Miracle-dz/UniFAR-FTUs huggingface dataset:
Method-of-Moments Solver for All-Metal Corrugated Structures: Leaky-Mode Analysis and Open-Stopband Suppression

A method-of-moments (MoMs) approach is proposed for the modal analysis of two-dimensional (2-D) all-metal leaky-wave structures periodic along one direction. The electric-field integral equations (EFIEs) for both TM and TE polarizations are formulated in the unit cell (UC) by employing a rapidly convergent Ewald representation for the relevant 2-D periodic Green’s function. The integral equations are discretized using triangular subsectional basis functions and a Galerkin testing scheme. Effective modal dispersive analyses are developed for a variety of leaky-wave structures. Real proper (bound) modes, as well as complex proper (backward leaky) and improper (forward leaky) modes, are investigated. The proposed method allows for the accurate design and tuning of asymmetric UCs required to suppress the open stopband (OSB), which cannot be easily carried out by means of full-wave simulations with commercial software. Validations are provided through alternative methods and in all cases the presented formulation shows high accuracy and versatility.
Predictor of the Postoperative Swelling After Craniotomy for Spontaneous Intracerebral Hemorrhage: Sphericity Index as a Novel Parameter

Objective Spontaneous intracerebral hemorrhage is a serious type of stroke with high mortality and disability rates. Surgical treatment options vary; however, predicting edema aggravation is crucial when choosing the optimal approach. We propose using the sphericity index, a measure of roundness, to predict the aggravation of edema and guide surgical decisions. Methods We analyzed 56 cases of craniotomy and hematoma evacuation to investigate the correlation between the sphericity index and patient outcomes, including the need for salvage decompressive craniectomy (DC). Results The patients included 35 (62.5%) men and 21 (37.5%) women, with a median age of 62.5 years. The basal ganglia was the most common location of hemorrhage (50.0%). The mean hematoma volume was 86.3 cc, with 10 (17.9%) instances of hematoma expansion. Cerebral herniation was observed in 44 (78.6%) patients, intraventricular hemorrhage in 34 (60.7%), and spot signs in 9 (16.1%). Salvage DC was performed in 13 (23.6%) patients to relieve intracranial pressure. The median follow-up duration was 6 months, with a mortality rate of 12.5%. The sphericity index was significantly correlated with delayed swelling and hematoma expansion but not salvage DC. Conclusions The sphericity index is a promising predictor of delayed swelling and hematoma expansion that may aid in the development of surgical guidelines and medication strategies. Further large-scale studies are required to explore these aspects and establish comprehensive guidelines.
2-D Transformer-Based Approach for Process Monitoring of Metal 3-D Printing via Coaxial High-Speed Imaging

Defects in the metal 3-D printing process exhibit randomness and low frequency, making them difficult to predict and control. This severely hinders the application of this technology in critical industrial fields. Extracting useful features from massive process monitoring data to ensure forming quality has become a popular research direction for intelligent additive manufacturing practitioners. In this study, a coaxial machine vision monitoring system is utilized to monitor the entire forming process of the melt track. More importantly, this study constructed a high-speed video dataset of typical metal 3-D printing working conditions by changing the powder layer thickness, which can serve actual industrial production. To promptly recognize unhealthy melt tracks from massive process monitoring data, a 2-D transformer-based framework named super frame feature pyramid transformer (SFFPT) is designed for video classification. This framework transforms the video understanding task into a 2-D feature map processing task, allowing the video classification task to be completed directly using only an image classifier. In comparison to state-of-the-art methods, SFFPT achieves the best classification accuracy in this study.
Students’ Errors in Solving Plane Geometry Problems Using E-Learning-based Diagnostic Tests

During the Covid-19 pandemic, the implementation of online and shift learning systems has been identified as one factor contributing to learning loss in mathematics problem-solving skills specifically in plane geometry problems. The primary objective of this research was to investigate the different types of errors committed by junior high school students in Banda Aceh, Indonesia, in solving plane geometry problems using e-learning diagnostic tests. A descriptive qualitative approach was adopted, and 24 Year 8 students were selected as subjects. The data were collected through tests and interviews, and data analysis involved data reduction, data presentation, data verification, and drawing conclusions. The findings revealed that the percentage of students' errors in solving conceptual problems, principal problems, and verbal problems were 76.39%, 68.75%, and 75%, respectively. Subsequently, the results from the second test and interviews with six students showed that students encountered several conceptual errors, including errors in stating angle symbols, errors in identifying the type of shape provided, errors in deducing information regarding properties, perimeter, and area of plane geometry, lack of knowledge regarding the application of the appropriate plane geometry formula, and inability to solve problems using the correct procedure. Principal errors were related to algebraic operations, while verbal errors involved translating problems and improper application of concepts and principles. The understanding of the types of errors will help teachers detect errors made the students early.
Effects of Myofascial Induction Therapy on Ankle Range of Motion and Pressure Pain Threshold in Trigger Points of the Gastrocnemius—A Clinical Trial

