bluuebunny/crossref_metadata_2025 huggingface dataset:
[Retracted] Pharmaceutical Reagent Inventory Strategy Based on Contract Shelf Life and Patient Demand
Authors: Li Lingling , Liu Zheng , Qian Qingshan , Zhao Zhao , Zhao Yuanjun
Published: April 2022

As the function and R&D level of in vitro diagnostic reagents continue to improve, the need for hospitals for in vitro diagnostic reagents in clinical diagnosis also keeps increasing. However, under the influence of management, process, technology, equipment, materials, employees, and other unexpected disturbing factors, the output of reagents often has random uncertainty, and it is difficult to provide the finished products required by orders on time, in quality and quantity. A secondary supply chain consisting of reagent manufacturers, distributors, and hospitals is constructed, and the inventory control models of in vitro diagnostic reagent supply chain under three strategies of centralized decision‐making, hospital‐owned inventory, and reagent distributor‐managed inventory are established, respectively, and the maximum expected returns of the supply chain system under different strategies are analyzed to achieve the optimal production decision of reagent manufacturers and the optimal procurement decision of hospitals. The results show that reducing the random output probability and patient demand uncertainty has a significant effect on improving the expected return of in vitro diagnostic reagent supply chain, and as the random output probability of reagent manufacturers and patient consumption demand uncertainty increase, the strategy of managing inventory by distributors in collaboration is always better than the strategy of managing inventory by hospitals' own warehouses, which can achieve higher expected return and better inventory quantity, but when the out‐of‐stock cost of hospitals is too high above a certain threshold, the hospital will tend to adopt the self‐inventory strategy.
Asymmetric Power Hazard Distribution for COVID-19 Mortality Rate under Adaptive Type-II Progressive Censoring: Theory and Inferences
Authors: El-Morshedy Mahmoud , El-Sagheer Rashad M. , Eliwa Mohamed S. , Alqahtani Khaled M.
Published: April 2022

This article investigates the estimation of the parameters for power hazard function distribution and some lifetime indices such as reliability function, hazard rate function, and coefficient of variation based on adaptive Type-II progressive censoring. From the perspective of frequentism, we derive the point estimations through the method of maximum likelihood estimation. Besides, delta method is implemented to construct the variances of the reliability characteristics. Markov chain Monte Carlo techniques are proposed to construct the Bayes estimates. To this end, the results of the Bayes estimates are obtained under squared error and linear exponential loss functions. Also, the corresponding credible intervals are constructed. A simulation study is utilized to assay the performance of the proposed methods. Finally, a real data set of COVID-19 mortality rate is analyzed to validate the introduced inference methods.
Intelligent Integration Method of Ideological and Political Education Resources Based on Deep Mining
Authors: Li Aiqin , Huang Yuzhen
Published: April 2022

An intelligent integration method of ideological and political education resources based on deep mining is proposed to address the problems of low accuracy, efficiency, and stability of resource integration in the current ideological and political education resource integration method. The ideological and political education resources are deeply mined by studying deep mining technology and applying the decision tree algorithm. Preprocess the text of ideological and political education materials after you've dug deep into the ideological and political education resources. Word segmentation based on user dictionary to remove rare words in ideological and political education resources. Based on the principle of educational information remote scheduling, the feature vector of ideological and political education resources is extracted, the characteristic variance contribution rate of ideological and political education resources is calculated, and the observable random vector of the characteristics of ideological and political education resources is obtained. To achieve intelligent integration of ideological and political education resources, the correlation degree is defined using the LDA model's subject text vector, and the text of ideological and political education resources is grouped using the bottom-up hierarchical clustering approach. The experimental results show that the proposed method has high accuracy and efficiency, and can effectively enhance the stability of the intelligent integration of ideological and political education resources.
Tuning of Estimated Sound Absorption Coefficient of Materials of Reverberation Room Method
Authors: Tang Yaochi , Chuang Xiang-Jun
Published: April 2022

