Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation Research on the Effect of Spreading Process Parameters on the Quality of Lunar Regolith Powder Bed in Additive Manufacturing

Lunar surface additive manufacturing with lunar regolith is a key step in in-situ resource utilization. The powder spreading process is the key process, which has a major impact on the quality of the powder bed and the precision of molded parts. In this study, the discrete element method (DEM) was adopted to simulate the powder spreading process with a roller. The three powder bed quality indicators, including the molding layer offset, voidage fraction, and surface roughness, were established. Besides, the influence of the three process parameters, which are roller’s translational speed, rotational speed, and powder spreading layer thickness on the powder bed quality indicators was also analyzed. The results show that with the reduction of the powder spreading layer thickness and the increase of the rotational speed, the offset increased significantly; when the translational speed increased, the offset first increased and then decreased, which resulted in an extreme value; with the increase of the layer thickness and the decrease of the translational speed, the values for voidage fraction and surface roughness significantly reduced. The powder bed quality indicators were adopted as the optimization objective, and the multi-objective parameter optimization was carried out. The predicted optimal powder spreading parameters and powder bed quality indicators were then obtained. Moreover, the optimal values were then verified. This study can provide informative guidance for in-situ manufacturing at the moon in future deep space exploration missions.
Object-Level Semantic and Velocity Feedback for Dynamic Occupancy Grids

LiDAR-basedframeworks combining dynamic occupancy grids and object-level tracking are a popular approach for perception of the environment in autonomous driving applications. This paper presents a novel backchannel from the object-level module to the grid-level module that procures the enhancement of overall performance. This feedback leads to an enhanced grid representation by the inclusion of two new steps that allow semantic classification of the occupied space and the improvement of the dynamic estimation. To this end, objects extracted from the grid are analyzed with respect to potential object classes and displacement. Class likelihoods are filtered over time at cell-level using particles and a naive Bayesian classifier. The displacement information is computed taking into account semantic information and comparing objects in consecutive frames. Then, it is used to obtain velocity measurements that are used to enhance grid's dynamic estimation. In contrast to other approaches in the literature seeking similar objectives, this proposal does not rely on additional sensing technologies or neural networks. The evaluation is conducted with real sensor data in challenging urban scenarios.
Combination of CAR‑T cell therapy and radiotherapy: Opportunities and challenges in solid tumors (Review)

Chimeric antigen receptor (CAR) T cell therapy has emerged as a new and breakthrough cancer immunotherapy. Although CAR-T cell therapy has made significant progress clinically in patients with refractory or drug-resistant hematological malignancies, there are numerous challenges in its application to solid tumor therapy, including antigen escape, severe toxic reactions, abnormal vascularization, tumor hypoxia, insufficient infiltration of CAR-T cells and immunosuppression. As a conventional mode of anti-tumor therapy, radiotherapy has shown promising effects in combination with CAR-T cell therapy by enhancing the specific immunity of endogenous target antigens, which promoted the infiltration and expansion of CAR-T cells and improved the hypoxic tumor microenvironment. This review focuses on the obstacles to the application of CAR-T technology in solid tumor therapy, the potential opportunities and challenges of combined radiotherapy and CAR-T cell therapy, and the review of recent literature to evaluate the best combination for the treatment of solid tumors.
Carceral and colonial domesticities: Subaltern case geographies of a Delhi rescue home

This article explores a relatively rare archival account of female subjectivity, experience, mobility, and voice within a carceral institution in late-colonial Delhi. The capital’s “Rescue Home” was created to house women and girls removed from the city’s brothels under new legislation. While no brothels were closed in the first year of the laws functioning, the home accepted 18 women and girls and detailed their circumstances and experiences in its 1940 report. It was able to forcibly detain girls and was run upon disciplinary and racial lines, like other colonial institutions. But its inhabitants were not subject to detailed surveillance. Rather, their lives were ones usually beyond recording or whose stories were actively silenced. The 1940 Rescue Home report provides us with rich details of the commonplace, quotidian struggles which women and girls faced in colonial Delhi. The 18 case geographies of the Home’s inhabitants help us understand how sexuality and motherhood, education and character, and race all shaped routes into the home and destinations when people left. The accounts tell us of a carceral governmentality with influence beyond the disciplinary institution’s walls, but also of female subjects who resisted, spoke back, and absconded. This relationship between forced immobility and willed mobility suggests that brothels and rescue homes were not just connected, through the intended transfer of inhabitants, but can be directly compared as carceral domesticities.
Two Neural Models for Multilingual Grammatical Error Detection

