bluuebunny/crossref_metadata_2025_split huggingface dataset:
Central configurations in the general four body coplanar problem with different masses
Authors: Shoaib Muhammad , Benhammouda Brahim , Steves Bonnie A. , Sweatman Winston L. 
Published: April 2023

Abstract
A generic method for finding central configurations in the general four body coplanar problem with four different masses is derived. New families of non-symmetrical central configurations with non-equal masses are found and all known symmetrical four body non-collinear central configurations are shown to be derivable subsets using this generic model. In this model, three masses are distributed on the vertices of a triangle. Then the fourth mass is allowed to be at any other point in the plane forming either convex or concave central configurations. In this general setting, a necessary condition is derived for the existence of central configurations and for requiring the masses to be positive. Using both analytical and numerical techniques, regions of possible central configurations can be derived. The special cases of four body central configurations investigated include isosceles trapezoids, right trapezoids, right quadrilaterals, convex kites and concave kites. In most of these special cases, the number of parameters of the problem can be reduced from four to two and hence the regions of existence can be shown graphically. A number of cases both convex and concave with no symmetry restrictions or equality of masses were also investigated to demonstrate the generality of the method for finding central configurations in the general four body coplanar problem with four different masses.
Development of lung cancer risk prediction models based on F-18 FDG PET images
Authors: Choi Kaeum , Park Jae Seok , Noh Hyunju , Park Sun Hyo , Kim Hyun Jung , Kim Hae Won 
Published: April 2023

Abstract
Background: We aimed to evaluate whether the degree of F-18 fluorodeoxyglucose (FDG) uptake in the lungs is associated with an increased risk of lung cancer and develop lung cancer risk prediction models using metabolic parameters on F-18 FDG positron emission tomography (PET).
Methods: We retrospectively included 585 healthy individuals who underwent F-18 FDG PET/CT scans for a health check-up. Individuals who developed lung cancer within 5 years of the PET/CT scan were classified into the lung cancer group (n=100); those who did not were classified into the control group (n=485). Clinical factors including age, sex, body mass index (BMI), and smoking history were collected. The standardized uptake value ratio (SUVR) and metabolic heterogeneity (MH) index were obtained in the bilateral lungs. Logistic regression models with clinical factors, SUVR and MH index were generated to quantify the probability of lung cancer development. The prediction models were validated using internal data set (n=210).
Results: The lung SUVR and lung MH index in the lung cancer group were significantly higher than those in the control group (p<0.001 and p<0.001, respectively). In the combined prediction model 1, age, sex, BMI, smoking history, and lung SUVR were significantly associated with lung cancer development (age: OR 1.07, p <0.001; male: OR 2.08, p=0.015; BMI: OR 0.93, p =0.057; current or past smoker: OR 5.60, p <0.001; lung SUVR: OR 1.13, p <0.001). In the combined prediction model 2, age, sex, BMI, smoking history, and lung MH index showed a significant association with lung cancer development (age: OR 1.06, p<0.001; male: OR 1.87, p=0.045; BMI: OR 0.93, p =0.010; current or past smoker: OR 4.78, p<0.001; lung MH index: OR 1.33, p<0.001). In the validation data, combined prediction model 1 and 2 exhibited very good discrimination (area under the receiver operator curve [AUC]: 0.867 and 0.901, respectively).
Conclusion: The metabolic parameters on F-18 FDG PET are related to an increased risk of lung cancer. Metabolic parameters can be used as biomarkers that provide information independent of clinical parameters related to lung cancer risk.
Urbanization Level and Tempo (Speed) in Tigray Regional State, Ethiopia
Authors: Tekulu Fikre Belay 
Published: April 2023

