Comprehensive Method Research Articles

Regional Evaluation Study of VFTO Interference to Secondary Side Cables Based on Cloud Model and MARCOS.

With the advent of the data era, most power secondary side equipment tends to be digitized. The power system needs more accurate numerical results to further improve its operating efficiency. Therefore, it is important to study the electromagnetic interferences of very fast transient overvoltage (VFTO) generated by gas-insulated switchgear (GIS). To protect the secondary side cable from interferences, the secondary side cable is wrapped with an outer shield and the shield is grounded. When the interference of VFTO comes, it will couple the interference current and interference voltage on the shield of the cable. By grounding, the interference is greatly discharged. However, due to the grounding resistance, there will be a potential difference between the grounding points at the two ends of the shield of the cable. This causes a corresponding interference current to flow through the shield, which will affect the transmission of signals inside the cable. In the actual substation, the resistivity of the soil, the ambient temperature and humidity of the area, and so on will have impacts on the grounding resistance. In addition, the irregularity of the cable arrangement and the time of the use of the cable will have impacts on the signal transmission of the cable. Based on the abovementioned issues, this article proposed a comprehensive assessment method based on the combination of the cloud model and measurement of alternatives and ranking according to compromise solution (MARCOS). The method brings the cloud model into MARCOS by the algorithm of the contribution of the cloud droplets. It overcomes the difficulty of cloud model quantification. By comparing the results of the proposed method with the actual conditions at the substation and the results of the common MARCOS assessment method, the validity of the method is verified, and a reference scheme is provided for substation optimization.

Comprehensive Flow Path Design Method for the Adaptive Cycle Engine Considering the Coupling Relation of Multiple Components

Abstract The adaptive cycle engine (ACE) has multiple coupled components on the same spool and complex bypass system, which makes it have more complex intercomponent coupling relation and hard to coordinate in the flow path design. In this study, the coupling relation of the ACE components and the component reference conditions are analyzed and determined, a multicomponent collaborative optimization design method is proposed to enable the quantitative evaluation of flow path design solutions. In this method, two optimization strategies are presented based on the different priorities of the intercomponent size coupling parameters, the intercomponent aerodynamic coupling parameter and the component performance in the optimization problem. ACE flow path solutions for various feasible design speed combinations are generated automatically considering the component performance and intercomponent coupling relation. According to an ACE flow path design case study, the design physical rotational speeds of low-pressure spool (NL,d) and high-pressure spool (NH,d) should be 7000 to 7600 r/min and 10,000 to 15,000 r/min, respectively. At NH,d = 12,000 r/min and NL,d = 7200 r/min, the high-pressure compression components and the fan components could be designed with the lowest aerodynamic load, respectively. NH,d is the key factor affecting the axial length of ACE. This method can be applied to other gas power plant designs.

Error Modeling and Accuracy Analysis of Parallel Robots for Spacecraft Docking Motion Simulation System

Background:: A large number of on-orbit docking dynamics experiments are conducted in the spacecraft docking motion simulation system with the development of Chinese space science and technology, and therefore, the requirement for high-precision parallel robots has increased. The pose accuracy of parallel robots is one of the most important problems in this application. Objective:: In order to evaluate the pose accuracy of parallel robots in advance, a comprehensive pose accuracy analysis method considering major error sources is presented in this paper, and the influence of major error sources on the pose accuracy is also investigated to summarize the statistics and propagation characteristics of the pose error. Methods:: The first-order error model for the parallel robot is established based on a generalized error model for each hydraulic cylinder and a sensitivity analysis method. Using the error model, a statistical approach to the parallel robot pose accuracy analysis is presented, and the influence of different parameter errors and different poses on the pose accuracy of the parallel robot is investigated. Sensitivity analysis is applied to evaluate the contribution of each parameter error to the position and orientation error of the parallel robot. An automated pose accuracy analysis program that computes and graphically displays the position and orientation error distributions and the sensitivity analysis results is developed. Results: The statistical analysis results of the influence of different parameter errors and different poses on pose accuracy are obtained by using the automated pose accuracy analysis program. The means of the position and orientation errors are close to zero. The standard deviations in the x and y directions are larger than those in the z direction, and these standard deviations are amplified with the increase of the parameter errors. For the given elevation, sensitivity analysis to various parameter errors is performed. It is found that the length error sensitivities of the hydraulic cylinders are less than one, and position error sensitivities of the hook joints A4, A5, and A6 are much greater than those of hook joints A1, A2 and A3. Conclusion:: The elemental error sources belong to one of two groups, i.e., those affecting the hydraulic cylinder length and those affecting the hook joints. The distributions of the position and orientation errors are consistent with statistics theories. The parallel robot is more sensitive to the errors affecting the hook joints than those affecting the hydraulic cylinder length. These will help the designers and users of the parallel robot understand the statistics and propagation characteristics of the pose error. Some recent patents on error modeling and kinematic calibration of parallel robots are also discussed in this article.

