National University System

This paper proposes a novel simulation-based framework for cascading failure analysis in cyber-physical power systems (CPPS), addressing the critical challenges of accurately modelling communication delays and dynamic power system responses. A queuing theory-based communication network model is developed to explicitly characterize the dynamic nature of end-to-end delays under fluctuating network traffic. This communication network model is then integrated with a dynamic power flow model that simulates power-frequency regulation during failures. This coupling models enables the characterisation of multi-timescale interactions during cascading failures and moves beyond conventional static or oversimplified analysis. Case studies validate that queuing delays significantly affect data availability, and the dynamic power flow model accurately responds to cyber-physical cascading failures with frequency deviations, verifying the framework's effectiveness.

Phycocyanin (C-PC) is a hydrophilic blue pigment found in cyanobacteria, rhodophytes, and cryptophytes. Its intense blue color and antioxidant capacity make phycocyanin an attractive substance for food and cosmetic industries. In this study, phycocyanin extracts from Arthrospira maxima, obtained by different extraction methods, were used to investigate the influence of preserving agents on thermostability of phycocyanin. C-PC remained stable at 60°C, but rapidly degraded at 70°C, and was completely lost at 80°C within 5 minutes. Addition of sorbitol, sucrose, and glucose at 32% (w/v) to phycocyanin extracted through ultrasonication and flocculation with chitosan in organic acid provided the highest stabilizing effects, with CR values of 96.67 ± 0.01 %, 83.41 ± 0.67 %, and 76.21 ± 1.34 %, respectively. The corresponding half-live values increased from 13.77 ± 0.21 min (control) to between 76.74 ± 5.12 and 614.83 ± 1.18 min, followed by xylitol, fructose, and maltose. Xylose, lactose, citric acid, and ascorbic acid showed no effects. The stabilizing effect of NaCl was dependent on pH of extracts, remaining stable at pH 6 – 7, but showing no significant effect at pH 3 – 4, and leading to precipitation. Incorporating 14 – 20% (w/v) NaCl in C-PC at pH 6.49 increased CR values from 88.61 ± 0.31 to 99.10 ± 0.01 %, with half-life values ranging from 172.03 ± 4.92 to 2291.19 ± 14.51 minutes. Conversely, the stabilizing effect of sugars was most effective in C-PC at pH 3 – 4, and least effective in C-PC obtained by freeze/thawing at pH 6 – 7.

This paper introduces a novel optimization method for designing rotated subarrays. Rotated subarrays typically employ modular configurations with large element spacing to enable cost-efficient phased array implementations. The main challenge is reducing grating lobes caused by the large spacing, which is a complex optimization problem. By carefully redesigning the optimization model, this non-convex problem is transformed into a convex form that can be solved efficiently. Numerical results demonstrate superior grating lobe suppression compared to existing methods, while full-wave electromagnetic simulations validate the approach.

PurposeThis research addresses a need in early childhood education for evidence-based teaching strategies that build emotional self-regulation skills in young children. The intervention assessed in this study focused on increasing the emotion vocabulary of preschool-aged students.Design/methodology/approachThis mixed-methods, quasi-experimental study evaluated the impact a dialogic reading approach combined with direct instruction of emotion words during a shared book-reading activity had on students' emotion vocabulary knowledge. The study was conducted in a licensed daycare center in a suburb of Chicago, Illinois, with ten four- and five-year-old students. Pre- and post-session surveys assessed the intervention's impact on the students' receptive and expressive vocabulary knowledge, and observation notes captured the students' responses to the intervention activities.FindingsThe results showed significant increases with small to medium effect sizes between the students’ pre- and post-session survey scores for both receptive and expressive emotion vocabulary knowledge, a strong positive correlation between the level of student engagement during the intervention and their emotion vocabulary assessment scores, and the impact other variables had on the intervention’s effectiveness.Practical implicationsThis research provides information on a culturally adaptable and quickly learned teaching strategy that could be used to build emotional self-regulation skills in the early childhood classroom.Originality/valueThis research uniquely applies this intervention as a universal strategy with preschool-aged children.

Compared with the conventional cone and cone-cylinder space targets, streamlined space targets (SSTs) have attracted significant attention due to low scattering performance. In this paper, a curved surface optimization method for SSTs is presented, which quantitatively demonstrates the radar cross section (RCS) reduction properties of SSTs; Then, the location and RCS characteristics of the sliding scattering center on the surface that is unique to SSTs are analyzed; Finally, a dynamic echo simulation algorithm is proposed based on scattering center characteristics. The simulation experiment shows that the generated SST echo reflects the characteristics of scattering centers and micro-motion accurately, which can provide data for the recognition of space targets.

