bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides
Authors: Kucharski Rafal , Cats Oded 
Published: January 2024

Abstract
Ride-pooling offers a promising alternative in the context of urban mobility, being an intermediate mode between private rides and public transport. However, its full potential to contribute towards sustainability and efficiency has arguably not been yet unlocked neither in practice nor in theory. We propose a novel hyper-pool algorithm for addressing the computational complexity issues associated with pooling passengers into high-occupancy rides. In our Amsterdam 2000 trip case-study we identify 40 hyper-pooled rides with an average occupancy 5.8 passengers per vehicle.
Hyper-pool is an analytical, offline, utility-driven ride-pooling algorithm for bundling individual trip requests into attractive shared rides of high-occupancy. We depart from the ride-pooling ExMAS algorithm where single rides are pooled into attractive door-to-door rides and propose two novel demand-side algorithms for further bundling individual demand towards more compact pooling. First, we generate stop-to-stop rides, with a single pick-up and drop off points optimal for all the travellers. Second, we bundle the stop-to-stop rides again, resulting with hyper-pooled rides which are compact enough to resemble public transport operations.
The algorithm is publicly available and reproducible. It is applicable for real-size demand datasets and opens new opportunities for unlocking the potential of ride-pooling.
Telemetry without collars: performance of fur- and ear-mounted satellite tags for evaluating the movement and behaviour of polar bears
Authors: Ross Tyler R. , Thiemann Gregory W. , Kirschhoffer BJ , Kirschhoffer Jon , York Geoffrey , Derocher Andrew E. , Johnson Amy C. , Lunn Nicholas J. , McGeachy David , Trim Vicki , Northrup Joseph M. 
Published: January 2024

Abstract

The study of animal movement provides insights into underlying ecological processes and informs analyses of behaviour and resource use, which have implications for species management and conservation. The tools used to study animal movement have evolved over the past decades, allowing for data collection from a variety of species, including those living in remote environments. Satellite-linked radio and GPS collars have been used to study polar bear (Ursus maritimus) ecology and movements throughout the circumpolar Arctic for over 50 years. However, due to morphology and growth constraints, only adult female polar bears can be reliably collared for long durations. Further, collars have proven to be safe and reliable but there has been opposition to their use, resulting in a deficiency in data across much of the species’ range. To bolster knowledge of movement characteristics and behaviours for polar bears other than adult females, while also providing an alternative to collars, we tested the use of fur- and ear-mounted telemetry tags that can be affixed to polar bears of any sex and age. We also used data collected from the tags to quantify the amount of time subadult and adult males spent resting versus traveling while on land. Our results show fur tags remained functional for shorter durations than ear tags, but had comparable positional error estimates and provided sufficient data to model different behavioural states. Further, as hypothesized, subadult and adult male polar bears spent the majority of their time resting while on land, likely as a means of conserving energy until the sea ice reforms in early winter. Fur tags provide promise as a shorter-term means of collecting movement data from free-ranging polar bears.
Augmenting SQL Injection Attack Detection via Deep Convolutional Neural Network
Authors: Sneha Sneha Baral BK , Singh Hakam 
Published: January 2024

Abstract
Advancing the systematic methods or algorithms is necessary because SQL injection attacks can be hazardous for the security of databases and various web applications. SQL injection can be a destructive security risks which targets vulnerable web applications. There were many techniques which was previously developed which is also known as traditional methods or techniques. Those techniques used to generally rely on the signature-based methods which struggle to adjust into new attack patterns. Therefore, different new techniques were introduced with integration of machine learning. SQL injection attack detection with the blend of machine learning facilitates improvement in cybersecurity providing the scalable and the proficient defense mechanism against the developing cyber-attack. This research paper provides a potential technique to the danger of SQL injection which is based on Machine Learning i.e. Deep Convolutional Neural Network (DCNN). The proposed model was trained on the large datasets which includes genuine as well as malicious SQL queries for assuring its ability to adapt different types of evolving attacks. We have used embedding layers and tokenization techniques for demonstrating SQL queries as numerical input for the model. It is made up of many convolutional layers and fully linked layers which is able to illustrate the complex patterns and the complex correlation that can be observed in SQL queries. Our approach to detect a SQL injection attack utilizing a DCNN illustrates the remarkable accuracy, precision, recall as well as F1 score. Additionally, we also had a look at the significances of using deep learning techniques in real-world scenarios along with the existing web application and the framework.
A Reference Current Control Strategy Based on SOGI and FBD Method for Shunt Active Power Filter
Authors: liu jijun , wan liang 
Published: January 2024

