Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Detailed Analysis of a 7-Level Cascaded H-Bridge Inverter in Symmetric and Asymmetric Configurations

This paper discusses the advantages of asymmetric multilevel inverters over traditional inverters and presents a comparative analysis between symmetric and asymmetric cascaded H-bridge multilevel topologies. The use of multilevel inverters is gaining importance in sectors such as electric transportation and renewable energy integration due to their ability to generate high-quality waveforms with lower harmonic content. However, the cost associated with multilevel inverters is a challenge due to the large number of required components. The paper focuses on a single-phase 7-level binary asymmetrical Cascaded H-Bridge Multilevel Inverter (CHBMIs) and compares its performance with the symmetrical counterpart in terms of both harmonic content and total harmonic distortion. In addition, the percentage of power absorbed by both bridges of the asymmetric converter is also analyzed. The results presented in this work show demonstrate the effectiveness of the proposed asymmetrical converter.
MODIFICACIONES DEL PERFIL DE PLAYAS EN LAS ISLAS BALEARES (PLAYAS DE CAN PICAFORT Y ES COMÚ DE MURO)

Mallorca’s economic and social situation makes that the coast area, with emphasis in sandy coasts, has been considered from its profitability and in any case from their environmental conditions, changeables and vulnerables. The perception exposed cause that this space is crowded along the high season (summer), against the perception have it along the low season (winter), becoming forgotten spaces. In this way, the present paper expect to show and prove the dynamism that these places extend along a seasonal scale, and about the different behaviours showed by the beaches studied, depending of the human impacts.
Machine Learning Based Method to Design a Facial Emotion Detection and Chat Bot System

One of the active areas of research trends is recognizing emotions in images. This project aims to identify facial emotions. The research concept in Emotion Recognition is included in the flow of our emotion recognition. These involve image acquisition, image pre-processing, face detection, feature extraction, and classification, with the machine being applied after the emotion have been classified. Our framework relies on already-existing still images. This project aims to improve automated facial emotion recognition and build interaction between the system and the user (bot).
Review of sediment transport and beach profile changes under storm surge

Research of sediment movement and beach profile evolution under storm surge are summarized and reviewed,including sediment transport movement under storm surge,the relative lag phenomenon of sediment movement,the beach profile evolution under storm surge and the recovery of storm profile,the influence of sea level rise on beach profile evolution and exploring the feasibility of Bruun rule under storm surge.In order to grasp it more accurately about sediment movement under the storm surge,the hysteresis effect and the mechanism of the regular pattern of beach profile evolution,it is imperative to carry out the corresponding laboratory research.As the generalized simulation of storm surge is the direction of further research to breakthrough,Bruun rule may no longer be applicable to the beach profile erosion research under storm surge when the sea level rises.
Measuring gaze behavior to characterize spatial awareness skill in Rocket League

Spatial awareness is the ability to perceive the spatial relationships among objects, including other people and one’s own body, in the surrounding environment. A strong sense of spatial awareness is an essential component of athletic performance, allowing players to move and react effectively in a fast-paced, dynamic environment. Characterizing spatial awareness with athletes on, for example, a soccer pitch presents major challenges, however. The eSports game, Rocket League, shares some feature of fast-paced, dynamic ball sports including an expectation of increased spatial awareness with increasing player skill. Because Rocket League is played on a screen, we sought to characterize player skill in terms of both objective game data and gaze behavior. We trained a YOLOv7 neural network to robustly identify the ball and players from each frame of gameplay to form areas of interest for gaze behavior characterization. We measured gameplay and gaze behavior from complete novices, players with some but limited gameplay exposure, and very experienced players. We see this pilot effort as a start to characterizing spatial awareness in Rocket League play as it develops with game skill, as well as an opportunity to examine how spatial awareness may transfer from the physical world to screen-based environments and vice-versa.
Surface mass balance and stable oxygen isotope ratios from shallow firn cores on Fimbulisen, East Antarctica

Abstract The mass balance of Antarctica is one of the crucial factors for determining sea-level change in a warming climate. The marginal zones of the continent, namely the ice shelves, are most sensitive to climate change. During the 2009/10 austral summer an extensive glaciological field campaign was carried out on Fimbulisen, an ice shelf in East Antarctica, to investigate its recent surface mass balance. Shallow (10–18 m) firn cores were drilled and accumulation rates and stable-isotope ratios determined. For firn-core dating, two different methods were compared: (1) seasonal variations of stable oxygen isotope ratios (δ18O), and (2) dielectric profiling, including using the volcanic eruptions of Pinatubo, Philippines (1991), and El Chichόn, Mexico (1982), as time markers. The mean annual accumulation for the period 1992–2009 ranges from 298 to 349 mmw.e. a–1. The interannual variability at the drilling sites is >30%. Accumulation rates show a weak decreasing trend during the past 20–30 years, which is statistically significant only for one of the cores. Stable-isotope ratios were compared to the snowfall temperature of Neumayer station. Neither the temperatures nor the δ18O values show any trend for the investigated time period.
IoT Attack Classification Based on Generative Adversarial Networks

