bluuebunny/crossref_metadata_2025 huggingface dataset:
The Optimization of Civic Education with the Assistance of Artificial Intelligence Devices
Authors: Luo Shuchen
Published: September 2022

With the advent of the era of artificial intelligence, a technological revolution has begun in the field of education, which has injected new momentum into moral education. Today, civic education is developing rapidly in countries around the world, while civic education in China is still relatively lagging behind. In China, we need to optimize civic education by building a comprehensive civic education system, implementing the concept of people-oriented, gathering the joint efforts of the whole society, expanding diversified civic education methods, and selecting and enriching the contents of civic education. Artificial intelligence technology enables higher education institutions to carry out civic education, enrich teaching contents, broaden teaching methods, and optimize teaching evaluation. However, technology-related innovations are dualistic in nature, with essential, technical, and value risks that hinder their effectiveness in civic education. This paper proposes strategies for optimization in terms of improving digital core risk response capabilities, establishing reliable technology risk response mechanisms, and maintaining a foundation for civic education response costs. Thus, weakening the risk factors of AI in civic education facilitates the transformation of AI and technology to the advantage of knowledge-based civic education. This paper discusses strategies and methods for optimizing smart education using AI devices and establishes a corresponding smart education platform to help students with their ideological education. Based on the analysis of actual cases, the paper discusses the effectiveness and advantages of the program.
Adsorption of Heavy Metals in Contaminated Water Using Zeolite Derived from Agro-Wastes and Clays: A Review
Authors: Kithinji Kinoti Ismael , Ogunah Joanne , Muturia M’Thiruaine Cyprian , Marangu Joseph Mwiti
Published: September 2022

Due to climate change and anthropogenic activities such as agriculture, mining, and urbanization, water contamination has become a very real modern problem. Modern solutions such as activated carbon, reverse osmosis, and ultrafiltration, among others, have been employed in the decontamination of water. These methods are, however, expensive to set up and maintain and therefore have proved a challenge to implement in developing countries. Zeolite materials exhibit excellent structural properties, such as high ion exchange capacity, porosity, and relative surface area, which make them attractive to water decontamination processes. However, conventional zeolites are expensive, and recent research has focused on utilizing low-cost materials such as agro-wastes and clays as raw materials for the synthesis of zeolites. This review aims to discuss the role of low-cost zeolites in their removal of heavy metals and the feasibility of agro-wastes and natural clays in the synthesis of zeolites. Recent research studies based on the synthesis of zeolites from clays and agro-wastes and their application in heavy metal removal have been reviewed and discussed. Agro-wastes such as rice husk ash and sugarcane bagasse ash and layered silicate clays such as kaolinite and smectites are particularly of interest to zeolite synthesis due to their high silica to alumina ratio. Zeolites synthesized through various methods such as hydrothermal, molten salt, and microwave irradiation synthesis have been discussed with their effect on the adsorption of various heavy metals.
An Alternate Approach to Generate Induced Pluripotent Stem Cells with Precise CRISPR/Cas9 Tool
Authors: Javaid Nasir , Choi Sangdun
Published: September 2022

The induced pluripotent stem cells (iPSCs) are considered powerful tools in pharmacology, biomedicine, toxicology, and cell therapy. Multiple approaches have been used to generate iPSCs with the expression of reprogramming factors. Here, we generated iPSCs by integrating the reprogramming cassette into a genomic safe harbor, CASH-1, with the use of a precise genome editing tool, CRISPR/Cas9. The integration of cassette at CASH-1 into target cells did not alter the pattern of proliferation and interleukin-6 secretion as a response to ligands of multiple signaling pathways involving tumor necrosis factor-α receptor, interleukin-1 receptor, and toll-like receptors. Moreover, doxycycline-inducible expression of OCT4, SOX2, and KLF4 reprogrammed engineered human dermal fibroblasts and human embryonic kidney cell line into iPSCs. The generated iPSCs showed their potential to make embryoid bodies and differentiate into the derivatives of all three germ layers. Collectively, our data emphasize the exploitation of CASH-1 by CRISPR/Cas9 tool for therapeutic and biotechnological applications including but not limited to reprogramming of engineered cells into iPSCs.
Specific Emitter Identification Based on a Hybrid Deep Neural Network for ACARS Authentication
Authors: Yin Liyan , Xiang Xin , Liu Kun , Liang Yuan
Published: September 2022

