Combining Serum Procalcitonin Level,Thromboelastography,and Platelet Count to Predict Short-term Development of Septic Shock in Intensive Care Unit

Objective Despite the recent advances in diagnosis and treatment,sepsis continues to lead to high morbidity and mortality.Early diagnosis and prompt treatment are essential to save lives.However,most biomarkers can only help to diagnose sepsis,but cannot predict the development of septic shock in high-risk patients.The present study determined whether the combined measurement of procalcitonin(PCT),thromboelastography(TEG)and platelet(PLT)count can predict the development of septic shock.Methods A retrospective study was conducted on 175 septic patients who were admitted to the intensive care unit between January 2017 and February 2021.These patients were divided into two groups:73 patients who developed septic shock were assigned to the septic shock group,while the remaining 102 patients were assigned to the sepsis group.Then,the demographic,clinical and laboratory data were recorded,and the predictive values of PCT,TEG and PLT count for the development of septic shock were analyzed.Results Compared to the sepsis group,the septic shock group had statistically lower PLT count and TEG measurements in the R value,K value,αangle,maximum amplitude,and coagulation index,but had longer prothrombin time(DT),longer activated partial thromboplastin time(APTT),and higher PCT levels.Furthermore,the Sequential Organ Failure Assessment(SOFA)score was higher in the septic shock group.The multivariate logistic regression analysis revealed that PCT,TEG and PLT count were associated with the development of septic shock.The area under the curve analysis revealed that the combined measurement of PCT,TEG and PLT count can be used to predict the development of septic shock with higher accuracy,when compared to individual measurements.Conclusion The combined measurement of PCT,TEG and PLT count is a novel approach to predict the development of septic shock in high-risk patients.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Demonstrating grating-based phase-contrast imaging of laser-driven shock waves

Single-shot X-ray phase-contrast imaging is used to take high-resolution images of laser-driven strong shock waves.Employing a two-grating Talbot interferometer,we successfully acquire standard absorption,differential phase-contrast,and dark-field images of the shocked target.Good agreement is demonstrated between experimental data and the results of two-dimensional radiation hydrodynamics simulations of the laser-plasma interaction.The main sources of image noise are identified through a thorough assessment of the interferometer’s performance.The acquired images demonstrate that grating-based phase-contrast imaging is a powerful diagnostic tool for high-energy-density science.In addition,we make a novel attempt at using the dark-field image as a signal modality of Talbot interferometry to identify the microstructure of a foam target.

Strong shock propagation for the finite-source circular blast in a confined domain

The circular explosion wave produced by the abrupt discharge of gas from a high-temperature heat source serves as a crucial model for addressing explosion phenomena in compressible flow.The reflection of the primary shock and its propagation within a confined domain are studied both theoretically and numerically in this research.Under the assumption of strong shock,the scaling law governing propagation of the main shock is proposed.The dimensionless frequency of reflected shock propagation is associated with the confined distance.The numerical simulation for the circular explosion problem in a confined domain is performed for validation.Under the influence of confinement,the principal shock wave systematically undergoes reflection within the domain until it weakens,leading to the non-monotonic attenuation of kinetic energy in the explosion fireball and periodic oscillations of the fireball volume with a certain frequency.The simulation results indicate that the frequency of kinetic energy attenuation and the volume oscillation of the explosive fireball align consistently with the scaling law.

Dynamic convergent shock compression initiated by return current in high-intensity laser-solid interactions

We investigate the dynamics of convergent shock compression in solid cylindrical targets irradiated by an ultrafast relativistic laser pulse.Our particle-in-cell simulations and coupled hydrodynamic simulations reveal that the compression process is initiated by both magnetic pressure and surface ablation associated with a strong transient surface return current with density of the order of 10^(17) A/m^(2) and lifetime of 100 fs.The results show that the dominant compression mechanism is governed by the plasma β,i.e.,the ratio of thermal pressure to magnetic pressure.For targets with small radius and low atomic number Z,the magnetic pressure is the dominant shock compression mechanism.According to a scaling law,as the target radius and Z increase,the surface ablation pressure becomes the main mechanism generating convergent shocks.Furthermore,an indirect experimental indication of shocked hydrogen compression is provided by optical shadowgraphy measurements of the evolution of the plasma expansion diameter.The results presented here provide a novel basis for the generation of extremely high pressures exceeding Gbar(100 TPa)to enable the investigation of high-pressure physics using femtosecond J-level laser pulses,offering an alternative to nanosecond kJ-laser pulse-driven and pulsed power Z-pinch compression methods.

