Characteristic Index Research Articles

Phenotypic characteristics of the mycelium of Pleurotus geesteranus using image recognition technology.

Phenotypic analysis has significant potential for aiding breeding efforts. However, there is a notable lack of studies utilizing phenotypic analysis in the field of edible fungi. Pleurotus geesteranus is a lucrative edible fungus with significant market demand and substantial industrial output, and early-stage phenotypic analysis of Pleurotus geesteranus is imperative during its breeding process. This study utilizes image recognition technology to investigate the phenotypic features of the mycelium of P. geesteranus. We aim to establish the relations between these phenotypic characteristics and mycelial quality. Four groups of mycelia, namely, the non-degraded and degraded mycelium and the 5th and 14th subcultures, are used as image sources. Two categories of phenotypic metrics, outline and texture, are quantitatively calculated and analyzed. In the outline features of the mycelium, five indexes, namely, mycelial perimeter, radius, area, growth rate, and change speed, are proposed to demonstrate mycelial growth. In the texture features of the mycelium, five indexes, namely, mycelial coverage, roundness, groove depth, density, and density change, are studied to analyze the phenotypic characteristics of the mycelium. Moreover, we also compared the cellulase and laccase activities of the mycelium and found that cellulase level was consistent with the phenotypic indices of the mycelium, which further verified the accuracy of digital image processing technology in analyzing the phenotypic characteristics of the mycelium. The results indicate that there are significant differences in these 10 phenotypic characteristic indices ( ), elucidating a close relationship between phenotypic characteristics and mycelial quality. This conclusion facilitates rapid and accurate strain selection in the early breeding stage of P. geesteranus.

Study of hooked fiber in cotton yarn based on tracer fiber method

PurposeThrough the tracer fiber method, we strive to more accurately obtain the hook degree, straightening degree, percentage and other characteristic indexes. In order to intuitively represent the hook state from sliver to yarn, and feed back production information in combination with quality test.Design/methodology/approachTaking the cotton fiber as an example, the hooked fibers were studied by using the tracer fiber method. Tracer fibers were made from cotton-type viscose fibers. Tracer fibers and combed cotton fibers were uniformly mixed for many times and used to produce the card sliver, semi-drawn sliver, drawn sliver, roving and yarn. With the help of ZF-20D ultraviolet analyzer, geometric parameters of hooked fibers were measured, and characterization indexes were calculated. And hook indexes and quality indexes were compared.FindingsBy redefining and reclassifying hooked fibers, the change of hooked fibers in the process was tracked and characterized carefully. Some hooks in card sliver are straightened but not eliminated, and will form longer zero-angled hooks in the subsequent process. The straightening degree and number of zero-angled hooks affect the evenness CV mainly.Originality/valueThe characterization of hooked fibers is important for reducing hooked fibers and spinning high quality yarns. There is no uniform standard for the characterization of hooked fibers at present. Most studies are about relationship between process and hook in carding and drawing. There is no research on hooked fibers in the whole spinning process. In the paper, hooked fibers were redefined and reclassified, the change of hooked fibers in the process was tracked and characterized carefully.

Influence of O2 on structural evolution of biochar in oxidative pyrolysis of cellulose

Oxidative pyrolysis could mitigate energy requirement in endothermic pyrolysis, but evolution of pyrolytic products might be impacted in oxidative atomosphere. This was investigated herein at 300–600 °C in a carrier gas of 11 vol% of oxygen with cellulose as a research object for the pyrolysis, as cellulose-derived aliphatic structures are prone to oxidization. The results indicated that O2 reacted with organics on cellulose/biochar even from 300 °C, decreasing biochar production while enhancing bio-oil formation. Oxidation of the organics on the biochar increased CO/CC ratio but did not increase oxygen content, as formation of the oxygen-containing aliphatic structures and their elimination via cracking took place in parallel, which in overall formed the biochar of higher C/O and C/H ratios, but not necessarily higher thermal stability. The abundance of the thermally unstable aliphatic structures was responsible for this, but it was also the reason for the improved comprehensive combustion characteristic index and combustion kinetics. Additionally, pyrolysis at 600 °C in presence of O2 formed the biochar with a specific surface area of 426.2 m2/g (mainly micropores), which was significantly higher than that of counterpart in nitrogen (28.9 m2/g). The in-situ IR characterization of the pyrolysis suggested that O2 favored formation of oxygen-containing species such as -OH, CO of varied forms, *CO2, olefinic CC but not aromatic CC.

