Exponential Decay Model Research Articles

Time-series based multi-criteria large-scale group decision making with intuitionistic fuzzy information and application to multi-period battery supplier selection

Battery is the core component of new energy vehicles. The choice of proper battery supplier is crucial for new energy vehicle companies since it directly affects the market share and core competitiveness. Some criteria of battery suppliers cannot be evaluated in the short period and need to be continuously evaluated at different periods (or time). Decision makers (DMs) may present some hesitancy when giving their evaluations. Thus, this paper formulates the battery supplier selection as a type of time-series based multi-criteria large-scale group decision making (LSGDM) with intuitionistic fuzzy information. A novel method is proposed to effectively resolve the battery supplier selection. Firstly, considering the importance of different periods, the comprehensive time weights are determined by combining the exponential decay model and the maximal entropy ordered weighted averaging (OWA) operator weights determining model. Secondly, DMs are divided into different clusters by using the proposed intuitionistic fuzzy Hamming distance-based fuzzy c-means (FCM) algorithm on each criterion. Combining the confidence levels of DMs and the corresponding clustering results, the DMs’ weights with respect to each criterion are determined. Then, the comprehensive criteria weights are obtained by integrating the intuitionistic fuzzy entropy and the constructed multi-objective programming model. Thirdly, the individual consensus level and group consensus degree at each period are defined, respectively. Then, a new identification approach is proposed to identify DMs to be adjusted. Subsequently, a total adjustment minimization model is built to adjust these DMs’ evaluation information. The optimal alternative is generated according to the collective global overall evaluations. Finally, a real example of batter supplier selection is demonstrated to verify the feasibility and superiority of the proposed method.

Modeling the antidepressant treatment response to transcranial magnetic stimulation using an exponential decay function

Recovery from depression often demonstrates a nonlinear pattern of treatment response, where the largest reduction in symptoms is observed early followed by smaller improvements. This study investigated whether this exponential pattern could model the antidepressant response to repetitive transcranial magnetic stimulation (TMS). Symptom ratings from 97 patients treated with TMS for depression were collected at baseline and after every five sessions. A nonlinear mixed-effects model was constructed using an exponential decay function. This model was also applied to group-level data from several published clinical trials of TMS for treatment-resistant depression. These nonlinear models were compared to corresponding linear models. In our clinical sample, response to TMS was well modeled with the exponential decay function, yielding significant estimates for all parameters and demonstrating superior fit compared to a linear model. Similarly, when applied to multiple studies comparing TMS modalities as well as to previously identified treatment response trajectories, the exponential decay models yielded consistently better fits compared to linear models. These results demonstrate that the antidepressant response to TMS follows a nonlinear pattern of improvement that is well modeled with an exponential decay function. This modeling offers a simple and useful framework to inform clinical decisions and future studies.

Beyşehir Gölü Populasyonundaki Sudak (Sander Lucioperca Linnaeus, 1758) Balıklarının Vücut Uzunluğu ile Canlı Ağırlıkları Arasındaki İlişkilerin Matematiksel Modellerle İncelenmesi

The aim of this study is to examine the relationship between the total length and standard lengths with live weight of the freshwater pikeperch growing in the Beyşehir Lake population using four mathematical models. 50 female fish materials of marketable size limit age were used. The data obtained were divided into two groups: estimation (70%) and test (30%). The applied models are exponential decay, exponential and Wood models, and in such research generally preferred power model. R2, R2adj, RMSE, AIC, BIC and MAPE values were used as goodness of fit tests and comparison criteria of the models. According to the study results, the total length gives more accurate estimates than a standard length in the estimating live weight. As a result, it was concluded that other models might provide better results than the power model, and they were suggested to be used in estimating the live weight of pikeperch fish.

