Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Risk, Effectiveness, and Benefits in Transportation Regulation

Abstract We review the practice of safety benefits analysis for federal transportation regulations in the USA. Using a case-study approach, we explore the linkages between risk assessment and benefits analysis, adding to previous work exploring these linkages for environmental health regulations. Challenges for calculating the benefits of transportation safety regulations arise because safety outcomes, like many noncancer health effects, typically do not have formal risk relationships like dose–response functions established for them. Analysts often rely on engineering or other expert judgments or resort to qualitative discussions to connect a regulatory intervention to its intended outcome. Challenges also arise when regulatory outcomes are intangible or do not have established metrics. Safety outcomes are not always measurable in concrete terms like mortality risk and may include difficult-to-operationalize concepts like “safety culture. ” If the outcome is not measurable, then quantifying or monetizing the expected effects of a regulation is not possible, and the ability to conduct robust qualitative discussions also may be limited. Economists evaluating benefits for safety regulations encounter limitations analogous to difficulties found in health regulations. To inform policymaking effectively, economists and safety experts could look to the relationship developed in environmental economics between economists and health scientists.
The Appraisal of Theories: Kuhn Meets Bayes

This paper claims that adoption of Bayes's theorem as the schema for the appraisal of scientific theories can greatly reduce the distance between Kuhnians and logical empiricists. It is argued that plausibility considerations, which Kuhn considered outside of the logic of science, can be construed as prior probabilities, which play an indispensable role in the logic of science. Problems concerning likelihoods, especially the likelihood on the "catchall," are also considered. Severe difficulties concerning the significance of this probability arise in the evaluation of individual theories, but they can be avoided by restricting our judgments to comparative assessments of competing theories.
Effect of Pelagic Sargassum on In Vitro Dry Matter and Organic Matter Degradation, Gas Production, and Protozoa Population

Simple Summary Despite studies conducted with other seaweed species on in vitro gas production, until now, no studies have been reported on the use of pelagic Sargassum as potential ruminant feed. Thus, the objective of the present study was to determine the effect of Sargassum inclusion, using tropical grass as substrate, on in vitro gas production kinetics. Additionally, heavy metals and macro- and microminerals were determined in Sargassum. For that, in vitro incubations were performed with different levels of Sargassum inclusion on a basal substrate (Stargrass hay). In vitro results showed that up to 30% pelagic Sargassum could be included in hay-based substrates from tropical grasses. Abstract This study determined the effect of pelagic Sargassum on in vitro dry matter and organic matter degradation, total gas production (TGP), and protozoa population. The treatments were different levels of Sargassum inclusion on a basal substrate (Stargrass hay; Cynodon nlemfuensis) as follows: T0 (control treatment based on Stargrass hay), T10 (90% Stargrass hay + 10% Sargassum), T20 (80% Stargrass hay + 20% Sargassum), and T30 (70% Stargrass hay + 30% Sargassum). Ruminal fermentation kinetics and protozoa population were determined during 72 h of in vitro incubations. Compared to control, dry matter degradability at 48 and 72 h and organic matter degradability at 24 and 48 h were higher in Sargassum treatments. TGP was lower with T20 at 48 h. The total population of protozoa and the concentration of Entodinium spp. were lower at T20 at 48 h and T30 at 72 h. Cl, S, Ca, K, and Zn (103, 5.97, 88.73, 285.70 g/kg, and 15,900 mg/kg) were high in Sargassum, reaching twice or even nine times higher than the contents in Stargrass (11.37, 1.60, 43.53, 87.73 g/kg, and 866.67 mg/kg). Overall, up to 30% pelagic Sargassum could be included in hay-based substrates from tropical grasses without negative effects on in vitro dry matter and organic matter degradability.
An extended stability theorem for nonlinear systems subject to slowly varying exogenous signals

Deals with the stability properties of nonlinear systems subject to slowly varying exogenous signals. First, a well-known stability result of linear time-varying systems is improved. Second, an extended stability theorem is established for nonlinear systems where exogenous signals are slowly-varying in the sense of moving-average. Finally, the application of this extended theorem to automated vehicle platoons is outlined.
Compact deep UV laser system at 222.5 nm by single-pass frequency doubling of high-power GaN diode laser emission