Lightweight convolutional neural networks for real-time lane segmentation

Autonomous driving depends on reliable perception systems that involve various perception modules and advanced computer vision techniques. One crucial component of these systems is lane detection, which traditional methods often rely on basic features like color or edges that are sensitive to lighting and perspective changes. Recently, convolutional neural networks (CNNs), have revolutionized lane detection. Nevertheless, existing methods still have some limitations, such as the need for pixel-level labeling and computational inefficiency for real-time applications. To address these challenges, this work leverages PP-LiteSeg for real-time semantic segmentation. PP-LiteSegs key elements are its Simple Pyramid Pooling Module (SPPM), Unified Attention Fusion Module (UAFM), and Flexible and Lightweight Decoder (FLD), which optimize lane detection efficiency. The FLD flexibly adjusts computational costs between the encoder and decoder, balancing efficiency and accuracy. The UAFM enhances feature representations using attention mechanisms, increasing segmentation accuracy. The SPPM efficiently aggregates contextual information while reducing computational complexity. The comprehensive method for lane segmentation achieves competitive results on popular lane detection datasets. The proposed model can adapt to different computational capabilities and significantly enhances lane detection efficiency for real-time applications.

A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation

The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

Metabolites rapid-annotation in mice by comprehensive method of virtual polygons and Kendric mass loss filtering: A case study of Dendrobium nobile Lindl

With significant advancements in high-resolution mass spectrometry, there has been a substantial increase in the amount of chemical component data acquired from natural products. Therefore, the rapid and efficient extraction of valuable mass spectral information from large volumes of high-resolution mass spectrometry data holds crucial significance. This study illustrates a targeted annotation of the metabolic products of alkaloid and sesquiterpene components from Dendrobium nobile (D. nobile) aqueous extract in mice serum through the integration of an in-houses database, R programming, a virtual metabolic product library, polygonal mass defect filtering, and Kendrick mass defect strategies. The research process involved initially establishing a library of alkaloids and sesquiterpenes components and simulating 71 potential metabolic reactions within the organism using R programming, thus creating a virtual metabolic product database. Subsequently, employing the virtual metabolic product library allowed for polygonal mass defect filtering, rapidly screening 1705 potential metabolites of alkaloids and 3044 potential metabolites of sesquiterpenes in the serum. Furthermore, based on the chemical composition database of D. nobile and online mass spectrometry databases, 95 compounds, including alkaloids, sesquiterpenes, and endogenous components, were characterized. Finally, utilizing Kendrick mass defect analysis in conjunction with known alkaloids and sesquiterpenes targeted screening of 209 demethylation, methylation, and oxidation products in phase I metabolism, and 146 glucuronidation and glutathione conjugation products in phase II metabolism. This study provides valuable insights for the rapid and accurate annotation of chemical components and their metabolites in vivo within natural products.