Acoustic communication and imaging sonar guided AUV docking: system infrastructure, docking methodology and lake trials

Autonomous underwater vehicle (AUV) docking technique attracts attention which leverages the long-term, on-station vehicle launch and recovery for the underwater missions. In this paper, an acoustic communication and imaging sonar guided docking method is proposed to improve the docking accuracy for AUV. In docking system, multibeam forward looking sonar (MFLS) is deployed on the submerged docking station to reduce the noise in acoustic images rather than being fixed on AUV, and the ultra short base line (USBL) transceiver with acoustic communication modules is used to achieve positioning while AUV is out of the view of MFLS. For the docking methodology, a dedicated three-stage docking strategy is presented to guide AUV to the docking station. In the homing stage, AUV navigates to a preset homing place not very far from the docking station via the way-point guidance and self-positioning of the on-board inertial navigation system (INS). In the next stage, USBL provides the relative position for AUV controller via acoustic channel. Moreover, an exponential decay model based filtering algorithm is proposed to eliminate the positioning outliers of USBL. It is expected that AUV will be guided into the perceptual field of MFLS in this stage. In the final stage, the docking accuracy in short range should be improved while USBL may generate outliers due to the louder noise. In this case, the relative positioning is implemented by using kernelized correlation filter (KCF), which is a fast objective tracking algorithm to keep track of the AUV feature in acoustic image until AUV navigates into the docking station or out of the view of sensor. To validate feasibility and performance of the proposed system infrastructure and docking methodology, the docking results are analyzed by contrast with the experiments only use USBL during lake trials.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied, the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions, electrode material degradation, and even thermal runaway. Accurate estimation and prediction of battery health conditions are crucial for battery safety management. In this paper, an end-cloud collaboration method is proposed to approach the track of battery degradation process, integrating end-side empirical model with cloud-side data-driven model. Based on ensemble learning methods, the data-driven model is constructed by three base models to obtain cloud-side highly accurate results. The double exponential decay model is utilized as an empirical model to output highly real-time prediction results. With Kalman filter, the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results. Subsequently, the whole framework can give an accurate prediction and tracking of battery degradation, with the mean absolute error maintained below 2%. And the execution time on the end side can reach 261 μs. The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Retrospective review of the effectiveness of orthokeratology versus soft peripheral defocus contact lenses for myopia management in an academic setting

To evaluate the relative efficacy of peripheral defocus contact lenses (PDCLs) and orthokeratology (OK) in a real-world clinical population, and compare these results with previous randomised controlled clinical trials. Records from a university practice were reviewed to identify children who were treated with OK or PDCLs. The analysed sample contained 273 visits from 77 patients. Annualised rates of axial length (AL) progression were calculated and used as the response variable in both linear mixed-effects (LME) and nonlinear regression models. On average, children were 10.7 years of age at baseline (p=0.14 between treatments), and most patients were female. More Asian children wore OK lenses compared with PDCLs (p < 0.01). At baseline, children had ~3.00 D of myopia and 0.75 D of astigmatism in both treatment groups (p > 0.20 between treatments). LME regression models using only baseline covariates showed no evidence that the annualised change in AL differed between treatments, with or without the inclusion of age, race, sex, baseline AL or spherical equivalent refractive error. Across all possible subsets of models, age at baseline was the best predictor of annualised AL change. There was no statistical difference between parameters of an exponential decay model fitted within treatment using follow-up age as a time-varying predictor, indicating that the rate of annualised change in AL was similar for OK and PDCL. Retrospective analysis of real-world clinical data found no difference in annualised AL growth between PDCL and OK. Importantly, the AL progression from this clinical setting is consistent with that reported in randomised clinical trials. Therefore, continued research of real-world performance is warranted to understand the safety and efficacy of modern myopia control treatments in the broader population.