Abstract
The study attempts to study the level and tempo or speed of urbanization in the Tigray regional state based on census data from 1994–2013 in Ethiopia. The level of urbanization at the regional level was found in 1994, 2007, and 2020 at 14.9%, 19.5%, and 26.7% respectively. Whereas the level of urbanization varies among the zones of the region, the higher level of urbanization was recorded in the Eastern zone followed by the Western, Central, and Southern Zone of Tigray. The tempo of urbanization was found 0.9 percent per annum at the regional level and the highest speed or tempo of urbanization shown was recorded in the Eastern zone of Tigray. The unbalanced urbanization among the zones results in socio-economic challenges. The study recommends several policy interventions given in the conclusion part aimed at judicious urbanization suitable for sustainable development.
Evaluation of Oxygen Extraction Rate As A Guide Red Blood Cell Transfusion Strategy in Intensive Care Unit: A Protocol For A Prospective Observational Study
Authors: Tüzen Ahmet Salih , Aksun Murat , Şencan Atilla , Girgin Senem , Gölboyu Birzat Emre , Kırbaş Gizem , Şanlı Ozan 
Published: April 2023

Abstract
Background

Restrictive and liberal transfusion approaches to hemoglobin targets have used when deciding on red blood cell transfusions in patients who do not have acute bleeding and have a hemodynamically stable course in the ICU. However, physiologic trigger points that evaluate tissue oxygenation when deciding on blood transfusion in patients have also been among the important topics of research in recent years. In this study, we will evaluate the O2ER, which is an important indicator of the balance between oxygen delivery and consumption in tissues. Whether oxygen extraction rate can be used as a trigger for blood transfusion will be determined by clinical outcomes in ICU patients. If physiologic transfusion targets are feasible, the risks of unnecessary transfusions can be avoided with individualized targets. Also, the decision to transfuse blood can be made without delay in patients requiring red blood cell transfusion.

Methods/Design

We will perform a prospective, single-center, observational cohort study of 65 patients receiving red blood cell transfusions in the intensive care unit. Markers such as CaO2, CcvO2, O2ER, AV-O2 difference and NIRS will be measured before and 15 minutes after transfusion. We will investigate whether blood transfusion is really necessary and the frequency of transfusion-related events occur in patients with an O2ER ratio less than 30% and equal to 30% or above. All patients will be followed up to 90 days after transfusion. Ethics committee approval was obtained from Izmir Katip Celebi University Non-Interventional Clinical Studies Institutional Review Board. All patients must provide written informed consent prior to enrollment in the study.

Discussion

The existence of a direct relationship between individualized, patient-based oxygen extraction rates and the decision to transfuse red blood cells and reduce transfusion-related complications may imply that physiologically based blood transfusion targets should be established and routinely incorporated into existing transfusion decision-making protocols.
Trial registration number
NCT05798130
An intrusion detection algorithm based on convolutional long-short-term-memory and auto-encoding
Authors: Tang Mingwei , Cui Jiyuan , Jiang Zhongyuan 
Published: April 2023

Abstract
An intrusion detection algorithm based on deep learning is one of the hot topics in this field. In spite of some important research results, some shortcomings remain. In addition, the lack of labeled training data makes it more difficult to detect unknown attacks. High-dimensional and massive data will reduce the detection accuracy of the model and increase its complexity during training. Moreover, the phenomenon of data imbalance can seriously affect classification accuracy and degrade detection performance for rare attacks. In order to deal with the above problems, an intrusion detection algorithm based on convolutional long-short-term-memory and auto-encoding (CLAE) is proposed. Firstly, we design an encoder network module consisting of two CNN layers and one LSTM layer. The encoder network can encode raw data and output compressed one-dimensional vectors. Secondly, a decoder network module is designed. The decoder network has all layers symmetrical to the encoder network. In order to recover the original input data, this module is able to learn high-level features. And then, an attention residual block is designed in this paper to extract the key information from the data. As a result of ARB, more important features can be identified, redundant features can be filtered, feature learning of the model can be made more efficient, and intrusion detection accuracy can be improved significantly. Finally, using the NSL-KDD dataset and UNSW-NB15 dataset, CLAE’s superiority in detecting unknown attacks is demonstrated. The experimental results show that the proposed model CLAE can achieve state-of-the-art results compared to other models.
ARM-Stream: Active Recovery of Miscategorizations in Clustering-Based Data Stream Classifiers
Authors: Cavalcanti Douglas Monteiro , Cerri Ricardo , de Faria Elaine Ribeiro 
Published: April 2023