This paper presents two neural models for multilingual grammatical error detection and their results in the MultiGED-2023 shared task. The ﬁrst model uses a simple, purely supervised character-based approach. The second model uses a large language model which is pretrained on 100 different languages and ﬁne-tuned on the provided datasets of the shared task. Despite simple approaches, the two systems achieved promising results. One system has the second best F-score; the other is in the top four of participating systems.
Duality, Robustness and 2-Stage Robust LP Decision Models. Application to Robust PERT Scheduling

The various robust linear programming models investigated so far in the literature essentially appear to be based either on what is referred to as ’rowwise’ uncertainty models or on ’columnwise’ uncertainty models (these basically assume that the rows - resp: the columns - of the constraint matrix are subject to changes within a well specified uncertainty set). We first address the question of whether LP duality can be used to convert a robust LP problem with, say, rowwise uncertainty, into a robust LP with columnwise uncertainty. We provide simple examples supporting the general statement that the dual of a given robust linear programming model is NOT equivalent to the robust version of the dual (assuming of course that uncertainty for the rows - resp: the columns - of the primal, is specified in exactly the same way as uncertainty for the corresponding columns - resp: rows - of the dual). Next, we further investigate this issue of duality in the context of robustness by considering the subclass of robust LP models with uncertainty limited to the right handside only. (this subclass does not appear to have been significantly investigated so far). In this context we introduce the concept of ’ two-stage robust LP model’ as opposed to the standard case (which might be referred to as ’single-stage robust LP model’) and we show how to derive both statements of (a) the dual to the robust model and (b) the robust version of the dual. The resulting expressions of the objective function to be optimized in both cases, though bearing some formal resemblance, appear to be clearly distinct. Moreover, from a complexity point-of-view, one appears to be efficiently solvable ( it reduces to a convex optimization problem) whereas the other, as a nonconvex optimization problem, is expected to be computationally difficult in the general case. As an application of the 2-stage robust LP model introduced here, we next investigate the robust PERT scheduling problem , considering two possible natural ways of specifying the uncertainty set for the task durations : the case where the uncertainty set is a scaled ball with respect to the L∞ norm; the case where the uncertainty set is a scaled Hamming ball of bounded radius (which, though leading to a quite different model, bears some resemblance to the well-known Bertsimas-Sim approach to robustness). We show that in both cases, the resulting robust optimization problem can be efficiently solved in polynomial time.
Design Optimization of a Variable Stiffness Actuator for Knee Exoskeleton Application

The field of wearable devices and exoskeletons for rehabilitation and assistance is one of the main fields where variable stiffness actuators (VSAs) are continuously incorporated. This is due to the need for such devices to adapt to erratic environments and mimic human motions. Recently, a passive revolute joint design with controllable variable stiffness was proposed by the authors. However, the previous design was only introduced as a proof of concept, with no further investigation of the design stiffness change capabilities for application utilization. In this work, we introduce an extended analysis and comprehensive parametric study of the system’s main design parameters. Based on the performed analysis, we propose an optimization framework for this design concept that allows implementation in knee exoskeletons. The main design parameters that affect the stiffness performance are defined. The proposed method allows the identification of these design parameters, which enables the redesign of the system according to the predefined user requirements, such as size, desired stiffness range, and stiffness change rate. A bench-top experimental setup is constructed to validate the obtained optimal design parameters. Results show the ability to achieve the desired stiffness performance for the intended application.
An Effective Supervised Machine Learning Approach for Indian Native Chicken’s Gender and Breed Classification

This study proposes a computer vision and machine learning (ML)-based approach to classify gender and breed in native chicken production industries with minimal human intervention. The supervised ML and feature extraction algorithms are utilized to classify eleven Indian chicken breeds, with 17,600 training samples and 4,400 testing samples (80:20 ratio). The gray-level co-occurrence matrix (GLCM) algorithm is applied for feature extraction, and the principle component analysis (PCA) algorithm is used for feature selection. Among the tested 27 classifiers, the FG-SVM, F-KNN, and W-KNN classifiers obtain more than 90% accuracy, with individual accuracies of 90.1%, 99.1%, and 99.1%. The BT classifier performs well in gender and breed classification work, achieving accuracy, precision, sensitivity, and F-scores of 99.3%, 90.2%, 99.4%, and 99.5%, respectively, and a mean absolute error of 0.7.
New paths in the string theory landscape

In this paper we investigate the properties of series of vacua in the string theory landscape. In particular, we study minima to the flux potential in type IIB compactifications on the mirror quintic. Using geometric transitions, we embed its onedimensional complex structure moduli space in that of another Calabi–Yau with h 1,1 = 86 and h 2,1 = 2. We then show how to construct infinite series of continuously connected minima to the mirror quintic potential by moving into this larger moduli space, applying its monodromies, and moving back. We provide an example of such series, and discuss their implications for the string theory landscape.
Black Hole Thermodynamics

