Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Hermite-based Variability Analysis Using Decoupling Technique

During the design and fabrication process of electronic systems, one of the major concerns is predicting the effect of the variability of their geometrical and physical parameters on the general performance of the designed circuits. To address the above difficulty, this thesis presents a new Hermite-based approach to circuit variability analysis using the Polynomial-Chaos (PC) paradigm. The new approach is aimed at limiting the growth of the computational cost of variability analysis with the increase in the number of random variables and the number of Hermite coefficients used to represent the circuit response in each random variable. The proposed method is based on deriving a closed-form for the structure of the augmented matrices generated by the PC approach. An algorithm is then developed to decouple the large augmented matrices into independent matrices that can be factorized in parallel. In addition, the model-order reduction (MOR) is applied to the circuit stochastic analysis using the proposed PC approach to show its ability to achieve an additional speed-up while still retaining the result accuracy. Several numerical examples are presented to demonstrate the computational efficiency of the proposed method and its ability to handle the increasing dimension of random space.
Sudden Infant Death Syndrome: Risk Factors and Newer Risk Reduction Strategies

Sudden infant death syndrome (SIDS) continues to be one of the top causes of infant death in the U.S. Despite significant public health initiatives focused on high-risk populations to enhance sleep environments and techniques. The SIDS rate has remained stable in recent years. Risk factors and newer risk reduction strategies for SIDS are the focus of this review article. We conducted a comprehensive literature search on Medline, Cochrane, Embase, and Google Scholar until July 2022. The following search strings and Medical Subject Heading (MeSH) terms were used: "SIDS," "Sudden Infant Death" and "SUID". We explored the literature on SIDS for its epidemiology, pathophysiology, the role of various etiologies and their influence, associated complications leading to SIDS, and preventive and treatment modalities. Despite a more than 50% drop-in rates since the start of the "Back to Sleep" campaign in 1994, sudden infant death syndrome (SIDS) continues to be the top cause of post-neonatal mortality in the United States, despite continued educational initiatives that support safe sleep and other risk reduction strategies. The new American Academy of Pediatrics guidelines for lowering the risk of SIDS include a lot of emphasis on sleeping habits, bedding, and environment but also include elements that are frequently ignored (i.e., prenatal care, smoking, alcohol and drug use, and childhood vaccinations). This study highlights these less-frequently discussed aspects and identifies treatments that have produced beneficial behavioral shifts that benefit newborns as well as their mothers' health and wellbeing.
Solar Coronal Modeling by Path-conservative HLLEM Riemann Solver

In this paper, we employ a path-conservative HLLEM finite-volume method (FVM) to solve the solar wind magnetohydrodynamics (MHD) systems of extended generalized Lagrange multiplier (EGLM) formulation with Galilean invariance (G-EGLM MHD equations). The governing equations of single-fluid solar wind plasma MHD are advanced by using a one-step MUSCL-type time integration with the logarithmic spacetime reconstruction. The code is programmed in FORTRAN language with Message Passing Interface parallelization in spherical coordinates with a six-component grid system. Then, the large-scale solar coronal structures during Carrington rotations (CRs) 2048, 2069, 2097, and 2121 are simulated by inputting the line-of-sight magnetic field provided by the Global Oscillation Network Group (GONG). These four CRs belong to the declining, minimum, rising, and maximum phases of solar activity. Numerical results basically generate the observed characteristics of structured solar wind and thus show the code’s capability of simulating solar corona with complex magnetic topology.
One-dimensional convolutional neural networks for Raman spectral analysis

A machine learning based approach has been developed to classify Raman spectroscopic data. The algorithm is based on a one dimensional neural network (1D-CNN) architecture which is trained with synthetic data that can incorporate sensor specific characteristics such as spectral range, spectral resolution and noise. The synthetic spectra are based on high SNR measurements which are then augmented by mixing target and background signatures. The CNN is trained to consider target representations in the presence of certain background materials including glass and HDPE. These additional target representations allow the CNN to make detections for materials taken through a container. Within this paper the performance of CNNs trained for Raman sensor systems has been evaluated using real data collected using the ThermoFisher FirstDefender. The evaluation data consists of various target chemicals (including explosives) and interferents (including household materials) collected through glass and plastic vials. The data was acquired with a controlled range of collection settings, including integration time and laser power, available on the unit. The performance of the 1D-CNN approach has demonstrated high classification accuracies, high probability of detection and low false alarm rates. Specifically, these metrics have been calculated as a function of signal to noise ratio. Additionally, a sensitivity analysis was conducted using an acetonitrile standard diluted in water which demonstrates the CNN’s capability of detecting all dilutions of acetonitrile down to weight concentrations of <1%. This sensitivity analysis was mirrored using a mixture of potassium chlorate and Vaseline. The CNN demonstrated detections down to 10% by weight of potassium chlorate.
Selective logging and shifting agriculture may help maintain forest biomass on the Yucatán peninsula

