bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fleeting Extinction? Unraveling the persistence of Noise Traders in Financial Markets with Learning and Replacement
Authors: Gerotto Luca , Pellizzari Paolo , Tolotti Marco 
Published: March 2024

Abstract
We describe an agent-based model of a financial market where agents can learn whether to buy costly information on returns, to use noise as if it were information, or to disregard any signals. We show that while learning alone drives all noise traders to extinction in stationary populations, allowing for small rates of replacement of existing agents with new ones suffices to generate substantial levels of persistent noise trading, with the equilibrium share of agents using irrelevant news reaching double digits. Remarkably, the presence of noise traders, when replacement is realistically considered, inflates the share of agents who use costly information relative to the benchmark scenario without replacement.
JEL Classification: C63 , D83 , G11
Going paperless: the strengths and limitations of electronic Research Ethics Information Management System in a health training institution in Tanzania
Authors: Mkumbwa Rebecca D. , Pancras Godwin , Sirili Nathanael S. 
Published: March 2024

Abstract

Over the years, the processing of research proposals for ethical approvals has been done manually through a review of printed proposals. The system faced many challenges which led to inefficiency of the review process, and thus longer turn-around time. To address these challenges, an electronic or web-based application for research proposal submission and processing was introduced. However, the advantages of going paperless and inherent drawbacks remain unsubstantiated. This study aimed to explore the strength and limitations of electronic Research Ethics Information Management Systems (REIMS) in a health training institution in Tanzania. An exploratory qualitative study was adopted where 16 faculty members of the Muhimbili University of Health and Allied Sciences were interviewed. The faculty were purposefully recruited to include only those who had been exposed both to the paper-based research ethics review system and electronic Research Ethics Information Management System. The faculty were stratified into two groups of researchers and members of the research ethics committee. Thematic analysis using inductive reasoning was used to analyze the gathered information. The strengths and limitations of REIMS were unveiled in this study as two important themes. The strengths included convenience in use of the system and improved records management. The imitations included demand for reliable ICT resources and limited reviewer-researcher interaction. The findings of this study underscore the strengths and limitations of transformation in handling research ethics review following an introduction of an electronic system and abandoning the paper-based system. Institutions aspiring to adapt the system can learn from this experience and devise ways to overcome the limitations. We recommend system automation, strengthening the institutional capacity and further studies to be conducted on the use of the REIMS.
Re-Resection and Deltopectoral Flap Reconstruction For Recurrent Anastomotic Esophageal Cancer
Authors: Yang Tzu-Yi , Li Chih-Ying , Tsao Chung-Kan , Chou Pin-Li , Chao Yin-Kai , Chiu Chien-Hung 
Published: March 2024

Abstract
Background
Anastomotic recurrence following esophagectomy is a rare occurrence. While complete resection yields better long-term outcomes, surgical management in this scenario poses challenges due to the impact of prior surgery and the fragile condition of patients. Here, we present an alternative surgical approach and reconstruction method to address this challenging scenario.
Case presentation:
Our patient, a 60-year-old man, developed anastomotic recurrence after neoadjuvant chemoradiotherapy and esophagectomy with gastric tube reconstruction via the retrosternal route. To tackle this issue, we employed an innovative surgical technique involving esophagogastrectomy via hemisternotomy, reconstruction using a deltopectoral fasciocutaneous local flap, and the establishment of a feeding jejunostomy. There were no major complications post-procedure, and the patient achieved good cancer control.
Conclusions
This approach not only offers significant technical advantages but also provides nutritional benefits. By adopting this novel approach, there is potential to enhance the current management of recurrent anastomotic esophageal cancer post-esophagectomy.
Fourier Feature Network for 3D Vessel Reconstruction from Biplane Angiograms
Authors: Wu Sean , Kaneko Naoki , Liebeskind David , Scalzo Fabien 
Published: March 2024