Microgrid F36ault Detection Method Based on Lightweight Gradient Boosting Machine–Neural Network Combined Modeling

The intelligent architecture based on the microgrid (MG) system enhances distributed energy access through an effective line network. However, the increased paths between power sources and loads complicate the system’s topology. This complexity leads to multidirectional line currents, heightening the risk of current loops, imbalances, and potential short-circuit faults. To address these challenges, this study proposes a new approach to accurately locate and identify faults based on MG lines. Initially, characteristic indices such as fault voltage, voltage fundamentals at each MG measurement point, and extracted features like peak voltage values in specific frequency bands, phase-to-phase voltage differences, and the sixth harmonic components are utilized as model inputs. Subsequently, these features are classified using the Lightweight Gradient Boosting Machine (LightGBM), complemented by the bagging (Bootstrap Aggregating) ensemble learning algorithm to consolidate multiple strong LightGBM classifiers in parallel. The output classification results of the integrated model are then fed into a neural network (NN) for further training and learning for fault-type identification and localization. In addition, a Shapley value analysis is introduced to quantify the contribution of each feature and visualize the fault diagnosis decision-making process. A comparative analysis with existing methodologies demonstrates that the LightGBM-NN model not only improves fault detection accuracy but also exhibits greater resilience against noise interference. The introduction of the bagging method, by training multiple base models on the initial classification subset of LightGBM and aggregating their prediction results, can reduce the model variance and prevent overfitting, thus improving the stability and accuracy of fault detection in the combined model and making the interpretation of the Shapley value more stable and reliable. The introduction of the Shapley value analysis helps to quantify the contribution of each feature to improve the transparency and understanding of the combined model’s troubleshooting decision-making process, reduces the model’s subsequent collection of data from different line operations, further optimizes the collection of line feature samples, and ensures the model’s effectiveness and adaptability.

To Evaluate the Degradation of Heat Damaged Concrete Specimens after Curing Using Non-Linear Ultrasound Technique

In this research, Non-linear Ultrasound (NLU) technique was used to evaluate the degradation on heat damaged cement mortar specimen behaving explicit nonlinear. Mortars of Portland cement type I (or normal cement, NC) were chosen to mold the specimens with water cement ratios of 0.4, 0.5 and 0.6, and size of 5x5x5cm-cube and 5x5x2cm-brick. The specimens were grouped for heating at different temperatures (30, 200, 300, 400, 600, 800℃ for 24 hours) to create degradations of different levels. After curing process in water for 28 days, mechanical property tests were carried out on each cube to get its stress-strain relation and strength. NLU tests were conducted on the brick specimens. The RITEC SNAP System with 250kHz/transmit and 500kHz/receive transducers was used to conduct the measurement of harmonics. The obtained amplitudes in spectrum of fundamental, 2nd and 3rd harmonics (A1, A2 and A3) and the amplitudes due to the background nonlinearity by facility (A01, A02 and A03) were analyzed to approach the nonlinear parameters for each specimen, such as the β and γ. The [(A01- A1),(A03/A 3 2 01 -A3/A13)] and[(A01-A1), |A02/A01 -A2/A12|] were applied as a nonlinear characteristic index to mark the quality of the microstructure of material, which could be related to the durability and workability of the material. The results showed a promised quantification method using NLU. Extended application can be expected.

Remote sensing monitoring and potential distribution analysis of Spartina alterniflora in coastal zone of Guangxi.

