bluuebunny/crossref_metadata_2025 huggingface dataset:
Human Mobility-based Individual-level Epidemic Simulation Platform
Authors: Fan Zipei , Yang Chuang , Zhang Zhiwen , Song Xuan , Liu Yinghao , Jiang Renhe , Chen Quanjun , Shibasaki Ryosuke
Published: March 2022

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers.
RDH-DES: Reversible Data Hiding over Distributed Encrypted-Image Servers Based on Secret Sharing
Authors: Xiong Lizhi , Han Xiao , Yang Ching-Nung , Xia Zhihua
Published: March 2022

Reversible Data Hiding in Encrypted Image (RDHEI) schemes may redistribute the data hiding procedure to other parties and can preserve privacy of the cover image. Recently, cloud computing technology has led to the rapid growth of networked media, and many multimedia rights are owned by multiple parties, such as a film's producer and multiple distributors. Thus, the data hiding task could be distributed to multiple distributed servers. Multi-party data hiding has become an important demand for networked media. In addition, it is essential to preserve multi-server and multi-message privacy and data integrity. However, most of the RDHEI schemes involve only one data hider. That inspired us to design the secure multi-party embedding over distributed encrypted-image servers as a solution for multi-party RDHEI applications. In this article, we propose a novel Reversible Data Hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing. The Chinese remainder theorem, secret sharing, and block-level scrambling are developed as a lightweight cryptography to generate the encrypted image shares. These shares are distributed to different image servers and are used to embed secret data in the proposed framework. The marked encrypted image can be constructed through the marked encrypted shares from different parties, and the decryption and extraction can be completed by the receiver. The experimental results and theoretical analysis have demonstrated that the proposed scheme is secure and effective.
Reading-Assistance Tools Among Deaf and Hard-of-Hearing Computing Professionals in the U.S.: Their Reading Experiences, Interests and Perceptions of Social Accessibility
Authors: Alonzo Oliver , Elliot Lisa , Dingman Becca , Lee Sooyeon , Al Amin Akhter , Huenerfauth Matt
Published: March 2022

Automatic Text Simplification (ATS) software aims at automatically rewrite complex text to make it simpler to read. Prior research has explored the use of ATS as a reading assistance technology, identifying benefits from providing these technologies to different groups of users, including Deaf and Hard-of-hearing (DHH) adults. However, little work has investigated the interests and requirements of specific groups of potential users of this technology. Considering prior work establishing that computing professionals often need to read about new technologies in order to stay current in their profession, in this study, we investigated the reading experiences and interests of DHH individuals with work experience in the computing industry in ATS-based reading assistance tools, as well as their perspective on the social accessibility of those tools. Through a survey and two sets of interviews, we found that these users read relatively often, especially in support of their work, and were interested in tools to assist them with complicated texts; but misperceptions arising from public use of these tools may conflict with participants’ desired image in a professional context. This empirical contribution motivates further research into ATS-based reading assistance tools for these users, prioritizing which reading activities users are most interested in seeing the application of this technology, and highlighting design considerations for creating ATS tools for DHH adults, including considerations for social accessibility.
Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation
Authors: Chen Xu , Zhang Ya , Tsang Ivor W. , Pan Yuangang , Su Jingchao
Published: March 2022

Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user’s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
Authors: Dey Spandan , Sahidullah Md , Saha Goutam
Published: March 2022

Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction. A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum since the early 2000s, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.
Probing and Visualizing Interfacial Charge at Surfaces in Aqueous Solution
Authors: Caniglia Giada , Tezcan Gözde , Meloni Gabriel N. , Unwin Patrick R. , Kranz Christine
Published: March 2022

Surface charge density and distribution play an important role in almost all interfacial processes, influencing, for example, adsorption, colloidal stability, functional material activity, electrochemical processes, corrosion, nanoparticle toxicity, and cellular processes such as signaling, absorption, and adhesion. Understanding the heterogeneity in, and distribution of, surface and interfacial charge is key to elucidating the mechanisms underlying reactivity, the stability of materials, and biophysical processes. Atomic force microscopy (AFM) and scanning ion conductance microscopy (SICM) are highly suitable for probing the material/electrolyte interface at the nanoscale through recent advances in probe design, significant instrumental (hardware and software) developments, and the evolution of multifunctional imaging protocols. Here, we assess the capability of AFM and SICM for surface charge mapping, covering the basic underpinning principles alongside experimental considerations. We illustrate and compare the use of AFM and SICM for visualizing surface and interfacial charge with examples from materials science, geochemistry, and the life sciences.
The Land–Sea Connection: Insights Into the Plant Lineage from a Green Algal Perspective
Authors: Bachy Charles , Wittmers Fabian , Muschiol Jan , Hamilton Maria , Henrissat Bernard , Worden Alexandra Z.
Published: March 2022

