Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case Report and Literature Review of a Rare Jejunal Solitary Peutz–Jeghers-Type Polyp Resected Endoscopically in an Elderly Patient Presenting with Subacute Gastrointestinal Bleeding

Solitary Peutz–Jeghers-type polyp (SPJP) is a rare hamartomatous lesion. It is considered a different entity from Peutz–Jeghers syndrome despite similar histopathological findings. It can be found in the GI tract but rarely in the jejunum. Jejunal SPJP is susceptible to necrosis, ulceration, and intussusception, resulting in GI bleeding or small bowel obstruction. We describe a case of subacute gastrointestinal bleeding secondary to jejunal SPJP to share our approach to this challenging case using therapeutic endoscopy. An 81-year-old male patient with a history of atrial fibrillation on warfarin with stable therapeutic INR levels presented with a 1-week history of melena, generalized fatigue, and shortness of breath on exertion and was found to have profound iron deficiency anemia. Esophageal gastroduodenoscopy and colonoscopy failed to identify the source of bleeding; however, single-balloon enteroscopy detected a 4 cm polyp with a stalk in the proximal jejunum. Endoscopic polypectomy was performed, and the whole polyp was removed. Histopathological examination was consistent with Peutz–Jeghers polyp. The genetic analysis was negative for STK11 mutation. Follow-up magnetic resonance enterography and video capsule endoscopy did not reveal any other polypoid lesion in the GI tract. The patient's symptoms resolved gradually, and his hemoglobin level returned back to normal levels within 6 months. To our knowledge, this is the first case of endoscopic polypectomy during balloon-assisted enteroscopy for jejunal SPJP.
GPU implementation of deformable pattern recognition using prototype-parallel displacement computation (ビジュアルコンピューティング論文特集号)

In this paper, for the reduction of the computation time of a deformable approach to pattern recognition, prototype-parallel displacement computation on GPUs (PPDC-GPU) is proposed. The displacement computation used in this study has the virtue of simplicity and consists of locally parallel processing, therefore it is suitable for the implementation on graphical processing units (GPUs). In the proposed method, large plates of image and displacement function are generated from input images, prototypes, and displacement functions on the main memory, and then these plates are transferred collectively to the video memory. After computing the displacement between the input image plate and the prototype image plate on the GPU, the displacement function plates are transferred back to the main memory. The simulation results show that PPDC-GPU reduces the computation time to less than 10% of the ordinary implementation on CPUs. This study especially focused on handwritten character recognition, since it is one of the most fundamental and important problems in the field of computer vision and pattern recognition. However the proposed framework can be widely applied to other problems, for instance, face recognition or object recognition.
Predicting Factors Related to Student Performance Using Decision Tree Algorithm

Student performance serves as a vital gauge for assessing the quality of education delivered by educational institutions. However, it is subject to the influence of various factors. To unravel the intricacies of these influences, this study employs a decision tree algorithm via the RapidMiner application as a powerful data mining method. The decision tree approach not only adds precision to the analysis but also makes the interpretation of results more intuitive, thereby assisting educational stakeholders in making informed decisions. Our research aims to identify learning strategies that could enhance student performance by investigating the relationship between multiple factors, including gender, parental educational background, and test preparation on students' passing test scores. The findings reveal that participation in test preparation courses has a significant impact on students' likelihood of passing tests. In the absence of test preparation, other variables such as gender and parental educational background come into play. These insights can serve as a valuable reference for educational institutions, guiding them to mandate test preparation courses to increase students' odds of academic success. This study contributes to the existing literature by demonstrating the efficacy of decision trees in dissecting the multi-layered factors affecting student performance, thereby offering a robust analytical tool for educational research.
Near-Native-Like Fluency as a Potential Pitfall in Speaking