In recent years, the continuous expansion of Spartina alterniflora (S. alterniflora) has caused serious damage to coastal wetland ecosystem. Mapping the coverage of S. alterniflora by remote sensing and analyzing its growth pattern pose great importance in controlling the expansion and maintaining the biodiversity of coastal wetlands in Guangxi. This study aimed to use harmonic regression to fit time series data of vegetation indices based on Landsat images, and the phenological features were extracted as the input of random forest model to distinguish S. alterniflora in coastal zone of Guangxi from 2009 to 2020. The influence of natural environmental factors on the distribution of S. alterniflora was evaluated by Maxent model, and the potential distribution was analyzed. The results showed that: (1) Based on the time series data of characteristic indices fitted by harmonic regression, the extraction of phenological features of S. alterniflora identification effect exhibited high accuracy (in the result of 2009, Overall Accuracy [OA] = 97.33%, Kappa = 0.95). (2) During 2009-2020, the S. alterniflora in coastal zone of Guangxi kept proliferating and expanding from east to west. The total area of S. alterniflora continued to increase while the growth rate showed a trend that increased first and then decreased. (3) The Maxent model shows good accuracy in simulating the habitat of S. alterniflora, with a potential distribution area of 14,303.39 hm2. The findings will be beneficial to the understanding of dynamic changes of S. alterniflora in coastal zone of Guangxi and provide a scientific reference for other coastal wetland studies on S. alterniflora expansion.

Effectiveness of homogenized nursing care intervention in postoperative bladder irrigation of urological system in primary hospitals-Taking the incidence of postoperative complications as an example: A retrospective study.

To explore the effect of homogenized nursing interventions for bladder irrigation after urological surgery in primary hospitals, and to further analyze the incidence of postoperative complications according to the different nursing methods. The experiment selected 110 patients with urological disorders who underwent bladder irrigation, and the subjects were admitted to the hospital from January 2020 to October 2023. The subjects were chronologically divided into a control group and an experimental group, with the admission time of the control group being between between January 2020 and October 2022. The experimental group was admitted between October 2022 and October 2023. Conventional nursing care and homogenized nursing care were used respectively, and the intervention effect of homogenized nursing care was assessed by comparing the patients' psychological state indexes, vital characteristic indexes, and the incidence of various postoperative complications under the 2 interventions. None of the indicators were statistically significant before the nursing intervention. In the comparison of nursing satisfaction and clinical comfort, the experimental group's nursing satisfaction was 94.5% and clinical comfort was 90.9%, which was significantly higher than that of the control group. In the comparison of the incidence of postoperative complications, the incidence of urinary retention in the experimental group was 1.82%, which was significantly lower than that of the control group. In addition, the total incidence of complications in the experimental group was 1.82%, which proved that the homogenized nursing intervention was effective in reducing the complications after urinary bladder irrigation. In multifactorial logistic regression analysis, after homogenized nursing intervention. The relative risks of the 4 complications were 1.836, 1.445, 1.993, and 2.138, respectively, which were significantly lower than those of conventional nursing intervention. In the ROC analysis, the AUC values of the 4 complications were 0.832, 0.731, 0.746, and 0.723, respectively. proving the superiority of homogenized care in preventing postoperative complications. Homogenized nursing intervention can effectively reduce the incidence of postoperative complications in patients with urological postoperative bladder irrigation, improve patients' symptoms and prognosis, and improve the quality of care and patients' quality of life.