Background: The myofascial induction technique (MIT) has been shown to increase shoulder range of motion (ROM) in breast cancer survivors and decrease pain pressure threshold over the radial nerve in patients with epicondylalgia. To the authors’ best knowledge, no study on trigger points and MIT has been published to date. The effect on ROM of latent trigger points is also unknown. Methods: A total of 20 twins with one latent trigger point of the gastrocnemius muscle were evaluated pre- and post-MIT in the calf. We measured static footprint variables in a pre–post study. Results: We found differences in PPT (p = 0.001) and no differences in ROM with knee flexed (p = 0.420) or stretched (p = 0.069). Conclusions: After Calf MIT, latent myofascial trigger points improve PPT but no change in ankle dorsiflexion with knee bent or knee flexed were found in non-restriction healthy subjects.
The Stellar CME–Flare Relation: What Do Historic Observations Reveal?

Solar coronal mass ejections (CMEs) and flares have a statistically well-defined relationship, with more energetic X-ray flares corresponding to faster and more massive CMEs. How this relationship extends to more magnetically active stars is a subject of open research. Here we study the most probable stellar CME candidates associated with flares captured in the literature to date, all of which were observed on magnetically active stars. We use a simple CME model to derive masses and kinetic energies from observed quantities and transform associated flare data to the Geostationary Operational Environmental Satellite 1–8 Å band. Derived CME masses range from ∼1015 to 1022 g. Associated flare X-ray energies range from 1031 to 1037 erg. Stellar CME masses as a function of associated flare energy generally lie along or below the extrapolated mean for solar events. In contrast, CME kinetic energies lie below the analogous solar extrapolation by roughly 2 orders of magnitude, indicating approximate parity between flare X-ray and CME kinetic energies. These results suggest that the CMEs associated with very energetic flares on active stars are more limited in terms of the ejecta velocity than the ejecta mass, possibly because of the restraining influence of strong overlying magnetic fields and stellar wind drag. Lower CME kinetic energies and velocities present a more optimistic scenario for the effects of CME impacts on exoplanets in close proximity to active stellar hosts.
Smart Recommendation for Unanimous People

- Creativity has become one of the optimal technologies to solve human life problems, and technology is used for facilitating human needs. People always seek and be more comfortable based on a similar mindset. Which in return helps them to build new ideas and thoughts. With the popularity of social networks and social media, many users like to share their reviews, ratings, experiences, and images. The factors that are most considered by social media platforms, like influence, search content and interest based on friends, bring connectivity for smart recommendation systems to establish the relation between the users with the help of the data collected from the users. Social factors like interpersonal interest similarity, interpersonal influence, and personal interest these factors are taken into consideration before recommending friends to users. This smart recommendation model, which we have used, is based on Latent Dirichlet Allocation (LDA) algorithm. The category of personal interest will help the users to club together and can recommend people to the user based on individualities. The interpersonal influence helps users to connect based on their interest towards learning and innovation; they can connect and discuss regarding their common interests.
Ras Association Domain Family 1A Gene Promoter Methylation as a Biomarker for Chronic Viral Hepatitis C-related Hepatocellular Carcinoma

Background One of the most prevalent aberrant epigenetic modifications found in hepatocellular carcinoma (HCC) is abnormal DNA methylation. Our study aimed to evaluate serum Ras association domain family 1A (RASSF1A) gene promoter methylation in patients with chronic viral hepatitis C (HCV)-associated liver cirrhosis with and without HCC as a potential new marker for the early detection of HCC. Methodology The 60 participants who participated in the trial were divided into the following three groups: 20 patients with newly diagnosed primary HCC on top of HCV-related liver cirrhosis, 20 patients with HCV-related liver cirrhosis, and 20 age- and sex-matched healthy individuals as a control group. All participants underwent methylation-specific polymerase chain reaction testing to detect the blood level of the RASSF1A gene’s methylated promoter. Results Methylated RASSF1A was found in 30% of patients with liver cirrhosis caused by HCV and in 65% of patients with HCC, but not in any of the controls. It was discovered that the serum methylation RASSF1A had an accuracy of 82.50% and an area under the curve (AUC) of 0.825 for separating HCC patients from healthy controls. With an AUC of 0.675 and an accuracy of 67.50%, it was able to differentiate patients with HCC from those with HCV-related liver cirrhosis. Additionally, there was no statistically significant association between RASSF1A methylation status and HCC mass size (p = 0.449). Conclusions Serum RASSF1A promoter methylation status detection could be useful for detecting HCC early, especially in high-risk individuals such as those with HCV.
3G-AN: Triple-Generative Adversarial Network Under Corse-Medium-Fine Generator Architecture