Evaluating the quality of visual explanations on chest X-ray images for thorax diseases classification

AbstractDeep learning models are extensively used but often lack transparency due to their complex internal mechanics. To bridge this gap, the field of explainable AI (XAI) strives to make these models more interpretable. However, a significant obstacle in XAI is the absence of quantifiable metrics for evaluating explanation quality. Existing techniques, reliant on manual assessment or inadequate metrics, face limitations in scalability, reproducibility, and trustworthiness. Recognizing these issues, the current study specifically addresses the quality assessment of visual explanations in medical imaging, where interpretability profoundly influences diagnostic accuracy and trust in AI-assisted decisions. Introducing novel criteria such as informativeness, localization, coverage, multi-target capturing, and proportionality, this work presents a comprehensive method for the objective assessment of various explainability algorithms. These newly introduced criteria aid in identifying optimal evaluation metrics. The study expands the domain’s analytical toolkit by examining existing metrics, which have been prevalent in recent works for similar applications, and proposing new ones. Rigorous analysis led to selecting Jensen–Shannon divergence (JS_DIV) as the most effective metric for visual explanation quality. Applied to the multi-label, multi-class diagnosis of thoracic diseases using a trained classifier on the CheXpert dataset, local interpretable model-agnostic explanations (LIME) with diverse segmentation strategies interpret the classifier’s decisions. A qualitative analysis on an unseen subset of the VinDr-CXR dataset evaluates these metrics, confirming JS_DIV’s superiority. The subsequent quantitative analysis optimizes LIME’s hyper-parameters and benchmarks its performance across various segmentation algorithms, underscoring the utility of an objective assessment metric in practical applications.

Feasibility, Repeatability, and Correlation to Lung Function of Phase-Resolved Functional Lung (PREFUL) MRI-derived Pulmonary Artery Pulse Wave Velocity Measurements.

Pulse wave velocity (PWV) in the pulmonary arteries (PA) is a marker of vascular stiffening. Currently, only phase-contrast (PC) MRI-based options exist to measure PA-PWV. To test feasibility, repeatability, and correlation to clinical data of Phase-Resolved Functional Lung (PREFUL) MRI-based calculation of PA-PWV. Retrospective. 79 (26 female) healthy subjects (age range 19-78), 58 (24 female) patients with chronic obstructive pulmonary disease (COPD, age range 40-77), 60 (33 female) patients with suspected pulmonary hypertension (PH, age range 28-85). 2D spoiled gradient echo, 1.5T. PA-PWV was measured from PREFUL-derived cardiac cycles based on the determination of temporal and spatial distance between lung vasculature voxels using a simplified (sPWV) method and a more comprehensive (cPWV) method including more elaborate distance calculation. For 135 individuals, PC MRI-based PWV (PWV-QA) was measured. Intraclass-correlation-coefficient (ICC) and coefficient of variation (CoV) were used to test repeatability. Nonparametric tests were used to compare cohorts. Correlation of sPWV/cPWV, PWV-QA, forced expiratory volume in 1 sec (FEV1) %predicted, residual volume (RV) %predicted, age, and right heart catheterization (RHC) data were tested. Significance level α = 0.05 was used. sPWV and cPWV showed no significant differences between repeated measurements (P-range 0.10-0.92). CoV was generally lower than 15%. COPD and PH patients had significantly higher sPWV and cPWV than healthy subjects. Significant correlation was found between sPWV or cPWV and FEV1%pred. (R = -0.36 and R = -0.44), but not with RHC (P-range -0.11 - 0.91) or age (P-range 0.23-0.89). Correlation to RV%pred. was significant for cPWV (R = 0.42) but not for sPWV (R = 0.34, P = 0.055). For all cohorts, sPWV and cPWV were significantly correlated with PWV-QA (R = -0.41 and R = 0.48). PREFUL-derived PWV is feasible and repeatable. PWV is increased in COPD and PH patients and correlates to airway obstruction and hyperinflation. 3 TECHNICAL EFFICACY: Stage 2.

Study on Resilience Evaluation for Construction Management of Major Railway Projects

The construction of major railway projects poses significant risks, which present considerable challenges to construction management. To accurately assess the level of construction management for these projects, this study incorporated resilience theory into the field. The grounded theory method was utilized to establish a resilience evaluation indicator system for managing a major railway project construction. Additionally, a resilience evaluation model based on the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method was proposed. This model was applied to evaluate the construction management resilience of a major railway project located in the mountainous region of southwest China. The results indicated that the project exhibits a very high overall level of construction management resilience. Specifically, it demonstrates high levels of ability to monitor and warn, an ability to resist absorption, and an ability to respond to emergencies. Additionally, it showcases high levels of ability to recover and rebuild, and an ability to learn to adapt. The evaluation results were consistent with the actual situation and verified the correctness and reliability of the method. Based on the aforementioned research findings, this paper puts forward recommendations on material redundancy and resource security from a resource perspective, and suggestions on organizational optimization and personnel capacity improvement from a subject perspective, thus indicating directions for enhancing the management level of major engineering railway constructions.

