bluuebunny/crossref_metadata_2025 huggingface dataset:
FREEDOM: Effective Surveillance and Investigation of Water-borne Diseases from Data-centric Networking Using Machine Learning Techniques
Authors: Pradeepa S. , Srinivasan Jaisaiarun P. , Anandalakshmi R. , Subbulakshmi P. , Vimal S. , Tarik A.
Published: April 2022

Worldwide, epidemics continue to be a concern on public health. Even with the technological advances, there are still barriers present in predicting the outbreaks. We propose a new methodology known as FREEDOM (Effective Surveillance and Investigation of Water-borne Diseases from data-centric networking using Machine Learning) to perform effective surveillance and investigation of water-borne diseases from social media with next-generation data. In the proposed model, we collected the data from the Twitter media, preprocessed the tweet content, performed hierarchical spectral clustering, and generated the frequent word set from each cluster through the apriori algorithm. At last, the inferences are extracted from the frequent word set through human intervention. From the experimental results, the support and confidence value of the outcome derived from the Apriori algorithm has exhibited the different water-borne diseases that are not listed in the WHO (World Health Organization), and the surveillance of those diseases with percentage ranking and has been achieved using the data-centric networking. They get aligned with precise results portraying real statistics. This type of analysis will empower doctors and health organizations (Government sector) to keep track of the water-borne diseases, their symptoms for early detection, and safe recovery thereby sufficiently reducing the death tolls.
A Method Based on Riccati Equation for the Vibration Analysis of Rods with Variable Cross-Sections
Authors: Pala Yaşar , Kahya Çağlar
Published: April 2022

In this study, the longitudinal vibration of rods with variable cross-sections is studied. For the analytical solution of the problem, a new analytical method based on a recently developed method on the Riccati differential equation is utilized. The governing equation is reduced to Hill’s type second-order ordinary differential equation. The transformed equations can readily be solved analytically for various cases according to the method. Seven cases have been considered, and the frequency equations for each case have been obtained. According to the method developed, the problem is solved in the simplest way. By using the present method, the reader can readily decide whether the problem is solved analytically or numerically. The present method can solve the problem of longitudinal vibration of rods having cross-sections of arbitrary shape. Finally, the method is also applied to the longitudinal vibration of stepped rods. Mode shapes are plotted for special values.
Generation of Random Fields for Image Segmentation Techniques: A Review
Authors: Pemula Rambabu , Vijaya Kumar Sagenela , Nagaraju C.
Published: April 2022

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs.
The Augmentation Data of Retina Image for Blood Vessel Segmentation Using U-Net Convolutional Neural Network Method
Authors: Erwin  , Safmi Asri , Desiani Anita , Suprihatin Bambang , Fathoni
Published: April 2022

The retina is the most important part of the eye. By proper feature extraction, it can be the first step to detect a disease. Morphology of retina blood vessels can be used to identify and classify a disease. A step, such as segmentation and analysis of retinal blood vessels, can assist medical personnel in detecting the severity of a disease. In this paper, vascular segmentation using U-net architecture in the Convolutional Neural Network (CNN) method is proposed to train a sematic segmentation model in retinal blood vessel. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used to increase the contrast of the grayscale and Median Filter is used to obtain better image quality. Data augmentation is also used to maximize the number of datasets owned to make more. The proposed method allows for easier implementation. In this study, the dataset used was STARE with the result of accuracy, sensitivity, specificity, precision, and F1-score that reached 97.64%, 78.18%, 99.20%, 88.77%, and 82.91%.
Padeep: A Patched Deep Learning Based Model for Plants Recognition on Small Size Dataset: Chenopodiaceae Case Study
Authors: Heidary-Sharifabad Ahmad , Zarchi Mohsen Sardari , Zarei Gholamreza
Published: April 2022

A large training sample is prerequisite for the successful training of each deep learning model for image classification. Collecting a large dataset is time-consuming and costly, especially for plants. When a large dataset is not available, the challenge is how to use a small or medium size dataset to train a deep model optimally. To overcome this challenge, a novel model is proposed to use the available small size plant dataset efficiently. This model focuses on data augmentation and aims to improve the learning accuracy by oversampling the dataset through representative image patches. To extract the relevant patches, ORB key points are detected in the training images and then image patches are extracted using an innovative algorithm. The extracted ORB image patches are used for dataset augmentation to avoid overfitting during the training phase. The proposed model is implemented using convolutional neural layers, where its structure is based on ResNet architecture. The proposed model is evaluated on a challenging ACHENY dataset. ACHENY is a Chenopodiaceae plant dataset, comprising 27030 images from 30 classes. The experimental results show that the patch-based strategy outperforms the classification accuracy achieved by traditional deep models by 9%.
Generalized mechanics of incompressible multiphase suspensions
Authors: Massoudi Mehrdad , Ahmadi Goodarz
Published: April 2022

