Miracle-dz/UniFAR-FTUs huggingface dataset:
Model and Control of Integrated Six-switch Buck-Boost and CLLLC Cascaded Converter

An integrated six-switch Buck-Boost and CLLLC cascaded converter (ISSBB-CLLLC) is proposed in this paper, which is suitable for high-voltage applications. To realize the flying capacitor voltage balance, current ripple optimization and output voltage regulation, a model of the converter is established, and a control strategy is proposed. Firstly, the balance of flying capacitor voltage in six-switch Buck-Boost circuit is analyzed, and a voltage balance control method (FCV-BCM) is proposed, which is implemented by alternatively using the redundant switching states. Next, a model of the ISSBB-CLLLC is derived and a dual closed-loop control based on phase shift control (DLC-PSC) is proposed to regulate the output voltage, which can further optimize the current ripple of inductor in six-switch Buck-Boost circuit as well. Finally, the experimental results are given to verify the effectiveness of the control strategies.
Spatial, Temporal, and Density-Dependent Components of Habitat Quality for a Desert Owl

Spatial variation in resources is a fundamental driver of habitat quality but the realized value of resources at any point in space may depend on the effects of conspecifics and stochastic factors, such as weather, which vary through time. We evaluated the relative and combined effects of habitat resources, weather, and conspecifics on habitat quality for ferruginous pygmy-owls (Glaucidium brasilianum) in the Sonoran Desert of northwest Mexico by monitoring reproductive output and conspecific abundance over 10 years in and around 107 territory patches. Variation in reproductive output was much greater across space than time, and although habitat resources explained a much greater proportion of that variation (0.70) than weather (0.17) or conspecifics (0.13), evidence for interactions among each of these components of the environment was strong. Relative to habitat that was persistently low in quality, high-quality habitat buffered the negative effects of conspecifics and amplified the benefits of favorable weather, but did not buffer the disadvantages of harsh weather. Moreover, the positive effects of favorable weather at low conspecific densities were offset by intraspecific competition at high densities. Although realized habitat quality declined with increasing conspecific density suggesting interference mechanisms associated with an Ideal Free Distribution, broad spatial heterogeneity in habitat quality persisted. Factors linked to food resources had positive effects on reproductive output but only where nest cavities were sufficiently abundant to mitigate the negative effects of heterospecific enemies. Annual precipitation and brooding-season temperature had strong multiplicative effects on reproductive output, which declined at increasing rates as drought and temperature increased, reflecting conditions predicted to become more frequent with climate change. Because the collective environment influences habitat quality in complex ways, integrated approaches that consider habitat resources, stochastic factors, and conspecifics are necessary to accurately assess habitat quality.
Exploiting Subword Permutations to Maximize CNN Compute Performance and Efficiency

Neural networks (NNs) are quantized to decrease their computational demands and reduce their memory foot-print. However, specialized hardware is required that supports computations with low bit widths to take advantage of such optimizations. In this work, we propose permutations on subword level that build on top of multi-bit-width multiply-accumulate operations to effectively support low bit width computations of quantized NNs. By applying this technique, we extend the data reuse and further improve compute performance for convolution operations compared to simple vectorization using SIMD (single-instruction-multiple-data). We perform a design space exploration using a cycle accurate simulation with MobileNet and VGG16 on a vector-based processor. The results show a speedup of up to $3.7\times$ and a reduction of up to $1.9\times$ for required data transfers. Additionally, the control overhead for orchestrating the computation is decreased by up to $3.9\times$.
The role of aerosol variations in anthropogenic ozone depletion at northern midlatitudes

