Point estimation and related classification problems for several Lindley populations with application using COVID-19 data.

The problems of point estimation and classification under the assumption that the training data follow a Lindley distribution are considered. Bayes estimators are derived for the parameter of the Lindley distribution applying the Markov chain Monte Carlo (MCMC), and Tierney and Kadane's [Tierney and Kadane, Accurate approximations for posterior moments and marginal densities, J. Amer. Statist. Assoc. 81 (1986), pp. 82-86] methods. In the sequel, we prove that the Bayes estimators using Tierney and Kadane's approximation and Lindley's approximation both converge to the maximum likelihood estimator (MLE), as n → ∞ , where n is the sample size. The performances of all the proposed estimators are compared with some of the existing ones using bias and mean squared error (MSE), numerically. It has been noticed from our simulation study that the proposed estimators perform better than some of the existing ones. Applying these estimators, we construct several plug-in type classification rules and a rule that uses the likelihood accordance function. The performances of each of the rules are numerically evaluated using the expected probability of misclassification (EPM). Two real-life examples related to COVID-19 disease are considered for illustrative purposes.

StreamSAXS: a Python-based workflow platform for processing streaming SAXS/WAXS data.

StreamSAXS is a Python-based small- and wide-angle X-ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user-friendly tool for analysis of both batch data files and real-time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug-in framework, which enables developers to extend the built-in workflow tasks. Another feature is the support for both already acquired and real-time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large-scale acquisition systems for end-to-end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios.

PCP auto count: a novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting.

Introdution: During development, planes of cells give rise to complex tissues and organs. The proper functioning of these tissues is critically dependent on proper inter- and intra-cellular spatial orientation, a feature known as planar cell polarity (PCP). To study the genetic and environmental factors affecting planar cell polarity, investigators must often manually measure cell orientations, which is a time-consuming endeavor. To automate cell counting and planar cell polarity data collection we developed a Fiji/ImageJ plug-in called PCP Auto Count (PCPA). Methods: PCPA analyzes binary images and identifies "chunks" of white pixels that contain "caves" of infiltrated black pixels. For validation, inner ear sensory epithelia including cochleae and utricles from mice were immunostained for ßII-spectrin and imaged with a confocal microscope. Images were preprocessed using existing Fiji functionality to enhance contrast, make binary, and reduce noise. An investigator rated PCPA cochlear hair cell angle measurements for accuracy using a one to five agreement scale. For utricle samples, PCPA derived measurements were directly compared against manually derived angle measurements and the concordance correlation coefficient (CCC) and Bland-Altman limits of agreement were calculated. PCPA was also tested against previously published images examining PCP in various tissues and across various species suggesting fairly broad utility. Results: PCPA was able to recognize and count 99.81% of cochlear hair cells, and was able to obtain ideally accurate planar cell polarity measurements for at least 96% of hair cells. When allowing for a <10° deviation from "perfect" measurements, PCPA's accuracy increased to 98%-100% for all users and across all samples. When PCPA's measurements were compared with manual angle measurements for E17.5 utricles there was negligible bias (<0.5°), and a CCC of 0.999. Qualitative examination of example images of Drosophila ommatidia, mouse ependymal cells, and mouse radial progenitors revealed a high level of accuracy for PCPA across a variety of stains, tissue types, and species. Discussion: Altogether, the data suggest that the PCPA plug-in suite is a robust and accurate tool for the automated collection of cell counts and PCP angle measurements.

Equivalent consumption minimization strategy based on global optimization of equivalent factor for hybrid tractor.

Due to the increase in emission requirements for non-road vehicles in many countries and the reduction of agricultural personnel, tractors are developing towards high horsepower and electrification. According to the working conditions of high-horsepower tractors, a hydromechanical continuously variable transmission (HMCVT) is designed for hybrid tractors. Taking a tractor equipped with this transmission as the research object, an equivalent factor global optimization model was established and a genetic algorithm was used to optimize the equivalent factor S offline to obtain the optimal equivalent factor of the tractor under different operating mileage and the initial state of charge (SOC) of battery. By using the optimized equivalent factor, the tractor can be in the charge depleting (CD) mode for a longer time on the premise of making full use of the energy in the battery, so as to improve the auxiliary ability of the motor in the whole operation cycle to reduce the fuel consumption of the tractor. The effectiveness of the control strategy is verified by MATLAB/Simulink and hardware in the loop (HIL) tests, and the fuel economy of tractors is improved by 2.939% and 3.909% respectively in the two tests.