A continuum theory for multiphase particulate suspensions in liquid media was formulated. The generalized fundamental balance laws for the particulate phases and the continuous fluid phase were presented. The thermodynamics of the multiphase system was studied, and the method of Lagrangian multipliers was employed to include the incompressibility of materials constraint and the criterion for fully saturated media. Constitutive equations were developed, which included micro-rotational and micro-dilatational effects. The basic equations of motion of different phases were derived and discussed. Special considerations were given to the case of incompressible constituents. Applications of this theory to nanofluids, dilute suspension flows, and multiphase sedimentations were also described.
Optimal Feedback Control for Modeling Human–Computer Interaction
Authors: Fischer Florian , Fleig Arthur , Klar Markus , Müller Jörg
Published: April 2022

Optimal feedback control (OFC) is a theory from the motor control literature that explains how humans move their body to achieve a certain goal, e.g., pointing with the finger. OFC is based on the assumption that humans aim at controlling their body optimally, within the constraints imposed by body, environment, and task. In this article, we explain how this theory can be applied to understanding Human-Computer Interaction (HCI) in the case of pointing. We propose that the human body and computer dynamics can be interpreted as a single dynamical system. The system state is controlled by the user via muscle control signals, and estimated from observations. Between-trial variability arises from signal-dependent control noise and observation noise. We compare four different models from optimal control theory and evaluate to what degree these models can replicate movements in the case of mouse pointing. We introduce a procedure to identify parameters that best explain observed user behavior. To support HCI researchers in simulating, analyzing, and optimizing interaction movements, we provide the Python toolboxOFC4HCI. We conclude that OFC presents a powerful framework for HCI to understand and simulate motion of the human body and of the interface on a moment-by-moment basis.
Construction of Economic Management Big Data Platform Based on Artificial Intelligence Algorithm Monitoring and Early Warning
Authors: Mao Shuzhen
Published: April 2022

The development of artificial intelligence and the emergence of big data have brought convenience to the development of various fields and also brought great influence to the economic field. There are many data sources for economic management, and the scale is huge, so how to manage these large-scale economic data has become an urgent problem to be solved. In addition, the issue of how to conduct security management on these large-scale data, protect the security of users’ accounts and property, and effectively monitor and prewarn economic market risks is also an urgent issue. This article aims to build an economic market risk monitoring and early warning platform through advanced science and technology such as artificial intelligence and big data to realize an intelligent risk control platform in the economic and financial fields, as well as a data-driven risk management model to create intelligent risk early warning and prevention and the response system to enhance the intelligent level of risk assessment, early warning, prevention, and disposal. Experiments show that the artificial intelligence algorithm monitoring and early warning economic management big data platform constructed in this article shows that its accuracy of economic risk prediction can reach more than 90%.
Rotor Transverse Slots’ Influence on Negative Sequence Loss and Heat Distribution Prediction of Large Generators Based on Improved Radial Basis Function Process Neural Network
Authors: Guo Wu , Guo Jian
Published: April 2022

Owing to the advantages of scientific computation and the data feature support provided by artificial intelligence technology, the theoretical exploration and application research of new computing methods for large generators, one of the most expensive energy equipment in a power system, has become a research hotspot toward solving the bearing limit and operation capacity under abnormal working conditions. Because of many factors affecting the distribution of negative sequence loss and temperature rise that have an extremely complex nonlinear relationship, the traditional calculation and prediction methods of negative sequence conditions cannot suitably reflect the time accumulation effect. Therefore, a prediction method of rotor steady-state negative sequence heating based on radial basis function process neural network is proposed in this paper, and a negative sequence working condition prediction model is established. Accordingly, this study focuses on the study of the relationship among negative sequence heating characteristics, negative sequence component proportion, and transverse slot; additionally, their influence degree, variation relationship, and main principles are further explored that provide a theoretical basis for the design and operation of large generators. As observed from the test results, the steady-state negative sequence condition prediction method based on the improved genetic algorithm radial basis function process neural network features high accuracy; it is a feasible prediction method, specifically for negative sequence conditions of large generators.
An ECC-Based Digital Signature Scheme for Privacy Protection in Wireless Communication Network
Authors: Zhang Ping , Li Yamin , Liu Muhua , Shang Youlin , Fu Zhumu
Published: April 2022

