bluuebunny/crossref_metadata_2025 huggingface dataset:
Empirical Analysis for Improving Food Quality Using Artificial Intelligence Technology for Enhancing Healthcare Sector
Authors: UmaMaheswaran S. K. , Kaur Gaganpreet , Pankajam A. , Firos A. , Vashistha Piyush , Tripathi Vikas , Mohammed Hussien Sobahi
Published: May 2022

Artificial intelligence or AI has a wide range of applications in healthcare and food industries. AI helps in different ways in medical industries, such as analysing the disease progression rate, effective prediction of treatment method, and proper disease diagnosis. Advantages of artificial intelligence in the food business include enhanced customer accessibility, improved technological innovation, readily accessible client requirements and comments, strategic advantage through unique products, and plenty others. Different AI technologies such as “Machine Learning (ML),” “Neural Language Processing (NLP),” “Rule-Based Expert Systems (RESs),” “Deep Learning (DL),” and so on are used in healthcare and food industries for big “medical data” analysis. This study has applied three critical variables to measure the application of AI in enhancing food quality (viz., usage of machine learning models, NLP models, etc.). This study has stated that these models support in enhancing the overall food quality in an effective manner. The present research analyses the importance of these AI technologies in enhancing service quality in healthcare and food industries. A primary survey-based data analysis has been done with 153 individuals taken from healthcare industries. Moreover, statistical analysis has been done in this research with SPSS software. Four independent variables are taken in this research, which are ML, NLP, RES, and DL. The service quality of healthcare has been taken as a dependent variable, and the effect of independent variables on “enhancing healthcare service” has been analysed. Secondary thematic analysis has been done to justify primary data. The results show that 43.79% of the individuals have supported DL and 56.86% have supported the treatment prediction ability AI. 37.9% of the individuals have also supported AI over traditional medications. Further analysis has shown that independent variables ML, DL, NLP, and RES have a strong positive correlation with improving SQ. These results have been justified by secondary journals, and it is proved that AI technologies enhance the service quality in healthcare and food sectors.
Effective Strategies for Optimizing College Students’ Innovation and Entrepreneurship Education under the Background of “Internet +”
Authors: Jia Fengzhen , Guo Zhaoying , Huo Jiaofei
Published: May 2022

It is an unavoidable requirement of higher education to thoroughly refine the scientific concept of development and explore the innovative and entrepreneurial education of college students. Wide and effective creative and business literacy training for college students is an important means to reduce the fierce competition for jobs and an effective way to further build an innovative country. The purpose of this paper is to conduct an in-depth study on effective strategies to optimize creative and business literacy training for college students in the context of Internet+. This paper examines how to optimize college students’ creative and business literacy training in the context of Internet+, explains the related concepts and development status of college students’ creative and business literacy training, and discusses the value and practical significance of college students’ creative and business literacy training as well as the current innovation of college students in China. Opportunities, challenges, and problems are encountered in entrepreneurship. Survey experiments were conducted. The results show that more than 83.6% of graduates have some idea about creative and business literacy training, but the satisfaction of college students with the current creative and business literacy training is only 58.2%. Finally, this article suggests five points for optimizing college students.
Optimization Analysis of the Emergency Logistics Identification Method Based on the Deep Learning Model under the Background of Big Data
Authors: Song Aifeng , Yang Xue , Ni Likun , Liu Chen , Yao Yujia , Pan Langxuan
Published: May 2022

Vehicle routing, which is effective and efficient, is a dominant aspect of supply chain management in general and deep learning (DL) in particular. It is also a right step towards the fuel conservation and environmental concern in disposing used commodities. Economics of logistics and transportation plays a major role in deciding the competitiveness of the product, either new or used, in the market. With the upward trends of fuel and logistics costs, manufacturing industries have little option other than keeping the cost of transportation the lowest. Many organizations now started implementing lesser expensive and proper transport modes to keep the maintenance of supply chain cost to the minimum. Proper handling of returned commodities to recover value without affecting the environment may need appropriate techniques or methodologies. This paper deals with the routing of vehicles with energy conservation as the agenda in the value recovering method named as repair service work. It is done through a big data-based deep learning model, in a multicommodity environment. Here, the transportation of commodities to repair service facilities is given an in-depth focus to reduce the energy use. The minimization of the distance traveled by the truck fleet reduces the energy consumption by the trucks. This article deals with the optimization of emergency logistic with the assistance of deep learning approach, whereas this approach attains 57.19 km with 6 optimized routes. The process of emergency flow control is attained effectively using deep learning approach.
Operation and Management Strategy of Online Car-Hailing Platforms Based on Big Data Diagnosis and Game Perspective
Authors: Luo Shunjun , Jia Faxian
Published: May 2022

