bluuebunny/crossref_metadata_2025_split huggingface dataset:
Video alignment using unsupervised learning of local and global features
Authors: Fakhfour Niloufar , ShahverdiKondori Mohammad , Mohammadzade Hoda 
Published: October 2023

Abstract
In this paper, we tackle the problem of video alignment, the process of matching the frames of a pair of videos containing similar actions. The main challenge in video alignment is that accurate correspondence should be established despite the differences in the execution processes and appearances between the two videos. We introduce an unsupervised method for alignment that uses global and local features of the frames. In particular, we introduce effective features for each video frame using three machine vision tools: person detection, pose estimation, and VGG network. Then, the features are processed and combined to construct a multidimensional time series that represents the video. The resulting time series are used to align videos of the same actions using a novel version of dynamic time warping named Diagonalized Dynamic Time Warping(DDTW). The main advantage of our approach is that no training is required, which makes it applicable for any new type of action without any need to collect training samples for it. For evaluation, we considered video synchronization and phase classification tasks on the Penn action dataset \citep{penn}. Also, for an effective evaluation of the video synchronization task, we present a new metric called Enclosed Area Error(EAE). The results show that our method outperforms previous state-of-the-art methods, such as TCC \citep{TCC}, and other self-supervised and weakly supervised methods.
Association of 8-hydroxy-2’-deoxyguanosine with motoric cognitive risk in elderly Chinese people: RUGAO longevity and aging cross-sectional study
Authors: Dai Qingqing , Ma Yajun , Liu Chang , Zhao Ruixue , Chen Qi , Chen Weijia , Wang Xiaofeng , Jiang Xiao-yan , Li shujuan 
Published: October 2023

Abstract
Background: Motor cognitive risk syndrome (MCR) is a pre-dementia and disability state combining objective slow walking speed and subjective cognitive complaints (SMCs). To explore the risk factors and the association between 8-hydroxy-2’-deoxyguanosine (8-OHdG) and motor cognitive risk syndrome (MCR) in Chinese community-dwelling elderly people.
Methods: A total of 1312 participants were involved in this study based on the data of the Rugao Longevity and Aging Study (RuLAS). The MCR was characterized by subjective memory complaints (SMCs) and slow walking speed. The SMCs were defined as a positive answer to the question ‘Do you feel you have more problems with memory than most?’ in a 15-item Geriatric Depression Scale. Slow walking speed was determined by one standard deviation or more below the mean value of the patient’s age and gender group. The plasma of 8-OHdG were measured by a technician in the biochemistry laboratory of the Rugao People’s Hospital during the morning of the survey.
Results: The prevalence of MCR was 7.9%. After adjusting the other covariates, the elder (OR 1.057, 95% CI 1.010–1.106; p= 0.018), cerebrovascular disease history (OR 2.155, 95% CI 1.206–3.850; p= 0.010), and elevated level of 8-OHdG (OR 1.007, 95% CI 1.002–1.012; p= 0.003) showed obvious relevance to MCR.
Conclusions: This study indicated These results may suggest that the elevated 8-OHdG was associated with the increased risks of MCR in elderly community.
Precise Object Detection in Challenging Foggy Driving Conditions With Deep Learning
Authors: REVATHY G , GURUMOORTHI E. , FELIX A YOVAN , RAGAVENDIRAN S D PRABU 
Published: October 2023

Abstract
Recent advancements in deep learning have led to significant improvements in driving perception. However, perceiving the environment accurately during risky situations like fog remains a major challenge. Existing methods for detecting foggy atmospheres struggle with both recognition speed and accuracy. In this study, we propose an object discovery network specifically designed for driving in foggy environments, utilizing a modified version of YOLOv5. Our approach involves building a mist detection network based on a modified ResNet-50 model. To address the lack of features in foggy scenes, we introduce a new feature enhancement module (FEM) and employ the skip attention mechanism. These additions allow the recognition network to focus on the most relevant features in foggy scenes, improving overall performance. Experimental results demonstrate that our suggested foggy multi-target recognition network outperforms the original YOLOv5 in terms of both accuracy and speed. When evaluated on the MS COCO vehicle dataset, our network achieves an impressive accuracy of 81.8%, showcasing its superiority over the original YOLOv5. This advancement in foggy environment perception holds great promise for enhancing driving safety and efficiency. Keywords: accuracy, deep learning, driving perception, foggy environment, mist detection, object discovery, recognition speed, ResNet-50, skip attention, YOLOv5.
Finding on Machine Learning Approaches for Classification of Ancient Ceramics - A Systematic Literature Review
Authors: Ling Ziyao , Delnevo Giovanni , Mirri Silvia 
Published: October 2023