A rat model of multicompartmental traumatic injury and hemorrhagic shock induces bone marrow dysfunction and profound anemia

Background:Severe trauma is associated with systemic inflammation and organ dysfunction.Preclinical rodent trauma models are the mainstay of postinjury research but have been criticized for not fully replicating severe human trauma.The aim of this study was to create a rat model of multicompartmental injury which recreates profound traumatic injury.Methods:Male Sprague-Dawley rats were subjected to unilateral lung contusion and hemorrhagic shock(LCHS),multicompartmental polytrauma(PT)(unilateral lung contusion,hemorrhagic shock,cecectomy,bifemoral pseudofracture),or na?ve controls.Weight,plasma toll-l ike receptor 4(TLR4),hemoglobin,spleen to body weight ratio,bone marrow(BM)erythroid progenitor(CFU-GEMM,BFU-E,and CFU-E)growth,plasma granulocyte colony-stimulating factor(G-CSF)and right lung histologic injury were assessed on day 7,with significance defined as p values<0.05(*).Results:Polytrauma resulted in markedly more profound inhibition of weight gain compared to LCHS(p=0.0002)along with elevated plasma TLR4(p<0.0001),lower hemoglobin(p<0.0001),and enlarged spleen to body weight ratios(p=0.004).Both LCHS and PT demonstrated suppression of CFU-E and BFU-E growth compared to naive(p<0.03,p<0.01).Plasma G-CSF was elevated in PT compared to both na?ve and LCHS(p<0.0001,p=0.02).LCHS and PT demonstrated significant histologic right lung injury with poor alveolar wall integrity and interstitial edema.Conclusions:Multicompartmental injury as described here establishes a reproducible model of multicompartmental injury with worsened anemia,splenic tissue enlargement,weight loss,and increased inflammatory activity compared to a less severe model.This may serve as a more effective model to recreate profound traumatic injury to replicate the human inflammatory response postinjury.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

Experimental and numerical study on protective effect of RC blast wall against air shock wave

Prototype experiments were carried out on the explosion-proof performance of the RC blast wall.The mass of TNT detonated in the experiments is 5 kg and 20 kg respectively.The shock wave overpressure was tested in different regions.The above experiments were numerically simulated,and the simulated shock wave overpressure waveforms were compared with that tested and given by CONWEP program.The results show that the numerically simulated waveform is slightly different from the test waveform,but similar to CONWEP waveform.Through dimensional analysis and numerical simulation under different working conditions,the equation for the attenuation rate of the diffraction overpressure behind the blast wall was obtained.According to the corresponding standards,the degree of casualties and the damage degree of the brick concrete building at a certain distance behind the wall can be determined when parameters are set.The above results can provide a reference for the design and construction of the reinforced concrete blast wall.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Effect of the interval between two shocks on ejecta formation from the grooved aluminum surface

This work focuses on the effect of the interval between two shocks on the ejecta formation from the grooved aluminum(Al_(1100))surface by using smoothed particle hydrodynamics numerical simulation.Two unsupported shocks are obtained by the plate-impact between sample and two flyers at interval,with a peak pressure of approximately 30 GPa for each shock.When the shock interval varies from 2.11 to 7.67 times the groove depth,the bubble velocity reduces to a constant,and the micro jetting factor R_(J) from spike to bubble exhibits a non-monotonic change that decreases initially and then increases.At a shock interval of 3.6 times the groove depth,micro jetting factor R_(J) from spike to bubble reaches its minimum value of approximately 0.6.While,the micro jetting factor R_(F) from spike to free surface decreases linearly at first,and stabilizes around 0.25 once the shock interval surpasses 4.18 times the groove depth.When the shock interval is less than 4.18 times the groove depth,the unloading wave generated by the breakout of the first shock wave is superimpose with the unloading part of the second shock wave to form a large tensile area.

Evolution of molecular structure of TATB under shock loading from transient Raman spectroscopic technique

By combination of the transient Raman spectroscopic measurement and the density functional theoretical calculations,the structural evolution and stability of TATB under shock compression was investigated.Due to the improvement in synchronization control between two-stage light gas gun and the transient Raman spectra acquisition,as well as the sample preparation,the Raman peak of the N-O mode of TATB was firstly observed under shock pressure up to 13.6 GPa,noticeably higher than the upper limit of 8.5 GPa reported in available literatures.By taking into account of the continuous shift of the main peak and other observed Raman peaks,we did not distinguish any structural transition or any new species.Moreover,both the present Raman spectra and the time-resolved radiation of TATB during shock loading showed that TATB exhibits higher chemical stability than previous declaration.To reveal the detailed structural response and evolution of TATB under compression,the density functional theoretical calculations were conducted,and it was found that the pressure make N-O bond lengths shorter,nitro bond angles larger,and intermolecular and intra-molecular hydrogen bond interactions enhanced.The observed red shift of Raman peak was ascribed to the abnormal enhancement of H-bound effect on the scissor vibration mode of the nitro group.

