Miracle-dz/UniFAR-FTUs huggingface dataset:
“A Commission from Heaven”: The Legacy of Lorenzo de Zavala’s Enlightenment Discourse on Texas

ABSTRACT This article examines how Texan Revolutionaries portrayed the Texas Revolution as a struggle for modernity. In particular, numerous Anglo-Texans created a narrative in which they cast Santa Anna at the helm of a counter-enlightenment restoring Ancien Régime values. The Revolutionaries drew upon the discourse of the Mexican politician and Texas co-founder Lorenzo de Zavala. This article contests the portrayal of the Texas Revolution as a White and Anglo movement. This manuscript contributes to the historiography by examining Zavala’s influence on the language of the Texas Revolution. Together, Zavala and the Texas Revolutionaries accused Santa Anna of fighting for monarchy and aristocracy. Furthermore, Texan revolutionaries argued the imminent restoration of Catholicism threatened their cherished liberties and freedoms. This was all the while Anglo-Texans portrayed their secession as a struggle for reason, virtue, and happiness. This analysis primarily utilizes the Mirabeau Lamar Papers to discern the sentiments and expressions of numerous individuals involved in the Texas Revolution. The following study contributes to the lacuna in the history of ideas in the Texas Republic. Furthermore, this article links Texas to the broader historiography of Atlantic Revolutions.
A Framework for Fusion of 3D Appearance and 2D Shape Cues for Generic Object Recognition

Abstract This paper addresses the problem of Generic Object Recognition (GOR) from arbitrary viewpointsby modeling the perceptual capability of human beings. We propose a novel framework which uses acombination of 3D appearance and 2D shape cues to recognize the object class as well as determineits pose. We propose a hierarchical framework for GOR, which combines two stages of processing.First, the 3D appearance model of the object is captured from multiple viewpoints using LinearSubspace Analysis techniques. These appearance cues are used to reduce the search space to a fewrank-ordered samples. We have used a decision-fusion based combination of 2D PCA and ICA tointegrate the complementary information of classiﬁers and improve appearance-based recognitionaccuracy. Shape matching is then performed on the reduced search space, using either distancetransform based correlation or shape context based matching. The proposed framework for GORuses a decision fusion technique, in which evidences from 3D appearance and 2D shape are combined(fused) to obtain the correct object class and its pose. Experiments were conducted using objects withcomplex appearance and shape characteristics, and the performance of the proposed framework hasbeen shown to be superior, using the COIL-100 and IGOIL (IITM Generic Object Image Library)databases. IGOIL database was also used to analyze the appearance manifolds along two orthogonalaxis of rotation. Performance degradation in case of noisy images has also been presented.Keywords: Generic Object Recognition, Linear Subspace Analysis, Appearance, Manifolds, Shape,Distance Transform, Shape Context, Classiﬁer Fusion
Meta-analysis of meat quality of cattle slaughtered with different subcutaneous fat thicknesses

Abstract The objective of this work was to evaluate, through meta-analysis, the impact of subcutaneous fat thickness on beef qualitative traits. Combined data from 13 studies were used, comprising 245 carcasses of male bovines slaughtered in Brazil. Effect size for all parameters was calculated as mean difference or standardized mean difference, at 95% probability. The meta-analysis of random and fixed effects was carried out when I2>50% and I2<50%, respectively, for each indicator separately, with means for fat thickness, classified in the “control” (3–6 mm), “low” (<3 mm), and “high” (>6 mm) groups. Carcasses in the “high” group showed greater intramuscular fat deposition, whereas those in the “low” group presented an intramuscular fat deposition similar to that of the control. When the fat thickness was below the recommended threshold of 3 mm, shear force increased. Subcutaneous fat thickness values greater than 6 mm induced a positive change in the perception of tenderness by the panel of evaluators. Beef carcasses with a subcutaneous fat thickness over 6 mm tend to accumulate more intramuscular fat content and are tenderer for consumers’ acceptance. Carcasses with a fat thickness less than 3 mm produce tougher meat.
Feature selection‐integrated classifier optimisation algorithm for network intrusion detection

