bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment and Treatment of Postmenopausal Osteoporosis: An Appraisal of International Guidelines
Authors: Chaiamnuay Sumapa
Published: June 2022

Recently, several updates in the assessment and treatment of postmenopausal osteoporosis guidelines were published. This review discusses international guidelines, including the algorithm for management of patients at risk of osteoporotic fracture from the International Osteoporosis Foundation (IOF) and the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) 2020, The Pharmacological Management of Osteoporosis in Postmenopausal Women: An Endocrine Society Clinical Practice Guideline (2019)/Guideline Update (2020), and the American Association of Clinical Endocrinologists (AACE)/American College of Endocrinology (ACE) guidelines for the diagnosis and treatment of postmenopausal osteoporosis-2020 update. Due to the recent COVID-19 pandemics, the joint guidance on osteoporosis management in the era of COVID-19 pandemic will also be included.

The criterion for diagnosis of osteoporosis has been published by AACE/ACE. All postmenopausal women of age ≥50 years old should be evaluated for osteoporosis risk. Those at very high risk for fracture are recommended to start treatment with anabolic agents and those at high risk for fracture are recommended to start treatment with antiresorptive agent such as bisphosphonates or denosumab. Intervention thresholds and treatment options vary from country to country. Physicians should individualize the treatment according to risks, benefits, patient preferences, as well as treatment accessibility.
Handling Adverse Effects of Digital Screen Time on Preschool Children: Looking beyond Limiting Screen Time
Authors: Goyal Ruchi
Published: June 2022

Author analyses excessive screen time issues with preschool children, and how counsellors can take a better counselling approach with parents while keeping in perspective Erik Erikson’s Theory of psychosocial development impacting the child by the age of 5 years. Preschool children spend more than 2.5 hours per day on digital screens; this is against recommended guidelines of less than 1 hour per day by * WHO and ** AAP. A literature review of research papers from online *** libraries by the author found that only a small percentage of children met the recommended guidelines resulting in the risk of language disorders, delayed development of physical, motor skills and cognitive abilities, obesity, sleep problems, depression and anxiety due to screen exposure. The loneliness of children is found to be the primary reason for the excessive screen time. The author suggests that counsellors help parents look beyond limiting a child’s screen time alone and improve the parental attitude toward the child’s behaviour which is the biggest barrier in managing a child’s screen time. The author’s contribution is the ‘SALT’ counselling approach emphasising listening Support, finding Alternatives, Loneliness and Time in this digital era to cater to all types of child learners - visual, auditory, and kinesthetic.

Why is your paper of interest to readers?

Exposure of preschool children to digital devices is more than double the recommended screen time, hampering a child’s critical personality development required as per Erik Erikson’s Theory. Counsellors need to look at other associated factors beyond screen time which affect children adversely and which are more important than just the screen time limitation prescribed as per government guidelines.

Focus: SE Asia and Outside SE Asia

(No IRB approval is sought as the paper is based on secondary research.)
When satisfiability solving meets symbolic computation
Authors: Bright Curtis , Kotsireas Ilias , Ganesh Vijay
Published: June 2022

The science of less-than-brute force.
PMAL: A Proxy Model Active Learning Approach for Vision Based Industrial Applications
Authors: Khan Abbas , Haq Ijaz Ul , Hussain Tanveer , Muhammad Khan , Hijji Mohammad , Sajjad Muhammad , De Albuquerque Victor Hugo C. , Baik Sung Wook
Published: June 2022

Deep Learning models’ performance strongly correlate with availability of annotated data; however, massive data labeling is laborious, expensive, and error-prone when performed by human experts.
Active Learning (AL)
effectively handles this challenge by selecting the uncertain samples from unlabeled data collection, but the existing AL approaches involve repetitive human feedback for labeling uncertain samples, thus rendering these techniques infeasible to be deployed in industry related real-world applications. In the proposed
Proxy Model based Active Learning technique (PMAL)
, this issue is addressed by replacing human oracle with a deep learning model, where human expertise is reduced to label only two small subsets of data for training proxy model and initializing the AL loop. In the PMAL technique, firstly, proxy model is trained with a small subset of labeled data, which subsequently acts as an oracle for annotating uncertain samples. Secondly, active model's training, uncertain samples extraction via uncertainty sampling, and annotation through proxy model is carried out until predefined iterations to achieve higher accuracy and labeled data. Finally, the active model is evaluated using testing data to verify the effectiveness of our technique for practical applications. The correct annotations by the proxy model are ensured by employing the potentials of explainable artificial intelligence. Similarly, emerging vision transformer is used as an active model to achieve maximum accuracy. Experimental results reveal that the proposed method outperforms the state-of-the-art in terms of minimum labeled data usage and improves the accuracy with 2.2%, 2.6%, and 1.35% on Caltech-101, Caltech-256, and CIFAR-10 datasets, respectively. Since the proposed technique offers a highly reasonable solution to exploit huge multimedia data, it can be widely used in different evolutionary industrial domains.
Cookie monster
Authors: Cranor Lorrie Faith
Published: June 2022