Quantitative Analysis Method of the Organizational Characteristics and Typical Types of Landscape Spatial Sequences Applied with a 3D Point Cloud Model

(1) Background: Sequential landscape changes give people experiences of dynamic beauty, and the key to creating spatial sequences lies in the organization of spatial changes. The purpose of this study is to use a 3D point cloud model to achieve a refined description of spatial sequences’ organizational characteristics from information acquisition to description and explore the quantitative interpretation methods of typical sequence organizational characteristics. (2) Methods: The proposed model contains three main steps: data acquisition and extraction, characteristic index system construction and data processing, and quantitative characterization analysis. A typology research method that combines quantitative induction with qualitative statistical verification is proposed. (3) Results: Seventy-two spatial sequence point cloud models of study cases are obtained; 4 indicators are established; 3 typical organization types are summarized, namely fluctuating, reversal and moderate type; and the characterization factors and threshold intervals for each sequence organization type are analyzed to validate the type classification result. (4) Conclusions: This research improves the accuracy of spatial data, the comprehensiveness of sequence organization characterization factors, and the reliability of classification results. It supplements the existing spatial sequence theoretical knowledge system and provides parameters that can be referred to in practical design.

Model construction and empirical of urban characteristic planning evaluation index system

With accelerated urbanisation, the importance of urban characteristic planning has become increasingly prominent. It has been a focus on how to conduct scientific urban characteristic planning evaluation to ensure high quality and sustainable development of urban characteristic planning. Therefore, this study proposed an urban characteristic planning evaluation index system based on support vector machine (SVM). The process uses hierarchical analysis to determine the relative weight values, calculates fuzzy values for indicators that cannot be directly and objectively evaluated and then introduces SVM for precise processing of interval data. The test results showed that the accuracy of the research method was 0.9760, 0.9600 and 0.9750 when taking the maximum bias value of 0.5 in the three datasets. The optimisation time of the research method for the parameters of the UCI (University of California Irvine) dataset remained within 650–750 ms. The evaluation results of the research method fit well with the expert evaluation results. The above results show that the research method has high data accuracy and operational efficiency in the evaluation of urban characteristic planning, which can make reasonable evaluation of urban characteristic planning. This method can provide reliable reference basis for government decision making.

Low-Cost Portable Road Smoothness Testing Method Based on Pseudo-Vibration Velocity Range

Road smoothness not only directly affects the safety and comfort of vehicle travel but also relates to the efficiency and cost-effectiveness of road maintenance. Traditional road smoothness detection methods usually require professional equipment and personnel, leading to high costs and cumbersome operations. Therefore, finding a low-cost, simple, and accurate method for detecting road smoothness is of great significance. This study uses vehicle-mounted acceleration sensors to detect road smoothness, establishing a correlation between driving vibration acceleration data and the international roughness index (IRI). For this research, a driving vibration acceleration data acquisition device was developed, and the driving acceleration data from the test sections were denoised and their feature values extracted. The pseudo-vibration velocity range was used as the characteristic index representing the road surface smoothness IRI value. Testing with different vehicle types showed that the method is applicable to both sedans and SUV models, yielding a relative error of 8.9% for the sedan smoothness test model and 6.7% for the SUV smoothness test model. This study contributes to conducting large-scale road smoothness detection at a low cost, improving the efficiency of road maintenance and operations.

The association of obesogenic environments with weight status, blood pressure, and blood lipids: A cross-sectional pooled analysis across five cohorts