In the era of technology, information security has gained significant importance, as intruders constantly conduct attacks to breach information systems. Intelligent network intrusion detection systems (NIDS) are promising for detecting malicious activities; however, it is required to apply feature selection (FS) and classifier optimisation (CO) using cost‐effective algorithms to build an accurate and efficient system. Although classifier‐dependent FS (CDFS) techniques and CO algorithms have been shown to perform well, they suffer from computational complexity, and their interdependencies negatively affect model performance. This study proposes the FS‐integrated classifier optimisation algorithm that incorporates FS during CO, enhances optimisation, and tackles the interdependency problem. Furthermore, since this algorithm does not use an iterative feature selection process, such as forward selection or backward elimination, it provides relatively less complexity than other CDFS techniques. Moreover, an application of the proposed methodology (NIDS) was implemented using the designed framework to validate the model in this problem domain. The proposed methodology achieved accuracies of 85.10%, 73.24% with one feature for the NSLKDD datasets, 83.45% with 32 features for the UNSW‐NB15 dataset, 99.41% with eight features, and 99.63% with 16 features for the CIC‐IDS2017 datasets. The results showed that the FS‐integrated optimisation algorithm had improved the accuracy of the classifier with fewer features. Furthermore, the proposed methodology outperformed other FS, ensemble learning, and deep learning‐based methods regarding detection accuracy and false alarm rate. In conclusion, the developed NIDS is an accurate, efficient, straightforward, feasible, and easy‐to‐implement system that can be created using limited computing power and time as a promising solution to protect traditional and modern computer networks.
The aMAP Score is an Independent Risk Factor for Intermediate-stage Hepatocellular Carcinoma: A Large Retrospective Cohort Study

Background: A less effective nomogram for patients with intermediate-stage hepatocellular carcinoma (HCC) to predict overall survival (OS) is available. This study aimed to investigate the role of age-male-albumin-bilirubin-platelet (aMAP) scores in the prognosis of patients with intermediate-stage HCC and develop an aMAP score-based nomogram to predict OS. Methods: Data on newly diagnosed intermediate-stage patients with HCC at Sun Yat-sen University Cancer Center between January 2007 and May 2012 were retrospectively collected. Independent risk factors affecting prognosis were selected by multivariate analyses. The optimal cut-off value for the aMAP score was determined using X-tile. The survival prognostic models were presented by the nomogram. Results: For the 875 patients with intermediate-stage HCC included, the median OS was 22.2 months (95% CI 19.6-25.1). Patients were classified into three groups by X-tile plots (aMAP score < 49.42; 49.42 ≤ aMAP score < 56; aMAP score ≥ 56). Alpha-fetoprotein, lactate dehydrogenase, aMAP score, diameter of main tumor, number of intrahepatic lesions, and treatment regimen were independent risk factors for prognosis. A predicted model was constructed with a C-index of 0.70 (95% CI: 0.68-0.72) in the training goup, and its 1-, 3-, and 5-year area under the receiver operating curve were: 0.75, 0.73, and 0.72. The validation group of the C-index is 0.82. Calibration graphs showed good consistency between the actual and predicted survival rates. The decision curve analysis suggested the clinical utility of the model, which may help clinicians guide clinical decision-making. Conclusion: The aMAP score was an independent risk factor for intermediate-stage HCC. The aMAP score-based nomogram has good discrimination, calibration, and clinical utility.
Encapsulation of Polyphenolic Compounds Based on Hemicelluloses to Enhance Treatment of Inflammatory Bowel Diseases and Colorectal Cancer

