Initial Threshold Research Articles

Experimental study on dilatancy behavior of soft rock under dynamic loading

Correct understanding of rock dilatation plays a non-negligible role in the safety of rock engineering and the efficiency of geological resources extraction. Although the significance of dilatation under quasi-static loading or unloading conditions is well-studied, its behavior under dynamic loads remains poorly understood. This is largely because conventional dynamic testing system fails to capture the volumetric strain evolution of rock under confinement. This study develops a transparent confining chamber in SHPB system with which high-speed 3D-DIC is capable of measuring both axial and circumferential strain history of dynamically compressed rock. The progressive failure stages and volumetric change of red sandstone under confining pressure of 3.5 MPa and strain rates of 129∼292 s−1 were studied and compared with those in quasi-static case. Results show that, the normalized stress thresholds of crack initiation and dilatancy in dynamic cases are much smaller than those in the quasi-static one. The volumetric variation of soft rock is characterized by four distinct stages: elastic contraction, pre-peak dilatancy, strain-softening dilatancy and strain-recovery dilatancy. The pre-peak elastic contraction is negligible compared to the post-peak dilation, and the ultimate dilation observed in dynamic compression is significantly greater than that in quasi-static compression. Increasing strain rate delays the onset of dilatancy, decreases the dilatant rate but remarkably extends the strain-softening dilatant process which is dominant in total dilation. A rate-dependent piecewise model incorporating key strain thresholds and a dilatancy rate factor was established to characterize the dynamic dilatancy behavior. The research results provide a new insight and possible method in dilation prediction for underground engineering subjected to dynamic loading.

A New Rock Brittleness Index Based on Crack Initiation and Crack Damage Stress Thresholds

A New Rock Brittleness Index Based on Crack Initiation and Crack Damage Stress Thresholds

Dependence of Positive Bias Stress Instability on Threshold Voltage and Its Origin in Solution-Processed Aluminum-Doped Indium Oxide Thin-Film Transistors.

The initial electrical characteristics and bias stabilities of thin-film transistors (TFTs) are vital factors regarding the practical use of electronic devices. In this study, the dependence of positive bias stress (PBS) instability on an initial threshold voltage (VTH) and its origin were analyzed by understanding the roles of slow and fast traps in solution-processed oxide TFTs. To control the initial VTH of oxide TFTs, the indium oxide (InOx) semiconductor was doped with aluminum (Al), which functioned as a carrier suppressor. The concentration of oxygen vacancies decreased as the Al doping concentration increased, causing a positive VTH shift in the InOx TFTs. The VTH shift (∆VTH) caused by PBS increased exponentially when VTH was increased, and a distinct tendency was observed as the gate bias stress increased due to a high vertical electric field in the oxide dielectric. In addition, the recovery behavior was analyzed to reveal the influence of fast and slow traps on ∆VTH by PBS. Results revealed that the effect of the slow trap increased as the VTH moved in the positive direction; this occured because the main electron trap location moved away from the interface as the Fermi level approached the conduction band minimum. Understanding the correlation between VTH and PBS instability can contribute to optimizing the fabrication of oxide TFT-based circuits for electronic applications.

Unexpectedly, triple super phosphate fertilizer induces maize drought resilience

Phosphorus fertilizer is commonly applied to soils in crop production as diammonium phosphate (DAP). To decrease ammonium addition to the environment, triple super phosphate (TSP) is being considered as a DAP replacement. This study was undertaken to compare the response of maize plants to soil fertilization with TSP vs. DAP under well-watered conditions, under soil-drying conditions, and root hydraulic conductance. It was found for well-watered conditions in a controlled environment that there was no difference in plant growth between DAP to TSP treatments. In soil dry-down experiments, however, the initiation of the decrease in transpiration rate was unexpectedly quite different between DAP and TSP treatments. The soil water content threshold for initiation of decrease in transpiration rate with DAP treatment (average fraction transpirable soil water for two experiments = 0.285) was consistent with common observations, but the threshold associated with TSP treatment occurred at an unusually high soil water content (average fraction transpirable soil water for two experiments = 0.545). The higher threshold with TSP resulted in an extended period of soil water use, i.e. soil water conservation. Soil water conservation resulting from the TSP treatment was associated with a 27% greater shoot mass accumulation following a 4-wk re-watering period after the water-deficit treatment than measured with the DAP treatment. The high threshold for decrease in transpiration resulting from TSP was consistent with measured lower root hydraulic conductance as compared to DAP treatment. The unexpected discovery of TSP-induced initiation of transpiration rate decrease at high soil water content is consistent with greater crop drought resilience.