With the increasing variety and quantity of aircraft, there is a potential threat to the security of the Aircraft Communications Addressing and Reporting System (ACARS) due to the lack of reliable authentication measures. This paper proposes a novel specific emitter identification (SEI) algorithm based on a hybrid deep neural network (DNN) for ACARS authentication. Our deep learning architecture is a combination of Deep Residual Shrinkage Network (DRSN), Bidirectional-LSTM (Bi-LSTM), and attention mechanism (AM), which perform the functions of local and global feature learning and feature focusing, respectively, so that the individual information hidden in the signal waveform can be thoroughly mined. We introduce soft thresholding as a nonlinear transformation in the DSRN to enhance robustness against noise and adopt a low-cost training strategy for new data using transfer learning. The proposed SEI algorithm is optimized and evaluated based on real-world ACARS signals captured in the Xianyang airport. Experimental results demonstrate that our algorithm can distinguish authorized entities from unauthorized entities and obtain an identification accuracy of up to 0.980. In addition, the design rationality and the superiority over other algorithms are verified through the experiments.
Computer Vision and the IoT-Based Intelligent Road Lane Detection System
Authors: Shashidhar R. , Arunakumari B. N. , Manjunath A. S. , Ahuja Neelu Jyoti , Hoang Vinh Truong , Tran-Trung Kiet , Belay Assaye
Published: September 2022

Many technical improvements have recently been made in the field of road safety, as accidents have been increasing at an alarming rate, and one of the major causes of such accidents is a driver’s lack of attention. To lower the incidence of accidents and keep safe, technological innovations should be made. One way to accomplish this is with IoT-based lane detection systems, which function by recognizing the lane borders on the road and then prompting the turning of the road. Because of the various road conditions that one can encounter when driving, lane detection is a difficult problem. An image processing-based method for lane detection has been proposed in this paper. In this regard, each frame from the video is extracted and image processing techniques are applied for the detection of lanes. The frame which is extracted from the video is then subjected to a Gaussian filter for the removal of noise. Subsequently, color masking has been used to process the frame to detect only the road lanes, whose edges are obtained by applying the canny edge detection algorithm. Afterward, the Hough transform has been applied to the region of interest to extrapolate the lines. Finally, the path is plotted along the lines, and turns are predicted by using the concept of vanishing points.
Word Recognition and Fluency Activities’ Effects on Reading Comprehension: An Iranian EFL Learners’ Experience
Authors: Pallathadka Harikumar , Xie Sen , Alikulov Salokhiddin , Sadruldeen Al-Qubbanchi Hasnaa , Hamoud Alshahrani Shadia , Yunting Zeng , Kargar Behbahani Hossein
Published: September 2022

Reading fluently is the goal of many L2 learners. The purpose of this study was to see how word recognition and fluency-building activities affect Iranian EFL students’ reading comprehension. To do so, 93 subjects with preintermediate command of English were selected. Then, they were divided into two experimental groups and one control group, each with 31 participants. In an eight-session period, the first experimental group (word recognition group) received word and phrase recognition activities, the second treatment group (fluency group) was exposed to fluency-oriented activities, while the control group was only exposed to the Farsi translation of the assigned texts. Using a randomized pretest-posttest comparison group design, we attempted to see how reading comprehension changes in response to the treatment. Therefore, a one-way between-groups ANOVA was used to test the null hypothesis. The findings revealed that both experimental groups significantly outperformed the control group in the posttest. There was no significant difference between the two experimental groups, according to post-hoc analyses, hence the efficacy of the two implemented techniques. This study implies that material developers should include such activities in their textbooks to enhance text comprehension, especially at the preintermediate level of language proficiency. Additionally, teachers can employ these techniques in their classes with their learners at the preintermediate level.
Potassium Nitrate Priming Effect on the Germination of Tomato (Lycopersicum esculentum. Mill) cvs. “Mersa” and “Tekeze-1”
Authors: Mebratu Addisalem
Published: September 2022

To compare the responses of two open-pollinated tomato varieties to germination parameters and shoot growth, seeds of open-pollinated tomato varieties, Mersa and Tekeze-1 were given osmopriming treatments with potassium nitrate (KNO3) in a lab setting. A completely randomized design with four replications was used. The analysis of variance revealed that both tomato varieties responded differently to priming with potassium nitrate concentrations significantly (P<0.05) for almost all measured traits. Potassium nitrate treatments also affected final germination percent (G%) and mean daily germination percent significantly (P<0.05) and germination index (GI) and shoot length (SL) (P<0.001). However, KNO3 did not have a significant effect on time for 50% germination (T50) and mean germination time (MGT). Variety Mersa showed better performance in germination percent (G%) and SL than Tekeze-1; while Tekeze-1 had shorter days for MGT and reached T50 faster than the Mersa variety. Potassium concentrations at 0.5% and 1.5% showed better germination results than the control and at 1% KNO3. The interaction of the Mersa variety with KNO3 at 1.5% and 1% resulted in the highest SL, but the Tekeze-1 variety had the highest shoot length at 0.5% KNO3 concentration, indicating that the varieties have different responses to the applied KNO3 rate. This study confirmed the possibility of enhancing seed germination through externally applied priming agents such as KNO3. Despite being a preliminary finding, this study demonstrated that tomato cultivars differ in how they react to KNO3 priming, and more research, using other tomato varieties and priming agents, is needed.
Sacubitril Valsartan Enhances Cardiac Function and Alleviates Myocardial Infarction in Rats through a SUV39H1/SPP1 Axis
Authors: Shen Jian-Fen , Fan Zhong-Bao , Wu Chun-Wei , Qi Guo-Xian , Cao Qiu-Yu , Xu Feng
Published: September 2022