Propagation Properties of Shock Waves in Polyurethane Foam based on Atomistic Simulations

Porous materials are widely used in the field of protection because of their excellent energy absorption characteristics.In this work,a series of polyurethane microscopic models are established and the effect of porosity on the shock waves is studied with classical molecular dynamics simulations.Firstly,shock Hugoniot relations for different porosities are obtained,which compare well with the experimental data.The pores collapse and form local stress wave,which results in the complex multi-wave structure of the shock wave.The microstructure analysis shows that the local stress increases and the local velocity decreases gradually during the process of pore collapse to complete compaction.Finally,it leads to stress relaxation and velocity homogenization.The shock stress peaks can be fitted with two exponential functions,and the amplitude of attenuation coefficient decreases with the increase of density.Besides,the pore collapse under shock or non-shock are discussed by the entropy increase rate of the system.The energy is dissipated mainly through the multiple interactions of the waves under shock.The energy is dissipated mainly by the friction between atoms under non-shock.

STABILITY OF TRANSONIC SHOCKS TO THE EULER-POISSON SYSTEM WITH VARYING BACKGROUND CHARGES

This paper is devoted to studying the stability of transonic shock solutions to the Euler-Poisson system in a one-dimensional nozzle of finite length.The background charge in the Poisson equation is a piecewise constant function.The structural stability of the steady transonic shock solution is obtained by the monotonicity argument.Furthermore,this transonic shock is proved to be dynamically and exponentially stable with respect to small perturbations of the initial data.One of the crucial ingredients of the analysis is to establish the global well-posedness of a free boundary problem for a quasilinear second order equation with nonlinear boundary conditions.

Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Accurate origin–destination(OD)demand prediction is crucial for the efficient operation and management of urban rail transit(URT)systems,particularly during a pandemic.However,this task faces several limitations,including real-time availability,sparsity,and high-dimensionality issues,and the impact of the pandemic.Consequently,this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network(PAG-STAN)for metro OD demand prediction under pandemic conditions.Specifically,PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices.Subsequently,a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices.Thereafter,PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic.Finally,a masked physics-guided loss function(MPG-loss function)incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability.PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios,highlighting its robustness and sensitivity for metro OD demand prediction.A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

Early peripheral perfusion index predicts 28-day outcome in patients with septic shock

BACKGROUND:To investigate the prognostic value of the peripheral perfusion index(PPI)in patients with septic shock.METHODS:This prospective cohort study,conducted at the emergency intensive care unit of Peking University People's Hospital,recruited 200 patients with septic shock between January 2023 and August 2023.These patients were divided into survival(n=84)and death(n=116)groups based on 28-day outcomes.Clinical evaluations included laboratory tests and clinical scores,with lactate and PPI values assessed upon admission to the emergency room and at 6 h and 12 h after admission.Risk factors associated with mortality were analyzed using univariate and multivariate Cox regression analyses.Receiver operator characteristic(ROC)curve was used to assess predictive performance.Mortality rates were compared,and Kaplan-Meier survival plots were created.RESULTS:Compared to the survival group,patients in the death group were older and had more severe liver damage and coagulation dysfunction,necessitating higher norepinephrine doses and increased fl uid replacement.Higher lactate levels and lower PPI levels at 0 h,6 h,and 12 h were observed in the death group.Multivariate Cox regression identifi ed prolonged prothrombin time(PT),decreased 6-h PPI and 12-h PPI as independent risk factors for death.The area under the curves for 6-h PPI and 12-h PPI were 0.802(95%CI 0.742-0.863,P<0.001)and 0.945(95%CI 0.915-0.974,P<0.001),respectively,which were superior to Glasgow Coma Scale(GCS),Sequential Organ Failure Assessment(SOFA)scores(0.864 and 0.928).Cumulative mortality in the low PPI groups at 6 h and 12 h was signifi cantly higher than in the high PPI groups(6-h PPI:77.52%vs.22.54%;12-h PPI:92.04%vs.13.79%,P<0.001).CONCLUSION:PPI may have value in predicting 28-day mortality in patients with septic shock.