Evaluation and monitoring of forest biomass are important to help combat global warming and conserve biodiversity. Above ground biomass (AGB) mapping has been effectively used to assess forest loss, degradation, and recovery in the tropics. In this study, temporal (2007, 2010, and 2015) AGB maps were developed for the Mayan Zone of the Yucatan Peninsula, México integrating vegetation data from the National Forest Inventory (NFI) with synthetic aperture radar (SAR) image data (ALOS PALSAR). We assess if degradation could be attributed to the land use categories present in the study area: selectively logged forest, shifting agriculture, permanent commercial cultivation, and conservation forest. Spatial autoregressive models are applied to determine differences in AGB dynamics between land use categories, compared to baselines of mature conserved forest. We find that forest biomass remains stable in the study area. AGB does not differ in selectively logged areas compared to conserved mature forests. Biomass losses are observed due to deforestation for commercial cropping and pasture. AGB in shifting agriculture areas, however, fluctuates and shows a slight gain from 2007 to 2015. AGB mapping using NFI data and SAR imagery has the potential for monitoring forest loss and degradation on the Yucatan Peninsula.
A Comparison of Cloud Classification Methodologies: Differences Between Cloud and Dynamical Regimes

Classifications of cloud data into Cloud Regimes (CRs) and compositing based on meteorological parameters, Dynamic Regimes (DRs), are often used in the analysis of clouds. We compare CR and DR classifications to understand the relative merits of these approaches and develop a comparison methodology for future studies. We apply the Self‐Organizing Map technique to International Satellite Cloud Climatology Project (ISCCP) D1 joint histograms to produce a CR and ERA‐Interim pressure vertical velocity output to produce a DR. The CR created improves the separation between high‐level CRs compared to previous work. Composites of ISCCP joint histogram data using the DR produce coherent groupings similar to those in the CR scheme particularly in regions of ascent. Both classifications display coherent geographical patterns and reproduce relationships between vertical velocity and cloud properties. However, the CR produces more coherent clusters with higher intracluster similarity and a greater range of independent cloud classes. Independent tests of composites using ISCCP FD output show that the regional variability of longwave cloud radiative effect for particular nodes are significantly higher in the DR than the CR scheme suggesting a poorer classification. Composite mean CloudSat reflectivity‐altitude joint histograms represent all major cloud types in the CR scheme, while the current DR grouping is less coherent and misses classes. This suggests that the CR scheme is a more useful classification than the DR scheme based solely on vertical velocity data. Contingency table analysis indicates a low association between these classifications, suggesting combining these schemes would be valuable.
Time-Frequency Duality of Biphotons for Quantum Optical Synthesis

Time-frequency duality, which enables control of optical waveforms by manipulating amplitudes and phases of electromagnetic fields, plays a pivotal role in a wide range of modern optics. The conventional one-dimensional (1D) time-frequency duality has been successfully applied to characterize the behavior of classical light, such as ultrafast optical pulses from a laser. However, the 1D treatment is not enough to characterize quantum mechanical correlations in the time-frequency behavior of multiple photons, such as the biphotons from parametric down conversion. The two-dimensional treatment is essentially required, but has not been fully demonstrated yet due to the technical problem. Here, we study the two-dimensional (2D) time-frequency duality duality of biphotons, by measuring two-photon distributions in both frequency and time domains. It was found that generated biphotons satisfy the Fourier limited condition quantum mechanically, but not classically, by analyzing the time-bandwidth products in the 2D Fourier transform. Our study provides an essential and deeper understanding of light beyond classical wave optics, and opens up new possibilities for optical synthesis in a high-dimensional frequency space in a quantum manner.
GC-MS and Bioassay-Guided Isolation of Xanthones from Mammea siamensis

Mammea siamensis (Miq.) T. Anders. (Calophyllaceae) plants have long been employed as an active integral composition in Thai traditional medicine. Additionally, phenylcoumarins and triterpenes were reported as major components in phytochemical research. This work explored the various parts of M. siamensis; barks, flowers, twigs, leaves, and young leaves; to determine their bioactive compounds. By using the GC-MS and bioassay guidance, two xanthones, 6-deoxyisojacareubin (1) and 1,5-dihydroxyxanthone (2), together with a mixture of phenylcoumarins, mammea A/AA cyclo D (3) and mammea A/AB cyclo D (4) have been isolated from the methanolic extract of young leaves. Their structures were identified by means of spectroscopic technique and by comparison with literature data. In particular, the current study was the first exposed report of xanthones 1 and 2 from the genus Mammea. Furthermore, compounds 1 and 2 and the methanolic young leaf extract had high antioxidant efficiency on DPPH and ABTS assays. The young leaf extract provided mild toxicity on the brine shrimp lethality test (BSLT) with LC50 value of 93.11 ± 1.37 µg/mL. In addition, the isolated compounds 1 and 2 were non-toxicity in BSLT assay. Therefore, the young leaf extract and the purified constituents 1 and 2 should be further studied and developed for using in pharmaceutical industries.
A boosted co‐training method for class‐imbalanced learning