Abstract

In this paper, a reference current control strategy based on second-order generalized integrator and FBD method for shunt active power filter is proposed. The method uses the second-order generalized integrator and the symmetrical component method to obtain the fundamental sequence component of the grid voltage. And then the FBD method is used to decompose the load current under the fundamental positive-sequence voltage reference to obtain the reference value of the compensation current. This method does not need coordinate transformation, is simple to calculate, and has clear physical meaning. It can compensate harmonic current, reactive current and negative-sequence current at the same time even under complex grid voltage and load and frequency offset. The proposed method is compared with the existing method under a variety of experimental conditions. The experimental results prove the effectiveness and accuracy of the proposed method.
The picture theory of seven pathways associated with COVID-19 in the real world
Authors: Lee Jong hoon , Sergi Consolato , Kast Richard E. , Kanwar Badar A. , Altschuler Eric L. , Bourbeau Jean , Oh Sangsuk , Sohn Mun-Gi , Lee Kun Ho , Coleman Michael D. 
Published: January 2024

Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces immune-mediated diseases. Interactions between the host and virus govern induction, resulting in multiorgan impacts In 2021, as normal life was challenging during the pandemic era, we analyzed SCI journals according to L. Wittgenstein's Tractatus Logi-co-Philosophicus. The pathophysiology of coronavirus disease 2019 (COVID-19) involves 1) the angiotensin-converting enzyme (ACE2) and Toll-like receptor (TLR) pathways starting with eight, from 2022.01.14., and rediscovered with nineteen, to 2024.01.10., 2) the neuropilin (NRP) pathway with seven and successful with twenty four, 3) the sterile alpha motif (SAM) and histidine-aspartate domain (HD)-containing protein 1 (SAMHD1) tetramerization pathway with two and successful with thirteen, 4) inflammasome activation pathways with five and successful with thirteen, 5) the cytosolic DNA sensor cyclic-GMP-AMP synthase (cGAS)/stimulator of interferon genes (STING) (cGAS–STING) signaling pathway with six and successful with eleven, 6) the spike protein pathway with fourteen and successful with twenty three, 7) the immunological memory engram pathway with thirteen and successful with eighteen, and 8) the excess acetylcholine pathway with three and successful with nine. We reconfirmed that COVID-19 involved seven (1–7) pathways and a new pathway involving excess acetylcholine. Therefore, it is necessary to therapeutically alleviate and block the pathological course harmoniously with modulating innate lymphoid cells (ILCs) if subsequent diverse SARS-CoV-2 variants are encountered in the future.
Anticancer effect of Epigallocatechin Gallate Loaded Nanoparticles on Head and Neck Cancer
Authors: Zadeh Zahra Khatib , Arab Samaneh , Kazemi Sohrab , Arabhalvaee Mohadeseh , Afraz Elham Sadat , Bahraminasab Marjan 
Published: January 2024

Abstract
Introduction:
Head and neck cancer, as one of the most common cancers, causes the death of many people worldwide every year. The current approaches to treat this cancer have not been successful, and recurrence, drug resistance development, side effects, and high treatment costs are important problems necessitating the need for more effective drugs and treatment approach. Epigallocatechin gallate (EGCG) is the most plentiful and biological-active catechin in green tea with proved anticancer effect. However, the stability, low bioavailability, and short half-life, limits its clinical use. The nanocarrier development may overcome these deficiencies by improving pharmacokinetics and pharmacodynamics. Therefore, this study aimed to examine the polyethylene glycol (PEG) nanoparticles containing EGCG for their anticancer activity.
Materials and methods
First, PEG nanoparticles loaded with EGCG were prepared, which were then characterized by dynamic light scattering (DLS), zeta potential, and Fourier transform infrared spectroscopy (FTIR). The toxicity of nanoparticles on the TSCC-1 cancer cell line was assessed by MTT and LDH assays. Cell migration rate, colony formation ability, the apoptosis rate, and the expression level of BAX, BCL2, and VEGF genes after treatment of cancer cells with drug-loaded particles were assessed. Moreover, the effect of nanoparticles on the spheroid growth of TSCC-1 cells in three-dimensional (3D) culture was investigated.
Results
The results of the FTIR assay demonstrate the presence of PEG nanoparticles containing EGCG. The size and zeta potential of the drug-loaded nanoparticles and nanoparticles without EGCG were 1.62 ± 17.53 nm and − 0.166 ± 0.169 mv, and 14 ± 2.3 nm and − 0.266 ± 0.169 mv, respectively. The synthesized nanoparticles showed sustained release of the drug. Moreover, the MTT assay showed the cytotoxicity of the nanoparticles was significant at a concentration of 80 µg/ml on TSCC-1 cells. The colony formation assay showed no colonies in the groups treated with nanoparticles containing EGCG compared to the control group. The scratch test also revealed the ability of the nanoparticles to inhibit cell migration. Furthermore, the induction of delayed apoptosis by 88.3 ± 3.18% was observed in the group treated with nanoparticles at a concentration of 80 µg/ml. In addition, the expression of BCL2 and VEGF gene significantly decreased and BAX gene increased. Furthermore, the study of cultivation in the 3D environment showed a decrease in the size and growth of cell spheroids in the nanoparticle-treated group compared to the control group.
Conclusion
The results show that PEG nanoparticles containing EGCG have significant anticancer activity (TSCC-1) and may be a suitable treatment option for the management of squamous cell carcinoma of the head and neck.
Artificial Intelligence Algorithms For Object Detection and Recognition In video and Images
Authors: Dakshinamoorthy Prabakar , Rajaram Gnanajeyaraman , garg Shruti , Murugan Prabhu , Manimaran A. , Sundar Ramesh 
Published: January 2024