Deep ultraviolet (DUV) lasers emitting below 300 nm are of great interest for many applications, for instance in medical diagnostics or for detecting biological agents. Established DUV lasers, e.g. gas lasers or frequency quadrupled solid-state lasers, are relatively bulky and have high power consumptions. A compact and reliable laser diode based system emitting in the DUV could help to address applications in environments where a portable and robust light source with low power consumption is needed. In this work, a compact DUV laser system based on single-pass frequency doubling of highpower GaN diode laser emission is presented. A commercially available high-power GaN laser diode from OSRAM Opto Semiconductors serves as a pump source. The laser diode is spectrally stabilized in an external cavity diode laser (ECDL) setup in Littrow configuration. The ECDL system reaches a maximum optical output power of 700 mW, maintaining narrowband emission below 60 pm (FWHM) at 445 nm over the entire operating range. By direct single pass frequency doubling in a BBO crystal with a length of 7.5 mm a maximum DUV output power of 16 μW at a wavelength of 222.5 nm is generated. The presented concept enables compact and efficient diode laser based light sources emitting in the DUV spectral range that are potentially suitable for in situ applications where a small footprint and low power consumption is essential.
A Power-Aware Control Strategy for an Elbow Effort-Compensation Device

This work presents a reactive control strategy for loading and sudden unloading of an elbow effort-compensation device controlled in force. Through this control strategy, in addition to an individual's forearm weight, an external load can be detected and adaptively compensated via a feed-forward force reference, facilitating the execution of arbitrary movements by the wearer. In case of a sudden contact/load loss, a power-aware strategy is implemented to immediately eliminate the portion of external loading in the force reference. The adaptive compensation of the external loads is achieved through an electromyography interface. Instead, to react to sudden load releases, we set a power limit on the tendon, and continuously measure it through an encoder and a load cell connected with the cable. Two sets of experiments are designed to test the proposed load-releasing method on a bench-top setup with 2 kg, and 3.9 kg, and a human subject with 0.5 and 1 kg. Next, the overall scenario including load-compensation and load-releasing are carried out on eight human subjects with 0.5 and 1 kg loads to evaluate the release and compensation time, and the effort reduction with respect to non-powered exoskeleton case. Results show that the average compensation/release time (payload) among subjects is measured as 0.98/0.91 seconds (0.5 kg), and 1/0.86 seconds (1 kg). The average effort reduction among the subjects are also reported as 66.4%, and 67.11% for 0.5 kg, and 1 kg, respectively.
Reconstruction and Resilience in Rwandan Education Programming: A News Media Review

This study evaluates news portrayals of resilience in the newly renewed Education for Sustainable Peace in Rwanda initiative. There remains a gap in understanding about how Rwanda's education approaches are portrayed and disseminated to the public by news media. This research uses an inductive coding analysis and layers Entman's framing theory to evaluate new media portrayals of resiliency in the reporting of domestic and international media outlets. This research demonstrates that there has been little media attention paid to Rwanda's education system outside of Rwanda, despite the newly revised programming. This research also finds that in media sources which do discuss Rwanda's education system, portrayals of approaches to resilience that have the potential for inclusivity are far more common than approaches which are top-down and exclusive. These findings serve to contribute to literature in both the context of Rwanda's place in global governance and the broader discussions of educational resiliency.
Factors affecting student success in a first-year mathematics course: a South African experience

In spite of sustained efforts tertiary institutions implement to try and improve student academic performance, the number of students succeeding in first-year mathematics courses remains disturbingly low. For most students, the gap between their mathematical capability and the competencies they are expected and need to develop to function effectively in these courses persists even after course instruction. In this study, an instrument for identifying and examining factors affecting student performance and success in a first-year Mathematics university course was developed and administered to 86 students. The overall Cronbach's Alpha coefficient for the questionnaire was found to be 0.916. Having identified variables from prior research known to affect student performance, factor analysis was used to identify variables exhibiting the greatest impact on student performance. The variables included prior academic knowledge, workload, student approaches to learning, assessment, student support teaching quality, methods and resources. From the analysis, students' perceptions of their workload emerged as the factor having the greatest impact on student's performance, followed by the matriculation examination score. The findings are discussed and strategies that can be used to improve teaching and contribute to student success in a first-year mathematics course in a South African context are presented.
Queues with non-stationary input stream: Ross's conjecture

