bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive value of ACEF II score for adverse prognosis in patients with coronary heart disease after percutaneous coronary intervention
Authors: Wu Siyi , Qiu Zhiqing , Lu Yaqiong , Wu Xiaoguang , Gao Ting , Xu Jie , Li Shuangshuang , Zhou Xiang
Published: March 2022

Objective

To investigate the predictive value of age, creatinine and ejection fraction (ACEF) II score for the incidence of major adverse cardiovascular and cerebrovascular events (MACCEs) in patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI).

Methods

A total of 445 patients with CHD who underwent PCI were consecutively enrolled. The receiver operating characteristic (ROC) curve was used to analyse the power of the ACEF II score in predicting MACCE. Kaplan-Meier survival curves and log-rank tests were chosen for survival analysis of adverse prognosis between groups. Finally, multivariate Cox proportional risk regression analysis was used to investigate independent risk factors for MACCEs in patients with CHD after PCI.

Results

There was a significantly higher incidence of MACCEs in patients with high ACEF II scores. The area under the ROC curve of ACEF II score was 0.718, suggesting it had ideal predictive value for MACCE risks. The ACEF II score had a best cut-off value of 1.461 (sensitivity 79.4%, specificity 53.7%). Survival analysis indicated that patients in the high-score group had a significantly lower cumulative MACCE-free survival rate. Multivariate Cox regression analysis showed that ACEF II scores ≥1.461, Gensini scores ≥61.5, age, cardiac troponin I and previous PCI were independent risk factors of MACCE in patients with CHD after PCI, while the utilisation of statins was an independent protective factor.

Conclusions

The ACEF II score has an ideal capacity for risk stratification in patients with CHD undergoing PCI and offers good predictive value for MACCE in the long term.
Telescope array bursts, radio pulses and axion quark nuggets
Authors: Liang Xunyu , Zhitnitsky Ariel
Published: March 2022

Telescope array (TA) experiment has recorded Abbasi et al. (Phys Lett A 381(32):2565–2572, 10.1016/j.physleta.2017.06.022, 2017), Okuda (J Phys Conf Ser 1181:012067, 10.1088/1742-6596/1181/1/012067, 2019) several short time bursts of air shower like events. These bursts are very distinct from conventional single showers, and are found to be strongly correlated with lightnings. In our previous work Zhitnitsky (J Phys G 48(6):065201, 10.1088/1361-6471/abd457, arXiv:2008.04325 [hep-ph], 2021) we proposed that these bursts represent the direct manifestation of the dark matter (DM) annihilation events within the so-called axion quark nugget (AQN) model. In the present work we suggest to test this proposal to search for the radio signals in frequency band $$\nu \in $$

ν
∈

(0.5–200) MHz which must be synchronized with the TA bursts. We argued that the conventional lightning-induced radio emission can be easily discriminated from the AQN-induced radio pulses discussed in this work.
Brexit and bots: characterizing the behaviour of automated accounts on Twitter during the UK election
Authors: Bruno Matteo , Lambiotte Renaud , Saracco Fabio
Published: March 2022

Online Social Networks (OSNs) offer new means for political communications that have quickly begun to play crucial roles in political campaigns, due to their pervasiveness and communication speed. However, the OSN environment is quite slippery and hides potential risks: many studies presented evidence about the presence of d/misinformation campaigns and malicious activities by genuine or automated users, putting at severe risk the efficiency of online and offline political campaigns. This phenomenon is particularly evident during crucial political events, as political elections. In the present paper, we provide a comprehensive description of the networks of interactions among users and bots during the UK elections of 2019. In particular, we focus on the polarised discussion about Brexit on Twitter, analysing a data set made of more than 10 millions tweets posted for over a month. We found that the presence of automated accounts infected the debate particularly in the days before the UK national elections, in which we find a steep increase of bots in the discussion; in the days after the election day, their incidence returned to values similar to the ones observed few weeks before the elections. On the other hand, we found that the number of suspended users (i.e. accounts that were removed by the platform for some violation of the Twitter policy) remained constant until the election day, after which it reached significantly higher values. Remarkably, after the TV debate between Boris Johnson and Jeremy Corbyn, we observed the injection of a large number of novel bots whose behaviour is markedly different from that of pre-existing ones. Finally, we explored the bots’ political orientation, finding that their activity is spread across the whole political spectrum, although in different proportions, and we studied the different usage of hashtags and URLs by automated accounts and suspended users, targeting the formation of common narratives in different sides of the debate.
Quantifying Differences Between Affine and Nonlinear Spatial Normalization of FP-CIT Spect Images
Authors: Castillo-Barnes Diego , Jimenez-Mesa Carmen , Martinez-Murcia Francisco J. , Salas-Gonzalez Diego , Ramírez Javier , Górriz Juan M.
Published: March 2022

Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson’s Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.
On normalized Laplacian eigenvalues of power graphs associated to finite cyclic groups
Authors: Rather Bilal A. , Pirzada S. , Chishti T. A. , Alghamdi Ahmad M.
Published: March 2022

For a simple connected graph G of order n, the normalized Laplacian is a square matrix of order n, defined as [Formula: see text], where [Formula: see text] is the diagonal matrix whose i-th diagonal entry is [Formula: see text]. In this paper, we find the normalized Laplacian eigenvalues of the joined union of regular graphs in terms of the adjacency eigenvalues and the eigenvalues of quotient matrix associated with graph G. For a finite group [Formula: see text], the power graph [Formula: see text] of a group [Formula: see text] is defined as the simple graph in which two distinct vertices are joined by an edge if and only if one is the power of the other. As a consequence of the joined union of graphs, we investigate the normalized Laplacian eigenvalues of the power graphs of the finite cyclic group [Formula: see text].
Systematic Review of Literature Documenting the Link between BMI, Sperm Parameters, and Leptin
Authors: Almabhouh Fayez A. , Singh Harbindar Jeet
Published: March 2022

Background: Leptin administration to rats causes adverse effects on sperm that resemble those reported in obese males. The purpose of this review is, therefore, to examine whether human and animal studies have taken this information on board and have attempted to link the poor sperm quality in obese or overweight individuals to leptin in their methodology, data analysis, and discussion. The review also documents the link between low sperm quality in the obese and leptin.

Method: A search was made for articles reporting body mass index (BMI), sperm parameters, and leptin, published until September 2020 in PubMed, Science Direct, Google Scholar, and Springer Link using a number of search terms.

Results: Forty-four of the 53 human and animal studies on BMI and sperm parameters reported abnormal sperm parameters in the obese but only 9 of those had measured serum leptin. None, however, attempted to link sperm abnormalities to leptin. Nine animal studies report of adverse effects of leptin on sperm in normal weight rats that were found to be similar to those reported in obese males.

Conclusion: Although none of the human studies had actually linked the raised leptin to sperm abnormalities in the obese, there is nevertheless, convincing evidence linking leptin to obesity-related poor sperm quality and male infertility. There is a need, therefore, to include measures that will help reduce the impact of raised leptin levels on male reproductive function, particularly, in obese infertile or subfertile males.
COVID-19 and Maternal Mental Health and Wellbeing
Authors: Buhagiar Rachel
Published: March 2022

The impact of the coronavirus infection (COVID-19) on the physical health of individuals has been receiving widespread attention, especially for high risk segments of the population, including pregnant women, new mothers and their babies. However, equally important are the psychological consequences of the pandemic. A growing body of evidence supports the notion that this global pandemic translates into a massive source of maternal stressful agents causing psychological burden for some, which may further lead to mental health issues. Indeed, perinatal mental health disorders, such as depression and anxiety, are increasingly recognized as concerning effects of the COVID-19 outbreak, with a higher prevalence rate compared to the pre-pandemic period being reported. Additionally, an increase in self-harming thoughts and deaths by suicide during lockdown have also been described amongst perinatal women. This paper provides an overview of evidence-based data on perinatal mental health outcomes during this global pandemic. The catastrophic circumstances related to these unprecedented times and how these are shaping the mental health of pregnant and postnatal women will be outlined, as well as the impact the latter might have on the caregiving quality and child developmental outcomes. Consequently, the importance of prioritizing perinatal mental health in the current and ongoing pandemic is highlighted.
The Effect of Audio-Visual Smiles on Social Influence in a Cooperative Human–Agent Interaction Task
Authors: Torre Ilaria , Carrigan Emma , Domijan Katarina , McDonnell Rachel , Harte Naomi
Published: March 2022

Emotional expressivity is essential for human interactions, informing both perception and decision-making. Here, we examine whether creating an audio-visual emotional channel mismatch influences decision-making in a cooperative task with a virtual character. We created a virtual character that was either congruent in its emotional expression (smiling in the face and voice) or incongruent (smiling in only one channel). People (N = 98) evaluated the character in terms of valence and arousal in an online study; then, visitors in a museum played the “lunar survival task” with the character over three experiments (N = 597, 78, 101, respectively). Exploratory results suggest that multi-modal expressions are perceived, and reacted upon, differently than unimodal expressions, supporting previous theories of audio-visual integration.
Gray-Box Shilling Attack: An Adversarial Learning Approach
Authors: Wang Zongwei , Gao Min , Li Jundong , Zhang Junwei , Zhong Jiang
Published: March 2022

