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The Wilson–Cowan model has been widely applied for the simulation of electroencephalography (EEG) waves associated with neural activities in the brain. The Runge–Kutta (RK) method is commonly used to numerically solve the Wilson–Cowan equations. In this paper, we focus on enhancing the accuracy of the numerical method by proposing a strategy to construct a class of fourth-order RK methods using a generalized iterated Crank–Nicolson procedure, where the RK coefficients depend on a free parameter c2. When c2 is set to 0.5, our method becomes a special case of the classical fourth-order RK method. We apply the proposed methods to solve the Wilson–Cowan equations with two and three neuron populations, modeling EEG epileptic dynamics. Our simulations demonstrate that when c2 is set to 0.4, the proposed RK4-04 method yields smaller errors compared to those obtained using the classical fourth-order RK method. This is particularly visible when the spectral radius of the connection matrix or the excitation-inhibition coupling coefficient is relatively large.

In this paper, we explore the iterated Crank–Nicolson (ICN) algorithm for the one-dimensional peridynamic model. The peridynamic equation of motion is an integro-differential equation that governs structural deformations such as fractures. The ICN method was originally developed for hyperbolic advection equations. In peridynamics, we apply the ICN algorithm for temporal discretization and the midpoint quadrature method for spatial integration. Several numerical tests are carried out to evaluate the performance of the ICN method. In general, the ICN method demonstrates second-order accuracy, consistent with the Störmer–Verlet (SV) method. When the weight is 1/3, the ICN method behaves as a third-order Runge–Kutta method and maintains strong stability-preserving (SSP) properties for linear problems. Regarding energy conservation, the ICN algorithm maintains at least second-order accuracy, making it superior to the SV method, which converges linearly. Furthermore, selecting a weight of 0.25 results in fourth-order superconvergent energy variation for the ICN method. In this case, the ICN method exhibits energy variation similar to that of the fourth-order Runge–Kutta method but operates approximately 20% faster. Higher-order convergence for energy can also be achieved by increasing the number of iterations in the ICN method.

Abstract Conductive grease has been considered as a potential solution in addressing electric erosion issue inside motor bearings. Understanding the conductive grease performance, especially the electric and tribological behaviour is crucial for its application. This paper combines bulk grease characterization and contact film study considering both electrical and tribological loading conditions. The results show that the electric performance for the selected conductive grease sample is electric field dependent, including both frequency and voltage amplitude. Its film impedance inside the contact area is closely related to the external electric field, running condition, and lubrication regime. The following film impedance calculation tells that not only bulk grease electric parameters, but the contact parameter, lubricant film contour and lubrication condition should be considered when performing the overall bearing electrical modelling.

Nowadays, with the wide installation of distributed energy resources and independent energy storage systems, prosumers as a new type of electricity market entity have emerged. Since numerous prosumers can significantly impact the carbon emission of the power grid, this paper proposes an improved carbon emission flow method for the power grid with prosumers. This method can accurately clarify the detailed distribution of electrical carbon emission flow in power grids. First, based on the power flow, prosumers’ impacts on the electrical carbon emission are quantified from three aspects that include the carbon emission sources, the network flow, and the indirect carbon emission individuals. Then, an improved power carbon emission flow model is proposed, in which the complex carbon emission intensity of prosumers is derived emphatically. Finally, case studies based on the IEEE 30-bus system verify the feasibility of the proposed method. This method provides a measurement basis for further research considering electrical carbon emissions.