Inflammatory bowel diseases (IBD) and colorectal cancer (CRC) are difficult to cure, and available treatment is associated with troubling side effects. In addition, current therapies have limited efficacy and are characterized by high costs, and a large segment of the IBD and CRC patients are refractive to the treatment. Moreover, presently used anti-IBD therapies in the clinics are primarily aimed on the symptomatic control. That is why new agents with therapeutic potential against IBD and CRC are required. Currently, polyphenols have received great attention in the pharmaceutical industry and in medicine due to their health-promoting properties. They may exert anti-inflammatory, anti-oxidative, and anti-cancer activity, via inhibiting production of pro-inflammatory cytokines and enzymes or factors associated with carcinogenesis (e.g., matrix metalloproteinases, vascular endothelial growth factor), suggesting they may have therapeutic potential against IBD and CRC. However, their use is limited under both processing conditions or gastrointestinal interactions, reducing their stability and hence their bioaccessibility and bioavailability. Therefore, there is a need for more effective carriers that could be used for encapsulation of polyphenolic compounds. In recent years, natural polysaccharides have been proposed for creating carriers used in the synthesis of polyphenol encapsulates. Among these, hemicelluloses are particularly noteworthy, being characterized by good biocompatibility, biodegradation, low immunogenicity, and pro-health activity. They may also demonstrate synergy with the polyphenol payload. This review discusses the utility and potential of hemicellulose-based encapsulations of polyphenols as support for treatment of IBD and CRC.
Strain Rate Changes during Stress Echocardiography Are the Most Accurate Predictors of Significant Coronary Artery Disease in Patients with Previously Treated Acute Coronary Syndrome

Background and Aims. Dobutamine stress echocardiography (DSE) is a well-established non-invasive investigation for the detection of ischemic myocardial dysfunction. The aim of this study was to evaluate the accuracy of myocardial deformation parameters measured by speckle tracking echocardiography (STE) in predicting culprit coronary artery lesions in patients with prior revascularization and acute coronary syndrome (ACS). Methods. We prospectively studied 33 patients with ischemic heart disease, a history of at least one episode of ACS and prior revascularization. All patients underwent a complete stress Doppler echocardiographic examination, including the myocardial deformation parameters of peak systolic strain (PSS), peak systolic strain rate (SR) and wall motion score index (WMSI). The regional PSS and SR were analyzed for different culprit lesions. Results. The mean age of patients was 59 ± 11 years and 72.7% were males. At peak dobutamine stress, the change in regional PSS and SR in territories supplied by the LAD showed smaller increases compared to those in patients without culprit LAD lesions (p < 0.05 for all). Likewise, the regional parameters of myocardial deformation were reduced in patients with culprit LCx lesions compared to those with non-culprit LCx lesions and in patients with culprit RCA legions compared to those with non-culprit RCA lesions (p < 0.05 for all). In the multivariate analysis, the △ regional PSS (1.134 (CI = 1.059–3.315, p = 0.02)) and the △ regional SR (1.566 (CI = 1.191–9.013, p = 0.001)) for LAD territories predicted the presence of LAD lesions. Similarly, in a multivariable analysis, the △ regional PSS and the △SR predicted LCx culprit lesions and RCA culprit lesions (p < 0.05 for all). In an ROC analysis, the PSS and SR had higher accuracies compared to the regional WMSI in predicting culprit lesions. A △ regional SR of −0.24 for the LAD territories was 88% sensitive and 76% specific (AUC = 0.75; p < 0.001), a △ regional PSS of −1.20 was 78% sensitive and 71% specific (AUC = 0.76, p < 0.001) and a △ WMSI of −0.35 was 67% sensitive and 68% specific (AUC = 0.68, p = 0.02) in predicting LAD culprit lesions. Similarly, the △ SR for LCx and RCA territories had higher accuracies in predicting LCx and RCA culprit lesions. Conclusions. The myocardial deformation parameters, particularly the change in regional strain rate, are the most powerful predictors of culprit lesions. These findings strengthen the role of myocardial deformation in increasing the accuracy of DSE analyses in patients with prior cardiac events and revascularization.
Speech Emotion Recognition Based on Attention MCNN Combined With Gender Information

Emotion recognition is susceptible to interference such as feature redundancy and speaker gender differences, resulting in low recognition accuracy. This paper proposes a speech emotion recognition (SER) method based on attention mixed convolutional neural network (MCNN) combined with gender information, including two stages of gender recognition and emotion recognition. (1) Using MCNN to identify gender and classify speech samples into male and female. (2) According to the output of the first stage classification, a gender-specific emotion recognition model is established by introducing coordinated attention and a series of gated recurrent network units connecting the attention mechanism (A-GRUs) to achieve emotion recognition results of different genders. The inputs of both stages are dynamic 3D MFCC features generated from the original speech database. The proposed method achieves 95.02% and 86.34% accuracy on EMO-DB and RAVDESS datasets, respectively. The experimental results show that the proposed SER system combined with gender information significantly improves the recognition performance.
algorithm for constructing Lyapunov functions

