Assessment of early factors for identification or prediction severe acute pancreatitis in pregnancy

BACKGROUND Acute pancreatitis in pregnancy(APIP)is a rare and serious condition,and severe APIP(SAPIP)can lead to pancreatic necrosis,abscess,multiple organ dysfunction,and other adverse maternal and infant outcomes.Therefore,early identification or prediction of SAPIP is important.AIM To assess factors for early identification or prediction of SAPIP.METHODS The clinical data of patients with APIP were retrospectively analyzed.Patients were classified with mild acute pancreatitis or severe acute pancreatitis,and the clinical characteristics and laboratory biochemical indexes were compared between the two groups.Logical regression and receiver operating characteristic curve analyses were performed to assess the efficacy of the factors for identification or prediction of SAPIP.RESULTS A total of 45 APIP patients were enrolled.Compared with the mild acute pancreatitis group,the severe acute pancreatitis group had significantly increased(P<0.01)heart rate(HR),hemoglobin,neutrophil ratio(NEUT%),and neutrophil–lymphocyte ratio(NLR),while lymphocytes were significantly decreased(P<0.01).Logical regression analysis showed that HR,NEUT%,NLR,and lymphocyte count differed significantly(P<0.01)between the groups.These may be factors for early identification or prediction of SAPIP.The area under the curve of HR,NEUT%,NLR,and lymphocyte count in the receiver operating characteristic curve analysis was 0.748,0.732,0.821,and 0.774,respectively.The combined analysis showed that the area under the curve,sensitivity,and specificity were 0.869,90.5%,and 70.8%,respectively.CONCLUSION HR,NEUT%,NLR,and lymphocyte count can be used for early identification or prediction of SAPIP,and the combination of the four factors is expected to improve identification or prediction of SAPIP.

P-SV wave elastic impedance and fluid identification factor in weakly anisotropic media

The P-SV wave reflection coefficients in VTI and HTI media were obtained by approximation of the Jilek（2002a and b）equation in orthotropic anisotropic media.An approximate equation for P-SV wave elastic impedance can be derived from the combination of the new coefficients with S-wave elastic impedance（Duffaut et al.,2000）.On this basis, the fluid identification factor in weakly anisotropic media was constructed and used to identify the Castagna and Smith（1994）lithologic combination and achieved good results. Finally,we specifically analyzed the anisotropic parameter impacts P-SV wave elastic impedance and fluid factor trends.

Impact factors identification of spatial heterogeneity of herbaceous plant diversity on five southern islands of Miaodao Archipelago in North China

Herbaceous plants are widely distributed on islands and where they exhibit spatial heterogeneity.Accurately identifying the impact factors that drive spatial heterogeneity can reveal typical island biodiversity patterns.Five southern islands in the Miaodao Archipelago,North China were studied herein.The spatial distribution of herbaceous plant diversity on these islands was analyzed,and the impact factors and their degree of impact on spatial heterogeneity were identified using CCA ordination and ANOVA.The results reveal 114 herbaceous plant species,belonging to 94 genera from 34 families in the50 plots sampled.The total species numbers on different islands were significantly positively correlated with island area,and the average a diversity was correlated with human activities,while the(3 diversity among islands was more affected by island area than mutual distances.Spatial heterogeneity within islands indicated that the diversities were generally high in areas with higher altitude,slope,total nitrogen,total carbon,and canopy density,and lower moisture content,pH,total phosphorus,total potassium,and aspect.Among the environmental factors,pH,canopy density,total K,total P,moisture content,altitude,and slope had significant gross effects,but only canopy density exhibited a significant net effect.Terrain affected diversity by restricting plantation,plantation in turn influenced soil properties and the two together affected diversity.Therefore,plantation was ultimately the fundamental driving factor for spatial heterogeneity in herbaceous plant diversity on the five islands.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Seroprevalence of Bartonella henselae and Identification of Risk Factors in China

Serum samples were tested for Bartonella henselae Ig G antibodies using indirect immunofluorescence assays.We then analyzed associated risk factors.Serum samples were considered positive when reactive at a dilution of more than 1:320.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

Model identification with BPNN on restrictive ecological factors of SRB for sulfate-reduction

The model of back-propagation neural network (BPNN) was presented to demonstrate the effect of restrictive ecological factors, COD/SO 4 2- ratio, pH value, alkalinity (ALK) and SO 4 2- loading rate (Ns), on sulfate reduction of Sulfate Reducing Bacteria (SRB) in an acidogenic sulfate reducing reactor supplied with molasses as sole organic carbon source and sodium sulfate as electron acceptor. The compare of experimental results and computer simulation was also discussed. It was shown that the method of BPNN had a powerful ability to analyze the ecological characteristic of acidogenic sulfate reducing ecosystem quantitatively.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