Acoustical materials are usually used for architectural design, ambient noise, and traffic noise to absorb sound. The sound absorption coefficient (SAC) is often used as the performance index of acoustical materials. It is measured using the reverberation room method (RRM) and the impedance tube method (ITM). The sound source of the RRM is a random incidence in various directions, whereas that of the ITM is a normal incident. The SAC measured by the RRM is more extensive, practical, and costly than ITM. Usually, the SAC measured by RRM is used as the commercially available acoustical materials index. However, RRM requires large-area test samples for measurement and a special sound field of reverberation room for test experiments. Therefore, this study aims to use the ITM to measure SAC. The measured SAC, frequency, thickness, and material density were employed as independent variables and substituted in the obtained multiple regression model to predict SAC obtained by RRM. In addition, the measurement characteristics of impedance tubes provide varisized calibers for different frequency ranges to measure a relatively accurate SAC. Based on the abovementioned characteristics, this study proposed a dual-model for estimating the SAC of the reverberation room method to achieve the best-estimated result. After experimental validation, the dual-model estimated result was compared with SAC from the reverberation room method. It was observed that the maximum absolute value of all errors did not exceed 0.16. Most of the absolute errors were below 0.1, proving the accuracy of this method. Therefore, the method proposed in this study can shorten the acoustical material development schedule for manufacturers and save the cost of the development process.
Research on Internet of Things Context-Aware Information Fusion Technology for Smart Libraries
Authors: Chen Xue , Hao Qing
Published: April 2022

In the information age, the rapid innovation and development of information technology plays an important supporting role in the dissemination and utilization of information. As an important concentration of literature information and knowledge resources, the library also actively introduces various advanced technologies, optimizes the service model, and transforms to intelligence. This paper expounds the development status of smart library and context-aware technology at home and abroad, analyzes the application of context-aware technology in smart library, constructs a framework of smart library based on context awareness, and puts forward some suggestions for smart library’s context-aware service.
Reduction of the Rank Calculation of a High-Dimensional Sparse Matrix Based on Network Controllability Theory
Authors: Zhao Chen , Liu Yuqing , Hu Li , Yuan Zhengzhong
Published: April 2022

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the Internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable.
Fabrication and Sc-Refinement of SiCp/A356 Composites by Vacuum Stirring Casting Method
Authors: Chen Fukun , Wang Kuangfei
Published: April 2022

Aluminum matrix composites reinforced with 10 vol.% SiC particles (SiCp/Al MMCs) were prepared throughthe vacuum stirring method from SiC particles preoxidated at high temperature, followed by posttreatments of remelting and Sc addition treatment. Major problems during vacuum stirring preparation were investigated. Results show that overoxidation of SiC particles may occur during the vacuum stirring process, which causes loss of SiC particles and increases in “impurity pores” after solidification or aggregation of SiC particles, leading to scarcely improved mechanical properties of the prepared SiCp/Al composites. After Sc addition treatment, the α-Al and eutectic Si phases were obviously refined. The composite matrix was strengthened. The result indicates that the maximum tensile strength of 10 vol.% SiCp/A356 composites with Sc addition treatment was 193.5 MPa, which was increased by 43% compared with the matrix alloy. Moreover, a fracture at particles was detected after Sc addition treatment. The stress could be effectively transferred to the SiC particles when the fracture occurred.
Deep Eutectic Solvents (DESs)-Ultrasonic-Assisted Extraction of Paeoniflorin and Paeonol from Moutan Cortex
Authors: Chen Zhuo , Zhang Jinnan , Yang Fuwei
Published: April 2022

Deep eutectic solvents (DESs-)-ultrasonic-assisted extraction technology was used to evaluate and optimize the extraction process of paeoniflorin and paeonol in Moutan Cortex. First of all, 10 kinds of choline chloride-based DESs were selected as potential extraction solvents, and the DES extraction effect of choline chloride: lactic acid was determined to be the best. Then, single-factor experiments were conducted to explore the effects of DES composition (molar ratio, water content, and ratio of material to liquid) and ultrasonic treatment (ultrasonic power, extraction time, extraction temperature, and vortex time) on the yield of paeoniflorin and paeonol in Moutan Cortex. On this basis, the response surface method was used to optimize the process of ultrasonic-assisted extraction of paeoniflorin and paeonol from Moutan Cortex with DESs. The results showed that the optimum process parameters of DESs-ultrasonic-assisted extraction of paeoniflorin and paeonol from Moutan Cortex were as follows: molar ratio of choline chloride to lactic acid was 1 : 2, water content was 30%, ratio of material to liquid was 1 : 30 (g/mL), ultrasonic power was 300 W, extraction time was 33 min, extraction temperature was 50°C, and vortex time was 5 min. Under these conditions, the yield sum of paeoniflorin and paeonol in the obtained Moutan Cortex was 9.279 mg/g. The difference between the experimental value and the theoretical value was 0.333 mg/g. The results showed that the response surface method can simulate and predict the yield of paeoniflorin and paeonol in Moutan Cortex under different extraction conditions, and it was feasible to optimize the process parameters. The results of this study show that DES has great potential to replace traditional solvents in the extraction of active components of the Moutan Cortex. The study on the extraction of active components of traditional Chinese medicine by DES is helpful to expand the scope of the application of DES in traditional Chinese medicine.
Enterprise Collaborative Integrated Management System Based on IoT Cloud Technology
Authors: Jia Dian , Wu Zhaoyang
Published: April 2022