Abstract Introduction Patient engagement, encompassing both patient experience and opportunities for involvement in care, has been associated with increased patient satisfaction and the overall quality of care. Despite its importance, there is limited knowledge regarding patient engagement in the transition from nondialysis‐dependent chronic kidney disease (CKD) to dialysis‐dependent treatment. This systematic review employs meta‐ethnography to synthesize findings from qualitative studies examining patients' experiences of engagement during this transition, with the aim of developing a comprehensive theoretical understanding of patient engagement in the transition from nondialysis‐dependent CKD to dialysis. Methods A systematic search of six databases, namely the Cochrane Library, PsycINFO, Scopus, Embase, PubMed and Web of Science was conducted to identify eligible articles published between 1990 and 2022. Meta‐ethnography was utilized to translate and synthesize the findings and develop a novel theoretical interpretation of ‘patient engagement’ during the transition to dialysis. Results A total of 24 articles were deemed eligible for review, representing 21 studies. Patient engagement during a transition to dialysis was found to encompass three major domains: psychosocial adjustment, decision‐making and engagement in self‐care. These three domains could be experienced as an iterative and mutually reinforcing process, guiding patients toward achieving control and proficiency in their lives as they adapt to dialysis. Additionally, patient engagement could be facilitated by factors including patients' basic capability to engage, the provision of appropriate education, the establishment of supportive relationships and the alignment with values and resources. Conclusions The findings of this review underscore the necessity of involving patients in transitional dialysis care, emphasizing the need to foster their engagement across multiple domains. Recommendations for future interventions include the provision of comprehensive support to enhance patient engagement during this critical transition phase. Additional research is warranted to explore the effects of various facilitators at different levels. Patient or Public Contribution The studies included in our review involved 633 participants (547 patients, 14 family members, 63 healthcare providers and 9 managers). Based on their experiences, views and beliefs, we developed a deeper understanding of patient engagement and how to foster it in the future.

Single-phase broken faults occur frequently, affecting the reliability of distribution network. In order to effectively identify the fault type of single-phase broken fault, this paper proposes a new identification method, which is based on the combination of variational mode decomposition and stacked auto encoder with double optimization (AO-VMD-PSO-SAE). Firstly, the zero sequence voltage, which collected in line, is decomposed into a set of variational modal components. Nextly, the stack automatic encoder is used to conduct unsupervised training on the denoised data to establish a depth learning model, and the AO optimization algorithm and the PSO optimization algorithm are used to determine the super parameters in the model.​ Finally, simulation results supported and the validity of the method was verified. What the results show is that the proposed model named AO-VMD-PSO-SAE can accurately predict the types of single-phase broken fault under noise interference.

Abstract Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data, the research on load forecasting under extreme weather events is still blank, so it is necessary to release a large-scale load dataset containing extreme weather events. The dataset includes electricity consumption data of industrial and commercial users under extreme weather events such as typhoons and extreme heat, which are collected at 15-minute intervals. The data is collected over six years from smart meters installed at the power entry points of users in southern China. The dataset consists of electricity consumption data from 386 industrial and commercial users in 17 industries, with more than 50 million records. During the recording period, extreme weather events such as typhoons and extreme heat are marked to form a total of 5,741 event records.

The pumping station is one of the critical parts of the hydraulic structure in China. Traditional forecasting methods are limited in accuracy, time-consuming, and high cost, resulting in limited data availability. Therefore, simulation model analysis based on soft computation is a realistic and valuable alternative. This article intends to use the BP neural network to predict the safe operation status of pump stations and optimize the initial threshold and weight information of the BP network using the sparrow search algorithm (SSA) to improve the accuracy and generalization ability of the model. In addition, to more accurately reflect the correlation between various influencing factors and the safe operation status of the pumping station, the entropy weight method and the analytic hierarchy process were used to obtain the comprehensive weights of each main influencing factor. The experimental results show that the SSA-BP model can accurately predict the safe operation status of pumping stations, and compared with other traditional models, the SSA-BP model has better convergence and higher accuracy. This model provides a new approach for predicting the safe operation of pumping stations and has particular reference significance for predicting the safe operation of other pumping stations.

Background China is one of the countries with the highest burden of stroke. Implementing multidimensional management guidelines will help clinicians practise evidence-based care, improve patient outcomes and alleviate societal burdens. This update of the 2019 edition will provide the latest comprehensive recommendations for the diagnosis and treatment of ischaemic cerebrovascular diseases.Methods We conducted a comprehensive search on MEDLINE (via PubMed) up to 31 August 2023. The writing team established the recommendations through multiple rounds of online and offline discussions. Each recommendation was graded using the evidence grading algorithm developed by the Chinese Stroke Association (CSA). The draft was reviewed and finalised by the CSA Stroke Guidelines Writing Committee.Results This update included revisions of 15 existing recommendations and 136 new recommendations in the following areas of stroke care: emergency assessment and diagnosis of ischaemic cerebrovascular disease, acute-phase reperfusion therapy, evaluation of underlying mechanisms, antithrombotic therapy, prevention and treatment of complications, and risk factor management.Conclusions This guideline updated the recommendations for the clinical management of ischaemic cerebrovascular disease from 2019.