Aiming at the security problems of wireless communication network and the shortcomings of Elliptic Curve Digital Signature Algorithm (ECDSA), this paper designed a forward secure digital signature scheme and proved that the scheme has forward security and unforgeability in the random oracle model. Experimental simulations are done in this paper, and the results show that the proposed scheme has the highest efficiency compared with the ECDSA scheme and the three existing forward secure schemes. This scheme not only meets the forward security and protects the users’ privacies but also reduces the requirement of computing power of the user in the wireless communication network.
Comprehensive Analysis of the Potential Prognostic Value of 11 Glycosylation-Related Genes in Head and Neck Squamous Cell Carcinoma and Their Correlation with PD-L1 Expression and Immune Infiltration
Authors: Chen Langxiong , Ling Yuefu , Yang Hong
Published: April 2022

Background. Head and neck squamous cell carcinoma (HNSCC) is one of the worst and most common malignant tumors. This study is aimed at studying the complex interaction between glycosylation-related genes and HNSCC. Methods. The Cancer Genome Atlas (TCGA) contains gene expression profile data of HNSCC and normal tissues, as well as patient survival and clinical data. Combining five glycosylation-related gene sets, bioinformatics was used to analyze the expression of glycosylation-related genes in TCGA-HNSCC datasets and to identify prognostic risk markers, analyze their prognostic value, and the influence of glycosylation-related genes on the tumor immune microenvironment. Results. Gene expression profiles and corresponding clinical information of 499 cases of HNSCC and 44 cases of adjacent tissues were obtained. Using 11 glycosylation-related genes to construct a prognostic risk score, the Kaplan-Meier curve analysis found that the overall survival of the high-risk group was significantly different than that of the low-risk group (

P
<
0.001

). ROC analysis was used to evaluate the prognostic efficacy of prognostic risk markers, and the results showed that the prognostic risk markers had good efficacy in predicting the prognosis of patients. We also found that there is a correlation between glycosylation-related genes, PD-L1, and immunocyte infiltration, and there is a dynamic effect between the change in the copy number of glycosylation-related genes and the number of tumor-infiltrating immune cells. Conclusions. Our research shows that glycosylation-related prognostic risk markers may be independent risk factors for the prognosis of HNSCC. We have found that there may be subtle links between glycosylation-related genes, PD-L1, and immunocyte infiltration, which has certain significance for exploring the occurrence and development of HNSCC and exploring the research of targeted therapy.
Research on Enterprise Financial Management and Prediction System Based on SaaS Model
Authors: Zhang Qianying , Zhou Fang
Published: April 2022

In order to supervise and forewarn the sustainable operation ability of enterprises efficiently and accurately, this paper proposes an enterprise financial management and forecasting system based on SaaS model. First of all, in order to continue to effectively predict and analyze the enterprise finance, first analyze and extract the report data in the financial system. Then, by building a deep belief network model to predict the enterprise financial data, in order to reduce the cost of enterprises, the financial system designed in this paper chooses the cloud technology service framework based on SaaS model. Finally, in order to analyze the risk identification performance of the financial management and prediction system in this paper, the risk sample data of an enterprise’s financial system is selected for simulation test. The results show that the correct rate of risk identification of the financial management system designed in this paper is higher than other comparison systems, which speeds up the speed of risk identification of the financial information management system, and has certain practical application value.
Lamotrigine Extraction and Quantification by UPLC-DAD in Plasma from Patients with Bipolar Disorder
Authors: Palacios-Magaña Claudia V. , Romero-Tejeda Elba M. , Fajardo-Robledo Nicté S. , González-Ortiz Luis J. , González-Mendez José G , Pacheco-Moisés Fermín P.
Published: April 2022

A sensitive and efficient analytical process for detecting lamotrigine in acidic solution based in ultra-high-performance liquid chromatography-diode array detector (UPLC-DAD) was developed; the stationary phase used was a C8, 150 × 4.6 mm, 2.6 µm. The mobile phase consisted of acetonitrile/acidified water (0.01% H3PO4 and 0.005% triethylamine, pH 2.4) (25 : 75 v/v). Limits of detection and quantification were 0.02 µg/mL and 0.05 µg/mL, respectively. The working interval for the evaluation of the method ranged from 0.05 to 12 µg/mL, and the linear fit of the experimental data has a value of r2≥0.98. Before quantifying lamotrigine in plasma of patients with bipolar disorder, lamotrigine was released from plasma proteins with a 0.2 M sodium hydroxide solution, and then proteins were removed by precipitation with acetonitrile. Afterward, the lamotrigine base was dissolved in ethyl acetate. This extract was reconstituted in potassium phosphate solution (pH 2.4) to obtain more than 98% of lamotrigine protonated in N2, which was detected and quantified as indicated above. The absolute percentage of lamotrigine recovery is  ≥80% for all tested concentration levels. The accuracy and precision of the method have %CV values <4% for the lamotrigine levels of 3, 6, and 9 µg/mL. The correlation coefficient for the used concentration range is 0.99. The analytical method is precise and sensitive to measure lamotrigine levels expected in plasma of BD patients and these levels were in the therapeutic dose range.
[Retracted] The Reaction Pathway of miR‐30c‐5p Activates Lipopolysaccharide Promoting the Course of Traumatic and Hemorrhagic Shock Acute Lung Injury
Authors: Li Jianmin , Pan Chanyuan , Tang Chao , Tan Wenwen , Liu Hui , Guan Jing
Published: April 2022