The present study explores the intriguing phenomenon of near-native-like fluency in second language speaking proficiency and its associated challenges. While attaining near-native-like fluency is often regarded as a pinnacle achievement in language acquisition, this study contends that it can be a double-edged sword, bringing about both benefits and pitfalls for language learners. Drawing from a comprehensive review of literature, this research delves into the multifaceted aspects of near-native-like fluency, including its cognitive and linguistic underpinnings, as well as the psychological and sociocultural factors that contribute to its development. It elucidates how near-native-like speakers often exhibit a remarkable command of grammar, pronunciation, and vocabulary, mirroring the proficiency of native speakers. However, the study also unveils potential pitfalls associated with near-native-like fluency, including issues related to identity and cultural authenticity. It scrutinizes the challenges faced by learners who strive to achieve such fluency and the dilemmas they encounter when their linguistic competence is perceived as indistinguishable from that of native speakers. Furthermore, this research examines the impact of near-native-like fluency on communication dynamics, highlighting instances where over-competence may lead to misunderstandings, alienation, or a loss of the learner's unique cultural perspective. It underscores the importance of recognizing that near-native-like fluency does not necessarily equate to cultural assimilation. In conclusion, this library research advocates for a more nuanced understanding of near-native-like fluency in second language acquisition. It emphasizes the need for language educators and learners alike to consider the potential pitfalls and complexities associated with this proficiency level. By addressing these challenges, a more inclusive and culturally sensitive approach to language learning and teaching can be developed, ultimately enhancing cross-cultural communication and fostering a deeper appreciation for linguistic diversity.
Pharmaceutical, Ethnopharmacological, Phytochemical and Synthetic Importance of Genus Aerva: A Review

Aerva plants are known widely for their exceptional medicinal uses. They have been employed conventionally for their medicinal properties by the common folk in several regions of the world. This indicates the efficacy of these remarkable herbs. They are also known for their multiple biological activities such as antioxidant, antibacterial, antilithiatic, hepatoprotective, antidiabetic, anticancer, antihyperlipidemic and nephroprotective potential. These pharmacological actions are associated with the presence of valuable nutrients and biochemical compounds such as sterols/terpenes, flavonoids, alkaloids, phenolics and sugars. The proper utilization of the substances obtained from these plants can enhance the finesse of the capabilities of these plants. These plants are not only medicinally important but are significantly serving several other scientific purposes. In the prospect of medicinal, scientific uses and the bioavailability of precious chemicals of Aerva species, a comprehensive review is compiled, which focuses on the detailed profile of biochemical compounds, pharmacological functions and biological activities of Aerva plants. The review also discusses the synthetic importance and the toxicity of these plants. The study aims to compile comprehensively what is already done and what more is needed to be done in the future investigations related to Aerva species.
GPS Error Modeling Based on Time Series and Research on the Accuracy of Point Positioning

A data processing algorithm was proposed to improve the GPS single point positioning accuracy.Full details were provided on how to use time series analysis in the modeling of the first order differential series for the GPS error.The AR(n) model which is adopted to describe the first order differential series for the GPS error was derived from the observed data,and model parameter values were obtained using the least square method;one step prediction for the error series was effectively achieved by conditional expectation;positioning accuracy of ordinary GPS receivers was improved remarkably through online adjustment to the observation data.The simulated results show efficiency and reliability of the proposed method.
Eco-Innovation in the Food Industry: Exploring Consumer Motivations in an Emerging Market

The utilization of eco-innovative products has witnessed a surge in adoption, driven by their inherent capacity to address pressing environmental concerns. To comprehensively fathom the underlying motivations propelling consumers to embrace these products, we conducted an in-depth investigation employing “The Not Company” (Chile) as a compelling case study. We conducted qualitative interviews with a cohort of 20 Chilean consumers, guided by the Theory of Planned Behavior theoretical framework. The research methodology harnessed the principles of thematic analysis, yielding insights that underscore the significance of key determinants in shaping consumers’ choices towards eco-innovative products. Specifically, our findings highlighted that consumer choices in this domain are profoundly influenced by their attitudes, subjective norms, and perceived behavioral control. Moreover, within these overarching categories, we unearthed sub-themes illuminating the intricate influences guiding consumer choices. These sub-themes encompassed beliefs about food manufacturing and packaging, the persuasive impact of social media and advertising, and the indelible impressions left by prior encounters with eco-innovative products. This study highlights consumers’ fundamental role in the broader eco-innovation landscape, particularly within the food industry context.
PENGARUH TINGGI TEMPAT TERHADAP TINGKAT SERANGAN HAMA PENGGEREK BUAH KAKAO (Conopomorpha cramerella Snellen) DI KABUPATEN PIDIE Effect of the Altitude on the Level of Cocoa Pod Borer (Conopomorpha Cramerella Snellen) Attack in Pidie District