Inscrutable cookie banners torment users while failing to inform consent.
Text Style Transfer
Authors: Hu Zhiqiang , Lee Roy Ka-Wei , Aggarwal Charu C. , Zhang Aston
Published: June 2022

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the text style transfer task (TST), which aims to change the stylistic properties of the text while retaining its independent content of style. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has developed because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models, and provide a comprehensive summary of the state of the art. We review existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study in which we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.
Report on the 1st International Workshop on Learning to Quantify (LQ 2021)
Authors: del Coz Juan Jose , Gonzalez Pablo , Moreo Alejandro , Sebastiani Fabrizio
Published: June 2022

The 1st International Workshop on Learning to Quantify (LQ 2021 - https://cikmlq2021.github.io/), organized as a satellite event of the 30th ACM International Conference on Knowledge Management (CIKM 2021), took place on two separate days, November 1 and 5, 2021. As the main CIKM 2021 conference, the workshop was held entirely online, due to the COVID-19 pandemic. This report presents a summary of each keynote speech and contributed paper presented in this event, and discusses the issues that were raised during the workshop.
Empirical Industrial Organization Economics to Analyze Developing Country Food Value Chains
Authors: Macchiavello Rocco , Reardon Thomas , Richards Timothy J.
Published: June 2022

Food value chains (FVCs) in developing countries are transforming rapidly, with some regions in the modern stage (led by supermarkets and large processors) and other regions in a transitional stage (led by midstream small and medium enterprises). With transformation, however, come market-performance issues related to monopoly and monopsony power, vertical bargaining, contracting, and other issues addressed by empirical industrial organization (EIO) researchers. Although the concepts and methods of EIO are evolving rapidly, the two bodies of literature on EIO and FVC transformation as part of the food markets and food industries branches of development economics have not sufficiently cross-pollinated. Applying tools of modern EIO to FVCs in developing countries is now relevant because of the transformation that has occurred and possibly due to the increasing availability of data from surveys of farms, processors, and wholesalers, and for some retailers, from scanner data. We review the transformation trends, the EIO themes and tools relevant to them, and the emerging data sources.
On The Stability of Passive Film for Al-Rich Multicomponent Bulk Metallic Glass Under Long-Term Immersion in Chloride Containing Solution
Authors: Wang Q. , Wang D. B. , Cui J. P. , Emori W. , Zhang S. D. , Wang J. Q.
Published: June 2022

In this paper, the electrochemical characteristics and passive film stability of an Al86Ni6Y4.5Co2La1.5 bulk metallic glass were systematically investigated in a chloride containing solution by the combination of electrochemical impedance spectroscopy and high-resolution transmission electron microscopy observations. The results demonstrated that the evolution of corrosion could be divided into five distinct zones: film growth in the initial immersion; a complete passive film with a dynamic and stable process; the occurrence of pitting on the passive film; accumulation of corrosion products and the formation of continuous channels to failure. Interestingly, it was noted that in the second zone, the passive film achieved the highest stability at the middle of immersion due to the synergistic effect of film doping and thickening induced by the alloying of Y/La and Ni/Co. Afterwards, a gradual degradation of the passive film was observed, which was associated with the partial devitrification of the Y2O3 and La2O3 amorphous passive film. Correspondingly, the film/metal matrix interface underwent a transition from a flat interface to a curved one during the immersion, inducing the failure of the passive film. Our findings provide guidance for the design of Al-based amorphous alloys with high corrosion resistance.
An Efficient Framework for Detection and Classification of IoT Botnet Traffic
Authors: Maurya Sandeep , Kumar Santosh , Garg Umang , Kumar Manoj
Published: June 2022

The Internet of Things (IoT) has become an integral requirement to equip common life. According to IDC, the number of IoT devices may increase exponentially up to a trillion in near future. Thus, their cyberspace having inherent vulnerabilities leads to various possible serious cyber-attacks. So, the security of IoT systems becomes the prime concern for its consumers and businesses. Therefore, to enhance the reliability of IoT security systems, a better and real-time approach is required. For this purpose, the creation of a real-time dataset is essential for IoT traffic analysis. In this paper, the experimental testbed has been devised for the generation of a real-time dataset using the IoT botnet traffic in which each of the bots consists of several possible attacks. Besides, an extensive comparative study of the proposed dataset and existing datasets are done using popular Machine Learning (ML) techniques to show its relevance in the real-time scenario.
Thermal Monitoring of Series and Parallel Connected Lithium-ion Battery Modules Using Fiber Optic Sensors
Authors: Atchison Hayden , Bailey Zachary , Wetz David , Davis Matthew , Heinzel John
Published: June 2022