Abstract

The design of classifiers involves learning the probability distribution of data classes from labeled samples. Traditionally, this problem assumes a single estimation of the distribution, but recent advancements in data generation have led to non-stationary data streams where the distribution is subject to concept drift and evolution. In such cases, classifiers that can incrementally update their estimates of the distribution are advantageous. Among them, clustering-based classifiers gain prominence for using unlabeled data in the update process. These classifiers generate new clusters from partially or completely unlabeled data, categorizing each as either an existing class extension or a new one. However, to determine class information through unlabeled data, these classifiers depend on assumptions about feature-class interaction that can become invalid as the distribution changes, leading to incorrect updates. To address this problem, we propose ARM-Stream, an active-learning framework for improving the reliability of clustering-based data stream classifier updates by providing label information where the process would otherwise fail. We demonstrate the effectiveness of ARM-Stream in detecting and correcting miscategorizations through experiments on synthetic and real-world datasets, integrating the framework into two clustering-based data stream classifiers from the literature: MINAS and ECHO. The source code used in the experiments is provided.
Accelerating Atmospheric Gravity Wave Simulations using Machine Learning
Authors: DONG Wenjun , Fritts DC , Lund Thomas , Liu Alan , Liu Hanli , Snively Jonathan 
Published: April 2023

Abstract

Gravity waves (GWs) and their associated multi-scale dynamics are known to play fundamental roles in energy and momentum transport and deposition processes throughout the atmosphere. GWs are well described by the Navier-Stokes equations, but solving these equations extending to very small scales remains daunting, and is limited by the computational costs of resolving the smallest important spatio-temporal features in a large-scale environment. This has led to developments of a wide variety of GW parameterization schemes for regional and global atmospheric models. Traditionally, GW parameterizations are based on linear theory, and no parameterization of secondary GWs (SGWs) has been developed to date. In addition, there remain many aspects of GW dynamics (e.g., GW self-acceleration, instability, GW breaking, SGW generation, and multi-scale interactions) that are important to describe, but cannot be addressed by linear theory or existing schemes.
Here we describe an initial, two-dimensional (2-D), machine learning model – the Compressible Atmosphere Model Neural Network (CAMNet) - intended as a first step toward a more general, three-dimensional, highly-efficient, model for applications to nonlinear GW dynamics description. CAMNet employs a physics-informed neural operator and GPU hardware to dramatically accelerate GW and SGW simulations applied to two GW sources to date. CAMNet is trained on high-resolution simulations by the state-of-the-art model Complex Geometry Compressible Atmosphere Model (CGCAM). Two initial applications to a Kelvin-Helmholtz instability source and mountain wave generation, propagation, breaking, and SGW generation in two wind environments are described here. Results show that CAMNet can capture the key 2-D dynamics modeled by CGCAM with high precision. Spectral characteristics of primary and SGWs estimated by CAMNet agree well with those from CGCAM. Our results show that CAMNet can achieve a several order-of-magnitude acceleration relative to CGCAM without sacrificing accuracy and suggests a potential for machine learning to enable efficient and accurate descriptions of primary and secondary GWs in global atmospheric models.
Representation of Fitzpatrick Skin Phototype in Dermatology Surgical Textbooks
Authors: Fimbres Denisse Cristina Porras , Jacobs Jennifer , Diamond Carrie , Rundle Chandler , Rodriguez-Homs Larissa , Presley Colby , Stamey Christopher 
Published: April 2023