Synthesis and Biological Activities of Novel Pyrazole Carboxamides Containing an Aryloxypyridyl Ethylamine Module.

Pyrazole carboxamide is widely utilized in agricultural crop protection. In this research, we synthesized two classes of compounds, namely, pyrazole-5-carboxamide (4a) and pyrazole-4-carboxamide (4b), which are distinguished by the inclusion of the N-1-(6-aryloxypyridin-3-yl) ethylamine skeleton. This design was inspired by the frequent occurrence of diaryl ether modules in pesticide molecules. The bioassay results revealed that some compounds 4a exhibit higher insecticidal activity (IA) than 4b, while some compounds 4b display stronger fungicidal activity compared to 4a. This suggests that pyrazolyl plays a crucial role in determining the selectivity of these compounds toward different biological species. Notably, compound 4a-14 not only retains the potent activity of tolfenpyrad, the exact lead compound of 4a, against Lepidoptera pest Plutella xylostella and Thysanoptera pest Frankliniella occidentalis but also shows excellent IA against pests with piercing-sucking mouthparts, such as Aphis craccivora Koch and Nilaparvata lugens. This research has important implications for the control of pests with piercing-sucking mouthparts and the development of new insecticides and fungicides. The findings highlight the potential of inhibitory complex I as an effective control target for these pests, particularly those that have developed resistance to traditional insecticides. Additionally, it sheds light on the binding mode of 4b-11 and complex II, which serves as a negative reference for the design of SDHI fungicides. The study emphasizes the significance of pyrazolyl in determining selectivity in biological species and identifies avenues for future research in enhancing the biological activity of amino modules. The discovery of (S)-4a-14 not only presents a promising candidate compound for pesticide development but also provides valuable insights into the inhibitory effect of a respiratory chain complex on piercing-sucking insect pests. These findings have important implications in both theory and practice, offering new directions for pest control strategies and pesticide and fungicide development.

Research on Global Optimization Algorithm of Integrated Energy and Thermal Management for Plug-In Hybrid Electric Vehicles.

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO-DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO-DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29-84.67%, and the accuracy of the global optimal solution is improved by 0.94-16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO-DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68-23.74% under various driving conditions, which increases the energy-saving potential by 0.55-3.26% compared to just doing the energy management.

A comprehensive review on isolated and non-isolated converter configuration and fast charging technology: For battery and plug in hybrid electric vehicle.

Electric vehicle systems are a promising future transportation system because they play an important role in reducing atmospheric carbon emission and have become a focal point of research and development in the present era. The emerging fast charging technology has the ability to have refueling experiences comparable to gasoline cars. This article discusses existing electric vehicle charging infrastructure with a particular emphasis on rapid charging technologies, which would be needed to meet current and potential EV refueling requirements. Various dc-dc converter topologies for battery electric and plug-in hybrid vehicles are compared and contrasted in this article in terms of performance, output power, current ripples, voltage ripples, conduction loss, recovery loss, switching frequency loss, reliability, durability, and cost. The architecture, benefits, and drawbacks of AC-DC and DC-DC converter topologies for rapid charging stations are also discussed in this article. Furthermore, this study addresses the crucial problems and difficulties associated with electric vehicle converters for direct current rapid charging. Eventually, technical and relevant contributions are provided for an electric vehicle system development.

An Open-Source ABAQUS Plug-In for Delamination Analysis of 3D Printed Composites.

This article presents the development and implementation of the Delamination Plug-in, an open-source tool for modeling delamination tests in the ABAQUS software. Specifically designed for stochastic modeling of 3D printed composites, the plug-in combines the benefits of the graphical user interface (GUI) and the programming of commercial finite element (FE) software. The Delamination Plug-in offers an effortless alternative to the time-consuming analytical modeling and GUI work involved in delamination tests and includes algorithms for several tests, such as the double cantilever beam, end-loaded split, end-notched flexure, and modified end-loaded split tests, solved using the virtual crack closure technique and the cohesive zone method. It enables the user to develop simulations for both simple symmetric laminates and generally layered laminates with additional thermal stresses. The applicability of the tool is demonstrated through its use in two distinct delamination problems, one for conventional and one for 3D printed composite laminates, and its results are compared to analytical models and experimental data from the open literature. The results demonstrate that the Delamination Plug-in is efficient and applicable for such materials. This establishes the tool as an important means of automating delamination analysis and for the development and testing of 3D printed composites, making it a valuable tool for both researchers and industry professionals.

A Design for Safety (DFS) Framework for Automated Inspection Risks in Metro Stations by Integrating a Knowledge Base and Building Information Modeling.