Abstract

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy.
Association of Renin-angiotensin pathway gene polymorphisms with COVID-19 susceptibility and severity in the Moroccan cohort
Authors: Yousfi Fatima-Zahra El , Hilali Samia El , Belayachi Jihane , Ennibi Khalid , Razine Rachid , Abouqal Redouane , Bouhouche Ahmed 
Published: January 2024

Abstract

Infection by the recent SARS-Cov-2 virus causes the COVID-19 disease with variable clinical manifestations ranging from asymptomatic or mild respiratory symptoms to severe respiratory distress and multiorgan failure. The renin-angiotensin system, responsible for maintaining homeostasis and governing several critical processes, has been considered the main system involved in the pathogenesis and progression of COVID-19. Here, we aimed to assess the possible association between variants in the RAS-related genes and COVID-19 susceptibility and severity in a sample of the Moroccan population. A total of 325 individuals were recruited in this study, with 105 outpatients, 107 hospitalized patients, and 118 healthy controls negative for SARS-CoV-2 infection, and subjected to NGS gene panel sequencing containing eleven RAS pathway genes. A total of 65 functional variants were identified including 63 missenses, 1 splice, and 1 INDEL. Most of them were rare, with 47 (72%) found in a single individual. According to the common disease/common variant hypothesis, five common candidate variants with MAF > 10% were identified (ACE2 rs2285666, TMPRSS2 rs12329760, AGT rs699 genes, ACE rs4341, and ACE rs4343). Statistical analysis showed that the ACE rs4343 AA genotype was associated with a 2.5-fold increased risk of severe COVID-19 (p = 0.026), and the T genotype of the ACE2 rs2285666 variant showed a borderline association with susceptibility to SARS-CoV-2 in males (p = 0.092). In conclusion, our results showed that the RAS pathway genes are highly conserved among Moroccans, and most of the identified variants are rare. Among the common variants, the ACE rs4343 polymorphism would lead to a genetic predisposition for severe COVID-19.
SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments
Authors: Jiao Shun , Wang Haiyan , Luo Jian 
Published: January 2024

Abstract
With the rapid development of Internet technology, the cloud-edge-end computing model has gradually become an essential new computing model. Under this model, terminal task offloading is crucial to task allocation and processing. Existing terminal task offloading solutions mainly focus on optimizing the offloading strategy to minimize system delay, transmission energy consumption, and computation energy cost. However, there are some problems such as the lack of a basis for judging whether to offload or not, the lack of analysis on the attributes of the terminal devices and the edge nodes, and the lack of optimization of load balancing. To address these issues, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is proposed for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. Subsequently, a set of vectors is derived based on the analysis. An Elite Artificial Bee Colony Offloading (E-ABCO) method is proposed, which optimizes offloading decisions by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Experiments show that the proposed model has better performance in convergence, delay and energy consumption.
Comparison of the market share of public and private hospitals under different Medical Alliances: an interrupted time-series analysis in rural China
Authors: Xiong Yingbei , Lin Kunhe , Yao Yifan , Zhong Zhengdong , Xiang Li 
Published: January 2024

Abstract

Background
China initiated the Medical Alliances (MAs) reform to enhance resource allocation efficiency and ensure equitable healthcare. In response to challenges posed by the predominance of public hospitals, the reform explores public-private partnerships within the MAs. Notably, private hospitals can now participate as either leading or member institutions. This study aims to evaluate the dynamic shifts in market share between public and private hospitals across diverse MAs models.
Methods
Data spanning April 2017 to March 2019 for Dangyang County's MA and January 2018 to December 2019 for Qianjiang County's MA were analyzed. Interrupted periods occurred in April 2018 and January 2019. Using independent sample t-tests, chi-square tests, and interrupted time series analysis (ITSA), we compared the proportion of hospital revenue, the proportion of visits for treatment, and the average hospitalization days of discharged patients between leading public hospitals and leading private hospitals, as well as between member public hospitals and member private hospitals before and after the reform.
Results
After the MAs reform, the revenue proportion decreased for leading public and private hospitals, while member hospitals saw an increase. However, ITSA revealed a notable rise trend in revenue proportion for leading private hospitals (p < 0.001). Member public and private hospitals experienced decreasing revenue proportions, with outpatient visits declining in member public hospitals (p < 0.05) and inpatient admissions dropping in member private hospitals (p < 0.001). The average length of stay in member private hospitals increased from 15.29 to 16.16 days post-reform, maintaining a monthly growth of 0.321 days (p < 0.01).
Conclusions
This study underscores the imperative to reinforce oversight and constraints on leading hospitals, especially private leading hospitals, to curb the trend of diverting patients from member hospitals. At the same time, for private hospitals that are at a disadvantage in competition and may lead to unreasonable prolongation of hospital stay, this kind of behavior can be avoided by strengthening supervision or granting leadership.
Evidence from Whole Genome Sequencing of Aerosol Transmission of SARS-CoV-2 almost Five Hours after Hospital Room Turnover
Authors: Charness Michael E. , Gupta Kalpana , Linsenmeyer Katherine , Strymish Judith , Madjarov Rebecca , Stack Gary 
Published: January 2024

