Search

Armed forces the world over have three primary functions – force development, force deployment and force employment. Defence policy plays a guiding role in all of these, but it is especially important in establishing the rationale for the creation of credible military deterrence. Usually, a defence policy presupposes the development of armed forces that are effective at executing their mandate, a condition that is measurablein terms of the discipline, skill and quality of the organisation. The study on which this article reports, used this concept to describe how change management – especially by focusing on incorporating effective prioritisation – could enhance the current defence policy. A general analysis of South African defence policy publications indicates that, indeed, the policymakers had thoroughly considered the effectiveness of the armed forces when they wrote the White Paper on Defence of 1996 and the Defence Review of 1998. By 2006, the South African Army had interpreted national defence policy and formulated a future strategy of its own very much in alignment with the ‘modern system’ approach of the original policy publications. The present study suggests that the principal reason for the large variance between defence policy, military capabilities, and real operational demands stems from the lack of effective prioritisation of defence. This article, therefore, concludes that the South African National Defence Force (SANDF) has been largely unsuccessful in complying with the demands of defence policy, irrespective of the fact that the policy by itselfmay be obsolete and/or inappropriate for the South African context. Furthermore, it is concluded that military effectiveness in meeting current operational demands is also doubtful. Finally, the schizophrenic organisational culture of the defence force may be the primary cause of its moving ever closer to reneging on its constitutional mandate.

This educational study aimed to explore the feasibility and acceptance of a literacy exercise adopted from the real world of scientific publishing in a cell and tissue biology course. For that purpose, a tertiary-level multimodality science course, which integrated a blended learning faculty and student lectures, journal club, and wet laboratory sessions including a research project as well as examinations, was complemented by essay writing of a review and peer reviewing of five manuscripts. All tasks contributed to the final course mark. Special emphasis was laid on the usability of different E-Learning applications for scientific writing and teacher- and peer-assessment procedures. Further, potential influences of student characteristics on their peer- and self-assessments as well as their acceptance of the feedback from their peers were evaluated. Seventy-five undergraduate students from different Bachelor programs were included in the study. Plagiarism check and double-blind assessments of the essays were performed using "Turnitin.com." Students self-assessed their paper and received feedback from five peers and the teacher. Peer assessment was more severe than the teacher- or self-assessment, and the peer mark correlated best with the final course mark. Students with better marks assessed more generously, and there had moderate tendencies for influences of gender and background on peer feedback behavior. The students perceived the writing and assessment exercises, especially being peer-assessed, as demanding, but rewarding and a great learning experience. The additional tasks were feasible using E-Learning technology, which should foster future biomedical courses to train writing skills and the ability to cope with different roles in the scientific community.

Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This paper introduces a detailed MATLAB (R2024a)/Simulink-based simulation platform that models a benchmark low-voltage industrial installation, including a six-pulse controlled rectifier, linear loads, and a capacitor bank for power factor correction. Systematic simulations are performed with the platform to examine the sensitivity of notch characteristics to key parameters within plausible ranges, such as short-circuit power at the point of common coupling, commutation reactance, firing angle, snubber circuits, and rated power of the rectifier. In addition, parameters such as the rated power of linear loads and the compensation power of the capacitor bank are examined. Other influencing parameters including background voltage unbalance and distortion are also modeled and considered. A comparative analysis with field measurements from German industrial LV networks validates the plausibility and suitability of the simulations. Building upon this platform, a Monte Carlo simulation approach is adopted to generate extensive datasets of realistic voltage notch waveforms by randomly varying these key parameters. A case study conducted under conditions typical of German LV networks demonstrates the applicability of the simulations. To support further research, the simulation platform and exemplary synthetic waveforms are provided alongside the paper, serving as a valuable tool for testing and designing strategies for analysis, detection, and monitoring of voltage notches. [ABSTRACT FROM AUTHOR]

