Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Real-Time Controller Design Test Bench for Power Converter Applications

In order to evaluate and validate the latest trends of power-hardware-in-loop (PHIL) test setup, the dc-dc buck converter is modelled within a real-time system where the simulation model of the converter is exported to FPGA NI PXIe with a time step of 250 ns. PHIL setup allows high flexibility, the benefit of graphical programming, and advanced investigation of the control system for a converter without any safety concern and with the possibility of testing against situations that rarely occur in the field. The LabVIEW-FPGA has been used as a prototyping environment for a digital controller with the help of OPAL-RT eHS software and NI hardware. Such collaboration enables other software such as MATLAB/Simulink, Multisim, PLECS, PSIM, and LabVIEW Co-simulation for accelerating innovative research and development. This research work presents a more efficient and effectual NI PXIE platform with at least ten times more FPGA capability. This paper highlights the hardware-software toolset’s performance and the proposed methodology by addressing regulation issues in dc-dc converters. For more satisfactory and reliable operation real-time simulation study of a dc-dc buck converter is evaluated at different parametric variations under the closed-loop PI controller. Finally, the executed model’s effectiveness for a closed loop buck converter with real DC loads is validated through the hardware-in-loop (HIL) laboratory setup.
Becoming a mother during the COVID‐19 national lockdown in Italy: Issues linked to the wellbeing of pregnant women

The COVID‐19 pandemic and consequent lockdown represent risk factors for the mental health of pregnant women. We explored the impact of COVID‐19 restriction policies on psychological health, analysing the predictive role of social support on maternal wellbeing. A total of 212 pregnant women recruited from two public hospitals in Italy were divided into two groups: (a) a pre‐COVID‐19 group composed of 141 expectant women (mean age = 34.6; SD = 4.3) at their third trimester before the national lockdown period; (b) a COVID‐19 group composed of 71 pregnant women (mean age = 33.3; SD = 4.5) at their third trimester during the COVID‐19 national lockdown. Participants completed two self‐report questionnaires: the Profile of Mood States and the Multidimensional Scale of Perceived Social Support. Moreover, the COVID‐19 group was asked to respond to an open question concerning worries about their pregnancies and COVID‐19. Results showed that pregnant women during COVID‐19 presented higher anxiety, depression and hostility, and lower vigour, than the pre‐COVID‐19 group. The main concerns were related to the effect of hospital restriction policies on childbirth and fears of contracting COVID‐19. Perceived partner social support represented a protective factor only for the pre‐COVID‐19 women. Limitations, strengths, and theoretical and clinical implications are discussed.
Multi-Convolution Feature Extraction and Recurrent Neural Network Dependent Model for Short-Term Load Forecasting

Load forecasting is critical for power system operation and market planning. With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a difficult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model’s superior performance, the proposed method is applied to Ireland’s load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model’s generalizability.
Optical haze of randomly arranged silver nanowire transparent conductive films with wide range of nanowire diameters

The effect of the diameter of randomly arranged silver nanowires on the optical haze of silver nanowire transparent conductive films was studied. Proposed simulation model behaved similarly with the experimental results, and was used to theoretically study the optical haze of silver nanowires with diameters in the broad range from 30 nm and above. Our results show that a thickening of silver nanowires from 30 to 100 nm results in the increase of the optical haze up to 8 times, while from 100 to 500 nm the optical haze increases only up to 1.38. Moreover, silver nanowires with diameter of 500 nm possess up to 5% lower optical haze and 5% higher transmittance than 100 nm thick silver nanowires for the same 10-100 Ohm/sq sheet resistance range. Further thickening of AgNWs can match the low haze of 30 nm thick AgNWs, but at higher transmittance. The results obtained from this work allow deeper analysis of the silver nanowire transparent conductive films from the perspective of the diameter of nanowires for va.
Design and testing of selective inactivators against an antifungal enzyme target