Acute lung injury (ALI) is an acute hypoxic respiratory failure caused by diffuse inflammatory injury in alveolar epithelial cells during severe infection, trauma, and shock. Among them, trauma/hemorrhagic shock (T/HS) is the main type of indirect lung injury. Despite a great number of clinical studies, indirect factor trauma/hemorrhagic shock to the function and the mechanism in acute lung injury is not clear yet. Therefore, it is still necessary to carry on relevant analysis in order to thoroughly explore its molecular and cellular mechanisms and the pathway of disease function. In our research, we aimed to identify potential pathogenic genes and do modular analysis by downloading disease‐related gene expression profile data. And our dataset is from the NCBI‐GEO database. Then, we used the Clusterprofiler R package, GO function, and KEGG pathway enrichment analysis to analyze the core module genes. In addition, we also identified key transcription factors and noncoding RNAs. Based on the high degree of interaction of potential pathogenic genes and their involved functions and pathways, we identified 17 dysfunction modules. Among them, up to 9 modules significantly regulate the response to bacterial‐derived molecules, and the response to lipopolysaccharide and other related functional pathways that mediate disease development. In addition, miR‐290, miR‐30c‐5p, miR‐195‐5p, and miR‐1‐3p‐based ncRNA and Jun, Atf1, and Atf3‐based transcription factors have a total of 80 transcription drivers for functional modules. In summary, this study confirmed that miR‐30c‐5p activates lipopolysaccharide response pathway to promote the pathogenesis of ALI induced by hemorrhagic shock. This result can be an important direction for further research on related deepening diseases such as acute respiratory distress syndrome (ARDS). It further provides a piece of scientific medical evidence for revealing the pathogenic principle and cure difficulty of acute lung injury and also provides important guidance for the design of therapeutic strategies and drug development.
Stability Analysis and Optimal Control Strategies of Giving Up Relapse Smoking Model with Bilinear and Harmonic Mean Type of Incidence Rates
Authors: Alkahtani Badr Saad T.
Published: April 2022

In this manuscript, a giving-up smoking model is develop using bilinear incidence rate, harmonic mean type of incidence rate and keeping in view the relapse factor associated to smoking. It is shown that the model' solution is bounded and positive for the appropriate initial data. The equilibria of the model are obtained, and it is proved that the smoking-free equilibrium is both locally and globally asymptotically stable for

R

0

less than unity. It is shown that the model has a positive light smoker present equilibrium whenever

R

0

is greater than one, and it is locally asymptotically stable if we have

1
<

R

0

<
1
+
2

β

1

μ
+
d
+
δ

/
Λ

β

2

. Conditions for the global stability of light smoker present equilibrium are rigorously investigated. Also, it is proved that the model has a smoking present equilibrium when

R

0

satisfies some condition which is investigated both for local and global behavior. By considering a few control measures, optimal control strategies are achieved with the help of Pontryagin’s maximum principle. The analytical results are verified numerically, and effectiveness of the control program is presented.
A New Model for Bias-Generating Agent-Based Simulation and Its Application to Election Systems: Allowing Agents to Make Mistakes for a Reason
Authors: Pan Jiateng , Yoshikawa Atsushi , Yamamura Masayuki
Published: April 2022

Several studies have proposed that vote tampering based on heuristic algorithms can manipulate voters’ votes. It can be found from the analysis of the poll results of the 2016 US election that the frequency of “Trump won,” which is generally considered a black swan phenomenon, is not low and even reached 16.8%. However, many models are unable to restore the generation of such a high frequency of black swan phenomena. In this study, the black swan phenomenon is successfully reproduced using a bias-generating agent-based election system model. By adjusting the tampering method, the frequency of the black swan phenomenon will change from 5% to 15%. From the simulation results, it can be observed that one of the possible causes of the black swan phenomenon is the tampering of the voting results, which leads to more biased voters, thus increasing the frequency of the winning elections. This study proposes that to obtain more realistic simulation results, it is necessary to introduce more realistic perceptual models for agents, rather than relying solely on random functions. Allowing agents to make mistakes for a reason should be an integral part of multi-agent-based simulation in the field of pairwise human simulation.
Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning
Authors: Meng Xu-Hong , Shi Hong-Ying , Shang Wei-Hong
Published: April 2022