The discovery in the early 1970s that black holes radiate as black bodies has radically aﬀected our understanding of general relativity, and oﬀered us some early hints about the nature of quantum gravity. In this chapter I will review the discovery of black hole thermodynamics and summarize the many independent ways of obtaining the thermodynamic and (perhaps) statistical mechanical properties of black holes. I will then describe some of the remaining puzzles, including the nature of the quantum microstates, the problem of universality, and the information loss paradox.
Retrograde ureteral catheterization under local anesthesia for emergency drainage in patients with infection and hydronephrosis secondary to ureteral calculi: Experience from a tertiary care hospital

Objectives: The aim is to evaluate the safety and efficacy of retrograde ureteral catheterization under local anesthesia in patients with urinary tract infections complicated by hydronephrosis caused by ureteral stone obstruction. Materials and Methods: From October 2020 to September 2021, a retrospective analysis of patients' medical records was performed. Records of past history, physical examination, laboratory tests, and imaging investigations were reviewed. Retrograde ureteric stent (RUS) was performed under local anesthesia using cystoscopes and guided by portable fluoroscopy. Real-time fluoroscopy was used to verify the double-J stent position and confirm a smooth process. The postoperative recovery and length of admission were also recorded. Results: A total of 14 patients with ureteral stone obstruction with infective hydronephrosis received 15 total emergency RUS procedures (one bilateral). Intraoperative findings, operation times, and infection signs were recorded and analyzed. All patients met systemic infection criteria, with a mean body temperature of 38.7°C ± 1.7°C. Leukocytosis was noted in 8 (57.1%) patients. Elevated C-reactive protein (8.5 ± 6.3 mg/L) and procalcitonin (24.1 ± 22.0 ng/mL) were found in 13 (92.9%) and 9 (64.3%) patients, respectively. Mean stone size was 8.5 ± 6.3 mm, mostly localized to the upper ureter (upper: 12; middle: 0; lower: 3). Mean operation time was 14.1 ± 4.3 min. After emergency drainage, all patients improved and were discharged after infection was controlled. The average length of admission was 6.2 ± 2.2 days. Conclusion: RUS under local anesthesia is safe and effective for treating infective hydronephrosis due to ureteral stone obstruction. A randomized controlled trial with a large sample remains necessary to validate these findings.
Speech Technology to Support Phonics Learning for Kindergarten Children at Risk of Dyslexia

We present the AiRO learning environment for kindergarten children at risk of developing dyslexia. The AiRO frontend, easy to use for pupils down to 5 years old, introduces each spelling task with pictural and auditive cues. AiRO responds to spelling attempts with phonetic renderings (synthetic voice). Below, we introduce the didactic and technical principles behind AiRO before presenting our first experiment with 49 kindergarten pupils. Our subjects were pre-and post-tested on reading an spelling. After four weeks of AiRO-based training the experimental group significantly out-performed the control group, suggesting that a new CALL-based pedagogical approach to prevent dyslexia for some children may be within reach.
A Mixed-Methods Explanatory Study of the Failure Rate for Freshman STEM Calculus Students

Abstract In this study we aimed to understand who was struggling in freshman calculus courses, and why. Concentrating on the Fall sections of the class, the best predictors for success were placement test results, the student’s own appraisal of the quality of mathematics teaching they received in high school, and the Motivated Strategies for Learning Questionnaire constructs that measure test anxiety, self-efficacy, and organization. However, the ability to accurately predict whether a student would fail was very low. Three groups of students of interest were identified: those yet to declare their major, non-traditional students, and, curiously, students who choose to enroll in the 10 a.m. section.
Evaluation of Skinfold Techniques in People with Down Syndrome: Development of a New Equation

The primary aim of this study was to evaluate the accuracy of skinfold thickness (SFT) measurements for the estimation of %Fat when compared to dual energy X-ray absorptiometry (DXA) in individuals with Down syndrome (DS). The secondary aim was to develop a new SFT-based body fat equation (SFTNICKERSON). SFT-based %Fat was estimated using a body fat equation from González-Agüero (SFTG-A) and body density conversion formulas from Siri (SFTSIRI) and Brozek (SFTBROZEK). Criterion %Fat was measured via DXA. SFTG-A, SFTSIRI, and SFTBROZEK were significantly lower than DXA (mean differences ranged from −7.59 to −13.51%; all p < 0.001). The SEE values ranged from 3.47% (SFTBROZEK) to 8.60% (SFTG-A). The 95% limits of agreement were greater than ±10% for all comparisons. Mid-axilla and suprailium were significant predictors of %Fat (both p < 0.05). %Fat SFTNICKERSON = 10.323 + (0.661 × mid-axilla) + (0.712 × suprailium). Age and all other skinfold sites were not statically significant in the regression model (all p > 0.05). Current findings indicate that SFTG-A, SFTSIRI, and SFTBROZEK erroneously place an individual with excessive adiposity in a normal healthy range. Accordingly, the current study developed a new equation (SFTNICKERSON) that can easily be administered in people with DS in a quick and efficient time frame. However, further research is warranted in this area.
Twitter Sentiment Analysis using Machine Learning