Class imbalance learning (CIL) has become one of the most challenging research topics. In this article, we propose a Boosted co‐training method to modify the class distribution so that traditional classifiers can be readily adapted to imbalanced datasets. This article is among the first to utilize pseudo‐labelled data of co‐training to enlarge the training set of minority classes. Compared with existing oversampling methods which generate minority samples based on labelled data, the proposed method has the ability to learn from unlabelled data and then decrease the risk of overfitting. Furthermore, we propose a boosting‐style technique which implicitly modifies the class distribution and combines it with co‐training to alleviate the bias towards majority classes. Finally, we collect two series of classifiers generated during Boosted co‐training to build an ensemble for the classification. It further improves the CIL performance by leveraging the strength of ensemble learning. By taking advantage of the diversity of co‐training, we also contribute a new approach to generating base classifiers for ensemble learning. The proposed method is compared with eight state‐of‐the‐art CIL methods on a variety of benchmark datasets. Measured by G‐Mean, F‐Measure, and AUC, Boosted co‐training achieves the best performances and average ranks on 18 benchmark datasets. The experimental results demonstrate the significant superiority of Boosted co‐training over other CIL methods.
Progress of SuperKEKB

SuperKEKB is designed based on the ‘nano-beam scheme’ with the target luminosity of 8×10 35 cm -2 s -1 . The design of the interaction region (IR) with a very low βy* at the interaction point (IP) is discussed. The status of the installation and the injector commissioning of SuperKEKB is reported.
Exact wavefield reconstruction on finite-difference grids with minimal memory requirements

ABSTRACTWavefield injection in finite-difference (FD) grids can be described by the method of multiple point sources. The method teaches how synthetically generated wavefields and wavefield constituents can be reconstructed from surface recordings using a combination of monopole and dipole sources on an injection surface surrounding the model. We show how to properly record surface wavefields and inject point sources in staggered FD grids, in a way that is consistent with the order of spatial accuracy of the FD scheme. The description is general and can be used for schemes of any order. Only one or two surface wavefields are required to reconstruct the original wavefields or wavefield constituents to numerical precision, independent of the order of spatial accuracy of the FD stencil. We have applied the method for the separation of up- and downgoing wavefields and for source wavefield reconstruction for reverse time migration. Our implementation enables accurate source wavefield reconstruction with optima.
Participation in Higher Education Classroom Discussions: How Students’ Identities Influence Perspective Taking and Engagement

This study examines the impact of students’ identities on how students participate in classroom discussions in postsecondary courses. Participation in such discussions is known to increase students’ learning, but, despite this, little is known about how students’ identities influence how and whether they choose to participate. Drawn from a larger study on the experiences of postsecondary instructors and students, this article focuses on students’ perspectives and experiences. Survey data were collected from undergraduate students enrolled in an interdisciplinary undergraduate program. Students’ race, religion, gender, and their first- or continuing-generation university status were found to have varying influences on their participation in classroom discussions. Most students with marginalized identities opted out of actively participating in discussions about contentious or sociopolitical topics. This study suggests that inclusive approaches to classroom discussion can be useful in promoting students’ engagement and academic learning.
Switching Frequency Minimized Harmonic Mitigation: A Multiobjective Optimized Modulation Strategy for High- Power Converters

In order to explore the minimum switching frequency required to regulate certain amount of harmonic components, a novel switching frequency minimized harmonic mitigation (SFMHM) model is proposed in this article, which can regulate $ {N}\mathbf{-1}$ harmonics with far less than N switching angles. By flexibly adjusting the threshold values, the proposed model can achieve both harmonic elimination and mitigation objectives. Based on pulsewidth modulation (PWM) discretization and supported by quadratic programming, the switching frequency in the proposed model is expressed as a quadratic objective function of the PWM waveform. The objectives as fundamental control and selected harmonic mitigations are realized by treating the numerical approximations of the Fourier coefficients as constraints, which are finally transformed into an optimization model with binary variables and can be easily solved by some optimization toolboxes, such as YALMIP. Some computing results show that, compared with the conventional selective harmonic elimination/mitigation (SHE/SHM) methods, the number of switching angles required to mitigate the same number of harmonics under the proposed method has been significantly reduced. The switching frequency can be reduced a lot compared with the conventional SHE/SHM methods, e.g., by 40% for some modulation indexes. Simulations and experiments verify the correctness of proposed SFMHM model.
Robust adaptive dynamic surface control of uncertain non-linear systems with output constraints

