Miracle-dz/UniFAR-FTUs huggingface dataset:
Sphingolipid Levels and Signaling via Resveratrol and Antioxidant Actions in Cardiometabolic Risk and Disease

Resveratrol (RSV) is a phenolic compound with strong antioxidant activity, which is generally associated with the beneficial effects of wine on human health. All resveratrol-mediated benefits exerted on different systems and pathophysiological conditions are possible through resveratrol’s interactions with different biological targets, along with its involvement in several key cellular pathways affecting cardiometabolic (CM) health. With regard to its role in oxidative stress, RSV exerts its antioxidant activity not only as a free radical scavenger but also by increasing the activity of antioxidant enzymes and regulating redox genes, nitric oxide bioavailability and mitochondrial function. Moreover, several studies have demonstrated that some RSV effects are mediated by changes in sphingolipids, a class of biolipids involved in a number of cellular functions (e.g., apoptosis, cell proliferation, oxidative stress and inflammation) that have attracted interest as emerging critical determinants of CM risk and disease. Accordingly, this review aimed to discuss the available data regarding the effects of RSV on sphingolipid metabolism and signaling in CM risk and disease, focusing on oxidative stress/inflammatory-related aspects, and the clinical implications of this relationship.
A Novel Optimal Voltage Vector Selection Control Strategy for Vienna Rectifier

At present, model predictive control (MPC) is widely used in power electronic converters. The objective function is typically utilized to select the optimal voltage vector, but the switching frequency is not fixed or too high during the selection process. Therefore, to address this issue, a novel optimal voltage vector selection control strategy for Vienna rectifier is proposed in this paper. Specifically, hysteresis link is introduced into finite control set-model predictive direct power control (FCS-MPDPC). The optimization criterion is changed from the traditional objective function tracking error minimization to the longest extension step in the hysteresis loop. As a result, the switching frequency can be reduced and the control algorithm can be simplified by redefining the optimization criteria of the rectifier output voltage vector. Moreover, according to the power control model of Vienna rectifier, three-level space vector pulse width modulation (SVPWM) is used to generate the switching function for the next cycle. Finally, an experimental platform for Vienna rectifier based on dSPACE hardware is built to verify the feasibility of the proposed strategy. Experimental results show that the control strategy can ensure the rectifier to run normally and exhibit good steady state performance.
Partial Reduplication in Some Austronesian Formosan Languages

0. Introduction Reduplicative infixation has been a hotly debated topic, especially the invariant shape and the position of reduplicants. In the Amis language and several Austronesian Formosan languages, the pattern of partial reduplication seems to be infixation – copying a bimoraic form from the right edge except the final consonant. The main concern of this paper will be to evaluate the functions of Alignment constraints in terms of the position and the size of reduplicants, utilizing Optimality Theory. Languages mentioned in this paper display a more complete typology for the mora alignment constraint (Chrowhurst 2004) where the evaluation applies on the mora tier. In addition, while the invariant shape of the reduplicant can be predicted through alignment interactions in several languages (Gafos 1998, Hendrick 2001, Chrowhurst 2004), Amis and Thao differ. A constraint referring to the size is still necessary. I propose that the reduplicant position can be accounted for by Alignment, but the invariant shape should appeal to the Generalized Template constraint Red=Stem in Amis and Thao. The organization of this paper is as follows. The first section presents the reduplicative patterns in Formosan languages Amis, Pazih, Paiwan and Thao. Section two displays the functions of Alignment constraints and illustrates an analysis regarding the positions of reduplicants. Section three is concerned with the templatic use in reduplication. I argue that the templatic constraint may not be substituted with Alignment interactions. The final section concludes the paper and discusses implications.
Nonlinear filtering algorithm based on local texture direction probability statistic model