With the continuous development of computer technology, analysis techniques based on various types of sports data sets are also evolving. One typical representative is image-based motion recognition technology, which enables video action recognition with a certain degree of feasibility. In basketball technical action videos, technical action has obvious characteristics. The athletes in the footage in sports videos are relatively fixed, and the scenes are relatively homogeneous, so technical action analysis of basketball technical action videos has certain advantages. However, there are many challenges in basketball technical action recognition, mainly including the fact that basketball techniques are numerous and complex. To address the above issues, this research proposes a 3D convolutional neural network framework that two different resolution image inputs are performed on the basketball technical action dataset. The experimental results show that the algorithmic process designed in this study is effective for action recognition on the basketball technical action dataset.
The Impact of Family Sustainable Livelihood and Environmental Cognition on Herdsmen’s Choice of Production Behavior
Authors: Wang Qihang , Miao Yinjia
Published: April 2022

This study analyzed the influence of sustainable livelihood and environmental cognition on herdsmen’s production behaviors on basis of logistic model. The consequences illustrated that the social capital followed by material capital, financial capital, and human capital has the greatest impact on herdsmen’s production behaviors, but the natural capital played a vital role in promoting herdsmen’s choice of raising breeding scale. The area and quality of grassland in herdsman families’ sustainable livelihood could encourage herdsmen to increase breeding scale, while the quantity of livestock, structure of house, the number of labor, the level of education, the status of health, and the income of families could promote herdsmen to restrain the breeding scale. The cognition of policy and ecological environment also had a significant positive effect on herdsmen restraining breeding scale.
Sum-Connectivity Coindex of Graphs under Operations
Authors: Ibraheem Muhammad , Bonyah Ebenezer , Javaid Muhammad
Published: April 2022

Topological indices or coindices are mathematical parameters which are widely used to investigate different properties of graphs. The operations on graphs play vital roles in the formation of new molecular graphs from the old ones. Let

Γ

be a graph we perform four operations which are

S

,

R

,

Q
,
 and 
T

and obtained subdivisions type graphs such that

S

Γ

,

R

Γ

,

Q

Γ

, and

T

Γ

, respectively. Let

Γ

1

and

Γ

2

be two simple graphs; then,

F

-sum graph is defined by performing the Cartesian product on

F

Γ

1

and

Γ

2

; mathematically, it is denoted by

Γ

1
+
F

Γ

2

, where

F
∈

S
,
R
,
Q
,
T

. In this article, we have calculated sum-connectivity coindex for

F

-sum graphs. At the end, we have illustrated the results for particular

F

-sum graphs with the help of a table consisting of numerical values.
Occupational Therapy-Based Energy Management Education in People with Post-COVID-19 Condition-Related Fatigue: Results from a Focus Group Discussion
Authors: Hersche Ruth , Weise Andrea
Published: April 2022

Persons with post-COVID-19 conditions have prolonged symptoms and longer-term consequences which can prevent them from returning to previous everyday functioning. Fatigue is the most frequent symptom reported in literature. Occupational therapists (OTs) are specialized in client-centered problem analysis, counseling, and education to recover occupational engagement and performance in everyday life. Since the beginning of the COVID-19 pandemic, OTs have been challenged to respond with services adequate to the needs of this patient group. Energy management education (EME) was initially developed for persons with multiple sclerosis-related fatigue and then made independent of diagnosis suitable to persons living with chronic disease-related fatigue. EME, a structured self-management education, is becoming a part of the new services. This study was aimed at exploring the initial experiences of OTs using the EME protocol and materials with persons with postacute COVID-19 and/or post-COVID-19 condition-related fatigue and gathering their recommendations for improvements and adaptions. One online focus group discussion took place in May 2021 with OTs experienced in using the EME protocol. The topics addressed were the institutional context of the OTs and their experiences during the treatment. A thematic analysis was performed. According to nine OTs working in different settings in Switzerland, the EME protocol is exploitable in both in- and outpatient settings and was judged appropriate by them, even if the EME materials can be improved. The main challenges for the OTs were the short period their patients had lived with fatigue; the discrepancy between self-concept, self-perception, and performance; and the insecurity, fear, and anxiety related to recovery. Further research is needed to include the perspective of EME participants and to measure quantitative outcomes such as fatigue impact, self-efficacy, occupational performance, and quality of life. Until the existing EME protocol is improved, it is applicable to persons with post-COVID-19 condition-related fatigue.
Development of Vegetative Propagation Strategies for Balanites aegyptiaca in the Sahel, Niger
Authors: Massaoudou Moussa , Habou Rabiou , Serge Félix Zounon Christian , Tougiani Abasse , Van Damme Patrick
Published: April 2022