Abstract

3D reconstruction of biplane cerebral angiograms remains a challenging, unsolved research problem due to the loss of depth information and the unknown pixelwise correlation between input images. The occlusions arising from only two views complicate the reconstruction of fine vessel details and the simultaneous addressing of inherent missing information. In this paper, we take an incremental step toward solving this problem by reconstructing the corresponding 2D slice of the cerebral angiogram using biplane 1D image data. We developed a coordinate-based neural network that encodes the 1D image data along with a deterministic Fourier feature mapping from a given input point, resulting in a slice reconstruction that is more spatially accurate. Using only one 1D row of biplane image data, our Fourier feature network reconstructed the corresponding volume slices with a peak signal-to-noise ratio (PSNR) of 26.32±0.36, a structural similarity index measure (SSIM) of 61.38±1.79, a mean squared error (MSE) of 0.0023±0.0002, and a mean absolute error (MAE) of 0.0364±0.0029. Our research has implications for future work aimed at improving backprojection-based reconstruction by first examining individual slices from 1D information as a prerequisite.
No-Reference Point Cloud Quality Assessment Based on Multi-Projection and Hierarchical Pyramid Network
Authors: Miao Yizhuang , Xiao Shuyan , Pan Lingjiao , Zhang Lin , Yang Zhengkai 
Published: March 2024

Abstract

Within the realms of 3D vision and computer graphics, point clouds play a pivotal role as a dominant technology in various applications. However, practical processes such as acquisition, storage, and transmission introduce diverse quality defects in point clouds, making point cloud quality assessment a pressing issue. Presently, research on no-reference methods for evaluating point cloud quality often overlooks multi-scale feature analysis, resulting in suboptimal performance. This study presents a novel approach for evaluating the quality of point clouds based on multi-projection and a hierarchical pyramid network without using any reference data. Specifically, the point cloud is initially projected onto multiple 2D images. Subsequently, a pyramid network is employed to decompose the point cloud into multiple layers, extracting features at various scales. These features are subsequently inputted into a quality regression module for obtaining predicted scores at diverse levels. Finally, a weighted average of predicted scores from different levels is calculated to derive a point cloud quality score that encompasses information from multiple scales. The proposed method is applied on two databases, namely SJTU-PCQA and WPC, to assess the quality of distorted point clouds. Our experiments demonstrate that the proposed method surpasses traditional NR-PCQA methods regarding accuracy and generalization.
Occurrence of neonicotinoid pesticides in outdoor air particulate matters from 2019 to 2021 in China
Authors: Du hairong , Yao Yunying , Yao Wei , Zhu Xiaoling 
Published: March 2024

Abstract
Neonicotinoid insecticides (NNIs), as a new type of insecticide, are now widely used in agriculture and daily life. Because of the problematic volatility of NNIs, few studies have evaluated them in atmospheric particulate matter. In this study, a total of 101 outdoor PM2.5 samples were collected from the Wuhan urban area from 2019 to 2021, and seven NNIs and three metabolites of NNIs were detected. The frequency of detection for all 10 substances was more than 60%, with DIN and IMI reaching 100%. DIN (53.3 pg/m3) and IMI (42.8 pg/m3) had higher median concentrations than did the other substances. The distributions of four neonicotinoids and one metabolite were significantly different (p<0.05) across the four seasons: spring, summer, fall and winter. The median concentration of imidacloprid-equivalent total neonicotinoids (IMIeq: generated by the relative potency factor method) was 256.1 pg/m3. Finally, the estimated daily intake (EDI) of NNIs via respiration was greater in infants and young children than in the rest of the population, suggesting that infants and young children are more likely to be exposed to the health effects of airborne residual PM2.5.
Aseptic Technique Training Course Teaching Practices Based on Mastery Learning
Authors: Luo Binlin , Ding Qiang , Chen Liling , Du Junjie , Wang Xiaozhi , Chen Huanhuan , Jiang Surong 
Published: March 2024