Study on the Process Optimization of Peanut Coat Pigment Staining of Poplar Wood

Wood staining is a crucial technique for enhancing the decorative effect of wood. Different mordants and mordant processes can influence the staining effect of wood. In this study, three types of mordants and mordant methods were selected to improve the color difference and colorfastness to the washing of poplar veneer, using green peanut pigment as the dye. An orthogonal test was conducted to investigate the effects of mordant temperature, mordant time, and mordant concentration on color difference and colorfastness to washing. Range and variance analysis were employed to determine these properties’ main factors. A fuzzy comprehensive evaluation method was used to evaluate and optimize the color difference value and colorfastness of washing. The results revealed that all three factors had significant impacts on both color difference and colorfastness to washing during the process of mordant staining. The optimal process conditions (temperature, concentration, time) for achieving desirable staining effects on poplar veneer were determined as 50 °C, 0.8%, and 2 h, respectively. Furthermore, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD) analyses demonstrated that the dye formed complexes with poplar wood, enabling it to adhere to wood grain apertures and tube walls effectively. Mordant treatment increased the crystallinity of stained wood significantly while improving its overall staining performance considerably. This study provides substantial data support for future optimization processes involving natural pigment coal staining in wood.

To Achieve a Win–Win Situation: Reorganizing and Enhancing Agroforestry Ecosystem Assets and Productivity to Inform Karst Desertification Control

The ongoing degradation of fragile ecosystems increasingly diminishes the availability of natural resources. Consequently, the conservation and utilization of ecosystem assets have emerged as a focal point of global research. This study focuses on integrating agroforestry ecosystem assets (AEA) with their capacity to provide ecosystem services, aiming to explore their interconnections and enhance their optimization. We employed a comprehensive literature review method, utilizing the Scopus database to select, analyze, and include 61 pertinent studies on AEA globally. The systematic literature review results show the following: (1) The overall number of published papers is showing an upward trend, indicating that research in this field is gradually expanding. The geographical focus of the literature is primarily in Europe and Asia, with academic institutions being the main contributors to this research. (2) Landmark research findings are mainly concentrated in the areas of identification, quantitative assessment, and decision management. Among these, quantitative assessment is the main focus, while the research on identification, decision management, and influencing factors is relatively limited and explores the relevant key scientific questions. (3) The above information highlights the key areas for improvement in the karst desertification control agroforestry ecosystem, focusing on two aspects: fragile habitats and human–land relationships. Furthermore, this review furnishes essential recommendations for agroforestry practitioners and policymakers across various regions, emphasizing the critical need to thoroughly evaluate and leverage the connections between the quantity and quality of ecosystem assets. Such an approach is pivotal for facilitating the strategic restructuring and optimization of agroforestry ecosystems. This, in turn, aims to elevate the sustainability of AEA utilization and enhance their ability to provide ecosystem services (ES).

On the fair scheduling of truck drivers in delivery companies: balancing fairness and profit

AbstractFairness is crucial in transportation systems to ensure that all drivers are treated equally and have the same opportunities. Fair payment policies, equal access to work opportunities, and fair scheduling are some of the policies delivery companies implement to ensure fairness between drivers. In this paper, we study a fair scheduling mixed-integer programming problem where we consider a bi-objective function that aims to maximize profit and improve fairness between drivers by minimizing the maximum deviation from the average driving time. To solve this problem, we employ the weighted comprehensive criterion method and propose an iterative population-based heuristic. The results show that the relative gap between the heuristic and exact approach is acceptable. We also report the fairness price which is the relative difference between total profit with and without incorporating fairness. We find out that improving fairness between drivers does not always lead to a significant reduction in total profit. When the reduction in total profit is important, we recommend formulating the scheduling problem differently where instead of minimizing the maximum deviation from the average driving time, drivers are rewarded when their driving times are longer than the average. We explore incorporating the cost of rewarding these drivers in the objective function.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Cardiopulmonary Exercise Testing in Evaluating Transthyretin Amyloidosis.