With the advent of the white-hot competition environment at home and abroad, enterprises must continuously improve their management level to reduce operating costs, so as to achieve the purpose of improving operating efficiency, highlighting competitive advantages, and improving core competitiveness. The purpose of this research is to realize the sharing and collaboration mechanism of government resources, enterprise resources, and other resources in the digital ecological environment, so as to realize the transformation of enterprise productivity and production mode to a higher-end mode, and enhance the innovation ability of enterprises. On the basis of cloud computing theory and service computing theory, this research has carried out service model innovation, analysis, and design and realizes a service-oriented open collaborative sharing, multiorganization, multitenant, and dynamically scalable intelligent enterprise collaborative innovation cloud service platform. By selecting 20 valid scoring data, 80% of the scores are distributed in 3-5 points, indicating that the collaborative innovation cloud service platform for smart enterprises proposed in this paper realizes collaborative sharing among multiple organizations and multiple tenants. It highlights the matchmaking between enterprises and high-quality innovation resources, alleviates the uneven distribution of innovation resources, bridges the gap between various innovation entities, and provides a scalable and sustainable evolution of collaborative innovation cloud service environment.
Prediction of the Short-Term Risk of New-Onset Renal Dysfunction in Patients with Type 2 Diabetes: A Longitudinal Observational Study
Authors: Xu Jianbo , Shan Xiaoyun , Xu Yina , Ma Yongjun , Wang Huabin
Published: April 2022

Background. Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D. Methods. This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated. Results. During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (

P
<
0.05

). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (

C
−
index
=
0.785

,

P
<
0.001

). This model was validated using the

K

-fold cross-validation method; the accuracy was

0.815
±
0.013

in training sets and

0.784
±
0.019

in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction. Conclusion. The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.
Role of the Long Intergenic Non-Protein-Coding RNA 1278/miR-185-5p/Cystatin SN Axis in Laryngeal Cancer Cells
Authors: Shen Bin , Ding Haibin , Ye Yanyan , Luo Baozhen
Published: April 2022

Laryngeal cancer accounts for 25%–30% of tumors in the head and neck. Cystatin SN (CST1) was revealed to show upregulated expression in this cancer, while its functions and upstream pathway remain unknown and need investigation. The current study was designed to solve this problem. We designed short hairpin RNAs targeting CST1 for the loss-of-function assays to probe the influences of CST1 in laryngeal cancer cell proliferation and motility. The upstream competitive endogenous RNA pattern of CST1 was searched using bioinformatics analysis and confirmed by luciferase reporter assays. The experimental results demonstrated that CST1 is a tumor facilitator in laryngeal cancer by stimulating cellular proliferative, migrative, and invasive abilities. CST1 is regulated by the long intergenic non-protein-coding RNA 1278 (LINC01278)/miR-185-5p axis. LINC01278 knockdown and miR-185-5p overexpression exert the same functions as CST1 knockdown to repress cancer cell proliferation, migration, and invasion. In conclusion, LINC01278 plays an oncogenic role in laryngeal cancer by suppressing miR-185-5p to enhance CST1 expression, which enriches the molecular mechanism for the carcinogenesis of laryngeal cancer.
Named Entity Recognition of Ancient Poems Based on Albert-BiLSTM-MHA-CRF Model
Authors: Zhou Faguo , Wang Chao , Wang Jipeng
Published: April 2022

The extraction and construction of the knowledge graph related to the entity of ancient poems are helpful to excavate the connection between ancient poems, and it is of great significance to inherit the traditional Chinese culture. This paper proposes an Albert-BiLSTM-MHA-CRF model for entity extraction in ancient poems. Based on the BiLSTM-CRF model, the author introduces the Albert pretraining model and the multihead self-attention mechanism to extract character vectors and enhance the generalization ability of word embedding vectors and the potential semantics between characters of the model, depending on weight and other feature extraction capabilities. The experiment is carried out in the corpus of ancient poetry, and the model is compared with Bert-BiLSTM-CRF, BiLSTM-CRF, and CRF model. The results show that the entity extraction effect of ancient poetry is significantly improved, and the harmonic average value is 97.17%. Compared with Bert model as the pretraining model, Albert model reduces the time by 19.56%.
Expression and Related Mechanisms of miR-100 and TRIB2 in COPD Patients
Authors: Mai Zhitao , Mi Ya , Jiang Mingming , Wan Shanzhi , Di Qingguo
Published: April 2022