Background: The (Corona Virus Disease) COVID infection became the source of serious disagreements between scientists, doctors, politicians and private citizens, directly after it’s detection in the population in January, 2020. The pandemic had some very unusual properties as well as the people’s reaction to it. It was feared, that the virus might be used, intentionally or accidentally, to interfere with the usual, democratic way of the upcoming election. Objective: Research the original COVID statistic and metadata to obtain better understanding of the pandemic including the biological (viral) as well as the social (human) aspects of it. Provide a professional, independent, non-biased view of the pandemic, that is not polluted by political and media suggestions. Materials and methods: Publicly available data-covering the first 5 months of the COVID pandemic, January, 2020-May, 2020 were collected and analyzed with the usual, common statistical methods, like student’s t-test and regression analyses. The political attitude of the states were characterized by a ratio (D/R) of the number of left, Democrat (D) and right, Republican (R) oriented citizens in the respective states, as determined by opinion research. Results: De novo statistical analyzes of publicly available data (covering the initial five months of the pandemic) suggests that the negative medical consequences of COVID pandemic were significantly more expressed in “Democrat-dominated” (D) states than in “Republican-dominated”, (R) states. The proportion of COVID test positive persons was higher in D-states (6.831 ± 1605, n=14 and 1,165 ± 323, n=13 respectively, p<0.001). The difference in mortality was even more striking, 8-times more person/million population died in COVID related conditions in solid D states than in solid R states (438 ± 128 and 59 ± 13; p<0.001, respectively). The COVID associated mortality increased two fold from right to left on the political scale of the states (2.7% ± 0.3 vs. 5.3% ± 0.5, p<0.001). The correlation between COVID deaths (mortality) and the age/pre-existing medical condition(s) of the diseased is very strong (R2= 0.98) and the overwhelming majority of COVID “victims” are persons who were close to death even independently of the epidemic. Conclusion: Alarming signals regarding the dangerousness of COVID infection seems to have originated in a few states with predominantly left oriented citizens (high D/R ratio). The COVID mortality statistic seems to be seen and analyzed with little or no respect for the context of mortality caused by other well-known diseases or even by the natural, age related deaths.

In radar electronic countermeasures, as the difference between jamming and targets continues to decrease, traditional methods that are implemented based on classical features are currently unable to meet the requirements of jamming detection. Compared with classical features such as texture, scale, and shape, shadow has better discernability and separability. In this paper, target shadow is investigated and applied to detect jamming in Synthetic Aperture Radar (SAR) images, and a SAR false target identification method based on shadow features is proposed. First, a difference image is generated by change detection, which can extract the shadow region in single-time SAR images. Then, a three-step differentiation condition is proposed, which can distinguish false targets from real targets. Simulated experimental results show that the proposed method can effectively extract the shadow region in SAR images and accurately distinguishreal and false targets. Furthermore, the potential of shadow in SAR image interpretation and electronic countermeasures is also demonstrated.

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique for studying gene expression patterns at the single-cell level. Inferring gene regulatory networks (GRNs) from scRNA-seq data provides insight into cellular phenotypes from the genomic level. However, the high sparsity, noise and dropout events inherent in scRNA-seq data present challenges for GRN inference. In recent years, the dramatic increase in data on experimentally validated transcription factors binding to DNA has made it possible to infer GRNs by supervised methods. In this study, we address the problem of GRN inference by framing it as a graph link prediction task. In this paper, we propose a novel framework called GNNLink, which leverages known GRNs to deduce the potential regulatory interdependencies between genes. First, we preprocess the raw scRNA-seq data. Then, we introduce a graph convolutional network-based interaction graph encoder to effectively refine gene features by capturing interdependencies between nodes in the network. Finally, the inference of GRN is obtained by performing matrix completion operation on node features. The features obtained from model training can be applied to downstream tasks such as measuring similarity and inferring causality between gene pairs. To evaluate the performance of GNNLink, we compare it with six existing GRN reconstruction methods using seven scRNA-seq datasets. These datasets encompass diverse ground truth networks, including functional interaction networks, Loss of Function/Gain of Function data, non-specific ChIP-seq data and cell-type-specific ChIP-seq data. Our experimental results demonstrate that GNNLink achieves comparable or superior performance across these datasets, showcasing its robustness and accuracy. Furthermore, we observe consistent performance across datasets of varying scales. For reproducibility, we provide the data and source code of GNNLink on our GitHub repository: https://github.com/sdesignates/GNNLink.

Prior research suggests that government policies can augment factor markets that fuel entrepreneurship. However, the impact of the transition from governments’ preferential policies for businesses to their sponsorship of firms is underexamined. We use an unanticipated policy transition in China in 2014 to shed light on this question. We examine how the consequences of the policy transition may vary between venture capital firms that receive government sponsorship (GVCs) and those that do not (IVCs). We use the novel synthetic difference-in-differences method to overcome the fundamental identification challenge that arises from the difficulty of constructing the appropriate counterfactual for GVCs. Our analysis reveals that, compared to the synthetic control, GVCs are associated with greater expansion of their inter-organizational networks manifested not only in direct connections but also in network communities (membership in cohesive network structure). Intriguingly, however, the policy transition does not lead to the superior economic performance of GVCs relative to synthetic control. We test possible mechanisms to uncover this puzzle and find that GVCs tend to make more geographically distant investments, which usually entail syndication partnerships but do not outperform proximate investments. We discuss the implications for the growing literature at the intersection of public-private partnership and entrepreneurship.