Forests have always been a source of wood, food, and medicine for the rural populations of the Sahel. Anthropogenic and animal pressures often lead to low tree recruitment rates and seedling survival. Under certain conditions, multipurpose species such as Balanites aegyptiaca have shown dramatic decreases in population numbers. The objective of the present study is to determine the natural colonisation behaviour of B. aegyptiaca and to develop vegetative propagation strategies. Surveys were carried out in the agroforest parklands of the Regional Centre for Agricultural Research of Maradi Research Station. An inventory was carried out in 1,500 m2 plots to determine the species’ regeneration methods. We then tested seed germination success, and suckering induction, air-layering, and stem cuttings were carried out to determine the techniques best suited for the propagation of B. aegyptiaca. It emerged from this study that in nature, B. aegyptiaca is propagated naturally by dispersion of seeds (81.2%), as well as by rejection of the strain (13.5%) and by suckering (5.2%). The germination test showed that heavy seeds (38.4%) germinated best because they have a higher nutrient reserve. With a success rate of 11.1%, distal suckers react better than proximal suckers (5.5%) because they quickly acquire relative independence from the mother plant. As for air-layering, stems with large diameters react better (53.3%) than those with small diameters (46.6%) because they have thicker bark and store a large amount of elaborate sap responsible for rhizogenesis. In the stem segment cutting test, cuttings of small diameter react better at 30.8% than those of large diameter (12.6%) because they have a higher number of meristematic tissues. The stem segment cuttings seem to be the best adaptable alternative to the natural propagation of B. aegyptiaca because it combines ease of use, low cost, and a significant success rate.
DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features
Authors: Barukab Omar , Khan Yaser Daanial , Khan Sher Afzal , Chou Kuo-Chen
Published: April 2022

In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.
Listeria Species Occurrence and Associated Risk Factors and Antibiogram of Listeria Monocytogenes in Milk and Milk Products in Ambo, Holeta, and Bako Towns, Oromia Regional State, Ethiopia
Authors: Borena Bizunesh Mideksa , Dilgasa Lemma , Gebremedhin Endrias Zewdu , Sarba Edilu Jorga , Marami Lencho Megersa , Kelbesa Kebede Abdisa , Tadese Nega Desalegn
Published: April 2022

A cross-sectional study was conducted to estimate the prevalence and associated risk factors of Listeria species and assess the antibiogram of Listeria monocytogenes (L. monocytogenes) isolated from milk and milk products from Holeta, Ambo, and Bako towns, Ethiopia. A total of 482 samples (384 milk, 35 cottage cheeses, 30 bulk tank milk, and 33 curdle milk) were collected using a systematic random sampling method and isolation and identification of Listeria species were done using standard microbiological techniques. An antimicrobial susceptibility test for L. monocytogenes was performed using the Kirby–Bauer disk diffusion technique. Descriptive statistics were used to summarize the prevalence of Listeria, while the Chi-square test and logistic regression were used to determine the association between the prevalence of Listeria and the risk factors and the magnitude of association, respectively. The overall isolation rate of Listeria species from milk and milk products was 7.67% (37/482; 95% confidence interval (CI): 5.46, 10.42). The highest prevalence of Listeria species (15.15%; 95% CI: 5.11–31.90) was detected in bulk tank milk and the lowest prevalence of Listeria species (6.67%; 95% CI: 0.82–22.07) and L. monocytogenes (0.00; 95% CI: 0.00–1.15) was found in curdled milk. The other species isolated were Listeria welshimeri 0.62% (3/482; 95% CI: 0.13–1.81), Listeria seeligeri 1.04% (5/482; 95% CI: 0.33–2.40), Listeria ivanovi 1.24%, (6/482; 95% CI: 0.45–2.68), and Listeria grayi 2.49% (12/482; 95% CI: 5.46–10.42). Univariable logistic regression showed that study town, herd size, farm size, number of lactating cows, and management system were the factors significantly associated with the isolation of Listeria species at farm level, while the intensive management system was the independent predictor at cow level in the multivariable model (adjusted odds ratio = 3.38,

P
=
0.046

). L. monocytogenes isolates showed the highest resistance against oxacillin (100%), amoxicillin (90.91%), and vancomycine (81.82%). L. monocytogenes showed a very high multidrug resistance (MDR) [81.82%]. In conclusion, the current study showed the widespread type of Listeria species MDR L. monocytogenes isolates in cow raw milk and milk products from Ambo, Holeta, and Bako towns, Oromia Regional State, Ethiopia.
Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction
Authors: Dong Yuanshuai , Zhang Yanhong , Hou Yun , Tong Xinlong , Wu Qingquan , Zhou Zuofeng , Cao Yuxuan
Published: April 2022