With the implementation of a series of preferential strategies by online car-hailing companies, the contradiction between online car-hailing and traditional taxis and passengers has become more and more intense. Coordinating the interests of the three parties has become increasingly important. In order to coordinate the contradiction between online car-hailing and traditional taxis and passengers and to manage the online car-hailing and traditional taxis reasonably, this paper conducts research on the operation and management strategy of online car-hailing platform based on big data diagnosis and game perspective. In order to solve the problem of online car-hailing platform operation and management strategy, this paper adopts a research method combining qualitative judgment and quantitative analysis and conducts research by combining specific logic deduction, field investigation, empirical research, mathematical analysis, and computer simulation. The results found that while the platform rate was reduced to 0.085, the daily income of online motorists increased from 170 yuan to 236 yuan, by 38.6%. In the event of a reduction in taxi fares to -3500, one hire the daily income of motorists increased from 134 yuan to 212 yuan, an increase of 57.8%. This shows that reducing the percentage of the platform has the greatest impact on the revenue of online car-hailing companies, and the recharge rebate strategy has the least impact on the revenue of online car-hailing companies. The strategy of reducing elementary money concessions can greatly increase the income of taxi drivers, but it also reduces nearly one-third of the income of taxi companies.
Insights of Heat and Mass Transfer in Magneto-Mixed Convective Sisko Nanofluid over a Wedge with Viscous Dissipation
Authors: Dadhich Yogesh , Jain Reema , Gyeltshen Sonam
Published: May 2022

The current analysis provides the important insights of Sisko nanofluid flow over a wedge with thermal radiation and viscous dissipation effects. The Buongiorno nanofluid model, which includes Brownian movement and thermophoresis, is taken into consideration. Momentum, temperature, and nanoparticle concentration equations are used to simulate the current problem. The suitable similarity variables are applied to the governing partial differential equations (PDEs) which yield the dimensionless ordinary differential equations (ODEs). The MATLAB function bvp4c has been used to resolve the resulting ODEs. The attributes of various flow parameters on the transfer rate of mass, heat, temperature, velocity, and nanoparticle concentration have been explored. The pressure gradient parameter boosts the mass transfer and velocity. Moreover, mixed convection leads to the decrement in thermal and nanoparticle concentration boundary layer. The obtained numerical findings are compared to published results in the literature by considering the particular cases to validate the current study and are seen to be in perfect accord.
Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices
Authors: Kshirsagar Pravin R. , Manoharan Hariprasath , Kasim Samir , Khan Asif Irshad , Alam Md Mottahir , Abushark Yoosef B. , Abera Worku
Published: May 2022

The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, analyze, and predict landslide distant areas, etc. A wireless sensor network comprises autonomous sensors geographically dispersed for monitoring physical or environmental variables, comprising temperature, sound, pressure, etc. This remote management service contains a monitoring system with more information and helps the user grasp the problem and work hard when WSN is a catastrophic event tracking prospect. This paper illustrates the effectiveness of Wireless Sensor Networks (WSN) and artificial intelligence (AI) algorithms (i.e., Logistic Regression) for landslide monitoring in real-time. The WSN system monitors landslide causative factors such as precipitation, Earth moisture, pore-water-pressure (PWP), and motion in real-time. The problems associated with land life surveillance and the context generated by data are given to address these issues. The Wireless Sensors Network (WSN) and Artificial Intelligence (AI) give the option of monitoring fast landslides in real-time conditions. A proposed system in this paper shows real-time monitoring of landslides to preternaturally inform people through an alerting system to risky situations.
Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT
Authors: Kulkarni Mrunalini Harish , Kulkarni Chaitanya , Babu K. Suresh , Rahin Saima Ahmed , Singh Shweta , Kumar D. Dinesh
Published: May 2022

As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear support vector machine (SVM) classifier pair. Feature-level fusion analysis was performed to detect probable abnormalities. The results of the experiments reveal that the SVM model offers significant benefits in disease prediction. In the SVM illness prediction model, the ABC algorithm has the best parameter optimization effect in terms of accuracy, time, and other factors. The suggested method outperformed the traditional SVM and BP neural network methods by 17.24 percent and 72.41 percent, respectively. It can lower the RMSE and improve assessment indicators like the precision recall rate and the F-measure, demonstrating the method’s validity and accuracy. As a result, it is frequently used in community health management, geriatric community monitoring, and clinical medical therapy in an industrial environment.
Fuzzy-Based Approach for Clustering Data with Multivalued Features
Authors: Prakash K L. N. C. , Vimaladevi M. , Chakravarthy V. Deeban , Narayana G. Surya , Srinivasulu Asadi
Published: May 2022

In analysis of data, objects have mostly been characterized by a set of characteristics known as attributes, which together contained only one value for each object. Besides that, a few attributes in reality could include with more than a single value; such as from a human beside multiple profession characterizations, practises, communication methods, and capabilities, in addition to shipping addresses, of that kind of attributes are referred to as multivalued attributes and are typically regarded as null attributes when data is processed employing machine learning procedures. Throughout this article, another similarity mechanism is introduced that is defined around including multivalued characteristics which can be used for grouping. We propose a model to analyse each factor’s relative prominence for different data collection challenges in order to enable the selection among the most suited multivalued elements. The suggested methodology is a clustering technique for development and evolution that employs fuzzy c-means clustering and retains the new and more effective membership component by implementing the proposed similarity metric. Clustering of multivalued variables using fuzzy c-means is the efficient grouping criteria that results; any methodology to group-related data appears viable. The results show that our assessment not only improves previous segmentation methods on the multivalued cluster-based architecture but also helps in the improvement of the standard similarity metrics.
5G Edge Network of Collaborative Computing Task-Scheduling Algorithm with Cloud Edge
Authors: Sui Weixin , Zhou Yimin , Zhu Sizheng , Xu Ye , Wang Shanshan , Wang Dan
Published: May 2022

