A prediction model for large vessel occlusion in suspected stroke patients based on available prehospital information

Objective:Early and accurate identification of large vessel occlusion(LVO)acute ischemic stroke(AIS)patients is critically important for stroke management.Practicable scales with simple items can facilitate prehospital paramedics distinguishing LVO-AIS patients with high efficiency and help to avoid unnecessary and costly delays.The current study aims to develop a screening tool to predict AIS-LVO patients based on prehospital available data.Method:A total of 251 suspected stroke patients who were transported to the emergency department of our hospital via emergency medical services were consecutively enrolled from August,2020 to January,2022.Data including demographic information,medical history,clinical manifestations,and vital signs were collected.A multivariate logistic regression model was developed based on statistically significant variables selected from univariate analysis.Result:Forty-two patients(16.7%)were diagnosed as LVO-AIS based on imaging validation at admission.A comprehensive model was developed with past medical history factors such as atrial fibrillation and coronary heart disease,vital signs such as systolic blood pressure,and prominent symptoms and signs such as gaze palsy,facial paralysis,and dysarthria.The model showed better diagnostic performance in terms of area under the receiver operating characteristic curves(0.884,95%CI,0.830-0.939),which was higher than other common prehospital prediction scales such as the Face,Arm,Speech,Time test(FAST),the Field Assessment Stroke Triage for Emergency Destination(FAST-ED)scale,and the Gaze-Face-Arm-Speech-Time test(G-FAST).Calibration curve analysis,decision curve analysis,and clinical impact curve analysis further validated the reliability,net benefit,and potential clinical impact of the prediction model,respectively.Conclusion:We conducted a prediction model based on prehospital accessible factors including past history of atrial fibrillation and coronary heart disease,systolic blood pressure,and signs such as gaze palsy,facial palsy,and dysarthria.The prediction model showed good diagnostic power and accuracy for identification of the high-risk patients with LVO and may become an effective tool for the LVO recognition in prehospital settings.Future studies are warranted to refine and validate the model further in order to enhance the accuracy and objectivity of clinical judgments.

Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model

Based on the high-resolution Regional Ocean Modeling System(ROMS) and the conditional nonlinear optimal perturbation(CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander(LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error(denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely,CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction.The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors.

Trend Prediction Method Based on the Largest Lyapunov Exponent for Large Rotating Machine Equipments

In order to predict electromechanical equipments＇ nonlinear and non-stationary condition effectively, max Lyapunov exponent is introduced to the fault trend prediction of large rotating mechanical equipments based on chaos theory. The predict method of chaos time series and two methods of proposing f and F are dis- cussed. The arithmetic of max prediction time of chaos time series is provided. Aiming at the key part of large rotating mechanical equipments-bearing, used this prediction method the simulation experiment is carried out. The result shows that this method has excellent performance for condition trend prediction.

A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

As the critical equipment,large axial-flow fan(LAF)is used widely in highway tunnels for ventilating.Note that any malfunction of LAF can cause severe consequences for traffic.Specifically,fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault.Thus,the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance(or noise).In order to overcome this problem,a novel early fault judgment method to predict the operation trend is proposed in this paper.The vibration-electric information fusion,the support vector machine(SVM)with particle swarm optimization(PSO),and the cross-validation(CV)for predicting LAF operation states are proposed and discussed.Finally,the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

CLIMATE PREDICTION EXPERIMENT FOR TROPICAL CYCLONE GENESIS FREQUENCY USING THE LARGE-SCALE CIRCULATION FORECAST BY A COUPLED GLOBAL CIRCULATION MODEL

Based on an analysis of the relationship between the tropical cyclone genesis frequency and large-scale circulation anomaly in NCEP reanalysis, large-scale atmosphere circulation information forecast by the JAMSTEC SINTEX-F coupled model is used to build a statistical model to predict the cyclogenesis frequency over the South China Sea and the western North Pacific. The SINTEX-F coupled model has relatively good prediction skill for some circulation features associated with the cyclogenesis frequency including sea level pressure, wind vertical shear, Intertropical Convergence Zone and cross-equatorial air flows. Predictors derived from these large-scale circulations have good relationships with the cyclogenesis frequency over the South China Sea and the western North Pacific. A multivariate linear regression(MLR) model is further designed using these predictors. This model shows good prediction skill with the anomaly correlation coefficient reaching, based on the cross validation, 0.71 between the observed and predicted cyclogenesis frequency. However, it also shows relatively large prediction errors in extreme tropical cyclone years(1994 and 1998, for example).

