Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Multiphysics Multiobjective Optimization for U-Shaped IPMSM Equipped With Eccentric Rotor

Benefiting from high efficiency, high-cost performance, and high operational reliability, interior permanent magnet synchronous motors (IPMSM) have attracted much attention in many emerging technology fields such as electric vehicles. To further enhance the comprehensive properties and engineering practicability, a U-shaped IPMSM equipped with an eccentric rotor is introduced, and a novel multiphysics multiobjective optimization model is innovatively proposed, studied, and developed. In this model, the key characteristics of electromagnetic, temperature, and mechanical fields are all selected as optimization objectives, realizing multiphysics collaborative optimization. First, nine key parameters that determine topology are selected as design variables, and five multiphysics characteristics are selected as optimization objectives or constraints. After that, in the data collection stage, segmented orthogonal experiments and sensitivity analysis are employed to ensure optimization efficiency. Then, the machine learning algorithm based on support vector regression and improved non-dominated sorting genetic algorithm (NSGA-III) are perfectly combined, and an effective and reliable optimal scheme is obtained while fully guaranteeing prediction accuracy. Finally, a comprehensive comparative analysis is carried out through simulation analysis and prototype experiments. The validity and merits of the studied U-shaped IPMSM and optimization model are excellently verified.
Computation of Supercritical Flow in Open Channels

The use of two explicit, shock-capturing, finite-difference schemes, Lax and MacCormack, for the solution of two-dimensional shallow-water equations in steady, supercritical flow is investigated. Special attention is given to the inclusion of the boundary conditions. A satisfactory agreement between the computed results and the analytical solutions and experimental measurements shows that many practical problems may be simulated by the procedures presented.
Efficient unmanned aerial vehicle formation rendezvous trajectory planning using Dubins path and sequential convex programming

ABSTRACT Trajectory planning of formation rendezvous of multiple unmanned aerial vehicles (UAVs) is formulated as a mixed-integer optimal control problem, and an efficient hierarchical planning approach based on the Dubins path and sequential convex programming is proposed. The proposed method includes the assignment of rendezvous points (high level) and generation of cooperative trajectories (low level). At the high level, the assignment of rendezvous points to UAVs is optimized to minimize the total length of Dubins-path-based approximate trajectories. The assignment results determine the geometric relations between the UAVs’ goals, which are used as equality constraints for generating trajectories. At the low level, trajectory generation is treated as a non-convex optimal control problem, which is transformed to a non-convex parameter optimization and then solved via sequentially performing convex optimization. Numerical experiments demonstrate that the proposed method can generate feasible trajectories and can outperform a typical nonlinear programming method in terms of efficiency.
Near-Infrared Spectroscopy-Guided, Individualized Arterial Blood Pressure Management for Carotid Endarterectomy under General Anesthesia: A Randomized, Controlled Trial

Abstract Background: Differences in blood pressure can influence the risk of brain ischemia, perioperative complications, and postoperative neurocognitive function in patients undergoing carotid endarterectomy (CEA). Methods: In this single-center trial, patients scheduled for CEA under general anesthesia were randomized into an intervention group receiving near-infrared spectroscopy (NIRS)-guided blood pressure management during carotid cross-clamping and a control group receiving standard care. The primary endpoint was postoperative neurocognitive function assessed before surgery, on postoperative days 1 and 7, and eight weeks after surgery. Perioperative complications and cerebral autoregulatory capacity were secondary endpoints. Results: Systolic blood pressure (p < 0.001) and norepinephrine doses (89 (54–122) vs. 147 (116–242) µg; p < 0.001) during carotid cross-clamping were lower in the intervention group. No group differences in postoperative neurocognitive function were observed. The rate of perioperative complications was lower in the intervention group than in the control group (3.3 vs. 26.7%, p = 0.03). The breath-holding index did not differ between groups. Conclusions: Postoperative neurocognitive function was comparable between CEA patients undergoing general anesthesia in whom arterial blood pressure during carotid cross-clamping was guided using NIRS and subjects receiving standard care. NIRS-guided, individualized arterial blood pressure management resulted in less vasopressor exposition and a lower rate of perioperative complications.
An Empirical Study of Locally Updated Large-scale Information Network Embedding (LINE)