Lithium-ion batteries are widely deployed in commercial and industrial applications. Continuous monitoring is necessary to prevent destructive results that can occur due to thermal runaway. Thermocouples and thermistors are traditional sensors used for thermally monitoring cells, modules, and batteries, but they only sense changes at the physical point where they are deployed. A high density of these sensors within a module or battery is desirable but also impractical. The study documented here shows that a commercial grade fiber optic sensor can be used as a practical replacement for multiple discrete thermocouples or strain gauges for a battery or module, to monitor a battery module at millimeter resolution along the fiber length. It is shown here that multiple fiber optic sensors can be series connected to allow for monitoring of a battery consisting of more than one module. In addition, it is shown that the same type of fiber can also be used to identify the onset of fault conditions by correlating the response in a fiber optic sensor suspended close to the module with an audible signature detected by a microphone at the time of failure. Early detection and identification of abnormal cell operation is demonstrated within batteries employing many cells.
Effect of caloric intake and macronutrient composition on intestinal cholesterol absorption and bile acids in patients with obesity
Authors: Hashem Anas Mohamad , Cifuentes Lizeth , Calderon Gerardo , Ricardo-Silgado Maria Laura , Gonzalez-Izundegui Daniel , Campos Alejandro , McRae Alison , Franks Shawna , Hurtado Maria Daniela , Burton Duane , Petterson Xuan-Mai , Lanza Ian R. , Camilleri Michael , Acosta Andres
Published: June 2022

We show how switching from a regular diet to an HCHF increases cholesterol absorption in patients with normal weight and obesity. The decrease in cholesterol absorption from an HCHF to an LCLF, on the other hand, was only seen in normal-weight controls, underlining the importance of body weight in this regulation. In addition, changes in caloric and fat content had an immediate and direct effect on hepatic bile acid production.
Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model
Authors: Mahto Dashrath , Yadav Subhash Chandra , Lalotra Gotam Singh
Published: June 2022

With the emergence of social media platforms, most people have changed their way of interacting. Perhaps, sharing day-to-day lifestyle updates is a trend substantially influenced by microblogging sites, specifically Twitter, Facebook, Instagram, and many more. Moreover, text and messages are the most preferred way for such interactions. Twitter is one of the most commonly used microblogging tools that enable people to express their thoughts, opinions, emotions, happiness, sadness, excitement, ideas, mental stress, and so on. Hence, the sentiment prediction furnished by such textual data becomes a complex and challenging task. In this research, the authors proposed a hybridization of the convolutional neural network and bi-directional long short-term memory model (named ConvBidirectional-LSTM), which aims to better the categorization of sentiments of text data. Then, this proposed hybrid ConvBidirectional-LSTM model is compared with the existing state-of-the-art models, GloVe-based CNN-LSTM and Hierarchical Bi-LSTM (HeBiLSTM) models. Furthermore, the performance of the proposed hybrid ConvBidirectional-LSTM model is evaluated on the US airline dataset using various performance parameters like accuracy, precision, recall, and

f

1

score. The proposed model outperformed the existing state-of-the-art models with an accuracy rate of 93.25% in sentiment prediction.
Study on the Metabonomics Mechanism of Mongolian Medical Andai Therapy on Healthy People
Authors: Sa QiLa , Bao LiHong , Hu YaGeTu , Bai Haihua , Bo AGuLa
Published: June 2022

Andai therapy is a traditional therapy combining body, mind, and language with Mongolian characteristics. In the form of singing and dancing, it is widely popular among people of all ages in Mongolian areas of Inner Mongolia. According to Mongolian medicine, Heyi is one of the three elements of human body, and it can maintain life activities, promote blood circulation, and improve the functions of the sensory and mental consciousness. Andai therapy stimulates the whole body nerves and Heyi through music and dance, improves Heyi and blood operation, strengthens physique, improves immunity, effectively promotes physical and mental health, and plays a role in preventing and treating diseases. Objective. In this study, gas chromatography-mass spectrometry (GC-MS) was used to explore the mechanism of Andai therapy, so as to provide a new research direction for taking targeted prevention and treatment measures for diseases. Methods. Using gas chromatography-mass spectrometry (GC-MS) on all its cases baseline plasma to the targeted metabonomics testing, the differential metabolites of the experimental group (receiving Andai therapy) and control group (without receiving Andai therapy), analysis-related metabolite function, and screening of metabolites and related pathways through adjusting mechanism to explore the related factors are compared, to study the mechanism of the influence of Mongolian medical Andai therapy on the metabolism of different healthy people. Results. The differences in metabolic numbers between the experimental group and the control group are 114, such as cyclohexylamine chlorinated acid, 2,4-2 aminobutyric acid bitter almond alcohol, l-methyl inosine, 2-picolinate, and 2-hydroxy-2-glutaric acid metabolite content of the control group that are significantly higher than the experimental group, experimental group’s other substance content is significantly higher than that of the control group, and two groups’ metabolite content was obviously different. The number of differential metabolites between the female experimental group and the female control group was 119, and the number of differential metabolites between the male experimental group and the male control group was 48.
A Study on Integrating Multimodal English and American Literature Resources Using Data Mining
Authors: Cai Lanlan , Zhuang Zhiqiang
Published: June 2022