Characteristics of Shale Reservoir and Sweet Spot Identification of the Lower Cambrian Niutitang Formation in Northwestern Hunan Province, China

The accumulation and productivity of shale gas are mainly controlled by the characteristics of shale reservoirs;study of these characteristics forms the basis for the shale gas exploitation of the Lower Cambrian Niutitang Formation(Fm),Southern China.In this study,core observation and lithology study were conducted along with X-ray diffraction(XRD)and electronic scanning microscopy(SEM)examinations and liquid nitrogen(N2)adsorption/desorption and CH4 isothermal adsorption experiments for several exploration wells in northwestern Hunan Province,China.The results show that one or two intervals with high-quality source rocks(TOC>2 wt%)were deposited in the deep-shelf facies.The source rocks,which were mainly composed of carbonaceous shales and siliceous shales,had high quartz contents(>40 wt%)and low clay mineral(<30 wt%,mainly illites)and carbonate mineral(<20 wt%)contents.The SEM observations and liquid nitrogen(N2)adsorption/desorption experiments showed that the shale is tight,and nanoscale pores and microscale fractures are well developed.BJH volume(VBJH)of shale ranged from 2.144×10^-3 to 20.07×10^-3 cm^3/g,with an average of 11.752×10^-3 cm3/g.Pores mainly consisted of opened and interconnected mesopores(2–50 nm in diameter)or macropores(>50 nm in diameter).The shale reservoir has strong adsorption capacity for CH4.The Langmuir volume(VL)varied from 1.63 to 7.39 cm^3/g,with an average of 3.95 cm^3/g.The characteristics of shale reservoir are controlled by several factors:(1)A deep muddy continental shelf is the most favorable environment for the development of shale reservoirs,which is controlled by the development of basic materials.(2)The storage capacity of the shale reservoir is positively related to the TOC contents and plastic minerals and negatively related to cement minerals.(3)High maturity or overmaturity leads to the growth of organic pores and microfractures,thereby improving the reservoir storage capacity.It can be deduced that the high percentage of residual gas in Niutitang Fm results from the strong reservoir storage capacity of adsorbed gas.Two layers of sweet spots with strong storage capacity of free gas,and they are characterized by the relatively high TOC contents ranging from 4 wt%to 8 wt%.

Blind Channel Identification for Cyclic-Prefixed MIMO-OFDM Systems with Virtual Carriers

This paper applies the repetition index scheme(RIS)to the channel identification of cyclic prefixed(CP)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)systems with virtual carriers(VCs)in the environment of the number of receive antennas being no less than that of transmit antennas.The VCs will cause a rank deficiency problem in computing the subspace information.With the subcarrier mapping matrix,the received signal is simplified to remove the rank deficiency.We use the RIS scheme to generate many times of equivalent symbols so the channel identification can converge with few received OFDM blocks.The RIS scheme will convert the white noise into non-white noise.With the Cholesky factorization,a noise whitening technique is developed to turn the non-white noise back to white noise.We further analyze the necessary conditions of identifiability of channel estimation.Simulations are performed to show the superiority of the proposed method.

Data fusion of radar and IFF for aircraft identification

The problem of identification of friend-or-foe aircraft in the actual application condition is addressed.A hybrid algorithm combining fuzzy neutral network with probability factor（FNNP）,multi-level fuzzy comprehensive evaluation and the DempsterShafer（D-S） theory is proposed.This hybrid algorithm constructs a complete process from generating the fuzzy database to the final identification,realizes the identification of friend-or-foe automatically if the training samples or expert’s experience can be obtained,and reduces the effect of uncertainties in the process of identification.At the same time,the whole algorithm can update the identification result with the augment of observations.The performance of the proposed algorithm is assessed by simulations.Results show that the proposed algorithm can successfully deduce the aircraft’s identity even if the observations have measurement errors.

A Novel Augmented UD Identification with Resetting Strategy

Augmented UD identification (AUDI) technique is derived from the traditional recursive least squares algorithm and had been developed rapidly during last decade. However, as the identification process evolves, AUDI algorithm falls easily into identification saturation, which means that AUDI algorithm cannot respond to time varying system parameters unless a set of very strong identification signals is utilized or a long identification period is occupied. To overcome such a difficulty, a novel resetting AUDI (RAUDI) strategy is advanced by resetting the augmented information matrix based on MF (Monitor Function) monitoring the conspicuous change of process parameters. The numeric experiment demonstrates that the RAUDI has a good performance in estimation of rapid parameter changes.