Abstract
Ceramics appraisal is a hot topic in the field of cultural relic collection, dating back to prehistoric times. Traditionally, there are primarily two types of ceramics appraisal methods, which are experience-based methods and technology-based methods. In practice, both methods would cause high costs and be time-consuming. This paper presents the results of a systematic literature review of 22 empirical studies that used machine and deep learning algorithms to classify and identify ancient ceramics, encompassing data collection processes to build datasets, feature extraction of ancient ceramics images, and the selection of machine learning algorithms. Major findings included that there has been a growing number of research projects on the use of machine and deep learning algorithms for the classification of ancient ceramics.
Examining the Impact of Digital Transformation on Economic Growth in India: A Study of the Post-Pandemic Era
Authors: SWARGIARY KHRITISH 
Published: October 2023

Abstract
In recent years, digital transformation has gained significant momentum globally, and its impact on various sectors, including economies, has been widely discussed. The COVID-19 pandemic further accelerated the adoption of digital technologies across industries and societies, transforming the way we live, work, and conduct business. This research article aims to investigate the relationship between digital transformation and economic growth in India in the post-pandemic era. We hypothesize that a higher level of digital transformation will positively influence economic growth, leading to improved productivity, innovation, and competitiveness.
Compressibility and pressure-induced structural evolution of kokchetavite, hexagonal polymorph of KAlSi3O8, by single-crystal X-ray diffraction
Authors: Romanenko Alexandr V. , Rashchenko Sergey V. , Korsakov Andrey V. , Sokol Alexander G. , Kokh Konstantin A. 
Published: October 2023

Abstract
Compressibility and pressure-induced structural evolution of kokchetavite, the hexagonal polymorph of KAlSi3O8, has been studied up to 11.8 GPa using synchrotron single-crystal X-ray diffraction. Two phase transitions were observed at pressures of ~0.3 and 10.4 GPa. Kokchetavite-I (as-synthesized, P6/mcc) transforms into kokchetavite-II with the P6c2 space group. Kokchetavite-II → kokchetavite-III phase transition at ~10.4 GPa is accompanied by a change of symmetry to probably orthorhombic. After pressure release, kokchetavite reverts to the initial single-crystal state with P6/mcc space group. A second-order Birch-Murnaghan equation of state was calculated for phase kokchetavite-II with coefficients V0 = 1486(3) Å3, K0 = 59(2) GPa.
Developing synthetic sewer pipe system for data-scarce domains in application for urban flood modeling
Authors: Dasallas Lea , An Hyunuk , Lee Seungsoo 
Published: October 2023

Abstract

In urban flood modeling, the accuracy of surface and subsurface flow calculations greatly depends on the parameterization of the drainage system. Incorporating the influence of the sewer pipe system is, therefore, integral to accurately simulating urban inundation during short-duration rainfall events. However, obtaining comprehensive data on sewer systems in developing countries is currently challenging. To mitigate this situation, we propose a method for developing a synthetic sewer network to supplement the representation of the sewer system in urban flood models, particularly in data-scarce domains. The model implements the concept of shallow water equation for surface flow and a 1D slot model for pipe flows with an interaction equation between them. We compare case studies with and without discharge interaction between the surface and hypothetical sewer system for the 2020 flood event in an urban subcatchment within the Marikina Basin, Philippines. Results show that the synthetic sewer pipe integration can capture the urban flood propagation more appropriately. Information such as flood depth and pipe flow discharge can aid in identifying flood-prone areas where sewer system parameters may require modifications. The proposed method can be used as an alternative to performing high-resolution urban flood simulations with limited availability of sewer network data.
Experimental study on the performance of the dielectric barrier discharge technique in the treatment of simulated groundwater with high iron and manganese content
Authors: Jin Yixin , Li Xuezhi , Ma Lida , Tian Wenjie , Zhang Linlin , Liu Aonan 
Published: October 2023