Recommender systems are essential components of many information services, which aim to find relevant items that match user preferences. Several studies have shown that shilling attacks can significantly weaken the robustness of recommender systems by injecting fake user profiles. Traditional shilling attacks focus on creating hand-engineered fake user profiles, but these profiles can be detected effortlessly by advanced detection methods. Adversarial learning, which has emerged in recent years, can be leveraged to generate powerful and intelligent attack models. To this end, in this article we explore potential risks of recommender systems and shed light on a gray-box shilling attack model based on generative adversarial networks, named
GSA-GANs
. Specifically, we aim to generate fake user profiles that can achieve two goals: unnoticeable and offensive. Toward these goals, there are several challenges that we need to address: (1) learning complex user behaviors from user-item rating data, and (2) adversely influencing the recommendation results without knowing the underlying recommendation algorithms. To tackle these challenges, two essential GAN modules are respectively designed to make generated fake profiles more similar to real ones and harmful to recommendation results. Experimental results on three public datasets demonstrate that the proposed GSA-GANs framework outperforms baseline models in attack effectiveness, transferability, and camouflage. In the end, we also provide several possible defensive strategies against GSA-GANs. The exploration and analysis in our work will contribute to the defense research of recommender systems.
SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection
Authors: Jiang Xue , Zhou Xuebing , Grossklags Jens
Published: March 2022

Federated Learning (FL) [
31
] is a decentralized learning mechanism that has attracted increasing attention due to its achievements in computational efficiency and privacy preservation. However, recent research highlights that the original FL framework may still reveal sensitive information of clients’ local data from the exchanged local updates and the global model parameters. Local Differential Privacy (LDP), as a rigorous definition of privacy, has been applied to Federated Learning to provide formal privacy guarantees and prevent potential privacy leakage. However, previous LDP-FL solutions suffer from considerable utility loss with an increase of model dimensionality. Recent work [
29
] proposed a two-stage framework that mitigates the dimension-dependency problem by first selecting one “important” dimension for each local update and then perturbing the dimension value to construct the sparse privatized update. However, the framework may still suffer from utility loss because of the insufficient per-stage privacy budget and slow model convergence.

In this article, we propose an improved framework,
SignDS-FL
, which shares the concept of dimension selection with Reference [
29
], but saves the privacy cost for the value perturbation stage by assigning random sign values to the selected dimensions. Besides using the single-dimension selection algorithms in Reference [
29
], we propose an Exponential Mechanism-based Multi-Dimension Selection algorithm that further improves model convergence and accuracy. We evaluate the framework on a number of real-world datasets with both simple logistic regression models and deep neural networks. For training logistic regression models on structured datasets, our framework yields only a

\( \sim \)

1%–2% accuracy loss in comparison to a

\( \sim \)

5%–15% decrease of accuracy for the baseline methods. For training deep neural networks on image datasets, the accuracy loss of our framework is less than

\( 8\% \)

and at best only

\( 2\% \)

. Extensive experimental results show that our framework significantly outperforms the previous LDP-FL solutions and enjoys an advanced utility-privacy balance.
CLC: A Consensus-based Label Correction Approach in Federated Learning
Authors: Zeng Bixiao , Yang Xiaodong , Chen Yiqiang , Yu Hanchao , Zhang Yingwei
Published: March 2022

Federated learning (FL)
is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants’ side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a
Consensus-based Label Correction approach (CLC)
in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is
https://github.com/bixiao-zeng/CLC.git
.
A Review on Source Code Documentation
Authors: Rai Sawan , Belwal Ramesh Chandra , Gupta Atul
Published: March 2022

Context:
Coding is an incremental activity where a developer may need to understand a code before making suitable changes in the code. Code documentation is considered one of the best practices in software development but requires significant efforts from developers. Recent advances in natural language processing and machine learning have provided enough motivation to devise automated approaches for source code documentation at multiple levels.

Objective:
The review aims to study current code documentation practices and analyze the existing literature to provide a perspective on their preparedness to address the stated problem and the challenges that lie ahead.

Methodology:
We provide a detailed account of the literature in the area of automated source code documentation at different levels and critically analyze the effectiveness of the proposed approaches. This also allows us to infer gaps and challenges to address the problem at different levels.