Characteristics of queues with non-stationary input streams are difficult to evaluate, therefore their bounds are of importance. First we define what we understand by the stationary delay and find out the stability conditions of single-server queues with non-stationary inputs. For this purpose we introduce the notion of an ergodically stable sequence of random variables. The theory worked out is applied to single-server queues with stationary doubly stochastic Poisson arrivals. Then the interarrival times do not form a stationary sequence (‘time stationary’ does not imply ‘customer stationary’). We show that the average customer delay in the queue is greater than in a standard M/G/1 queue with the same average input rate and service times. This result is used in examples which show that the assumption of stationarity of the input point process is non-essential.
Psychological Capital and Work Engagement: A Mediated Moderation Model of Positive Emotions and Emotional Intelligence

The current study sought to investigate and add critical consequences of work engagement to the literature on psychological capital in the healthcare sector of Pakistan. For better understanding of the mechanism, Positive Emotions (PE) is added as potential mediator between the relationship of psychological capital (PsyCap) and work engagement. Also, the moderating role of emotional intelligence is studied. A total of 370 registered nurses working in tertiary public and private hospitals of Lahore participated in the survey. Using convenience sampling technique, the data were collected through a standardized and structured questionnaire using cross-sectional time frame. Results revealed that PE partially mediated the relationship between PsyCap and work engagement. Moderation revealed a statistically significant moderation of EI between PsyCap and PE. Also, it was worth noting that mediating effect of positive emotions among psychological capital and work engagement was moderated by emotional intelligence at all three levels of moderation. Workplace interventions need to be developed and implemented to increase the psychological capital and work engagement of registered nurses. Findings from this research have implications for registered nurses and hospital management as well as the literature of positive psychology. The results underlined the vital role of sub dimensions of PsyCap, EI and PE in developing work engagement among registered nurses.
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction

Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply–demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data’s security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users’ privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user’s wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.
One-Class Adversarial Fraud Detection Nets With Class Specific Representations

Most fraud detection algorithms identify the anomaly by learning from a small number of existing fraud samples, therefore they are often ineffective when deal with complex and unknown situations. This article proposes a new one-class classification model called one-class adversarial fraud detection nets with class specific representations (CS-OCAN), which consists of modified autoencoders and Complementary generative adversarial networks (GAN). Firstly, the two-iteration framework is designed to make reasonable use of the reference data generated by Complementary GAN. Secondly, an additional loss function is added in the latent space of the autoencoder, which transforms the semi-supervised problem into a supervised problem that aims to maximize inter-class distances between two classes and minimize intra-class variances. We have conducted experiments on UMD Wikipedia dataset and Credit card fraud detection dataset. Experimental results show that our method CS-OCAN has significantly improved the detection accuracy and stability compared with the state-of-the-art one-class classification models.
A Rocky Mountain Storm. Part I: The Blizzard—Kinematic Evolution and the Potential for High-Resolution Numerical Forecasting of Snowfall

Abstract Over the 3-day period of 24–26 October 1997, a powerful winter storm was the cause of two exceptional weather phenomena: 1) blizzard conditions from Wyoming to southern New Mexico along the Front Range of the Rocky Mountains and 2) hurricane-force winds at the surface near Steamboat Springs, Colorado, with the destruction of about 5300 ha of old-growth forest. This rare event was caused by a deep, cutoff low pressure system that provided unusually strong, deep easterly flow over the Front Range for an extended period. The event was characterized by highly variable snowfall and some very large snowfall totals; over a horizontal distance of 15 km, in some cases, snowfall varied by as much as 1.0 m, with maximum total snowfall depths near 1.5 m. Because this variability was caused, in part, by terrain effects, this work investigates the capability of a mesoscale model constructed in terrain-following coordinates (the Regional Atmospheric Modeling System: RAMS) to forecast small-scale (meso γ), orogr.
An Identity-Based Proxy Re-Encryption Scheme With Single-Hop Conditional Delegation and Multi-Hop Ciphertext Evolution for Secure Cloud Data Sharing