Intrusion detection method based on improved social network search algorithm

The network security problem in today's world is becoming more and more prominent, and intrusion detection as a branch in the field of network security has been developed tremendously. At present, back propagation (BP) neural network is widely used in intrusion detection. However, its weights and thresholds are randomly initialized, so that fall into local optimal after training. To solve this problem, an intrusion detection model based on improved social network search (ISNS) algorithm to optimize BP neural network is proposed. The model first uses chaotic mapping and elite mechanism to improve the original Social Network Search (SNS) algorithm, and then uses the ISNS algorithm to filter the initial weights and thresholds of the BP neural network, and constructs the neural network with the optimal values to avoid falling into local optimum. The experimental results show that the proposed method solves the problem that the traditional BP neural network is easy to fall into local optimum. At the same time, the ISNS_BP model achieves better classification performance than other models, and the accuracy on the NSL-KDD and UNSW-NB15 datasets reaches 98.62% and 93.97%, respectively.

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.

Low-altitude small target detection in sea clutter background based on improved CEEMDAN-IZOA-ELM

To effectively detect low-altitude small targets under complex sea surface environment, an innovative method has been developed. This method harnesses the chaotic characteristics of sea clutter and employs a combination of Adaptive Noise Complete Ensemble Empirical Modal Decomposition (CEEMDAN), Adaptive Wavelet Thresholding (AWT), and Polynomial Fitting Filtering (SG) for denoising sea clutter data. Subsequently, the Improved Zebra Optimization Algorithm-Extreme Learning Machine (IZOA-ELM) detector is utilized to identify low-altitude small targets amidst the sea clutter background. To begin, the CEEMDAN method is applied to disentangle the measured sea clutter data into a set of Intrinsic Mode Functions (IMFs). Afterwords, the Refined Composite Multiscale Dispersion Entropy (RCMDE) is computed for each individual IMF. This process categorizes the IMFs into three distinct components: noise-dominant, signal-noise mixture, and signal-dominant segments. The noise-dominate of IMF component is subjected to denoising through AWT, the signal-noise mixture of IMF components are processed using SG filtering, while the signal-dominant of IMF remains unaltered. The denoised sea clutter signal is reconstructed by concatenating the denoised and unprocessed IMFs. Based on the chaotic nature of sea clutter signals, first-order sea clutter data is transformed into high-dimensional data through phase space reconstruction. The initial weights and thresholds of the ELM are optimized through the IZOA to establish an optimal prediction model. This model is then used to detect small, low-altitude targets by analyzing the prediction error. The algorithm's effectiveness in noise removal is validated using IPIX and SPRR measured sea clutter data, demonstrating a significant improvement in the root mean square of prediction error (RMSE) by one order of magnitude after denoising compared to the pre-denoising state. Furthermore, we observed that the IZOA-ELM method can be effectively applied to detect small targets at low altitudes across various sea conditions. However, when the sea state is complex and greatly affected by the surrounding noise, an effective approach is to first employ CEEMDAN-AWT-SG to denoise the original signal, and then utilize IZOA-ELM for target detection.

