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This paper presents a detailed literature review on switched reluctance motor (SRM) and drive systems in electric vehicle (EV) powertrains. SRMs have received increasing attention for EV applications owing to their reliable structure, fault tolerance ability and magnet free design. The main drawbacks of the SRM are torque ripple, low power density, low power factor and small extended speed range. Recent research shows that multi-stack conventional switched reluctance motors (MSCSRM) and multi-stack switched reluctance motors with a segmental rotor (MSSRM-SR) are promising alternative solutions to reduce torque ripples, increase torque density and increase power factor. Different winding configurations such as single-layer concentrated winding (SLC), single layer mutually coupled winding (SLMC), double layer concentrated winding (DLC), double layer mutually coupled winding (DLMC) and fully-pitched winding (FP) are introduced in the literature in recent years to increase average torque and to decrease torque ripples. This research analyzes winding methods and structure of the SRMs, including conventional and segmental rotors. They have been compared and assessed in detail evaluation of torque ripple reduction, torque/power density increase, noise/vibration characteristics and mechanical structure. In addition, various drive systems are fully addressed for the SRMs, including conventional drives, soft-switching drives, drives with standard inverters and drives with an integrated battery charger. In this paper, the SRM control methods are also reviewed and classified. These control methods include strategies of torque ripple reduction, fault-diagnosis, fault-tolerance techniques and sensorless control. The key contributions of this paper provide a useful basis for detailed analysis of modeling and electromechanical design, drive systems, and control techniques of the SRMs for EV applications.

In electric vehicle (EV) applications, hybrid excitation of switched reluctance motors (HESRMs) are gaining popularity due to their advantages over other EV motors. The benefits include control flexibility, simple construction, high torque/power density, and the ability to operate over a broad speed range. However, modern HESRMs are constructed by increasing the air gap flux density with permanent magnets (PMs) in the excitation system in order to generate more electromagnetic torque. This study aims to investigate a new topology for increasing the torque capabilities of HESRM without the use of permanent magnets (PMs) or other rare-earth components. This paper provides a comprehensive evaluation of the static and dynamic characteristics, software analysis using the Ansys 2D finite element method (FEM), and an experimental demonstration of the real-time motor with an advanced control strategy in MATLAB/Simulink. Our simulation and experimental results for a machine with 12/8 poles and a machine rating of 1.2 kW indicate that the HESRM designed without PMs has greater torque capability and efficiency than the conventional SRM. The proposed HESRM without PMs has a high torque/power density and a higher torque per ampere across the entire speed range, making it suitable for EV applications.

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions.

• Mitochondrial respiration and tricarboxylic acid (TCA) cycle activity are required during salt stress in plants to provide ATP and reductants for adaptive processes such as ion exclusion, compatible solute synthesis and reactive oxygen species (ROS) detoxification. However, there is a poor mechanistic understanding of how salinity affects mitochondrial metabolism, particularly respiratory substrate source. • To determine the mechanism of respiratory changes under salt stress in wheat leaves, we conducted an integrated analysis of metabolite content, respiratory rate and targeted protein abundance measurements. Also, we investigated the direct effect of salt on mitochondrial enzyme activities. • Salt-treated wheat leaves exhibit higher respiration rate and extensive metabolite changes. The activity of the TCA cycle enzymes pyruvate dehydrogenase complex and the 2-oxoglutarate dehydrogenase complex were shown to be directly salt-sensitive. Multiple lines of evidence showed that the γ-aminobutyric acid (GABA) shunt was activated under salt treatment. • During salt exposure, key metabolic enzymes required for the cyclic operation of the TCA cycle are physiochemically inhibited by salt. This inhibition is overcome by increased GABA shunt activity, which provides an alternative carbon source for mitochondria that bypasses salt-sensitive enzymes, to facilitate the increased respiration of wheat leaves.

The parallel reaction monitoring (PRM) assay has emerged as an alternative method of targeted quantification. The PRM assay is performed in a high resolution and high mass accuracy mode on a mass spectrometer. This review presents the features that make PRM a highly specific and selective method for targeted quantification using quadrupole-Orbitrap hybrid instruments. In addition, this review discusses the label-based and label-free methods of quantification that can be performed with the targeted approach.

Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.

