In this paper the Llama-2 and GPT-2 large language models are evaluated for their fundamental understanding of basic due process concepts. The reference implementations and versions fine-tuned on judicial opinions were prompted with questions addressing due process issues. The results were evaluated by an attorney.

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio.

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data was sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weatherunderground for the years 2020-2021. An analysis was performed concerning the seasonal, monthly and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station and a deployed wind turbine as well to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. It was observed that a seasonal and monthly complemetarity was observed between the wind and solar. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio.

Due to the rise of the IoV, a massive volume of data is generated and shared among connected vehicles, resulting in a wide range of security threats. Therefore, mutual authentication is vital to protect the integrity of the collected data and the driver's privacy in the IoV ecosystem. The proposed work responds to this need and presents a scheme that authenticates the vehicle and the driver to a cloud service managed by the fleet vehicle's owner. The vehicle is authenticated using its biometric signature generated by the Physical Unclonable Functions (PUFs), and the driver is authenticated using his biological biometrics. We evaluated the proposed scheme's performance using formal and informal security analysis. We evaluated the resilience of the proposed scheme against well-known attacks. We also compared the proposed scheme's computational complexity and communication cost with other related existing studies. The results demonstrate that the proposed scheme is robust against common attacks with less computation complexity compared to the current solutions in the literature.

In the Internet of Vehicles (IoV) system, the need for vehicles to authenticate themselves dynamically makes them vulnerable to physical, side channel, and cloning attacks. This article presents a lightweight, two-factor mutual authentication scheme and key agreement protocol suitable to the IoV system to address these issues. The proposed scheme uses Physical Unclonable Functions (PUF) to achieve the desired security requirements. The proposed scheme is organized using a four-layer model with four main communicating entities: the Vehicle (V), Road Side Unit (RSU), Central RSU (CRSU), and a Trusted Authority (TA). In the proposed scheme, a vehicle needs to authenticate itself only once, when it enters the coverage area of a CRSUx or handoff seamlessly between different RSUs that are under the same coverage of the CRSUx. Security and performance analysis of the proposed scheme are conducted to validate the proposed solution. The results demonstrate that the proposed scheme is robust against various attacks with less computation complexity compared to the current solutions in the literature.

The ubiquity of unmanned aerial vehicles (UAVs) or drones is posing both security and safety risks to the public as UAVs are now used for cybercrimes. To mitigate these risks, it is important to have a system that can detect or identify the presence of an intruding UAV in a restricted environment. In this work, we propose a radio frequency (RF) based UAV detection and identification system by exploiting signals emanating from both the UAV and its flight controller, respectively. While several RF devices (i.e., Bluetooth and WiFi) operate in the same frequency band as UAVs, the proposed framework utilizes a semi-supervised learning approach for the detection of UAV or UAV's control signals in the presence of other wireless signals such as Bluetooth and WiFi. The semi-supervised learning approach uses stacked denoising autoencoder and local outlier factor algorithms. After the detection of UAV or UAV's control signals, the signal is decomposed by using Hilbert-Huang transform and wavelet packet transform to extract features from the time-frequency-energy domain of the signal. The extracted feature sets are used to train a three-level hierarchical classifier for identifying the type of signals (i.e., UAV or UAV control signal), UAV models, and flight mode of UAV.

In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal to noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.

The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we propose a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7% and 86% at a signal-to-noise ratio of 30 dB and 18 dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment.

The purpose of this preliminary study was to determine smartphone usage, expressed level of interest, and intent to use mHealth apps among adults with comorbid type 2 diabetes (T2D) and depression. A convenience sample of adults (N=35) completed a Demographic and Mobile App Survey and the CESD-R-10. A majority reported using mobile apps (n=23, 65.7%) and felt comfortable or very comfortable using mobile apps (n=14, 46.7%). However, few respondents used a health app (n=6, 17.1%) or a diabetes-specific app for diabetes management (n=3, 8.6%). Adjusted, age and education were the two variables that independently impacted app use; those aged less than 55 years as well as those with a graduate degree were more likely to use apps. Being younger and having an advanced degree increased the odds of using a diabetes-specific app. The findings suggest that adults with T2D are amenable to using mHealth apps to manage diabetes.

The emergence of drones has added new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a drone. For this reason, people can take advantage of these aircraft to intrude into restricted or private areas. A Drone Detection and Identification (DDI) system is one of the ways of detecting and identifying the presence of a drone in an area. DDI systems can employ different sensing technique such radio frequency (RF) signals, video, sounds and thermal for detecting an intruding drone. In this work, we propose a machine learning RF-based DDI system that uses low band RF signals from drone-to-flight controller communication. We develop three machine learning models using the XGBoost algorithm to detect and identify the presence of a drone, the type of drones and the operational mode of drones. For these three XGBoost models, we evaluated the models using 10-fold cross validation and we achieve average accuracy of 99.96%, 90.73% and 70.09% respectively.