Research on performance state evaluation of circuit breaker energy storage spring based on intelligent algorithm

The performance state evaluation method of circuit breaker energy storage spring mainly judges its performance state indirectly by measuring the pre-tightening force or pre-pressure of the spring. However, there may be some errors in this indirect measurement method, which will affect the accuracy of the evaluation results. Therefore, the performance state evaluation based on intelligent algorithm is proposed. Select the evaluation characteristic quantity of performance state, calculate the energy storage spring impulse according to the momentum theorem, and obtain the pressure value of the closing energy storage spring through the pressure sensor as the evaluation quantity reflecting the energy storage spring performance state. The BP neural network is established, and the fireworks algorithm is applied to the BP neural network to optimize the initial weight and threshold, so as to realize the performance state evaluation of energy storage spring based on BP neural network. The experimental results show that the spring energy release speed of the proposed method is in the range of 0 - 1.0m/s, and the estimated spring pressure value is basically consistent with the actual value.

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks; Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good; Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.

Exploring Audiologic Outcomes in Sudden Sensorineural Hearing Loss: A Retrospective Analysis.

Objective This retrospective study aims to present the audiologic outcomes of patients aged 18 years and above who underwent treatment for sudden sensorineural hearing loss (SSNHL) at the tertiary Hospital Central Sur Petróleos Mexicanos in Mexico City, Mexico, between January 2000 and December 2015. Main outcome measures The main outcome measures were patient demographics (age, sex, comorbidities) time from symptom onset to diagnosis and treatment initiation, initial threshold, treatment details (type, dosage, duration), adverse effects, audiometry at diagnosis and at the end of treatment, follow-up duration, and pure-tone average. Results A total of 72 patients were included, with a mean follow-up duration of four months. Comorbidities such as type 2 diabetes mellitus, hypertension, and hypertriglyceridemia were observed in a significant portion of patients. However, these conditions and the use of salvage therapy and adjuvant drugs did not impact hearing recovery. A longer delay from symptom onset to medical attention was associated with a lower gain in decibels (p=0.307). Diabetic patients who received steroid treatment showed a significant gain of at least 15 dB, indicating the greatest benefit in this subgroup. Conclusions Adjuvant drugs may be unnecessary and ineffective in treating SSNHL. Metabolic disorders may be linked to the development of SSNHL. Steroid treatment is the only effective therapeutic option for improving hearing recovery in diabetic patients. Early initiation of treatment after symptom onset is crucial for maximizing auditory recovery.

Patternable Process-Induced Strain in 2D Monolayers and Heterobilayers.

Strain engineering in two-dimensional (2D) materials is a powerful but difficult to control approach to tailor material properties. Across applications, there is a need for device-compatible techniques to design strain within 2D materials. This work explores how process-induced strain engineering, commonly used by the semiconductor industry to enhance transistor performance, can be used to pattern complex strain profiles in monolayer MoS2 and 2D heterostructures. A traction-separation model is identified to predict strain profiles and extract the interfacial traction coefficient of 1.3 ± 0.7 MPa/μm and the damage initiation threshold of 16 ± 5 nm. This work demonstrates the utility to (1) spatially pattern the optical band gap with a tuning rate of 91 ± 1 meV/% strain and (2) induce interlayer heterostrain in MoS2-WSe2 heterobilayers. These results provide a CMOS-compatible approach to design complex strain patterns in 2D materials with important applications in 2D heterogeneous integration into CMOS technologies, moiré engineering, and confining quantum systems.

Experimental Study on the Pre-Peak Mechanical and Seepage Characteristics of Granite