Sacubitril valsartan (lcz696) has been demonstrated as a substitute for angiotensin-converting enzyme inhibitors and angiotensin receptor blockers for the treatment of heart failure. This research is aimed at examining the effects of lcz696 and its target molecules on myocardial infarction (MI). A rat model of MI was induced by left anterior descending artery ligation and treated with lcz696. Lcz696 treatment significantly reduced cardiac injury and heart failure, restored the left ventricular fractional shortening and ejection fraction, and reduced oxidative stress and inflammatory responses in rat myocardium. By analyzing the heart failure-related GSE47495 dataset and performing gene ontology (GO) functional enrichment analysis, we obtained histone lysine methyltransferase SUV39H1 and secreted phosphoprotein 1 (SPP1) as two molecules implicated in the oxidative stress and inflammation processes. An elevation of SUV39H1 whereas a decline of SPP1 were detected in cardiac tissues after lcz696 treatment. Enrichments of SUV39H1 and H3K9me3 at the SPP1 promoter were identified by chromatin immunoprecipitation assay. SUV39H1 catalyzed H3K9me3 modification to suppress the expression of SPP1. Preconditioning of SUV39H1 silencing blocked the protective roles of lcz696, but SPP1 silencing alleviated the myocardial injury. In conclusion, this study demonstrates that lcz696 enhances cardiac function and alleviates MI in rats through a SUV39H1/SPP1 axis.
Biliary Cripple and the Spectrum of Complications following Cholecystectomy: A Case Report
Authors: Shrestha Anish Kumar , Sah Jayant Kumar , Ghimire Bikal , Bhandari Aashna , Shrestha Anisha
Published: September 2022

Bile duct injury is a complication seen during cholecystectomy. Here, we highlight the occurrence of bile duct injury (BDI) during an open cholecystectomy who underwent hepaticojejunostomy (HJ), later presenting with a stricture of HJ. Percutaneous transhepatic biliary drainage (PTBD) was performed which led to the development of hepatic artery injury.
The Application of the Depth Model of Precise Matching between People and Posts Based on Ability Perception in Human Resource Management
Authors: Cao Hui
Published: September 2022

To revitalize talents and give full play to the maximum utility of HR (human resources), it is not enough to accumulate talents alone. HR must be effectively allocated to realize the matching of people and posts. Competency is a personal characteristic of an organization that distinguishes its performance level in a specific job and organizational environment. In order to solve the problem that job seekers’ job-seeking ability is difficult to match the job requirements, this paper combines neural network with traditional HRM (human resource management) algorithm based on ability perception and designs a depth model of accurate matching of people and posts in HR field, which can improve the quality of data training of traditional algorithm. The results show that compared with other algorithms, the

F
1

value of the proposed algorithm is obviously improved, and the proposed algorithm performs best, with the

F
1

value of 0.829. In this paper, the method of global network plus local network is used, which can effectively improve the hidden features of data and then improve the matching degree and recommendation accuracy of the algorithm.
Application of Wireless Sensor Network Data Fusion Technology in Mechanical Fault Diagnosis
Authors: Zhao Xinle , Shen Zuhui , Jing Zhisheng
Published: September 2022

In order to solve the problem that a large number of vibration signals cannot be transmitted in real time in the application of wireless sensor networks (WSNs) in mechanical fault diagnosis, a mechanical fault diagnosis method based on multilevel and hierarchical information fusion of WSNs was proposed. In this method, the cluster tree network structure is used to expand the coverage of network monitoring, and WSNs information fusion is divided into three levels: data-level fusion, feature-level fusion, and decision-level fusion. The terminal node performs data-level fusion on the original vibration information to extract feature information; the cluster-head node performs feature-level fusion on the feature information to obtain pattern recognition results; and the gateway node performs decision-level fusion on the recognition results to evaluate the running status of mechanical equipment. The results show that the slight damage fault of the bearing inner ring can be accurately diagnosed by decision-level fusion based on four groups of probability distribution functions. According to the statistics of 30 test results, the fault recognition rate is 83.3%. The method can be applied to mechanical fault diagnosis effectively.
The Whistleblower’s Dilemma: An Evolutionary Game Analysis of the Public Health Early Warning System
Authors: Cao Feng , Zhang Ling , Wu Yihan
Published: September 2022

Although the ability to manage public health emergencies in China has improved significantly, there are still many challenges to the existing information transmission mechanism in pandemic early warning systems. In this context, a tripartite evolutionary game model composed of the local government, the whistleblower, and the public is formulated. By using Matlab, the dynamic evolution path of the game model is stimulated under different conditions. Stable strategies for an early warning system for public health emergencies are also explored. The results indicate that the cost of whistleblowing, the cost of response, and the benefit of attention significantly influence strategic decisions among three parties. This study highlights the importance of whistleblowing in managing public health emergencies. Yet, our findings provide theoretical support for policy recommendations for promoting public health emergency preparedness.
Cost and Clinical Benefits Associated with Oncotype DX® Test in Patients with Early-Stage HR+/HER2- Node-Negative Breast Cancer in the Netherlands
Authors: de Jongh Felix E. , Efe Reva , Herrmann Kirsten H. , Spoorendonk Jelle A.
Published: September 2022