Characterization of Vegetation Loss and Impact on Network Performance at $V$-Band Frequencies

Wireless communication systems using millimeter-wave (mmWave) frequencies can be used for high-throughput applications such as fixed wireless access, where static line-of-sight links are used to provide internet connectivity. The directive antennas are typically mounted on building facades above street level. Therefore, the wireless links are mainly subject to attenuation due to atmospheric absorption, rain, and vegetation. In this letter, we present vegetation loss measurements at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$V$</tex-math></inline-formula> -band frequencies ranging from 50 to 75 GHz, using a spectrum analyzer-based channel sounder. Existing vegetation models, including the vegetation-dependent exponential decay (VED) model, are validated based on the measured vegetation loss. Furthermore, IEEE 802.11ad transceivers are used for the validation of the vegetation models, and to evaluate the influence of vegetation on the network performance via packet error rate (PER) and throughput measurements.

Investigation of Unsteady Rotor–Stator Interaction and Deterministic Correlation Analysis in a Transonic Compressor Stage

Abstract Unsteady blade row interaction (UBRI) has a large impact on performance in high-speed axial compressors. Thus, the influence of UBRI should be accurately predicted in compressor routine design. In this study, the unsteady rotor–stator interaction in a transonic single-stage compressor, NASA Stage 35, is studied by steady and unsteady Reynolds-Averaged Navier–Stokes methods. First, the UBRI is analyzed. Periodic separation bubbles on the stator suction surface induced by the rotor wake are observed. Comparisons between steady and unsteady results show that the radial distribution of flow angle near the hub and tip region of the stator is strongly influenced by UBRI. Second, deterministic correlations are analyzed based on the average-passage equation system. The distribution of deterministic stress and the relationship between spatial correlations and deterministic correlations are analyzed. Results show that a strong nonlinear interaction caused by the rotor wake and the stator potential field is found in the rotor–stator gap, which is responsible for the generation of spatial–time correlation. Then, the anisotropy of deterministic correlations is analyzed using the Lumley triangle. Results show that the deterministic stress is highly anisotropic in the stator passage, which can be considered in the modeling using a similar strategy of turbulence modeling. At last, the relationships between the harmonics and decay rate of deterministic correlations are analyzed. The decay rate of deterministic correlation is highly related to the corresponding harmonics, and the higher-order harmonic exhibits a higher decay rate. A modified exponential decay model is proposed for deterministic correlation based on harmonics, which provides improved performance.

Noise tailoring for robust amplitude estimation

A universal fault-tolerant quantum computer holds the promise to speed up computational problems that are otherwise intractable on classical computers; however, for the next decade or so, our access is restricted to noisy intermediate-scale quantum (NISQ) computers and, perhaps, early fault tolerant (EFT) quantum computers. This motivates the development of many near-term quantum algorithms including robust amplitude estimation (RAE), which is a quantum-enhanced algorithm for estimating expectation values. One obstacle to using RAE has been a paucity of ways of getting realistic error models incorporated into this algorithm. So far the impact of device noise on RAE is incorporated into one of its subroutines as an exponential decay model, which is unrealistic for NISQ devices and, maybe, for EFT devices; this hinders the performance of RAE. Rather than trying to explicitly model realistic noise effects, which may be infeasible, we circumvent this obstacle by tailoring device noise using randomized compiling to generate an effective noise model, whose impact on RAE closely resembles that of the exponential decay model. Using noisy simulations, we show that our noise-tailored RAE algorithm is able to regain improvements in both bias and precision that are expected for RAE. Additionally, on IBM’s quantum computer ibmq_belem our algorithm demonstrates advantage over the standard estimation technique in reducing bias. Thus, our work extends the feasibility of RAE on NISQ computers, consequently bringing us one step closer towards achieving quantum advantage using these devices.