Abstract
Given the importance of skin of color (SoC) representation in dermatologic education and training, this study quantified representation of Fitzpatrick skin phototypes (FST) in core dermatology surgery textbooks. Images within Surgery of the Skin: Procedural Dermatology, Dermatologic Surgery, and Facial Reconstruction after Mohs Surgery were categorized according to the Fitzpatrick skin phototype (FST) depicted and the dermatologic surgery topic addressed. 1501 images were analyzed, with only 5.6% of the images categorized as FST IV-VI representing SoC. Several topics (11/29, 37.9%) identified did not include images with SoC. Increasing access to high-quality images of SoC can enhance appreciation of various skin conditions, especially those predominant in SoC, by dermatologic trainees and clinicians.
Influence of electrode reactions on electroosmotic flow and ion transport in a microchannel
Authors: Sun Runze , Ma Chicheng , Al-Anzi Bader , Sauret Emilie , Gu Yuantong , Li Zirui 
Published: April 2023

Abstract
Electroosmotic flow (EOF) is a universal phenomenon in most microfluidic systems when an external electric field exists along charged channel walls. The mechanism of ion transport and fluid flow in such systems has been extensively studied, largely based on simplified models without consideration of electrode reactions and water dissociation. In order to study the effects of these electrochemical reactions, we build an electrokinetic model with full consideration of these processes, namely electrochemistry (EC) model, and compare its performance with that of the traditional electrokinetic (EK) model. Our results show that electrode reactions alter the electric potential and reduce the current, causing a significant reduction in EOF velocity. These potential changes and EOF reduction are driven almost entirely by electrode reactions because the difference between the results from the EC model and those from the EK model with potential adjustment induced by chemical reactions is slight. In addition, the participation of ions in electrode reactions leads to notable alterations in their concentration within the microchannel and significant pH change, which are totally ignored in the traditional EK model. It is found that at a typical applied electric field of 50 V/cm, the EOF velocity in the EC model is 64% of that in the EK model. This difference in velocity decreases to only 1.9% as the EK model considers electric potential shifts caused by electrode reactions. In the microchannel, the Cl− concentration drops by approximately 50% while the OH− increases, leading to a pH growth of 3.5. The results presented in this work can improve the understanding of electrode effects on the physicochemical properties of EOF systems, providing essential guidance for manipulating fluid flow and amphoteric molecular transport in various microfluidic systems.
Patients with brain metastasis from colorectal cancer
Authors: Yang Yu , Yang Yang , Wang Nianfei , Wu Xiuwei , Zhang Mingjun 
Published: April 2023

Abstract
Background
This study aimed to retrospectively evaluate the treatment strategies and possible prognostic factors in patients with brain metastases (BMs) from colorectal cancers (CRC).
Methods
We retrospectively reviewed 40 patients with BMs from CRC who were treated at our center between July 2011 and May 2022. Clinicopathological characteristics and clinical outcomes were analyzed.
Results
The median follow-up time was 3 (range, 0.5–28) months. The median survival time after diagnosis of BMs was 4 months. The 1-year survival rate was 9.5%. In the univariate analysis, the absence of extracranial metastases was significantly associated with better overall survival (OS) but not intracranial progression-free survival (iPFS), while Eastern Cooperative Oncology Group performance status, recursive partitioning analysis (RPA) classification and local treatment of BMs were significantly associated with iPFS and OS. Multivariate analysis showed that only local treatment of BMs influenced the iPFS and OS. In addition, the OS was better in patients with intracranial benefit. The median time interval between the diagnosis of the primary tumor and BMs was 28 (range, 2–107) months. Among these BMs, 16.2% of the BM occurred within the first year after diagnosis of the primary tumor, 43.2% in the first 2 years, and 67.6% in the first 3 years.
Conclusions
local treatment of BMs was an independent prognostic factor for patients with BMs from CRC. Earlier detection followed by an aggressive local therapeutic approach for BMs had a great influence on treatment outcomes as well as the long-term prognosis and quality of life.
Swin-RGC: Swin-Transformer with Recursive Gated Convolution for substation equipment non-rigid defect detection
Authors: Li Hui , Zhang Jie , Li Rui , Zhang Hui , Zou Le , Liu Shujuan 
Published: April 2023