The study of English and American literature is a significant part of curriculum for foreign language majors. In classrooms, multimodal theory teaching is often utilized, in which the single-modal interactive manner of textual or teacher’s vocal transmission is complemented and augmented by other multimodal teaching techniques, i.e., visual and auditory techniques. The information resources of English and American literature have dramatically expanded as a result of today’s information explosion and mature network technology. The lack of resources is not the bottleneck that prevents English and American literature from becoming studied. On the other hand, there are too many resources that depend on data mining technologies. This paper develops an integration analysis platform based on data mining for integrating the content of English and American literature resources and uses the optimized Apriori algorithm to process multichannel data. Using web technologies provides a single data management system that optimizes and queries the target data. We measure the quality of the target data of English and American literature resources and at the same time obtain the grey correlation degree of the data by calculation and assign a value function to find. The proposed method can optimize the target data by calculating the grey correlation degree of the acquired data and assigning a value function to it. The contribution value of the target data and the similarity discrimination threshold are obtained, which are used for calculating the total value. The simulation outcomes demonstrate that the proposed method can effectively simplify the data structure, enhance the data accuracy and quality, improve the query efficiency, and has high applicability and high-quality robustness.
Logistics Service Supply Chain Model Applying Artificial Intelligence and Big Data Analysis
Authors: Chen Lingling , Zhang Yuanyuan , Wang Zhiping
Published: June 2022

The logistics service chain is a specialized service chain produced along with the continuous update and development of the logistics industry. It is an inevitable direction choice that is naturally formed when the degree of logistics specialization continues to increase. This paper aims to study how to analyze and study the logistics service supply chain model based on artificial intelligence and big data analysis, and describe the data mining algorithm. This paper puts forward the problem of logistics service supply chain, which is based on big data analysis, and then elaborates around its related concepts and algorithms, and conducts case design and analysis on supplier selection. The experimental results show that the customer satisfaction of supplier C has reached 86%, the accuracy rate of supplier D's logistics operations is as high as 90%, and the logistics transportation cost of supplier B is only 0.38 yuan/ton

∗

km. In the end, the three of them were selected as company Z's transportation suppliers.
On the Existence of Solutions for a Class of Schrödinger–Kirchhoff‐Type Equations with Sign‐Changing Potential
Authors: Yang Guocui , Zhou Jianwen , Duan Shengzhong
Published: June 2022

In this paper, we consider the following Kirchhoff problem where a, b > 0 are constants, λ is a positive parameter, and 4 < p < 6. Under suitable assumptions on V(x), the existence of nontrivial solution is obtained via variational methods. The potential V(x) is allowed to be sign‐changing.
Bioinformatics Approach Predicts Candidate Targets for SARS‐CoV‐2 Infections to COPD Patients
Authors: Che Li , Chen Guangshu , Cai Xingdong , Xie Zhefan , Xia Tingting , Zhang Wei , Liu Shengming
Published: June 2022

COVID‐19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID‐19 due to the specific lung receptor ACE2 of SARS‐CoV‐2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID‐19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N‐35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N‐35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID‐19.
CNN Based Multiclass Brain Tumor Detection Using Medical Imaging
Authors: Tiwari Pallavi , Pant Bhaskar , Elarabawy Mahmoud M. , Abd-Elnaby Mohammed , Mohd Noor , Dhiman Gaurav , Sharma Subhash
Published: June 2022

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.
Functional MRI Changes in Patients after Thyroidectomy under General Anesthesia
Authors: Yang Xilun , Yu Bing , Ma Ling
Published: June 2022