In this monograph we develop an algorithm for constructing Lyapunov functions for arbitrary switched dynamical systems \(\dot{\mathbf{x}} = \mathbf{f}_\sigma(t,\mathbf{x})\), possessing a uniformly asymptotically stable equilibrium. Let \(\dot{\mathbf{x}}=\mathbf{f}_p(t,\mathbf{x})\), \(p\in \mathcal{P}\), be the collection of the ODEs, to which the switched system corresponds. The number of the vector fields \(\mathbf{f}_p\) on the right-hand side of the differential equation is assumed to be finite and we assume that their components \(f_{p,i}\) are \(\mathcal{C}^2\) functions and that we can give some bounds, not necessarily close, on their second-order partial derivatives. The inputs of the algorithm are solely a finite number of the function values of the vector fields \(\mathbf{f}_p\) and these bounds. The domain of the Lyapunov function constructed by the algorithm is only limited by the size of the equilibrium's region of attraction. Note, that the concept of a Lyapunov function for the arbitrary switched system \(\dot{\mathbf{x}} = \mathbf{f}_\sigma(t,\mathbf{x})\) is equivalent to the concept of a common Lyapunov function for the systems \(\dot{\mathbf{x}}=\mathbf{f}_p(t,\mathbf{x})\), \(p\in\mathcal{P}\), and that if \(\mathcal{P}\) contains exactly one element, then the switched system is just a usual ODE \(\dot{\mathbf{x}}=\mathbf{f}(t,\mathbf{x})\). We give numerous examples of Lyapunov functions constructed by our method at the end of this monograph.
A Remedy to Losing Time Synchronization at D-PMUs, H-PMUs, and WMUs in Event Location Identification in Power Distribution Systems

Recent advancements in synchronized sensor technologies has introduced an unprecedented level of visibility in power distribution systems. Apart from the Distribution-level Phasor Measurement Units (D-PMUs), a.k. a. micro-PMUs, that have been widely used in recent years, other synchronized sensors have also been developed in this field, including Harmonic Phasor Measurement Units (H-PMUs) and Waveform Measurement Units (WMUs). However, in practice, it is common for these sensors to lose time synchronization over some periods of time and for different reasons. In this paper, we propose new and customized solutions to tackle loss of time synchronization in D-PMUs, H-PMUs, and WMUs, whereby addressing the unique challenges in each case. Our focus is on solving the event location identification problem, for different types of steady-state and transient events. We show that, our methods can maintain high accuracy in event location identification, despite losing time synchronization, whether we use D-PMUs, H-PMUs, or WMUs.
Isolation of Plant growth beneficial endophytic bacterial strains from medicinal plants Gymnema sylvestre and Tabernamontana divaricate

Endophytes are microorganisms that colonize within the living plant cell and mostly have a symbiotic association with the host plant. There is a growing interest to recognize the involvement of the endophytes in the production of the growth promoting bioactive compounds and secondary metabolites that are produced by the plants. Medicinal plants are known to produce several phytochemicals with potential biological activities. In the current study the endophytic bacterial diversity of two valuable medicinal plants Gymnema sylvestre, and Tabernamontana divaricate are studied. The study aimed at understanding the involvement of the endophytic bacterial community in growth beneficial trait of the host plant viz. IAA( Indole Acetic Acid) production, Phosphate solubilisation and their capacity to grow on Nitrogen free medium so as to assert their role in Nitrogen fixation. A total of 14 bacterial strains were isolated from both the taxa. 11 isolates from Gymnema sylvestre and 03 from Tabernamontana divaricate. The morphological and biochemical characterization of the isolates were performed. It was found that 13 out of 14 isolates were capable of producing IAA, 04 isolates were able to grow on Nitrogen free media and only one was capable of Phosphate solubilisation. The study showed that the isolated strains of endophytes have immense plant growth promoting potential which can be used for improvement of growth of crops.
Research on a Design Method of Generative Model for Cavity Section Shape of an Anechoic Coating