Safety issues have always been of great concern to the metro construction industry. Numerous studies have shown that safety issues are closely related to the design phase. Many safety problems can be solved or improved by developing the design. This study proposes a structured identification method for safety risks based on the metro design specifications, journal literature, and expert experience. A safety knowledge base (KB) for the design was established to realize safety knowledge sharing and reusing. The KB has been developed into Building Information Modeling (BIM) software as an inspection plug-in to achieve automated analysis and retrieval of safety risks. The designers are provided with a visualization of risk components to locate and improve the pre-control measures of the design. Subsequently, the process of design for safety (DFS) database creation was demonstrated with a metro station project, and the feasibility of applying the KB to safety checking in BIM was verified. In response to the inspection results, safety risks in the construction phases can be eliminated or avoided by standardizing and improving the design.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

Comparison of a Scaled Cadaver-Based Musculoskeletal Model With a Clinical Upper Extremity Model.

Reliably and accurately estimating joint/segmental kinematics from optical motion capture data has remained challenging. Studies objectively characterizing human movement patterns have typically involved inverse kinematics and inverse dynamics techniques. Subsequent research has included scaled cadaver-based musculoskeletal (MSK) modeling for noninvasively estimating joint and muscle loads. As one of the ways to enhance confidence in the validity of MSK model predictions, the kinematics from the preceding step that drives such a model needs to be checked for agreement or compared with established/widely used models. This study rigorously compares the upper extremity (UE) joint kinematics calculated by the Dutch Shoulder Model implemented in the AnyBody Managed Model Repository (involving multibody kinematics optimization (MKO)) with those estimated by the Vicon Plug-in Gait model (involving single-body kinematics optimization (SKO)). Ten subjects performed three trials of (different types of) reaching tasks in a three-dimensional marker-based optical motion capture laboratory setting. Joint angles, processed marker trajectories, and reconstruction residuals corresponding to both models were compared. Scatter plots and Bland-Altman plots were used to assess the agreement between the two model outputs. Results showed the largest differences between the two models for shoulder, followed by elbow and wrist, with all root-mean-squared differences less than 10 deg (although this limit might be unacceptable for clinical use). Strong-to-excellent Spearman's rank correlation coefficients were found between the two model outputs. The Bland-Altman plots showed a good agreement between most of the outputs. In conclusion, results indicate that these two models with different kinematic algorithms broadly agree with each other, albeit with few key differences.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

High-yield ectoine production in engineered Corynebacterium glutamicum by fine metabolic regulation via plug-in repressor library.

Ectoine is a high-value protective and stabilizing agent with different applications in biopharmaceuticals, biotechnology, and fine chemicals. Here, efficient production of ectoine in Corynebacterium glutamicum was achieved by combination of metabolic engineering and plug-in repressor library strategy. First, the ectBAC cluster from Pseudomonas stutzeri was introduced into strain K02, and the titer of the obtained strain was 2.12 g/L. Metabolic engineering was then performed for further optimization, including removal of competing pathways (pck and ldh knockout), deletion of glycolysis repressor (sugR knockout), and enhancement of precursor supply (overexpression of Ecasd and CglysCS301Y). Next, two repressor libraries were designed for targeted flux control to improve ectoine production. Finally, strain CB5L6 produced 45.52 g/L ectoine and had the highest yield in C. glutamicum. For the first time, plug-in repressor library was employed to engineer C. glutamicum for metabolites production, which will provide a guideline for the construction of microbial cell factories.

A Dual-Adaptive Equivalent Consumption Minimization Strategy for 4WD Plug-In Hybrid Electric Vehicles.

Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%.

Inference for transition probabilities in non-Markov multi-state models.

Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling assumption is that the multi-state stochastic process is Markovian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regression analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research.

SAIBR: a simple, platform-independent method for spectral autofluorescence correction.

Biological systems are increasingly viewed through a quantitative lens that demands accurate measures of gene expression and local protein concentrations. CRISPR/Cas9 gene tagging has enabled increased use of fluorescence to monitor proteins at or near endogenous levels under native regulatory control. However, owing to typically lower expression levels, experiments using endogenously tagged genes run into limits imposed by autofluorescence (AF). AF is often a particular challenge in wavelengths occupied by commonly used fluorescent proteins (GFP, mNeonGreen). Stimulated by our work in C. elegans, we describe and validate Spectral Autofluorescence Image Correction By Regression (SAIBR), a simple platform-independent protocol and FIJI plug-in to correct for autofluorescence using standard filter sets and illumination conditions. Validated for use in C. elegans embryos, starfish oocytes and fission yeast, SAIBR is ideal for samples with a single dominant AF source; it achieves accurate quantitation of fluorophore signal, and enables reliable detection and quantification of even weakly expressed proteins. Thus, SAIBR provides a highly accessible low-barrier way to incorporate AF correction as standard for researchers working on a broad variety of cell and developmental systems.