The Sanshandao Gold Mine is currently in the deep mining stage. The ground pressure on the surrounding rocks is gradually becoming more considerable, and at the same time, threatened by the overlying seawater, the possibility of mine water inrush accidents is increasing. In this study, the MTS815 rock triaxial seepage test system was employed for the triaxial compression testing and stress–seepage coupled testing of granite under different confining pressures. The results show that granite’s pre-peak mechanical evolution under different confining pressures is divided into four stages (the crack closure stage, linear elasticity stage, stable crack expansion stage, and unstable crack expansion stage). With the increase in the confining pressure, the crack initiation threshold, crack damage threshold, and peak threshold gradually increased, but the closure threshold had no corresponding change. Moreover, in the loading process, the permeability curve first decreased and then increased, and the confining pressure suppressed the peak permeability of granite. Finally, based on the test results, stress sensitivity analysis was carried out, and it was found that polynomials fit the relationship between permeability and effective stress better. Granite’s permeability showed strong stress sensitivity at medium confining pressures. The stress sensitivity of the permeability of granite decreased with increasing effective stress at medium and high confining pressures, while it tended to increase at low confining pressures.

Prediction Model for Safe Operation of Pumping Stations Optimized by the Sparrow Search Algorithm and BP Neural Network

The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations.

Nivolumab in patients (pts) with advanced gastrointestinal (GI) cancers with high plasma tumor mutational burden (pTMB): Results from a SCRUM-Japan GOZILA phase II trial.

98 Background: TMB is a biomarker for immune checkpoint inhibitors (ICIs). Analysis of circulating tumor DNA (ctDNA) has the potential to non-invasively identify pts likely to benefit from ICIs by assessing pTMB. We conducted a phase II basket trial as part of the SCRUM-Japan GOZILA umbrella/basket trial to evaluate the efficacy and safety of nivolumab monotherapy in pts with advanced GI cancers with high pTMB. Methods: Eligibility criteria included histologically confirmed unresectable or recurrent GI cancers; ECOG PS of 0 or 1; refractory or intolerant to the standard therapies; and high pTMB identified by Guardant360 CDx, a 74-gene ctDNA assay. Pts received intravenous nivolumab monotherapy of 360 mg every 3 weeks until progressive disease. The primary endpoint was objective response rate (ORR) per investigator assessment. The pTMB score was calculated by adjustment of mutation count by tumor fraction, and initial pTMB thresholds were determined based on previously reported ORR for ICI treatment for each GI cancer in the cohort. The cohort 2 proceeded only for esophageal cancer, and an ultra-high TMB cohort was created to include pts in the top 2% for pTMB scores. Results: Fifty-one pts with high pTMB in the cohort 1/2 (n=32) and the ultra-high TMB cohort (n=19) were enrolled. The median duration of follow-up was 7.5 months (range, 0.9–51.3). Deficient mismatch repair (dMMR) or high microsatellite instability (MSI-H) was observed in 1 pt in cohort 1/2 and 2 in ultra-high TMB cohort. The ORR was 12.5% and 21.1%, with median duration of response of 14.0 months and not reached, respectively. In an exploratory analysis of the overall population, pts with esophageal cancer had the highest ORR of 33.3% (4/12), followed by pancreatic (1/6, 16.7%) and colorectal cancer (3/27, 11.1%). Of 2 pts with proficient MMR/non-MSI-H colorectal cancer who had a response, 1 had a POLE mutation. Pts with response tended to have higher pTMB score (median, 26 vs. 8). dMMR/MSI-H (HR=0.16; 95% CI, 0.02–1.18), PD-L1 CPS ≥1 (HR=0.55; 95% CI, 0.29–1.01), and absence of liver metastasis (HR=0.36; 95% CI, 1.48–5.14) were significantly associated with longer progression-free survival. Treatment-related grade 3–4 adverse events occurred in 3 pts (9%) in the cohort 1/2 and 3 (16%) in the ultra-high TMB cohort, with no treatment death in either cohort. Conclusions: Nivolumab demonstrated antitumor activity in pretreated pts with advanced GI cancer and high pTMB. These results suggest that the use of pTMB potentially identify patients who may benefit from ICI treatment. Clinical trial information: UMIN000033182 .