The colonization of land by plants generated opportunities for the rise of new heterotrophic life forms, including humankind. A unique event underpinned this massive change to earth ecosystems—the advent of eukaryotic green algae. Today, an abundant marine green algal group, the prasinophytes, alongside prasinodermophytes and nonmarine chlorophyte algae, is facilitating insights into plant developments. Genome-level data allow identification of conserved proteins and protein families with extensive modifications, losses, or gains and expansion patterns that connect to niche specialization and diversification. Here, we contextualize attributes according to Viridiplantae evolutionary relationships, starting with orthologous protein families, and then focusing on key elements with marked differentiation, resulting in patchy distributions across green algae and plants. We place attention on peptidoglycan biosynthesis, important for plastid division and walls; phytochrome photosensors that are master regulators in plants; and carbohydrate-active enzymes, essential to all manner of carbohydratebiotransformations. Together with advances in algal model systems, these areas are ripe for discovering molecular roles and innovations within and across plant and algal lineages.
Neural Signaling in Cancer
Authors: Keough Michael B. , Monje Michelle
Published: March 2022

Nervous system activity regulates development, homeostasis, and plasticity of the brain as well as other organs in the body. These mechanisms are subverted in cancer to propel malignant growth. In turn, cancers modulate neural structure and function to augment growth-promoting neural signaling in the tumor microenvironment. Approaching cancer biology from a neuroscience perspective will elucidate new therapeutic strategies for presently lethal forms of cancer. In this review, we highlight the neural signaling mechanisms recapitulated in primary brain tumors, brain metastases, and solid tumors throughout the body that regulate cancer progression.
The Electrochemical Detection of 4-chloro-2-methylphenoxyacetic Acid (MCPA) Using a Simple Activated Glassy Carbon Electrode
Authors: Yu Tian , Fenelon Orla , Herdman Karen M. , Breslin Carmel B.
Published: March 2022

4-Chloro-2-methylphenoxyacetic acid (MCPA) is one of the pesticides most widely used to control broadleaf weeds in arable and horticultural crops and it leaches readily into groundwater bodies causing pollution. In this study, a sensor was fabricated by the simple activation of a glassy carbon electrode (GCE) in an aqueous phosphate solution by cycling the GCE between –2.0 and 2.4 V vs SCE. The activated GCE exhibited very good detection of MCPA, with a linear concentration range extending from 1 to 850 μM (cyclic voltammetry) and a limit of detection (LOD) of 0.008 μM, which was obtained using differential pulse voltammetry. A more thermodynamically favoured oxidation of MCPA was observed at the activated GCE, with an approximate shift of 110 mV in the peak potential to lower potentials. Very good reproducibility and stability were achieved, with the sensor giving similar peak currents over a 30-day immersion period. Good selectivity was achieved in the presence of nitrates, nitrites, sulfates and structurally related compounds such as nitro-phenols. The promising performance of the activated GCE in the sensing of MCPA was attributed to the generation of oxygenated functional groups and an increased surface area arising from the local dissolution of the GCE during activation.
Review—Polymeric/Small Organic Molecules-Based Electrochromic Devices: How Far Toward Realization
Authors: Jamdegni Monika , Kaur Amarjeet
Published: March 2022

Electrochromic devices have emerged as new age technology devices due their low power consumption, eye friendly working modes and simple structure with easily adjustable features. Such devices have a vast application prospect in areas like energy efficient building architecture, flexible displays, next generation smart wearable, satellite thermal control, etc. Organic electrochromic material offers several advantages like rich color, stretchable and flexible architecture, facile synthesis and easy modification of optical properties. However, there are certain technical issues like cyclic stability, durability, uniform color performance, etc., which are still to be resolved before large scale commercialization of the organic electrochromic devices. In recent years, various research teams are making efforts in developing new methods and technologies to overcome such issues. In this review, we have summarized these innovative efforts taken in the last decade. This review can help researchers to analyse and compare different techniques adopted towards improving the performance of organic electrochromic devices.
Effect of Cerium Chloride on the Self-Corrosion and Discharge Activity of Aluminum Anode in Alkaline Aluminum-air Batteries
Authors: Harchegani R. K. , Riahi A. R.
Published: March 2022