Abstract
Groundwater with high concentrations of iron and manganese is harmful to the human body and modern industries. Conventional methods of treating iron and manganese have the disadvantage of high costs and complex control processes. A dielectric barrier discharge (DBD) water treatment system was built to remove iron and manganese from groundwater in this paper. Single-factor experiments were used to analyze the effect of discharge power, initial iron and manganese concentrations, gas flow rate, liquid flow rate, initial pH, flocculant and capture agent process parameters on iron and manganese removal rates. It was found that Fe2+ removal rates can reach 97.31% and Mn2+ removal rates can reach 81.42% when the initial concentrations of Fe2+ and Mn2+ are 6 and 3 mg/L, respectively. The use of 10 mg/L NaHCO3 or 5 mg/L Ca(OH)2 increased the removal rate of Fe2+ to 99% but had no effect on the removal rate of Mn2+. It was found that the optimum treatment time for Mn2+ was different for different process parameters, mainly in the range of 9–15 min, which needs to be noted in future production applications. This work shows that DBD can effectively remove iron and manganese ions from groundwater and analyzes the removal mechanism.
A new approach for sensitivity analysis of the StormWater Management Model applied in an airport
Authors: Peng Jing , Zhao Hucheng , Luo Zihang , Ouyang Jie , Yu Lei 
Published: October 2023

Abstract

Sensitivity analysis of urban flood model parameters is important for efficient and accurate flood simulation. In order to explore the problems of large sampling parameters and nonlinear correlation between input and output variables, this paper proposed a new correlation analysis approach. The type, strength, and the order of sensitive parameters to the four outputs are analyzed using the proposed approach. The results show that the R values of Manning-N are biggest, its distribution is linear in heat maps, and the Manning-N has a strong linear correlation with Average Depth, Hour of Maximum Flooding, and Time to Peak. For Average Depth, the second sensitive parameter is Conductivity. For Hour of Maximum Flooding, the second and third more sensitive parameters are Conductivity and N-perv; however, there are certain nonlinear correlations from heat maps. For Total Inflow, the R values of each parameter are between 0.021 and 0.534. Most sensitive parameters are none; however, the more sensitive parameters are Conductivity, N-perv, and initial deficit. For Time to Peak, the second and third more sensitive parameters are N-perv and N-Imperv; however, there are certain non-linear correlations from heat maps. The results can provide theoretical guidance for application and parameter calibration of SWMM in airport.
A Novel Brevinin2 HYba5 Peptide against Polymicrobial Biofilm of
Staphylococcus aureus and Enterococcus faecalis
Authors: Radhakrishnan Megha Periyappilly , Suryaletha Karthika , Joseph Iype , George Sanil , Thomas Sabu 
Published: October 2023

Background:
Brevinin2 HYba5 (Peptide 29) is a novel cationic peptide identified from
an endemic frog, Hydrophylax bahuvistara. Staphylococcus aureus and Enterococcus faecalis are
troublesome biofilm-forming pathogens associated with nosocomial and community-acquired
infections and contribute to the severity of infections associated with implanted devices and chronic
wounds. Co-existence of both pathogens in biofilm mode contributes to an increased antibiotic
resistance, treatment failure and hence persistent disease burden. Identifying a novel and stable, less
toxic compound targeting multispecies biofilm with a lower probability of acquiring resistance in
comparison to antibiotics is highly warranted.

Objective:
Evaluate the activity of Brevinin2 HYba5 against S. aureus and E. faecalis mixed
biofilm.

Methods:
The anti-biofilm activity of peptide 29 was tested by Crystal violet assay, Confocal laser
scanning Microscopy (CLSM) and MTT Assay. Cytotoxicity of the peptide was tested in RBC and
L929 fibroblast cell line. Biofilm inhibitory activity of the peptide was evaluated at different
temperatures, pH, serum and plasma concentrations. The antibiofilm potential of the peptide was
tested against polymicrobial biofilm by Fluorescent in situ hybridisation (FISH) and plate counting
on HiCromeTM UTI Agar media.