In this observational cross-sectional study, we investigated the relationship between combined obesogenic neighbourhood characteristics and various cardiovascular disease risk factors in adults, including BMI, systolic blood pressure, and blood lipids, as well as the prevalence of overweight/obesity, hypertension, and dyslipidaemia. We conducted a large-scale pooled analysis, comprising data from five Dutch cohort studies (n = 183,871). Neighbourhood obesogenicity was defined according to the Obesogenic Built-environmental CharacterisTics (OBCT) index. The index was calculated for 1000m circular buffers around participants’ home addresses. For each cohort, the association between the OBCT index and prevalence of overweight/obesity, hypertension and dyslipidaemia was analysed using robust Poisson regression models. Associations with continuous measures of BMI, systolic blood pressure, LDL-cholesterol, HDL-cholesterol, and triglycerides were analysed using linear regression. All models were adjusted for age, sex, education level and area-level socio-economic status. Cohort-specific estimates were pooled using random-effects meta-analyses. The pooled results show that a 10 point higher OBCT index score was significantly associated with a 0.17 higher BMI (95%CI: 0.10 to 0.24), a 0.01 higher LDL-cholesterol (95% CI: 0.01 to 0.02), a 0.01 lower HDL cholesterol (95% CI: −0.02 to −0.01), and non-significantly associated with a 0.36 mmHg higher systolic blood pressure (95%CI: −0.14 to 0.65). A 10 point higher OBCT index score was also associated with a higher prevalence of overweight/obesity (PR = 1.03; 95% CI: 1.02 to 1.05), obesity (PR = 1.04; 95% CI: 1.01 to 1.08) and hypertension (PR = 1.02; 95% CI: 1.00 to 1.04), but not with dyslipidaemia. This large-scale pooled analysis of five Dutch cohort studies shows that higher neighbourhood obesogenicity, as measured by the OBCT index, was associated with higher BMI, higher prevalence of overweight/obesity, obesity, and hypertension. These findings highlight the importance of considering the obesogenic environment as a potential determinant of cardiovascular health.

The relationship between landscape pattern and plant species diversity in east Dongting lake wetland based on different Eco-environment

Exploring the impact factors associated with biodiversity and the relationship between them has always been a concerned issue in recent years. However, the previous research mostly focus on theoretical layer. Accordingly, the relationship between landscape pattern and biodiversity is to be analyzed in this research. The landscape pattern determines the function and ecological process of the landscape, and affects the species flow, information flow and energy flow in the landscape. Land use patterns has inevitably left an impact on the landscape pattern. Landscape pattern determines the function and ecological process of landscape and thus plays a significant role in biodiversity. East Dongting Lake National Nature Reserve is taken as the research object of the paper, and the remote sensing image data of three different time periods are collected, including 2000, 2010 and 2020. With an interpretation of the vegetation landscape pattern changes inside the protected area to collect and analyze the vegetation coverage. By comparing landscape patterns and the dynamic changes of land use in different periods of time, the correlation between landscape pattern characteristics and regional biodiversity is to be analyzed. Research shows: (1) From 2000 to 2020, the vegetation coverage of East Dongting Lake increased, but the landscape shape, scale, diversity and uniformity index decreased to varying degrees. (2) At the class level of landscape type, the relationship between landscape index and biodiversity is different. A complex relationship between farmland landscape and biodiversity. There is a significant positive correlation between the index of grassland landscape type and the index of regional biodiversity. (3) The correlation analysis results at the landscape level show that the landscape characteristic index is positively correlated with the regional biodiversity index. The grassland landscape in the area is the main habitat of biological species. At the same time, as the main grain producing area, the impact of farmland landscape cannot be ignored. This study has certain theoretical guiding significance for the protection and management of biodiversity in the region in terms of maintaining landscape pattern in particular the grassland landscape area and increasing vegetation coverage in the process of land use.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Fault Identification Method of Transformer Winding based on Gramian Angular Difference Field and Convolutional Neural Network

Background:: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field. Objective:: This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily life. Methods:: By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into characteristic index and then into a three-dimensional image by moving window calculation method and Gramian Angular difference field transformation. The fault classification is realized by a convolutional neural network. Results:: The accuracy of the final model for slice classification reached 100%. Conclusion:: Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.