Cognitive changes affecting elderly patients following surgery under anesthesia have drawn significant attention and have been investigated in considerable depth. Resting‐state functional magnetic resonance imaging (rs‐fMRI) can be used to assess changes in brain functional connectivity (FC) associated with postoperative changes in cognition, a common complication in seniors undergoing surgery. In this study, we recruited 20 patients over 55 of age and scheduled an elective thyroidectomy under general anesthesia to assess perioperative changes in brain FC density (FCD) in patients undergoing thyroidectomy under general anesthesia using rs‐fMRI. All 20 patients underwent a series of clinical, quantitative, neurological, and neuropsychological tests and fMRI examinations on the day before surgery (Day 0) and 7 days after surgery (Day 7). The following tests were conducted on all patients: the Minimental State Examination (MMSE), the digit symbol substitution test (DSST), the trail making test (part A), the verbal fluency test, and Warrington’s recognition memory test (WRMT). FMRI data were acquired using a 3T MR system; the FCD values were calculated using the REST software package. We used paired t‐tests to compare the FCD between Day 7 and Day 0. A value of p < 0.05 was considered to reflect statistical significance. The postoperative FCD was significantly reduced in the supplementary motor area (SMA). Analyses of the percentage changes of errors in the WRMT revealed a significant and negative correlation with the mean percentage change of FCD in the SMA (Spearman’s r = −0.54, 95% CI: (‐0.80, ‐0.12), p = 0.014). Postoperative changes in FCD in the SMA may be associated with the perioperative neurocognitive changes in patients undergoing partial thyroidectomy under general anesthesia.
MSAAM: A Multiscale Adaptive Attention Module for IoT Malware Detection and Family Classification
Authors: Wang Changguang , Zhao Ziqiu , Wang Fangwei , Li Qingru
Published: June 2022

Nowadays, the attack and defense of malware have presented asymmetric characteristic threats, which has disrupted the pace of IoT research. Traditional detection and family classification methods based on feature extraction, as well as the classical machine learning algorithms, have been afflicted with the problems of high time consuming and unbalanced numbers of malware samples. This paper designs a universal and effective Multiscale Attention Adaptive Module called MSAAM that can combine local and global feature information. It can automatically adjust the arrangement and proportion of channel and spatial submodules by auxiliary classifiers according to actual tasks. The traditional CliqueNet uses a circular feedback structure to improve the DenseNet, optimizes the information flow in a deep network, enhances the utilization of its parameters, and uses a multiscale strategy to prevent a sharp increase of its parameters. As a result, it shows a good effect in the study of image classification. By replacing the attention module in the traditional CliqueNet with the designed MSAAM, we present a new method to process the produced gray-scale images converted from the malware and thus get better results in malware processing. The improved CliqueNet runs on the benchmark datasets of MalImg and Microsoft’s BIG 2015 to verify our presented method. After validation on the experimental benchmark datasets, the detection accuracy reaches 99.8%, while the family classification accuracy reaches 99.2% and 98.2% on the above two datasets, respectively. The presented method can solve the problem of unbalanced samples in malware family classification and is also effective against obfuscation attacks.
Intuitionistic Fuzzy Fixed Point Theorems in Complex-Valued
b
-Metric Spaces with Applications to Fractional Differential Equations
Authors: Tabassum Rehana , Shagari Mohammed Shehu , Azam Akbar , Mohamed OM Kalthum S. K. , Bakery Awad A.
Published: June 2022

Among various efforts in advancing fuzzy mathematics, a lot of attentions have been paid to examine novel intuitionistic fuzzy analogues of the classical fixed point results. Along this direction, the idea of intuitionistic fuzzy mapping (IFM) is used in this paper to establish some fixed point (FP) results in complex-valued

b

-metric spaces. Moreover, from application perspective, one of our results is rendered to provide an existence condition for a solution of Caputo-type fractional differential equations. A few nontrivial illustrations are also furnished to authenticate and indicate the usability of the presented results.
[Retracted] Biologically Synthesized Peptides Show Remarkable Inhibition Activity against Angiotensin‐Converting Enzyme: A Promising Approach for Peptide Development against Autoimmune Diseases
Authors: Mujtaba Nosheen , Jahan Nazish , Jamal Adil , Abrar Shazia , Kiran Shumaila , Rasool Atizaz , Hossain Md Belal , Bahwerth Fayez Saeed , Nomani Ibtesam , Iqbal Khalid Javed
Published: June 2022