Objectives. Patients with early-stage HR+/HER2- N0 breast cancer may receive adjuvant chemotherapy in combination with surgery. However, chemotherapy does not always lead to improved survival and incurs high healthcare costs and increased adverse events. To support decision-making regarding adjuvant chemotherapy, genomic profile testing performed with tests such as the Oncotype DX® test can help healthcare practitioners decide whether chemotherapy provides any benefit to these patients. As such, a cost-consequence model was developed with the aim to estimate the economic impact of using different gene expression tests or no testing, in patients with node-negative early-stage breast cancer. Methods. A cost-consequence model was developed to estimate the economic impact of three different scenarios in the Dutch setting: (1) Oncotype DX® test, (2) MammaPrint®, and (3) and no genomic profile testing. The model included chemotherapy costs, administration costs, short- and long-term adverse event costs, productivity loss, genomic profiling testing costs, cost of cancer recurrence, and hospitalization costs. Results. A treatment paradigm with Oncotype DX resulted in average savings per patient of €6,768 vs. a paradigm with MammaPrint and €13,125 vs. a paradigm with no genomic testing. Furthermore, due to less patients receiving adjuvant chemotherapy through better targeting by the Oncotype DX test, fewer adverse events, sick days, practice visits, and hospitalizations were required compared to MammaPrint and no genomic profiling. Conclusions. Testing with Oncotype DX test in Dutch clinical practice in patients with early-stage breast cancer proved to be cost-saving versus MammaPrint and no genomic profiling tests. Introducing the Oncotype DX test to the Dutch setting will likely reduce the economic resources that are required.
Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques
Authors: Pandey Saroj Kumar , Kumar Gaurav , Shukla Shubham , Kumar Ankit , Singh Kamred Udham , Mahato Shambhu
Published: September 2022

In cardiac rhythm disorders, atrial fibrillation (AF) is among the most deadly. So, ECG signals play a crucial role in preventing CVD by promptly detecting atrial fibrillation in a patient. Unfortunately, locating trustworthy automatic AF in clinical settings remains difficult. Today, deep learning is a potent tool for complex data analysis since it requires little pre and postprocessing. As a result, several machine learning and deep learning approaches have recently been applied to ECG data to diagnose AF automatically. This study analyses electrocardiogram (ECG) data from the PhysioNet/Computing in Cardiology (CinC) Challenge 2017 to differentiate between atrial fibrillation (AF) and three other rhythms: normal, other, and too noisy for assessment. The ECG data, including AF rhythm, was classified using a novel model based on a combination of traditional machine learning techniques and deep neural networks. To categorize AF rhythms from ECG data, this hybrid model combined a convolutional neural network (Residual Network (ResNet)) with a Bidirectional Long Short Term Memory (BLSTM) network and a Radial Basis Function (RBF) neural network. Both the F1-score and the accuracy of the final hybrid model are relatively high, coming in at 0.80% and 0.85%, respectively.
Seroprevalence of Anti‐tTg‐IgA among Symptomized Celiac Disease Patients and Their Correlation with Rotavirus Infection
Authors: Sadiq Asma , Khan Jadoon , Ullah Irfan , Basharat Nosheen , Ali Sajid , Din Ahmad Ud , Ali Ijaz , Farid Arshad , Shah Muddaser , Abdel-Daim Mohamed M. , Albadrani Ghadeer M. , Khan Imtiaz Ali
Published: September 2022

Celiac disease (CD) is a chronic inflammatory disorder in the intestinal tract as a response to the use of gluten in genetically predisposed individuals. It is a worldwide problem, with a high prevalence rate in North America. This is a descriptive cross‐sectional study involving 1090 samples collected from different hospitals of Rawalpindi and Islamabad, Pakistan, from January 2019 to December 2019. In this study, 1090 blood samples screened for seroprevalence of anti‐tTG antibodies in CD symptomatic patients via ELISA (enzyme‐linked immunosorbent assay). 1090 fecal samples from the same CD patients were collected and tested for the presence of rotavirus (RV) via ELISA and RT‐PCR. Of the 1090 patients tested for seroprevalence of anti‐tTG antibodies, 112/1090 (10.3%) were found to be positive. Of the 112 anti‐tTG‐positive patients, 78/112 (70%) were positive for RV via ELISA and 74/112 (66.1%) were RV positive via RT‐PCR. A statistically significant association was reported between rotavirus infection and celiac disease (p˂0.05). Anti‐tTG antibodies were higher in age group 6 (12‐18 years) patients (18.4%) and at minimum in age group 3 (1‐3 years) patients (4.8%). However, there was a statistically insignificant relationship between group age and CD prevalence (p > 0.05). The highest CD prevalence was noted during winter season (19.6%) and the lowest (3.0%) during fall/autumn. Our study findings demonstrate that Pakistan has a high prevalence of CD compared to other studies. Further studies in the fields of environmental risk factors and treatment with more advanced serological and histopathological studies are needed in the future.
A Novel Approach Based on Generative Adversarial Network for Interference Detection in Wireless Communications
Authors: He Shoushuai , Zhu Lei , Yao Changhua , Zeng Weijun , Qin Zhen
Published: September 2022