Abstract

This research aims to explore aseptic technique training course teaching practices based on mastery learning in the field of medical education. Aseptic techniques are indispensable in health care services, which makes it crucial to provide training for medical students to master these skills proficiently. Traditional teaching methods have focused primarily on theoretical knowledge, whereas mastery learning emphasizes student engagement, autonomous study, and practical application. In designing the course, we first establish clear learning objectives and plans, breaking the content into a series of graduated tasks and skills. Students progressively grasp the essentials of aseptic techniques through video demonstrations, lecture explanations, and targeted deliberate practice until they meet the minimum standards for passing the course. Timely feedback and peer assessment are highlighted in the course, with students' progress being evaluated through on-site practical examinations to ensure that the learning objectives of each stage are achieved. This comprehensive assessment method helps promptly identify and correct issues in the learning process, thereby enhancing teaching outcomes. In summary, an aseptic technique training course based on mastery learning provides medical students with an efficient and systematic learning approach to enhancing their ability to apply aseptic techniques in real clinical settings, thus improving the quality and safety of clinical operations.
Evaluation Of Segment Anything Model (SAM) For Automated Labelling in Machine Learning Classification of UAV Geospatial Data
Authors: Parulekar Bhargav , Singh Nischal , Ramiya Anandakumar M 
Published: March 2024

Abstract
With the present trend toward digitization in many areas of urban planning and development, accurate object classification is becoming increasingly vital. To develop machine learning models that can effectively classify the broader region, it is crucial to have accurately labeled datasets for object extraction. However, the process of generating sufficient labeled data for machine learning models remains challenging. A recently developed AI-assisted segmentation approach called Segment Anything Model (SAM) offers a solution to enhance the semi-automated labeling of complex and intricate image structures. By utilizing SAM, the accuracy and consistency of annotation results can be improved, while also significantly reducing the time required for annotation. This paper aims to assess the efficiency of SAM annotated labels for training machine learning models using high-resolution remote sensing data captured by UAVs (Unmanned Aerial Vehicles) in the peri-urban region of Anad, Kerala, India. A comparative analysis was conducted to evaluate the performance of training datasets generated using SAM and semi-automated labeling with existing tools. Multiple machine learning models, including Random Forest, Support Vector Machine, and XGBoost, were employed for this analysis. The results, as indicated by the classification accuracy of the machine learning models, demonstrate that the SAM-based segmentation approach is more efficient in generating semantic labels for accurately training the models.
Polydiacetylene Based Bio Sensing Strategy for the Detection of Ndv and Csf Antibodies for Diva Strategy
Authors: Nachinarkiniyan Deepa. , Mahalingam Vidhya. , Das Priyadip , Pabisetti Vayunandana Kishore , Kirubaharan John , Juliet Asokan Vimala 
Published: March 2024

Abstract
Influenza viruses have become a major threat to humans, animals and agriculture posing a huge serious economic loss. In this connection, a rapid, high sensitive and cost effective detection method is the need of the day to keep a regular surveillance and to control the spreading of the viruses. Newcastle disease virus (NDV) is most prevalent virus in avian species causing disastrous losses in worldwide poultry industries. Despite of the availability of several vaccines, eradication of the disease still remains a challenge due to the inability of the conventional vaccines to differentiate the infected from vaccinated animals (DIVA).This led to the need for the development of marker vaccines which in conjunction with diagnostic tools that are able to differentiate the infected animals from the vaccinated animals. The limitations possessed by conventional diagnostic tools has paved the way to the implementation of biosensors for its rapid, cost effective, on site detecting features. This research work advocates, the development of a bio sensing strategy for the detection of anti-bodies developed when the chickens exposed to Newcastle disease virus (NDV) and Classical Swine Fever (CSF) using peptide-functionalized polydiacetylene (PEP-PDA) based on the unique color variation (bio-chromism) of the PDA. The PDA vesicle is formed from the ratio of 10, 12-pentacosadionic acid (PCDA), 1, 2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) using a peptide of NDV and CSF attached to the surface of the vesicle. The specificity, sensitivity and proficiency of the method is evaluated. The developed method is very cost effective, advantageous than existing methods.
Retention strategies in a dominant market
Authors: Amorim Andreia 
Published: March 2024

