Faculty leadership practices in graduate hybrid education

Hybrid education is an instructional delivery format that includes both online learning and traditional classroom learning and is often seen as the best of two worlds. It is one of the fastest-growing trends in higher education because of its countless educational benefits. Studies reviewed about hybrid learning focused on various areas, including student engagement, student attitudes, faculty experiences, learning effectiveness, and supporting technology tools. However, a review of the literature revealed scant studies focused on faculty leadership, especially graduate-level education faculty\u27s leadership strategies and their influence on teaching. Faculty leadership is a critical component that is directly related to effective teaching and school success (Berry et al., 2010). Therefore, the purpose of this study was to discover and identify faculty leadership practices used in graduate hybrid courses at a U.S.-based nonprofit university. Qualitative data related to faculty experiences were gathered to offer insights about leadership practices in graduate-level hybrid education. Aligned with the theoretical framework of this paper - Kouzes and Posner\u27s (2017) 5 exemplary leadership disciplines: model the way, inspire a shared vision, challenge the process, enable others to act, and encourage the heart, 21 significant leadership practices of faculty applying in graduate-level hybrid education were discovered. These findings indicated that leadership practices could be considered in graduate-level hybrid education to further the concept of teaching and learning

Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption

We study the infinite-horizon restless bandit problem with the average reward criterion, under both discrete-time and continuous-time settings. A fundamental question is how to design computationally efficient policies that achieve a diminishing optimality gap as the number of arms, $N$, grows large. Existing results on asymptotical optimality all rely on the uniform global attractor property (UGAP), a complex and challenging-to-verify assumption. In this paper, we propose a general, simulation-based framework that converts any single-armed policy into a policy for the original $N$-armed problem. This is accomplished by simulating the single-armed policy on each arm and carefully steering the real state towards the simulated state. Our framework can be instantiated to produce a policy with an $O(1/\sqrt{N})$ optimality gap. In the discrete-time setting, our result holds under a simpler synchronization assumption, which covers some problem instances that do not satisfy UGAP. More notably, in the continuous-time setting, our result does not require any additional assumptions beyond the standard unichain condition. In both settings, we establish the first asymptotic optimality result that does not require UGAP.Comment: 29 pages, 4 figure

A Gyro Signal Characteristics Analysis Method Based on Empirical Mode Decomposition

It is difficult to analyze the nonstationary gyro signal in detail for the Allan variance (AV) analysis method. A novel approach in the time-frequency domain for gyro signal characteristics analysis is proposed based on the empirical mode decomposition and Allan variance (EMDAV). The output signal of gyro is decomposed by empirical mode decomposition (EMD) first, and then the decomposed signal is analyzed by AV algorithm. Consequently, the gyro noise characteristics are demonstrated in the time-frequency domain with a three-dimensional (3D) manner. Practical data of fiber optic gyro (FOG) and MEMS gyro are processed by the AV method and the EMDAV algorithm separately. The results indicate that the details of gyro signal characteristics in different frequency bands can be described with the help of EMDAV, and the analysis dimensions are extended compared with the common AV. The proposed EMDAV, as a complementary tool of the AV, which provides a theoretical reference for the gyro signal preprocessing, is a general approach for the analysis and evaluation of gyro performance

Watermarking for Neural Radiation Fields by Invertible Neural Network

To protect the copyright of the 3D scene represented by the neural radiation field, the embedding and extraction of the neural radiation field watermark are considered as a pair of inverse problems of image transformations. A scheme for protecting the copyright of the neural radiation field is proposed using invertible neural network watermarking, which utilizes watermarking techniques for 2D images to achieve the protection of the 3D scene. The scheme embeds the watermark in the training image of the neural radiation field through the forward process in the invertible network and extracts the watermark from the image rendered by the neural radiation field using the inverse process to realize the copyright protection of both the neural radiation field and the 3D scene. Since the rendering process of the neural radiation field can cause the loss of watermark information, the scheme incorporates an image quality enhancement module, which utilizes a neural network to recover the rendered image and then extracts the watermark. The scheme embeds a watermark in each training image to train the neural radiation field and enables the extraction of watermark information from multiple viewpoints. Simulation experimental results demonstrate the effectiveness of the method

Steganography for Neural Radiance Fields by Backdooring

The utilization of implicit representation for visual data (such as images, videos, and 3D models) has recently gained significant attention in computer vision research. In this letter, we propose a novel model steganography scheme with implicit neural representation. The message sender leverages Neural Radiance Fields (NeRF) and its viewpoint synthesis capabilities by introducing a viewpoint as a key. The NeRF model generates a secret viewpoint image, which serves as a backdoor. Subsequently, we train a message extractor using overfitting to establish a one-to-one mapping between the secret message and the secret viewpoint image. The sender delivers the trained NeRF model and the message extractor to the receiver over the open channel, and the receiver utilizes the key shared by both parties to obtain the rendered image in the secret view from the NeRF model, and then obtains the secret message through the message extractor. The inherent complexity of the viewpoint information prevents attackers from stealing the secret message accurately. Experimental results demonstrate that the message extractor trained in this letter achieves high-capacity steganography with fast performance, achieving a 100\% accuracy in message extraction. Furthermore, the extensive viewpoint key space of NeRF ensures the security of the steganography scheme.Comment: 6 pages, 7 figure