One of the key impediments to aluminum (Al) as an anode in alkaline Al-air batteries is self-corrosion, which limits the battery’s efficiency due to the capacity loss and lifespan reduction. Thus, it is vital to find an efficient electrolyte additive that reduces self-corrosion in Al anodes. In this study, the effect of adding 0.5 to 1.5 wt% of cerium chloride to 4 mol l−1 KOH electrolyte on the self-corrosion of pure Al anode was investigated using electrochemical experiments. The results show that the addition of cerium chloride to the electrolyte reduces self-corrosion of the Al anode with a negligible effect on the anode activity. Cerium chloride forms cerium hydroxide (Ce (OH)3) in the alkaline electrolyte, which is adsorbed on the Al surface. Therefore, the corrosion potential increased, and self-corrosion current density decreased. As the cerium chloride concentration increased, the Al anode efficiency increased from 43.8% to 76.1%, and the capacity density increased from 1294 to 2244 mAh g−1. Furthermore, increasing the immersion time of the Al anode in the electrolyte containing cerium chloride increased the self-corrosion resistance and provided the self-healing properties for the anode.
A Novel Electrochemical-Biosensor Microchip Based on MWCNTs/AuNPs for Detection of Glycated Hemoglobin (HbA1c) in Diabetes Patients
Authors: Ponsanti Khanittha , Ngernyuang Nipaporn , Tangnorawich Benchamaporn , Na-Bangchang Kesara , Boonprasert Kanyarat , Tasanarong Adis , Saeheng Teerachat , Hanwattanakul Akkaracha , Pechyen Chiravoot
Published: March 2022

This study reports an electrochemical sensor based on multiwalled carbon nanotubes/gold nanoparticles (MWCNTs/AuNPs) for detection of total hemoglobin (Hb) and glycated hemoglobin (HbA1c) in blood samples. Measurement of glycated hemoglobin percentage (%HbA1c) is crucial to the monitoring of glycemia in diabetes patients. The %HbA1c, which refers to the concentration ratio of HbA1c to total hemoglobin (Hb), has been widely used for accurate determination of stage-wise diabetes. The gold nanoparticles (AuNPs) used in the sensor were synthesized using a biological method employing passion fruit peel extracts. The resulting AuNPs are spherical with diameter approximately 18 nm, and these can be mixed with MWCNTs in various ratios to form MWCNTs/AuNPs composites. TEM imaging indicates that gold nanoparticles decorate the surface of the MWCNTs. Coating of a screen-printed carbon electrode (SPCE) with MWCNTs/AuNPs affords the novel MWCNTs/AuNPs/SPCE electrode, which exhibits a linear response over a 0.186–2.044 g dl−1 HbA1c concentration range, with the correlation value of 0.96. The electrode can reliably be utilized for detection of total Hb and HbA1c in blood samples, which highlights its promise as a reliable approach for sensitive point-of-care testing (POCT) of HbA1c in diabetes patients.
Robust Bottom-Up Gold Filling of Deep Trenches and Gratings
Authors: Josell D. , Osborn W. A. , Williams M. E. , Miao H.
Published: March 2022

This work extends an extreme variant of superconformal Au electrodeposition to deeper device architectures while exploring factors that constrain its function and the robustness of void-free processing. The unconventional bottom-up process is used to fill diffraction gratings with trenches 94 μm deep and 305 μm deep, with aspect ratios (height/width) of just below 20 and 15, respectively, in near-neutral 0.16 mol∙l−1 Na3Au(SO3)2 + 0.64 mol∙l−1 Na2SO3 electrolyte containing 50 μmol∙l−1 Bi3+. Although the aspect ratios are modest compared to previously demonstrated void-free filling beyond AR = 60, the deepest trenches filled exceed those in previous work by 100 μm—a nearly 50% increase in depth. Processes that substantially accelerate the start of bottom-up deposition demonstrate a linkage between transport and void-free filling. Final profiles are highly uniform across 65 mm square gratings because of self-passivation inherent in the process. Electron microscopy and electron backscatter diffraction confirm the fully dense Au and void-free filling suggested by the electrochemical measurements. X-ray transmission “fringe visibility” averages more than 80% at 50 kV X-ray tube voltage across the deeper gratings and 70% at 40 kV across the shallower gratings, also consistent with uniformly dense, void-free fill across the gratings.
Performance Analysis of Deep Learning Models for Binary Classification of Cancer Gene Expression Data
Authors: Majumder Subhasree , Yogita  , Pal Vipin , Yadav Anju , Chakrabarty Amitabha
Published: March 2022