Cardiopulmonary exercise testing (CPET) has an established role in the assessment of patients with heart failure. However, data are lacking in patients with transthyretin (ATTR) amyloidosis. To use CPET to characterize the spectrum of functional phenotypes in patients with ATTR amyloidosis and assess their association with the cardiac amyloid burden as well as the association between CPET parameters and prognosis. This single-center study evaluated patients diagnosed with ATTR amyloidosis from May 2019 to September 2022 who underwent CPET at the National Amyloidosis Centre. Of 1045 patients approached, 506 were included and completed the study. Patients were excluded if they had an absolute contraindication to CPET or declined participation. The mean (SD) follow-up period was 22.4 (11.6) months. Comparison of CPET parameters across disease phenotypes (ATTR with cardiomyopathy [ATTR-CM], polyneuropathy, or both [ATTR-mixed]), differences in CPET parameters based on degree of amyloid infiltration (as measured by cardiovascular magnetic resonance [CMR] with extracellular volume mapping), and association between CPET parameters and prognosis. Among the 506 patients with ATTR amyloidosis included in this study, the mean (SD) age was 73.5 (10.2) years, and 457 participants (90.3%) were male. Impairment in functional capacity was highly prevalent. Functional impairment in ATTR-CM and ATTR-mixed phenotypes (peak mean [SD] oxygen consumption [VO2], 14.5 [4.3] mL/kg/min and 15.7 [6.2] mL/kg/min, respectively) was observed alongside impairment in the oxygen pulse, with ventilatory efficiency highest in ATTR-CM (mean [SD] ventilatory efficiency/volume of carbon dioxide expired slope, 38.1 [8.6]). Chronotropic incompetence and exercise oscillatory ventilation (EOV) were highly prevalent across all phenotypes, with both the prevalence and severity being higher than in heart failure from different etiologies. Worsening of amyloid burden on CMR was associated with decline in multiple CPET parameters, although chronotropic response and EOV remained abnormal irrespective of amyloid burden. On multivariable Cox regression analysis, peak VO2 and peak systolic blood pressure (SBP) were independently associated with prognosis (peak VO2: hazard ratio, 0.89 [95% CI, 0.81-0.99; P = .03]; peak SBP: hazard ratio, 0.98 [95% CI, 0.97-0.99; P < .001]). In this study, ATTR amyloidosis was characterized by distinct patterns of functional impairment between all disease phenotypes. A high prevalence of chronotropic incompetence, EOV, and ventilatory inefficiency were characteristic of this population. CPET parameters were associated with amyloid burden by CMR and with peak VO2, and SBP, which have been shown to be independent predictors of mortality. These findings suggest that CPET may be useful in characterizing distinct patterns of functional impairment across the spectrum of amyloid infiltration and predicting outcomes, and potentially offers a more comprehensive method of evaluating functional capacity for future prospective studies.

Influencing factor analysis and enterprise evaluation of lithium-ion battery recycling based on improved DEMATEL method

Spent lithium-ion battery (LIB) recycling can create great pollution to the environment. Understanding the safety, environment, technique, and regulation factors’ impact on the recycling process is crucial. Due to the complexity of the relevant factors, and there is a certain degree of correlation and dependence between the factors, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyze the factors’ degree of impact in this study. As the experts are ambiguous about some relations between the factors, it is impossible to conduct integrated evaluation. The improved DEMATEL method is proposed in this study to make up the missing relations. Further, the weights of the factors will be calculated. In the improved DEMATEL method, the numerical scale of a linguistic term set is introduced. Therefore, the numerical scale used by experts can not only be uniform and symmetrical, but can also be non-uniform symmetric, non-uniform asymmetric, etc. Finally, both reusing and recycling companies are included in this study and their factors’ importance weights were analyzed with the fuzzy comprehensive evaluation method.