Abstract The real‐time carbon emission estimation of generators helps quantify the carbon emission costs and reduce power system emissions of power generation. Accurate estimation relies on the accuracy of the carbon emission factors (EFs) and power generation measurements. The dynamic carbon emission factor (DEF) of generators was proposed recently as a linear function of the output power. However, there is a significant deviation between the modelled DEFs and the actual measurement EFs, especially when the output power is low. This paper first presents the general definition of the DEF to characterize the emissions and focuses on the unit‐level DEF (UDEF). The piecewise non‐linear UDEF (P‐UDEF) model is then proposed, which can better represent the unit emission characteristics. Then an accurate piecewise linear cost approximation method is proposed considering the segment points and extreme points of both P‐UDEF and generation costs function. Last, the system carbon emissions and costs estimation are estimated by combined economic emission dispatch (CEED), and the reduction potential is evaluated. Case studies on an IEEE 30‐bus system with piecewise linear cost functions show that the proposed P‐UDEF can realize real‐time emission and cost estimation as well as reduce the total system emissions by considering the incomplete combustion cost of generating units.

BackgroundThe optimal control thresholds for systolic blood pressure (SBP) and diastolic blood pressure (DBP) in patients with white matter hyperintensity (WMH) are still unclear.MethodA longitudinal retrospective study of patients with brain magnetic resonance imaging (MRI) scans with intervals of more than 3 years was conducted. Blood pressure records during hospitalization and from outpatient visits between baseline and the last MRI scan were collected. The outcome was the change in total WMH from baseline to the final visit.ResultsAmong the 965 patients with MRI scans, 457 patients with detailed longitudinal blood pressure records were ultimately included and classified into the WMH absent group (n = 121), mild WMH group (n = 126), and moderate to severe WMH group (n = 210). Both baseline and longitudinal mean SBP, DBP, and SBP SD were significantly associated with WMH severity (p

Measurement(s) carbon dioxide emissions Technology Type(s) fossil fuel consumption

A novel temporal subgridding technique is proposed for the finite-difference time-domain (FDTD) method to solve two-dimensional Maxwell’s equations of electrodynamics in the TEz mode. Based on the subgridding FDTD algorithm with a separated spatial and temporal interface, our method focuses on the temporal subgridding region, as it is the main source of late-time instability. Different from other subgridding algorithms that work on the interpolation between coarse and fine meshes, our method stabilizes the solution by using iterative updating equations on the temporal coarse–fine mesh interface. This new method presents an alternative approach aimed at improving the stability of the subgridding technique without modifying the interpolation formulas. We numerically study the stability of the proposed algorithm via eigenvalue tests and by performing long-term simulations. We employ a refinement ratio of 2:1 in our study. Our findings indicate the stability of the conventional temporal subgridding FDTD algorithm with a magnetic field (Hz) interpolation. However, when electric fields (Ex and Ey) are utilized in interpolation, late-time instability occurs. In contrast, the proposed iteration-based method with an electric field interpolation appears to be stable. We further employ our method as the forward problem solver in the Through-the-Wall Radar (TWR) imaging application.

As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. It is worth studying on how to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate. In this article, a data enhancement method and a framework are proposed to assist wind power forecasting. The proposed method uses the super-resolution perception technology to first detect the completeness and correctness of historical meteorological and wind power data collected by industrial devices. Then, the detected errors are corrected and the missing data are recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data become complete high-frequency data. Based on the enhanced complete high-frequency data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on public datasets are used to demonstrate the effectiveness of the proposed method and framework. With the proposed method and framework, higher frequency data with more detailed information can be achieved, thereby providing support for accurate wind power prediction that was not possible before.