Quantitative Prediction of Concentrated Regions of Large and Superlarge Deposits in China

Identification and quantitative prediction of large and superlarge mineral deposits of solid mineral resources using the mineral resource prediction theory and method with comprehensive information is carried out nationwide in China at a scale of 1∶5 000 000. Using deposit concentrated regions as the model units and concentrated mineralization anomaly regions as prediction units, the prediction is performed on GIS platform. The technical route and research method of locating large and superlarge mineral deposits and principle of compiling attribute table of independent variables and functional variables are proposed. Upon methodology study, the qualitative locating and quantitative predicting mineral deposits are carried out with quantitative theory Ⅲ and characteristic analysis, respectively, and the advantage and disadvantage of two methods are discussed. This research is significant for mineral resource prediction in ten provinces of western China.

Prediction of methane emissions during the extraction of close-to-roof layer of a seam of large thickness

Prediction of methane emissions at the stage of longwall planning constitutes the basis for the determination of the appropriate method and parameters of ventilation and selection of prevention means including the methane drainage technol- ogy. The growth of methane saturation of coal seams with the extraction depth, with simultaneously increasing output concen- tration, contributes to the increase of the quantity of methane emitted into longwall areas. The subject matter of the article has been directed at the predicted quantity of methane emissions into planned longwalls with roof caving in the layer of seams adjacent to the roof of large thickness. The performed prognostic calculations of methane emissions into the longwall working were referred to two sources, i.e. methane liberated during coal mining by means of a cutter-loader and methane originating from the degasification of the floor layer destressed by the longwall conducted in the close-to-roof layer. The calculations of predictions allow to refer to the planned longwall, on account of the emitting methane, with possible and safe output quantity. Planning of extraction in the close-to-roof layer of a seam of large thickness with roof caving is especially important in con- ditions of increasing methane saturation with the depth of deposition and should be preceded by a prognostic analysis for de- termining the extraction possibilities of the planned longwall.

Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander （KLM） state using the conditional nonlinear optimal parameter perturbation （CNOP-P） method. Because of their relatively large uncertainties, three model parameters were considered： the interracial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 （SPG2） algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation （CNOP-I） method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.

Spatial-temporal dynamics of bacterioplankton communities in the breeding area of large yellow croaker Larimichthys crocea in Sansha Bay,China

As an important spawning ground for large yellow croaker Larimichthys crocea,Sansha Bay,South China Sea has been a research hotspot.However,studies on the influence of the bacterioplankton community and assessments of its seasonal succession of bacterioplankton in different sea areas in Sansha Bay are still limited.To address the issue,we use 16S rRNA gene amplicon sequencing and functional prediction to investigate the spatial-temporal dynamics of the bacterioplankton community in three distinct areas,i.e.,Breeding Area(BA),Yantian Harbor(YH),and Bay Margin(BM)of Sansha Bay.Results show that the structure of the bacterioplankton community in Sansha Bay had a significant seasonal succession.Moreover,the representative zero-radius Operation Taxon Units in different seasons were significantly different among the three selected sea areas.Specifically,during the breeding season,bacterioplankton communities in BA were characterized by compound-degrading bacteria,such as Rhodococcus and Owenweeksia,while in YH and BM,animal parasites or symbionts such as Vibrio and Arcobacter were dominant.Furthermore,the redundancy analysis and Spearman correlation analysis further explained that water temperature,dissolved oxygen,and ammonia nitrogen were the main environmental factors responsible for the difference.In addition,the bioindicator functions screened by Functional Annotation of Prokaryotic Taxa and random forest machine learning mainly relied on compound degradation,nitrite oxidation,and photoheterotrophy.The compound-degradationcorresponded bacterioplankton genera such as Rhodococcus had relatively higher abundance in BM,while Nitrospina corresponding to nitrite oxidation tended to be abundant in YH and BA.Based on the spatial and temporal variation in the composition and function of bacterioplankton,our findings provide a basis for understanding the theory of bacterioplankton community structure in the inner-bay habitat of the large yellow croaker in Sansha Bay.