Background. Chronic obstructive pulmonary disease (COPD) is one of the most common chronic respiratory diseases in the world. COPD is a general term for a class of lung diseases, including emphysema, chronic bronchitis, and refractory asthma. It is characterized by irreversible airflow obstruction and chronic tracheal inflammation. Objective. This study aimed to investigate the expression and related mechanisms of miR-100 and TRIB2 in patients with COPD. Methods. We collected the serum of patients admitted to our hospital and healthy volunteers undergoing physical examination at the same time, pulmonary fibroblasts were purchased for the experiments, miR-100 was overexpressed, and TRIB2 expression was inhibited in cells. The miR-100 and TRIB2 expression levels in serum and cells were detected by qRT-PCR and Western blot, cell proliferation and apoptosis were detected by CCK-8 and flow cytometry, and the relationship between miR-100 and TRIB2 was explored by the dual-luciferase report. Results. The miR-100 expression in the serum of the COPD group was expressed normally, while the TRIB2 expression was expressed abnormally

p
<
0.05

. The AUC of serum miR-146a and TRIB2 for COPD diagnosis were 0.965 and 0.954, respectively. Overexpressing miR-100 and inhibiting the TRIB2 expression could decrease cell proliferation and increase apoptosis rate. According to the dual-luciferase report, miR-100 and TRIB2 had a targeted regulatory relationship. Rescue experiments showed that overexpressing TRIB2 could reverse the changes of cell proliferation and apoptosis caused by overexpression of miR-100. Conclusion. miR-100 and TRIB2 were expressed abnormally in serum of COPD patients, and miR-100 could inhibit proliferation of pulmonary fibroblasts and promote their apoptosis.
Association Rule Mining Algorithm in College Students’ Quality Evaluation System
Authors: Lei Jing
Published: April 2022

An association rule mining algorithm is an algorithm that mines the association between things and is often used to mine the association knowledge between things. Association rule mining algorithms can find potential connections between different qualities of college students from the data of college students’ life and learning, which can help teachers discover the problems and their own strengths of different students and achieve teaching according to their aptitude. The purpose of this paper is to solve some problems related to the associative rule extraction algorithm and to investigate the impact of applying the associative rule extraction algorithm in a college student quality assessment system. Based on the algorithm, a quality assessment system for college students has been developed. A modified script-based associative rule extraction algorithm is used to find the correlation between the quality and the ability of college students. The quality assessment data of college students are analyzed and studied. The results show that the use of associative rule extraction algorithms to assess the quality and ability of college students can improve the efficiency of the test by 24% and the accuracy of the test score by 33% and reduce the probability of outliers in the scoring process by 27%. It can be seen that the association rule extraction algorithm can be applied to college students’ quality assessment system and also reduces the probability of encountering obstacles in accuracy and performance assessment. At the same time, this experiment also proves the robustness and feasibility of the algorithm in this paper.
Analysis of Hazard Factors Affecting the Quality of Life for Lung Cancer Patients after Chemotherapy
Authors: Wen Lianying , Liao Xiaohong , Cao Yanjin , Liu Yanxiu , Wu Wei , Chen Yan , Liu Huiyu
Published: April 2022

Objective. To explore the hazard factors affecting the quality of life for patients with lung cancer after chemotherapy, so as to provide evidence-based clinical proof to improve their quality of life. Methods. The clinic data of lung cancer patients treated with chemotherapy from November 2020 to November 2021 in our hospital were selected for retrospective analysis. A questionnaire survey was administered to 74 patients who met the inclusion criteria. The general condition questionnaire made by the department was used to analyzed the patients’ demographic characteristics and disease status. The quality of life questionnaire for Chinese cancer patients receiving chemobiotherapy (QLQ-CCC) was used to evaluate the patients’ quality of life. Results. The research objects selected for this study scored an average of (94.53 ± 22.65) points in the quality of life. It was found by logistic regression analysis that age, education level, marital status, pathological types, monthly family income, and metastasis were the independent influencing factors that lowered the quality of life of lung cancer patients after chemotherapy (P < 0.001). Conclusion. Chemotherapy can lead to a decline in the quality of life of lung cancer patients, and there are many factors at play. Therefore, medical and nursing staff should focus on the overall condition of patients when carrying out clinical treatment.
A Cross-Cultural Examination of the Sexting Motives and Attitudes: Bosnia and Herzegovina vs. Croatia
Authors: Dodaj Arta , Sesar Kristina , Novak Tihana
Published: April 2022