Results:
The peptide 29 could inhibit biofilm formation of S. aureus and E. faecalis individually as
well as in polymicrobial biofilm at 75 μM concentration. The peptide maintained its antibiofilm
potential at different temperatures, serum and plasma concentrations. Activity of the peptide was
high at acidic and neutral pH but found to get reduced towards alkaline pH. The peptide is nonhemolytic
and does not exhibit significant cytotoxicity against the L929 fibroblast cell line (92.80%
cell viability).

Conclusion:
The biofilm inhibition property makes peptide 29 a promising candidate for the
management of S. aureus and E. faecalis biofilm, especially in catheter-associated devices to
prevent the initial colonization and thus can ease the burden of pathogenic biofilm-associated
infections.
Efficacy and Safety of Different Antibiotic Therapies for Bone and Joint Infections:
A Network Meta-analysis of Randomized Controlled Trials
Authors: Shi Xiangwen , Wu Yipeng , Ni Haonan , Guo Minzheng , Cheng Qi , Xu Yongqing 
Published: October 2023

Background::
Although an increasing number of antibiotics are being used to treat bone and joint
infections, their specific efficacy remains controversial. Thus, we aimed to systematically compare the efficacy
and safety of antibiotic therapies for orthopedic infections.

Methods::
PubMed, Embase, The Cochrane Library, and Web of Science databases were searched from inception
to April 2022. Two authors independently and rigorously conducted the screening, data extraction, and
quality assessment of the relevant studies. All the extracted data were evaluated using traditional metaanalysis
and network meta-analysis by STATA SE 16.0.

Results::
A total of eleven randomized controlled trials (RCTs) involving 1,063 patients were included for data
analysis. The analysis results from the NMA indicated that in terms of the clinical effectiveness rate, linezolid
(OR: 1.75, 95% CI: 1.01 to 3.02) showed significant efficacy compared to ampicillin/sulbactam. With regard
to the microbiological eradication rate, linezolid showed significant efficacy compared to cephalosporins (OR:
8.13, 95% CI: 1.16 to 57.09) and quinolones (OR: 3.51, 95% CI: 1.18 to 10.49). Similar findings were obtained
for subgroup populations with diabetic foot infections (DFI). However, linezolid was significantly related
to higher adverse events than ampicillin/sulbactam (OR: 3.25, 95% CI: 1.68 to 6.30) and cephalosporins
(OR: 18.29, 95% CI: 1.59 to 209.76).

Conclusion::
Linezolid appeared to be the most promising treatment regimen for staphylococcal bone and
joint infections. However, due to the overall limited evidence, the research results need further high-quality
RCTs for confirmation.
Gut Microbiota Exchange in Domestic Animals and Rural-urban People Axis
Authors: Nezhadi Javad , Ahangarzadeh Rezaee Mohammad , Asghari Ozma Mahdi , Ganbarov Khudaverdi , Samadi Kafil Hossein 
Published: October 2023

Abstract:
In recent years, one of the most critical topics in microbiology that can be addressed is
microbiome and microbiota. The term microbiome contains both the microbiota and structural
elements, metabolites/signal molecules, and the surrounding environmental conditions, and the
microbiota consists of all living members forming the microbiome. Among; the intestinal microbiota
is one of the most important microbiota, also called the gut microbiota. After colonization,
the gut microbiota can have different functions, including resistance to pathogens, maintaining
the intestinal epithelium, metabolizing dietary and pharmaceutical compounds, and controlling
immune function. Recently, studies have shown that the gut microbiota can prevent the formation
of fat in the body. In this study, we examined the gut microbiota in various animals, including
dogs, cats, dairy cows, sheep, chickens, horses, and people who live in urban and rural
areas. Based on the review of various studies, it has been determined that the population of microbiota
in animals and humans is different, and various factors such as the environment, nutrition,
and contact with animals can affect the microbiota of people living in urban and rural areas.
DLC-ac4C: A Prediction Model for N4-acetylcytidine Sites in Human
mRNA Based on DenseNet and Bidirectional LSTM Methods
Authors: Jia Jianhua , Cao Xiaojing , Wei Zhangying 
Published: October 2023

Introduction:
N4 acetylcytidine (ac4C) is a highly conserved nucleoside modification
that is essential for the regulation of immune functions in organisms. Currently, the identification
of ac4C is primarily achieved using biological methods, which can be time-consuming and laborintensive.
In contrast, accurate identification of ac4C by computational methods has become a more
effective method for classification and prediction.