This study aims to look at the effect of altitude on the level of pest infestation C. cramerella, so getting information as a data source control of pests C. cramerella future. This study was conducted in garden cocoa folk in Pidie district, starting from January to March 2015. This study used survey method. Factors examined in this study was the effect of altitude on the level of C. cramerella attack, carried out with purposive sampling method. Altitude is divided into four categories (A = 0-200 m dpl, B = 201-400 m dpl, C = 401-600 m dpl, dan D = 601-800 m dpl). Altitude affects the level of attacks pests C. cramerella, the highest attack at the location A to 76.77%, and attacks decreased in the location D is 31%. PBK altitude affects the population, the highest population found in the location A with an average 328.60 compared to the location D is only 95.80. There is a positive correlation between the temperature with an attack percentage of fruit and pests C. cramerella population, but the humidity showed a negative correlation to the percentage of fruit attacks and pests C. cramerella populations. Keywods: Altitude place, Conopomorpha cramerella Snellen, Cocoa.
Adaptive Texture and Spectrum Clue Mining for Generalizable Face Forgery Detection

Although existing face forgery detection methods achieve satisfactory performance under closed within-dataset scenario where training and testing sets are created by the same manipulation technique, they are vulnerable to samples created by unseen manipulation techniques under cross-dataset scenario. To solve this problem, in this work, we propose a novel adaptive texture and spectral clue mining (ATSC) approach for generalizable face forgery detection. It adaptively adjusts the parameters depended on input images to mine specific intrinsic forgery clues on both spatial and frequency domains. Specifically, ATSC customizes a Texture Clue Mining Module and a Spectrum Clue Selecting Module. The former module exploits instance-aware dynamic convolution on spatial domain to dynamically assemble multiple parallel convolutional kernels based on the learned image-dependent attention maps for effectively capturing subtle texture artifacts on spatial domain. A customized attention loss is also applied to the attention maps as supervision to precisely localize forgery artifacts whilst retain useful background information from suspicious and non-suspicious regions, which drives the module to explore all potential crucial clues and learning robust texture-related forgery feature. Moreover, the latter module applies adaptive frequency filtering mechanism on DCT-based frequency signals, which selects frequency information of interest to capture refined spectrum clues on frequency domain in an input-adaptive manner. Equipped with the above two modules, ATSC can learn more generalizable forgery features for face forgery detection. Extensive experimental results demonstrate the superior generalization ability of the proposed ATSC over various state-of-the-art methods on the challenging benchmarks.
Improved YOLOv5s With Coordinate Attention for Small and Dense Object Detection From Optical Remote Sensing Images

The objects in optical high-resolution remote sensing images (HRRSIs) are usually tiny, dense, and exist in complex backgrounds, which brings great challenges to accurate object detection. This article presents an improved YOLOv5s network-based technique for remote sensing object recognition to overcome these difficulties. First, unnecessary residual modules are pruned from the cross-stage partial layer of conventional YOLOv5s and a refined residual coordinate attention module is incorporated to enhance the feature representation of the densely packed small objects in HRRSIs by introducing the residual structure and the mix pooling operation instead of the existing average pooling. Second, since various scales of objects are present in HRRSIs, the algorithm of differential evolution is adopted to replace the traditional K-means for generating a variety of anchor boxes in different sizes. Third, we replace the commonly used complete intersection over union (IoU) loss function in YOLOv5s with the AW-IoU loss function based on both α-IoU and wise-IoU. AW-IoU could expedite bounding box regression and focus more on regular anchor boxes. Finally, instead of nonmaximum suppression (NMS), the SCYLLA (S-IoU) soft-NMS is employed to eliminate the redundant duplicate boxes to detect the dense objects in remote sensing images. Experimental results on the NWPU VHR-10 dataset demonstrate that the proposed YOLOv5s method performs well compared with state-of-the-art algorithms.
An Inquiry into Disparities in Teenage Birth Rates Between Douglas County and Pueblo County, Colorado