Abstract

With the developments in technology, firms can gather information about consumers' purchase history which can be use to price discrimination accordingly - Behaviour-Based Price Discrimination (BBPD). Introducing the capacity of firms to implement retention strategies, the aim of this paper is to analyze the effects of BBPD where firms apply retention strategies under a switching costs approach. Assuming that there is a dominant position in the market with one firm with a higher inherit market share, retention strategies are good for consumers and bad for profits.
Keywords: retention strategies, price discrimination, dominant firm.
An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection
Authors: Chen Qisheng , Omote Kazumasa 
Published: March 2024

Abstract

Nowadays, machine learning is used in many fields. Not only in fields such as image recognition, machine learning is also used for malicious detection. Especially in recent years, there have been many studies using machine learning for malicious URL detection to replace traditional blacklists. In order to compare the performance of the malicious URLs detection method, researches used the F-score or other detection accuracy to evaluate, but there are some difficulties in evaluating the URL embedding method used in malicious URLs detection because the detection accuracy is also effect by machine learning or deep learning models and data sets. An evaluation method of URL embedding method that is not affected by other factors is particularly important. In this paper, we proposed an intrinsic evaluation method for URL embedding method that is not affected by machine learning models or deep learning models and data sets. Besides, We analyse some URL embedding methods according to intrinsic and extrinsic methods and offer a guidance in selecting suitable embedding methods in URL by analysing the results.
ITPNet: Initial Trajectory Prediction Network - A deep learning framework for predicting initial trajectories to warm start optimization-based motion planners
Authors: Natarajan Sankaranarayanan , Kirchner Frank 
Published: March 2024

Abstract
Motion planning for robot manipulators in a cluttered environment is one of the most researched areas in the field of robotics.For high degrees of freedom (DOF) robotic systems, optimization-based motion planners are often preferred for trajectory planning, as they are computationally efficient and provide a smooth, locally optimal solution.To converge to an optimal solution, an optimization-based motion planner needs a good initial trajectory. Finding an initial trajectory, which is not far away from the basin of attraction of the optimum is not a trivial task. In this work, we propose an Initial Trajectory Prediction Network (ITPNet), a deep neural network framework for predicting an initial trajectory to warm start optimization-based motion planners.Given a planning task in the form of task and environment features, the ITPNet predicts the best initial trajectory for warm starting an optimization-based motion planner. Two different task features: joint and Cartesian features, and three types of environment features extracted using: Principal Component Analysis (PCA), Variational Autoencoder (VAE), and Signed Spatial Distance (SSD) techniques are compared.The learned models are evaluated on an upper-torso humanoid system in two different scenarios. The results show that the model, using the Cartesian task and the SSD-based environment features, efficiently learns the mapping between the planning tasks and the optimal trajectories. Warm-starting the planner with the predicted initial trajectory, even for an unseen environment results in a higher success rate and requires fewer iterations.
Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19
Authors: LHIADI REDOUANE , Amaanaoui Taoufiq , Kaaouachi Abdelali , Jaddar Abdessamad 
Published: March 2024

Abstract
The COVID-19 pandemic has prompted governments and specialists worldwide to collaborate in search of effective solutions and strategies for containment and eventual societal recovery. With advancements in equipment capabilities, remote communications, and on-board/distributed computing, information-based technologies are playing an increasingly critical role in identifying, detecting, and diagnosing potential COVID-19 cases. This study aims to explore factors for early diagnosis, tracking, and identification of COVID-19 spread, focusing on data collection and discussing opportunities for improvement. The study recognizes that deep learning models are well-suited for mitigating the impact of COVID-19, given the availability of a large volume of pandemic data through various technologies and collaborative efforts. While deep learning and big data approaches may not have been extensively implemented or clinically tested, they offer quick responses and valuable insights to medical staff and decision-makers. However, designing deep learning algorithms for COVID-19 presents numerous challenges. The quality and quantity of COVID-19 datasets need further improvement, demanding ongoing efforts from the research community to enhance data quality and reliability.
Addressing treatment switching bias in the ALTA-1L trial with g-methods: exploring the impact of model specification
Authors: Tawil Amani Al , McGrath Sean , Ristl Robin , Mansmann Ulrich 
Published: March 2024