Persistently rising atmospheric greenhouse gas concentrations challenge dominant Liberal hopes that science and multilateralism might deliver rational, global climate outcomes. Emerging Realist climate approaches that take geopolitics and national interests more seriously have yet to explore Morgenthau’s concern that ‘scientism’ – exaggerated faith in scientific rationality to solve political problems – would lead to disastrous underestimations of power and irrationality. Recently, Realists have mooted ‘solar geoengineering’ designs as a ‘lesser evil’ option to deliberately cool the Earth independently of emissions reductions. However, assessments of solar geoengineering prospects barely factor in Realist concerns, focusing instead on idealised scientific modelling of bio-physical effects and Liberal governance scenarios. To explore how geoengineering techno-science would be ‘translated’ into security assessments, geopolitical logics were elicited through interviews and group discussions with (mainly Arctic-oriented) national security professionals. Security experts reframe solar geoengineering in three significant ways: (a) from a climate ‘global public good’ to a source of geopolitical leverage and disruption; (b) from a risk-reduction tool to a potential source of distrust and escalation; and (c) from a knowledge-deficit problem solvable by more research, to a potential disinformation vector. This expands Realist scholarship on climate change and identifies serious risks to ongoing scientific and commercial pursuit of such technologies.

The objective of this study was to develop a novel analytical chemistry method, comprised of a coupled high-performance liquid chromatography–gas chromatography/mass spectrometry system (LC–GC/MS) with low detection limits and high selectivity, for the identification and determination of oxygenated polycyclic aromatic hydrocarbons (OPAHs) and polycyclic aromatic hydrocarbons (PAHs) in urban air and diesel particulate matter. The linear range of the four OPAHs, which include 9,10-anthraquinone, 4 H -cyclopenta[def]phenanthrene-4-one, benzanthrone, and 7,12-benz[a]anthraquinone, was 0.7 pg–43.3 ng with limits of detection (LODs) and limits of quantification (LOQs) on the order of 0.2–0.8 and 0.7–1.3 pg, respectively. The LODs in this study are generally lower than values reported in the literature, which can be explained by using large-volume injection. The recoveries of the OPAHs spiked onto glass fiber filters using two different pressurized liquid extraction (PLE) methods were in the ranges of 84–107 and 67–110 %, respectively. The analytical protocols were validated using the following National Institute of Standards and Technology standard reference materials: SRM 1649a (Urban Dust), SRM 1650b (Diesel Particulate Matter), and SRM 2975 (Diesel Particulate Matter, Industrial Forklift). The measured mass fractions of the OPAHs in the standard reference materials (SRMs) in this present study are higher than the values from the literature, except for benzanthrone in SRM 1649a (Urban Dust). In addition to the OPAHs, 44 PAHs could be detected and quantified from the same particulate extract used in this protocol. Using data from the literature and applying a two-sided t test at the 5 % level using Bonferroni correction, significant differences were found between the tested PLE methods for individual PAHs. However, the measured mass fractions of the PAHs were comparable, similar to, or higher than those previously reported in the literature.

The National Institute of Standards and Technology (NIST) generates and maintains thousands of Standard Reference Materials (SRMs) to serve commerce worldwide. Many SRMs contain metrologically traceable mass fractions of known organic chemicals and are commercially available to aid the analytical chemistry community. One such material, NIST SRM 1957 Organic Contaminants in Non-Fortified Human Serum, was one of the first materials issued by NIST with measurements for per- and polyfluoroalkyl substances (PFAS) listed on the Certificate of Analysis and was commercially available in 2009. Since the release of SRM 1957, nearly 400 units have been sold to date, and over 50 publications related to PFAS measurements have included this material for multiple analytical purposes, such as a quality control material, for interlaboratory comparison, as an in-house comparison tool, for inter- and intra-day measurement accuracy, as an indicator of isomeric patterns of PFAS, and for other uses. This perspective details the ways SRM 1957 is utilized by the analytical community and how data have been reported in the literature. A discussion on accurately comparing SRM data to generated data is included. Furthermore, we conducted an in-depth investigation around additional applications for NIST SRMs, such as a matrix-matched reference material, and for the identification of targeted compounds during high-resolution mass spectrometry data collection. Ultimately, this manuscript illustratively describes the ways to utilize a NIST SRMs for chemicals of emerging concern.

BackgroundQuality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.Aim of reviewThis tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.Key scientific concepts of reviewSystem suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.