Abstract

Experimental evidence suggests that SARS-CoV-2 remains viable within aerosols with a half-life of approximately 1-3 hours, though changes in aerosol microenvironment may shorten viability to minutes. However, it remains unclear how long airborne SARS-CoV-2 can transmit infection. Whole genome sequencing of nasopharyngeal samples obtained from patients on an outbreak unit suggested in-room transmission of the delta variant, AY3 lineage, of SARS-CoV-2 to two patients admitted 1 hour, 43 minutes and 4 hours, 45 minutes after discharge of an asymptomatic infected patient. These findings suggest that airborne SARS-CoV-2 may transmit infection for nearly 5 hours, even in a hospital setting.
Regularized Robust Strategic Asset Allocation under Stochastic Variance-Covariance of Asset Returns
Authors: Kusuda Koji , Kikuchi Kentaro 
Published: January 2024

Abstract
This study considers a finite-time robust consumption-investment problem under a quadratic security market model with stochastic variances and covariances of asset returns, as well as stochastic interest rates, market price of risk, and inflation rates. Because the optimal portfolio is proportional to the inverse of the stochastic variance-covariance matrix of asset returns, it becomes unstable when near-singularity of the variance-covariance matrix occurs. We propose a regularized consumption-investment problem in which the near-singularity risk is introduced into the budget constraint equation as a regularization term. We show that the optimal regularized portfolio can be stable and decomposed into the product of the ''standard deviation,'' ''correlation,'' and ''investment control'' factors. As the optimal regularized robust portfolio contains an unknown function that is a solution to a nonlinear PDE, we derive an approximate optimal regularized portfolio. Our numerical analysis shows that the market timing effects in the approximate optimal regularized asset allocation are significant and nonlinear, and all factors contribute to these market timing effects.
JEL classification: C61, G11
TGR5 protects against pSNL-induced mechanical allodynia by alleviating neuroinflammation in the injured nerves of male mice
Authors: Shi Wen-Ge , Yao Yao , Liang Ya-Jing , Lei Jie , Feng Shi-Yang , Tian Yue , Zhang Zi-Xian , Cai Jie , Xing Guo-Gang , Fu Kai-Yuan 
Published: January 2024

Abstract

Neuropathic pain is a pervasive medical challenge that currently lacks effective treatment solutions. Molecular changes occurring at the site of peripheral nerve damage contribute to the development of peripheral and central sensitization, which are critical components of neuropathic pain. This study aimed to investigate the role of the G protein-coupled bile acid receptor (GPBAR1, also known as TGR5) in the peripheral mechanisms underlying neuropathic pain induced by partial sciatic nerve ligation (pSNL) in male mice. TGR5 was upregulated in injured nerves and colocalized predominantly with macrophages. Peri-sciatic nerve administration of the TGR5-specific agonist INT-777 provided sustained relief from mechanical allodynia. Transcriptome sequencing revealed that pain relief was primarily attributable to reduced neuroinflammation. This finding was corroborated by a reduction in myeloid cells and proinflammatory mediators (including CCL3, CXCL9, IL-6, and TNF-α), accompanied by an increase in the percentage of anti-inflammatory M2 macrophages following INT-777 administration. Furthermore, myeloid cell-specific TGR5 knockdown in the sciatic nerve following pSNL exacerbated both mechanical allodynia and neuroinflammation. This is substantiated by data from the bulk RNA-seq and upregulated expression levels of inflammatory mediators (including CCL3, CCL2, IL-6, TNF-α and IL-1β), as well as increased monocytes/ macrophages in the injured nerve. Besides, the activation of microglia in the ipsilateral dorsal horn of spinal cord induced by pSNL altered when TGR5 in the sciatic nerve was manipulated. In summary, TGR5, present in injured nerves, plays a protective role and offers potential as a target for treating neuropathic pain.