Aerosol surface area distributions inferred from satelliteborne 1-μm extinction measurements are used as input to a two-dimensional model to study the effects of heterogeneous chemistry upon anthropogenic ozone depletion at northern midlatitudes. It is shown that short-term (interannual) and longer-term (decadal) changes in aerosols very likely played a substantial role along with trends in anthropogenic chlorine and bromine in both triggering the ozone losses observed at northern midlatitudes in the early 1980s and increasing the averaged long-term ozone depletions of the past decade or so. The use of observed aerosol distributions enhances the calculated ozone depletion due to halogen chemistry below about 25 km over much of the past decade, including many periods not generally thought to be affected by volcanic activity. Direct observations (especially the relationships of NO X /NO Y and ClO/Cl y ratios to aerosol content) confirm the key aspects of the model chemistry that is responsible for this behavior and demonstrate that aerosol changes alone are not a mechanism for ozone losses in the absence of anthropogenic halogen inputs to the stratosphere. It is also suggested that aerosol-induced ozone changes could be confused with 11-year solar cycle effects in some statistical analyses, resulting in an overestimate of the trends ascribed to solar activity. While the timing of the observed ozone changes over about the past 15 years is in remarkable agreement with the model predictions that explicitly include observed aerosol changes, their magnitude is about 50% larger than calculated. Possible chemical and dynamical causes of this discrepancy are explored. On the basis of this work, it is shown that the timing and magnitude of future ozone losses at midlatitudes in the northern hemisphere are likely to be strongly dependent upon volcanic aerosol variations as well as on future chlorine and bromine loading.
Detection of Hypertrophic Cardiomyopathy from Echocardiography: A Survey of Current Approaches

Hypertrophic cardiomyopathy (HCM) is a genetic disorder characterized by the thickening of the heart walls, which can lead to heart failure and other complications. Echocardiography is a non-invasive diagnostic tool that can be used to detect HCM. In this study, we survey the existing approaches for detection of HCM from echocardiography. We reviewed the literature on various techniques used to analyze echocardiogram videos and images, including traditional image processing methods, machine learning techniques, and deep learning methods. We also discussed the advantages and disadvantages of each approach, as well as the challenges that need to be addressed in the future. The results of this study show that deep learning methods, particularly those based on convolutional neural networks (CNNs), have shown promise for the detection of HCM from echocardiography. However, further research is needed to improve the performance of these methods and to make them more widely applicable in clinical practice. Overall, this study provides a comprehensive overview of the current state of the art in HCM detection from echocardiography and highlights the areas that require further research.
An Efficient Deep Learning Technique for Intrusion Detection in Wireless Sensor Network

Predicting intrusion detection in a wireless sensor network (WSN) involves using machine learning algorithms to analyze network traffic data and predict whether an intrusion or security breach will occur. The goal of intrusion prediction is to provide advanced warning of potential attacks and allow for proactive measures to be taken to prevent or mitigate the attack's impact. Several machines and deep learning algorithms can be used for intrusion prediction in WSNs. This research describes a practical deep-learning approach for Intrusion Detection in WSN based on artificial neural networks. Spyder IDE of python with version 3.7 is used for the simulation work. The results of the simulations demonstrate that the anticipated model is accurate 99.99% of the time.
Evaluation of the applicability of the Estimated State of the Global Ocean for Climate Research (ESTOC) data set

A long‐term data synthesis experiment was conducted for the period 1957–2011 using a modified quasi‐global four‐dimensional variational data assimilation system that was originally developed to improve the representation of the deep ocean, including a unique method for anomaly assimilation. The overall characteristics of the resulting ocean state estimate, which is dynamically consistent without any artificial sources or sinks for heat and salt, are evaluated in the Pacific Ocean. It is shown that the data set better represents the comprehensive ocean state: the mean state of the water mass distribution and volume transport and components of temporal variability from the sea surface to the bottom on interannual to multidecadal timescales. This suggests that the data set can be used to examine interactions between temporal variations throughout the entire depth range and is useful for understanding ocean physics and its role in the climate system.
Influence of the rds,on temperature dependency of SiC MOSFETs on the optimal switching cell mechanical layout

The design of power electronic systems is typically performed based on optimisation procedures. In this paper, the influence of the temperature dependant on-state resistance on the optimal switching cell mechanical layout is investigated. A standardised half-bridge switching cell is modelled and optimised to discuss the importance of establishing general layout recommendations.
Machine learning approaches to study the structure-activity relationships of LpxC inhibitors