Many sexting studies conducted in the Western cultures have shown that the percentage is higher in less traditional cultures. However, the generalizability of this phenomenon to non-Western cultures has not been extensively researched. The purpose of this study is to examine and explain cross-cultural differences in sexting behavior among subjects from Bosnia and Herzegovina and Croatia. A mixed methods approach was used. The first, qualitative phase included focus groups with two groups of high school students from Bosnia and Herzegovina and Croatia (

N
=
57

), aged 15 to 19. In the second, quantitative phase, questionnaires were sent to 440 young adults with an average age of 21 years (

SD
=
3.8

). From the interviews with the young people, nonconsensual sexting was perceived as less beneficial. Sexting was viewed as a double standard. The quantitative study revealed gender and country differences in attitudes toward sexting and motives for sexting. Positive attitudes toward sexting were found to predict different forms of sexting motives in both samples. In the sample from Bosnia and Herzegovina, age was found to predict sexting with instrumental motives and body image reinforcement motives. Gender, on the other hand, was found to be predictive of instrumental sexting motives in the Croatian sample and body image reinforcement sexting motives in the Bosnia and Herzegovina sample. This study illustrates the value of cross-cultural approaches combined with mixed methods as a design to study sexting behavior.
Promoting Competitiveness of Green Brand of Agricultural Products Based on Agricultural Industry Cluster
Authors: Liu Yishu , Wang Xia
Published: April 2022

Agricultural industrial clusters are the basis for the formation of regional brands of agricultural products and also an effective way to enhance the regional brand value and competitiveness of agricultural products. Based on the agricultural industry cluster, carry out regional brand building of agricultural products, emphasizing that the production of agricultural products from raw material bases to product sales in specific administrative areas, and even the collaborative production services of relevant agricultural technology research and development institutions are connected to the local network, establishing a regional first-class agricultural product brand. The brand grows with the development of regional production bases. Therefore, this paper is aimed at exploring the research on the promotion of green brand competitiveness of agricultural products based on the theory of agricultural industrial clusters, analyzing the internal connection between agricultural product agglomeration effect and brand competitiveness, and exploring its advantages and disadvantages. Using this strategy, this paper will use the research method of specific analysis of specific problems, design an analysis model construction experiment based on the influencing factors of agricultural industrial clusters, and draw experimental conclusions through the analysis and comparison of experimental data. Through theoretical innovation and exploration, a model for the rapid development of green agricultural product brands can be found. The results show that the use of questionnaires to explore the differences in the planting characteristics of green agricultural products in industrial clusters can account for 35% of the brand competitiveness, promote the rapid development of the brand, and enhance the technological innovation ability by 19%. Therefore, combined with the characteristics of the current era, with the help of the national agricultural aid policy, fully absorb the transformation, actively innovate, and improve the level of agricultural cultivation. Analyze different application difficulties, explore development prospects, give full play to the competitiveness of agricultural products, realize high-yield green crops, use these theories as a guide to innovate and integrate, provide valuable experience clusters for the wide application of agricultural industrialization, and achieve sustainable regional economic structure and ecological environment.
College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future
Authors: Zhang Yu
Published: April 2022

With the global integration and the development of science and technology, higher requirements have been put forward for English teaching in China. Society urgently needs English talents with high English cross-cultural communication ability and strong self-learning ability. The concept of “Teaching for the Future” (TTF) advocates the use of technology as a tool for teaching and learning. In the context of the “big data” era, the rapid development of network information technology has spawned a series of new educational models. Based on the SPOC teaching model, this paper constructs a new cross-cultural teaching model for college English and conducts experiments on two selected classes of the same grade in a university. The experimental results show that students increase their interest in learning English culture and enhance their ability to communicate and collaborate. Therefore, the integration of future education and network technology represented by SPOC will play a certain role in promoting the cross-cultural teaching of English in colleges and universities.
Systematic Analysis and Modelling of Profit Maximization on Carsharing
Authors: Giorgione Giulio , Kliazovich Dzmitry , Bolzani Luca , Ciari Francesco , Viti Francesco
Published: April 2022