Evaluation method for the impact of digital finance on the environmental performance of manufacturing industry

ABSTRACT A new evaluation method for the impact of digital finance on the environmental performance of the manufacturing industry is proposed to improve the accuracy of evaluation results and provide reliable data for enterprise development. Establish a scientific and comprehensive evaluation index system that includes environment, cost-effectiveness, life cycle, and sustainable development. Collect sample data through indirect measurement and direct measurement methods through questionnaire surveys and standardize each data through the measurement of digital economy development level to eliminate differences between different indicators. Based on the constructed evaluation indicator system and sample data collection results, the weights of each evaluation indicator are determined by establishing a projection pursuit model. The fuzzy comprehensive evaluation method and the analytic hierarchy process are used to obtain the fuzzy comprehensive evaluation results of environmental performance based on the projection pursuit model to evaluate the impact of digital finance on the environmental performance of the manufacturing industry. The experimental results show that compared with traditional business models, digital business models can effectively reduce energy consumption, operating costs, and logistics transportation costs of enterprises, improve resource utilization efficiency, and outperform traditional methods in evaluation accuracy, recall rate, and efficiency, verifying the effectiveness of this method.

Case identification of non-traumatic brain injury in youth using linked population data

BackgroundPopulation-level administrative data provides a cost-effective means of monitoring health outcomes and service needs of clinical populations. This study aimed to present a method for case identification of non-traumatic brain injury in population-level data and to examine the association with sociodemographic factors.MethodsAn estimated resident population of youth aged 0–24 years was constructed using population-level datasets within the New Zealand Integrated Data Infrastructure. A clinical consensus committee reviewed the International Classification of Diseases Ninth and Tenth Editions codes and Read codes for inclusion in a case definition. Cases were those with at least one non-traumatic brain injury code present in the five years up until 30 June 2018 in one of four databases in the Integrated Data Infrastructure. Rates of non-traumatic brain injury were examined, both including and excluding birth injury codes and across age, sex, ethnicity, and socioeconomic deprivation groups.ResultsOf the 1 579 089 youth aged 0–24 years on 30 June 2018, 8154 (0.52%) were identified as having one of the brain injury codes in the five-years to 30 June 2018. Rates of non-traumatic brain injury were higher in males, children aged 0–4 years, Māori and Pacific young people, and youth living with high levels of social deprivation.ConclusionThis study presents a comprehensive method for case identification of non-traumatic brain injury using national population-level administrative data.

Comprehensive evaluation of the organic-rich saline lacustrine shale in the Lucaogou Formation, Jimusar sag, Junggar Basin, NW China

To explore a comprehensive evaluation method for saline lacustrine shale, samples were collected from the shale strata of the Lucaogou Formation in the Jimusar Sag, Junggar Basin. Organic geochemical and mineralogical parameters were assessed through TOC evaluation, rock pyrolysis, and XRD. Quantitative analysis of pore structure was achieved using a combined low-pressure N2 adsorption and mercury intrusion. Optical microscopy and FE-SEM analyzed macerals and pore morphology, respectively, while 2D NMR identified varied hydrogen-containing components in the shale. The findings revealed predominance of inorganic pores in saline lacustrine shale strata, with a lower occurrence of organic pores. The saline lacustrine sediments were in the late stage of oil generation. The siltstone and type III3 carbonate felsic shale displayed an abundance of type II1 organic matter, low vitrinite and inertinite contents, high macroporous volumes and surface areas, and they possessed a high content and fluidity of free oil, despite being lower maturity (0.85%<Ro<1.1%), making them attractive for shale oil exploration. Finally, we propose a comprehensive evaluation method for saline lacustrine shale strata considering free oil content (26% contribution), pore structure (23% contribution), maturity (16% contribution), macerals (13% contribution), free oil mobility (12% contribution), and organic matter type (10% contribution), encompassing various lithofacies.

A Comprehensive Competitiveness Decision Method for Electricity Retailers Considering the Integration of Device Operation, Investment Planning, and Marketing Strategy

A Comprehensive Competitiveness Decision Method for Electricity Retailers Considering the Integration of Device Operation, Investment Planning, and Marketing Strategy

A Comprehensive Detection Method for the Lateral Movement Stage of APT Attacks

A Comprehensive Detection Method for the Lateral Movement Stage of APT Attacks