Angiotensin‐converting enzyme (ACE) regulates several biological functions besides its vital role in immune functions. ACE is elevated in immune cells in inflammatory diseases including atherosclerosis, granuloma, chronic kidney disease, and also autoimmune diseases, like multiple sclerosis, rheumatoid arthritis, and type I diabetes. No significant information prevails in the literature regarding the isolation, identification, and profiling of potential ACE inhibitory peptides. In the present study, indigenous crop varieties like seeds (peanut, corn, oat, sunflower, chickpea, parsley, cottonseed, papaya, sesame, and flaxseed) were used to evaluate their ACE inhibition activity. Variables including hydrolysis time, enzyme‐to‐substrate ratio (E/S), pH, and temperature were standardized to acquire the most suitable and optimum ACE inhibition activity. Seeds of cotton, chickpea, and peanuts displayed remarkably maximum ACE inhibition activity than other plants. The study disclosed that maximum ACE inhibitory activity (86%) was evaluated from cottonseed at pH 8.0, temperature of 45°C, hydrolysis time of 2 hrs, and enzyme to the substrate (E/S) ratio of 1 : 5 followed by peanuts (76%) and chickpea (55%). SDS‐PAGE confirmed that vicilin protein is present in cottonseed and peanut seed while cruciferin and napin proteins are present in chickpeas. LC‐MS/MS analysis disclosed potential novel peptides in hydrolyzed cottonseed that can be ascribed as potential ACE inhibitors which have never been reported and studied earlier. The current study further showed that cottonseed peptides due to their promising ACE inhibitory activity can be a valuable source in the field of ACE inhibitor development.
Nanoprocessing of Industrial Rejects for Controlling Operational Energy of Buildings
Authors: Tembhurkar Sandeep , Nayar Priyanka , Azad Naumanuddin , Ralegaonkar Rahul , Madurwar Mangesh
Published: June 2022

This study investigates the pozzolanic potential of industrial waste, which fails to meet the chemical composition as defined by the various international standards, e.g., IS: 3812 (Part 1): 2017 and ASTM C618‐19 to adopt as a supplementary cementitious material. The effects of mechanical nanoprocessing on pozzolanic reaction efficiency, impact on energy efficiency, and construction affordability were studied. The result of mechanical milling shows the improvement in pozzolanicity, and the physicomechanical characteristics of novel concrete incorporated with identified industrial waste are comparable with control concrete. The building energy simulation was carried out using the BIM software for the house, modelled with controlled concrete, and novel concrete incorporating the identified milled industrial waste. The peak cooling load and building material cost of a novel concrete model house were 34% and 9.09% less than the conventional concrete. The study reveals that the chemical characterization provided in international standards shall not be the only criteria to decide the suitability of the materials to use as supplementary cementitious material, whereas the same can be examined and improved through various treatments, and mechanical nanoprocessing may be one of the best options. Cement production and utilization affect the environment. To reduce the concern, pozzolanic investigation of industrial waste is necessary, which can address the issue of uncontrolled extraction of natural resources, emission, disposal, and pollution globally.
Integration and Dissemination of Sports Big Data Based on Blockchain
Authors: Wang Li’ e , Liu Fenshan
Published: June 2022

With the ceaseless expanding of big data and the Internet, it has more and more affected people’s life. The collection and construction of information has become the front-end technology of current industrial development. News reports and self-media channels are also more extensive. As one of the sections of news reports, sports has also encountered opportunities and challenges in its dissemination. As an innovative technology, blockchain has brought new impetus to the development of sports industry. It is also driving new technological upgrades and visual experiences. Blockchain is a chain data structure that combines data blocks in a sequential manner according to time sequence and is a cryptographically guaranteed untameably and unforgeable distributed ledger. Sports communication has more development opportunities in modern life. Therefore, the purpose of this paper is to explore a new path of physical integration and dissemination based on the theory of blockchain, analyze the development and integration path in the application of blockchain technology, and explore their respective advantages and disadvantages. This article will use the research method of specific analysis of specific problems to make data comparison and draw a conclusion, to carry out theoretical innovation and exploration, so as to find a suitable model to promote the development of sports industry. The so-called “sports big data” refers to the analysis and application of data in sports scenarios. Combined with competitive sports, the significance of data analysis lies in improving the level of competition and preventing sports injuries. The research results show that sports companies using blockchain technology have increased their communication efficiency by 37%, and audience popularity has increased by 15% year on year. The decentralized and anonymous features of blockchain technology ensure the originality and enthusiasm of sports data news teams. Therefore, through the analysis of the selection of application scenes and technologies of the sports industry by the blockchain technology, combined with the characteristics of the current era, the transformation is fully absorbed and combined with a new perspective. This paper analyzes different application dilemmas and explores the development prospects, thus putting forward the implementation path of physical industry blockchain technology application.
The Positioning of Mental Health Education in Social Work under the Healthy China Strategy
Authors: Wang Xiao , Gu Manyi
Published: June 2022