The effect of citrus planting orientation on the gradient and incidence of huanglongbing

AbstractSuccessful primary spread of huanglongbing (HLB) within the citrus orchard occurs by the dispersal of the Asian citrus psyllid (ACP; Diaphorina citri) carrying the HLB bacterium “Candidatus Liberibacter asiaticus” (CLas) from external areas to blocks located in the orchard periphery. Higher ACP population is usually concentrated in a strip of up to 200 m from the orchard's edge. Consequently, a higher number of HLB‐symptomatic trees is also detected in this edge strip. Control strategies should be intensified in this strip to prevent further ACP dispersal and therefore to avoid more infected trees in inner blocks. The effect of the citrus planting direction, either parallel or perpendicular to the orchard's edge, was evaluated by comparing the HLB gradient and incidence in blocks located at the orchard's edge by the analysis of satellite images. Six areas with two neighbouring blocks (one planted in parallel and the other in a perpendicular direction) were evaluated in two periods. The exponential decay model was fitted to the HLB incidence at each distance in each block. Parameters from the model (a and b parameters) were similar between blocks planted in parallel (0.60 and 0.018 per metre, on average) and perpendicular (0.75 and 0.016 per metre, on average) planting directions. The total HLB incidence in blocks with a perpendicular planting direction (±30.3%) was higher than in blocks with a parallel planting direction (±22.8%). Overall, planting parallel to the orchard's edge is beneficial in reducing HLB incidence in orchards. This strategy can be integrated into HLB management programmes.

Identifying Longitudinal CD4:CD8 Ratio Trajectories Indicative of Chronic Renal Disease Risk among People Living with HIV: An Application of Growth Mixture Models.

The incidence of chronic kidney disease (CKD) is increasing among people living with HIV (PLWH). Routine monitoring of indicators such as CD4:CD8 ratio might improve the early detection of CKD. Our objective was to identify clinically relevant CD4:CD8 ratio trajectories indicative of CKD risk. Participants were ≥ 18 years old, initiated antiretroviral therapy between 2000 and 2016, and were followed for ≥6 months until 31 March 2017 or last contact date. Outcome was incidence of CKD. Growth mixture models (GMMs) and decay models were used to compare CD4:CD8 ratio trajectories. Following GMM, 4547 (93.5%) participants were classified in Class 1 with 5.4% developing CKD, and 316 (6.5%) participants were classified in Class 2 with 20.9% developing CKD. The final model suggested that participants in Class 2 had 8.72 times the incidence rate of developing CKD than those in Class 1. Exponential decay models indicated a significant CD4:CD8 ratio decline among Class 2 participants who developed CKD. Among those who developed CKD in Class 2, starting at 5.5 years of follow-up, the slope of their ratio trajectory curve changed significantly, and the rate of decline increased dramatically. Routine monitored CD4:CD8 ratios can be an effective strategy to identify early CKD risk among PLWH.

Testing of Roller-Crimper-and-Undercutting-Blade-Equipped Prototype for Plants Termination

The use of roller crimpers to terminate plants and obtain a natural mulch before cash crop establishment has been identified as a valid and sustainable approach to control weeds. Several enhancements have been evaluated to improve and speed up plant termination to avoid delays in cash crop planting and consequent yield losses, which can occur with standard roller crimpers. In the present study, a new prototype machine provided with a roller crimper and an undercutting blade, allowing it to simultaneously crimp plant stems and cut root systems, has been designed, realized, and tested. The aim of the research was therefore to evaluate the effectiveness of the prototype for plant termination and to compare it with a commercial roller crimper. The termination was performed on a spontaneous vegetation cover (weeds). A monophasic exponential decay model to evaluate the weed termination rate over time was performed. The fitted model showed that the prototype is able to achieve a greater and faster weed devitalization compared to the commercial roller crimper, with a lower plateau (0.23 vs. 5.35 % of greenness of plant material, respectively) and higher constant of decay (1.45 vs. 0.39 day−1, respectively). Further studies are needed to evaluate the prototype’s effectiveness in relation to different soil textures, moisture conditions, and amounts of plant biomass to manage, to further improve the machine and extend its use in a broad range of situations, including cover crop termination.

Enhancement of Biomass Conservation and Bioethanol Production of Sweet Sorghum Silage by Constructing Synergistic Microbial Consortia.