The damage of road auxiliary facilities poses a major hidden danger to driving safety. It is urgent to study a method that can automatically detect the damage of the road auxiliary facilities and provide help for the maintenance of traffic safety auxiliary facilities. In the method for identifying the absence of road auxiliary facilities based on deep convolutional network for image segmentation and image region correction, the PointRend model based on the deep convolutional networks (CNN) is first used to achieve the pixel‐level fine segmentation of the auxiliary facilities area, and then, the multiple images in the same image are segmented. In anti‐glare panel area, on the largest outer polygon estimated by the convex hull algorithm, the optimal outer quadrilateral is determined according to the distance between the vertices, and then, the anti‐glare panel area correction is completed by affine transformation and finally through the image one‐dimensional projection mapping and adjacent shading. The distance correlation between the boards realizes the identification and positioning of the missing light‐shielding board. The highway anti‐glare panel missing recognition method based on deep convolution image segmentation and correction uses the vertex distance to quickly determine the external quadrilateral, which is suitable for estimating the shape of the area in a dynamic scene. After actual testing and verification, it can accurately and efficiently identify the disease of the anti‐glare plate. Compared with traditional image segmentation methods, the method using the PointRend target segmentation model has better segmentation quality for target details, and it is more robust when dealing with background interference.
The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology
Authors: Fan Rong , Sun Suqin , Shan Yuanli , Zhang Qingjiang , Li Jiazhuo , Ma Guoqing , Pan Limin
Published: April 2022

In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.
Bayesian Inference of Ammunition Consumption Based on Normal‐Inverse Gamma Distribution
Authors: Liu Haobang , Shi Xianming , Chen Xiaojuan , Li Yuan , Zhao Mei , Jiang Yongchao
Published: April 2022

To address the problems of high cost of new ammunition experiment, few data of field test and low accuracy of consumption prediction, this article proposes a Bayesian estimation method of ammunition consumption based on normal‐inverse gamma distribution, and estimates the hyperparameters in the prior distribution through the prior information from the consumption of ammunition under different damage degrees of point targets, based on the normal distribution phenomenon of ammunition consumption at each damage degree. It is to establish a Bayesian estimation model for ammunition consumption under different damage degrees according to field test data based on Bayesian formula and solve for its posterior distribution. The example proves that the estimation results of ammunition consumption for point target with different damage degrees based on this method is more scientific and reasonable according to various prior information.
Poisson XLindley Distribution for Count Data: Statistical and Reliability Properties with Estimation Techniques and Inference
Authors: Ahsan-ul-Haq Muhammad , Al-Bossly Afrah , El-Morshedy Mahmoud , Eliwa Mohamed S.
Published: April 2022

In this study, a new one-parameter count distribution is proposed by combining Poisson and XLindley distributions. Some of its statistical and reliability properties including order statistics, hazard rate function, reversed hazard rate function, mode, factorial moments, probability generating function, moment generating function, index of dispersion, Shannon entropy, Mills ratio, mean residual life function, and associated measures are investigated. All these properties can be expressed in explicit forms. It is found that the new probability mass function can be utilized to model positively skewed data with leptokurtic shape. Moreover, the new discrete distribution is considered a proper tool to model equi- and over-dispersed phenomena with increasing hazard rate function. The distribution parameter is estimated by different six estimation approaches, and the behavior of these methods is explored using the Monte Carlo simulation. Finally, two applications to real life are presented herein to illustrate the flexibility of the new model.
Toward Labor Legal Risk Assessment Based on Unbiased Iterative Brunching Decision Tree Algorithm
Authors: Yan Sui
Published: April 2022

In recent years, with the advancement of China’s labor laws and regulations, especially the strengthening of workers’ awareness of legal rights protection, enterprise labor risk is rising. It is essential for trade unions to help enterprises establish and improve the risk assessment mechanism of labor and employment on time, work hard from the four links of prediction, preexamination, forecast, and prevention urge enterprises to employ workers under the law, effectively reduce labor disputes, avoid the rise of conflicts, and maintain the stability of enterprises and society. In the background of building a harmonious society, the employment of enterprises has been paid more attention by the government, industry, and schools. Particularly for enterprises, market competition largely comes down to talent competition. In the global talent shortage, legal employment and risk prevention have been placed in front of enterprises. This is especially true for state-owned enterprises. More, under the prevailing situation of protecting workers’ rights, interests, and increasing legal awareness of enterprises and workers, it is more prominent for state-owned enterprises to carry the mission and responsibility of building a harmonious society. Its employment plays a leading role in the whole society or industry. The pragmatic purpose of this work is to deal with the risk management-related theory to the study of legal risk prevention and control of employment using the method of risk identification related to the current domestic and foreign through decision tree algorithm (DTA) about the ocean enterprise employee to assess international agreements and legal rules. Given the ocean enterprise employee specifically the seafarers’ employment combined with industry norms, the ideas, and measures of legitimate employment, this study has a strong theoretical and practical guiding importance for practical work.
Influence of Nursing Intervention Based on Risk Assessment Model on Self-Efficacy and Postoperative Rehabilitation of Surgical Patients
Authors: Yang Yanfang , Chen Peng , Jiao Cuili
Published: April 2022