With the rapid growth of wireless devices, the communication environment gets complex. The detection of interference or unauthorized signals can improve spectrum efficiency, which is a key technology for limited spectrum resources. Traditional detection methods analyze the parameter characteristics of the received signal. But it is difficult to detect interference with the same time and frequency as the original signal by those feature engineering. As a classical problem in deep learning, anomaly detection is usually solved by supervised learning. But a more challenging situation is to detect unknown or invisible anomalies. It means that the number of abnormal samples is insufficient and the data is highly biased toward the normal samples. In this paper, a wireless communication interference detection algorithm based on generative adversarial network (GAN) is proposed. In the semi-supervised learning scenario, the algorithm detects the time-frequency overlapped interference by the reconstruction strategy. The generator adopts the encoder-decoder-encoder architecture. In the training process, the model jointly learns the data distribution of normal samples by minimizing the distance in both the signal space and the latent space. In the inference phase, a large distance metric implies an abnormal sample. Experiments on simulated communication datasets show the superiority of the proposed algorithm.
Scheduling Method for Agricultural IOT Business Based on Improved Multiobjective Evolutionary Algorithm
Authors: Zhang Kewang , Shu Zhixu
Published: September 2022

In the modern society where technology is advancing every day, the agricultural industry is also undergoing innovation, and the Internet of Things (IoT) based on machine learning algorithms adds new vitality and yields increasing directions to this ancient industry. This study analyzes and processes data based on improved multiobjective algorithms for the application of IoT in agriculture and establishes the relevant algorithmic models. The components of IoT are introduced, and it is determined that information flow, capital flow, logistics, and Internet are the main reasons why it can be generated. After establishing an improved multiobjective evolutionary algorithm model with good convergence and diversity, the embedded multichannel sensor collection device measured in this experiment in the same cultivated environment has a more stable collection data cycle compared to the external sensor. The embedded multichannel sensor has better stability, so this sensor is selected for this study to monitor parameters such as soil moisture content and oxygen content. The IoT requires timely communication and consultation among users, and the actual experiment found that the use of ultrashort waves with a frequency of 230 MHz is the most stable and efficient.
Construction of a Cuprotosis-Related Gene-Based Model to Improve the Prognostic Evaluation of Patients with Gastric Cancer
Authors: Han Chunyan , Zhang Kai , Mo XinKai
Published: September 2022

Background. Gastric cancer (GC) is one of the most serious gastrointestinal malignancies with bad prognosis. The association between GC and cuprotosis-related genes has not been reported. Methods. The clinical and RNA expression of patients with GC were downloaded from TCGA database. The CIBERSORT package was used to quantify the abundance of specific cell types. Using the Cox regression analysis, we conducted a prognostic nomogram model based on cuprotosis-related differential genes in GC. We evaluated the prognostic power of this model using the Kaplan-Meier (K-M) survival curve analysis, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis. Results. The plasma cells, monocytes, and mast cells in GC tissue were significantly less than those in adjacent tissue (

p
<
0.05

), while T cell CD4 memory activated macrophage M0, macrophage M1, and macrophages in GC tissue. The number of M2 was significantly more than that in the adjacent tissue (

p
<
0.05

). Additionally, GC patients in the test group, the training group, and all the sample groups had shorter survival time with the increase of the risk factor (

p
<
0.05

). The nomogram of GC based on cuprotosis prognosis-related genes was conducted. The AUC of the nomogram to predict 1-, 3-, and 5-year survival rate was 0.618, 0.618, and 0.625, respectively. Conclusion. A novel cuprotosis-related gene signature impacts on the prognosis of GC. Our research provides new insights and potential targets for studying the link between GC and cuprotosis point, thereby providing new insights into understanding the molecular mechanism of GC.
Speech Dialogue System-Based Environmental Resource Allocation and Online English Teaching and IoT Applications
Authors: Wang Kunfeng
Published: September 2022

For the online teaching of English and the allocation of environmental resources, the conventional methods are difficult to adapt to the increased load in time when the business volume increases, and their practicability has obvious shortcomings. Aiming at this problem, this paper puts forward the environmental resource allocation and online English teaching based on the voice dialogue system. Based on the English teaching content, the vocabulary information table and dialogue information table which are input into the voice dialogue system are designed. In the user’s usage, the dialogue sent by the user is matched with, and each group of matching results is taking as the target. Environmental resources are allocated for each group of matching results with the help of the dispatcher in the voice dialogue system, and the optimal allocations of single goal and multiple goals are realized. On this basis, the online teaching architecture is designed, and the above contents are deployed to the teaching architecture. It supports the multichannel interaction between users and the online English teaching platform and realizes independent online English learning. The experimental results show that the teaching method based on the voice dialogue system has stable environmental resource allocation, low blocking rate, and high teaching quality score, and the overall practicability has been significantly improved.
An Analysis on the Markovian Model for Production Management and Establishment of New Pharmacies Using Hyper Graph
Authors: Vinola Anitha Florence F. , Padma G. , Velusamy Prabu
Published: September 2022

