Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Federated Reinforcement Learning Framework with Proximal Policy Optimization for Autonomous Driving

Reinforcement learning (RL) is widely used in autonomous driving tasks. The approach is well-suited for autonomous driving tasks because the environment is constantly changing and unpredictable. Autonomous vehicles adapt to changes and make safe and efficient decisions in real time. There has been a growing interest in using RL to develop autonomous vehicles that operate in complex and challenging environments, such as city streets and highways. To overcome the limit of the centralized model which needs more data transmission delay and computational complexity, we incorporated the proximal policy optimization (PPO) algorithm into every agent on simulation and integrated the federated learning (FL) for multiple clients to train with shared model weights. The model reduced the amount of data needed to train a model by sharing data from multiple vehicles in different scenarios for autonomous driving. In this study, we combined the different adaptive optimization with FL aggregation techniques on autonomous driving to implement and compare their performance. The experimental results showed that faster-accelerated coverage than local training, and FedYogi was significantly better than the other baselines.
Teaching Information Literacy and Critical Thinking Skills in Chat GPT Time

In this article, the unprecedented popularity of Chat GPT is acknowledged, generating positive interest but also raising concerns about AI’s potential to replace humans. Despite factual mistakes and hallucinations, Chat GPT’s enduring presence is emphasized, acknowledged with the record-breaking sales growth and Generative AI market projected almost 50% YoY growth. Amid controversies, particularly in education with New York City Public Schools banning its use due to concerns over cheating and setback of critical thinking and slowdown of problem-solving skills development, the author advocates for incorporating Chat GPT into the learning process. The article proposes an ‘upgrade’ of a Class designed in 2016, reinforcing the usage of mental models as the cognitive framework for effective prompt formulation. By effectively using Chat GPT in their Class assignment students will master its use for any given task, simultaneously enforcing generic skills needed for successful study and easier employment.
Current Stress Minimization in Dual Active Bridge Converter for Ultra-fast Charging of Electric Vehicles

This paper introduces a novel phase-shift modulation technique for isolated dual active bridge (DAB) converter utilized in ultra-fast charging of electric vehicles. The primary objective of this technique is to minimize the current stress in the converter, thereby improving its efficiency and reducing device losses. This can contribute to the development of more efficient and reliable power conversion systems for ultra-fast EV charging stations. The ability to regulate power through precise phase-shift control enhances the charging experience, allowing for faster charging times while maintaining optimal efficiency. Furthermore, the simplicity of implementation adds to the feasibility and scalability of integrating this technique into ultra-fast EV charging infrastructure. The proposed modulation technique employs two phase shift delays unlike the conventional single-phase shift (SPS) technique, which has only one delay angle. To evaluate the performance of the proposed modulation technique, a comparative analysis was conducted with the traditional SPS approach, demonstrating that the new modulation technique achieves better performance by reducing current stress and offering simplicity in implementation.
Exploring Business Applications of Internet Technology: Impact of Epidemic Risk Anxiety on Willingness to Pay for Synchronous Online Course Using UTAUT2 Model

With the advancement of the Internet of Things (IoT), Artificial intelligence (AI), and network technology, synchronous online courses are considered a viable substitute for physical training, particularly. In synchronous learning, the incorporation of IoT, coupled with real-time big data collection and analysis has facilitated the exploration of innovative educational concepts and the development of emerging technology application systems. Consequently, many free or paid synchronous online course platforms have emerged in recent years. For website operators in the competitive environment, the challenge lies in how to entice consumers to transition from free usage to a willingness to pay for their services. Additionally, during the stringent COVID-19 pandemic, consumers' perception of pandemic-related risks effectively increased their willingness to pay. Using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, we explored the factors influencing users' willingness to pay for access or related moderating elements. Additionally, gender, age, and prior experience interfered with their willingness to pay were investigated. 596 questionnaires were collected, and the data were analyzed by AMOS software. The results showed that habit, performance expectancy, facilitating conditions, and consumers' risk perception significantly affected consumers' willingness to pay. Groups with more than one year experience. and respondents over 30 years old were more likely to pay as they perceived anxiety from the pandemic. We provided specific suggestions for both potential consumers and platform designers to realize how to improve their decision-making process regarding their involvement in synchronous online course platforms. Valuable insights for internet-based interface and platform designers were offered to enhance users' willingness to pay.
Power Management of a Smart Transformer based Ship Microgrid