Antimicrobial resistance (AMR) has emerged as one of the global threats to human health in the 21st century. Drug discovery of inhibitors against novel targets rather than conventional bacterial targets has been considered an inevitable strategy for the growing threat of AMR infections. In this study, we applied quantitative structure-activity relationship (QSAR) modeling to the LpxC inhibitors to predict the inhibitory activity. In addition, we performed various cheminformatics analysis consisting of the exploration of the chemical space, identification of chemotypes, performing structure-activity landscape and activity cliffs as well as construction of the Structure-Activity Similarity (SAS) map. We built a total of 24 QSAR classification models using PubChem and MACCS fingerprint with 12 various machine learning algorithms. The best model with PubChem fingerprint is the Extremely Gradient Boost model (accuracy on the training set: 0.937; accuracy on the 10-fold cross-validation set: 0.795; accuracy on the test set: 0.799). Furthermore, it was found that the best model using the MACCS fingerprint was the Random Forest model (accuracy on the training set: 0.955; accuracy on the 10-fold cross-validation set: 0.803; accuracy on the test set: 0.785). In addition, we have identified eight consensus activity cliff generators that are highly informative for further SAR investigations. It is hoped that findings presented herein can provide guidance for further lead optimization of LpxC inhibitors.
An Analysis of Spatial Correlation in RIS-Assisted Wireless Networks

In this paper, we analyze the impact of spatial correlation between the elements of reconfigurable intelligent surface (RIS) arrays considering both phase-optimized arrays providing a quadratic signal-to-noise ratio (SNR) gain as well as arrays without phase optimization providing a linear gain. The analysis shows that while spatial correlation degrades performance when the RIS array is phase-optimized, it significantly improves performance in the case of arrays without phase optimization. We develop analytic SNR expressions, which show that in RIS arrays without phase optimization, spatial correlation leads to the appearance of a quadratic term whose magnitude increases with the amount of correlation, and we provide simulation results, which corroborate the analytic results. Our findings suggest that spatial correlation can be exploited to reduce the number of pilots needed for channel estimation and signal detection, thus reducing both the receiver complexity and the overhead required for configuration of the RIS array.
NITSRI-2D: A Surface Potential Based SPICE Compatible Model for pH-Sensitive FETs Based on 2-D Materials

We present a novel surface-potential (SP) based compact model for pH-sensitive FETs based on 2D materials, developed at the National Institute of Technology Srinagar (NITSRI). The Poisson’s equation along with site-binding theory and Gouy-chapman-stern approach is used for handling the device electrostatics towards the electrolyte side while the electrostatics in the device is addressed by employing 2-D density of states and Fermi-Dirac statistics. The proposed model is built on our prior work wherein we exhaustively demonstrated a Verilog-A implementation of our physics-based 2D material FET model, having obtained explicit surface-potential expressions using the Lambert-W function and Halley’s correction method, while subsequently computing the drain current using the drift-diffusion transport model. Experimental data belonging to a MoS2-based ISFET is used for validating the proposed model and the experimental results show a very good match with model results illustrating its robustness and predictive ability.
Improved Foreground Modelling for Bayesian 21 cm Power Spectrum Estimation with BayesEoR

Estimating the power spectrum (PS) of the redshifted 21 cm signal from the Epoch of Reionization (EoR) has proven difficult due to the presence of bright astrophysical foregrounds (FGs). Existing techniques to mitigate FGs during PS estimation under-utilize the data and do not properly account for the covariance between the observed EoR and FG signals. We are developing a Bayesian framework, BayesEoR, that jointly models the instrument, FGs, and 21 cm signal and marginalizes over their uncertainties to enable extraction of statistically robust and unbiased estimates of the EoR PS. In this paper, we present a brief overview of our approach to Bayesian PS estimation and the results of a recent study in which we used BayesEoR to analyze a set of detailed simulations containing mock EoR and realistic FG signals. Due to computational constraints, the forward model of the instrument in BayesEoR, which involves application of the instrument primary beam in the image domain, is restricted to model a subset of the sky. We find that, when the sky emission outside of the modelled domain is downweighted by the beam at the level of the dynamic range between the EoR and FGs, BayesEoR can model visibilities which see the whole sky using only a subset of the sky. We also present several techniques that can be used to mitigate FG contamination during EoR PS estimation while ameliorating computational costs.
Thermal Analysis of Aluminum Electrolytic Capacitors Considering the Insulation Resistance