Findings:
(1) The research community focused on method-level summarization. (2) Deep learning has dominated the past five years of this research field. (3) Researchers are regularly proposing bigger corpora for source code documentation. (4) Java and Python are the widely used programming languages as corpus. (5) Bilingual Evaluation Understudy is the most favored evaluation metric for the research persons.
INN: An Interpretable Neural Network for AI Incubation in Manufacturing
Authors: Chen Xiaoyu , Zeng Yingyan , Kang Sungku , Jin Ran
Published: March 2022

Both artificial intelligence (AI) and domain knowledge from human experts play an important role in manufacturing decision making. Smart manufacturing emphasizes a fully automated data-driven decision-making; however, the AI incubation process involves human experts to enhance AI systems by integrating domain knowledge for modeling, data collection and annotation, and feature extraction. Such an AI incubation process not only enhances the domain knowledge discovery but also improves the interpretability and trustworthiness of AI methods. In this article, we focus on the knowledge transfer from human experts to a supervised learning problem by learning domain knowledge as interpretable features and rules, which can be used to construct rule-based systems to support manufacturing decision making, such as process modeling and quality inspection. Although many advanced statistical and machine learning methods have shown promising modeling accuracy and efficiency, rule-based systems are still highly preferred and widely adopted due to their interpretability for human experts to comprehend. However, most of the existing rule-based systems are constructed based on deterministic human-crafted rules, whose parameters, such as thresholds of decision rules, are suboptimal. Yet the machine learning methods, such as tree models or neural networks, can learn a decision rule based structure without much interpretation or agreement with domain knowledge. Therefore, the traditional machine learning models and human experts’ domain knowledge cannot be directly improved by learning from data. In this research, we propose an interpretable neural network (INN) model with a center-adjustable sigmoid activation function to efficiently optimize the rule-based systems. Using the rule-based system from domain knowledge to regulate the INN architecture not only improves the prediction accuracy with optimized parameters but also ensures the interpretability by adopting the interpretable rule-based systems from domain knowledge. The proposed INN will be effective for supervised learning problems when rule-based systems are available. The merits of the INN model are demonstrated via a simulation study and a real case study in the quality modeling of a semiconductor manufacturing process. The source code of this work is hosted here:
https://github.com/XiaoyuChenUofL/Interpretable-Neural-Network
.
Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning
Authors: Zhang Lin , Fan Lixin , Luo Yong , Duan Ling-Yu
Published: March 2022

The rapid development of modern artificial intelligence technique is mainly attributed to sufficient and high-quality data. However, in the data collection, personal privacy is at risk of being leaked. This issue can be addressed by federated learning, which is proposed to achieve efficient model training among multiple data providers without direct data access and aggregation. To encourage more parties owning high-quality data to participate in the federated learning, it is important to evaluate and reward the participant contribution in a reasonable, robust, and efficient manner. To achieve this goal, we propose a novel contribution estimation method: Intrinsic Performance Influence-based Contribution Estimation (IPICE). In particular, the class-level intrinsic performance influence is adopted as the contribution estimation criteria in IPICE, and a neural network is employed to exploit the non-linear relationship between the performance change and estimated contribution. Extensive experiments are conducted on various datasets, and the results demonstrate that IPICE is more accurate and stable than the counterpart in various data distribution settings. The computational complexity is significantly reduced in our IPICE, especially when a new party joins the federation. IPICE assigns small contributions to bad/garbage data and thus prevent them from participating and deteriorating the learning ecosystem.
Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform
Authors: Li Ke , Guo Bin , Liu Jiaqi , Wang Jiangtao , Ren Haoyang , Yi Fei , Yu Zhiwen
Published: March 2022

Recently,fake newshas been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about the dynamic evolution of crowd opinions, and usually neglect the issue of uneven arrival of data in the real world. To address these issues, in this article, we focus on a kind of approach for fake news detection, namelyprogressive detection, which can be achieved by thedynamic Probabilistic Graphical Model. Based on the observation on real-world datasets, we adaptively improve the Kalman Filter to theLabeled Variable Dimension Kalman Filter(LVDKF) that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly. It can take sequential data as input, distill the dynamic evolution knowledge regarding a post, and utilize crowd wisdom from users’ responses to achieve progressive detection. Then we derive the formulas using the Forward, Backward, and EM Algorithm, and we design a dynamic detection algorithm using Bayes’ theorem. Finally, we design experimental scenarios simulating progressive detection and evaluate LVDKF on two public datasets. It outperforms the baseline methods in these experimental scenarios, which indicates that it is adequate for progressive detection.
Theoretical Analysis of Critical Conditions for Crack Formation and Propagation, and Optimal Operation of SOECs
Authors: Wang Yudong , Virkar Anil V. , Khonsari M. M. , Zhou Xiao-Dong
Published: March 2022

A theoretical analysis on crack formation and propagation was performed based on the coupling between the electrochemical process, classical elasticity, and fracture mechanics. The chemical potential of oxygen, thus oxygen partial pressure, at the oxygen electrode-electrolyte interface (