Stochastic models are mainly used to rectify the real‐life problems in different areas of the busy world such as marketing, trading, networking, communicating media, and challenging biomedical inventions, for the survival of mankind. Any business which runs on the basis of getting more profit with less investment includes all the resources for production and the man power too, during the current century. To bring the equality in business gain and man power, many stochastic models have been introduced since the 1940s. The different states and the steady state probabilities are discussed by the Markov models to overcome the insufficiency of man power. An Iterative Hidden Markov Model has been introduced for effective production by considering the observable states depend on hidden states under the circumstances of real‐world probabilities are known and unknown. This paper mainly deals with the various machines, pharmacies, and different methods, namely, Nonadjacency Search Method and Adjacency Search Method applied through the Hyper graph. Comparison made between Nonadjacency Search Method and Adjacency Search Method through hyper assignments using cliques helps us to obtain tremendous profit through effective production and to start new pharmacies, instead of predictions through computational methods. Results were discussed based on the information obtained from Apollo pharmacies.
Multiplicative Attributes Derived from Graph Invariants for Saztec4 Diamond
Authors: Aftab Muhammad Haroon , Siddique Imran , Asamoah Joshua Kiddy K. , El-Wahed Khalifa Hamiden Abd , Hussain Muhammad
Published: September 2022

This study consists of developing some closed and updated formulas derived from multiplicative graph invariants such as general Randic index for λ0 = {±1, ± 1/2}, ordinary general geometric‐arithmetic (OGA), general version of harmonic index (GHI), sum connectivity index (SI), general sum connectivity index (GSI), 1st and 2nd Gourava and hyper‐Gourava indices, (ABC) index, Shegehalli and Kanabur indices, 1st generalised version of Zagreb index (GZI), and forgotten index (FI) for the subdivided Aztec diamond network. Aztec diamond is constructed based on the squares boxes. These square boxes are placed at the centre point and nourish the condition |s − (1/2)| + |r − (1/2)| ≤ n. Furthermore, we put a new vertex of degree‐2 at each edge of the small boxes, squares in shapes. A new structure is obtained that has the same properties as its parental graph and is called a subdivided Aztec diamond and symbolised as Saztecn. Subsequently, we compute the multiplicative topological attributes to get some new formulas. For this purpose, a simple, connected, and the finite graph is considered by supposing it Y as the graph of the Saztecn. The order and size have also been discussed in this study and found three different kinds of edges (2, 2), (2, 3), and (2, 4) for computing. The discussion on the networks mentioned above provides us with essential results that can be used in the determination of bio and physio activities and can be interspersed with the molecular compounds and their graphical structures better to understand their physical as well as biological properties.
Growth, Nutrient Retention, Waste Output, and Antioxidant Capacity of Juvenile Triangular Bream (Megalobrama terminalis) in Response to Dietary Selenium Yeast Concentration
Authors: Wu Yubo , Ma Hengjia , Fu Donghuan , Zhu Hui , Wang Xiujuan , Ren Xing
Published: September 2022

An 8-week feeding trial was conducted to investigate the effects of different dietary selenium yeast levels on growth, nutrient retention, waste output, and antioxidant capacity of juvenile triangular bream (Megalobrama terminalis). Five isonitrogenous (320 g/kg crude protein) and isolipidic (65 g/kg crude lipid) diets were formulated, with supplementation of graded levels of selenium yeast at 0 (diet Se0), 1 (diet Se1), 3 (diet Se3), 9 (diet Se9), and 12 g/kg (diet Se12). No significant differences were found in initial body weight, condition factor, visceral somatic index, hepatosomatic index, and whole body contents of crude protein, ash, and phosphorus among fish fed different test diet. The highest final body weight and weight gain rate were found in fish fed diet Se3. The specific growth rate (SGR) is closely related to dietary selenium (Se) concentrations with a relationship described as

SGR
=
–
0.0043
 
S

e

2

+
0.1062
 
Se
+
2.661

. Higher feed conversion ratio was found, while lower retention efficiencies of nitrogen and phosphorus were found in fish fed diets Se1, Se3, and Se9 than in fish fed diet Se12. Contents of selenium in whole body, vertebra, and dorsal muscle increased with dietary supplementation of selenium yeast increased from 1 mg/kg to 9 mg/kg. Lower nitrogen and phosphorous waste was found in fish fed diets Se0, Se1, Se3, and Se9 than in fish fed diet Se12. Fish fed diet Se3 exhibited the highest activities of superoxide dismutase, glutathione peroxidase, and lysozyme while the lowest malonaldehyde content in both the liver and kidney. Our results showed that the optimal dietary selenium requirement for triangular bream should be 12.34 mg/kg based on the nonlinear regression on SGR, and fish fed diet Se3 in which selenium concentration (8.24 mg/kg) was close to the optimal requirement displayed the best growth performance, feed nutrient utilization, and antioxidant capacity.
Influencing Factors of College Students’ Use of Sports Apps in Mandatory Situations: Based on UTAUT and SDT
Authors: Guo Jian
Published: September 2022