A simplified decision feedback Chebyshev function link neural network with intelligent initialization for underwater acoustic channel equalization

IntroductionIn shallow-water environments, the reliability of underwater communication links is often compromised by significant multipath effects. Some equalization techniques such as decision feedback equalizer, and deep neural network equalizer suffer from slow convergence and high computational complexity.MethodsTo address this challenge, this paper proposes a simplified decision feedback Chebyshev function link neural network equalizer (SDF-CFLNNE). The structure of the SDF-CFLNNE employs Chebyshev polynomial function expansion modules to directly and non-linearly transform the input signals into the output layer, without the inclusion of hidden layers. Additionally, it feeds the decision signal back to the input layer rather than the function expansion module, which significantly reduces computational complexity. Considering that, in the training phase of neural networks, the random initialization of weights and biases can substantially impact the training process and the ultimate performance of the network, this paper proposes a chaotic sparrow search algorithm combining the osprey optimization algorithm and Cauchy mutation (OCCSSA) to optimize the initial weights and thresholds of the proposed equalizer. The OCCSSA utilizes the Piecewise chaotic population initialization and combines the exploration strategy of the ospreywith the Cauchy mutation strategy to enhance both global and local search capabilities. RseultsSimulations were conducted using underwater multipath signals generated by the Bellhop Acoustic Toolbox. The results demonstrate that the performance of the SDFCFLNNE initialized by OCCSSA surpasses that of CFLNN-based and traditional nonlinear equalizers, with a notable improvement of 2-6 dB in terms of signal-to-noise ratio at a bit error rate (BER) of 10−4 and a reduced mean square error (MSE). Furthermore, the effectiveness of the proposed equalizer was validated using the lake experimental data, demonstrating lower BER and MSE with improved stability. DiscussionThis underscores thepromise of employing the SDFCFLNNE initialized by OCCSSA as a promising solution to enhance the robustness of underwater communication in challenging environments.

Online monitoring system of material moisture content based on the Kalman filter fusion algorithm in air-impingement dryer

A Kalman filter fusion algorithm was proposed, and an online monitoring system was developed for real-time monitoring of the moisture content of materials in an air-impingement dryer. The Kalman filter algorithm was used to estimate the optimal state of the original detection values of the weighting sensor and air velocity sensor. A backpropagation (BP) neural network fusion model was established, where the weight detection value, elastic substrate temperature, air velocity, and impingement distance were considered inputs and the real weight of the material was the output. The optimal topology of the BP neural network was selected, and the initial weights and thresholds of the BP neural network were optimized using a genetic algorithm. The coefficient of determination (R2) and root mean square error (RMSE) of the optimized BP neural network fusion model were 0.9995 and 4.9, respectively. The Kalman filter fusion algorithm, which can realize online monitoring of moisture content, was established using the Kalman filter algorithm and fusion model. Moreover, an online monitoring system for material moisture content was developed, validation experiments were carried out, and the R2 and RMSE of the nine sets of validation experiments were 0.9963 and 0.78, respectively. The monitoring system satisfied the requirements of material moisture content detection accuracy in the drying process. The developed monitoring system is greatly important for improving the automation level of the drying equipment for fruits and vegetables. The proposed Kalman filter fusion algorithm also provides a reference for other multifactor fusion detection.

Treatment outcomes for idiopathic sudden sensorineural hearing loss in dialysis patients

Idiopathic sudden sensorineural hearing loss (ISSNHL) is challenging for both nephrologists and otolaryngologists treating patients undergoing dialysis. This single-center, retrospective, observational study investigated the treatment outcomes of patients with ISSNHL undergoing dialysis, enrolling 700 patients (47 undergoing and 653 not undergoing dialysis) diagnosed with ISSNHL between January 2005 and December 2021 at Asan Medical Center, Republic of Korea. To balance pre-existing clinical characteristics, 1:5 propensity score matching (PSM) was performed with the patients who were not undergoing dialysis. Treatment included high-dose systemic steroid therapy or intra-tympanic steroid injections. The pure tone average of the groups was compared before and 2 weeks and 2 months after treatment. The hearing-improvement degree was evaluated using Siegel's criteria. Before PSM, age, prevalence of diabetes or hypertension, initial hearing threshold at each frequency level (0.5, 1, 2, and 4 kHz), and treatment strategies exhibited significant between-group differences. However, in the PS-matched cohort, none of the confounders showed significant between-group differences. Two months after steroid treatment, the non-dialysis patient group demonstrated significantly higher average improvement in pure tone audiometry (P = 0.029) and greater percentage of complete response according to Siegel's criteria. This study suggests that treatment outcomes for ISSNHL are significantly poorer for patients undergoing than for those not undergoing dialysis.