LLM4CP:Adapting Large Language Models for Channel Prediction

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

Anatomical changes in the somatosensory system after large sensory loss predict strategies to promote functional recovery after spinal cord injury

Among cases of spinal cord injury are injuries involving the dorsal column in the cervical spinal cord that interrupt the major cutaneous afferents from the hand to the cuneate nucleus（Cu）in the brainstem.Deprivation of touch and proprioceptive inputs consequently impair skilled hand use.

Use of subsequent PET/CT in diffuse large B-cell lymphoma patients in complete remission following primary therapy

Interim 18F-fluorodeoxyglucose(FDG) positron emission tomography/computed tomography(I-PET/CT) is a powerful tool for monitoring the response to therapy in diffuse large B-cell lymphoma(DLBCL). This retrospective study aimed to determine when and how to use I-PET/CT in DLBCL. A total of 197 patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone(R-CHOP) were enrolled between October 2005 and July 2011; PET/CT was performed at the time of diagnosis(PET/CT0), after 2 and 4 cycles of chemotherapy(PET/CT2 and PET/CT4, respectively), and at the end of treatment(F-PET/CT). According to the International Harmonization Project for Response Criteria in Lymphoma, 110 patients had negative PET/CT2 scans, and 87 had positive PET/CT2 scans. The PET/CT2-negative patients had significantly higher 3-year progression-free survival rate(75.8% vs. 38.2%) and 3-year overall survival rate(93.5% vs. 55.6%) than PET/CT2-positive patients. All PET/CT2-negative patients remained negative at PET/CT4, but 3 were positive at F-PET/CT. Among the 87 PET/CT2-positive patients, 57 remained positive at F-PET/CT, and 32 progressed during chemotherapy(15 at PET/CT4 and 17 at F-PET/CT). Comparing PET/CT4 with PET/CT0, 7 patients exhibited progression, and 8 achieved partial remission. Comparing F-PET/CT with PET/CT0, 10 patients exhibited progression, and 7 achieved partial remission. In conclusion, our results indicate that I-PET/CT should be performed after 2 rather than 4 cycles of immunochemotherapy in DLBCL patients. There is a limited role for subsequent PET/CT in the detection of relapse in PET/CT2-negative patients, but repeat PET/CT is required if the PET/CT2 findings are positive.

Definition,prediction,prevention and management of patients with severe ischemic stroke and large infarction

Severe ischemic stroke carries a high rate of disability and death.The severity of stroke is often assessed by the degree of neurological deficits or the extent of brain infarct,defined as severe stroke and large infarction,respectively.Critically severe stroke is a life-threatening condition that requires neurocritical care or neurosurgical intervention,which includes stroke with malignant brain edema,a leading cause of death during the acute phase,and stroke with severe complications of other vital systems.Early prediction of high-risk patients with critically severe stroke would inform early prevention and treatment to interrupt the malignant course to fatal status.Selected patients with severe stroke could benefit from intravenous thrombolysis and endovascular treatment in improving functional outcome.There is insufficient evidence to inform dual antiplatelet therapy and the timing of anticoagulation initiation after severe stroke.Decompressive hemicraniectomy(DHC)<48 h improves survival in patients aged<60 years with large hemispheric infarction.Studies are ongoing to provide evidence to inform more precise prediction of malignant brain edema,optimal indications for acute reperfusion therapies and neurosurgery,and the individualized management of complications and secondary prevention.We present an evidence-based review for severe ischemic stroke,with the aims of proposing operational definitions,emphasizing the importance of early prediction and prevention of the evolution to critically severe status,summarizing specialized treatment for severe stroke,and proposing directions for future research.