The study aims to explore the effect of nursing intervention based on risk assessment model on self-efficacy and postoperative rehabilitation of surgical patients. The study applied a risk assessment model to the behavioral intervention of rehabilitation exercise in patients after percutaneous coronary intervention (PCI). A total of 157 patients were selected from a certain hospital, with 52 cases in the experimental group and 55 cases in the control group. A comprehensive cardiac rehabilitation intervention strategy based on TTM is given by researchers trained with professional knowledge, including dividing the stages of behavior change, developing a rehabilitation exercise plan through exercise teaching and exercise training, diet and lifestyle guidance, and urging patients to take regular medication and regular follow-up. The stage of rehabilitation movement behavior change improved. The preexperiment health status was (35.39 ± 14.31), and the overall health of the control group was (32.59 ± 15.41). Overall health status and QOL scores were relatively low in both the groups. The intervention group scored higher than the control group. After coronary stent implantation, the heart restores its original pumping function, various organs and tissues receive effective blood perfusion, and the symptoms of myocardial infarction such as profuse sweating, dizziness, fatigue, and precordial discomfort are effectively relieved. The physical discomfort can gradually improve or even recover to the functional state before the onset of the disease. Before the intervention, the self-efficacy score of the intervention group was (23.68 ± 4.49). After the intervention, the self-efficacy score of the intervention group was (26.21 ± 4.09), while the self-efficacy score of the control group before the intervention was (22.21 ± 4.1). After the intervention, the self-efficacy score of the control group was (25.23 ± 4.41). Although the self-efficacy score of the control group was improved, the difference was small, and the intervention effect was not obvious. It indicates that risk assessment intervention can improve patients’ self-efficacy level and quality of life of postoperative rehabilitation patients.
Computation of Resolvability Parameters for Benzenoid Hammer Graph
Authors: Ahmad Ali , Al-Hossain Ahmad Al-Nashri
Published: April 2022

A representation of each vertex of a network into distance‐based arbitrary tuple form, adding the condition of uniqueness of each vertex with reference to some settled vertices. Such settled vertices form a set known as resolving set. This idea was delivered in various problems of computer networking as well as in chemical graph theory. Due to its huge implications, many new variants were introduced such as edge resolving set, fault‐tolerant version of edge, and vertex resolving set and its generalization named as partition resolving set. In this work, we addressed all these variants for a benzenoid chemical structure named a hammer graph. Moreover, we proved that all the above variants are independent of the size and order of this graph.
Therapeutic Efficacy of Flavonoids in Allergies: A Systematic Review of Randomized Controlled Trials
Authors: de Almeida Brasiel Poliana Guiomar , Guimarães Fernanda Verdini , Rodrigues Patrícia Machado , Bou-Habib Dumith Chequer , Carvalho Vinicius de Frias
Published: April 2022

Objective. To assess the clinical efficacy of flavonoid supplements on allergic diseases. Design. Systematic review. Data Sources. MEDLINE/PubMed, Scopus, Web of Science, and Embase databases were searched from inception to September 2021. Eligibility Criteria for Selecting Studies. Eligible study designs were randomized controlled trials that investigated the effect of flavonoids applied to allergic diseases. Results. This review included 15 randomized controlled trials, including allergic rhinitis/cedar pollinosis (

n
=
10

), asthma (

n
=
3

), and atopic dermatitis (

n
=
2

). A total of 990 participants aged 6 to 69 years were included in these studies. Globally, 12 studies (80%) revealed some benefits of flavonoids (isolate or combined with other compounds) in allergic patients, while three studies (20%) reported no statistically significant impact on symptom scores and/or lung function. No severe adverse events related to treatment were reported. According to the GRADE system, the outcomes evaluated were of low to moderate quality of evidence. Conclusions. Overall, this review suggests that the administration of flavonoids may provide a viable strategy for mitigating allergic symptoms. Future trials with high methodological quality are needed to establish definitive conclusions. This trial is registered with PROSPERO registration no. CRD42021237403.