The problem of embedding very large information networks into low-dimensional vector spaces is useful in many tasks such as visualization, node classification, and link prediction. This paper studies the novel network embedding method called the ''LINE'' , which optimizes a carefully designed objective function that preserves both the local and global network structures. In order to rule out the instability on border vertices' embeddings and its influence on core vertices, we only compute the core graph with LINE after peeling out those border vertices. Then we compute the embedding of peeled nodes with locally updated process. This paper also tried to interpret and visualize the local update process with logistic regression, and optimize the local update process by adding prior and intercept to the objective function. Finally, this paper demonstrate the embeddings on several multi-label network classification tasks for social networks such as BlogCatalog and YouTube. Our results show that the optimized LINE outperforms the initial methods 5% in F1-score with YouTube dataset and speed up the convergence time.
Awn Image Analysis and Phenotyping Using BarbNet

Consideration of the properties of awns is important for the phenotypic description of grain crops. Awns have a number of important functions in grasses, including assimilation, mechanical protection, and seed dispersal and burial. An important feature of the awn is the presence or absence of barbs—tiny hook-like single-celled trichomes on the outer awn surface that can be visualized using microscopic imaging. There are, however, no suitable software tools for the automated analysis of these small, semi-transparent structures in a high-throughput manner. Furthermore, automated analysis of barbs using conventional methods of pattern detection and segmentation is hampered by high variability of their optical appearance including size, shape, and surface density. In this work, we present a software tool for automated detection and phenotyping of barbs in microscopic images of awns, which is based on a dedicated deep learning model (BarbNet). Our experimental results show that BarbNet is capable of detecting barb structures in different awn phenotypes with an average accuracy of 90%. Furthermore, we demonstrate that phenotypic traits derived from BarbNet-segmented images enable a quite robust categorization of 4 contrasting awn phenotypes with an accuracy of >85%. Based on the promising results of this work, we see that the proposed model has potential applications in the automation of barley awns sorting for plant developmental analysis.
Penetration Effect: Exotic Behavior of a Wave in Anisotropic Media

Plane harmonic wave propagation along an interface between vacuum and a semi-infinite uniaxial anisotropic medium is considered. It is shown that there is a bulk wave within an anisotropic medium in this case. It is also proved for the first time that a reflected wave must propagate perpendicularly to an interface. Moreover, a reflected wave is absent in the case of ordinary wave propagation.
A Generalized Noise Reconstruction Approach for Robust DOA Estimation

A generalized noise reconstruction approach is proposed for improved direction-of-arrival (DOA) estimation. First, coarse estimation of the noise component in the received data is obtained based on cumulative sum of an approximate first-order finite difference series, where the generalized pattern search (GPS) algorithm is used to find the best initial value. The objective function is constructed with the total noise power estimated by the least squares (LS) method. After that, a new cumulative sum constructed with the solution from the previous step is used to accurately reconstruct the noise component in the data. Finally, robust DOA estimation is achieved through the denoised data. In simulations, different cases of noise, such as non-uniform noise, colored noise, impulsive noise, and K-distribution sea clutter, are considered. TI frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar data is also used to demonstrate the effectiveness of the proposed approach.
Sweet Basil between the Soul and the Table—Transformation of Traditional Knowledge on Ocimum basilicum L. in Bulgaria

The study tracks the utilization of Ocimum basilicum L. (sweet basil)—a garden plant popular for its ritual and ornamental value in the past, that is currently applied in various forms and ways as medicine, food, insect repellent, etc.—in Bulgaria. Previous data for Bulgarian rural home gardens showed a significant number of preserved local landraces; however, it remained unclear how people perceive the large varietal diversity of this species and how the traditions related to its use are preserved. We combined a literature review on the cultural value of sweet basil and the breeding of local genetic resources with an online questionnaire, directed to adult laypeople, that sought to access different aspects of past (recalled) and present use and related knowledge. The identification skills of the participants were tested using images of local plant landraces and foreign varieties. Responses from 220 participants showed that potted “Genovese”-type individual was most frequently identified as sweet basil (89.9%), followed by two examples of local landraces in flower. Participants who grow sweet basil or used it in more varied ways had significantly better identification skills. Ocimum basilicum was most frequently reported as food, while ritual/symbolic use was preserved while devalued during the Communism regime (1945–1989). Food and religious uses were negatively associated in the past, but presently, the tendency is completely reversed. Preferences for the informal exchange of seeds and seed-saving practices were discussed.
Editorial: Community series in adaptor molecules in T cell signaling, volume II