μ

O

2

OE∣El

) was investigated as a function of transport properties, electrolyte thickness and operating conditions (e.g., steam concentration, constant current, and constant voltage). Our analysis shows that: a lower ionic area specific resistance (ASR),

r

i

O
E

,

and a higher electronic ASR (

r

e

O
E

) of the oxygen electrode/electrolyte interface are in favor of suppressing crack formation. The

μ

O

2

O
E
∣
E
l

,

thus local pO2, are sensitive towards the operating parameters under galvanostatic or potentiostatic electrolysis. Constant current density electrolysis provides better robustness, especially at a high current density with a high steam content. While constant voltage electrolysis leads to greater variations of

μ

O

2

O
E
∣
E
l

.

Constant current electrolysis, however, is not suitable for an unstable oxygen electrode because

μ

O

2

O
E
∣
E
l

can reach a very high value with a gradually increased

r

i

O
E

.

A crack may only occur under certain conditions when

p

O

2

T
P
B

>

p

c
r

.
Enhancement of Anodically Treated Stainless Steel by NiFeP-Catalyst Electrodeposition as Bifunctional Electrodes for Water Electrolysis
Authors: Cartagena Santiago , Bedoya-Lora Franky E. , Calderón Jorge A.
Published: March 2022

Electrochemical water splitting, using bifunctional and easily scalable electrodes made of abundant materials, is one of the most important milestones towards the storage of intermittent energy generated from renewable sources. In this work, we demonstrated the synthesis of amorphous NiFeP catalysts for H2 and O2 evolution using a very simple and scalable electrodeposition method. Corrosion resistant stainless steel was used as substrate, which is subject of improvement by a facile anodic treatment in an electrolyte comprised of sulfuric acid and glycerol. This treatment increased the surface area approx. six-fold by dissolving parts of the stainless steel substrate. Electrodes with NiFeP catalyst supported on pretreated substrates reported overpotentials of 305 and 210 mV at ∣10∣ mA cm−2 for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), respectively. The electrodes were stable for 24 h at a current density of 400 mA cm−2, which resembles real operation for alkaline electrolyzers, with a slight increase in the overpotentials due to the accumulation of bubbles at the surface.
Construction of a Health Management Model for Early Identification of Ischaemic Stroke in Cloud Computing
Authors: Yang Yuying , Chang Qing , Chen Jing , Zhou Xiangkun , Xue Qian , Song Aixia
Published: March 2022

Knowledge discovery and cloud computing can help early identification of ischaemic stroke and provide intelligent, humane, and preventive healthcare services for patients at high risk of stroke. This study proposes constructing a health management model for early identification and warning of ischaemic stroke based on IoT and cloud computing, and discusses its connotation, constructive ideas, and research content so as to provide reference for its health management in order to develop and implement countermeasures and to compare the awareness of early stroke symptoms and first aid knowledge among stroke patients and their families before and after the activity. The rate of awareness of early symptoms and first aid among stroke patients and their families increased from 36% to 78%, and the difference was statistically significant (

P
<
0.05

) before and after the activity.
Parking Permit Scheme for Morning Commute considering Parking Search
Authors: Xu Duo , Sun Huijun
Published: March 2022

Improving parking efficiency is essential to promoting the reform in urban transportation. But the large amount of deadweight costs caused by the parking is often underestimated because it is difficult to measure. Based on the existing investigations from the small fraction of cruising vehicles, this paper explores the influencing factors of the parking issue and describes it by the user equilibrium model. Then, two types of permit management schemes were proposed, lot-based and spot-based. By analyzing their performance in reducing system cost, three conclusions were drawn. Firstly, parking search leads to traveler’s schedule and location adjustments, raises the trip cost, reduces the parking lot occupancy, and makes the parking issues “invisible.” Secondly, permit scheme levels up managers’ control, and it performs well in reducing deadweight loss, but only by eliminating the search cost, the deadweight loss can be fundamentally reduced. Thirdly, reducing parking search needs information guidance; with the rapid growth of urban parking demand, managers should make a transition to the permit scheme with parking information.
Effect of Uterine Artery Ligation and Uterine Artery Embolization on Postpartum Hemorrhage Due to Uterine Asthenia after Cesarean Section and Its Effect on Blood Flow and Function of Uterine and Ovarian Arteries
Authors: Liu Wufen , Yin Wei
Published: March 2022