Purpose: The clinical values of C-reactive protein (CRP) and serum amyloid A (SAA) to distinguish non-severe from severe influenza in children are rarely reported. Methods: Baseline characteristics and laboratory results were collected and analyzed. Receiver operating characteristic (ROC) curve analysis was used for combined detection of indicators for children with influenza, and scatter-dot plots were used to compare the differences between non-severe and severe influenza. Results: Children with influenza B had more bronchitis and pneumonia (P < 0.05) and children with influenza A had more other serious symptoms (P = 0.015). Lymphocyte count, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), CRP, and SAA performed differently among children with influenza A and B. Joint detection of SAA and other indicators could better separate healthy children from children with influenza than single indicator detection. The CRP and SAA levels of children with severe influenza B infection and SAA levels of children with severe influenza A infection were significantly elevated compared with children with non-severe influenza (P < 0.05). Conclusions: SAA and CRP could be potential indicators in distinction and severity assessment for children with influenza; however, age should be taken into account when using them in children with influenza B.

With the increasing number of river-crossing pipeline projects, many studies have focused on the safety and stability of embankment slopes in horizontal directional drilling crossing embankment construction. In addition, it is challenging to study the influence of key processes in directional drilling construction on the antisliding stability of embankment slopes. A model of horizontal directional drilling crossing the Shaying River embankment was developed by using the numerical simulation software FLAC3D, and the strength reduction method is used to analyze the antisliding stability of the embankment after crossing the embankment. In this study, the effect of the position of the whipstock point on the antisliding stability of the embankment slope was considered. The analysis shows that when the inclined point is located below the center of the embankment, the anti-sliding stability of the embankment is the most unfavorable, and the construction should try to avoid the whipstock point below the center of the embankment. It was also determined that when the vertical distance between the oblique point and the crest of the embankment is exceeding 13 m, the impact on the antisliding stability of the embankment can be ignored.

Objective: In a paternity case, the D12S391 locus was reported as a mismatch. To confirm the existence of mutations and mutations come from father or mother. Methods: STR and next-generation sequencing technology were used to validate the sequence. Results: NGS showed the loss of one adenine between the 19.3 allele of the child and allele 20 of the mother. Conclusion: The NGS can be applied in the paternity to validate the mutation.

In this work, we develop a numerical method for solving the three dimensional hydrodynamic electron fluid Maxwell equations that describe the electron gas dynamics driven by an external electromagnetic wave excitation. Our numerical approach is based on the Finite-Difference Time-Domain (FDTD) method for solving the Maxwell’s equations and an explicit central finite difference method for solving the hydrodynamic electron fluid equations containing both electron density and current equations. Numerical results show good agreement with the experiment of studying the second-harmonic generation (SHG) from metallic split-ring resonator (SRR).

As the advantages of course-based undergraduate research experiences (CUREs) become widely accepted as a mainstream high-impact practice for undergraduate STEM education, it is paramount for instructors to be trained in effective practices for CURE development and implementation. The Mentoring the Integration of Research Into the Classroom (MIRIC) Network is a community of practice dedicated to providing current and future instructors with long-term support in implementing CUREs tailored to specific academic environments. Through the use of virtual synchronous all-network discussions as well as participant-selected affinity groups, MIRIC provides instructors with a dynamic and diverse community of educators in which to engage in long-term conversation in best practices in CURE development and implementation. Survey data on MIRIC participants reveals both success in providing instructors with the tools they need to succeed in CURE development and inspiration for evolving network focus and direction in the future.