Hounsfield Units as an Independent Predictor of Failed Percutaneous Drainage of Spinal Tuberculosis Paraspinal Abscess Under Computed Tomography Guidance.

To investigate the value of Hounsfield units (HUs) as an independent predictor of failed percutaneous drainage of spinal tuberculosis paraspinal abscess under computed tomography (CT) guidance. A retrospective analysis was conducted on 61 patients who underwent CT-guided percutaneous drainage for spinal tuberculosis paraspinal abscess between October 2017 and October 2020. Preoperative CT scans were used to measure the HUs of the abscess. Patients were categorized into successful drainage (n = 49) and failed drainage (n = 12) groups. Statistical analysis involved independent sample t-tests and chi-square tests to compare between the 2 groups. Binary logistic regression was performed to identify independent predictive factors for drainage failure. Receiver operating characteristic (ROC) curves were employed to ascertain risk factor thresholds and diagnostic performance. Among the patients, 49 experienced successful drainage while 12 faced drainage failure. The mean HUs of abscesses in the failed drainage group were significantly higher than those in the successful drainage group (p < 0.001). ROC analysis revealed an area under the curve of 0.897 (95% confidence interval, 0.808-0.986) for predicting drainage failure based on HUs. The optimal HU cutoff value for predicting drainage failure was 22.3, with a sensitivity of 91.7% and specificity of 69.4%. HUs are an independent predictor of failed percutaneous drainage of spinal tuberculosis paraspinal abscess under CT guidance. The HU value of 22.3 can be used as an initial screening threshold for predicting the success or failure of drainage.

Application of QGA-BP Neural Network in Debt Risk Assessment of Government Platforms

How to correctly understand the existence of local government debt, study its risk classification and impact, give full play to the “dual nature” of debt with a full-caliber indicator system, and avoid debt risks to the greatest extent. That is the research direction of this article. In order to improve the accuracy and efficiency of risk assessment and effectively reduce the debt risk of government platform companies, a risk assessment method based on optimized back-propagation (BP) neural network is proposed. First, the method uses quantum genetic algorithm (quantum genetic algorithm, QGA) to adjust and determine the initial weight and threshold of BP neural network and realize the optimization of BP neural network model parameter setting. Then, the QGA-BP debt risk assessment of government platforms is verified that it performs well in the debt risk prediction of government platform companies, and its prediction accuracy and prediction speed are improved.

Study on the Relationship between Crack Initiation and Crack Bifurcation in Walnut Shells Based on Energy

Clarifying the dissipated energy required for crack expansion is an effective way to control material crushing. Therefore, based on the material fracture probability model and fractal theory, the energy range required for crack extension was determined, and the morphology of the cracks was quantified. This study investigates the influence of walnut size on crack propagation characteristics; this includes its effects on the crack initiation threshold energy, representing resistance to crack initiation, and the crack bifurcation threshold energy, representing resistance to crack bifurcation. The results show that crack extension has a well-defined threshold energy below which cracks do not initiate or bifurcate. The size of walnuts significantly impacts crack propagation characteristics, showing that both crack initiation threshold energy and crack bifurcation threshold energy decrease with increasing walnut sizes. In addition, there is a positive correlation function between the crack initiation threshold energy and the crack bifurcation threshold energy. The experimental results can offer fresh insights into material fracture prediction and serve as a reference for numerical simulations.