COPYRIGHT © 2023 Pongcharoen, Spurkland and Baldari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Editorial PUBLISHED 26 July 2023 DOI 10.3389/fimmu.2023. 1243039
WORKPLACE SUPPORT AND WELL-BEING AMONG WORKING PARENTS

Background and Purpose: Workplace support is vital in ensuring positive psychological well-being of working parents. The balancing act between work and family may affect the well-being of working parents due to strenuous responsibilities. Hence, this study intends to examine the relationship between workplace support and the well-being of working parents. Methodology: The study employs a cross-sectional survey where 146 working parents were recruited. A multiple regression analysis was employed. Findings: Current study found workplace support was a significant predictor of working parents’ well-being (F(1, 134) =2.70. , p < . 01), while all the predictors variables (organizational [r = .18, p < .05], supervisory [r = .17, p < .05] and co-worker support [r = .17, p < .05]) showed positive significant positive relationship with working parents’ well-being. Contributions: Current research provided insight toward the development of career counselling and understanding of the implication of workplace supports for healthier working parents. Furthermore, this study also contributed to the literature of human development and Super lifespan theory based on the current human development cycle. Keywords: Workplace support, supervisory support, co-worker support, working parents and psychological well-being. Cite as: Ying, C. Z. , & Mohamad Nor, A. (2023). Workplace support and well-being among working parents. Journal of Nusantara Studies, 8(1), 46-67. http://dx.doi. org/10.24200/jonus.vol8iss1pp46-67
A D-Band Waveguide Diplexer Based on Copper Additive Manufacturing

This article presents a D-band waveguide diplexer fabricated using micro metal additive manufacturing (M-MAM) technology. The diplexer features two sixth-order filters with passbands of 130–134 GHz and 141–148.5 GHz, respectively, for a crucial RF application band. Cylindrical resonators were employed in the filter design, and a nine-layer process was developed specifically for this frequency band, allowing the diplexer to be manufactured as a single piece. The process was optimized to minimize photoresist deformation during repeated thermal cycles, so a larger waveguide can be fabricated. To ensure accurate measurements, a fixture equipped with a straight-through calibration channel was also designed and manufactured. The measured insertion losses and return losses of both channels are less than 2 dB and greater than 15 dB, respectively, and the center frequency shifts are negligible at less than 0.15%. The excellent correlation between simulations and measurements is a result of the carefully designed process and its high level of accuracy.
Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.
Remote Assessment of Parkinson’s Disease Patients Amidst the COVID-19 Lockdown in Mexico

The COVID-19 pandemic introduced unprecedented challenges in managing patients with Parkinson’s disease (PD) due to disruptions in healthcare services and the need for social distancing. Understanding the effects of COVID-19 on PD symptoms is crucial for optimizing patient care. We conducted a comprehensive analysis of the data obtained during the period of COVID-19 lockdown, comparing it with analogous timeframes in 2018 and 2019. Our objective was to examine the influence of this unique circumstance on both motor and non-motor symptoms in patients with PD. Telemedicine was employed to assess symptoms using the Movement Disorder Society-sponsored Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Our findings revealed a notable worsening of symptoms, evidenced by a significant increase in the total MDS-UPDRS score. Specifically, there was an increase in Part III scores, reflecting changes in motor function. However, no differences were observed in Parts I or II, which pertain to non-motor symptoms. Additionally, patient satisfaction and the feasibility of telemedicine consultations were high, highlighting the efficacy of this alternative approach during the pandemic. The COVID-19 pandemic had a discernible impact on PD symptoms, with a significant worsening of motor symptoms observed during the lockdown period. Telemedicine was a valuable tool for remote assessment and follow-up, ensuring continuity of care for individuals with PD in the face of pandemic-related challenges.
Characterization of Shape Curving Effects on Multi-Band Fractal Antenna Design

The modern telecommunication systems require antennas with the wider bandwidths, the smaller dimensions, and the ability to resonate on several frequencies of interest. The fractal antennas have the capability to cater to all of the aforementioned requirements. Moreover, the fractal geometries tend to have improved level of the impedance and the standing wave ratio on the reduced physical area, in comparison with the non-fractal Euclidean geometries. This paper focuses on the design of multiband fractal patch antennas, which can resonate at different frequencies at the same time. It is demonstrated by computer simulation results that the significant number of resonant frequencies increases with the increase in number of iterations in the fractal geometry. Besides, some simulation results are presented using the fractal geometries to give an insight into the frequency response variations with respect to the change in the curved shape of the patch of the fractal antenna.
The Journey of Biorthogonal Logical Relations to the Realm of Assembly Code