In recent years, Generative Adversarial Networks (GANs) have gained worldwide interest and have marked a breakthrough in deep learning, encouraging detailed studies in generating artificial images. A new Generative Adversarial Networks (GAN) is proposed to unveil how Human visual perception takes place, focusing on how human beings perceive images, firstly, coarse structures and then their details. The network called 3G-AN consists of three generation stages and a single Discriminator. In this paper, a novel three-branch generator is proposed, which takes into account Coarse, Medium, and Fine structure of a given image. Coarse RGB decomposition image provides the general structure, while Medium RGB stage provides general-fine structure. Finally, Fine RGB decomposition provides fine details of the image. The proposal is tested on MNIST, CIFAR10, and Celebrity faces databases, generating realistic images with almost no anomalies. The RGB decomposition into coarse, medium, and fine, allows to understand the composition of an image from a structural point of view. The qualitative analysis carried out in this research paper outperforms the six most competitive models existing in the literature.
SEMANTIC SIMULTANEOUS LOCALIZATION AND MAPPING

SEMANTIC SIMULTANEOUS LOCALIZATION AND MAPPING Sean L. Bowman George J. Pappas Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Recent advances in object recognition and semantic scene understanding, however, have made this information easier to extract than ever before, and the recent proliferation of robots in human environments demand access to reliable semantic-level mapping and localization algorithms to enable true autonomy. Furthermore, loop closure recognition based on low-level features is often viewpoint dependent and subject to failure in ambiguous or repetitive environments, whereas object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks. In this thesis, we present two solutions that incorporate semantic information into a full localization and mapping pipeline. In the first, we propose a solution method using only single-image bounding box object detections as the semantic measurement. As these bounding box measurements are relatively imprecise when projected back into 3D space and difficult to associate with existing mapped objects, we first present a general method to probabilistically compute data associations within an estimation framework and demonstrate its improved accuracy in the case of high-uncertainty measurements. We then extend this to the specific case of semantic bounding box measurements and demonstrate its accuracy in indoor and outdoor environments. Second, we propose a solution based on the detection of semantic keypoints. These semantic keypoints are not only more reliably positioned in space, but also allow us
Comparing Different Sticky Traps to Monitor the Occurrence of Philaenus spumarius and Neophilaenus campestris, Vectors of Xylella fastidiosa, in Different Crops

Simple Summary Spittlebugs are the main vectors of the quarantine bacterium Xylella fastidiosa (Wells et al.) in Europe. Knowledge of the occurrence and population density of spittlebugs in different Xylella-susceptible crops has therefore become crucial to implement preventive and containment vector control strategies in order to reduce the risk of new outbreaks and the spread of the bacterium in infected areas. So far, sweep nets have been the most widely used method to monitor spittlebugs in different vegetation compartments in Europe. In this study, the attractiveness of different sticky traps, compared to sweep nets, has been examined. The obtained results suggest that the two methods should be integrated to achieve an accurate estimation of the presence and abundance of spittlebugs throughout the whole season when adults are present in the crops. Abstract Following the detection of the quarantine bacterium Xylella fastidiosa (Wells et al.) in the Apulia region (southern Italy) and the identification of spittlebugs as the main vector species that contributes to its epidemic spread, monitoring activities have been intensified in an attempt to implement vector control strategies. To date, sweep nets have been the most widely used sampling method to monitor adult spittlebug populations. Field experiments were carried out, during 2018 and 2019, to evaluate the effectiveness of sticky traps in capturing spittlebugs in different woody crops. The attractiveness of different traps was compared: four colored sticky traps (white, red, blue, and yellow), with the yellow sticky traps having three different background patterns (plain yellow, yellow with a black circle pattern, and yellow with a black line pattern). In addition, the efficiency of the yellow sticky traps was evaluated by placing the traps on the ground or hanging them from the canopies in orchards with different spittlebug population densities. Trap catches of Philaenus spumarius (Linnaeus) and Neophilaenus campestris (Fallén) (Hemiptera: Aphrophoridae) were compared with those collected using sweep nets. The two spittlebug species showed a similar response to the colored traps and were mainly attracted to the yellow sticky traps. Captures throughout the adult season indicated that an accurate estimation of the presence and abundance of spittlebugs can be obtained by integrating the two sampling methods. Moreover, sweep nets appeared to be more efficient in collecting adults soon after their emergence, while the use of sticky traps was more efficient in the rest of the adult season when the use of traps can significantly expedite vector monitoring programs.
Electric Vehicle Charging Scheduling Strategy for Supporting Load Flattening Under Uncertain Electric Vehicle Departures