Objective. To investigate the efficacy of uterine artery ligation (UAL) and uterine artery embolization (UAE) in the treatment of uterine asthenia postpartum hemorrhage (PPH) after cesarean section and its effect on uterine and ovarian artery blood flow and function. Methods. 100 patients with uterine asthenia PPH after cesarean section in our hospital from January 2018 to November 2020 were randomly divided into 50 cases in the UAL group and 50 cases in the UAE group. They were followed up for 12 months. The bleeding volume, operation time, immediate hemostasis rate, and hemostasis effective rate; lochia clearance time and menstrual rehydration time; RI and S/D; and serum FSH, E2, and LH levels were compared between the two groups. Results. Compared with the UAL group, the amount of bleeding in the UAE group was significantly increased and the operation time was significantly shortened (p<0.05). There was no significant difference in the immediate hemostatic rate and hemostatic effective rate between the two groups (p>0.05). There was no significant difference in lochia clearance time and menstrual rehydration time between the two groups (p>0.05). There was no significant difference in RI and S/D between the two groups (p>0.05). Compared with before the operation, the levels of FSH and LH in the two groups decreased significantly, and the level of E2 increased significantly (p<0.05). There was no significant difference between the two groups (p>0.05). Conclusion. The efficacy of UAL and UAE in the treatment of PPH with uterine asthenia after cesarean section and its effect on the blood flow and function of uterine and ovarian arteries are equivalent, but the amount of bleeding in UAL is less and the operation time of UAE is shorter. The appropriate operation method can be selected according to the actual situation.
A Target Recognition Method Based on Multiview Infrared Images
Authors: Zhang Junyi , Rao Yuan
Published: March 2022

Infrared image target recognition provides an important means of night traffic management and battlefield environment monitoring. With the improvement of the performance of infrared sensors and the popularization of applications, it becomes possible to obtain multiview infrared images of the same target in the same scene. A target recognition method combining multiview infrared images is proposed. At first, the internal correlation analysis of multiview infrared images is performed based on the nonlinear correlation information entropy (NCIE). The view subset from all the multiview images with the largest NCIE is selected as candidate samples for the subsequent target recognition. The joint sparse representation (JSR) is used to classify all infrared images in the candidate view subset. JSR can effectively investigate the internal correlation of multiple related sparse representation problems and improve the reconstruction accuracy and classification capabilities. In the experiments, the tests are performed on the collected infrared images of multiple types of traffic vehicles, under the conditions of original, noisy, and occluded samples. The effectiveness and robustness of the proposed method can be verified by comparative analysis.
A Fast Hybrid Classification Algorithm with Feature Reduction for Medical Images
Authors: Mahmoud Hanan Ahmed Hosni , AlArfaj Abeer Abdulaziz , Hafez Alaaeldin M.
Published: March 2022

In this paper, we are introducing a fast hybrid fuzzy classification algorithm with feature reduction for medical images. We incorporated the quantum-based grasshopper computing algorithm (QGH) with feature extraction using fuzzy clustering technique (

C

-means). QGH integrates quantum computing into machine learning and intelligence applications. The objective of our technique is to the integrate QGH method, specifically into cervical cancer detection that is based on image processing. Many features such as color, geometry, and texture found in the cells imaged in Pap smear lab test are very crucial in cancer diagnosis. Our proposed technique is based on the extraction of the best features using a more than 2600 public Pap smear images and further applies feature reduction technique to reduce the feature space. Performance evaluation of our approach evaluates the influence of the extracted feature on the classification precision by performing two experimental setups. First setup is using all the extracted features which leads to classification without feature bias. The second setup is a fusion technique which utilized QGH with the fuzzy C-means algorithm to choose the best features. In the setups, we allocate the assessment to accuracy based on the selection of best features and of different categories of the cancer. In the last setup, we utilized a fusion technique engaged with statistical techniques to launch a qualitative agreement with the feature selection in several experimental setups.
Effects of Cannabidiol on Parkinson’s Disease in a Transgenic Mouse Model by Gut-Brain Metabolic Analysis
Authors: Zhao Jinchuan , Gao Xin , Zhao Liangyou , Wang Yiqing , Zhang Jingbo , Liu Shumin
Published: March 2022