With extensive number of power electronically interfaced loads and generations, ship microgrids exhibit similar traits as terrestrial microgrids. The replacement of conventional power transformers with smart transformers in ship microgrid adds flexibility to the system. However, the presence of large number of smart power converters in the ship power systems introduce power quality problems, voltage and frequency violations, etc. Moreover, large dynamic loads in ships augment the challenges for ship power operators. This necessitates a proper control and power management strategy for a ship microgrid. This paper presents a power management strategy for a diesel generator driven smart transformer based ship microgrid. The proposed power management strategy aims to reduce the power drawn from diesel generator by minimizing the LVAC bus power, while maintaining the bus voltages within desired limits. Simulation results validate the effectiveness of the proposed strategy.
A Reconfigurable Power Converter for 6-Phase PMSM for Light Weight Electric Vehicle Application

This paper proposes a reconfigurable converter for a 6-phase permanent magnet synchronous motor (PMSM) associated with lightweight electric vehicles. It takes two 3-phase voltage source converters (VSI) to drive a 6-phase PMSM motor. In this paper, the same converters are reconfigured as the battery charger without adding extra semiconductor switches with the existing converter. The topology is altered in charging mode to create a front-end totem pole boost power factor correction (PFC) rectifier that is cascaded with a DC-DC converter through a DC link. The PFC state is followed by a half-bridge (HB) full-bridge (FB) dual active bridge (DAB) converter topology for providing isolation during the battery charging and desired step down in dc voltage for low voltage battery charging. The same converter is also used to charge the auxiliary battery without adding extra circuitry to the converter. The proposed converter eliminates the extra converter requirement for the charging application that results in overall less switch count, and the whole system’s weight, volume, and price are all decreased at the same time. The concept is verified on PLECS simulation software using a battery of 60 V, 400 Ah, with a charger rating of 1.5 kW for the charging mode of operation, and a 6-phase PMSM of 2 kW for the propulsion mode of operation.
Use of remote sensing, GIS and C++ for soil erosion assessment in the Shakkar River basin, India

ABSTRACT Soil is an essential resource for human livelihoods. Soil erosion is now a global environmental crisis that threatens the natural environment and agriculture. This study aimed to assess the annual rate of soil erosion using distributed information for topography, land use and soil, with a remote sensing (RS) and geographical information system (GIS) approach and comparison of simulated with observed sediment loss. The Shakkar River basin, situated in the Narsinghpur and Chhindwara districts of Madhya Pradesh, India, was selected for this study. The universal soil loss equation (USLE) with RS and GIS was used to predict the spatial distribution of soil erosion occurring in the study area on a grid-cell basis. Thematic maps of rainfall erosivity factor (R), soil erodibility factor (K), topographic factor (LS), crop/cover management factor (C), and conservation/support practice factor (P) were prepared using annual rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and a satellite image of the study area in the GIS environment. The annual rate of soil erosion was estimated for a 15-year period (1992–2006) and was found to vary between 6.45 and 13.74 t ha−1 year−1, with an average annual rate of 9.84 t ha−1 year−1. The percentage deviation between simulated and observed values varies between 2.68% and 18.73%, with a coefficient of determination (R 2) of 0.874.
Design and Simulation of Flight Control System for Fixed-Wing Drone Circling a Specific Point

Following a predefined circle path is a common mission for a drone. We calculated the track angle of the circular path for a fixed-wing drone and designed a lateral control system to achieve a fixed-radius path following. We developed autopilot hardware and software to validate the flight control system using Hardware-in-the-Loop (HIL) simulations. For the flight control system, we calculated the sum of the track angle of the circular flight path and the output of the radius controller for lateral control, which can be used as the control command to ensure seamless tracking during loitering. The lateral control command independently manages the aileron and the rudder. Using a 32-bit multi-core autopilot and Ground Control Station (GCS) software equipped with previously validated HIL, we simulated and compared the results. Findings indicated that using ailerons alone resulted in a minimum loiter radius of 40 m and using only the rudder resulted in an 85-meter radius with safe flight.
Power Quality Improvement for PV Array Reconfiguration based Grid Tied System

This work presents an efficient control scheme for a two-stage single-phase grid-tied PV system under partial shading conditions. The primary objective of this work is to improve photovoltaic (PV) power plant performance under partial shade conditions by increasing the electrical energy generation feeding to the grid. Therefore, this work proposes a PV array reconfiguration algorithm to enhance the PV power under partial shading conditions. Additionally, an enhanced MPPT algorithm is employed to optimize power extraction from the total cross tied (TCT) PV array. Moreover, to improve the power quality under nonlinear load conditions for a grid-connected mode, a cascaded complex variable complex coefficient filter-based approach, along with a sliding mode current controller, is proposed. The proposed reconfiguration algorithm efficacy is validated through extensive MATLAB simulations by considering the various random shading patterns. Further, the injected grid current THD remains within the limits prescribed by the IEEE-1547 standard while ensuring unity power factor operation.
A Finite Control Set Model Predictive Control for Grid Connected Three Phase Inverter with LCL Filter