Aim:
To the best of our knowledge, although there are several computational methods for ac4C locus
prediction, the performance of the models they constructed is poor, and the network structure
they used is relatively simple and suffers from the disadvantage of network degradation. This study
aims to improve these limitations by proposing a predictive model based on integrated deep learning
to better help identify ac4C sites.

Methods:
In this study, we propose a new integrated deep learning prediction framework, DLCac4C.
First, we encode RNA sequences based on three feature encoding schemes, namely C2 encoding,
nucleotide chemical property (NCP) encoding, and nucleotide density (ND) encoding. Second,
one-dimensional convolutional layers and densely connected convolutional networks
(DenseNet) are used to learn local features, and bi-directional long short-term memory networks
(Bi-LSTM) are used to learn global features. Third, a channel attention mechanism is introduced to
determine the importance of sequence characteristics. Finally, a homomorphic integration strategy
is used to limit the generalization error of the model, which further improves the performance of the
model.

Results:
The DLC-ac4C model performed well in terms of sensitivity (Sn), specificity (Sp), accuracy
(Acc), Mathews correlation coefficient (MCC), and area under the curve (AUC) for the independent
test data with 86.23%, 79.71%, 82.97%, 66.08%, and 90.42%, respectively, which was significantly
better than the prediction accuracy of the existing methods.

Conclusion:
Our model not only combines DenseNet and Bi-LSTM, but also uses the channel attention
mechanism to better capture hidden information features from a sequence perspective, and
can identify ac4C sites more effectively.
The Surging Mechanistic Role of Angiotensin Converting Enzyme 2 in
Human Pathologies: A Potential Approach for Herbal Therapeutics
Authors: Gupta Priyadarshini , Rani Vibha 
Published: October 2023

Abstract:
Advancements in biological sciences revealed the significant role of angiotensin-converting
enzyme 2 (ACE2), a key cell surface receptor in various human pathologies. ACE2 is a
metalloproteinase that not only functions in the regulation of Angiotensin II but also possesses
some non-catalytic roles in the human body. There is considerable uncertainty regarding its protein
expression, despite its presence in virtually all organs. The level of ACE2 expression and its
subcellular localisation in humans may be a key determinant of susceptibility to various infections,
symptoms, and outcomes of numerous diseases. Therefore, we summarize the distribution
and expression pattern of ACE2 in different cell types related to all major human tissues and organs.
Moreover, this review constitutes accumulated evidences of the important resources for further
studies on ACE2 Inhibitory capacity via different natural compounds in order to understand
its mechanism as the potential drug target in disease pathophysiology and to aid in the development
of an effective therapeutic approach towards the various diseases.
Finding Integrative Medication for Neuroblastoma and Glioblastoma
through Zebrafish as A Model of Organism
Authors: Barati Mohammad , Modarresi Chahardehi Amir , Hosseini Yasaman 
Published: October 2023

Abstract:
As far as malignant tumors of the central nervous system are concerned, glioblastoma
(GB) and neuroblastoma (NB) are the most prevalent, aggressive, and fatal forms in adult and pediatric
populations, respectively. NB is the most prominent childhood extracranial compact neoplasm
in pediatrics when the embryo develops from undifferentiated neural crest cells. Regarding
malignant primary brain tumors, GB is the most lethal and difficult to treat. Currently, there are
few effective treatments available for either condition. Research using zebrafish is relatively new
in the field of animal cancer studies, and the first results show promise. In particular, integrated genomic
investigations of NB and GB have revealed the potential of the zebrafish model in elucidating
the roles of specific genetic changes in the development of this fatal childhood malignancy.
Hence, this study examines the possibility of zebrafish as a model organism for discovering integrative
medicines for these types of cancer. This model is an excellent animal model for study due
to its transparency, ease of genetic modification, ethics and financial benefits, and preservation of
the primary brain areas andbloodbrain barrier (BBB). This review provides recent developments
in the zebrafish model of NB and GB to illustrate the benefits of using them in cancer studies as a
model of the organism. This approach provides novel insights into delivering individualized treatment
and enhancing outcomes for people coping with central nervous system malignancies.