Parkinson’s disease (PD) is a common neurodegenerative disease characterized by a disorder of the dopaminergic system in the midbrain, causing classical PD motor symptoms. The therapeutic effect of cannabidiol (CBD) on PD has been a research frontier in recent years. However, the pathogenesis of PD and the therapeutic mechanism of cannabinoid remain unclear. To further study the causes of PD and the effect of CBD on PD, we exposed the PD transgenic mouse model to CBD and then estimated the motorial and postural coordination through a modified swim test. Afterwards, the mechanism was investigated via the histopathology of substantia nigra and the gut-brain metabolic analysis in the approach of UHPLC-TOF-MS. The results showed that CBD significantly improved motor deficits of PD model and protected the substantia nigra area. The metabolic function of fatty acid biosynthesis, arginine biosynthesis/metabolism, butanoate (ketone body) metabolism, β-alanine metabolism, and pantothenate/CoA biosynthesis was highlighted in the pathological and therapeutic process along the gut-brain axis. In conclusion, CBD could attenuate PD via the neuroprotective effect on the midbrain. The attenuation of the central nervous system in turn improved motor performance of PD, which might be partially induced by the metabolic interaction between the gut-brain. In view of gut-brain metabolomics, the mechanism of PD pathogenesis and the effect of CBD on PD are highly related to the biosynthesis and metabolism of energy and essential substance.
Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets
Authors: Mishra Shubha , Shukla Piyush , Agarwal Ratish
Published: March 2022

Fake news, or fabric which appeared to be untrue with point of deceiving the open, has developed in ubiquity in current a long time. Spreading this kind of data undermines societal cohesiveness and well by cultivating political division and doubt in government. Since of the sheer volume of news being disseminated through social media, human confirmation has ended up incomprehensible, driving to the improvement and arrangement of robotized strategies for the recognizable proof of wrong news. Fake news publishers use a variety of stylistic techniques to boost the popularity of their works, one of which is to arouse the readers’ emotions. Due to this, text analytics’ sentiment analysis, which determines the polarity and intensity of feelings conveyed in a text, is now being utilized in false news detection methods, as either the system’s foundation or as a supplementary component. This assessment analyzes the full explanation of false news identification. The study also emphasizes characteristics, features, taxonomy, different sorts of data in the news, categories of false news, and detection approaches for spotting fake news. This research recognized fake news using the probabilistic latent semantic analysis approach. In particular, the research describes the fundamental theory of the related work to provide a deep comparative analysis of various literature works that has contributed to this topic. Besides this, a comparison of different machine learning and deep learning techniques is done to assess the performance for fake news detection. For this purpose, three datasets have been used.
Two Mixed-Ligand Cu(II) Polymers: Treatment Activity on Acute Cerebral Infarction Combined with Alteplase
Authors: Liu Wen-Ping , Xue Tao , Li Yangchun , Cui Ruoyu
Published: March 2022

With the reaction between Cu(NO3)2·3H2O and 4-(2

′

,3

′

-dicarboxylphenoxy) benzoic acid (H3L), a semirigid trichromatic acid ligand, in the presence of N-donor co-ligand 1,2-bis(4-pyridyl) ethylene (bpe), two novel mixed-ligand coordination polymers (CPs), i.e., [Cu(H2L)2(bpe)(H2O)2]bpe·(H2O)4 (1) and [Cu3(L)2(bpe)3(H2O)4]·(H2O)6 (2), have been created under different solvent systems. Furthermore, the biological activity of compounds was evaluated, and the related mechanism was explored as well. Firstly, the enzyme-linked immunosorbent assay (ELISA) was conducted to measure the content of coagulation factors in the serum after compound treatment. In addition to this, the real-time reverse transcription-polymerase chain reaction (RT-PCR) assay was conducted, and the inflammatory response in the brain was also determined.
Advanced Cognitive Algorithm for Biomedical Data Processing: COVID-19 Pattern Recognition as a Case Study
Authors: Elhoseny Mohamed , Tarek Zahraa , EL-Hasnony Ibrahim M.
Published: March 2022

Automated disease prediction has now become a key concern in medical research due to exponential population growth. The automated disease identification framework aids physicians in diagnosing disease, which delivers accurate disease prediction that provides rapid outcomes and decreases the mortality rate. The spread of Coronavirus disease 2019 (COVID-19) has a significant effect on public health and the everyday lives of individuals currently residing in more than 100 nations. Despite effective attempts to reach an appropriate trend to forecast COVID-19, the origin and mutation of the virus is a crucial obstacle in the diagnosis of the detected cases. Even so, the development of a model to forecast COVID-19 from chest X-ray (CXR) and computerized tomography (CT) images with the correct decision is critical to assist with intelligent detection. In this paper, a proposed hybrid model of the artificial neural network (ANN) with parameters optimization by the butterfly optimization algorithm has been introduced. The proposed model was compared with the pretrained AlexNet, GoogLeNet, and the SVM to identify the publicly accessible COVID-19 chest X-ray and CT images. There were six datasets for the examinations: three datasets with X-ray pictures and three with CT images. The experimental results approved the superiority of the proposed model for cognitive COVID-19 pattern recognition with average accuracy 90.48, 81.09, 86.76, and 84.97% for the proposed model, support vector machine (SVM), AlexNet, and GoogLeNet, respectively.
Design and Implementation of an Integrated Management System for Backfill Experimental Data
Authors: Kou Yunpeng , Liu Yuhang , Li Guoqing , Hou Jie , Luan Liming , Wang Hao
Published: March 2022