One of the main challenges of the low frequency, broadband and strong sound absorption performance of an anechoic coating is the design of the cavity section shape. A generative-based design method was investigated in this study. Two generative-based models were trained to design the cavity section shape of anechoic coatings with a highly efficient absorption coefficient. The design results were compared in terms of mode collapse and average sound absorption coefficients. The results show that the WGAN-GP model can stably generate 100 new designs in only 3 seconds, dramatically speeding up the design process.
Transforming Information Extraction: AI and Machine Learning in Optical Character Recognition Systems and Applications Across Industries

– Optical Character Recognition (OCR) technology has served as a transformative force in the field of data extraction and digitization, enabling the conversion of printed and handwritten text into machine-readable formats. The integration of artificial intelligence (AI) and machine learning (ML) techniques has further enhanced OCR capabilities, improving accuracy, speed, and adaptability across various industry sectors. This paper explores the evolution of OCR technology, its applications, and future prospects. We discuss key developments and innovations that have shaped the OCR landscape, notable use cases across industries such as finance, healthcare, manufacturing, logistics, legal, and retail, and the challenges that remain to be addressed. Through this comprehensive analysis, we highlight the transformative impact of AI-embedded OCR technology on data management, operational efficiency, and compliance, offering insights into the potential benefits and considerations for implementing these advanced algorithms in different sectors. Furthermore, we discuss the challenges that remain to be addressed to fully realize the potential of AI-embedded OCR and its implications for future research and development.
Study of Forming Performance and Characterization of DLP 3D Printed Parts

In order to explore the effect of printing parameter configurations on the forming performance of Digital Light Processing (DLP) 3D printed samples, printing experiments were carried out on the enhanced adhesion and efficient demolding of DLP 3D printing devices. The molding accuracy and mechanical properties of the printed samples with different thickness configurations were tested. The test results show that when the layer thickness increases from 0.02 mm to 0.22 mm, the dimensional accuracy in the X and Y directions increases first and then decreases, while the dimensional accuracy in the Z direction decreases, and the dimensional accuracy is the highest when the layer thickness is 0.1 mm. The mechanical properties of the samples decline with an increasing layer thickness of the samples. The mechanical properties of the 0.08 mm layer thickness are the best, and the tensile, bending, and impact properties are 22.86 Mpa, 48.4 Mpa, and 35.467 KJ/m2, respectively. Under the condition of ensuring molding accuracy, the optimal layer thickness of the printing device is determined to be 0.1 mm. The analysis of the section morphology of samples with different thicknesses illustrates that the fracture of the sample is a river-like brittle fracture, and there are no defects such as pores in the section of samples.
High-Precise Metallic Helical Microstructure Fabrication by Rotational Nanorobotic Manipulation System With Tilted Mandrel Compensation

Metallic helical microstructures are significant components applied in many fields, such as mechatronics, photonics, microelectromechanical systems, and biomedical engineering, but their fabrication with high precision remains a challenge. Herein, we report a general controllable approach for high-precise micro metallic helical structure fabrication by a self-developed rotational nanorobotic manipulation system. To reduce the fabrication error induced from the tilted mandrel, first, we propose an end-effector trajectory planning method to precompensate the tilted angle during rotation. Further, a hybrid vision and position feedback strategy is also developed to automate the fabrication process for obtaining a helical structure with uniform pitch distance consequently. The experimental results verify that our method is able to fabricate various micro helical structures with different dimensions with high precision up to 6 $\bm {\mu }$m (less than two pixel equivalent). The potential application of the helical structure as high-precise force sensor is also demonstrated for soft biomaterial (hemostatic sponges) characterization. This research paves an effective solution for the high-precise 3-D helical structure fabrication, which would prompt their applications in engineering and inspire other microstructure manufacturing as well.