The success of carsharing as a relatively new and more sustainable way of traveling is moving private car ownership towards a service use model. Competitivity is an essential aspect of this service and ways to increase profit while offering the most appealing service are still getting explored. Among others, dynamic pricing strategies can be designed to increase profit by attracting more users, selling more rental hours, or maximizing fleet utilization. In this paper, we propose an experimental method aimed at developing a model for maximizing service profit. Using agent-based modeling to generate realistic scenarios, we analyze pricing as a function of the potential demand (i.e., number of members) and supply (hours of booking supplied). The process of reaching the maximum profit consists of testing various combinations of pricing-demand and pricing-supply ranges in order to find the values that maximize the profit for every demand and supply level. Once the optimal prices are known, a polynomial fitting and an optimization method are used to generate a functional form linking all the maximal profit obtaining the advised price to offer for any specific supply levels. Results show how the profit only slightly depends on the variability of the potential demand, while it strongly depends on the amount of supply. It is then shown how it is possible to obtain a linear relation that maximizes the profit in the function of the price offered once the supply is given.
A 3D Peridynamic Model to Simulate Indirect Tensile Failure in Marble
Authors: Tan Wenwu , Zhou Keping , Zhou Shitong , Ren Weicheng
Published: April 2022

Driven by the commitment of achieving peak carbon dioxide emissions before 2030 and carbon neutrality before 2060, China is promoting the optimization of its energy and industry structures. In the electricity supply industry, a large number of different types of sophisticated large and giant hydropower stations are planned or being constructed in western China. In future decades, hydropower may take the dominant position in China’s electricity supply. Rock fracturing behavior plays a vital role in geotechnical hazard prevention and geotechnical engineering design. With the construction of hydropower stations, rock mechanics investigation will be of great concern. This study presents a hybrid model that coupled the finite element method and peridynamic theory to simulate the indirect tensile strength of marble. Sensitivity analyses of the loading rate, mesh size, and fracture energy release rate are performed. The numerical results indicate that both the loading rate and mesh size significantly affect the numerical representation of rock properties. After the calibration of the fracture energy release rate, Brazilian tensile strength modeling is successfully conducted, and the numerical results are consistent with the experimental results.
Construction and Implementation of Procedural Nursing System for General Surgery Laparoscopic Surgery Based on Deep Learning
Authors: Zheng Yonghong , Cheng Xufang , Shao Jingtao
Published: April 2022

In order to explore the construction and implementation effect of a procedural nursing system for laparoscopic surgery in general surgery based on deep learning, this article selects 150 cases of laparoscopic surgery patients admitted to our hospital from January 2020 to January 2021 for research. According to the time of enrollment, the control set and the study set were included in order, with 75 cases in each set. The control set was given routine nursing methods, and the research set was given the management of programmed nursing system based on deep learning. The nursing quality, pain, postoperative recovery, and incidence of complications were compared between the two sets. Logistic regression multivariate analysis of the risk factors for postoperative complications in patients undergoing laparoscopic surgery in general surgery was performed. Based on deep learning, the construction of the procedural nursing system for laparoscopic surgery in general surgery is applied to the nursing management of general surgery laparoscopic surgery, which can improve the quality of care and the VAS score of the patient's pain level, and reduce the incidence of complications. Underlying diseases and routine nursing are risk factors for complications of general surgery laparoscopic surgery, suggesting that corresponding prevention and control work should be done in the procedural nursing of general surgery laparoscopic surgery based on deep learning.
A Projection-Free Adaptive Momentum Optimization Algorithm for Mobile Multimedia Computing
Authors: Wang Lin , Zhou Yangfan , Wang Xin , Ji Zhihang , Liu Xin
Published: April 2022

In mobile multimedia applications, deep learning has received significant interest. Due to the limited computation and storage resources of mobile devices, however, general training methods are hardly suited for mobile multimedia computing. For this reason, we propose an adaptive momentum training (FWAdaBound) algorithm to reduce computation and storage cost, where the Frank-Wolfe method is employed. Furthermore, we rigorously prove the regret bound in order that

O

T

3
/
4

can be achieved, where

T

is a time horizon. Finally, the convergence, cost-reduction, and generalization ability of FWAdaBound are validated through various experiments on public datasets.
One Health Perspective of Salmonella Serovars in South Africa Using Pooled Prevalence: Systematic Review and Meta-Analysis
Authors: Ramatla Tsepo , Tawana Mpho , Onyiche ThankGod E. , Lekota Kgaugelo E. , Thekisoe Oriel
Published: April 2022