The efficient storage of materials before bioethanol production could be key to improving pretreatment protocol and facilitating biodegradation, in turn improving the cost-effectiveness of biomass utilization. Biological inoculants were investigated for their effects on ensiling performance, biodegradability of silage materials, and final bioethanol yield from sweet sorghum. Two cellulolytic microbial consortia (CF and PY) were used to inoculate silages of sweet sorghum, with and without combined lactic acid bacteria (Xa), for up to 60 days of ensiling. We found that the consortia notably decreased pH and ammonia nitrogen content while increasing lactic acid/acetic acid ratios. The microbes also functioned in synergy with Xa, significantly reducing lignocellulose content and improving biomass preservation. First-order exponential decay models captured the kinetics of nonstructural carbohydrates and suggested high water-soluble carbohydrate (grams per kilogram dry matter [DM]) preservation potential in PY-Xa (33.48), followed by CF-Xa (30.51). Combined addition efficiently improved enzymatic hydrolysis and enhanced bioethanol yield, and sweet sorghum treated with PY-Xa had the highest ethanol yield (28.42 g L-1). Thus, combined bioaugmentation of synergistic microbes provides an effective method of improving biomass preservation and bioethanol production from sweet sorghum silages. IMPORTANCE Ensiling is an effective storage approach to ensure stable year-round supply for downstream biofuel production; it offers combined facilities of storage and pretreatment. There are challenges in ensiling sweet sorghum due to its coarse structure and high fiber content. This study provides a meaningful evaluation of the effects of adding microbial consortia, with and without lactic acid bacteria, on changes in key properties of sweet sorghum. This study highlighted the bioaugmented ensiling using cellulolytic synergistic microbes to outline a cost-effective strategy to store and pretreat sweet sorghum for bioethanol production.

Biodiesel-diesel blend optimized via leave-one cross-validation based on kinematic viscosity, calorific value, and flash point

This study investigated the kinematic viscosity, calorific value, and flash point of three biodiesel–diesel blends (Luoping rape oil biodiesel, corn oil biodiesel, and catering waste oil biodiesel). Novel prediction equations for the calorific value were established based on the kinematic viscosity and flash point, and an optimal mixing ratio of biodiesel–diesel blends was obtained. Lederer’s equation, theoretical calorific values calculated using mass fractions, and the Hu-Burns equation, which are based on mixing ratio, are effective for predicting the kinematic viscosity, calorific value, and flash point of the three biodiesel–diesel blends. The mean absolute percentage error (MAPE) values of Lederer’s equation were 0.44%, 0.33%, and 0.68%, which were considerably higher than the results predicted using Krisnangkura equation. The MAPE values of the theoretical calorific values of mass fraction were 0.09%, 0.06%, and 0.11%. The MAPE values of Hu-Burns equation were 1.10%, 1.50%, and 1.00%, and the error was also smaller than that of Wickey-Chittenden equation and Chevro equation, which are consistent with the experimental results. A prediction model of kinematic viscosity, calorific value, and flash point values was established using Lederer’s equation, mass fraction theory calorific value, and Hu-Burns equation through the leave-one-out cross-validation method. The power exponent model was the most suitable for simulating the relationship between kinematic viscosity and calorific value, and the maximum errors of the three blends were 0.55%, 0.81%, and 0.90%, and the MAPE values were 0.16%, 0.39%, and 0.38%. The exponential decay model was the most suitable for simulating the relationship between the flash point and calorific value; the maximum errors of the three biodiesel blends were 0.98%, 0.68% and 0.58%; and the MAPE values were 0.51%, 0.25%, and 0.30%. Therefore, the calorific values obtained using these two models were highly accurate. Considering kinematic viscosity, flash point, and calorific value as the optimization indexes, the optimization interval was determined, and the optimal biodiesel mass fraction mixing ratios were 24.4%, 25.1%, and 25.2%. This study has important practical implications for the efficient utilization of biodiesel–diesel blended fuel. • Kinematic viscosity, calorific value and flash point are predicted by mixing ratio. • The calorific value is predicted by kinematic viscosity and flash point respectively. • A model for predicting the calorific value of biodiesel blended fuel is proposed. • The predicted calorific value error of biodiesel blending fuel is less than 1%. • The biodiesel blending proportion of the three blended fuels is about 25%.