Sentinel lymph node biopsy in oral cavity cancer using indocyanine green: A systematic review and meta-analysis

This meta-analysis was conducted to evaluate the value of indocyanine green (ICG) in guiding sentinel lymph node biopsy (SLNB) for patients with oral cavity cancer. An electronic database search (PubMed, MEDLINE, Cochrane Library, Embase, and Web of Science) was performed from their inception to June 2020 to retrieve clinical studies of ICG applied to SLNB for oral cavity cancer. Data were extracted from 14 relevant articles (226 patients), and 9 studies (134 patients) were finally included in the meta-analysis according to the inclusion and exclusion criteria. The pooled sentinel lymph node (SLN) sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 88.0% (95% confidence interval [CI], 74.0-96.0), 64.0% (95% CI, 61.0-66.0), 2.45 (95% CI, 1.31-4.60), 0.40 (95% CI, 0.17-0.90), and 7.30 (95% CI, 1.74-30.68), respectively. The area under the summary receiver operating characteristic curve was 0.8805. In conclusion, ICG applied to SLNB can effectively predict the status of regional lymph nodes in oral cavity cancer

Effects of synthetic colloids on oxidative stress and inflammatory response in hemorrhagic shock: comparison of hydroxyethyl starch 130/0.4, hydroxyethyl starch 200/0.5, and succinylated gelatin

INTRODUCTION: This study compared the effects of hydroxyethyl starch 130/0.4, hydroxyethyl starch 200/0.5, and succinylated gelatin on oxidative stress and the inflammatory response in a rodent hemorrhagic shock model. METHODS: Sodium pentobarbital-anesthetized adult male Wistar rats (200 g to 220 g) were subjected to a severe volume-controlled hemorrhage using arterial blood withdrawal (30 mL/kg to 33 mL/kg) and resuscitated with a colloid solution at the same volume as blood withdrawal (hydroxyethyl starch 130/0.4, hydroxyethyl starch 200/0.5, or succinylated gelatin). Arterial blood gas parameters were monitored. Malondialdehyde (MDA) content and myeloperoxidase (MPO) activity in the liver, lungs, intestine, and brain were measured two hours after resuscitation. The levels of tumor necrosis factor-alpha (TNF-α) and interleukin-6 in the intestine were also measured. RESULTS: Infusions of hydroxyethyl starch 130/0.4, but not hydroxyethyl starch 200/0.5 or succinylated gelatin, significantly reduced MDA levels and MPO activity in the liver, intestine, lungs and brain, and it also inhibited the production of TNF-α in the intestine two hours after resuscitation. However, no significant difference between hydroxyethyl starch 200/0.5 and succinylated gelatin was observed. CONCLUSIONS: Hydroxyethyl starch 130/0.4, but not hydroxyethyl starch 200/0.5 or succinylated gelatin, treatment after hemorrhagic shock ameliorated oxidative stress and the inflammatory response in this rat model. No significant differences were observed after hydroxyethyl starch 200/0.5 or succinylated gelatin administration at doses of approximately 33 mL/kg

An Effective Conversation-Based Botnet Detection Method

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial-of-Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high-speed network environment makes botnet detection more difficult. To solve these problems, we improve the progress of packet processing technologies such as New Application Programming Interface (NAPI) and zero copy and propose an efficient quasi-real-time intrusion detection system. Our work detects botnet using supervised machine learning approach under the high-speed network environment. Our contributions are summarized as follows: (1) Build a detection framework using PF_RING for sniffing and processing network traces to extract flow features dynamically. (2) Use random forest model to extract promising conversation features. (3) Analyze the performance of different classification algorithms. The proposed method is demonstrated by well-known CTU13 dataset and nonmalicious applications. The experimental results show our conversation-based detection approach can identify botnet with higher accuracy and lower false positive rate than flow-based approach

Identification, molecular evolution, codon bias, and expansion analysis of NLP transcription factor family in foxtail millet (Setaria italica L.) and closely related crops

The NODULE-INCEPTION-like protein (NLP) family is a plant-specific transcription factor (TF) family involved in nitrate transport and assimilation in plants, which are essential for improving plant nitrogen use efficiency. Currently, the molecular nature and evolutionary trajectory of NLP genes in the C4 model crop foxtail millet are unknown. Therefore, we performed a comprehensive analysis of NLP and molecular evolution in foxtail millet by scanning the genomes of foxtail millet and representative species of the plant kingdom. We identified seven NLP genes in the foxtail millet genome, all of which are individually and separately distributed on different chromosomes. They were not structurally identical to each other and were mainly expressed on root tissues. We unearthed two key genes (Si5G004100.1 and Si6G248300.1) with a variety of excellent characteristics. Regarding its molecular evolution, we found that NLP genes in Gramineae mainly underwent dispersed duplication, but maize NLP genes were mainly generated via WGD events. Other factors such as base mutations and natural selection have combined to promote the evolution of NLP genes. Intriguingly, the family in plants showed a gradual expansion during evolution with more duplications than losses, contrary to most gene families. In conclusion, this study advances the use of NLP genetic resources and the understanding of molecular evolution in cereals