Asymptotic Normality for Plug-In Estimators of Generalized Shannon's Entropy.

Shannon's entropy is one of the building blocks of information theory and an essential aspect of Machine Learning (ML) methods (e.g., Random Forests). Yet, it is only finitely defined for distributions with fast decaying tails on a countable alphabet. The unboundedness of Shannon's entropy over the general class of all distributions on an alphabet prevents its potential utility from being fully realized. To fill the void in the foundation of information theory, Zhang (2020) proposed generalized Shannon's entropy, which is finitely defined everywhere. The plug-in estimator, adopted in almost all entropy-based ML method packages, is one of the most popular approaches to estimating Shannon's entropy. The asymptotic distribution for Shannon's entropy's plug-in estimator was well studied in the existing literature. This paper studies the asymptotic properties for the plug-in estimator of generalized Shannon's entropy on countable alphabets. The developed asymptotic properties require no assumptions on the original distribution. The proposed asymptotic properties allow for interval estimation and statistical tests with generalized Shannon's entropy.

The Chinese plug-in electric vehicles industry in post-COVID-19 era towards 2035: Where is the path to revival?

The sudden Coronavirus Disease reported at the end of 2019 (COVID-19) has brought huge pressure to Chinese Plug-in Electric Vehicles (PEVs) industry which is bearing heavy burden under the decreasing fiscal subsidy. If the epidemic continues to rage as the worst case, analysis based on System Dynamics Model (SDM) indicates that the whole PEVs industry in China may shrink by half compared with its originally expected level in 2035. To emerge from the recession, feasible industrial policies include (1) accelerating the construction of charging infrastructures, (2) mitigating the downtrend of financial assistance and (3) providing more traffic privilege for drivers. Extending the deadline of fiscal subsidy by only 2 years, which has been adopted by the Chinese central government, is demonstrated to achieve remarkable effect for the revival of PEVs market. By contrast, the time when providing best charging service or most traffic privilege to get the PEVs industry back to normal needs to be advanced by 10 years or earlier. For industrial policy makers, actively implementing the other two promoting measures on the basis of existing monetary support may be a more efficient strategy for Chinese PEVs market to revive from the shadow in post-COVID-19 era.

Optimization of sensing-pad functionalizing strategy toward separative extended-gate FET biosensors for PSA detection.

In this study, separative extended-gate AlGaAs/GaAs high electron mobility transistor (HEMT) biosensor is proposed for prostate-specific antigen (PSA) detection. Zinc oxide nanotetrapods (T-ZnO) with a four-leg structure is introduced onto the sensing pad to form a three-dimensional (3D) and concave detection front. Compared with common plane front, the elaborately-designed-3D and concave detection front can offer more biological modification sites and decrease the Debye volume, resultantly improving both the detection scope and sensitivity. Anti-PSA probes can be fixed at any sites of T-ZnO via a chemical bio-functionalization method, which facilitates better detection performance by comparison with a physical modification scheme. On the other hand, the T-ZnO nanostructures with the four-leg configuration are capable of releasing the stress and erosion effect of the solution on the plane Au film, contributing to a great improvement in the reliability of the biosensors. The optimized biosensors with chemical bio-functionalized T-ZnO detection front demonstrate good linear current/voltage response to label-free PSA target in the concentration range from 5 fg/ml~5 ng/ml and a sensitivity variation ~ 1.3% dec-1.

PRETUS: A plug-in based platform for real-time ultrasound imaging research.

We present PRETUS - a Plugin-based Real Time UltraSound software platform for live ultrasound image analysis and operator support. The software is lightweight; functionality is brought in via independent plug-ins that can be arranged in sequence. The software allows to capture the real-time stream of ultrasound images from virtually any ultrasound machine, applies computational methods and visualizes the results on-the-fly. Plug-ins can run concurrently without blocking each other. They can be implemented in C++ and Python. A graphical user interface can be implemented for each plug-in, and presented to the user in a compact way. The software is free and open source, and allows for rapid prototyping and testing of real-time ultrasound imaging methods in a manufacturer-agnostic fashion. The software is provided with input, output and processing plug-ins, as well as with tutorials to illustrate how to develop new plug-ins for PRETUS.