Nonparametric Tests of Tail Behavior in Stochastic Frontier Models

This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature

Redox-Sensitive Calcium/Calmodulin-Dependent Protein Kinase IIα in Angiotensin II Intra-Neuronal Signaling and Hypertension

Dysregulation of brain angiotensin II (AngII) signaling results in modulation of neuronal ion channel activity, an increase in neuronal firing, enhanced sympathoexcitation, and subsequently elevated blood pressure. Studies over the past two decades have shown that these AngII responses are mediated, in part, by reactive oxygen species (ROS). However, the redox-sensitive target(s) that are directly acted upon by these ROS to execute the AngII pathophysiological responses in neurons remain unclear. Calcium/calmodulin-dependent protein kinase II (CaMKII) is an AngII-activated intra-neuronal signaling protein, which has been suggested to be redox sensitive as overexpressing the antioxidant enzyme superoxide dismutase attenuates AngII-induced activation of CaMKII. Herein, we hypothesized that the neuronal isoform of CaMKII, CaMKII-alpha (CaMKIIα), is a redox-sensitive target of AngII, and that mutation of potentially redox-sensitive amino acids in CaMKIIα influences AngII-mediated intra-neuronal signaling and hypertension. Adenoviral vectors expressing wild-type mouse CaMKIIα (Ad.wtCaMKIIα) or mutant CaMKIIα (Ad.mutCaMKIIα) with C280A and M281V mutations were generated to overexpress either CaMKIIα isoform in mouse catecholaminergic cultured neurons (CATH.a) or in the brain subfornical organ (SFO) of hypertensive mice. Overexpressing wtCaMKIIα exacerbated AngII pathophysiological responses as observed by a potentiation of AngII-induced inhibition of voltage-gated

Detecting cyberattacks in industrial control systems using online learning algorithms

Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusion detection systems that serve as the first line of defense by monitoring and reporting potentially malicious activities. Classical machine-learning-based intrusion detection methods usually generate prediction models by learning modest-sized training samples all at once. Such approach is not always applicable to industrial control systems, as industrial control systems must process continuous control commands with limited computational resources in a nonstop way. To satisfy such requirements, we propose using online learning to learn prediction models from the controlling data stream. We introduce several state-of-the-art online learning algorithms categorically, and illustrate their efficacies on two typically used testbeds---power system and gas pipeline. Further, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance problem that is pervasive in industrial intrusion detection systems. Our experimental results indicate that the proposed algorithm can achieve an overall improvement in the detection rate of cyberattacks in industrial control systems

Health-Promoting Properties of Eucommia ulmoides

Eucommia ulmoides (EU) (also known as “Du Zhong” in Chinese language) is a plant containing various kinds of chemical constituents such as lignans, iridoids, phenolics, steroids, flavonoids, and other compounds. These constituents of EU possess various medicinal properties and have been used in Chinese Traditional Medicine (TCM) as a folk drink and functional food for several thousand years. EU has several pharmacological properties such as antioxidant, anti-inflammatory, antiallergic, antimicrobial, anticancer, antiaging, cardioprotective, and neuroprotective properties. Hence, it has been widely used solely or in combination with other compounds to treat cardiovascular and cerebrovascular diseases, sexual dysfunction, cancer, metabolic syndrome, and neurological diseases. This review paper summarizes the various active ingredients contained in EU and their health-promoting properties, thus serving as a reference material for the application of EU

AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection

The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Systems (IDSs), which employ offline training to safeguard IoT systems. However, such static IDSs struggle with real-world scenarios where IoT system behaviors and attack strategies can undergo rapid evolution, necessitating dynamic and adaptable IDSs. In response to this challenge, we propose AOC-IDS, a novel online IDS that features an autonomous anomaly detection module (ADM) and a labor-free online framework for continual adaptation. In order to enhance data comprehension, the ADM employs an Autoencoder (AE) with a tailored Cluster Repelling Contrastive (CRC) loss function to generate distinctive representation from limited or incrementally incoming data in the online setting. Moreover, to reduce the burden of manual labeling, our online framework leverages pseudo-labels automatically generated from the decision-making process in the ADM to facilitate periodic updates of the ADM. The elimination of human intervention for labeling and decision-making boosts the system's compatibility and adaptability in the online setting to remain synchronized with dynamic environments. Experimental validation using the NSL-KDD and UNSW-NB15 datasets demonstrates the superior performance and adaptability of AOC-IDS, surpassing the state-of-the-art solutions. The code is released at https://github.com/xinchen930/AOC-IDS