This article presents a finite control set model predictive control (FCS-MPC) for a grid connected three phase inverter with LCL filter to control the active and the reactive power injection into grid.The LCL filter has advantages of attenuating the dominant harmonics.But it can produce strong resonance with grid current.Therefore an appropriate design of LCL filter is needed. Moreover, two different methods of FCSMPC has been implemented, namely, control in alpha-beta $(\alpha-\beta)$ frame, D-Q and modified D-Q frame.This Presented FCS model predictive control has one step prediction horizon with one bit lag delay. Therefore the overall prediction becomes two. The FCSMPC is tested and compared with PI controller (feed-forward decoupling control) in MATLAB/SIMULINK platform and the results are presented.
Analyzing Hybrid C4.5 Algorithm for Sentiment Extraction over Lexical and Semantic Interpretation

Internet-based social channels have turned into an important information repository for many people to get an idea about current trends and events happening around the world. As a result of Abundance of raw information on these social media platforms, it has become a crucial platform for businesses and individuals to make decisions based on social media analytics. The ever-expanding volume of online data available on the global network necessitates the use of specialized techniques and methods to effectively analyse and utilize this vast amount of information. This study's objective is to comprehend the textual information at the Lexical and Semantic level and to extract sentiments from this information in the most accurate way possible. To achieve this, the paper proposes to cluster semantically related words by evaluating their lexical similarity with respect to feature and sequence vectors. The proposed method utilizes Natural Language Processing, semantic and lexical clustering and hybrid C4.
Assessing the invasion potential of five common exotic vine species in temperate Australian rainforests

To compare the capacity of native and exotic vine species established under a rainforest canopy, a comparison of growth rates and resource allocation was made amongst five exotic vine species that are serious and common invaders and two common native vine species under two light conditions reflective of edge and interior canopy conditions. All species experienced heavy reductions in growth parameters in the low-light treatment, but three exotic species showed stronger growth under the low light. All exotic species had higher plasticity in leaf morphology showing a significant increase in SLA under low light. Native vines may have a lower capacity to change leaf morphology in shade, as a result of local adaptation to edge habitats. Higher SLA under both low and high light conditions suggests that exotic vines species are able to exploit a range of forest conditions better than the native species. Three species, Anredera cordifo-lia , Araujia sericifera and Cardiospermum grandiflorum , appear particularly capable of invading rainforest interiors. Individuals produced few leaves, focusing resources on roots and stems suggesting a response to reach the canopy quickly. With their long-distance seed dispersal, plasticity in leaf SLA and high RGR, these species appear most likely to invade undisturbed rainforest.
Reliable Active Learning via Influence Functions

Due to the high cost and time-consuming nature of collecting labeled data, having insufficient labeled data is a common challenge that can negatively impact the performance of deep learning models when applied to real-world applications. Active learning (AL) aims to reduce the cost and time required for obtaining labeled data by selecting valuable samples during model training. However, recent works have pointed out the performance unreliability of existing AL algorithms for deep learning (DL) architectures under different scenarios, which manifests as their performance being comparable (or worse) to that of basic random selection. This behavior compromises the applicability of these approaches. We address this problem by proposing a theoretically motivated AL framework for DL architectures. We demonstrate that the most valuable samples for the model are those that, unsurprisingly, improve its performance on the entire dataset, most of which is unlabeled, and present a framework to efficiently estimate such performance (or loss) via influence functions, pseudo labels and diversity selection. Experimental results show that the proposed reliable active learning via influence functions (RALIF) can consistently outperform the random selection baseline as well as other existing and state-of-the art active learning approaches.
Evolution of the Medicare IPPS to a Hospital Episode-of-Care Payment System

differences between E-IPPS and IPPS+Q across patient populations and types of hospitals. In analyzing patient socioeconomic status (SES), the percent of total IPPS hospital payments in penalties for hospitals caring for lower SES patients compared to hospitals caring for higher SES patients was 7.7% lower for E-IPPS, but 51% higher for IPPS+Q. Across different types of hospitals, the analysis revealed a general pattern that under E-IPPS, teaching hospitals, high Disproportionate Share Hospitals and rural hospitals have a lower percent of total IPPS hospital payments in penalties than under IPPS+Q. Large-size hospitals and large urban hospitals have a higher percent of total IPPS hospital payments in penalties under E-IPPS than under IPPS+Q.
Guidelines to Model-Based Usability Evaluation