The Study of Medium- and Short-term Prediction for Artux Earthquake (M_S=6.9) and Usunan Earthquake (M_S=5.8)

In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic risk regions are judged based on long- and medium-term seismic risk regions and annual seismic risk regions determined by national seismologic analysis, combined with large seismic situation analysis. We trace and analyze the seismic situation in large areas, and judge principal risk regions or belts of seismic activity in a year, by integrating the large area’s seismicity with geodetic deformation evolutional characteristics. As much as possible using information, we study synthetically observational information for long-medium- and short-term (time domain) and large-medium -small dimensions (space domain), and approach the forecast region of forthcoming earthquakes from the large to small magnitude. A better effect has been obtained. Some questions about earthquake prediction are discussed.

五种院前卒中筛查量表对院内急性大血管闭塞性缺血性卒中的预测价值

目的探讨并分析5种院前卒中筛查量表对院内急性大血管闭塞性缺血性卒中的预测价值。方法回顾性纳入2015年1月—2022年12月,因非卒中相关疾病收入东台市人民医院并在住院期间因疑似卒中症状激活院内卒中绿色通道的所有患者。采用ROC曲线评估筛查量表对院内急性大血管闭塞性缺血性卒中的预测价值。5种筛查量表分别为卒中现场评估和分类转运量表(field assessment stroke triage for emergency destination,FAST-ED)、快速动脉闭塞评估量表(rapid arterial occlusion evaluation,RACE)、洛杉矶运动量表(Los Angeles motor scale,LAMS)、辛辛那提院前卒中严重程度量表(Cincinnati prehospital stroke severity scale,CPSSS)和院前急性卒中严重程度量表(prehospital acute stroke severity scale,PASS)。结果本研究共纳入174例患者,其中54例(31.0%)患者诊断为急性大血管闭塞性缺血性卒中。RACE(AUC 0.888,95%CI 0.849~0.928)和LAMS(AUC 0.859,95%CI 0.812~0.905)对急性大血管闭塞性缺血性卒中的预测效能最高,其次是FAST-ED(AUC 0.820,95%CI 0.768~0.872)、CPSSS(AUC 0.810,95%CI 0.756~0.863)和PASS(AUC 0.786,95%CI 0.727~0.844),三者之间差异无统计学意义。结论院前卒中筛查量表在预测院内急性大血管闭塞性缺血性卒中方面均显示出良好的预测能力,其中RACE和LAMS的预测效能最高。

2018年浙江梅雨降水异常偏少的大尺度环流特征及前兆信号分析

2018年浙江梅雨降水异常偏少,梅雨期平均环流特征欧亚中高纬度为“两槽一脊型”,西太平洋副热带高压(以下简称西太副高)偏东偏北,东亚高空急流偏北,浙江以南缺少低空急流的支持,不利于形成持续降水。6月上、中旬水汽输送不足、南北气流辐合较弱、东亚高空急流偏南、南海夏季风偏强、印度季风偏弱是梅雨开始偏晚的重要原因。梅汛期主要形成了三次降水过程(过程Ⅰ、过程Ⅱ和过程Ⅲ)和一次降水间歇过程,过程Ⅰ和过程Ⅱ表现为南北气流辐合型降水,过程Ⅲ为台风降水。系统分析了与不同过程相对应的大尺度环流及其演变特征,发现由于冷空气活动偏弱、南海夏季风偏强、西南水汽输送偏弱,过程Ⅰ的强度强于过程Ⅱ。在环流分析基础上,进一步挖掘与浙江梅雨有密切关联的海洋、大气和陆面信号,发现对该年梅雨异常偏弱有重要指示意义的前兆信号为冬、春季Ni?o关键区海温指数为正异常且北太平洋中部海温呈现负异常、冬季西南印度洋海温为正异常、春季喀拉海-巴伦支海海冰偏少、4-5月南半球环状模指数和北极涛动指数分别处于正位相和负位相。基于浙江梅雨序列,依托相关系数和同号率两个指标筛选出从前冬到春季稳定维持或有所增强的气候信号,利用多元线性回归、多因子综合判别、联合诊断三种方法分别构造可冬季发布和春季发布的梅雨预测模型,发现线性模型整体上能够较好地预测梅雨降水距平,特别是对降水偏少情形指示意义突出。