Effect of nano-graphene lubricating oil on particulate matter of a diesel engine

Nano-graphene lubricating oil with appropriate concentration shows excellent performance in reducing friction and wear under different working conditions of diesel engines, and has been widely concerned. Lubricating oil has a significant impact on particulate matter (PM) emissions. At present, there are few studies on the impact of nano-graphene lubricating oil on the physicochemical properties of PM. In order to comprehensively evaluate the impact of nano-graphene lubricating oil on diesel engines, this paper mainly focused on the effects of lubricating oil nano-graphene additives on the particle size distribution and physicochemical properties of PM. The results show that, compared with pure lubricating oil, the total number of nuclear PM and accumulated PM of nano-graphene lubricating oil is significantly increased. The fractal dimension of PM of nano-graphene lubricating oil increases and its structure becomes more compact. The average fringe separation distance of basic carbon particles decreases, the average fringe length increases. The degree of ordering and graphitization of basic carbon particles are higher. The fringe tortuosity of basic carbon particles decreases, and the fluctuation of carbon layer structure of basic carbon particles decreases. Aliphatic substances in PM are basically unchanged, aromatic components and oxygen functional groups increase. The initial PM oxidation temperature and burnout temperature increase, the maximum oxidation rate temperature and combustion characteristic index decrease, and the activation energy increases, making it more difficult to oxidize. This was mainly caused by the higher graphitization degree of PM of nano-graphene lubricating oil and the increased content of aromatic substances.

Analysis of the Investment Features of Stocks of Valuation System with Chinese Characteristics Based on Hierarchical Cluster Method

The ultimate goal of stock investment is to achieve profitability, and risk avoidance is the most important foundation for achieving profitability. Accurately evaluating the value of stocks in the market is one of the key factors for successful investment. Under the background of proposing the Valuation System with Chinese Characteristics, a reasonable analysis of the investment features of stocks of Valuation System with Chinese Characteristics can bring opportunities to investors. To this end, first construct the characteristic indices of stocks of Valuation System with Chinese Characteristics — Price Earnings Ratio (PE), Price-to-Book Ratio (PB), and Dividend Yield Ratio. Then, based on the characteristic indices, use the hierarchical cluster method to perform clustering analysis on the stocks of Valuation System with Chinese Characteristics. Afterwards, by optimizing the clustering results, all stocks of Valuation System with Chinese Characteristics were classified into four categories. According to the classification analysis of the investment features of the stocks, it can be concluded that the issuing company of the first category of stocks has recently been operating poorly, so it is not recommended to invest; The value of stocks in the second category is overestimated and it is not recommended to invest; The third category of stocks are high growth stocks, and it is recommended to invest with caution; The stocks in the fourth category have the characteristics of undervaluation and high dividends, and it is recommended to invest.

A novel coal purification-combustion system: Product and efficiency of coal purification process

A novel coal purification-combustion technology is presented to realize the efficient and clean utilization of coal under carbon peaking and carbon neutrality goals. Central to this technology is the implementation of a purification process, the product characteristics, and energy properties of which are the focus of this study. An experimental test of coal purification was conducted using Shenmu coal. The results demonstrate increased coal reactivity following the moderate temperature activation (MTA) process. The high temperature reduction (HTR) phase promotes the production of higher amounts of amorphous carbon, reduces aromatic carbon content, and further decreases fuel particle size. The composite combustion characteristic index(S) of the purified fuel reaches its peak when the excess air coefficient of secondary air (λ2) is 0.36. As λ2 increases from 0.1 to 0.36, the energy in the gas product of the purification system increases from 13700 kJ/kg coal to 25351 kJ/kg coal, while exergy increases from 6530 kJ/kg coal to 11030 kJ/kg coal. The energy efficiency of the purification process ranges from 74.74 % to 80.88 %, with exergy efficiency varying between 60.51 % and 70 %. These findings offer significant insights into refining the coal purification process, ultimately contributing towards maximizing the utility of coal resources.

Influence of frequency characteristics of seismic ground motion on the fragility of an RC frame structure

Ground-motion frequency characteristics have a significant impact on structural damage. A quantitative description of the impact of ground-motion spectral features on structures is an urgent topic that requires solutions. In this study, the ratio of peak displacement to peak acceleration (R d2a ) is used as a representative index of the ground-motion spectral characteristics. The seismic records collected from the Kik-net strong-motion array in Japan are grouped according to their R d2a values. By adopting the incremental dynamic analysis method, the seismic fragility curves of a six-story RC frame structure loaded by inputting seismic records with different R d2a values are calculated using the OpenSees platform. The influence of R d2a values on the structural fragility is analyzed. Ground-motion records of liquefied sites are collected, and the peak ratios of seismic records at the liquefied sites are analyzed to reveal the differences in the fragility of the six-story RC structures at liquefied and non-liquefied sites. The results of this study are expected to serve as a reference for the evaluation of structural fragility and to understand the effect of ground-motion frequency characteristics on the fragility of RC frame structures.