Systemic infections from fungal organisms are becoming increasingly difficult to treat as drug resistance continues to emerge. To substantially expand the antifungal drug landscape new compounds must be identified and developed with novel modes of action against previously untested drug targets. Most drugs block the activity of their targets through reversible, noncovalent interactions. However, a significant number of drugs form irreversible, covalent bonds with their selected targets. While more challenging to develop, these irreversible inactivators offer some significant advantages as novel antifungal agents. Vinyl sulfones contain a potentially reactive functional group that could function as a selective enzyme inactivator, and members of this class of compounds are now being developed as inactivators against an antifungal drug target. The enzyme aspartate semialdehyde dehydrogenase (ASADH) catalyzes a key step in an essential microbial pathway and is essential for the survival of every microorganism examined. A series of vinyl sulfones have been designed, guided by molecular modeling and docking studies to enhance their affinity for fungal ASADHs. These newly synthesized compounds have been examined against this target enzyme from the pathogenic fungal organism Candida albicans. Vinyl sulfones containing complementary structural elements inhibit this enzyme with inhibition constants in the low‐micromolar range. These inhibitors have also led to the rapid and irreversible inactivation of this enzyme, and show some initial selectivity when compared to the inactivation of a bacterial ASADH. The best inactivators will serve as lead compounds for the development of potent and selective antifungal agents.
Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration

A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. We find that response predictions for salamander ganglion cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration in the salamander retina translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields. Author Summary For understanding how sensory systems operate in the natural environment, an important goal is to develop models that capture neuronal responses to natural stimuli. For retinal ganglion cells, which connect the eye to the brain, current standard models often fail to capture responses to natural visual scenes. This shortcoming is at least partly rooted in the fact that ganglion cells may combine visual signals over space in a nonlinear fashion. We here show that a simple model, which not only considers the average light intensity inside a cell’s receptive field but also the variance of light intensity over space, can partly account for these nonlinearities and thereby improve current standard models. This provides an easy-to-obtain benchmark for modeling ganglion cell responses to natural images.
Degradation of WTAP blocks antiviral responses by reducing the m6A levels of IRF3 and IFNAR1 mRNA

N6‐methyladenosine (m6A) is a chemical modification present in multiple RNA species and is most abundant in mRNAs. Studies on m6A reveal its comprehensive roles in almost every aspect of mRNA metabolism, as well as in a variety of physiological processes. Although some recent discoveries indicate that m6A can affect the life cycles of numerous viruses as well as the cellular antiviral immune response, the roles of m6A modification in type I interferon (IFN‐I) signaling are still largely unknown. Here, we reveal that WT1‐associated protein (WTAP), one of the m6A “writers”, is degraded via the ubiquitination‐proteasome pathway upon activation of IFN‐I signaling. With the degradation of WTAP, the m6A levels of IFN‐regulatory factor 3 (IRF3) and interferon alpha/beta receptor subunit 1 (IFNAR1) mRNAs are reduced, leading to translational suppression of IRF3 and instability of IFNAR1 mRNA. Thus, the WTAP‐IRF3/IFNAR1 axis may serve as negative feedback pathway to fine‐tune the activation of IFN‐I signaling, which highlights the roles of m6A in the antiviral response by dictating the fate of mRNAs associated with IFN‐I signaling.
Designing Voronoi Constellations to Minimize Bit Error Rate

In a classical 1983 paper, Conway and Sloane presented fast encoding and decoding algorithms for a special case of Voronoi constellations (VCs), for which the shaping lattice is a scaled copy of the coding lattice. Feng generalized their encoding and decoding methods to arbitrary VCs. Less general algorithms were also proposed by Kurkoski and Ferdinand, respectively, for VCs with some constraints on their coding and shaping lattices. In this work, we design VCs with a cubic coding lattice based on Kurkoski's encoding and decoding algorithms. The designed VCs achieve up to 1.03 dB shaping gains with a lower complexity than Conway and Sloane's scaled VCs. To minimize the bit error rate (BER), pseudo-Gray labeling of constellation points is applied. In uncoded systems, the designed VCs reduce the required SNR by up to 1.1 dB at the same BER, compared with the same VCs using Feng's and Ferdinand's algorithms. In coded systems, the designed VCs are able to achieve lower BER than the scaled VCs at the same SNR. In addition, a Gray penalty estimation method for such VCs of very large size is introduced.
State-of-the-Art Review on Soft-Switching Technologies for Non-Isolated DC-DC Converters

The soft-switching technology is widely used in DC-DC converters, DC-AC inverters, and AC-DC rectifiers. Soft-switching non-isolated DC/DC converters (Buck, Boost, and Buck-Boost) are the most active research area of the soft-switching technology, and fruitful results are achieved. However, there is currently no comprehensive review for the soft-switching technology of non-isolated DC/DC converters. This paper introduces soft-switching topologies for non-isolated DC/DC converters, and presents classification and characteristic of all kinds of soft-switching technologies. Some soft-switching technologies of derivative converters (non-inverting Buck-Boost converters and multiphase converters) are also presented. Finally future trends and generalized constitution methods of the soft-switching topology for non-isolated DC/DC converters are concluded. The soft-switching theory and constitution methods summarized in this article are expected to be able to guide the proposing of new soft-switching technologies for DC-DC converters.
FACTORS ASSOCIATED WITH MORTALITY IN HOSPITALIZED ELDERS IN AN INTERNAL MEDICINE DEPARTMENT