The Internet of Things (IoT) technology is widely used in various areas. Due to design constraints, IoT devices have low power consumption at a low cost. However, with increased risks to information security, IoT devices are easy targets of cyberattacks. These attacks render infrastructure devices unable to provide efficient information exchange. At the same time, the establishment of a deep learning (DL) model requires a large amount of data and takes a lot of time. Therefore, in this study, we proposed a method based on generative adversarial networks (GANs) for the classification of IoT attacks. In the original data set of 3 million records, only 50 thousand records were taken to train the GAN model. The generator automatically generated similar data and the discriminator in training, and the classification accuracy reached 80%.
Electrochemical Detection of Hormones Using Nanostructured Electrodes

Hormones regulate several physiological processes in living organisms, and their detection requires accuracy and sensitivity. Recent advances in nanostructured electrodes for the electrochemical detection of hormones are described. Nanostructured electrodes’ high surface area, electrocatalytic activity, and sensitivity make them a strong hormone detection platform. This paper covers nanostructured electrode design and production using MOFs, zeolites, carbon nanotubes, metal nanoparticles, and 2D materials such as TMDs, Mxenes, graphene, and conducting polymers onto electrodes surfaces that have been used to confer distinct characteristics for the purpose of electrochemical hormone detection. The use of aptamers for hormone recognition is producing especially promising results, as is the use of carbon-based nanomaterials in composite electrodes. These materials are optimized for hormone detection, allowing trace-level quantification. Various electrochemical techniques such as SWV, CV, DPV, EIS, and amperometry are reviewed in depth for hormone detection, showing the ability for quick, selective, and quantitative evaluation. We also discuss hormone immobilization on nanostructured electrodes to improve detection stability and specificity. We focus on real-time monitoring and tailored healthcare with nanostructured electrode-based hormone detection in clinical diagnostics, wearable devices, and point-of-care testing. These nanostructured electrode-based assays are useful for endocrinology research and hormone-related disease diagnostics due to their sensitivity, selectivity, and repeatability. We conclude with nanotechnology–microfluidics integration and tiny portable hormone-detection devices. Nanostructured electrodes can improve hormone regulation and healthcare by facilitating early disease diagnosis and customized therapy.
Listening Without Understanding : Central Bank Transparency, Financial Markets and the Crowding Out of Private Information

The trend of monetary policy transparency has recently extended itself to the practice of providing guidance on the likely direction of policy rates. There is a risk that communicating the central bankâ€™s own outlook for interest rates actually undermines the financial marketsâ€™ ability to predict monetary policy. This paper analyzes this risk using the Diamond (1985) model of a financial market, which includes both costly private information acquisition and a costless public signal. We demonstrate that a sufficiently precise signal from the central bank can result in a deterioration of the financial marketâ€™s ability to predict monetary policy through the crowding out of private information acquisition. Central banks could alleviate this risk with a policy of limiting the guidance offered to the financial market in order to leave sufficient scope for private information acquisition.
Generating Synthetic Population Using Transformer Based Networks

Microsimulation models are widely used in various fields. However, accessing individual-level data can be challenging due to confidentiality concerns and high costs. To address this, researchers often generate synthetic populations based on available data of the target population. In this paper, we present a novel Transformer-based approach for generating both numerical and categorical demographic attributes in synthetic populations. Our model achieves good fit with minor errors on the test set and preserves population heterogeneity, as indicated by the number of unique individuals in the real and synthetic populations (< 0.23% difference). Compared to the actual data, the synthetic population is moderately diverse, with an average distance of 0.00636 and standard deviation of 0.13836 between synthetic agents and real data. Our approach has potential applications in various domains, and we believe it can contribute to the development of more accurate and representative microsimulation models.
SoftMAC: Differentiable Soft Body Simulation with Forecast-based Contact Model and Two-way Coupling with Articulated Rigid Bodies and Clothes