Salmonella is a bacterium that is commonly associated with food-borne infections and is regarded as one of the most important pathogens in public health. Salmonella serovars, particularly Typhimurium and Enteritidis, which are widely distributed globally, mainly result in outbreaks commonly linked to the consumption of animal products. This study is a systematic review and meta-analysis of studies reporting the prevalence of Salmonella serovars from one health perspective that included human, environmental, and animal samples in South Africa. PubMed, ScienceDirect, African Journals Online, and Scopus databases were used to conduct extensive searches of articles which were ultimately included or excluded following the Systematic Reviews and Meta-Analysis (PRISMA) guidelines. According to the data obtained in this review, the overall pooled prevalence estimates (PPE) of Salmonella serovars detection were 79.6%, 61.6%, 56.5%, and 43.2% for human, environment, animal, and environment/animal samples in South Africa, respectively. The majority of the studies (50%) used the polymerase chain reaction (PCR) technique for the detection of Salmonella serovars, followed by culture methods (26.7%), while 20% used serotyping. The PPE for nontyphoidal Salmonellae (NTS) was 65.6% and 34.4% for Salmonella Typhimurium and Salmonella Enteritidis, respectively. Our data further shows that 3 serovars, namely, Salmonella Typhimurium, Salmonella Enteriditis, and Salmonella Hadar, have been isolated from animals, humans, and the environment in South Africa. Our results highlight the ongoing spread of Salmonella spp. especially on animals which might end up infecting humans via direct contact with infected animals or eating infected animal products. This calls for deliberate “One Health” epidemiological studies in order to document information on the transmission between humans, animals, and the environment. This will ultimately result in the formulation of a consolidated salmonellosis control policy by the environmental, human, and veterinary health sectors.
Artificial Neural Network for Folk Music Style Classification
Authors: Ning Qinliang , Shi Junyan
Published: April 2022

Folk music style classification is of great significance. Traditional folk music style classification has difficulties in feature selection, and the existing folk music style methods based on deep learning also have shortcomings. In this paper, we use artificial neural networks to classify folk music styles and transform audio signals into a sound spectrum. In this paper, we use artificial neural networks to classify folk music styles and transform audio signals into a sound spectrum to avoid the problem of manually selecting features. Further, we combine the characteristics of the music signal and a variety of music data enhancement methods to enhance the music data. The proposed model can extract elements of the sound spectrum that are more closely associated with a certain music style category. Experimental results reveal that the proposed method achieves a high accuracy rate, which verifies the effectiveness of our model.
Pothole Detection Using Deep Learning: A Real‐Time and AI‐on‐the‐Edge Perspective
Authors: Asad Muhammad Haroon , Khaliq Saran , Yousaf Muhammad Haroon , Ullah Muhammad Obaid , Ahmad Afaq
Published: April 2022

Asphalt pavement distresses are the major concern of underdeveloped and developed nations for the smooth running of daily life commute. Among various pavement failures, numerous research can be found on pothole detection as they are injurious to automobiles and passengers that may turn into an accident. This work is intended to explore the potential of deep learning models and deploy three superlative deep learning models on edge devices for pothole detection. In this work, we have exploited the AI kit (OAK‐D) on a single‐board computer (Raspberry Pi) as an edge platform for pothole detection. Detailed real‐time performance comparison of state‐of‐the‐art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny‐YOLOv4, YOLOv5, and SSD‐mobilenetv2) for pothole detection is presented. The experimentation is performed on an image dataset with pothole in diverse road conditions and illumination variations as well as on real‐time video captured through a moving vehicle. The Tiny‐YOLOv4, YOLOv4, and YOLOv5 evince the highest mean average precision (mAP) of 80.04%, 85.48%, and 95%, respectively, on the image set, thus proving the strength of the proposed approach for pothole detection and deployed on OAK‐D for real‐time detection. The study corroborated Tiny‐YOLOv4 as the befitted model for real‐time pothole detection with 90% detection accuracy and 31.76 FPS.
Construction and Path Analysis of Enterprise Green Technology Innovation Model Using Data Mining
Authors: Chen Xiuxiu , Bu Hua
Published: April 2022

With the rapid increase of resource consumption and ecological protection demand, the economic growth model of China has also changed to focus on efficiency and quality. In the future, the circular economic model will be the main direction of sustainable development. According to the analysis of international development trends, green consumption has become the development trend of the times. Some developing countries also put forward the concept of green trade in the process of trade. It results in a severe living environment and challenges for some Chinese enterprises. For this challenge, China should grasp the economic development trend and change the business model by adopting the green technology innovation model. It will reduce consumption and pollution, improve efficiency, and, also, build the enterprise green technology innovation model to greatly improve the production efficiency and resource utilization of the enterprises. This paper uses data mining technology to establish a mathematical model of cluster analysis. It describes the process of enterprise technological innovation in detail and analyzes the power source of enterprise technological innovation and several commonly used models. Through this model, the enterprise technological innovation ability can be comprehensively improved. It focuses on the analysis of the enterprise green technology innovation path based on data mining. It analyzes the enterprise green technology innovation path from the points of reducing transaction costs and improving the allocation efficiency of innovation elements. According to this path, the enterprise transaction costs can be reduced to a certain extent and the allocation efficiency of enterprise elements can also be realized.
Synthesis and Numerical Analysis of Compliant Devices: A Topology Optimization Approach for Mechanisms and Robotic Systems
Authors: Premanand S. , Arunkumar G. , Sundramurthy Venkatesa Prabhu
Published: April 2022