Sports apps are third‐party applications for smartphones or wearables that can help users record fitness data and guide their exercise behavior. Many Chinese college students are compelled to use sports apps for running exercises to improve their physical health and cultivate extracurricular exercise habits; however, the acceptance and use of sports apps by college students in mandatory situations requires elucidation. We explored the influencing factors of university students’ behavioral intention and usage behavior to use sports apps in mandatory situations by combining the unified theory of acceptance and use of technology and the Self‐Determination Theory. A questionnaire survey was conducted among 249 students of Liaoning University of Technology by using non‐probabilistic convenient sampling. Data analysis was performed by employing partial least squares structural equation modeling. The results showed that (1) the research model explained 66% (R2 = 0.66) of the variance in behavioral intention and 30% (R2 = 0.30) of the variance in usage behavior; (2) performance expectancy, effort expectancy, social influence, and autonomous motivation significantly positively affected behavioral intention, while controlled motivation negatively affected behavioral intention; and (3) behavioral intention, autonomous motivation, and controlled motivation significantly positively affected usage behavior. The influence of facilitating conditions on usage behavior was non‐significant. The results will help technical developers and schools to better understand the influencing factors of college students’ use of sports apps in mandatory situations, and formulate corresponding improvement strategies and policies to further promote the role sports apps play in college students’ exercise behavior.
Image Extraction of Tailings Pond Guided by Artificial Intelligence Support Vector Machine
Authors: Wu Xiaoyun
Published: September 2022

The large number of tailings ponds in my country, coupled with various man-made and natural factors in recent years, leads to the frequent occurrence of tailings pond accidents, causing serious harm. Mastering the number and distribution of tailings ponds is of great significance to prevent tailings pond accidents and carry out emergency management of tailings ponds. For the identification and monitoring of tailings ponds, the traditional survey methods are mainly based on ground surveys, and it is difficult to achieve large-scale and high-frequency detection. With the development of artificial intelligence technology, based on the support vector machine (SVM) method, the automatic extraction of remote sensing image (RSI) information has been realized, and remarkable achievements have been made in the field of remote sensing. This paper takes the tailings pond as the research goal, and based on the analysis of the characteristics of the tailings pond in the RSI, the method of SVM target detection is used to realize the rapid and high-precision identification of the tailings pond. The main contents of this paper are as follows: (1) The accident risk of tailings ponds at home and abroad and the status quo of tailings pond monitoring technology are introduced. (2) The relevant theory of SVM is learned, and the kernel function and corresponding parameter selection method of SVM are discussed in a multiclassification problem. (3) On the basis of determining the kernel function, parameters, and features, the trained model is compared with the original model. The results show that the SVM detection model proposed in this paper has excellent performance in tailings pond image recognition.
Animation Design of Multisensor Data Fusion Based on Optimized AVOD Algorithm
Authors: Ding Li , Wei Guobing , Zhang Kai
Published: September 2022

With the development of artificial intelligence, Internet of things, machine learning, and many other technologies, animation design task based on algorithm theory has become a research hotspot in the field. In recent years, perception technology has gradually become the key technology of animation design, and it is also the key research content in the current field. Whether the perception system can design animation quickly and accurately is the key of research. Compared with other algorithms, using AVOD (Aggregate view object detection) algorithm for animation design has obvious advantages. The original AVOD algorithm has some problems, such as low clustering efficiency, insufficient depth of feature extraction network, and occupying a large amount of memory. Based on this, this paper proposes to use the googleNet network and initial model of k-means++ to extract features and establish an optimized AVOD algorithm. At the same time, in order to illustrate the effectiveness of the optimization method, two typical cases are introduced to provide scientific guidance and reference for the research in this field.
Comparison of Predator-Prey Model and Hawk-Dove Game for Modelling Leukemia
Authors: Sultana Mariam , Khan Fareeha Sami , Khalid M. , Al-moneef Areej A. , Ali Ali Hasan , Bazighifan Omar
Published: September 2022

Game theory is an excellent mathematical tool to describe the interaction between the immune system and cancerous leukocytes

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. The feature of cancerous leukocytes to differentiate and mutate to give rise to leukemia is in the domain of ecological models as well. In this work, the dynamic of leukemia is described and compared by two models: firstly by a simple probabilistic mathematical model using the zero-sum two player game of Hawk and Dove, and secondly by Leslie Predator Prey model of ecology. The main goal of this study is to compare the results of both models and then discuss the treatment of leukemia i.e., Hematopoietic Stem cell transplant with the best model among them. Hawk and Dove model also describes the cell to cell interaction of cancerous leukocytes and healthy leukocytes