As the number of social media users is being higher, many people are sharing various opinions andeach country’s real-time situation by online. Also, the influence of online information is increasingto such an extent that the individual’s actual behavior or situation can be estimated. In this situation,researches to analyze through social media are being actively carried out in order to identify problemsin real life. In this research, we proved that we can infer actual behavior or situation based onindividual social media activities. This research focused on the Twitter platform that is actively usedto express individual emotions in social media platforms. We analyzed tweets of Donald Trump andHillary Clinton who were the 45th presidential candidates of the United States of America. Severalmethodologies like sentiment analysis, topic modeling, and machine learning were used to provecorrelation between Donald Trump’s tweets and his behavior. Through experiment, it proved notonly we can adjust classification and clustering algorithms but also Decision Tree was the most accuratealgorithm. Finally, we proposed the possibility of applying the above method to a system fordetecting anomaly symptoms by concentrating on negative messages. It is expected to provide socialmedia users with sufficient awareness of online activities.
Modified Kernel Functions by Geodesic Distance

When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into the classification method at hand. A common prior knowledge is that many datasets are on some kinds of manifolds. Distance-based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for a support vector machine (SVM) which incorporates geodesic distance and therefore is applicable in cases where such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art methods, such as SVM-based Euclidean distance.
Selection of Auto-focus Function in Micro Visual System

The algorithm of general focusing function is put forward,and a series of experiments is tested.Focusing character with different lighting and different content is validated and analyzed.The experiment result shows that the functions of gradient energy,Tenegrad,and Laplacian possess the stable,highly sensitive characteristic curve.It's better to use in small range with high accuracy;the functions of Variance and entropy have a large focusing range,good linearity and low sensitivity,so they are propitious to focus in large range with lower accuracy.So,there has better performance curve in background lighting than in reflection lighting.It's appropriate that using the functions of gradient energy or Tenegrad to focus for the image with more high frequency information,and using Laplacian function for the image with slightly high frequency information.
Supervised structural learning of semiparametric regression on high‐dimensional correlated covariates with applications to eQTL studies

Expression quantitative trait loci (eQTL) studies utilize regression models to explain the variance of gene expressions with genetic loci or single nucleotide polymorphisms (SNPs). However, regression models for eQTL are challenged by the presence of high dimensional non‐sparse and correlated SNPs with small effects, and nonlinear relationships between responses and SNPs. Principal component analyses are commonly conducted for dimension reduction without considering responses. Because of that, this non‐supervised learning method often does not work well when the focus is on discovery of the response‐covariate relationship. We propose a new supervised structural dimensional reduction method for semiparametric regression models with high dimensional and correlated covariates; we extract low‐dimensional latent features from a vast number of correlated SNPs while accounting for their relationships, possibly nonlinear, with gene expressions. Our model identifies important SNPs associated with gene expressions and estimates the association parameters via a likelihood‐based algorithm. A GTEx data application on a cancer related gene is presented with 18 novel eQTLs detected by our method. In addition, extensive simulations show that our method outperforms the other competing methods in bias, efficiency, and computational cost.
Hollow-cathode lamps as optical frequency standards: the influence of optical imaging on the line-strength ratios

Hollow cathode discharge lamps (HCLs) have been successfully used in recent years as calibration sources of optical astronomical spectrographs. The numerous narrow metal lines have stable wavelengths, which makes them well suited for m/s calibration accuracy of high-resolution spectrographs, while the buffer-gas lines are less stable and less useful. Accordingly, an important property is the metal-to-gas line-strength ratio (Rmetal/gas). Processes inside the lamp cause the light to be emitted from different regions between the cathode and the anode leaing to the emission of different beams with different values of Rmetal/gas. We used commercially- available HCLs to measure and characterize these beams with respect to their spatial distribution, their angle of propagation relative to the optical axis, and their values of Rmetal/gas. We conclude that a good imaging of an HCL into a fiber-fed spectrograph would consist of an aperture close to its front window in order to filter out the parts of the beam with low Rmetal/gas, and of a lens to collimate the important central beam. We show that Rmetal/gas can be further improved with only minor adjustments of the imaging parameters, and that the imaging scheme that yields the highest Rmetal/gas does not necessarily provide the highest flux.