In order to more effectively make use of local features to restore the noise-infected image, a nonlinear filtering algorithm based on local texture direction probability statistic model was proposed. The proposed algorithm utilizes Radon transform to determine texture direction probability density distribution of local areas of images, and then applies probability statistic model to estimate the middle pixel's gray value according to its neighbour pixels. The performance of the proposed method was evaluated with several sets of images contaminated by pulse noise and Gaussian noise. The experimental results show the superiorities of this method including the abilities of denoising and preserving edges and details of images especially for images with pulse noise and Gaussian noise.
Lesson Learned in Developing and Implementation a Global Business Strategy in Agriculture Companies: A Comparative Study

The consumption of fruits in the world is increasing, the increasing human population is also one of the factors for the increasing demand for fruits globally. Companies engaged in the agricultural industry are also required to always be able to serve customer requests. Based on a comparative study of two agricultural companies, this study aims to provide a study on the development and implementation of global business strategies for agricultural companies in determining the design of global business strategies by looking at companies that have succeeded globally. The companies studied were Dole and Chiquita. This study discusses the resources, capabilities, and competitive advantages of the two companies. This study also describes the attractive industry of the agriculture industry and shows how the two companies formulate their strategies to earn superior returns. The data used in this study is qualitative data. Since this study is based on a comparative analysis based on case studies, conclusions drawn from these cases may be difficult to generalize. The research findings are expected to provide lessons that can be learned by companies interested in developing business in the agricultural industry. Companies can learn from the strategies of both companies to achieve above-average returns.
Voltage and Reactive Power Regulation With Synchronverter-Based Control of PV-STATCOM

This paper presents a synchronverter based photovoltaic (PV) system as STATCOM (PV-STATCOM), in which inverter exchanges active and reactive power with the grid. The proposed system provides reactive power compensation during daytime as well as nighttime. Additional operating modes for PV-STATCOM are proposed to enhance its capability. The proposed controller is compared with conventional control based PV-STATCOM on the basis of its performance with different tests. The key difference between conventional control and synchronverter is that synchronverter emulates inertia to control the frequency and voltage of the system. It is observed that the proposed system reduces frequency deviation and provides satisfactory regulation of the point of common coupling (PCC) voltage during full STATCOM mode and partial STATCOM mode. The low voltage ride through (LVRT) test is also performed on the proposed system. In the weak grid situation, the proposed PV-STATCOM performs in a more stable manner than the conventional PV-STATCOM in both reactive power and PCC voltage control mode. The study is performed on EMTDC/PSCAD simulation software. Real Time Control Hardware In Loop (CHIL) test is performed on Real Time Digital Simulator (RTDS) with a microcontroller to validate the proposed controller strategy and control design.
The Ancient Olive Trees (Olea europaea L.) of the Maltese Islands: A Rich and Unexplored Patrimony to Enhance Oliviculture

A prospecting campaign in the Maltese Islands has ensured the survival of several ancient olive trees (Olea europaea L.), genetically distant from known cultivars. Most of these plants were abandoned or partially cultivated. A two-year evaluation of fruit characteristics and compositions was performed on samples collected from the main representatives of these indigenous genotypes. Analyses were carried out using Gas Chromatography, High-Performance Liquid Chromatography and Near Infrared Spectrometry. Among the fruit samples, a wide range of variations was observed. Some of the genotypes showed fruit traits suitable for table olive production. This is the case of samples with a pulp/pit ratio higher than four, such as 1Wardija, 1Caritas, 1Plattini, 1Bingemma Malta and 3Loretu, whilst 1Bidni, 1Mellieha, 2Qnotta, 3Loretu, 1Bingemma Malta and 1Caritas were suitable for dual purpose. The total phenol content ranged from 6.3 (1Wardija) to 117.9 (2Mtarfa) g/kg of fresh pulp. The average percentage of MUFA was quite low for most of the varieties. These genotypes, which presumably originated in the Maltese Islands and are well adapted to the local pedo-climatic conditions, are being propagated for the following evaluation of their bio-agronomical performance (production, suitability to intensive cultivation, environmental sustainability, product quality, etc.). The purpose is to select, among these local genotypes, the most outstanding varieties, in terms of phenolic and FA profile and agronomical potential, to spread into cultivation, thereby contributing to an increase in the quality of the local table and olive oil production, strongly linked to the territory.
Simple adaptive control – a stable direct model reference adaptive control methodology – brief survey