3 Chapter 1: Introduction 4 Chapter 2: Literature Review 6 Chapter 3: Methods 44 Chapter 4: Results 46 Chapter 5: Conclusion and Reccomendations 65 Works Cited 71 Appendix A 80 Appendix B 81 Appendix C 87 Appendix D 90 AN INQUIRY INTO DISPARITIES IN TEENAGE BIRTH RATES 3
Bioremediation of Oil-contaminated Soil Using Chicken Manure

ABSTRACT In less developed countries, the prevalence of soil contaminated with used lubricating oil is high and the situation worsens with the economic advancement. The contamination has been shown to adversely affect the environment and human health. To mitigate, bioremediation could be adopted to tackle the problem of hydrocarbon-contaminated soil. Thus, this experimental research carried out the bioremediation using chicken manure in soils contaminated with 5%, 10% and 20% w/w used lubricating oil for a 42-day composting period. To compare, this research also experimented with the 5%, 10% and 20% oil-contaminated soils untreated with chicken manure. The results showed that the highest total petroleum hydrocarbons (TPHs) reduction efficiency of >60% was achieved in the 5% oil-contaminated compost remediated with chicken manure. The highest biodegradation rate of lubricating oil of 0.023–0.0025 day−1 as measured by the first-order kinetics could also be achieved under the 5% oil contamination condition with the application of chicken manure. The findings highlight the prospect of chicken manure as a proper nutrient for enhanced remediation of hydrocarbon-contaminated soils, particularly of low contamination concentrations.
Penentuan Harga Opsi Asia Dengan Model Binomial Dipercepat

Abstract— Penentuan harga opsi Asia yang dikembangkan oleh T.S. Klessen dipelajari sebagai perbaikan atas model binomial Hull-White. Sebagai pembanding, dibahas pula model Costabile Massabo Russo.Letak perbedaan dari ketiga model tersebut adalah pemilihan rata-rata representatif yang berbeda pada setiap nodenya.Rata-rata representatif ini merupakan rata-rata yang dapat mewakili ratarata sebenarnya pada lintasan harga saham opsi Asia. Setelah diperoleh rata-rata representatif, dengan proses backwardrecursion serta interpolasi linear, harga opsi Asia tipe Eropa maupun Amerika dapat ditentukan. Pada model binomial dipercepat, dilakukan pula ekstrapolasi Richardson terhadap harga opsi.Hal ini dimaksudkan agar dapat mempercepat kekonvergenan terhadap timestep yang cukup besar.Hasil yang diperoleh kemudian dibandingkan dengan metode Monte Carlo teknik reduksi varians. Keywords— Opsi Asia, Model Binomial, Model Diskrit, Ekstrapolasi Richardson
Morphogenetic Metasurface Engineering for Computational Localization Applications

This paper presents a bio-inspired generative model allowing the emergence of self-organized spatial patterns exploited for the design of frequency-diverse metasurface antennas. The latter radiate patterns that are weakly correlated in space and frequency are adapted to the direction of arrival detection applications operating in a simplified hardware-oriented approach. The generative model studied automates the design of these metasurfaces by ensuring statistical control of certain key properties. To this end, demonstration antennas are evaluated in terms of quality factor and radiation efficiency. In line with previous demonstrations of computational localization techniques, a set of simulations is proposed to study the performance of new classes of frequency-diversity metasurfaces, referred to as morphogenetic.
Autogeneration of Mission-Oriented Robot Controllers Using Bayesian-Based Koopman Operator