The aim of this prospective cohort study was to determine the factors associated with mortality in elders admitted to an Internal Medicine Department of a public hospital in Lima. During 2019, 360 patients over 60 years of age were consecutively evaluated by applying the Comprehensive Geriatric Assessment tools and reviewing their medical records. During hospitalization, 45 (12.5%) died. There was a higher frequency of anemia (p = 0.043), hypoalbuminemia (p = 0.006), pressure ulcers (p = 0.003), high comorbidity (p < 0.001), functional dependence (p < 0.001) and malnutrition (p = 0.002) among deceased patients compared to non-deceased. In the multivariate model, the presence of delirium on admission was associated with a higher risk of hospital mortality (RR = 3.64; 95% CI: 1.51 8.76, p = 0.004). Additional studies are required in our country to assess the prognostic value of delirium and other geriatric syndromes on mortality and other adverse outcomes.
Acute pancreatitis in patients with COVID-19 is more severe and lethal: a systematic review and meta-analysis

Abstract Background Emerging evidence suggests an association between acute pancreatitis and COVID-19. Our objective is to conduct a systematic review and meta-analysis to evaluate whether COVID-19 affects the severity and outcomes associated with acute pancreatitis. Methods Cochrane guidelines and PRISMA statement were followed for this review. Digital dissertation bases were searched and all studies comparing the outcomes of acute pancreatitis amongst patients with and without COVID-19 were included. We compared the etiology, severity, length of hospital stay and mortality associated with acute pancreatitis in patients with and without COVID-19. Results Four observational studies with a total of 2,419 patients were included in the review. Presence of COVID-19 significantly increased the odds of mortality (OR 4.10, 95% CI 2.03–8.29) in patients with acute pancreatitis. These patients also had an increased incidence of severe pancreatitis (OR 3.51, 95% CI 1.19–10.32), necrotizing pancreatitis (OR 1.84, 95% CI 1.19–2.85) and a longer length of hospital stay (OR 2.88, 95% CI 1.50–5.52), compared to non-COVID patients. Patients with COVID-19 were more likely to have an unknown or idiopathic etiology of acute pancreatitis (OR 4.02, 95% CI 1.32–12.29), compared to non-COVID-19 patients. Conclusion Current evidence suggests that COVID-19 adversely impacts the morbidity and mortality associated with acute pancreatitis. SARS-CoV-2 may be a causative agent for acute pancreatitis. Further population-based studies are needed to confirm or refute these findings.
Voltage Correlation Based Single Pole-to-Ground Fault Detection of MMC-HVDC Transmission Line

Through analysis of various fault voltage characteristics of modular multilevel converter high voltage direct current (MMC-HVDC) transmission system, it shows that the positive and negative voltage relationships of direct current transmission lines with single pole-to-ground faults are obviously different from other faults. Hence, Pearson correlation coefficient is used to describe the degree of difference in this work. In the case of single pole-to-ground fault, correlation coefficient is positive and close to 1, while as for other failures, correlation coefficient is negative and close to −1. This article analyzes the mechanism of correlation coefficient to withstand transition resistance and verifies the effectiveness of the proposed method under high resistance grounding fault. A novel scheme is proposed by combining the quantity sum of voltage variation characteristics, which can effectively identify fault pole. The proposed scheme needs smaller time window and owns high efficiency, as well as great robustness to transition resistance.
Polar Shaping Codes for Costly Noiseless and Noisy Channels

We propose a shaping code based on polar codes. For a costly noiseless channel, we show that the total cost of the proposed polar shaping code approaches the optimal total cost as block length grows. We also consider shaping for costly noisy discrete memoryless channels (DMCs). We first give an upper bound on the rate that can be achieved with a specified symbol occurrence probability distribution over a DMC. Then we formulate an optimization problem whose solution gives a lower bound on the optimal total cost for a costly noisy DMC. We compute the lower bound for the costly $M$-ary erasure channel. Finally, we propose polar shaping codes for costly noisy channels that achieve the lower bound by adapting polar codes for asymmetric channels proposed by Honda and Yamamoto.
A Binary Decomposition and Transmission Schemes for the Peak-Constrained IM/DD Channel