Authentication is a key challenge in Internet of Things (IoT), and it is a foundation for data integrity and confidentiality. Security and privacy preservation are crucial for the integration of IoT in our daily life. Due to the resource-constrained nature of IoT sensors, it is not applicable to utilize traditional cryptography techniques in IoT domains. Wireless network devices are used for IoT in a wide range of applications. In this paper, we propose an efficient and secure authentication architecture for IoT. Compared to the existing constrained applications Protocols, the proposed architecture increases efficiency by minimizing the number of message exchanges and implementing lightweight cryptosystem. Also, the proposed architecture increases security by eliminating any key storage on device memory as well as third party certificate authority. The main idea of the proposed architecture is to integrate lightweight cryptography and hardware security approaches to be the basis to face the current security challenges.

This chapter describes the formal modeling and machine-checking of a bump-in-the-wire device that secures field device communications in industrial control networks. Field devices serve as the connection points between computer-based control systems and the physical processes being controlled. Industrial control network traffic is routinely checked for transmission errors, but limited mechanisms are available for combating attacks that exploit industrial control protocols to target critical infrastructure assets. This chapter focuses on a bump-in-the-wire solution that can be retrofitted on field devices to provide security functionality. The TLA+ formal specification language in combination with the isolation guarantees provided by the seL4 microkernel are used to demonstrate that the bump-in-the-wire solution provides important security and liveness properties. The resulting machine-checked system correctly applies hash-based message authentication to verify the authenticity of incoming messages while being resistant to attacks.

This paper presents a comprehensive framework for the active and incremental learning of link quality (LQ) in robot networks. Mobile robots need foresight into the quality of their wireless links in order to proactively optimize routing, plan mobility routes, avoid disconnects, and make other network optimizations. However, the task of predicting LQ is nontrivial. Many environmental factors that influence the quality of radio wave propagation are dynamic, and thus, robots must continually learn and update their prediction models while they operate online. Prior work in the field uses online learning algorithms for predicting LQ, but this approach is costly in terms of energy and network capacity because of the need for a consistent stream of LQ labels to be transmitted from the receiver to the transmitter. Hence, this paper introduces a framework to reduce these overhead expenses by incorporating active learning to selectively label only a portion of the samples from the data stream. The framework also uses incremental training batches to conserve labeling resources, and updates the batches using change detection and forgetting mechanisms to mitigate concept drift. Experimental results reveal that the framework reduces label queries by up to 21.5% and prediction error by up to 9% after periods of concept drift.

BACKGROUND:In the United States, approximately 700 women die annually from pregnancy-related complications in the first year after birth; a significant number of the deaths occur after hospital discharge. Although postpartum monitoring is important, the standard practice is for one healthcare evaluation at 6 weeks post-birth. Most women are not aware of signs of postpartum complications.AIM:The aim of the pilot study was to develop a prototype of a mobile app aimed at increasing a new mother's ability to monitor her own health after childbirth.DESIGN:The design used mixed methods and procedures from human-centred design in an iterative process.METHODS:Data were collected by the researchers from January - May 2019 in a hospital that serves primarily low income and underserved women in the southern US. Three groups of women provided data related to health education preferences or their reaction to a mock-up or prototype mobile app. Several women completed the Mobile App Rating Scale (MARS; N = 22).RESULTS:Themes from interviews indicated that women (N = 5) preferred electronic health education and that they used apps to monitor their pregnancies. Other new mothers (n-5) described their overall reaction to the proposed features of the app which was incorporated into the design of the app that was tested by the third group of new mothers (N = 22) who were positive about interactions with the app. The MARS scores for the app were positive.CONCLUSIONS:New mothers indicated that they would be willing to use an app to monitor their own postpartum health.IMPACT:Data from the pilot study informed the development of a prototype mobile app that can now be used in a clinical trial with new mothers to monitor their own health and report concerns to healthcare providers.

Public safety and cybersecurity are integrated endeavors as pervasive computing puts interconnected devices everywhere. STEM training in computing and cyber security might occur within the traditional public safety framework, both of criminological study and practical implementation. We tested the responsiveness to cybersecurity training for law enforcement from this traditional perspective. Our findings suggest great potential within this group for expanding effective cyber security protections for communities through STEM computing and cyber security education. They further suggest a commensurate need for training and support to realize this potential.

The usage of wireless networks to teleoperate a robot in operational scenarios or safety-critical applications has increased significantly in recent years. In these environments, it is important to maintain a steady connection between the control center and the robot. One of the ways to ensure this steady connection is to estimate the Link Quality (LQ) between the robot and control center. Estimating the LQ can help in alerting an operator about a potential failure in a communication link. However, it is costly in terms of network resources to transmit control packets that will track or monitor the LQ over the network (i.e., it increases the network traffic). It becomes a more difficult task to estimate LQ when mobility of the robot is a constraint and the environment in which the robot is functioning is dynamic. In this work, we propose an offline machine learning based model that detects the operational environment of a robot using both radio and network parameters. Based on this environment, LQ is predicted. Our results show that we can use received signal strength, signal quality, expected transmission count and communication distance to predict LQ and characterize the operational environment of a robot. We achieved an F1-score of 81% on the test data using our model to classify the operational environment. The mean absolute error of 0.09 was obtained for predicting Throughput Potential Ratio (TPR).