In recent years, the world mental health movement has developed rapidly, and people are paying more and more attention to mental health. China has clearly put forward the major task of comprehensively promoting the construction of a Healthy China and building a Healthy China by 2035. However, in contrast, the research on mental health in Western countries started earlier than in my country, and for a long period of time, my country has followed the research results of Western mental health ideas. Every different culture has its own unique psychological content. Due to factors such as values, habitual behavior, region, and cultural background, it is necessary to deeply explore the mental health thought resources in Chinese culture and study the mental health thought in Chinese culture. It provides new directions and ideas for our country’s psychological counseling and treatment, as well as mental health education and ideological and political education. This article starts from the basic concept of mental health education and sorts out the current research status at home and abroad. By analyzing the main ideas of mental health education work in the context of Healthy China, it explores the positioning of mental health education in social work, and finally, on the basis of strengthening the combination of Chinese culture and mental health thinking, making full use of localized mental health thought and applying it to practice, and drawing inspiration for the study of mental health thought in Chinese culture.
Efficacy and Safety of Bojungikgi-Tang for Persistent Allergic Rhinitis: A Study Protocol for a Randomized, Double-Blind, Placebo-Controlled, Phase II Trial
Authors: Lee Su Won , Choi Jin Kwan , Lyu Yee Ran , Yang Won Kyung , Kim Seung Hyung , Kim Je Hyun , Kim Si Yeon , Kang Weechang , Jung In Chul , Lee Beom Joon , Choi Jun Yong , Kim Taesoo , Park Yang Chun
Published: June 2022

Background. Allergic rhinitis (AR) is a common disease, and conventional medications are often insufficient for treatment. Bojungikgi-tang (BJIGT) is an herbal medicine widely used in traditional medicine and has anti-inflammatory and immunoregulatory effects. We hypothesize that BJIGT would improve nasal symptoms in patients with persistent AR (PAR). Methods. This is a randomized, double-blind, placebo-controlled, phase II trial. A total of 105 patients, identified with perennial allergens, with a history of PAR and a mean total nasal symptom score (TNSS) ≥ 5 during the run-in period will be recruited from Daejeon Korean Medicine Hospital. Participants will be randomly assigned to a high-dose BJIGT group, standard-dose BJIGT group, or control group (placebo) in a 1 : 1 : 1 allocation ratio after a week run-in period. The treatment medication will be taken three times per day for 4 weeks. The primary outcome measure is the mean change in the TNSS before and after medication. The secondary outcome measures include the Korean Allergic Rhinitis-Specific Quality of Life Questionnaire, total IgE and eosinophil count, overall assessment of AR, pattern identification questionnaire for AR, and Sasang constitution. Discussion. The aim of this study is to investigate the efficacy and safety of BJIGT in the treatment of PAR and to determine the suitable dosage of BJIGT. Therefore, we planned a randomized, controlled, phase II trial of two different doses of BJIGT compared with placebo, and the results of this study are expected to provide evidence for the use of BJIGT as a treatment of PAR. Trial Registration. The National Clinical Trial Registry Clinical Research Information Service, CRIS, KCT0006616, https://cris.nih.go.kr/cris/search/detailSearch.do/20706.
Prediction of Prognosis and Molecular Mechanism of Ferroptosis in Hepatocellular Carcinoma Based on Bioinformatics Methods
Authors: Xiong Yuanpeng , Ouyang Yonghao , Fang Kang , Sun Gen , Tu Shuju , Xin Wanpeng , Wei Yongyang , Xiao Weidong
Published: June 2022

Background. As an iron-dependent type of programmed cell death, ferroptosis plays an important role in the pathogenesis and progression of hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) have been linked to the prognosis of patients with HCC in a number of studies. Nevertheless, the predictive value of lncRNAs (FRLs) associated with ferroptosis in HCC has not been fully elucidated. Methods. Download RNA sequencing data and clinical profiles of HCC patients from The Cancer Genome Atlas (TCGA) database. The FRLs associated with prognosis were determined by Pearson’s correlation analysis. After that, prognostic signature for FRLs was established using Cox and LASSO regression analyses. Meanwhile, survival analysis, correlation analysis of clinicopathological features, Cox regression, receiver operating characteristic (ROC) curve, and nomogram were used to analyze the FRL signature’s predictive capacity. The relationship between signature risk score, immune cell infiltration, and chemotherapy drug sensitivity is further studied.Results. In total, 93 FRLs were found to be of prognostic value in patients with HCC. A five-FRL signature comprising AC015908.3, LINC01138, AC009283.1, Z83851.1, and LUCAT1 was created in order to enhance the prognosis prediction with HCC patients. The signature demonstrated a good predictive potency, according to the Kaplan-Meier and ROC curves. The five-FRL signature was found to be a risk factor independent of various clinical factors using Cox regression and stratified survival analysis. The high-risk group was shown to be enriched in tumorigenesis and immune-related pathways according to GSEA analysis. Additionally, immune cell infiltration, immune checkpoint molecules, and half-inhibitory concentrations differed considerably between risk groups, implying that this signature could be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. Conclusion. The five-FRL risk signature is helpful for assessing the prognosis of HCC patients and improving therapy options, so it can be further applied clinically.
Effects of Routine Health Care Combined with Oral Vitamin D on Linear Growth in 5-Year-Old Children
Authors: Zeng Qingwen , Liu Yangyang
Published: June 2022