In spite of successful proofs of stability and even successful demonstrations of performance, the eventual use of model reference adaptive control methodologies in practical real‐world systems has met a rather strong resistance from practitioners and has remained very limited. Apparently, the practitioners have a hard time understanding the conditions that can guarantee stable operations of adaptive control systems under realistic operational environments. Besides, it is difficult to measure the robustness of adaptive control system stability and allow it to be compared with the common and widely used measure of phase margin and gain margin that is utilized by present, mainly LTI, controllers. Furthermore, recent counterexamples seem to show that adaptive systems may diverge even when all required conditions are fulfilled. This paper attempts to revisit the fundamental qualities of the common direct model reference adaptive control methodology based on gradient and to show that some of its basic drawbacks have been addressed and eliminated within the so‐called simple adaptive control methodology. The sufficient conditions that guarantee stability are clearly stated and lead to similarly clear proofs of stability. The main claim of the paper is that if sufficient information exists for a robust classical design control, same information can be used to implement robust simple adaptive controllers. As many real world applications show, the added value of using add‐on adaptive control techniques, the use is pushing the desired performance beyond any previous limits. The paper also shows that the previous counterexamples to model reference adaptive control become just simple, successful, and stable applications of simple adaptive control. Copyright © 2013 John Wiley & Sons, Ltd.
Genome-Based Characterization of Hybrid Shiga Toxin-Producing and Enterotoxigenic Escherichia coli (STEC/ETEC) Strains Isolated in South Korea, 2016–2020

The global emergence of hybrid diarrheagenic E. coli strains incorporating genetic markers from different pathotypes is a public health concern. Hybrids of Shiga toxin-producing and enterotoxigenic E. coli (STEC/ETEC) are associated with diarrhea and hemolytic uremic syndrome (HUS) in humans. In this study, we identified and characterized STEC/ETEC hybrid strains isolated from livestock feces (cattle and pigs) and animal food sources (beef, pork, and meat patties) in South Korea between 2016 and 2020. The strains were positive for genes from STEC and ETEC, such as stx (encodes Shiga toxins, Stxs) and est (encodes heat-stable enterotoxins, ST), respectively. The strains belong to diverse serogroups (O100, O168, O8, O155, O2, O141, O148, and O174) and sequence types (ST446, ST1021, ST21, ST74, ST785, ST670, ST1780, ST1782, ST10, and ST726). Genome-wide phylogenetic analysis revealed that these hybrids were closely related to certain ETEC and STEC strains, implying the potential acquisition of Stx-phage and/or ETEC virulence genes during the emergence of STEC/ETEC hybrids. Particularly, STEC/ETEC strains isolated from livestock feces and animal source foods mostly exhibited close relatedness with ETEC strains. These findings allow further exploration of the pathogenicity and virulence of STEC/ETEC hybrid strains and may serve as a data source for future comparative studies in evolutionary biology.
1H NMR-Based Metabolic Profiling to Follow Changes in Pomelo Cultivars during Postharvest Senescence