Proxy re- encryption (PRE) provides a promising solution for applications requiring efficient and secure ciphertext conversion, such as secure cloud data sharing. While the one with the function of ciphertext evolution is more desirable for some long-term schemes involving key update. Recently, Yao et al. constructed an identity-based conditional proxy re- encryption scheme simultaneously supporting authorization and ciphertext evolution. However, its delegated ciphertext can be re- authorized, which may cause the authorization to exceed the original delegator’s expectation. In contrast, the single-hop PRE authorization scheme may be more appropriate for systems requiring better control over authorization. In this paper, an identity-based proxy re- encryption scheme with single-hop conditional delegation and multi-hop ciphertext evolution (IBPRE-SHCD-MHCE) is proposed. Also, its description, construction, and security analysis are given. The analysis and experimental comparison results show that the proposed scheme has improved the computational efficiency and storage consumption compared with the two underlying schemes.
Intersecting Inequalities in Education and on the Labour Market: Gender and Migration Background in Comparative Perspective

Gender and migration background have received ample attention in stratification research. Yet quantitative studies of inequality in education and on the labour market frequently examine them in isolation rather than from an intersectional perspective. I therefore ask: Do gender and migration background create additive disadvantages (the double jeopardy scenario), or do they interact to create specific inequalities faced by persons with specific combinations of characteristics (the intersectional perspective)? Results from two studies show that there is little variation in gender gaps by migration background in the domain of education across 9 countries, whereas gender gaps in labour market outcomes in Germany vary substantially across migrant groups and generations.
A Real-Time, Mesoscale Numerical Weather-Prediction System Used for Research, Teaching, and Public Service at The Pennsylvania State University

A mesoscale modeling system is being applied on an experimental basis at The Pennsylvania State University (Penn State) for production of real-time, high resolution, numerical weather forecasts for the northeastern United States. The initial model experimentation is being supported by Penn State. It is believed to be the first time that a real-time, three-dimensional mesoscale model has been run routinely at an American university, although mesoscale models have been run in real time in government laboratories. A version of the Penn State/NCAR mesoscale model is employed, using a two-way interacting nested grid with a fine-grid increment of 30 km, a coarse-grid increment of 90 km, and 15 computational levels. The forecast cycle is initiated automatically by the Department of Meteorology's Digital Equipment Corporation VAX 8350 system when all the required 0000 UTC surface and upper-air National Weather Service (NWS) data have been received, quality checked, and archived. Lateral boundary conditions are ex.
ICOE: A Lightweight Group-Consensus-Based Off-Chain Execution Model for Smart Contract-Based Industrial Applications

Programmable smart contracts facilitate the application of blockchain to many areas of industry, e.g., supply chains, energy, and healthcare. The off-chain execution model for blockchain smart contracts is a promising approach to achieve scalability for industrial applications, which executes each smart contract in one of the small-scale consensus groups. However, previous work based on such a model cannot efficiently handle cross-group contract transactions that involve multiple cross-group invocations between smart contracts because of the heavyweight state synchronization and repeated executions. In this article, we propose interactive consensus-based off-chain execution (ICOE), a lightweight group-consensus-based off-chain execution model, for cross-group contract transactions without the need of state synchronization and duplicate executions and also prove its correctness. The key novelty of our ICOE is to let the invoked smart contract only executed in its residing group and only return the execution result to the invoking smart contract while leveraging the two-phase commit protocol to guarantee consistent commits across multiple groups within the same invoking smart contract transaction. We implement ICOE and the extensive experimental results show that our ICOE achieves a 14× improvement of throughput on average compared with the state-of-the-art model.
Survey of Model-Based Security Testing Approaches in the Automotive Domain

Modern connected or autonomous vehicles (AVs) are highly complex cyber-physical systems. As a result of the high number of different technologies and connectivity features involved, testing these systems to identify security vulnerabilities is a big challenge. Security testing techniques, such as penetration testing, are often manual methods that are applied comparatively late in the vehicle development process. Thus, vulnerabilities are only detected late or after development, leading to higher costs and more patching effort. To reduce the amount of testing resources in general and enable early and automated testing, model-based testing methods have been established in several domains, such as information technology and the automotive domain. The transfer of model-based testing approaches to automotive security testing could help to detect vulnerabilities earlier than other, manual methods by automatically generating, executing, or simulating security tests. In this study, we review the literature on model-based test approaches in the automotive domain. First, we consider security-independent approaches to obtain an overview of applied models, formalisms, test selection criteria, and test generation techniques. In addition, we investigate, whether and how model-based approaches are applied for automotive security testing. Overall, we identified 63 publications related to model-based testing and 29 publications with regard to model-based security testing. The aim of this study is to provide an overview and direct comparison between these approaches. In this manner, the state of model-based security testing in the automotive domain, current challenges, and potential research areas are determined.