The classification of patients as cancer and normal patients by applying the computational methods on their gene expression profiles is an extremely important task. Recently, deep learning models, mainly multilayer perceptron and convolutional neural networks, have gained popularity for being applied on this type of datasets. This paper aims to analyze the performance of deep learning models on different types of cancer gene expression datasets as no such consolidated work is available. For this purpose, three deep learning models along with two feature selection method and four cancer gene expression datasets have been considered. It has resulted in a total of 24 different combinations to be analyzed. Out of four datasets, two are imbalanced and two are balanced in terms of number of normal and cancer samples. Experimental results show that the deep learning models have performed well in terms of true positive rate, precision, F1-score, and accuracy.
An SLP Vectorization Method Based on Equivalent Extended Transformation
Authors: Feng Jingge , He Yeping , Tao Qiuming , Ma Hengtai
Published: March 2022

SIMD extensions provide an efficient energy consumption platform to support mobile systems. How to use SIMD instructions to improve program performance is a challenge. SLP (superword level parallelism) is an efficient solution to exploit the parallelism, oriented to SIMD, between statements in the basic blocks, and it has been widely used in almost all the mainstream compilers. SLP relies on finding isomorphic statements to pack together into vectors. However, the capability of autovectorization for nonisomorphic statements is insufficient. In this paper, we introduce SLP-E, a novel autovectorization method that can automatically vectorize the codes which contain nonisomorphic statements, translate the nonisomorphic statements into the isomorphic statements by equivalent extended transformation of expressions, and vectorize the isomorphic statements. SLP-E improves the application scope and benefits of SLP. We implement the SLP-E in LLVM and compare it with prior approaches. A set of applications that benefit from autovectorization are taken from the SPEC CPU 2017 benchmark to compare our approach and prior techniques. Experimental results show that SLP-E achieves more than 43.9% speedup, on average, over other similar methods.
Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis
Authors: Jasti V. Durga Prasad , Zamani Abu Sarwar , Arumugam K. , Naved Mohd , Pallathadka Harikumar , Sammy F. , Raghuvanshi Abhishek , Kaliyaperumal Karthikeyan
Published: March 2022

Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.
Characterization of the Biochemical Potential of Moroccan Onions (Allium cepa L.)
Authors: Brahimi Amal , El Ouardi Mohamed , Kaouachi Amal , Boudboud Abdelhakim , Hajji Lhoussain , Hajjaj Hassan , Mazouz Hamid
Published: March 2022

Allium cepa L. remains the most cultivated Allium species in Morocco and around the world. With the purpose of making the first biochemical characterization of Moroccan onions, several biochemical components have been measured in eleven onion ecotypes. Onions were collected as seeds from different geographical origins and cultivated in the same environment, to eliminate the influence of the environment on biochemical expression. Moisture, total phenols, flavonoids, antioxidant activity, total and reducing sugars, and sulfur dioxide were the biochemical properties of interest. Except for moisture, the eleven onion ecotypes revealed a highly significant variation in terms of the studied biochemical characters. The total phenol and flavonoid content ranged from 5.94 to 11.22 mg equivalent gallic acid/g dry weight and 0.67 to 1.52 mg equivalent quercetin/g dry weight, respectively. The antioxidant activity of the studied onions showed a strong correlation with the polyphenols (

R

2

=
0.7189

), especially with the flavonoids (

R

2

=
0.8063

). The sulfur dioxide content parted from 85.60 to 30.43 ppm when measured using the Monier-Williams distillation method. The current results show that there is no correlation between total sugars and reducing sugars. In conclusion, these findings present a clear biochemical profile of Moroccan onion ecotypes, as well as confirm, for the first time, the presence of a clear variation between the biochemical profiles of Moroccan onion ecotypes, which could be useful for future valorization programs.
miR-195 Inhibits Proliferation and Enhances Apoptosis of OSCC Cells via Targeting TLR4
Authors: Wang Jianguo , Song Renyou , Wang Chunmei , Zhang Shuangsheng , Zhang Yanqi , Zhu Yanlong , Zhao Gang
Published: March 2022