Abstract

Background: Treatment switching in randomized clinical trials introduces challenges in performing causal inference. Intention To Treat (ITT) analyses often fail to fully capture the causal effect of treatment in the presence of treatment switching. Consequently, decision makers may instead be interested in causal effects of hypothetical treatment strategies that do not allow for treatment switching. For example, the phase 3ALTA-1L trial showed that brigatinib may have improved Overall Survival (OS) compared to crizotinib if treatment switching had not occurred. Their sensitivity analysis using Inverse Probability of Censoring Weights(IPCW), reported a Hazard Ratio (HR) of 0.50 (95% CI, 0.28–0.87), while their initial ITT analysis estimated an HR of 0.81 (0.53–1.22).
Methods: We used a directed acyclic graph to depict the clinical setting of the ALTA-1L trial in the presence of treatment switching, illustrating the concept of treatment-confounder feedback and highlighting the need for g-methods. In a re-analysis of the ALTA-1L trial data, we used IPCW and the parametric g-formula to adjust for baseline and time-varying covariates to estimate the effect of two hypothetical treatment strategies on OS: “always treat with brigatinib” versus “always treat with crizotinib”. We conducted various sensitivity analyses using different model specifications and weight truncation approaches.
Results: Applying the IPCW approach in a series of sensitivity analyses yielded Cumulative HRs (cHRs)ranging between 0.38 (0.12, 0.98) and 0.73 (0.45,1.22) and Risk Ratios (RRs) ranging between 0.52 (0.32,0.98) and 0.79 (0.54,1.17). Applying the parametric g-formula resulted in cHRs ranging between 0.61(0.38,0.91) and 0.72 (0.43,1.07) and RRs ranging between 0.71 (0.48,0.94) and 0.79 (0.54,1.05).
Conclusion: Our results consistently indicated that our estimated ITT effect estimate (cHR: 0.82 (0.51,1.22)may have underestimated brigatinib’s benefit by around 10-45 percentage points (using IPCW) and 10-20 percentage points (using the parametric g-formula) across a wide range of model choices. Our analyses underscore the importance of performing sensitivity analyses, as the result from a single analysis could potentially stand as an outlier in a whole range of sensitivity analyses.
Trial registration: Clinicaltrials.gov Identifier: NCT02737501 on April 14, 2016
A sport, mint az életmentés és a testi- és lelki egészségmegőrzés eszköze
Authors: Mayer Krisztina 
Published: March 2024

Ez a tanulmány az idősebb, szakértő tűzoltók rekreációs sporttevékenységébe enged betekintést, különös tekintettel a motivációra. Mennyit sportolnak hetente átlagosan a vonulós tűzoltók? Mióta végeznek rendszeres sporttevékenységet? Mi a motivációjuk? Jelen kutatás ezekre a kérdésekre keresi a választ kvantitatív módszert alkalmazva. A vizsgálatban vonulós tűzoltók vettek részt (46 fő, átlagos életkor 47,87 ±3,35). A vizsgálatba kerülés kritériuma volt a legalább 45 éves életkor és a legalább 15 éves tűzoltó szakmai tapasztalat. Az eredmények arra engednek következtetni, hogy a vizsgált vonulós állományt elsősorban a kikapcsolódás és a mindennapi stresszel való megküzdés motiválja a sporttevékenységekben való részvételre. E mellett megjelent a munka iránti elkötelezettség is, mely a rendszeres spottevékenységben való részvételre sarkallja a tűzoltókat.