In the new era, the arrival of 5G has added a lot of fun and beauty to our lives, allowing us to enjoy the happiness brought by the network even more. The 5th Generation mobile technology (5G for short) is a new generation of broadband mobile communication technology with features such as higher speed, lower latency, and massive connectivity. Moreover, this is a network infrastructure that, in fact, realizes the interconnections among humans, computer machines, and sensors/things, i.e., Internet of things. The world can now be characterized as a whole global village. The 5 G, mobile edge computing networks, and cloud-edge collaborative computing allow us to communicate and share information on the cloud networks, allowing us to better understand the global village. Based on cloud-edge collaborative computing for 5G edge networks, this study builds a task scheduling algorithm, which will help us to check network defects and make better use of the 5G technology to serve customers. Experimental results demonstrate the extra efficiency of the proposed algorithm over the closest rivals.
Angelica dahurica Extracts Attenuate CFA-Induced Inflammatory Pain via TRPV1 in Mice
Authors: Zhu Chan , Wang Meiyuan , Guo Jun , Su Shu Lan , Yu Guang , Yang Yan , Zhou Yuan , Tang Zongxiang
Published: May 2022

Angelica dahurica, belonging to the family Apiaceae, is a well-known herbal medicine. The roots of Angelica dahurica are commonly used for the treatment of headache, toothache, abscess, furunculosis, and acne. However, little is known about their analgesic molecular mechanism underlying pain relief. In this study, we used behavioral tests to assess the analgesic effect of the ADE (Angelica dahurica extracts) on CFA (complete Freund’s adjuvant)-induced inflammatory pain mice models. TRPV1 (Transient Receptor Potential Cation Channel Subfamily V Member 1) protein activity in dorsal root ganglion (DRG) was assessed with a calcium imaging assay. TRPV1 expression was detected with western blot and immunohistochemistry. Then, we examined the constituents of ADE using combined ultra-performance liquid chromatography-quadrupole time-of-light mass spectrometry (UPLC/Q−TOF−MS). Our results showed that ADE effectively attenuated mechanical and thermal hypersensitivities in CFA-induced inflammatory pain model in mice. ADE also significantly reduced the activity and the protein expression of TRPV1 in DRG from CFA mice. Therefore, ADE might be an attractive and suitable analgesic agent for the management of chronic inflammatory pain.
Evolution of Frozen Section in Carcinoma Breast: Systematic Review
Authors: Rana Manjit Kaur , Rana Amrit Pal Singh , Sharma Uttam , Barwal Tushar Singh , Jain Aklank
Published: May 2022

Background. The frozen section (FS) has been a good technique in surgical management of breast lesions since many years. But complete agreement and cooperation have not been achieved everywhere among surgeons and pathologists especially in the developing countries. FS undergoes continuous criticism due to various shortcomings but continued to be evaluated especially in developing countries. Objectives. This review was conducted to synthesize information on the use of frozen section in carcinoma breast. Data Sources. The MEDLINE database for frozen section since its origin and its implication in recent breast surgery techniques was studied. Study Eligibility Criteria. Sixty-five articles were reviewed with complete analysis on FS in both benign and malignant breast lesions. Study Appraisal and Synthesis Methods. The analysis of frozen section was done as a diagnostic tool in breast lesions, margin status in breast conservative surgery in carcinoma breast, and sentinel lymph node and use of immunohistochemistry for sentinel lymph node FS. Results. It was analysed that the FS gives accurate results in margin status analysis, decreasing rerecurrence. Conclusion. The accuracy of FSA, low recurrence rate, avoidance of reoperation, and good cosmesis are the key points of its use in breast conservative surgery. Its use in sentinel lymph node biopsy (SLNB) is equivocal. However, application of immunohistochemistry on frozen section of SLNB is an evolving trend in today’s era.
Density Perturbation and Cosmological Evolution in the Presence of Magnetic Field in
f

R

Gravity Models
Authors: Chakraborty Samarjit , Guha Sarbari
Published: May 2022

In this paper, we have investigated the density perturbations and cosmological evolution in the FLRW universe in the presence of a cosmic magnetic field, which may be assumed to mimic primordial magnetic fields. Such magnetic fields have sufficient strength to influence galaxy formation and cluster dynamics, thereby leaving an imprint on the CMB anisotropies. We have considered the FLRW universe as a representative of the isotropic cosmological model in the