Asarum sieboldii Miq., one of the three original plants of TCM ASARI RADIX ET RHIZOMA, is a perennial herb distributed in central and eastern China, the Korean Peninsula, and Japan. Methyleugenol has been considered as the most important constituent of Asarum volatile oil, meanwhile asarinin is also employed as the quality control standard of ASARI RADIX ET RHIZOMA in Chinese Pharmacopeia. They both have shown wide range of biological activities. However, little was known about genes involved in biosynthesis pathways of either methyleugenol or asarinin in Asarum plants. In the present study, we performed de novo transcriptome analysis of plant tissues (e.g., roots, rhizomes, and leaves) at different developmental stages. The sequence assembly resulted in 311,597 transcripts from these plant materials, among which 925 transcripts participated in ‘secondary metabolism’ with particularly up to 20.22% of them falling into phenylpropanoid biosynthesis pathway. The corresponding enzymes belong to seven families potentially encoding phenylalanine ammonia-lyase (PAL), trans-cinnamate 4-monooxygenase (C4H), p-coumarate 3-hydroxylase (C3H), caffeoyl-CoA O-methyltransferase (CCoAOMT), cinnamoyl-CoA reductase (CCR), cinnamyl alcohol dehydrogenase (CAD), and eugenol synthase (EGS). Moreover, 5 unigenes of DIR (dirigent protein) and 11 unigenes of CYP719A (719A subfamily of cytochrome P450 oxygenases) were speculated to be involved in asarinin pathway. Of the 15 candidate CADs, four unigenes that possessed high FPKM (fragments per transcript kilobase per million fragments mapped) value in roots were cloned and characterized. Only the recombinant AsCAD5 protein efficiently converted p-coumaryl, coniferyl, and sinapyl aldehydes to their corresponding alcohols, which are key intermediates employed not only in biosynthesis of lignin but also in that of methyleugenol and asarinin. qRT-PCR revealed that AsCAD5 had a high expression level in roots at three developmental stages. Our study will provide insight into the potential application of molecular breeding and metabolic engineering for improving the quality of TCM ASARI RADIX ET RHIZOMA.

The high incidence of emerging infectious diseases has highlighted the importance of effective immunization strategies, especially the stochastic algorithms based on local available network information. Present stochastic strategies are mainly evaluated based on classical network models, such as scale-free networks and small-world networks, and thus are insufficient. Three frequently referred stochastic immunization strategies-acquaintance immunization, community-bridge immunization, and ring vaccination-were analyzed in this work. The optimal immunization ratios for acquaintance immunization and community-bridge immunization strategies were investigated, and the effectiveness of these three strategies in controlling the spreading of epidemics were analyzed based on realistic social contact networks. The results show all the strategies have decreased the coverage of the epidemics compared to baseline scenario (no control measures). However the effectiveness of acquaintance immunization and community-bridge immunization are very limited, with acquaintance immunization slightly outperforming community-bridge immunization. Ring vaccination significantly outperforms acquaintance immunization and community-bridge immunization, and the sensitivity analysis shows it could be applied to controlling the epidemics with a wide infectivity spectrum. The effectiveness of several classical stochastic immunization strategies was evaluated based on realistic contact networks for the first time in this study. These results could have important significance for epidemic control research and practice.

A new diphenyl ether derivative, namely barceloneic lactone D (1), along with five known compounds (2-6) were isolated from the solid cultures of an endolichenic fungus, Preussia africana. The structure of new compound was elucidated by HR-ESI-MS, 1D and 2D NMR spectroscopy. All the isolates were evaluated for their antifungal activities. All compounds showed definite inhibitory activities with inhibition rate ranging from 9.2% to 79.2%. [ABSTRACT FROM AUTHOR]

A diagnosis of chronic kidney disease (CKD) may increase the risk for depression. The network perspective focuses on dynamic relationships among individual symptoms, which could advance our understanding of the development of depression during the transition to a diagnosis of CKD. The aim of this study was to use network analysis to examine the longitudinal associations of depressive symptoms from before to after a diagnosis of CKD. Method: The analytic sample included 1,386 participants from the Chinese Health and Retirement Longitudinal Study. Participants were aged 45 years or older and reported a doctor's diagnosis of CKD in any wave of interviews between 2011 and 2018. Depressive symptoms were measured by the 10-item version of the Center for Epidemiological Studies Depression. Cross-lagged panel network analysis was conducted to examine relationships between symptoms at three time points: prediagnosis; onset of diagnosis, and postdiagnosis). Results: After controlling for other symptoms and covariates, feeling unable to get going and less happiness at prediagnosis were the most predictive of other symptoms at the diagnosis of CKD. Feeling effortful to do everything and depressed mood at the diagnosis of CKD were the most predictive of other symptoms at postdiagnosis. Conclusions: Fatigue (i.e., feeling unable to get going, feeling effortful to do everything), less happiness, and depressed mood were central symptoms during the transition to a diagnosis of CKD. These findings highlight the benefits of identifying and managing these central symptoms to reduce the risk of activating other depressive symptoms. [ABSTRACT FROM AUTHOR]