Identification Model for Needy Undergraduates Based on FFM

In recent years, as the enrollment rate of Chinese colleges has increased year by year, the identification of needy undergraduates has become increasingly important. However, the traditional way to identify college students with financial difficulties mainly relies on manual review and collective voting, which easily causes subjectivity and randomness. To alleviate the problem above, this paper establishes an automatic identification model for needy undergraduates based on the 1842 questionnaires collected from undergraduates in WHUT. Firstly, this paper filters the questionnaire preliminary using the local outlier factor algorithm. Secondly, this paper combines mutual information, Spearman rank correlation coefficient and distance correlation coefficient by rank-sum ratio to select features for eliminating noise from irrelevant features. Thirdly, this paper trains filed-aware factor machine model and compares it with other models, such as Logistic Regression, SVM, etc. Eventually, this paper finds that filed-aware factor machine performers much better than other models in the identification of needy undergraduates, and prominent features affecting the identification of needy undergraduates are the year of the family income, cost of living provided parents, etc.

Structural Damage Identification via Pseudo Strain Energy Density and Wavelet Packet Transform

Based on strain signals, a new time-domain methodology for detecting the beam local damage has been developed. The pseudo strain energy density （PSED） is defined and used to build two major damage indexes, the average pseudo strain energy density （APSED） and the average pseudo strain energy density rate （APSEDR）. Probability and mathematical statistics are utilized to derive a standardized damage index. Furthermore, by applying the analytic relation between the strain energy release rate and the stress intensity factor, an analytic solution of crack depth is derived. For the dynamic strain signals, the wavelet packet transform is used to pre-process measured data. Finally, a numerical simulation indicates that this method can effectively identify the damage location and its absolute severity.

The Quantitative Identification of Regional Mine and Natural Earthquakes and Its Application

Based on the Morlet wavelet transform and digital data from the Fushun and Beizhen seismic stations, Liaoning Province, we put forward a new method in the paper, called time-frequency energy attenuation factor （α-value）. The characteristics of the α-value and its variation with magnitude of natural and mine earthquakes are studied, and the statistic relations between the α-value and specific earthquake magnitude are obtained. From the results, some conclusions can be drawn as follows： （1） in general, the α-values of mine and natural earthquakes of the same intensity have obvious difference and the ranges of their variation do not overlap each other; （2） the α-value decreases with the increase of earthquake magnitude, and the α-value of mine earthquake decreases faster than that of natural earthquake; （3） based on the earthquake magnitude and on the relations between the α-value and earthquake magnitude, we can distinguish the mine earthquakes from the natural ones; （4） the difference in focal mechanism of mine and natural earthquakes would be the main cause for obvious difference of the α-value; （5） the α-value variation is relatively steady for mine and natural earthquakes that occur in the same region, but it has obvious regional difference. The above results are of inspirational meaning for the study of abnormal change of the α-value before strong earthquakes.

Investigation on Pathogenic Factors of Tobacco Root-Knot Nematode Disease in Gengma County,Yunnan Province

Tobacco root-knot nematode disease has caused severe damage in Geng- ma County, Yunnan Province. In order to identify the pathogenic factors of the tobac- co root-knot nematode disease in this county, the pathogenic nematodes, hosts and environment of tobacco fields in Mengsa, Hepai and Sipaishan 3 main tobacco-grow- ing towns in Gengma County were investigated and analyzed based on the local re- lated field survey on tobacco root-knot nematode disease in this county in 2012. The results showed the incidence and severity of the tobacco root-knot nematode disease were all higher than those of previous years. dominant pathogens of the tobacco root-knot The species identification showed the nematode disease were Meloidogyne arenaria and M. javanica in Gengma County. The lacking of disease-resistant culti- vars, poor management and climatic anomaly were the main causes of the tobacco root-knot nematode disease in Gengma. According to the occurrence characteristics of the disease, the agricultural prevention-based control measures were proposed.