Objective. The aim is to evaluate the effects of routine health care combined with oral vitamin D on linear growth in 5-year-old children. Materials and Methods. The 5-year-old children who received routine health care in Shiyan Maternal and Child Health Care Hospital from January 2019 to January 2021 were retrospectively analyzed. They were divided into the supplement group and the non-supplement group according to whether or not they received oral vitamin D, and reasons for not taking oral vitamin D and its influence on children’s linear growth were analyzed. Results. A total of 368 children were enrolled, including 228 children in the supplement group, accounting for 61.96%. The analysis of the influencing factors of vitamin D supplementation showed that the proportion of children with well-educated parents and living in cities and towns was higher (all

P
<
0.05

). Comparing the general situation of children in the two groups, it was found that the height, weight, and head circumference of children in the supplement group were notably higher than those in the non-supplement group (all

P
<
0.05

). At age 4 and 5, the height of the supplement group was significantly higher than that of the non-supplement group (all

P
<
0.001

). Linear analysis showed that the relationship between height and age in the supplement group was Y (height, cm) = 10.07 × X (age, years) + 61.18, while that in the non-supplement group was Y (height, cm) = 8.296 × X (age, years) + 62.81, with significant difference (all

P
<
0.05

). Serum 25-hydroxyvitamin D concentration in the supplement group was significantly higher than that in the non-supplement group, and the proportion of children ≥75 nmol/L was evidently higher than that in the supplement group (all

P
<
0.05

). In addition, the incidence of vitamin D-related hypercalcemia in the supplement group was significantly higher than that in the non-supplement group (all

P
<
0.05

). Conclusion. There are still more children without vitamin D supplement, which is closely related to their parents’ education background and place of residence. Additionally, vitamin D supplementation can promote growth and improve 25-hydroxyvitamin D levels in children, but with the risk of related complications.
Investigation of Relaxation Time on Viscoelastic Two‐Dimensional Flow Characteristics Using FreeFem++
Authors: Khan Muhammad Sabeel , Memon M. Asif , Bonyah Ebenezer
Published: June 2022

Viscoelastic fluid flow models have shown promising scope in modeling the behavior of many industrial materials such as polymeric materials, microfluidics, biological liquids, gels, plastic melts, and geomaterials. The relaxation time in these models is of great physical significance. In this article, we study the impact of relaxation time on the viscoelastic flow characteristics in a two‐dimensional baffled cavity. To the best of authors’ knowledge, relaxation time impact on the chosen flow characteristics in the present context has not been studied and presented in the literature before. The constitutive theory of upper convected Maxwell viscoelastic flow incorporating the viscosity ad relaxation time is taken into consideration. To this account, the flow governing PDEs are derived, and an unsteady variational numerical approach based on classical variational setting is presented. A numerical algorithm based on characteristic Galerkin finite elements method is designed and implemented using the programming language FreeFem++. Computations are carried out and drag and lift forces along with other parameters of interests are calculated. Impact of relaxation time on these flow characteristics are studied and analyzed. The relaxation time Rf is assumed to be in the range of 0 ≤ Rf ≤ 1 × 10 − 3. The flow simulations are carried out for large Reynolds number in the range of 200 ≤ Re ≤ 5000. In addition to the application of FreeFem++, some new and interesting features of the flow characteristics are presented and discussed.
A New Model of Multiple Intelligence for Teaching English Informatics in the IoT Scenario
Authors: Ning Yang
Published: June 2022

This paper presents an in-depth study on the new mode of intelligent multidistance teaching of English with the help of virtual scenes of the Internet of Things. The virtual simulation technology is integrated into the traditional IoT teaching, and the professional education of IoT application technology is tapped; from the analysis of the current situation of IoT skills teaching and the feasibility of carrying out virtual simulation teaching, the “four-driven” design principle is proposed, and the teaching design is combined with the virtual simulation technology teaching, and the case design of the skill-based virtual simulation technology teaching of experience, demonstration, interaction, and assessment in the virtual environment is given. This paper presents the case design of virtual simulation teaching in a virtual environment with experience, demonstration, interaction, and assessment, and the multidimensional effect evaluation of IoT skills teaching and researches the application of virtual simulation to IoT skills teaching through the above four aspects. In this paper, a framework for distributed collaborative computing is built using an asynchronous message queue MQ, which enables multiple nodes to serve a task through task splitting. The DeepCluster module can effectively cluster the time series by deep representation learning and obtain the typical variation of time series patterns. In the task offloading module of the framework, a task offloading decision algorithm based on a value-constrained multi 0–1 backpacking model is designed to minimize task processing latency with an optimal offloading solution. The system test results show that the proposed distributed computing framework and offloading decision algorithm can significantly reduce the processing latency of large tasks.