A thermal analysis method for aluminum electrolytic capacitors considering the effect of insulation resistance is proposed in this paper, and its thermal stress model with the introduction of both series resistance and insulation resistance is given. According to experimental tests, the results illustrate the non-negligible effect of insulation resistance on capacitance loss and temperature when the aluminum electrolytic capacitor is operated in the high-voltage and high-temperature environment.
Lithium-ion Battery SOH Estimation with Varying Amount of Battery Operation Data

This work estimates SOH of lithium-ion batteries, aged by a forklift driving profile, based on multiple linear regression and compares the estimation accuracy at three levels. Unlike previous research, this work uses dynamic and field data rather than public datasets. The influence of data size and the position to extract features on the SOH estimation accuracy was researched. It is found that extracting features from smaller voltage segments contains more information. The estimation accuracy can be improved by 24.5% MAPE after the Box-Cox transformation.
Aborigines and environment in holocene Australia: Changing paradigms

Recent syntheses of Australian prehistory have emphasised the antiquity of colonisation of the continent and the changes associated with the Pleistocene/Holocene transition (Jones 1968, 1977, 1979; Bowdler 1977; Urry 1978; White and O'Connell 1979; Horton 1981), whereas the last 5000 years have received far less attention (but see Mulvaney 1975, Flood 1983 and Lourandos 1983a). This is perhaps understandable given the considerable antiquity of occupation and the scale of Pleistocene environmental changes. However, these more dramatic episodes have tended to divert attention from less marked but perhaps equally important Holocene environmental changes and subsequent human adaptations.
The relationship between work accident rates and economic activity: Evidence from Peru (2016-2021)

The study analyzes the relationship between work accident rates and economic activity (GDP per worker) in Peru. The empirical strategy is based on fixed-effects regressions and vector autoregression models (VAR) for panel-data settings on a sample of 15 Peruvian industries during the period 2016-2021. The main results confirm the unidirectional causal relationship in the short-term from GDP per worker to the rate of work accidents. Additionally, the findings suggest that this relationship is heterogeneous across industries: the reported causal relationship is confirmed for agriculture, fishing, utilities, construction, manufacturing, and transport and warehousing. In line with the increased awareness on how economic activity impacts work accidents, policy implications and future research avenues are discussed.
Bias Spectroscopy of Negative Differential Resistance in Ge Nanowire Cascode Circuits

In this paper, we exploit the nanometer scale properties of Ge based Schottky barrier field-effect transistors (SBFETs) with monocrystalline Al contacts, fusing the concept of reconfiguration and negative differential resistance (NDR) in a single device. Temperature dependent bias spectroscopy is used to investigate the electronic transport in the NDR regime leading to profound understanding of the involved physical transport mechanisms. Importantly, the obtained SBFETs are capable of shifting the NDR-peak by electrostatic gating. Thus, a cascode of such devices results in overlapping NDR regions, which allows the realization of new circuit topologies beyond the capabilities of conventional CMOS.
FIU Digital Commons FIU Digital

A new approach to enhance the bandwidth of Tightly Coupled Dipole Arrays (TCDA) is presented. The new design achieves the integration of a semi-resistive Frequency Selective Surface network (FSS) composed of a non-resistive low-pass FSS and two resistive band-stop FSSs. The integration of this FSS network within a dual-polarized Tightly Coupled Dipole Array (TCDA) led to an increased impedance bandwidth of 28:1 from 0.20GHz to 5.6GHz. Notably, the use of an FSS superstrate allowed for scanning down to 60 ◦ at VSWR < 3 in the E-plane and VSWR < 4 in the D-and H-planes. Additionally, the strategic use of the inserted low-pass FSS reduces the resistive effects above 2.5GHz for improved average efficiency. A 12 × 12 array prototype was fabricated and tested to verify the bandwidth and gain of a finite array. The simulated radiation efficiency was demonstrated to be 83%, on average, across the band.
Perception Sensors Based Fusion System and Method for Controlling Vehicle