A methodological approach for weighting factors in land suitability assessment: a tool for facilitating spatial planning

The increasing demand for infrastructural facilities in hill areas needs efficient spatial planning at the local level through land suitability assessment,which is influenced by a number of factors. There has been little systematic empirical work for identification and evaluation of the factors affecting spatial planning decisions in hill areas. The present study focuses on identifying the critical factors for land suitability assessment at the local level in hill areas and determining their percentage influence, which has been done in two stages. In the first stage, the comprehensive list of 21 factors from the reported literature was prepared which was further condensed to the critical factors. In the second stage, the percentage influence of the critical factors was calculated by analyzing the experts’ opinions collected through a questionnaire survey. Analytic Hierarchy Process(AHP) was used for the analysis of the questionnaire for weighting the critical factors. The slope was identified with the highest weight followed by aspect, surface runoff, elevation, and vegetation,whereas groundwater table, existing utilities,accessibility, soil type, and land use have comparatively less weights. The results of the study were used for the identification of locations for builtup facilities of an educational campus located in hill areas of Himachal Pradesh, India.

Hazardous and Harmful Factors on Construction Sites and Their Prevention

In this paper, the status quo of safety production on construction sites and the hazardous and harmful factors in the construction process were analyzed, and corresponding suggestions were proposed to make up for the shortcomings of the safety production assurance system standards on construction sites.

冲击地压主控因素及孕灾机制

随着我国煤矿逐步向深部开采转移,冲击地压灾害日趋严重。在冲击地压从机理认知走向防冲工程进程中,首要的任务是厘清冲击地压孕灾主控因素,并进行风险程度判识。在上百个冲击地压矿井致灾评价分析基础上,提出了导致冲击地压发生的4类客观主控因素:煤岩冲击倾向性、开采深度、坚硬顶板、地质构造,以及3类人为主控因素:煤柱、采空区及采掘卸荷,并对各主控因素对冲击地压孕灾的力学机制进行了分析讨论。在客观主控因素方面,煤岩冲击倾向性是煤岩积聚变形能进而诱发冲击破坏的自身属性;开采深度与巷道围岩内积聚的变形能呈正相关关系,是冲击地压发生的必要条件;坚硬顶板大尺度周期断裂形成的冲击动载及动能是诱发冲击地压的“导火索”;地质构造对冲击地压的影响显著,对于断层构造而言,断层两盘将在开采扰动造成的突发卸荷影响下产生相对“回弹”;煤层变薄区等效弹性模量变大,超前支承压力呈“双峰值”分布,使得冲击影响范围扩大。在人为主控因素方面,煤柱作为高应力集中区,其尺寸、倾角及相对位置等将直接影响冲击地压发生的概率和强度;采空区会诱发超前支承压力集中区内围岩积聚弹性能的突然释放,尤其是在采高较大、顶板垮落不充分的情况下;采掘卸荷会导致应力集中区快速“迁移”,引发煤体内弹性应变能的大量释放,是造成冲击地压的重要外因条件。在此基础上,对山东新汶、山东鲁西、内蒙古鄂尔多斯、陕西彬长、新疆和甘肃等矿区冲击地压孕灾主控因素差异性,进行了对比分析,强调按层次对矿井、采区和工作面进行冲击地压主控因素及其影响程度判识重要性,构建了从降能、释能、阻能到抗能的冲击地压治理工程路径。

基于DEMATEL-AISM的铁路工程建设风险识别影响因素与优化策略研究

铁路工程建设大型化、复杂化和系统化的发展趋势下,潜在的风险因素日益繁多,铁路工程建设高质量和可持续发展之路充满挑战。风险识别作为风险管理的首要环节,是应对和控制铁路工程建设风险的关键,受到项目内外部多方面因素的影响,且现有实践中没有系统的指导,导致风险识别工作易存在疏漏。基于此,通过文献研究和因子分析,提取铁路工程建设风险识别的核心影响因素,运用决策与试验评价实验室和解释结构模型的方法,构建对抗多级递阶结构模型(DEMATEL-AISM),计算铁路工程建设风险识别各影响因素的重要程度及其耦合关系。研究结果表明:铁路工程建设风险识别的效果受到直接层、间接层和根源层3层因素共同作用,其中,危险源监测分析、人员风险意识、风险调查措施、风险应对能力、铁路建设目标、预期风险后果和风险识别技术是影响风险识别结果的关键因素,在铁路工程建设风险管理工作的开展中应重点关注和控制。基于模型结果,分析风险识别影响因素的层级结构及因果关系,并从风险内部控制、项目一体化风险管理体系构建和动态风险预警机制设立3个方面,提出了铁路工程建设风险识别的建议和策略。研究结果有助于提高大型铁路工程建设风险辨识结果的全面性和科学性,对构建铁路工程建设风险识别体系具有一定参考价值和借鉴意义。