This paper develops a binary decomposition for the peak-constrained intensity-modulation with direct-detection (IM/DD) channel. Then, it uses this decomposition, which we call a Vector Binary Channel (VBC), to propose simple binary coding schemes. The VBC consists of $N$ bit-pipes and, different from the Avestimehr-Diggavi-Tse linear deterministic model, captures interactions between bit-pipes thus preserves capacity. Two practical coding schemes are then designed for the VBC. The first is an Independent Coding scheme which encodes independently and decodes successively while ignoring the dependence between noises on different bit-pipes. The second improves upon the first by exploiting the knowledge of noises on previously decoded bit-pipes as a state for the current decoding operation. The achievable rates of the schemes are compared numerically with capacity, and shown to perform well with the second scheme approaching capacity for signal-to-noise ratio (SNR) > 2.5 dB. The schemes can be implemented using practical capacity-achieving codes for the binary symmetric channel (BSC) such as polar codes. Methods to improve the schemes at low-SNR are discussed towards the end of the paper.
Breeding Success of Cape Vultures (Gyps coprotheres) at Colonies in the Tswapong Hills, Botswana

Abstract. The endangered Cape Vulture (Gyps coprotheres), a southern African endemic, breeds on precipitous cliffs in various habitats. Some colonies of this cliff-nesting species are found in the southeast (Mannyelanong Hill) and east (Tswapong Hills and adjacent complexes) of Botswana. Of six breeding sites studied in the Tswapong Hills complex from 1992 to 1999, only two large colonies near the villages of Goo-Moremi (Bonwalenong site) and Goo-Tau (Manong Yeng site) and one small colony near Lerala (Kukubye site) still hold breeding vultures. In 2017 and 2018 we used direct observations to estimate the breeding population and breeding success of Cape Vultures at the Bonwalenong and Manong Yeng colonies, and we compared our findings with data from the 1990s. We identified 261 nests in 2017 and 362 nests in 2018, and monitored them throughout the breeding season. Breeding success at the two sites differed significantly between the 2 yr of our study (2017 and 2018). However, breeding success at Manong Yeng in 2017–2018 did not differ significantly from that documented in the earlier study (1992–1999). Our results also suggested relatively stable numbers of breeding pairs in the Tswapong Hills between 1992–1999 and 2017–2018. Cape Vultures in the Tswapong Hills require continued monitoring and research to better understand population dynamics and the key factors influencing breeding success and nestling survival.
The Research of SEIJR Model With Time-Delay Based on 2019-nCov

A global epidemic disease known as the novel coronavirus (2019-nCov) had seriously hit the most area around the whole world causing unpredictable loss of manpower and finance during the past year. Modeling the spread and development of infectious diseases represented by new Coronavirus has become an important part of public health work in the world. Estimation of possible infection population and prospective suggestion of handling spread based on existing data is crucial. In this article, we build a more applicable model called SEIJR with a log-normal distributed time delay to forecast the trend of spreading considering the biology parameters obtained based on Chinese clinical data in Wuhan and the real spread feature of 2019-nCov in Italy. Adopting Particle Swarm Optimization (PSO), we estimate the early period average spreading velocity (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\alpha _{0}}$ </tex-math></inline-formula>) and implement inversion analysis of time point (<inline-formula> <tex-math notation="LaTeX">$\boldsymbol {T_{0}}$ </tex-math></inline-formula>) when the virus first hit Italy. Based on fixed <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\alpha _{0}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {T_{0}}$ </tex-math></inline-formula>, we then obtained the average spreading velocity <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\alpha _{1}}$ </tex-math></inline-formula> after the area lockdown using PSO. The result shows that it will help address the infection by generating the prediction trends of different <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {\alpha }$ </tex-math></inline-formula> which we considered. Finally, our research applies Logistic regression, Neural Network embedding LSTM layer, which is two representative machine learning algorithms, to directly predict future infection trends and compare the forecast with results yielded by mathematical model adopting differential equations. Not only solved the complex, nondifferentiable equation of the epidemic model, this research also performs well in inversion analysis based on PSO which conveys informative outcomes for further discussion on precautious action. The comparison with the machine learning algorithms shows that the 2019-nCov based epidemic dynamics assumption is reasonable and helpful to the mathematical model, which is better than the data-driven machine learning algorithms. Code can be freely downloaded from <uri>https://github.com/Summerwork/2019-nCov-Prediction</uri>.