The topology optimization design invariably shall be used in various applications such as four bar mechanisms, robotics designs, aircraft engineering designs, and many other mechanical innovative systems for improving the efficiency in the system. This research paper emphasizes more on general topology optimization design for a rectangular domain in which numerically analyzed with defined boundary conditions. Furthermore, the same setting geometry has been taken for sensitivity analysis to find the objective stress and nonstress zones. Then, the geometry is topology optimized to analyze stress, safety factor, output deflection, and mass reduction. Also this research work focuses more on topology optimization, design synthesis, and objective function comparison in different materials. Hence, the results are suitable volume and mass reduction in various robotic devices. Validation and comparison of compliance base materials will further support the paper to extend the work for experimental analysis. This final topology optimized and validated device can be manufactured and forced to experimental fatigue endurance test cycle test condition in static and dynamic state. The results outshoot the final destination of this optimization design.
Construction and Evaluation of Transformation Mode of the Achievements in Science and Technology in Colleges under the Background of Artificial Intelligence
Authors: Li Zhibin , Xu Yadan
Published: April 2022

The main objective of this work is to construct and evaluate the transformation mode of scientific and technological achievements in colleges and under the background of the wireless network for which an artificial intelligence backpropagation neural network (BPNN) model is utilized. Firstly, the work introduces the transformation mode of scientific and technological achievements. Secondly, the work establishes the evaluation system of the transformation mode of scientific and technological achievements in colleges and describes the steps of the fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) in detail. Thirdly, it determines the weight value of the evaluation indexes of the transformation of scientific and technological achievements in colleges and conducts the consistency and fuzzy comprehensive evaluation of these evaluation indexes. Finally, to further verify the scientificity and practicality of the index system, an artificial intelligence BPNN model is implemented to further evaluate the system. It is evident from the results that FCE of the transformation level of scientific and technological achievements in colleges shows that the excellent credibility accounts for 32.73%, the good accounts for 53.24%, the medium accounts for 14.33%, and the poor is 0. The transformation of scientific and technological achievements in colleges is generally at the upper-medium level. The calculation of the weight of the indicators indicates that all the indicators established greatly impact the transformation mode of scientific and technological achievements in colleges. The results of the BPNN model show that the learning and prediction ability of the evaluation model has reached a high level, which verifies the scientificity and practicality of the evaluation index system. The exploration provides a reference basis for the construction and evaluation of the transformation model of scientific and technological achievements in colleges.
Cognitive Impairment and Kidney Transplantation: Underestimated, Underrecognized but Clinically Relevant Problem
Authors: Golenia Aleksandra , Malyszko Jacek S. , Malyszko Jolanta
Published: April 2022

Background: Chronic kidney disease (CKD) affects the crosstalk between organs in the body and vast majority of studies were devoted to the interactions between the kidneys and the cardiovascular system. As of today, there is more evidence of the kidney and the central nervous system connections. Summary: Indeed, CKD and in particular dialysis therapy is linked to the increased prevalence of neurological complications, such as cerebrovascular disorders, movement disorders, cognitive impairment, and depression. Both traditional cardiovascular risk factors (such as diabetes, hypertension, and lipid disorders), nontraditional risk factors (such as uremic toxins, anemia, and secondary hyperparathyroidism) may predispose CKD patients to neurological disorders. Likewise, cognitive problems occur more commonly in kidney transplant recipients, regardless of age, than in the general population, but the prevalence is still understudied. Cognitive impairment is associated with a higher risk of hospitalization, mortality, decreased quality of life, or health care costs in kidney transplant recipients. Here, we review (i) the potential clinical impact of kidney transplantation on cerebrovascular and neurological complications, (ii) evaluation of patients with cognitive impairment for kidney transplantation (iii) the potential impact of cognitive impairment on waitlisted and transplanted patients on patient care, and (iv) unmet medical needs. Key Messages: Cognitive impairment in kidney transplant recipients is an underestimated, underrecognized but clinically relevant problem. The screening for cognitive declines after kidney transplantation is not yet a routine practice. Several prospective and cross-sectional studies reported improvement across some of the assessed cognitive domains after transplantation.