1
+
3

covariant formalism for

f

R

gravity. The propagation equations have been determined and analyzed, where we have assumed that the magnetic field is aligned uniformly along the

x

-direction, resulting in a diagonal shear tensor. Subsequently, the density perturbation evolution equations have been studied, and the results have been interpreted. We have also indicated how these results change in the general relativistic case and briefly mentioned the expected change in higher-order gravity theories.
Adsorptive Behavior of Tartaric Acid Treated Holarrhena antidysenterica and Citrullus colocynthis Biowastes for Decolourization of Congo Red Dye from Aqueous Solutions
Authors: Basharat Sumaira , Rehman Rabia , Basharat Sara
Published: May 2022

The aim of the present work is to eradicate Congo red (CR) dye from aqueous solutions since the dye compounds are harmful to human life and the environment leading to detrimental results. For this purpose, Holarrhena antidysenterica (HA) and Citrullus colocynthis (CC) adsorbents were used for the adsorptive removal of Congo red dye from wastewaters. The unmodified adsorbents (U-HA and U-CC) were chemically modified using tartaric acid (TA). Morphological structures were examined by FTIR and SEM. Batch adsorption studies were tested at a variety of pH, time exposure, temperatures, and adsorbent dosages. Thermodynamic parameters such as Gibbs free energy (

∆

G

°

), enthalpy (

∆

H

°

), entropy changes (

∆

S

°

), and energy of activation (

E

a

) were also calculated. The results revealed that tartaric acid-Citrullus colocynthis (TA-CC) gave optimum conditions of time of contact (35 min), temperature conditions (40°C), pH (3), and dosage of adsorbent (1.6 g) for maximum dye removal. Tartaric acid-Holarrhena antidysenterica (TA-HA) gave equilibrium time of contact (30 min), temperature (40°C), and pH optimum (2) along with a 1.6 g dosage of adsorbent. Mechanistic understanding of adsorption isotherm provided that the Langmuir model was followed by raw and modified adsorbents. Maximum adsorption capacities

Q

max

attained were 60.61 (mg g-1), 128.21 (mg g-1), 87.71 (mg g-1), and 131.57 (mg g-1), respectively, for U-HA, TA-HA, U-CC, and TA-CC. The results of kinetic modeling displayed a high value of

R

2

(0.99) along with minimal error (RMSE) for dye removal showing that the pseudo-second-order kinetic model has acceptable accuracy. Fourier transform infrared proposed the electrostatic, pi-pi interactions, and hydrogen bonding as dominant adsorption mechanisms at acidic pH, respectively. Rate-determining steps comprise both surface and intraparticle diffusions. Thermodynamics indicated that the dye adsorption of CR is spontaneous, exothermic, and favorable in nature. These agricultural wastes due to specific points such as low cost, availability, and high removal rates of adsorption are highly competent for the expulsion of anionic dye like CR from wastewaters.
Stability Analysis and Optimal Control Strategies of an Echinococcosis Transmission Model
Authors: Yang Run , Zhao Jianglin , Yan Yong
Published: May 2022

This paper presents a deterministic compartmental model for echinococcosis transmission dynamics. The basic reproduction number of the model determines the existence and stability of the disease-free and disease-endemic equilibrium points. We further formulate the optimal control problem and obtain the necessary conditions to minimize the number of infected individuals and the associated costs. Numerical simulations show that optimal control strategies can significantly reduce the number of infected individuals to lower levels. Environmental disinfection may be essential for the elimination of infections. The results of this study will be beneficial for the prevention and control of echinococcosis in the Ganzi Tibetan Autonomous Prefecture and other areas of China.
Intelligent Running Posture Detection Based on Artificial Intelligence Combined with Sensor
Authors: Lai ZhiRong , Wang Mingming , Wang Longhai , Zhou Yining
Published: May 2022

In order to avoid injuries caused by incorrect running posture to a greater extent and reduce the impact on athletes’ performance and physical health, on the basis of artificial intelligence sensors, the author studies the accurate detection of intelligent running motion posture. Using artificial intelligence sensors, an adaptive error quaternion unscented Kalman filter (DAUKF) algorithm is designed. The attitude analysis and recognition system based on the inertial measurement unit can not only measure the motion information of human body but also obtain the motion characteristic data and movement state of the human body through the analysis of posture data. Use the error quaternion and gyroscope drift error to establish the equation of state, the measurement values of the accelerometer and magnetometer are used to establish the observation equation, and the fading memory method is introduced to adaptively adjust the observation noise covariance, so as to reduce the interference of the system itself and the environment on attitude detection. Experimental results show that the proposed method improves the attitude detection accuracy, effectively suppresses the influence of drift error and dynamic observation noise, and provides a foot attitude detection scheme suitable for long-distance running.
Scientific Computing Evaluation of Interactive Product Art Design under the Background of User Experience Evaluation Analysis Model
Authors: Peng He , Qin Cheng
Published: May 2022