This study investigated metabolite changes in three pomelo cultivars during postharvest senescence using 1H NMR-based metabolic profiling. Three pomelo cultivars, ‘Hongroumiyou’, ‘Bairoumiyou’ and ‘Huangroumiyou’, abbreviated as “R”, “W” and “Y” according to the color of their juice sacs, were stored at 25 °C for 90 days, and NMR was applied to determine the metabolite changes in juice sacs during storage. Fifteen metabolites were identified, including organic acids, sugars, amino acids, fatty acids, phenols and naringin. Partial least squares discriminant analysis (PLS-DA) was used to screen the significant metabolites according to the variable importance for the projection (VIP) scores in three pomelo cultivars during 90 days of storage. Additionally, eight metabolites, naringin, alanine, asparagine, choline, citric acid, malic acid, phosphocholine and β-D-glucose, were screened to be the crucial biomarkers with VIP > 1. The undesirable flavor of “bitter and sour” during the 60 days of storage was mainly attributed to the naringin, citric acid and sugars. According to the correlation analysis, the citric acid content determined by NMR showed a significantly positive relationship with that analyzed by HPLC. These findings suggested that NMR technology was accurate and efficient for metabolomic analysis of pomelo fruit, and the 1H NMR-based metabolic profiling can be efficient during quality evaluation and useful for improving the fruit flavor quality during postharvest storage.
Machine Learning in Urban Tree Canopy Mapping: A Columbia, SC Case Study for Urban Heat Island Analysis

As the world’s urban population increases to the predicted 70% of the total population, urban infrastructure and built-up land will continue to grow as well. This growth will continue to have an impact on the urban heat island effect in all of the world’s cities. The urban tree canopy has been found to be one of the few factors that can lessen the effects of the urban heat island effect. This study seeks to accomplish two objectives: first, we examine the use of a commonly used machine learning classifier (e.g., Support Vector Machine) for identifying the urban tree canopy using no-cost high resolution NAIP imagery. Second, we seek to use Land Surface Temperature (LST) maps derived from no-cost Landsat thermal imagery to identify correlations between canopy loss and temperature hot spot increases over a 14-year period in Columbia, SC, USA. We found the SVM imagery classifier was highly accurate in classifying both the 2005 imagery (94.3% OA) and the 2019 imagery (94.25% OA) into canopy and other classes. We found the color infrared image available in the 2019 NAIP imagery better for identifying canopy than the true color images available in 2005 (97.8% vs. 90.2%). Visual analysis based on the canopy maps and LST maps showed temperatures rose near areas where tree canopy was lost, and urban development continued. Future studies will seek to improve classification methods by including other classes, other ancillary data sets (e.g., LiDAR), new classification methods (e.g., deep learning), and analytical methods for change detection analysis.
Single minimal path based backup path for multi-state network

Backup path is an important mechanism to sustain the reliability of a multi-state network. As a popular backup path method, the double minimal path based backup path algorithm can improve the multi-state network’s reliability when the main paths fail. However, this algorithm cannot work efficiently when a single main minimal path fails. To improve the reliability in the first main minimal path failure case, we propose a single minimal path based backup path algorithm. In the single minimal path based backup path algorithm, two disjoint minimal paths are used as the main routing pair to transmit the data, and one single minimal path, which is disjoint with the main minimal paths, acts as the backup path. In the second main minimal path failure case, we propose a double–single minimal path based backup path algorithm to improve the multi-state network reliability. To develop the single minimal path based backup path and the double–single minimal path based backup path algorithms, this article first formulates the multi-state network reliability analysis problem. Then, a solution procedure is proposed to calculate the multi-state network reliability. Furthermore, numerical examples are given to validate the effectiveness of the algorithms. Finally, some comparisons are made between the single minimal path based backup path/double–single minimal path based backup path and the double minimal path based backup path/double minimal path based backup path algorithms. The comparison results indicate that the single minimal path based backup path and the double–single minimal path based backup path algorithms lead to considerable improvement in terms of the multi-state network reliability in the first and the second main minimal path failure cases, respectively, which are verified by both the mathematical analysis and numerical experiments.
A Digital PLL-Based Phase Modulator With Non-Uniform Clock Compensation and Non-linearity Predistortion