Usability evaluation is usually performed at the final phases of the software development process, when there is at least a system prototype – functional or not. Doing this evaluation earlier – at the design phase – can decrease the costs to repair eventual problems. For this kind of evaluation, artifacts as usage scenarios, interaction and task models from Human-Computer Interaction area are used. And as methods, formal and automatic usability evaluation ones are usually used. Considering that, this kind of artifacts and methods are not widely used, this paper focuses on the proposal of an informal evaluation method to be used with widely known diagrams: guideline review applied to UML diagrams. The method could be considered an easy and inexpensive alternative to be used by the development team during the design process of interactive systems.
Stability Analysis of Standing Matter Wave Dark Solitons in a Coupled Bose-Einstein Condensate

: We study the existence and stability of standing matter waves in two quasi one-dimensional parallel coupled Bose-Einstein condensates in the presence of a magnetic trap. The system is modeled by linearly coupled Gross-Pitaevskii equations. In particular, the coupled dark soliton solutions are considered. The effect of changing strength of the magnetic trap on the stability of dark solitons is studied. It is found that the presence of a magnetic trap does not affect the stability of coupled dark solitons.
Next generation science standards : for states, by states

Next Generation Science Standards identifies the science all K-12 students should know. These new standards are based on the National Research Council's A Framework for K-12 Science Education. The National Research Council, the National Science Teachers Association, the American Association for the Advancement of Science, and Achieve have partnered to create standards through a collaborative state-led process. The standards are rich in content and practice and arranged in a coherent manner across disciplines and grades to provide all students an internationally benchmarked science education. The print version of Next Generation Science Standards complements the nextgenscience.org website and: * Provides an authoritative offline reference to the standards when creating lesson plans * Arranged by grade level and by core discipline, making information quick and easy to find * Printed in full color with a lay-flat spiral binding * Allows for bookmarking, highlighting, and annotating
Performance-dependent movement: An alternative driver of adaptive divergence

It is a tenet of evolutionary biology that local adaptation is driven by natural selection, while it is hindered by gene flow. This is because random movements between populations disrupt the match between phenotype and the local environment. However, if individuals moved to the environments where they have higher ecological performance, movements between populations could facilitate local adaptation. Here we show that movements of individuals towards their phenotype-dependent optimal breeding areas rapidly result in adaptive population divergence. We manipulated local ecological performance in a wild population of Eurasian tree sparrows by creating an artificial ecological trait that gave differential access to a new food source. Individuals exhibited a very strong preference for the breeding sites where they had the highest ecological performance. This promoted higher reproductive success, local adaptation, assortative mating, and reproductive isolation with respect to the novel trait. Our results experimentally show how local adaptation can be achieved by directed movements of individuals, if they settle in the environment to which they are best adapted. Considering this mechanism of adaptation will improve our understanding of how populations and species adapt and diverge. This may be especially relevant for biodiversity management under global change, where organisms face rapid and novel environmental changes.
Dancer in the Dark: Synthesizing and Evaluating Polyglots for Blind Cross-Site Scripting

Cross-Site Scripting (XSS) is a prevalent and well known security problem in web applications. Numerous methods to automatically analyze and detect these vulnerabilities exist. However, all of these methods require that either code or feed-back from the application is available to guide the detection process. In larger web applications, inputs can propagate from a frontend to an internal backend that provides no feedback to the outside. None of the previous approaches are applicable in this scenario, known as blind XSS (BXSS). In this paper, we address this problem and present the ﬁrst comprehensive study on BXSS. As no feedback channel exists, we verify the presence of vulnerabilities through blind code execution. For this purpose, we develop a method for synthesizing polyglots , small XSS payloads that execute in all common injection contexts. Seven of these polyglots are already sufﬁcient to cover a state-of-the-art XSS testbed. In a validation on real-world client-side vulnerabilities, we show that their XSS detection rate is on par with existing taint tracking approaches. Based on these polyglots, we conduct a study of BXSS vulnerabilities on the Tranco Top 100 , 000 websites. We discover 20 vulnerabilities in 18 web-based backend systems. These ﬁndings demonstrate the efﬁcacy of our detection approach and point at a largely unexplored attack surface in web security.