The scheduled electric vehicle (EV) charging flexi‐ bility has great potential in supporting the operation of power systems, yet achieving such benefits is challenged by the uncer‐ tain and user-dependent nature of EV charging behavior. Exist‐ ing research primarily focuses on modeling the uncertain EV arrival and battery status yet rarely discusses the uncertainty in EV departure. In this paper, we investigate the EV charging scheduling strategy to support load flattening at the distribu‐ tion level of the utility grid under uncertain EV departures. A holistic methodology is proposed to formulate the unexpected trip uncertainty and mitigate its negative impacts. To ensure computational efficiency when large EV fleets are involved, a distributed solution framework is developed based on the alter‐ nating direction method of multipliers (ADMM) algorithm. The numerical results reveal that unexpected trips can severely dam‐ age user convenience in terms of EV energy content. It is fur‐ ther confirmed that by applying the proposed methodology, the resultant critical and sub-critical user convenience losses due to scheduled charging are reduced significantly by 83.5% and 70.5%, respectively, whereas the load flattening performance is merely sacrificed by 17%.
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks

Recently, deep learning has become a de facto standard in machine learning with convolutional neural networks (CNNs) demonstrating spectacular success on a wide variety of tasks. However, CNNs are typically very demanding computationally at inference time. One of the ways to alleviate this burden on certain hardware platforms is quantization relying on the use of low-precision arithmetic representation for the weights and the activations. Another popular method is the pruning of the number of filters in each layer. While mainstream deep learning methods train the neural networks weights while keeping the network architecture fixed, the emerging neural architecture search (NAS) techniques make the latter also amenable to training. In this paper, we formulate optimal arithmetic bit length allocation and neural network pruning as a NAS problem, searching for the configurations satisfying a computational complexity budget while maximizing the accuracy. We use a differentiable search method based on the continuous relaxation of the search space proposed by Liu et al. (arXiv:1806.09055). We show, by grid search, that heterogeneous quantized networks suffer from a high variance which renders the benefit of the search questionable. For pruning, improvement over homogeneous cases is possible, but it is still challenging to find those configurations with the proposed method. The code is publicly available at this https URL and this https URL
Urbanisation-induced changes in the morphology of birds from a tropical city

ABSTRACT Urbanisation is accompanied by major environmental changes that impact the structure and functioning of communities and ecosystems, bringing new selective regimes for animal species and for eco-evolutionary dynamics. We aimed to evaluate whether urban intensification promotes ecomorphological changes in birds from a large city in Central Brazil. Analyses were performed on a set of 1314 individuals of 35 species, captured along a gradient of urban intensification. We found significant morphological changes associated with urban intensification by evaluating ten ecomorphological traits and body mass of the species assemblage. Beak length showed the most dramatic changes, and was significantly shorter as a function of urban intensification, mainly in individuals of insectivorous and omnivorous species. These results reinforce the notion that environmental changes caused by human activities in dense urban environments promote new selective pressures in resident bird species.
Spillovers in Fields of Study: Siblings, Cousins, and Neighbors

We use admission lotteries for higher education studies in the Netherlands to investigate whether someone’s field of study influences the study choices of their younger peers. We find that younger siblings and cousins are strongly affected. Also younger neighbors are affected but to a smaller extent. These findings indicate that a substantial part of the correlations in study choices between family members can be attributed to spillover effects and are not due to shared environments. Our findings contrast with those of recent studies based on admission thresholds, which find no sibling spillovers on field of study (major) choices. Because we also find spillovers from lottery participants at the lower end of the ability distribution, the contrasting findings cannot be attributed to the different research designs (leveraging admission lotteries versus admission thresholds). We believe that the different findings are due to the small differences in quality be-tween universities in the Netherlands, making differences in the prestige of fields of study more prominent.
Fipronil Degradation in Soil by Enterobacter chengduensis Strain G2.8: Metabolic Perspective

Fipronil is an insecticide widely used in the agricultural and veterinary sectors for its efficacy in pest control. The presence of fipronil in the environment is mainly due to agricultural and domestic practices and is frequently found in different types of environmental matrices in concentrations ranging from µg/L to mg/L and can be hazardous to non-target organisms due to its high toxicity. This study was carried out to obtain and characterize microorganisms from soil which are capable of biodegrading fipronil that could be of great biotechnological interest. For this purpose, bioprospecting was carried out using fipronil (0.6 g/L) as the main source of carbon and nitrogen for growth. Once obtained, the strain was identified by sequencing the 16S ribosomal RNA (rRNA) gene and the capacity to degrade fipronil was monitored by GC-MS. Our study showed a presence in soil samples of the strain identified as Enterobacter chengduensis, which was able to metabolize fipronil and its metabolites during the mineralization process. Enterobacter chengduensis was able to biodegrade fipronil (96%) and its metabolites fipronil-sulfone (92%) and fipronil-sulfide (79%) in 14 days. Overall, the results of this study provided a bacterium with great potential that could contribute to the degradation of fipronil in the environment.