Model-based robot controllers require customized control-oriented models, involving expert knowledge and trial and error. Remarkably, the Koopman operator enables the control-oriented model identification through the input–output mapping set, breaking through the barriers of the customization services. However, in recent years, research on Koopman-based robot control has mostly focused on lifting function construction, deviating from the original intention of improving the controller performance. Thus, we propose a robot controller autogeneration framework using the Bayesian-based Koopman operator, significantly releasing labor and eliminating the design obstacle. First, we introduce the Koopman-based system identification method and offer the basic lifting function design criteria. Then, a Bayesian-based optimization strategy with resource allocation is designed, which allows for the simultaneous optimization of the lifting function and the controller. Next, taking model-predictive control (MPC) as an example, a mission-oriented controller autogeneration framework is developed. Simulation and experimental results indicate that, under various robots and data sources, the proposed framework can effectively generate the robot controllers and perform with a far greater level of mission accuracy than the unoptimized Koopman-based MPC. Meanwhile, the proposed technique exhibits an obvious compensation effect against disturbances, demonstrating its practicability in robot control.
Reactive Oxygen Species Damage Bovine Endometrial Epithelial Cells via the Cytochrome C-mPTP Pathway

After parturition, bovine endometrial epithelial cells (BEECs) undergo serious inflammation and imbalance between oxidation and antioxidation, which is widely acknowledged as a primary contributor to the development of endometritis in dairy cows. Nevertheless, the mechanism of oxidative stress-mediated inflammation and damage in bovine endometrial epithelial cells remains inadequately defined, particularly the molecular pathways associated with mitochondria-dependent apoptosis. Hence, the present study was designed to explore the mechanism responsible for mitochondrial dysfunction-induced BEEC damage. In vivo, the expressions of proapoptotic protein caspase 3 and cytochrome C were increased significantly in dairy uteri with endometritis. Similarly, the levels of proapoptotic protein caspase 3, BAX, and cytochrome C were markedly increased in H2O2-treated BEECs. Our findings revealed pronounced BEEC damage in dairy cows with endometritis, accompanied by heightened expression of cyto-C and caspase-3 both in vivo and in vitro. The reduction in apoptosis-related protein of BEECs due to oxidant injury was notably mitigated following N-acetyl-L-cysteine (NAC) treatment. Furthermore, mitochondrial vacuolation was significantly alleviated, and mitochondrial membrane potential returned to normal levels after the removal of ROS. Excessive ROS may be the main cause of mitochondrial dysfunction. Mitochondrial permeability transition pore (mPTP) blockade by cyclophilin D (CypD) knockdown with CSA significantly blocked the flow of cytochrome C (cyto-C) and Ca2+ to the cytoplasm from the mitochondria. Our results indicate that elevated ROS and persistent opening of the mPTP are the main causes of oxidative damage in BEECs. Collectively our results reveal a new mechanism involving ROS-mPTP signaling in oxidative damage to BEECs, which may be a potential avenue for the clinical treatment of bovine endometritis.
Continuous Integration pipeline for ROS-based application using AWS Robomaker

This paper presents the implementation of Continuous Integration CI pipeline for Robotic Operating System (ROS) based software application using Amazon Web Services (AWS) CodePipeline and AWS RoboMaker. The simulation platform is designed by integrating the ROS application with AWS RoboMaker to simulate the application using Turtlebot3 as reference software using Gazebo as the simulation platform. The pipeline is setup using AWS CodePipeline with GitHub as software source for the ROS application and integrated with AWS RoboMaker to implement the CI pipeline for automatic testing and integration of the incoming software commits. A custom test case is defined and used to validate the efficiency of the software during the testing phase of the pipeline. The test case validates the rotation feature of the turtlebot. The implementation includes creating a robot app and simulation app in RoboMaker using Docker to containerize the Turtlebot3 ROS app to the Elastic Container Registry (ECR) and source the robot app from it. The paper also discusses the steps involved in setting up the pipeline, integrating the ROS application with RoboMaker, and integrating the CodePipeline with RoboMaker. The results show that the pipeline provides an efficient way to test and validate the ROS application before deployment, ensuring a high-quality product.