Logical relations appeared to be very fruitful for the development of modular proofs of compiler correctness. In this field, logical relations are parametrized by a high-level type system, and are even sometimes directly relating low level pieces of code to high-level programs. All those works rely crucially on biorthogonality to get extensionality and compositionality properties. But the use of biorthogonality in the definitions also complicates matters when it comes to operational correctness. Most of the time, such correctness results amount to show an unfolding lemma that makes reduction more explicit than in a biorthogonal definition. Unfortunately, unfolding lemmas are not easy to derive for rich languages and in particular for assembly code. In this paper, we focus on three different situations that enable to reach step-by-step the assembly code universe: the use of Curry-style polymorphism, the presence of syntactical equality in the language and finally an ideal assembly code with a notion of code pointer.
Semi-parametric Geographically Weighted Regression Modelling using Linear Model of Coregionalization

Geographically Weighted Regression is a weighted analysis regression for local or spatially varying parameters, therefore each location has different regression parameters. In its application, one often finds a condition that needs some global parameters. Geographically Weighted Regression that has local and global parameters is called Semi-parametric Geographically Weighted Regression. This study modelled Semi-parametric Geographically Weighted Regression using Linear Model of Coregionalization to assist spesification of local and global parameters. Linear Model of Coregionalization represented spatial variability proportion at different spatial distances and spatial dependence of parameters. High spatial dependence variables were as local parameters while the other variables were as global parameters. The data used was poverty data in North Sulawesi Province. The results of Geographically Weighted Regression and Semi-parametric Geographically Weighted Regression models were compared based on Akaike Information Criterion Corrected and Mean Square Prediction Error. It showed that Semi-parametric Geographically Weighted Regression model was better than Geographically Weighted Regression.
Energy-resolved pulse profiles of accreting pulsars: Diagnostic tools for spectral features

We introduce a method for extracting spectral information from energy-resolved light curves folded at the neutron star spin period (known as pulse profiles) in accreting X-ray binaries. Spectra of these sources are sometimes characterized by features superimposed on a smooth continuum, such as iron emission lines and cyclotron resonant scattering features. We address here the question on how to derive quantitative constraints on such features from energy-dependent changes in the pulse profiles. We developed a robust method for determining in each energy-selected bin the value of the pulsed fraction using the fast Fourier transform opportunely truncated at the number of harmonics needed to satisfactorily describe the actual profile. We determined the uncertainty on this value by sampling through Monte Carlo simulations a total of 1000 faked profiles. We rebinned the energy-resolved pulse profiles to have a constant minimum signal-to-noise ratio throughout the whole energy band. Finally we characterize the dependence of the energy-resolved pulsed fraction using a phenomenological polynomial model and search for features corresponding to spectral signatures of iron emission or cyclotron lines using Gaussian line profiles. We apply our method to a representative sample of NuSTAR observations of well-known accreting X-ray pulsars. We show that, with this method, it is possible to characterize the pulsed fraction spectra, and to constrain the position and widths of such features with a precision comparable with the spectral results. We also explore how harmonic decomposition, correlation, and lag spectra might be used as additional probes for detection and characterization of such features.
Gaining New Insights into Fundamental Biological Pathways by Bacterial Toxin-Based Genetic Screens

Genetic screen technology has been applied to study the mechanism of action of bacterial toxins—a special class of virulence factors that contribute to the pathogenesis caused by bacterial infections. These screens aim to identify host factors that directly or indirectly facilitate toxin intoxication. Additionally, specific properties of certain toxins, such as membrane interaction, retrograde trafficking, and carbohydrate binding, provide robust probes to comprehensively investigate the lipid biosynthesis, membrane vesicle transport, and glycosylation pathways, respectively. This review specifically focuses on recent representative toxin-based genetic screens that have identified new players involved in and provided new insights into fundamental biological pathways, such as glycosphingolipid biosynthesis, protein glycosylation, and membrane vesicle trafficking pathways. Functionally characterizing these newly identified factors not only expands our current understanding of toxin biology but also enables a deeper comprehension of fundamental biological questions. Consequently, it stimulates the development of new therapeutic approaches targeting both bacterial infectious diseases and genetic disorders with defects in these factors and pathways.