This study presents a novel constraints transformation-based robust adaptive dynamic surface control (DSC) scheme for tracking control of a class of uncertain non-linear systems with output constraints. The considered systems are in semi-strict feedback form that contain not only parametric uncertainties, but also general non-linear function uncertainties as well as unknown input gains. In the proposed approach, a constraints transformation technique is firstly introduced to convert the original constrained system into an equivalent one without constraints. By ensuring the stability of the transformed system, it suffices to guarantee the constraints are not transgressed. To attain this, a decoupled DSC design methodology is then developed to stabilise the transformed system and a systematic design procedure is established. Through rigorous analysis, it is shown that the proposed control scheme can achieve output tracking objective and guarantee that all the closed-loop signals remain bounded while simultaneously preventing the prescribed output constraints from being violated. Simulation studies illustrate the efficiency and feasibility of the proposed approach.
An adaptive digital filter method for measuring the radial error motion of ultra-precision spindle

To ensure the nanometer-level running accuracy of the ultra-precision spindle, the radial error motion should be precisely measured. However, the asynchronous errors induced by the measurement instrument will mix in the measured data and significantly deteriorate the measurement precision. In this study, a novel adaptive digital filter method (ADFM) is proposed to suppress the asynchronous errors and lower requirements for the precision of measurement instruments based on the Donaldson reversal method. Specifically, the Gaussian mixture model is first used to investigate the characteristics of asynchronous errors and obtain appropriate initial filter parameters. The least mean square algorithm is utilized to adaptively optimize the filter parameters. Monte Carlo simulation results indicate that ADFM can suppress asynchronous errors by more than 90% compared with the traditional methods. Experimental results show that the standard deviation uncertainty is reduced by more than 60%. Besides, the measurement results of two sensors with different precision verify the feasibility of ADFM.
A model of point target temperature estimation based on multispectral infrared

At present, when the infrared sensor detects the targets remotely, the target appears as a spot in the image plane, the geometry information is difficult to obtain, and the surface brightness temperature becomes an effective feature for the target recognition. However, due to the long distance of the target, the weak signal and the complex transmission path, the temperature features are difficult to extract accurately, which brings great uncertainty to the target recognition. Based on the principle of multi-spectral infrared radiation temperature measurement, this paper establishes a BP network model to estimate the point target temperature. Experiments show that the accuracy of extracting the faint targets temperature characteristics can be effectively improved, which shows great support for target recognition.
Givers of Great Dinners Know Few Enemies: The Impact of Food Sufficiency and Food Sharing on Low-intensity Household Conflict in Eastern Democratic Republic of Congo

Abstract Our study establishes a linkage between household food sufficiency and food sharing behaviour with the reduction of low-intensity, micro level conflict using primary data from 1763 households of eastern Democratic Republic of Congo. We develop a theoretical explanation of such behaviour using the seminal theories of dissatisfaction originating from food insecurity and the reciprocity of gifts in economic anthropology. We first examine if food sufficient households are less likely to engage in low-intensity conflict. Following, we investigate possible heterogeneous effects of food sufficiency, conditional on food sharing behaviour. Using propensity score matching, we find that food sufficiency reduces household conflict risk by an average of around 10 percentage points. Upon conditioning on food sharing behaviour, we find that conflict risk in the subpopulation of food sufficient households is 13.8 percentage points lower for households that share their food while the effects disappear for households that do not share their food. Our results hold through a rigorous set of robustness checks including doubly robust estimator, placebo regression, matching quality tests and Rosenbaum bounds for hidden bias. We conclude that food sufficiency reduces low-intensity conflict for households only in the presence of food sharing behaviour and offer explanations and policy prescriptions.
The effect of audit quality on transfer pricing aggressiveness and firm risk: Evidence from Southeast Asian countries

Abstract This research examines the relationship between audit quality and transfer pricing aggressiveness (TPAG), the impact of TPAG on firm risk, and the indirect effect of audit quality on firm risk through TPAG. The research is important in reaffirming the auditor-client relationship, increasing companies’ understanding of the advantages and disadvantages of transfer pricing in their strategies, and advancing research on audit quality and firm risk considering the tax aspects of multinational companies. Data were collected from non-financial publicly listed companies in three Southeast Asian countries, Indonesia, Malaysia, and Singapore, from 2014 to 2018, totaling 1,470 firm-year, using the panel data regression. The result indicates that multinational companies audited by high-quality (Big-N) audit firms exhibit higher levels of TPAG. Moreover, companies that employ more TPAG practices are likely to have lower firm risk because their transfer pricing transactions are efficient. This research also highlights that better audit quality indirectly reduces firm risk by influencing TPAG.