With the increase in environmental awareness worldwide, the filling mining method has attracted extensive attention because this method can realize safe and green mining in underground metal mines. Recycling waste tailings in stopes to control underground rock movement, and surface settlement can reduce waste environmental pollution while ensuring mining safety. Although many test data are required to support the formulation of the mine backfill scheme, the advanced management tool of backfill test data is insufficient. In this study, a new data management method that is suitable for backfill experiments is proposed. First, this study analyses the main system requirements, including experimental business process modeling, experimental process combing, and a multidimensional query of experimental data. Then, the backfill test business flow and data flow are summarized to establish the backfill test business model and experimental index system. Then, many system functions are designed, including backfill experiment management, experimental data query, backfill knowledge maintenance, and system management. Finally, a backfill test data management system is developed based on B/S architecture. Developing a data interface, having a built‐in test formula and customizing a multidimensional data analysis enable the system to solve the problems in data collection, data accounting, and data analysis. After being put into use in the Backfilling Engineering Laboratory of a group in Shandong, this system improved the data‐sharing rate and utilization rate and provided a convenient data management tool for the laboratory.
Bioconversion of Keratin Wastes Using Keratinolytic Microorganisms to Generate Value-Added Products
Authors: Anbesaw Muhammed Seid
Published: March 2022

The management of keratinous wastes generated from different industries is becoming a major concern across the world. In each year, more than a billion tons of keratin waste is released into the environment. Despite some trials that have been performed and utilize this waste into valuable products, still a huge amount of keratin waste from different sources is a less explored biomaterial for making valuable products. This indicates that the huge amount of keratin waste is neither disposed properly nor converted into usable products rather thrown away to the environment that causes environmental pollution. Due to the introduction of this waste associated with different pathogenic organisms into soil and water bodies, human beings and other small and large animals are affected by different diseases. Therefore, there is a need for modern and ecofriendly approaches to dispose and convert this waste into usable products. Hence, the objective of this review is to give a concise overview regarding the degradation of keratin waste by biological approaches using keratinase producing microorganisms. The review also focuses on the practical use of keratinases and the economical importance of bioconverted products of keratinous wastes for different applications. Various researches have been studied about the source, disposal mechanisms, techniques of hydrolysis, potential use, and physical and chemical properties of keratin wastes. However, there is negligible information with regard to the use of keratin wastes as media supplements for the growth of keratinolytic microorganisms and silver retrieval from photographic and used X-ray films. Hence, this review differs from other similar reviews in the literature in that it discusses these neglected concerns.
Deep Learning-Based Classification for Melanoma Detection Using XceptionNet
Authors: Lu Xinrong , Firoozeh Abolhasani Zadeh Y. A.
Published: March 2022

Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 new cases were found in 2018. While cancer diagnosis is based on interventional methods such as surgery, radiotherapy, and chemotherapy, studies show that the use of new computer technologies such as image processing mechanisms in processes related to early diagnosis of this cancer can help the physicians heal this cancer. This paper proposes an automatic method for diagnosis of skin cancer from dermoscopy images. The proposed model is based on an improved XceptionNet, which utilized swish activation function and depthwise separable convolutions. This system shows an improvement in the classification accuracy of the network compared to the original Xception and other dome architectures. Simulations of the proposed method are compared with some other related skin cancer diagnosis state-of-the-art solutions, and the results show that the suggested method achieves higher accuracy compared to the other comparative methods.
Threshold Dynamics for a Time-Periodic Viral Infection Model with Cell-to-Cell Transmission and Drug Treatments
Authors: Xu Jinhu , Huang Guokun
Published: March 2022

In this study, a time-periodic viral infection model incorporating cell-to-cell infection and antiretroviral therapy has been investigated. The basic reproduction number

ℛ

0

has been defined as a threshold parameter which governs whether or not the disease dies out. Theoretical results indicate that the disease goes to extinction if

ℛ

0

<
1

and otherwise the disease will uniformly persist. The global stabilities of the equilibria for the corresponding autonomous model have been investigated by constructing suitable Lyapunov functions. Moreover, numerical simulations have been carried out to validate the obtained results. The results show that cell-to-cell infection mode may be a barrier to curing the viral infection and increasing the efficacy of protease inhibitors for blocking cell-to-cell infection which will benefit to weaken the severity of the viral infection.