Entrapment of Mo-reducing Bacterium Increase its Resistance towards Mercury

In ruminants, even trace amounts of molybdenum can be lethal. In areas with high pollution, molybdenum levels in soil and mine tailings can exceed 20,000 ppm. Bioremediation of molybdenum can be challenging when toxic mercury is also present. This research presents a novel approach using dialysis tubing and the molybdenum-reducing activity of Enterobacter sp. strain Dr. Y13 for molybdenum removal from aqueous solutions. Molybdenum blue (Mo-blue), produced during enzymatic reduction, is insoluble in dialysis tubing and this can be a twofold advantage as a method of removal and as a method to protect bacterial cells from heavy metal inhibition, especially mercury. In this experiment, we assess the toxicity-shielding effect of dialysis tubing for molybdenum reduction to Mo-blue by this bacterium in the presence of mercury. As the concentrations of mercury were increased, both free and immobilized cells were strongly inhibited. Modelling using the dissociationone phase exponential decay model gave an IC50 value for the immobilized form of 0.1107 mg/L (95% confidence interval from 0.073 to 0.217 while the IC50 value for the free cell system was 0.023 mg/L (95% C.I. from 0.019 to 0.028). Since the confidence interval for the IC50 values did not overlap, the immobilized system gave better protection from mercury than the free cell system. Toxicity to free cells was higher than toxicity to cells trapped in dialysis tubes, suggesting that trapping Mo-reducing cells may be an effective strategy for bioremediation of water or wastewater contaminated with multiple heavy metals.

Immobilization of Bacillus sp. Strain Neni-8 in Dialysis Tubing Reduced Copper Toxicity to the Molybdenum Reduction Process

In ruminants, even trace amounts of molybdenum can be lethal. In areas with high pollution, molybdenum levels in soil and mine tailings can exceed 20,000 ppm. Bioremediation of molybdenum can be challenging when toxic copper is also present. This research presents a novel approach using dialysis tubing and the molybdenum-reducing activity of Bacillus sp. strain Neni-8 for molybdenum removal from aqueous solutions. Molybdenum blue (Mo-blue), produced during enzymatic reduction, is insoluble in dialysis tubing and this can be a twofold advantage as a method of removal and as a method to protect bacterial cells from heavy metal inhibition, especially copper. In this experiment, we assess the toxicity-shielding effect of dialysis tubing for molybdenum reduction to Mo-blue by this bacterium in the presence of copper. As the concentrations of copper were increased, both free and immobilized cells were strongly inhibited. Modelling using the dissociationone-phase exponential decay model gave an IC50 value for the immobilized form of 0.1107 mg/L (95% confidence interval from 0.073 to 0.217 while the IC50 value for the free cell system was 0.023 mg/L (95% C.I. from 0.019 to 0.028). Since the confidence interval for the IC50 values did not overlap, the immobilized system gave better protection from copper than the free cell system. Toxicity to free cells was higher than toxicity to cells trapped in dialysis tubes, suggesting that trapping Mo-reducing cells may be an effective strategy for the bioremediation of water or wastewater contaminated with multiple heavy metals.

A simple model for viral decay dynamics and the distribution of infected cell life spans in SHIV-infected infant rhesus macaques

The dynamics of HIV viral load following the initiation of antiretroviral therapy is not well-described by simple, single-phase exponential decay. Several mathematical models have been proposed to describe its more complex behavior, the most popular of which is two-phase exponential decay. The underlying assumption in two-phase exponential decay is that there are two classes of infected cells with different lifespans. However, with the exception of CD4+ T cells, there is not a consensus on all of the cell types that can become productively infected, and the fit of the two-phase exponential decay to observed data from SHIV.C.CH505 infected infant rhesus macaques was relatively poor. Therefore, we propose a new model for viral decay, inspired by the Gompertz model where the decay rate itself is a dynamic variable. We modify the Gompertz model to include a linear term that modulates the decay rate. We show that this simple model performs as well as the two-phase exponential decay model on HIV and SIV data sets, and outperforms it for the infant rhesus macaque SHIV.C.CH505 infection data set. We also show that by using a stochastic differential equation formulation, the modified Gompertz model can be interpreted as being driven by a population of infected cells with a continuous distribution of cell lifespans, and estimate this distribution for the SHIV.C.CH505-infected infant rhesus macaques. Thus, we find that the dynamics of viral decay in this model of infant HIV infection and treatment may be explained by a distribution of cell lifespans, rather than two distinct cell types.