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after diagnoses and the condition of the patient before and after treatments. In this work, Hematopoietic Stem cell transplant is discussed by using concepts of a zero-sum three player game. Also, both models will be characterized by determining the stability properties, identifying basins of attraction, and locating the equilibrium points to see, at what extent the patient’s survival is possible with leukemia in its body. Results for both models will be presented graphically.
Alternative Teaching Methods in Electricity and Magnetism
Authors: Rendon Justine Dave L. , Doloretos Nona Lyn , Capilitan Lenilyn B. , Dumaan Darlene L. , Mamada Mary Joy D. , Mercado Justine C.
Published: September 2022

Electricity and magnetism are known as an important topics in the physics and scientific curriculum at all levels, including elementary, secondary, and tertiary. Children can learn more about electricity and magnetism via discussion and activities. Electricity and magnetism also have applications in many aspects of our daily life as one of the fundamental areas and key concerns in physics. Traditional teaching methods are considered ineffective. Pupils who were taught using traditional methods or approaches, according to Jelicic et al. (2017), have a poor comprehension of basic electromagnetic principles and are often resistant to teachers' instruction and our education now is dependent on technology. In this review, the different alternative teaching methods used and preferred by other schools are being examined and its effect in teaching Electricity and Magnetism. The included studies in this literature review proved that the preferred teaching method to motivate and appreciate the importance of learning Electricity and Magnetism. Hence, students learn more through understanding concepts and the performance of things through active participation.
Human Resource Management Practices and Job Satisfaction: Basis for Development of a Teacher Retention Framework
Authors: Mondejar Hans Christian U. , Asio John Mark R.
Published: September 2022

The objective of this research paper is to examine the association between Human Resource Management (HRM) practices and teachers' Job Satisfaction (JS) of private academic institutions in Olongapo City, Philippines. The investigators used a descriptive-correlation research design among 170 conveniently chosen respondents. The study used an adopted and modified research instrument wherein the gathered data underwent descriptive and inferential analysis. The study revealed that private academic institutions practice recruitment and selection processes, evaluate employee performance, provide training and development, appropriate compensation, career planning opportunities, and employee safety, health, and welfare. Furthermore, respondents were satisfied with their supervisors, coworkers, working conditions, compensation and responsibilities; job itself, advancement, security, and recognition. Inferential analysis revealed that the association between HRM practices and job satisfaction is highly significant. The investigators used human resources management practices and job satisfaction to develop a teacher retention framework based on these results.
Assessing the Impact of the American Heart Association’s Research Portfolio: A Scientific Statement From the American Heart Association
Authors: Creager Mark A. , Hernandez Adrian F. , Bender Jeffrey R. , Foster Mary H. , Heidenreich Paul A. , Houser Steven R. , Lloyd-Jones Donald M. , Roach William H. , Roger Véronique L. ,   on behalf of the American Heart Association Research Committee
Published: September 2022

A task force composed of American Heart Association (AHA) Research Committee members established processes to measure the performance of the AHA’s research portfolio and evaluated key outcomes that are fundamental to the overall success of the program. This report reviews progress that the AHA research program has had in achieving its goals relevant to the research programs in the AHA’s research portfolio from 2008 to 2017. Comprehensive performance metrics were identified to assess the impact of AHA funding on researchers’ career progress and research outcomes. Metrics included bibliometric analysis (ie, tracking of publications and their impact) and career development measures (ie, subsequent grant funding, intellectual property, faculty appointment/promotion, or industry position). Publication rates ranged from ≈0.5 to 4 publications per year, with a strong correlation between number of publications per year and later career stage. The Field-Weighted Citation Index, a metric of bibliometric impact, was between 1.5 and 3.0 for all programs, indicating that AHA awardee publications had a higher citation impact compared with similar publications. To gain insight into the career progression of AHA awardees, a 2-year postaward survey was distributed. Of the Postdoctoral Fellowship recipient respondents, 72% obtained academic research positions, with the remaining working in industry or government research settings; 72% of those in academic positions obtained additional funding. Among respondents who were Beginning Grant-in-Aid and Scientist Development Grant awardees, 45% received academic promotions and 83% obtained additional funding. Measuring performance of the AHA’s research portfolio is critical to ensure that its strategic goals are met and to show the AHA’s commitment to high-quality, impactful research.
Reconstructing Lokono Contributions to Science
Authors: Rybka Konrad
Published: September 2022

Since the first colonial encounter, Indigenous people have shared their unique systems of knowledge with colonial actors. Yet, their roles in colonial research and their contributions to Western science remain both obscured by the careers of their collaborators and segmented by the bounds of the disciplines they helped advance. This article is an attempt at reconstructing and recentering the life work of Johannes Karwafodi, a Lokono man from the early-twentieth-century colony of Suriname who worked with Surinamese, Dutch, and international scholars, most notably, the Penard brothers (anthropology, zoology), De Goeje (linguistics, anthropology), Stahel (ethnobotany), and Abbenhuis (anthropology). At the same time, this in memoriam is a contribution to the study of science making in the colony, its actors, formats, methods, and conditions, written with the view to contextualizing its outcomes more broadly and stimulating new research into the roles that Indigenous people played in creating them.