Abstract
Substation equipment defects are important factors affecting the safe operation of power grids. However, many non-rigid defects have low detection accuracy and poor robustness,due to boundary ambiguity, irregular shape and tiny size. To address these problems,we propose a swin-transformer with recursive gated convolution framework for substation equipment non-rigid defect. Firstly, in order to effectively detect non-rigid defect objects to improve the discriminability of image features, we design the Swin-Transformer with Recursive Gated Convolution(Swin-RGC) framework to extract the interaction features between spaces in the deep model. Secondly, to avoid the loss of object location information, the Task-aligned One-stage Object Detection(TOOD) head is improved by fusing Coordinate Attention modules. Finally, a substation equipment defect detection dataset is established to provide a baseline for detecting non-rigid defects in substation power equipment. Experiment results on our dataset demonstrate that our proposed method achieves the performance of 69.9% Mean Average Pricision (mAP) in the substation equipment non-rigid defect detection, which outweighs the state-of-the-art approaches.
Can Rare Cancer Drugs Expect Sales in Japan?: A Prescription Pattern Analysis of Drugs for Chronic Myelogenous Leukemia and Neuroendocrine Tumor
Authors: Shibata Shoyo , Noguchi Emi , Matsushita Maiko , Suzuki Takeshi , Ozaki Koken 
Published: April 2023

Despite high unmet medical needs, investment in rare cancer drug development has stagnated, likely because the potential market for such drugs is small. In this context, we hypothesized that rare cancer drugs could achieve a higher sales margin. A dataset was created from publicly available information obtained from the IQVIA Solutions Japan K.K. Pharmaceutical Market database on the website of the Pharmaceuticals and Medical Devices Agency/Ministry of Health, Labour and Welfare of Japan. The total amount of sales and prescription volumes between 2010 and 2016 for drugs whose indications include chronic myelogenous leukemia (CML) and neuroendocrine tumor (NET) were investigated. Regarding drugs for CML, the sales and prescription volumes of imatinib have been decreasing every year, whereas those of dasatinib and nilotinib have been increasing. Regarding drugs for NET, the sales and prescription volumes of sunitinib, everolimus, and streptozocin have been increasing every year. The present study revealed two sales models for the development of rare cancer drugs. First, sales amounts can be assured if clinical positioning with other existing drugs is sufficiently clear. Second, obtaining a label for rare cancers can stimulate drug development for more common cancers. These findings suggest that rare cancer drugs can offer high market value and profit potential; thus, to meet high unmet medical needs, clinical development programs for the development of rare cancer drugs should be promoted. https://doi.org/10.21423/jrs-v07shibata
Activated banana peel macrocomposite adsorbent for river water treatment: isotherm and kinetic studies
Authors: Apandi Najeeha Mohd , Muhamad Mimi Suliza , Wee Yek Tan , Sunar Norshuhaila Mohamed , Nagarajah Ramathasan 
Published: April 2023

Abstract
This study investigates the potential of a hybrid process combining sand filtration column with activated banana peels macrocomposite (ABPM) adsorbent for river water treatment. Scanning electron microscopy (SEM) analysis displayed an irregular structure and high cavities of the banana peel adsorbent surface that caters to the deposition of contaminants, while energy-dispersive X-ray analysis detected major elements of the adsorbent, such as calcium, oxygen, silicon, and carbon. Fourier-transform infrared analysis of the banana peels adsorbent showed the presence of hydroxyl, acyl, amine, and alkene groups that were responsible for the adsorption process. The sand filtration column experiment was investigated to find out the removal of turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and ammonia nitrogen (AN) where it resulted in the highest removal efficiency operated at a flowrate of 1.15 ml/s with &gt;90% turbidity, 44% COD, 87% BOD, 75% TSS, and 54% AN removal. The adsorption isotherm was best described by the Langmuir model (R2 &gt; 0.98) compared to the Freundlich model (R2 &gt; 0.95). The pseudo-first-order kinetic model was the best fit for all the experimental data. The combination of sand filtration column with ABPM adsorbent is an efficient treatment solution for improving river water quality.