Fragmentation modeling of gas-phase ionic liquid clusters in high-voltage electric field

Cluster fragmentation subjected to a high-voltage electric field may play an important role in controlling the novel ionic liquid (IL) electrospray propulsion but has received little attention. In this work, the micro-fragmentation mechanisms of three kinds of IL clusters are numerically investigated using both static and dynamic approaches based on the quantum mechanical principles. Firstly, it reveals that the Coulomb interaction dominates the total inter-fragment energy for all three IL dimers, whereas the hydrogen-bonding interaction is identified as the main cause for the much slower break-up rate of the 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM-BF4]) dimer. In addition to the electrostatic interaction, the van der Waals interaction is a secondary energy component to which only a small fraction of atoms contribute significantly, regardless of IL cluster type. We find, only when the applied electric field is larger than 1 V/nm, the total inter-fragment energies of all three IL clusters start to dramatically reduced, indicating the possible occurrence of massive break-up. Secondly, ab initio molecular dynamics simulation further confirms that the [EMIM-BF4] negatively charged dimer is the most stable of three IL clusters under the identical conditions. As far as the high-voltage electric field is concerned, the one-parameter exponential function of break-up ratio versus time, routinely used in the field-free region, is not valid. Finally, on the basis of simulated results, a compound exponential-decay model is proposed, and the derived differential-sum equations from constant electric fields can be further applied to the non-uniform electric field.

Vegetation Loss at D-Band Frequencies and New Vegetation-Dependent Exponential Decay Model

With the potential of fixed wireless access (FWA) networks as an alternative to optical fiber, it is necessary to determine vegetation loss at millimeter wave frequencies. In this article, we present vegetation loss measurement results for different types of vegetation, including trees, hedges, and forests, at frequencies ranging from 110 to 170 GHz. An experimental method is proposed to determine the average loss per meter vegetation depth (VD) for different vegetation types. Average losses at 140 GHz range from 0.2 dB/m for an open forest, and up to 9.8 dB/m for dense hedges. As there is a large variance of vegetation loss for different vegetation types, we propose a novel vegetation-dependent exponential decay (VED) model, expressing vegetation loss as a function of VD, frequency, and vegetation density, which is expressed by the plant area index (PAI) parameter. The VED vegetation loss model can be used for network design, and to perform accurate link budget calculations.

Mechanical Strength Decay Evaluation of Excavation Unloaded Rock Mass under Freeze-Thaw Conditions

The quality of excavated and unloaded rock masses on steep and high slopes in cold regions is prone to deterioration, which in turn affects the long-term stability and safety of excavated slopes. Based on a triaxial unloading-damage test of sandstone, the unloading quantity was used to analyze the evolution law of unloading damage; a freeze-thaw cycle test of the unloaded-damaged samples was carried out, and it was found that the average change in porosity and the reloading peak strength damage rate after freeze-thaw increased with the increase in the number of freeze-thaw cycles, and the porosity change characteristics were independent of the value of the confining pressure at the time of the unloading damage. An exponential decay model was used, and based on the average change in porosity after freeze-thaw, a freeze-thaw strength decay model that can take into account the effect of confining pressure was established, and its ability to predict the strength decay of unloaded-damaged rock samples after freeze-thaw was verified by experimental data. The research results provide a reference for the evaluation of freeze-thaw degradation of unloaded rock masses during slope excavation in cold regions.