Art comes from life, and art products derived from it are also closely related to daily life. With the development and improvement of social economy, art products are more and more favored and recognized by people. In other words, there will be higher requirements for the design of art products, including whether the design can bring people a visual feast, meet people's spiritual needs, and have practical functions or not. Based on these needs and deficiencies, this paper proposes a scientific computing evaluation of interactive product art design based on the user’s experience evaluation analysis model, combining the corresponding design strategies with interactive algorithms. In addition, by integrating the design business logic and simulation experiments of interactive art products, this paper discusses its fuzzy structure from the perspective of interaction, and analyzes fuzzy cognition and decision-making clustering strategies. The purpose of this is to support decision analysis and realize the effectiveness of user’s fuzzy cognitive strategies. The simulation experiment results show that users can directly observe the results according to the categories after applying the user’s experience evaluation analysis model, which greatly improves the efficiency and quality of product design. These findings clearly verify that the user’s experience evaluation analysis model proposed in this paper has a significant impact on interactive product art design and also provide convenience for scientific computing evaluation.
Factors Associated with the Prevalence of Hepatitis B among Volunteer Blood Donors at Jimma Blood Bank, South Ethiopia
Authors: Yigezu Hamba , Temam Juhar , Bajiro Mitiku , Tesfaye Jule Leta , Nagaprasad N. , Roy Arpita , Saka Abel , Ramaswamy Krishnaraj
Published: May 2022

Background. Hepatitis B is a severe, widespread infectious disease of the liver that affects millions of people around the world. It is one of the life-threatening liver infections caused by the hepatitis B virus (HBV). HBV is the cause of up to 80% of cases of primary liver cancer. Due to the potential risk associated with HBV infection, it is important to study the factors which are associated with the seropositive volunteers. Objective. The purpose of this study was to identify factors associated with seropositivity for the hepatitis B virus among volunteers who donated blood at the Jimma Blood Bank in southern Ethiopia. Methods. Cross-sectional research was conducted on blood donors who came to the Jimma Blood Bank to donate their blood. Three hundred and fifty-nine volunteer blood donors who arrived at the Jimma Blood Bank were investigated face-to-face in order to collect sociodemographic characteristics and risk factors for HBV infection. The data were analyzed using statistical software SPSS version 20.0. The association between the risk factor for HBV infection and HBV infection was determined using chi-square tests. Result. In total, there were 359 participants; their mean age was 22.5, among which 161 (44.8%) were males. Out of 359 volunteers, 13 (3.6%) were seropositive for HBsAg. The test positivity rate among males was 7/198 (3.54%), while the rate among females was 6/161 (3.7%). More than 3/4 of those who tested positive were under the age of 40. Chi-square analysis showed that volunteers whose income was between 12 and 26.84 USD were less likely to have the infectious disease than those whose income was less than 11.84 USD per month (

p
=
0.042

). Conclusion. The prevalence of HBV was found to be 3.6% among selected volunteers. It was found that, out of 20 volunteers, 13 had infection. Chi-square analysis showed that HBV infection was associated with low monthly income and the use of unsafe therapeutic injections.
Simulation Research on Thermal Characteristics and Experimental Study on Electronic Control Board of the Miniature High-Speed Digital Valve
Authors: Yang Meisheng
Published: May 2022

The miniature high-speed digital valve is essentially a kind of miniature system that contains many nonlinear factors and involves multifield coupling among electromagnetic, mechanical, and liquid ones. In this study, the dimension of an electromagnetic structure is normalized into a unified variable, and the numerical analysis method is used to optimize the values of each parameter size on the premise of satisfying the requirements. The electronic control board is the control signal input equipment of the valve. The input changes of voltage and current directly affect the thermal characteristics of the valve. The electronic control board is the power source of the digital valves, and their high speed partially depends on the characteristics of the electronic control board. In this study, a control board for miniature digital valves was developed, which realizes the rapid rise and fall of current and meets the design requirements. A new electronic control board is manufactured, and a control platform for the electronic control board is built. Experiments demonstrate that there is no delay in the current rise and fall, showing that the valve achieves its dynamic response target. Using the finite element analysis software Workbench to simulate it numerically, the temperature field distribution in the original structure is not uniform, which affects the magnetic properties of the electromagnetic field. To address this disadvantage, this study proposes to insulate the heat source with thermal insulation material, which will not affect the temperature distribution of the entire electromagnetic components. The simulations show that the proposed method is effective and reliable.
Classification of Ancient Buddhist Architecture in Multi-Cultural Context Based on Local Feature Learning
Authors: Wu Yali
Published: May 2022

Ancient Buddhist architecture plays an important role in the development of Chinese architectural culture. Under the background of multiculturalism, the ancient Buddhist architectural style has also been influenced to varying degrees. In order to realize automatic classification of ancient Buddhist architecture under multi-cultural background, this paper proposes an automatic classification algorithm based on local feature learning. Firstly, the ancient Buddhist architecture images are gridded, so that the backbone network can obtain relatively flat ancient Buddhist architecture image blocks. At the same time, the backbone network can learn more local details. Then, the grid reconstruction module is designed to strengthen the connection between the features of each block and highlight the distinguishing detail features. The accuracy of ancient Buddhist architecture classification can be effectively improved through image meshing and mesh reconstruction. Experiment and analysis are carried out by using the dataset of ancient Buddhist architecture images on the Internet. Experimental results show that the proposed algorithm has better recognition accuracy and robustness than other comparison algorithms.
Evaluation and Stratification for Chinese International Education Quality with Deep Learning Model
Authors: He Min
Published: May 2022