Differentiable physics simulation provides an avenue to tackle previously intractable challenges through gradient-based optimization, thereby greatly improving the efficiency of solving robotics-related problems. To apply differentiable simulation in diverse robotic manipulation scenarios, a key challenge is to integrate various materials in a unified framework. We present SoftMAC, a differentiable simulation framework that couples soft bodies with articulated rigid bodies and clothes. SoftMAC simulates soft bodies with the continuum-mechanics-based Material Point Method (MPM). We provide a novel forecast-based contact model for MPM, which effectively reduces penetration without introducing other artifacts like unnatural rebound. To couple MPM particles with deformable and non-volumetric clothes meshes, we also propose a penetration tracing algorithm that reconstructs the signed distance field in local area. Diverging from previous works, SoftMAC simulates the complete dynamics of each modality and incorporates them into a cohesive system with an explicit and differentiable coupling mechanism. The feature empowers SoftMAC to handle a broader spectrum of interactions, such as soft bodies serving as manipulators and engaging with underactuated systems. We conducted comprehensive experiments to validate the effectiveness and accuracy of the proposed differentiable pipeline in downstream robotic manipulation applications. Supplementary materials and videos are available on our project website at https://sites.google. com/view/softmac.
Why Did the Energy Intensity Fall in China's Industrial Sector in the 1990s? The Relative Importance of Structural Change and Intensity Change

There have been a variety of studies investigating the relative importance of structural change and real intensity change to the change in China’s energy consumption in the 1980s. However, no detailed analysis to date has been done to examine whether or not the increased energy efficiency trend in the 1980s still prevailed in the 1990s. This article has filled this gap by investigating the change in energy consumption in China’s industrial sector in the 1990s, based on the data sets of value added and end-use energy consumption for the 29 industrial subsectors and using the newly proposed decomposition method of giving no residual. Our results clearly show that the overwhelming contributor to the decline in industrial energy use in the 1990s was the decline in real energy intensity, indicating that the trend of real energy intensity declines in the 1980s at the 2-digit level was still maintained in the 1990s. This conclusion still holds even if we lower the growth rate dramatically in line with the belief that the growth rate of China’s GDP may be overestimated.
Assessing the Near-Term Risk of Climate Uncertainty:Interdependencies among the U.S. States.

Policy makers will most likely need to make decisions about climate policy before climate scientists have resolved all relevant uncertainties about the impacts of climate change. This study demonstrates a risk-assessment methodology for evaluating uncertain future climatic conditions. We estimate the impacts from responses to climate change on U.S. state- and national-level economic activity from 2010 to 2050. To understand the implications of uncertainty on risk and to provide a near-term rationale for policy interventions to mitigate the course of climate change, we focus on precipitation, one of the most uncertain aspects of future climate change. We use results of the climate-model ensemble from the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report (AR4) as a proxy for representing climate uncertainty over the next 40 years, map the simulated weather from the climate models hydrologically to the county level to determine the physical consequences on economic activity at the state level, and perform a detailed 70-industry analysis of economic impacts among the interacting lower-48 states. We determine the industry-level contribution to the gross domestic product and employment impacts at the state level, as well as interstate population migration, effects on personal income, and consequences for the U.S. trade balance.more » We show that the mean or average risk of damage to the U.S. economy from climate change, at the national level, is on the order of $1 trillion over the next 40 years, with losses in employment equivalent to nearly 7 million full-time jobs. « less
Applying Deep Reinforcement Learning for Self-organizing Network Architecture

Inefficient resource allocation and unstable connection quality for mobile devices are the primary challenges of Self-Organizing Networks (SON). Frequent handovers between base stations result in a network burden imbalance. In contrast, unstable connection quality causes disconnection or signal interference between mobile devices and base stations, influencing network performance and reliability. In recent years, wireless communication technology has extensively used Reinforcement Learning (RL) to obtain the optimal strategy through continuous interaction between agents and their environments. Deep Reinforcement Learning (DRL) is based on Deep Neural Networks (DNN) to handle increasingly complex network situations. We proposed a SON architecture based on DRL in response to the aforementioned challenges. We described how the agent learns the optimal parameter settings through training based on various network scenarios to develop handover strategies and enhance overall network performance and resource utilization. The proposed framework can be applied to the present Fifth Generation (5G) network.
Relightable Gaussian Codec Avatars

The fidelity of relighting is bounded by both geometry and appearance representations. For geometry, both mesh and volumetric approaches have difficulty modeling intricate structures like 3D hair geometry. For appearance, existing relighting models are limited in fidelity and often too slow to render in real-time with high-resolution continuous environments. In this work, we present Relightable Gaussian Codec Avatars, a method to build high-fidelity relightable head avatars that can be animated to generate novel expressions. Our geometry model based on 3D Gaussians can capture 3D-consistent sub-millimeter details such as hair strands and pores on dynamic face sequences. To support diverse materials of human heads such as the eyes, skin, and hair in a unified manner, we present a novel relightable appearance model based on learnable radiance transfer. Together with global illumination-aware spherical harmonics for the diffuse components, we achieve real-time relighting with spatially all-frequency reflections using spherical Gaussians. This appearance model can be efficiently relit under both point light and continuous illumination. We further improve the fidelity of eye reflections and enable explicit gaze control by introducing relightable explicit eye models. Our method outperforms existing approaches without compromising real-time performance. We also demonstrate real-time relighting of avatars on a tethered consumer VR headset, showcasing the efficiency and fidelity of our avatars.
Minilag Filter for Jitter Elimination of Pose Trajectory in AR Environment