Taxi demand prediction has lately piqued academic attention due to its vast potential for use in expansive intelligent transportation systems. On the other hand, the majority of earlier methods merely took into account forecasting taxi demand in origin locations, omitting to simulate the particular circumstances of customers traveling to a destination. This research study presume that it is inefficient to pre-allocate taxis to each location based just on origin demand. This research study offers a significant and fascinating challenge in this work termed taxi origin-destination demand prediction, which tries to estimate future taxi demand between all area pairings. Its main challenges are figuring out how efficiently a variety of contextual data are gathered in order to understand demand trends. In this study, origin and destination views were among the classification tasks where a unique Deep Neural Network (DNN) with Deep learning-based models outperform conventional machine learning techniques. Comprehensive testing and analysis on a large dataset clearly demonstrate that, for forecasting taxi origin-destination demand, our DNN beats other evaluated approaches.
Effect of Irrigation Water Salinity and Sodicity and Water Table Position on Water Table Chemistry Beneath Atriplex lentiformis and Hordeum marinum

Coal bed methane (CBM) extraction in Montana and Wyoming's Powder River Basin (PRB) produces large quantities of modestly saline-sodic water. Such water can cause dispersion of clays, degradation of soil structure, and osmotic stress in plants. This study assessed effects of irrigation water quality and water table position on water chemistry of closed columns, simulating a perched or a relatively shallow water table. The experiment presents an assessment of potential salt loading in areas where shallow or perched water tables prevent leaching or where artificial drainage is not possible. Water tables were established in sand filled PVC columns (0.20 m diameter × .005 m wall thickness × 1.22 m tall) at 0.38, 0.76, and 1.14 m below the surface, after which columns were planted to one of three species, two halophytic Atriplex spp. and Hordeum marinum Huds. (maritime barley), a glycophyte. Results for the two Atriplex ssp. did not differ significantly, and therefore, only results from Atriplex lentiformis (Torr.) S. Wats. (big saltbush) and H. marinum are presented in this article. Columns were irrigated for 224 d; irrigation water supplied every 7 d was equal to evapotranspiration (ET) + 100 mL. This amount replaced the water lost to ET between sampling, plus the volume of the water sample. Irrigation water representing one of two irrigation sources was used: Powder River (PR) [electrolytic conductivity (EC) = 0.19 Sm−1, sodium adsorption ratio (SAR) = 3.5] or CBM water (EC = 0.35 Sm−1, SAR = 10.5). Irrespective of species, continuous irrigation with CBM and PR water led to salt loading over time, the extent of which was proportional to the salinity and sodicity of applied water. Water in columns planted to A. lentiformis with water tables maintained at 0.38 m depth consistently had significantly greater EC and SAR values than those with 0.76 and 1.14 m water table positions. Elevated EC and SAR values most likely reflect the shallow rooted nature of A. lentiformis, which resulted in enhanced ET with the water table close to the soil surface. H. marinum's deep rooted nature and subsequent ability to utilize water from a variety of depths did not produce any significant differences in EC and SAR as a result of varying water table positions.
Power Control and Voltage Balancing Strategies for a Five-Level T-Type Inverter

This paper presents the performance of a grid-tied five-level inverter (T5). Its structure requires fewer power components than the Neutral Point Clamped (NPC) and Flying Capacitor (FC) structures with equivalent voltage levels. The objective is to evaluate the PI, LMI (Linear Matrix Inequality) and SM (Sliding Mode) controllers for d- and q-axis current reference tracking. Each of these controllers is associated with a Space Vector Modulation technique (SVPWM) that provides DC-side capacitor voltage balancing. Efficiency and size are optimized as no additional components are required. The simulation of the complete system is performed on MATLAB/Simulink.