In the process of human communication, language learning and communication play a fundamental, leading, broad, and long-lasting role. It serves as a link and a bridge between countries and peoples, allowing for greater understanding and camaraderie. The significance of Chinese in international commercial and cultural exchanges has been more obvious in the current era, as China’s comprehensive strength continues to improve. Its cultural value and practical value have been continuously improved, the international community has an increasing demand for learning Chinese, and the cause of international Chinese language education has developed rapidly. China must strengthen the international dissemination of Chinese, so that the world can better understand and accept China, so that China can better integrate into the world. In this context, how to evaluate and stratify the quality of Chinese international education has become an important research topic. Relying on the hot deep learning technology in recent years, this work designs a neural network for evaluating the quality of international Chinese education. The content of this work is as follows: aiming at the serious defect of the current mainstream feature classification network that only uses the top-level features extracted by a single convolution layer to classify, which leads to the loss of classification accuracy. A multiscale feature pyramid fusion network is built in this paper, starting with the working mechanism of a convolutional neural network. It is capable of fully extracting and combining the representations of the network’s shallow and deep outputs, based on first- and second-order characteristics of global and local discriminative region information. Second, the network structure has the bottleneck layer module and the batch normalization layer module, both of which are made up of varying numbers of

1
×
1
 

convolution kernels.
Mining and Application of Tourism Online Review Text Based on Natural Language Processing and Text Classification Technology
Authors: Xu Hongsheng , Lv Yanqing
Published: May 2022

This paper firstly describes the research status of online review text mining and finds out the problems existing in the mining and application of tourism texts. Aiming at these problems, this paper proposes a text mining method for tourism online reviews based on natural language processing and text classification technology. The first step is to analyze the validity of the online review text; the purpose is to remove the invalid text and improve the mining efficiency of the online review text. The second step is to conduct a comprehensive evaluation of scenic spots and hotels based on text classification technology and sentiment analysis. The comprehensive evaluation indicators are established for the five core service contents. High-quality scenic spots and hotels are selected according to the ranking of comprehensive evaluation. The third step is to propose a mining method of tourism hot words based on natural language processing for the selected high-quality tourist locations. The obtained hot words can intuitively show the impression of tourists on the scenic spot. The fourth step is to use mutual information combined with the left and right entropy to discover new words and to mine service characteristics of high-quality scenic spots and hotel from the new words. Finally, the proposed new methods are tested on the crawled tourism online review texts. The experimental results show that the novel comprehensive evaluation method proposed in this paper can truly and objectively select high-quality scenic spots and hotels and provide an important basis for the decision-making of tourism management. On this basis, hot words and new words can be effectively excavated from relevant online review texts, and travel impressions can be fed back from various aspects and angles.
A Hyaluronan and Proteoglycan Link Protein 1 Matrikine: Role of Matrix Metalloproteinase 2 in Multiple Myeloma NF-κB Activation and Drug Resistance
Authors: Mark Christina , Warrick Jay , Callander Natalie S. , Hematti Peiman , Miyamoto Shigeki
Published: May 2022

The NF-κB signaling pathway plays key roles in inflammation and the pathogenesis of many solid and hematologic malignancies, including multiple myeloma, a malignancy of the plasma cells. While proteasome inhibitors, such as bortezomib, employed in multiple myeloma treatments may inhibit NF-κB signaling pathways, multiple myeloma cells often become drug resistant in part due to non–cell autonomous mechanism(s) from the multiple myeloma tumor microenvironment. We previously found that fragments of, but not full-length, hyaluronan and proteoglycan link protein 1 (HAPLN1), produced by multiple myeloma bone marrow stromal cells (BMSC), activate an atypical bortezomib-resistant NF-κB pathway in multiple myeloma cells. In our current study, we found that multiple myeloma cells promote HAPLN1 expression and matrix metalloproteinase 2 (MMP2) activity in cocultured BMSCs and MMP2 activity is higher in BMSCs established from multiple myeloma patients’ BM aspirates relative to normal equivalents. Moreover, MMP2 cleaves HAPLN1 into forms similar in size to those previously observed in patients with multiple myeloma with progressive disease. Both HAPLN1 and MMP2 in BMSCs were required to enhance NF-κB activation and resistance to bortezomib-induced cell death in cocultured multiple myeloma cells. We propose that MMP2-processing of HAPLN1 produces a matrikine that induces NF-κB activation and promotes bortezomib resistance in multiple myeloma cells.