The Role of Stochastic Fermi-type Particle Acceleration in the Inner Jets of Active Galactic Nuclei

High-resolution radio observations of nearby active galactic nuclei have revealed extended, limb-brightened structures in their inner jets. This ties in with other multiwavelength observations from radio to X-ray and gamma ray, indicating that a structured jet model is required. While electrons need to be kept energized to account for the observed features, the underlying particle acceleration mechanism is still unclear. We explore the role of stochastic Fermi-type particle acceleration, i.e., classical second-order Fermi and shear acceleration, for understanding the multiwavelength observations of the inner jets of M87. An analytical Fokker–Planck description is adopted to infer characteristic spectral indices and cutoff energies for these two mechanisms. We focus on electron synchrotron radiation as the dominant emission process. We find that the multiwavelength observations of M87 can be satisfactorily accounted for in a framework, where the X-rays are produced at a larger distance from the core than the radio emission region. This provides further support to multizone, broadband emission modeling. We use our findings to also comment on the acceleration of cosmic rays entrained in the sheath.

Prediction quality of patients’ composited outcomes of interest and how much variations on the outcomes explained by the patients’ sodium levels

Background: Hypernatremia can be caused by medications, glucose/urea-related hyperosmolarity, overdiuresis, compartmental fluid distribution imbalances, iatrogenic hypertonic saline excess, renal concentrating performance defects, overdiuresis, and hypermetabolic states. Patients who are in critical condition, encompassing ailments of the heart, liver, lungs, brain, and kidneys, frequently manifest hyponatremia (sodium < 135 mEq/L). Similar to hypernatremia, hyponatremia signifies the severity of the illness and a bleak prognosis. Hyponatremia is less lethal than central pontine myelinolysis, which overcorrects sodium concentrations urgently. Aim: The objective of this research endeavour was to investigate the strength of correlations, the extent to which the independent variables account for the overall variability in the dependent variable, and the accuracy of the dependent variable's predictions. In addition, the purpose of this test was to extract the coefficients required to present the Binary Logistic Regression models that were investigated. Methods: Between May 2018 and May 2021, this study examined two Na-based prognosticators for overall mortality in critically ill patients admitted to the King Hussein Medical Centre. An investigation was conducted on 2155 cases utilising Receiver Operating Characteristic (ROC) and Sensitivity Indices Test analyses to ascertain the most effective cut-off points, specificities, sensitivity, predictive values, likelihood ratios, Youden and accuracy indices, and mortality rates. The research discovered that serial termination points with lower values suggested more robust evidence for a positive actual state, whereas higher values suggested less robust evidence for a negative actual state. A Binary Logistic Regression (BLgR) test was performed to examine correlations, the extent of total variations in the dependent variable, and the accuracy of the prediction for each Na-related mortality prognosticator in relation to overall mortality. The analysis was conducted utilising version 25 of SPSS for Windows. Results: The research revealed that the AUROC for cNa was considerably greater in magnitude compared to its prognosticator that is genealogically related to Na. 129.35 mEq/l and 131.05 mEq/l were the optimal cut-off points, TPRs, TNRs, PPVs, NLRs, YIs, and AIs for the two comparative Na investigated statutes. The BLgR models were developed in order to simulate the relationship between the corrected and measured Na levels of patients and their overall mortality rate. For critically ill patients, the probabilities of overall mortality at the optimal cutoff operating Na levels were 79.59 and 81.62 percent, respectively.