大跨度桥梁钢桥面板跨尺度疲劳损伤评估方法

大跨度斜拉桥正交异性钢桥面板的顶板与纵肋焊接构造细节在车辆荷载作用下易产生疲劳损伤进而导致服役性能降低、影响行车安全。为评估大跨度桥梁钢桥面板的疲劳性能,提出基于细观损伤力学的大跨度钢桥疲劳损伤跨尺度评估方法;推导了基于细观损伤力学的钢桥面板疲劳损伤演化模型,在此基础上,结合实测交通数据,实现了基于Monte-Carlo法的随机车流模拟;最后,将提出的方法应用于一座大跨度三塔斜拉桥。研究结果表明,大跨度斜拉桥钢桥面板体系焊缝周围区域的累积疲劳损伤程度明显高于桥面板体系的其他部位;顶板与纵肋焊接构造细节的疲劳损伤累积呈现明显的非线性,预测的疲劳寿命远小于Miner线性疲劳损伤累积准则的结果。

量化未来与预测科学的前景

目前的社会科学研究多集中在过去和现在的时间向度,且研究重心往往集中在描述和解释两个方面。在大模型等人工智能技术的辅助之下,通用人工智能时代将快速到来,世界可能会进入一种“智能失重”的状态。关于未来的研究,文学想象和哲学思辨虽非常重要,但还远远不够,思考如何将实证方法引入未来研究至关重要。量化未来就是用定量的思路来测量未来,具体可以在实验方法和结构估计两方面实现突破。量化未来的可能性议题主要体现在对通用人工智能的应对上。通用人工智能到来之后,超风险社会似乎不可避免。有效调速主义是一种理想方案,而量化未来则可被视为有效调速主义的一种操作性版本。最终,人们可能会在这种对未来相对精准的把握中实现一种有助于决策的预测科学。

基于深度学习的超大直径盾构姿态预测研究

传统的盾构姿态纠偏措施多是在盾构机实际轴线已经偏离设计轴线之后采取的被动控制措施,具有一定的滞后性,而盾构姿态纠偏不及时会给施工过程和完成后的隧道本身带来严重的危害。为了准确预测盾构姿态偏差,为提前纠偏提供决策支持,本文依托江阴—靖江长江隧道超大直径盾构施工项目,提出一种基于CNN-EMD-LSTM的深度学习模型,该模型既能捕捉时间序列的维度特征和时变特征,又能提高盾构姿态数据分解重构方法的预测精度;通过消融实验对CNN-EMD-LSTM模型中每个部分的重要性进行探讨,并对CNN-EMD-LSTM模型在不同窗口长度、不同滑动步长下的预测效果进行对比。研究结果表明:CNN-EMD-LSTM模型对超大直径盾构姿态的预测效果较好;可以通过调节不同推进区间的压力进行盾构姿态纠偏;CNN-EMD-LSTM模型中各个部分按重要性从大到小排序依次为EMD、CNN、LSTM;窗口长度过大或过小都会增大模型预测误差,而滑动步长越小模型的预测效果越好。

复杂大电网非预设性运行工况下的继电保护动作适应性仿真分析

非预设性运行工况下的突发事件多,导致继电保护逻辑的适应性较差,不合理区域较多,为此,进行复杂大电网非预设性运行工况下继电保护动作适应性仿真分析。分析历史常规故障,标记故障点最常发生位置,构建零序、负序方向元件模型、距离元件模型、纵联电流差动元件模型,观察复杂大电网非预设性运行工况下的电网参数变化情况,第一时间掌握电网的耦合性,不断选择电网所需的继电保护动作逻辑,确认继电保护动作适应性。仿真实验表明,将故障点设置在保护区域内和区域外,继电保护动作能够根据故障选择不同保护模型进行故障母线切除或者保护不动作,适应性好。