Implications:

HAPLN1 and MMP2 produced by BMSCs obtained from patients with multiple myeloma promote NF-κB activity and resistance to bortezomib toxicity in multiple myeloma cells, uncovering their potential as biomarkers or therapeutic targets to address bortezomib resistance in patients with multiple myeloma.
Mast Cell Activation Syndromes: Collegium Internationale Allergologicum Update 2022
Authors: Valent Peter , Hartmann Karin , Bonadonna Patrizia , Niedoszytko Marek , Triggiani Massimo , Arock Michel , Brockow Knut
Published: May 2022

Mast cell activation syndromes (MCASs) are defined by systemic severe and recurrent mast cell activation, usually in form of anaphylaxis, a substantial, event-related increase of the serum tryptase level beyond the individual’s baseline and a response of the symptomatology to drugs directed against mast cells, mast cell-derived mediators, or mediator effects. A number of predisposing genetic conditions, underlying allergic and other hypersensitivity states, and related comorbidities can contribute to the clinical manifestation of MCASs. These conditions include hereditary alpha tryptasemia, mastocytosis with an expansion of clonal KIT-mutated mast cells, atopic diathesis, and overt IgE-dependent and IgE-independent allergies. Several of these conditions have overlapping definitions and diagnostic criteria and may also develop concomitantly in the same patient. However, although criteria and clinical features overlap, each of these conditions is characterized by a unique constellation of variables and diagnostic criteria. Since two, three, or more conditions can coexist in the same patient, with obvious clinical implications, it is of crucial importance to diagnose the variant of MCAS precisely and to take all accompanying, underlying and potentially complicating conditions, and comorbidities into account when establishing the management plan. Indeed, most of these patients require multidisciplinary investigations and only a personalized treatment approach can lead to an optimal management plan providing an optimal quality of life and low risk of anaphylaxis.
A Case of Papuloerythroderma Successfully Treated with Dupilumab
Authors: Mizuno Ayaka , Habe Koji , Matsushima Yoshiaki , Kondo Makoto , Yamanaka Keiichi
Published: May 2022

Papuloerythroderma is an erythroderma characterized by the composition of dense paving stone shape papules and intertriginous uninvolved skin on the abdominal wall and is often intractable and accompanied by itching. Topical or oral corticosteroids are treatment measures, but immunosuppressive drugs are sometimes required. Herein, we report a case of papuloerythroderma treated with dupilumab, a completely humanized immunoglobulin monoclonal antibody against interleukin-4 receptor subunit α (IL-4Rα) of IL-4 and IL-13 receptors, with rapid and marked improvement. Dupilumab is one of the treatment options to treat refractory papuloerythroderma.
Urothelial Bladder Carcinoma in Students at a Dental Technical College: A Report of 3 Cases
Authors: Suditu Nicolaie , Negru Irina , Novac Bogdan
Published: May 2022

Bladder cancer is one of the main types of neoplasia affecting men, with the highest incidence reported toward the end of the seventh decade of life. Unlike other malignancies, bladder cancer is attributable to specific widely occurring carcinogenic risk factors in 60–70% of cases, and numerous professions have been linked to higher rates of the disease. The present study includes the cases of three male graduates (mean age, 23 years) from the same dental technical college, two of whom were students at the Faculty of Dental Medicine of the Iași University of Medicine and Pharmacy (Iași, Romania) at the time of diagnosis. The individuals were occupationally exposed for a mean of 43.66 months. Histopathological examinations following resection indicated the presence of benign lesions (1 case) and malignant lesions of low aggressiveness (2 cases). The patients’ outcomes were favorable, and there was no tumor recurrence over a mean observation period of 56 months. The aim of the present study was to highlight the young age at which these patients developed bladder tumors under similar etiological conditions and over short periods of exposure to known occupational risk factors. This shorter time of exposure to risk factors makes it even harder to establish causality with the occurrence of bladder tumors. However, the present cases could lead to a suspicion of a direct association between the exposure and the tumors or an entirely coincidental occurrence.
Hand Dermatitis Secondary to Exposure to Butternut Squash
Authors: Shah Milaan A. , Feldman Steven R.
Published: May 2022

Cucurbita moschata, also known as butternut squash, is a common ingredient in numerous seasonal recipes but is an uncommon cause of cutaneous reactions. We present a 28-year-old male who developed dry and flaking skin of his right palm and fingers after coming in contact with butternut squash, which does not typically serve as an allergen that precipitates contact dermatitis. Given the unilateral localization of the dermatitis, timeline of the development of symptoms, and history of contact with butternut squash, the patient likely developed contact dermatitis of the right hand in response to exposure to butternut squash. Contact dermatitis with butternut squash appears to be an uncommon phenomenon, but it may occur more often and not be reported.
Successful Treatment of the TEMPI Syndrome with Pomalidomide Plus Dexamethasone Followed by Autologous Stem Cell Transplantation
Authors: Kawamura Shunto , Tamaki Masaharu , Nakamura Yuhei , Kawamura Masakatsu , Takeshita Junko , Yoshino Nozomu , Misaki Yukiko , Yoshimura Kazuki , Matsumi Shimpei , Gomyo Ayumi , Okada Yosuke , Akahoshi Yu , Kusuda Machiko , Kameda Kazuaki , Tanihara Aki , Kimura Shun-ichi , Nakasone Hideki , Kako Shinichi , Kanda Yoshinobu
Published: May 2022

