Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents

In vehicle–pedestrian accidents, the preimpact conditions of pedestrians and vehicles are frequently uncertain. The incident data for a crash, such as vehicle deformation, injury of the victim, distance of initial position and rest position of accident participants, are useful for verification in MAthematical DYnamic MOdels (MADYMO) simulations. The purpose of this study is to explore the use of an improved optimization algorithm combined with MADYMO multibody simulations and crash data to conduct accurate reconstructions of vehicle–pedestrian accidents. The objective function of the optimization problem was defined as the Euclidean distance between the known vehicle, human and ground contact points, and multiobjective optimization algorithms were employed to obtain the local minima of the objective function. Three common multiobjective optimization algorithms—nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA), and multiobjective particle swarm optimization (MOPSO)—were compared. The effect of the number of objective functions, the choice of different objective functions and the optimal number of iterations were also considered. The final reconstructed results were compared with the process of a real accident. Based on the results of the reconstruction of a real-world accident, the present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. In addition, when all vehicle-pedestrian-ground contacts were considered, the results showed a better match in terms of kinematic response. NSGA-II converged within 100 generations. This study indicated that multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions

Analysis of forensic autopsy cases associated with epilepsy: Comparison between sudden unexpected death in epilepsy (SUDEP) and not-SUDEP groups

Background and aimsEpilepsy is a common and chronic neurological disorder characterized by seizures that increase the risk of mortality. SUDEP is the most common seizure-related category of death. The study aimed to evaluate the key characteristics between SUDEP and not-SUDEP death cases.MethodsA retrospective study of forensic autopsy cases from 2002 to 2021, performed by the Academy of Forensic Science (Ministry of Justice, China), identified a total of 31 deaths associated with epilepsy. We compared the different characteristics between individuals who died of SUDEP (SUDEP group) and individuals with epilepsy died suddenly due to unrelated causes (not-SUDEP group).Results and conclusions13 cases met the general accepted definition of SUDEP; and 18 cases were classified as not-SUDEP. The mean age of the not-SUDEP group was significantly higher than that of the SUDEP groups (p < 0.05) and there were more cases without a clear cause of epilepsy in the SUDEP group than in the not-SUDEP group (p < 0.05). Death position differed significantly between the two groups, with more cases dying in the prone position in the SUDEP group (p < 0.05). Complete autopsies were performed in 24 of the 31 cases. There were no significant differences in heart, lungs and brain weights, or in ventricular thickness (p > 0.05) between the SUDEP and not-SUDEP groups. In addition, compared to the not-SUDEP group, the SUDEP group featured a significantly more cases with coronary lesions (grades 1-3, p < 0.05). Neuropathological lesions were identified in 12 of the 13 SUDEP cases (92.3%), cardiac lesions were present in 10 cases (76.9%) and pulmonary edema and pulmonary congestion were present in all cases. The primary cause of death in 13 of the 31 cases was seizure disorder or epilepsy. The primary mechanism of death in SUDEP group was mainly asphyxia while that in the not-SUDEP group was cardiopulmonary failure (p < 0.05). Patients in the prone position had a significantly higher risk of asphyxia than those who were not. Here, we investigated the key characteristics between SUDEP and not-SUDEP death cases, which may help to facilitate forensic diagnosis in presumed SUDEP cases

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

A critical role of RBM8a in proliferation and differentiation of embryonic neural progenitors

BACKGROUND: Nonsense mediated mRNA decay (NMD) is an RNA surveillance mechanism that controls RNA stability and ensures the speedy degradation of erroneous and unnecessary transcripts. This mechanism depends on several core factors in the exon junction complex (EJC), eIF4A3, RBM8a, Magoh, and BTZ, as well as peripheral factors to distinguish premature stop codons (PTCs) from normal stop codons in transcripts. Recently, emerging evidence has indicated that NMD factors are associated with neurodevelopmental disorders such as autism spectrum disorder (ASD) and intellectual disability (ID). However, the mechanism in which these factors control embryonic brain development is not clear. RESULT: We found that RBM8a is critical for proliferation and differentiation in cortical neural progenitor cells (NPCs). RBM8a is highly expressed in the subventricular zone (SVZ) of the early embryonic cortex, suggesting that RBM8a may play a role in regulating NPCs. RBM8a overexpression stimulates embryonic NPC proliferation and suppresses neuronal differentiation. Conversely, knockdown of RBM8a in the neocortex reduces NPC proliferation and promotes premature neuronal differentiation. Moreover, overexpression of RBM8a suppresses cell cycle exit and keeps cortical NPCs in a proliferative state. To uncover the underlying mechanisms of this phenotype, genome-wide RNAseq was used to identify potential downstream genes of RBM8a in the brain, which have been implicated in autism and neurodevelopmental disorders. Interestingly, autism and schizophrenia risk genes are highly represented in downstream transcripts of RBM8a. In addition, RBM8a regulates multiple alternative splicing genes and NMD targets that are implicated in ASD. Taken together, this data suggests a novel role of RBM8a in the regulation of neurodevelopment. CONCLUSIONS: Our studies provide some insight into causes of mental illnesses and will facilitate the development of new therapeutic strategies for neurodevelopmental illnesses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13064-015-0045-7) contains supplementary material, which is available to authorized users

Does EVA performance evaluation improve the value of cash holdings? Evidence from China

AbstractThis paper investigates the influence of the economic value added (EVA) performance evaluation, issued in 2010 by the State-owned Assets Supervision and Administration Commission of the State Council, on the value of the cash holdings of central state-owned enterprises (CSOEs). We find that EVA performance evaluation has some influence on the overinvestment of CSOE cash holdings and significantly increases the value of CSOE cash holdings compared with the cash holdings of local state-owned enterprises. The greater value of CSOE cash holdings derives from underinvestment modification and overinvestment restraint. The value of cash holdings increases more for companies with better accounting performance. Thus, the EVA performance evaluation policy increases CSOE efficiency. This study contributes to the emerging literature related to cash holdings and the economic consequences of the EVA performance evaluation policy. It expands the literature related to investor protection in countries experiencing economic transition

Unbiased state and fault estimation for discrete-time complex networks with time delays

In this paper, the joint state and fault estimation problem for a class of discrete-time complex networks with time delays is investigated. The information on the dynamics of the fault is not required with an appropriate structure of the proposed fault estimator. Unbiased state and fault estimation can be obtained simultaneously at each node only with locally available information, thereby avoiding the requirement of a centre node which collects information from every node. Considering the effects of time delays and the interconnections between different nodes, an upper bound of the estimation error covariance is calculated, and the desired filter parameters are obtained in a recursive manner such that the trace of the bound is minimised. The existence conditions of the developed filter are provided explicitly as well. Finally, a simulation example is employed to demonstrate the state estimation and fault diagnosis performances of the given strategy.</p