Purpose: The purpose of this study was to describe new mothers' knowledge related to maternal mortality. Study Design and Methods: Using a cross-sectional design, new mothers were recruited from a postpartum unit of an academic health sciences center where the population was predominately low-income women. Before hospital discharge, they answered questions on their knowledge of potential postpartum complications that could lead to maternal mortality. Questions were based on recommendations from an expert nursing panel. Descriptive statistics were used for data analysis. Results: One hundred twenty new mothers participated. Results indicated that most new mothers knew that they should watch for heavy bleeding, a severe headache, and swelling after hospital discharge. However, fewer participants knew that a new mother could experience feelings that she could harm herself or her baby, have blood clots larger than a baby's hand, a temperature of 100.4 °F or higher, and odor with vaginal discharge. Courses of action new mothers would take if experiencing any of the warning signs included 18% of mothers would take no action, 76.7% would tell their boyfriend/husband/partner, 72.5% would inform their mother. Only 60% who would call the labor and delivery unit. Only 38% of the sample knew that pregnancy-related complications can occur for up to 1 year after birth, and 13% of mothers reported not knowing that complications can occur for up to 6 weeks postpartum. Clinical Implications: Our findings provide a foundation to enhance postpartum education for new mothers and their families and to potentially decrease rates of maternal mortality in the United States.

Abstract Purpose: The purpose was to critique existing parenting apps using established criteria and health literacy guidelines. Study Design: Descriptive methodology was used. Methods: The Apple App Store was searched using the terms parenting, child health, and infant health. To be included, the apps had to have relevant content (parenting, child health, or infant health), be in English, and contain parent education. After eliminating apps that failed to meet inclusion criteria from the original 203 apps, 46 apps were reviewed. The Patient Education Materials Assessment Tool was used to evaluate the health literacy subscales called Understandability and Actionability. Content analysis included Authority, Objectivity, Accuracy, Timeliness, and Usability. Results: The majority of the apps (70%) were in English only. The price ranged from free to $4.99. The purpose, target audience, and topics varied. Although all included apps were for parents, some were for more targeted groups of parents. The source of the information was not presented in 26% of the apps. Most apps took the user to a Web site or an article to read. Functionality of the apps was limited, with none of them providing a customized experience. Clinical Implications: Much development and research is needed before mobile health (mHealth) solutions can be recommended by nurses caring for new parents. It is critical that consumers and interdisciplinary professionals be involved in the early design phase of the product to ensure that the end product is acceptable and usable and that it will lead to healthy behaviors.

Purpose: The purpose was to critique existing parenting apps using established criteria and health literacy guidelines.Study Design: Descriptive methodology was used.Methods: The Apple App Store was searched using the terms parenting, child health, and infant health. To be included, the apps had to have relevant content (parenting, child health, or infant health), be in English, and contain parent education. After eliminating apps that failed to meet inclusion criteria from the original 203 apps, 46 apps were reviewed. The Patient Education Materials Assessment Tool was used to evaluate the health literacy subscales called Understandability and Actionability. Content analysis included Authority, Objectivity, Accuracy, Timeliness, and Usability.Results: The majority of the apps (70%) were in English only. The price ranged from free to $4.99. The purpose, target audience, and topics varied. Although all included apps were for parents, some were for more targeted groups of parents. The source of the information was not presented in 26% of the apps. Most apps took the user to a Web site or an article to read. Functionality of the apps was limited, with none of them providing a customized experience.Clinical Implications: Much development and research is needed before mobile health (mHealth) solutions can be recommended by nurses caring for new parents. It is critical that consumers and interdisciplinary professionals be involved in the early design phase of the product to ensure that the end product is acceptable and usable and that it will lead to healthy behaviors.

Higher layer applications, such as routing protocols and robot navigation systems, commonly depend upon link quality (LQ) estimates for improving the efficiency and reliability of wireless communications. LQ estimation is especially critical for maintaining connectivity in mobile ad hoc networks, which tend to be less reliable than infrastructure networks due to their decentralized and dynamic nature. However, estimating LQ for applications higher than the physical layer is challenging due to the underlying dynamics of wireless propagation and the mismatched temporal perspectives between the layers. Due to its relevance and difficulty, a significant research effort has been devoted to developing empirical methods for accurately estimating LQ. The goal of this survey is to provide a comprehensive review of the existing approaches to LQ estimation in IEEE 802.11-based ad hoc and mesh networks, with some exceptions that include sensor networks. The survey organizes the literature according to the different fundamental techniques, and also compares them in terms in terms of strengths and weaknesses. Finally, we conclude with the latest developments in LQ estimation, which involve machine learning, and provide recommendations for future work in the field.