Impacts of Comorbidity and Mental Shock on Organic Micropollutants in Surface Water During and After the First Wave of COVID-19 Pandemic in Wuhan (2019–2021), China

The first pandemic wave of coronavirus disease 2019(COVID-19)induced a considerable increase in several antivirals and antibiotics in surface water.The common symptoms of COVID-19 are viral and bacterial infections,while comorbidities(e.g.,hypertension and diabetes)and mental shock(e.g.,insomnia and anxiety)are nonnegligible.Nevertheless,little is known about the long-term impacts of comorbidities and mental shock on organic micropollutants(OMPs)in surface waters.Herein,we monitored 114 OMPs in surface water and wastewater treatment plants(WWTPs)in Wuhan,China,between 2019 and 2021.The pandemic-induced OMP pollution in surface water was confirmed by significant increases in 26 OMP concentrations.Significant increases in four antihypertensives and one diabetic drug suggest that the treatment of comorbidities may induce OMP pollution.Notably,cotinine(a metabolite of nicotine)increased 155 times to 187 ngL1,which might be associated with increased smoking.Additionally,the increases in zolpidem and sulpiride might be the result of worsened insomnia and depression.Hence,it is reasonable to note that mental-health protecting drugs/behavior also contributed to OMP pollution.Among the observed OMPs,telmisartan,lopinavir,and ritonavir were associated with significantly higher ecological risks because of their limited WWTP-removal rate and high ecotoxicity.This study provides new insights into the effects of comorbidities and mental shock on OMPs in surface water during a pandemic and highlights the need to monitor the fate of related pharmaceuticals in the aquatic environment and to improve their removal efficiencies in WWTPs。

Short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function

BACKGROUND Hepatectomy is the first choice for treating liver cancer.However,inflammatory factors,released in response to pain stimulation,may suppress perioperative immune function and affect the prognosis of patients undergoing hepatectomies.AIM To determine the short-term efficacy of microwave ablation in the treatment of liver cancer and its effect on immune function.METHODS Clinical data from patients with liver cancer admitted to Suzhou Ninth People’s Hospital from January 2020 to December 2023 were retrospectively analyzed.Thirty-five patients underwent laparoscopic hepatectomy for liver cancer(liver cancer resection group)and 35 patients underwent medical image-guided microwave ablation(liver cancer ablation group).The short-term efficacy,complications,liver function,and immune function indices before and after treatment were compared between the two groups.RESULTS One month after treatment,19 patients experienced complete remission(CR),8 patients experienced partial remission(PR),6 patients experienced stable disease(SD),and 2 patients experienced disease progression(PD)in the liver cancer resection group.In the liver cancer ablation group,21 patients experienced CR,9 patients experienced PR,3 patients experienced SD,and 2 patients experienced PD.No significant differences in efficacy and complications were detected between the liver cancer ablation and liver cancer resection groups(P>0.05).After treatment,total bilirubin(41.24±7.35 vs 49.18±8.64μmol/L,P<0.001),alanine aminotransferase(30.85±6.23 vs 42.32±7.56 U/L,P<0.001),CD4+(43.95±5.72 vs 35.27±5.56,P<0.001),CD8+(20.38±3.91 vs 22.75±4.62,P<0.001),and CD4+/CD8+(2.16±0.39 vs 1.55±0.32,P<0.001)were significantly different between the liver cancer ablation and liver cancer resection groups.CONCLUSION The short-term efficacy and safety of microwave ablation and laparoscopic surgery for the treatment of liver cancer are similar,but liver function recovers quickly after microwave ablation,and microwave ablation may enhance immune function.

Thermally-induced cracking behaviors of coal reservoirs subjected to cryogenic liquid nitrogen shock

The benefits of using cryogenic liquid nitrogen shock to enhance coal permeability have been confirmed from experimental perspectives.In this paper,we develop a fully coupled thermo-elastic model in combination with the strain-based isotropic damage theory to uncover the cooling-dominated cracking behaviors through three typical cases,i.e.coal reservoirs containing a wellbore,a primary fracture,and a natural fracture network,respectively.The progressive cracking processes,from thermal fracture initiation,propagation or cessation,deflection,bifurcation to multi-fracture interactions,can be well captured by the numerical model.It is observed that two hierarchical levels of thermal fractures are formed,in which the number of shorter thermal fractures consistently exceeds that of the longer ones.The effects of coal properties related to thermal stress levels and thermal diffusivity on the fracture morphology are quantified by the fracture fractal dimension and the statistical fracture number.The induced fracture morphology is most sensitive to changes in the elastic modulus and thermal expansion coefficient,both of which dominate the complexity of the fracture networks.Coal reservoir candidates with preferred thermal-mechanical properties are also recommended for improving the stimulation effect.Further findings are that there exists a critical injection temperature and a critical in-situ stress difference,above which no thermal fractures would be formed.Preexisting natural fractures with higher density and preferred orientations are also essential for the formation of complex fracture networks.The obtained results can provide some theoretical support for cryogenic fracturing design in coal reservoirs.

Transformer-based correction scheme for short-term bus load prediction in holidays

To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduce complexity and capture inherent characteristics more effectively.Gated residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data.Ultimately,a pre-trained structure is incorporated to reduce computational complexity.Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is outstanding.Meanwhile,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.