In AR applications, the jitter of virtual objects can weaken the sense of integration with the real environment. This jitter is often caused by noise in the pose obtained by 3D tracking or localization methods, especially in monocular vision systems without IMU support. Filtering the pose is an effective method to eliminate jitter, however, it can also cause significant lag in the filtered pose, seriously degrading the AR experience. Existing filters struggle to simultaneously reduce jitter while maintaining low lag. In this paper, we propose a novel Minilag filter, which achieves excellent pose smoothing while significantly reducing the lag through backtracking update and compensation strategies, and has excellent real-time performance. We represent the rotation in the pose in the Lie algebra and filter it in locally Euclidean space, ensuring that the filtering of rotation is consistent with that of vectors. We also analyze the noise distribution and characteristics in the tracked pose, providing a theoretical basis for setting filter parameters. We evaluated the proposed filter using both objective mathematical metrics and a user study, and the experimental results demonstrate that our method achieves state-of-the-art performance.
Invited perspective: “Natural hazard management, professional development and gender equity: let’s get down to business.”

Women constitute a minority in the geoscience professional environment (around 30%, e.g., UNESCO, 2015; 15 Gonzales, 2019; Handley et al., 2020), and as a consequence, they are underrepresented in disaster risk reduction 16 (DRR) planning. Zaidi and Fordham (2021) pointed out that the Sendai Framework for Disaster Risk Reduction 2015– 17 2030 (SFDRR) has failed to effectively promote women and girls' inclusion in disaster policy. In addition, it represents 18 a missed opportunity to tackle gender (even beyond female-male dichotomy) based issues in DRR. Nevertheless, 19 practical actions have been promoted and applied in several contexts with promising results, but often they only remain 20 lessons learned in localised environments (Zaidi and Fordham, 2021). Instead, the global gender gap index, which 21 includes political empowerment, economic participation and opportunity, educational attainment, health, and survival, 22 reveals that average distance completed to parity is at only 68% in 2019. Although the gap closing rate has constantly 23 improved, it will take about 135.6 years to close it completely (WEF, 2021). These numbers do not yet account for 24 2020-2021 data, where the global pandemic has more strongly impacted women, their career, their opportunities, and 25 their health in comparison with men (e.g., Alon et al., 2020; Chandler et al., 2021; Yildirim and Eslen-Ziya, 2021). 26
Assessment of Nitrate and Pesticide Impacts to Bedrock Aquifers in Upland Agricultural Settings of the Western Kentucky Coal Field: Kentucky Senate Bill 271 Revised Contract Reports (1995-2000)

This report is a compilation of five Kentucky Senate Bill 271 Education and Research Program contract reports summarizing work completed between 1995 and 2000. Original reports were submitted to the University of Kentucky College of Agriculture. In project year 1995-96, efforts began to assess the impact of current agricultural practices for growing corn, soybean, alfalfa, and pasture on groundwater quality in a Western Kentucky Coal Field upland bedrock setting. A farm in Henderson County was selected as the project site at which nitrate-nitrogen (nitrate-N) concentrations in the domestic well water were consistently four times greater than the maximum contaminant level of 10 mg/L. Four existing domestic wells and one stream were sampled for field measurements (pH, electrical conductivity, temperature, and Eh), chloride, sulfate, nitrate-N, and herbicides. In project year 1996-97, soil cores were collected in each of the agricultural fields (row crop, alfalfa, and pasture), the farmstead area, and an abandoned dairy feedlot. Also, monitoring wells were installed in the corn (eight wells), pasture (eight wells), and alfalfa (three wells) fields. Monitoring wells were sampled for field measurements, anions, metals, nutrients, herbicides, and nitrogen and oxygen isotopes. In project year 1997-98, 20 wells and four seeps were sampled for field measurements, anions, metals, nutrients, herbicides, and isotopes. Soil cores collected during the 1996-97 project year were analyzed for texture, Mehlich 3 extractable nutrients (phosphorus, potassium, calcium, magnesium, and zinc), pH in water, organic matter and total nitrogen by dry combustion, and potassium chloride extractable ammonium-nitrogen (ammonium-N) and nitrate-N. In project year 1998-99, sampling of all wells and seeps continued. Three additional wells were installed near an abandoned dairy feedlot. Each well was sampled for field measurements, anions, metals, nutrients, herbicides, and isotopes. Forty-six soil cores
Mapping Human Whole-Brain Structural Networks with

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.