Highlights: A python-based tool called DL-SCAN for automated cell segmentation and thorough analysis of fluorescence microscopy image data. A user-friendly interface with several pre- and post-processing options to streamline the analysis workflow. Single- and multi-cell analysis options. DL-SCAN provides valid segmentation results on both synthetic and real biological data. DL-SCAN tracks changes in cells' morphology over time in response to pathological events such as ischemia. DL-SCAN generates a range of statistics, such as rise time, rise rate, decay time, decay rate, duration, and amplitude of time traces with a few clicks. With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program's ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+ and Ca2+ imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types. [ABSTRACT FROM AUTHOR]

Cryptocurrencies (cryptos) have detrimental environmental effects due to their massive energy consumption. While several green crypto coins have been launched, the mainstream—environmentally unfriendly coins—still lead the market. In this study, we explore if influencer marketing, specifically fear‐of‐missing‐out (FOMO) appeals, can contribute to promoting green crypto coin purchases to encourage sustainable investments. The results of five studies (two lab experiments using neurobiological sensors, two online experiments, and a discrete choice experiment) show that social media postings conveying FOMO appeals decrease (increase) green crypto (normal crypto) coin investments. The results further show that message congruence and emotional value perceptions mediate this effect. Finally, we demonstrate that the observed results hold when adding governance and regulatory crypto support as well as the crypto price path evolution to the choice. The results provide notable theoretical contributions and implications for policymakers concerned with fostering more sustainable consumption behavior in the crypto context. [ABSTRACT FROM AUTHOR]

We report a method for serial X-ray crystallography at X-ray free-electron lasers (XFELs), which allows for full use of the current 120-Hz repetition rate of the Linear Coherent Light Source (LCLS). Using a micropatterned silicon chip in combination with the high-speed Roadrunner goniometer for sample delivery, we were able to determine the crystal structures of the picornavirus bovine enterovirus 2 (BEV2) and the cytoplasmic polyhedrosis virus type 18 polyhedrin, with total data collection times of less than 14 and 10 min, respectively. Our method requires only micrograms of sample and should therefore broaden the applicability of serial femtosecond crystallography to challenging projects for which only limited sample amounts are available. By synchronizing the sample exchange to the XFEL repetition rate, our method allows for most efficient use of the limited beam time available at XFELs and should enable a substantial increase in sample throughput at these facilities.

An accurate self-assessment of student work can enhance student learning and subsequently improve academic performance. Instructors can facilitate this process by providing "standards" that students can utilize as feedback when self-evaluating their understanding. Traditional forms of feedback, such as marked assessment tasks, are limited in their ability to serve as standards, as they do not adequately capture variations corresponding to different levels of understanding. To develop a complex understanding in physiology, students have to integrate concepts pertaining to different subcomponents of body systems. The present study attempted to ascertain if exposing students to variations in complexity would refine their ability to self-evaluate their understanding and capacity to integrate concepts. Students were tasked to answer an essay-length, open-ended physiology question to expose their current understanding of the topic. The change in students' self-marking of their answer before and after being exposed to the variations in conceptual understanding of the topic were used to determine whether improvements in self-evaluation accuracy occurred. These variations were presented as instructor-generated answers to the open-ended question, framed using the structure of the observed learning outcome (SOLO) taxonomy. Student scores in the integrative questions of the end-of-semester exam were used as a measure of student ability to integrate concepts. Findings indicated that this intervention led to improvements in student self-evaluation and exam performance, and the positive outcomes were replicated across multiple iterations of the activity.

The growth of renewables in public energy networks requires suitable strategies to assess the stable operation of the respective power electronic devices, e.g., inverters. Different assessment methods can be performed with regard to the available knowledge and the assessment objective, e.g., a specific frequency range or the input signal characteristics that are typically classified into small-signal and large-signal disturbances. This paper addresses the limits of the measurement-based small-signal stability analysis in the harmonic frequency range of commercially available single-phase inverters for photovoltaic applications. The harmonic stability is analyzed, and the results for a sinusoidal background voltage and distorted background voltages are assessed based on measurements. The measurements prove that even in the harmonic frequency range, the harmonic stability analysis can only provide a sufficient but not a necessary condition in terms of the statement towards an instable operation. [ABSTRACT FROM AUTHOR]