TEMPI syndrome is a rare disease associated with plasma cell neoplasms. Although previous studies have reported that bortezomib is effective as a first-line treatment for TEMPI syndrome, some cases are refractory to this treatment. Pomalidomide, a kind of immunomodulatory drug, is widely used for the treatment of relapsed or refractory multiple myeloma and could be administered without dose modification in patients with renal dysfunction. We present a case of bortezomib-refractory TEMPI syndrome with renal insufficiency that was successfully treated with a combination of pomalidomide and low-dose dexamethasone with minimal adverse effects, followed by autologous hematopoietic stem cell transplantation (ASCT). To the best of our knowledge, this is the first case of TEMPI syndrome that was successfully treated with pomalidomide. Pomalidomide may be suitable for patients who do not respond to a proteasome inhibitor-based treatment. In addition, a subsequent ASCT could also be effective for achieving a further treatment response.
A Rat Model of Esophagogastric Anastomotic Stricture
Authors: Li Nan , Fei Xiang , Li Chunguang , Zhao Tiejun , Jin Hai , Chen Hezhong
Published: May 2022

Esophagogastric anastomosis stricture is one of the most common postoperative complications after esophagectomy; yet, its pathogenesis is still not fully understood, and the treatment and prevention of anastomotic stricture are limited due to the lack of a proper animal model. The insufficient blood supply in the gastric tube is considered a risk factor for postoperative anastomotic strictures. In this study, we used thermal imaging to develop a stable rodent model with esophagogastric anastomotic stricture caused by ischemia. Briefly, 30 male Sprague-Dawley rats have been divided into the control group and the ischemia group. The esophagogastric ischemia anastomosis was performed with the help of intraoperative thermal imaging to identify the poor perfusion area. An unpaired t test with Welch’s correction was used to analyze the difference between the two groups. On postoperative day 84, in the control group, no anastomosis stricture was observed, while in the ischemia group, 12 out of 15 animals (80%) developed obvious anastomosis stricture which could not let a 2.7-mm endoscope pass through. The diameter of the anastomosis in the control group and the ischemia group were 2.80 ± 0.15 mm and 1.73 ± 0.44 mm (p < 0.01), respectively (evaluated by endoscopy examination and barium radiography). H&E stain and Masson’s trichrome showed that the anastomosis in the ischemia group had more connective tissue hyperplasia and collagen deposition than control group. Thus, this new rat model can be used as a platform to further investigate the potential interventions for prevention of esophagogastric anastomotic stricture.
A Potential Research Target for Scleral Remodeling: Effect of MiR-29a on Scleral Fibroblasts
Authors: Yang Qianying , Lv Sha , Zhu Huirong , Zhang Liming , Li Hua , Song Shengfang
Published: May 2022

Introduction: The purpose of this study was to determine whether miR-29a regulates cell survival and apoptosis and the expression of phosphatase and tensin homolog deleted on chromosome 10 (PTEN), MMP-2, and collagen I in scleral fibroblasts. Methods: We transfected scleral fibroblasts with the miR-29a mimic and inhibitor. The effects of miR-29a on cell proliferation and apoptosis were determined using the CCK-8 assay and flow cytometry, respectively. Quantitative polymerase chain reaction (qPCR) was used to determine whether miR-29a regulates the mRNA levels of PTEN, MMP-2, and collagen I. The protein expression of PTEN, MMP-2, and collagen I was also assessed by western blot analysis. Results: The results of CCK-8 showed that, at 0, 24, 48, and 72 h after transfection, the relative optical density values in the mimic group were 0.233 ± 0.005, 0.380 ± 0.008, 0.650 ± 0.040, and 0.906 ± 0.032, and in the inhibitor group were 0.272 ± 0.011, 0.393 ± 0.029, 0.597 ± 0.059, and 0.950 ± 0.101, respectively. The flow cytometry results showed that the apoptosis rates of each group were as follows: the mimic group (0.043 ± 0.007), the NC group (0.040 ± 0.006), the inhibitor group (0.032 ± 0.003), the inhibitor NC group (0.027 ± 0.010), the lipofectamine group (0.027 ± 0.005), and the blank group (0.031 ± 0.009). The qPCR results indicated that in the mimic group, PTEN (0.795 ± 0.182, p = 0.2783), MMP-2 (0.621 ± 0.105, p = 0.0033), and COL1A1 (0.271 ± 0.100, p = 0.0002) expression decreased, whereas in the inhibitor group, PTEN (1.211 ± 0.100, p = 0.2614), MMP-2 (1.161 ± 0.053, p = 0.1190), and COL1A1 (1.7040 ± 0.093, p = 0.0003) increased. Western blot analysis showed that in the mimic group, the expression of PTEN (0.392 ± 0.039, p < 0.0001), MMP-2 (0.577 ± 0.017, p < 0.0001), and COL1A1 (0.072 ± 0.006, p < 0.0001) protein decreased, whereas in the inhibitor group, PTEN (1.043 ± 0.042, p = 0.9413), MMP-2 (1.397 ± 0.075, p = 0.0002), and COL1A1 (1.935 ± 0.081, p < 0.0001) expression increased. Conclusion: MiR-29a inhibits the expression of PTEN, MMP-2, and collagen I on scleral fibroblasts, which may provide a basis studies in sclera.