In this article, we present a low-power digital phase-locked loop (PLL)-based phase modulator targeting low error vector magnitude (EVM). We introduce a new non-uniform clock compensation (NUCC) scheme to tackle an EVM degradation resulting from the beneficial use of a time-varying sampling clock that is re-timed to the phase-modulated carrier. We also employ a phase-domain digital predistortion (DPD) to combat the intrinsic non-linearity of an LC-type digitally controlled oscillator (DCO), thus avoiding the complications of frequency-dependent calibrations. The prototype, implemented in 40-nm CMOS, modulates the carrier in the range of 2.7–3.9 GHz from a 40-MHz reference. The measured EVM is −47 dB for a 60-Mb/s 64-PSK modulation under the case that the phase-modulated output is frequency-divided by <inline-formula> <tex-math notation="LaTeX">$K=8$ </tex-math></inline-formula>, i.e., when the DCO exhibits the most significant non-linearity due to the large fractional FM bandwidth. When <inline-formula> <tex-math notation="LaTeX">$K=8$ </tex-math></inline-formula> or 4, the measured EVM remains below −43 dB across the carrier-frequency tuning range and without re-calibrating the DCO non-linearity.
Current and Previous Green Technologies, Their Efficiency, Associated Problems, and Success Rates to Mitigate M. aeruginosa in Aquatic Environments

Frequent M. aeruginosa outbreaks pose a major risk to public health and have a detrimental effect on aquatic ecosystems. Researchers are looking into ways to stop and control M. aeruginosa blooms, a problem that affects both the aquatic environment and human health significantly. It is important to develop proper monitoring methods to identify M. aeruginosa blooms. However, the existing control and monitoring techniques have some drawbacks that limit the field’s applicability. Therefore, we must improve current methods for effectively monitoring and controlling M. aeruginosa blooms. Mitigation strategies should be customized for particular bodies of water utilizing techniques that are fast, economical, and field-applicable. This review critically identifies and evaluates green technologies, especially those focused on the presence of M. aeruginosa in freshwater, and compares and discusses problems with these green technologies. Furthermore, they were characterized and ranked according to their cost, effectiveness, and field applicability. A few suggestions for improvements were provided, along with ideas for future research projects that would take anticipated environmental changes into account.
NOTES ON WEIGHTING IN REGRESSION

These notes were written for a workshop for Urban Institute researchers, especially those using the 1997 NSAF. The purpose of the workshop was to discuss the circumstances under which weighting in regression is appropriate and to explain why most regression models based on the NSAF should be weighted. Researchers generally, and economists in particular, are often taught not to use weights in a regression context, but instead to develop models that incorporate the factors used in sampling. These notes emphasize the importance for NSAF researchers of a caveat to that teaching: If the data are sampled based on either the outcome of interest or a factor that is a strong indicator of that outcome, then weighting is preferable to including such a variable as a control in the regression.
Boundary-Aware Bilateral Fusion Network for Cloud Detection

Cloud detection is one of the key technologies in the field of remote sensing. Although extensive deep learning-based cloud detection methods achieve good performance, their detection results in confusing areas, such as cloud boundaries and thin clouds, are often not satisfactory due to the potential interclass similarity and intraclass inconsistency of objects. To this end, we propose a boundary-aware bilateral fusion network (BABFNet), which effectively enhances cloud detection in confusing areas by introducing a boundary prediction branch as an auxiliary. To avoid the loss of details, the boundary prediction branch is designed to run at full resolution with a shallow architecture, while some semantic enhancement modules (SEMs) are used to supplement high-level semantic information by introducing multilevel encoder features of the cloud detection branch. This feature sharing in turn drives the cloud detection branch to focus more on cloud boundaries during training. At the end of the network, a bilateral fusion module (BFM) is added for information complementarity between features from these two branches. The features from the cloud detection branch provide multiscale features to the boundary prediction branch for more accurate boundary prediction, while the features from the boundary prediction branch further serve as prior knowledge to help the cloud detection branch aggregate contextual information. To verify the effectiveness of the proposed method, we select four different networks as cloud detection branches and conduct comparative experiments on two public datasets, GF-1 WFV and moderate-resolution imaging spectroradiometer (MODIS). The experimental results show that the proposed method significantly enhances cloud detection in confusing areas.