- and 3-fold symmetries of EhVo and EhV1, respectively. The distribution of duration time before the forward step was fitted well by the sum of two exponential decay functions with distinct time constants. Furthermore, occasional backward steps smaller than 36° were observed. Small backward steps were also observed during three long ATP cleavage pauses of BR350K. These results indicate that EhVo and EhV1 do not share pausing positions, Na+ and ATP bindings occur at different angles, and the coupling between EhVo and EhV1 has a rigid component.

Energy loss of α-particle in the non-equilibrium plasma of deuterium mixed with lithium

The stopping power of compressed and highly ionized deuterium (D) plasma mixed with lithium (Li), in which the electron and ion temperatures are non-equilibrium (i.e., ), to the injected α-particles is studied. Wide ranges of temperature (0.5–10 keV) and density (1–500 g cm−3) of D–Li mixing plasmas are considered. The numerical results show that, in the temperature equilibrium state, the change ratio of stopping power (denoted by η) increases positively with the increase of the fraction of Li (denoted by x Li). However, in some non-equilibrium dense plasma states, η may increase negatively with the increase of x Li. The corresponding nonlinear response of η to x Li may become more (less) remarkable if , which suggests that the collective excitation in the stopping power of D–Li mixing plasmas in which () will be enhanced (suppressed) due to the non-equilibrium temperature effect. As a result, the change ratio of deposition depth of α-particles (denoted by χ) for the non-equilibrium case of rather than seems to be closer to that of equilibrium cases. Furthermore, our results show that more than 60% of energy of injected α-particles is deposited into the electron species, and the energy deposited into the electron component (E e/E 0) is a monotonic decay function of x Li. In addition, relative to the equilibrium results, we find that E e/E 0 will slightly increase (decrease) when (). The findings and analysis in this work indicate that the non-equilibrium implosion with may be more controllable than that with , which may be useful for the advanced development of fusion propulsion systems, in which DLi is a potential fuel option.

Research on dust dispersion law of fully mechanized mining faces under different inclinations and tracking closed dust control method

Based on the gas–solid two-phase flow theory, numerical simulation of the dust dispersion law of fully mechanized mining work under different inclination angles and comparative analysis of field-measured data show that with the increase of working face inclination, the inclination of airflow into the unmined zone increases from 25° to 50° and the maximum wind speed increases from 2.16 to 2.25 m/s after the mixing of cutting turbulent wind and system ventilation. Meanwhile, the range of high-concentration dust clusters, suspension time, lateral migration intensity, and deposition zone increase to varying degrees; dust clusters increases from 62.02 to 202.46 m3. When X < 53.96 m, the dust concentration in the sidewalk-breathing zone shows a sine function with the length of the working face, and when X ≥ 53.96 m, it satisfies the exponential decay function. Based on this, the tracking closed dust control technology is proposed. Combining the offset angle of the airflow and t the gathering position of dust mass, the wind curtain angle and air velocity are automatically controlled to ensure that the dust is restricted to one side of the cable trough.