The aim of this research was to assess the function of microribonucleic acid (miR)-195 in the apoptosis and proliferation of oral squamous cell carcinoma (OSCC) cells as well as its action mechanism. The downstream target protein of miR-195 was predicted using the biological software. A quantitative polymerase chain reaction (qPCR) was implemented to examine the changes in expressions of miR-195 and its target protein toll-like receptor 4 (TLR4) in OSCC cell lines (TSCCA, Tca8223, Tb3.1, and CAL-27) and normal adult human gingival fibroblasts (HGFs), and the relation between their expressions was assessed. The expressions of phosphorylated proteins in nuclear factor-κB (NF-κB) pathway were determined through western blotting. miR-195 was expressed at a noticeably lower level in four OSCC cells than in HGFs, and the lowest level appeared in CAL-27 cells. Compared with miR-195 control, the miR-195 mimic could obviously raise the expression of miR-195. In CAL-27 cells with high expression of miR-195, the proliferation was inhibited and the apoptosis was evidently enhanced. OSCC cells exhibited evidently reduced protein and mRNA expression of TLR4, and miR-195 expression was inversely associated with TLR4 expression. It was uncovered from the dual-luciferase reporter assay that cells with wild-type TLR4 had prominently weakened luciferase activity relative to cells with mutant-type TLR4, revealing that the direct target of miR-195 is TLR4. The NF-κB pathway was impeded in cells that lowly expressed TLR4. miR-195 blocks the NF-κB pathway via inhibiting the expression of TLR4 in OSCC cells, thereby exerting an antitumor effect.
Sustainable Development Goals (SDGs) Priorities of Senior High School Students and Global Public: Recommendations for Implementing Education for Sustainable Development (ESD)
Authors: Yuan Xinqun , Yu Le , Wu Hao , She Hanshi , Luo Jie , Li Xiaowen
Published: March 2022

Education for Sustainable Development (ESD) contributes to the achievement of the Sustainable Development Goal (SDGs) when facing major global development issues. It includes formal, informal, and nonformal education, with an emphasis on a shift from “teaching” to “learning.” Using a data tracking approach and a questionnaire, this study aims to provide theoretical and practical suggestions for implementing ESD in senior high schools. Taking a Chinese senior high school as an example, the students in the school surveyed are compared with the global public in different language regions with respect to attention priorities for SDGs; students’ performance between the attention priorities and learning situation related to SDGs and subjects integrated into SDGs between learned and nominated are also analyzed. Gender equality and good health and well-being are the SDGs that students and the public both care most about. And the majority of SDGs receive less concern of the students and the public. By comparing the average amount of news about the SDGs in seven languages, the amount of news in English is far greater than other languages. Besides, different linguistic news has similarities and differences in their attention priorities to the SDGs. The students in the school surveyed only show greater learning performance in one SDGs, climate action. Only politics and economics, geography, and biology are the subjects with outstanding learning performance and high nominations by the students. The languages used to promote SDGs represent different populations and geographical distribution. The high school students’ attention to SDGs is affected by different linguistic media with the global public views, but it also has its particularities. Regarding the deficiency in SDGs awareness and learning performance, the implementation of ESD displays a nonideal status. It is also revealed that ESD should be strengthened. Additionally, during the process of ESD implementation, it is crucial to take full advantage of the combination of formal, informal, and nonformal education, advancing media campaigns for all 17 SDGs as well as fully mobilizing all course subjects to integrate SDGs. To achieve the curriculum reorientation of ESD, all subjects are recommended to connect to SDGs through unique characteristics. Considering the priorities of the students and the public, this research appeals to strengthening ESD through formal, informal, and nonformal channels under the influence from the global development and different linguistic regions’ media promotion. The recommendations can be used as an initiative and a reference for ESD implementation.
Existence of Solutions for a p‐Kirchhoff‐Type Problem with Critical Exponent
Authors: Benchira Hayat , El Mokhtar M. El Mokhtar Ould , Matallah Atika
Published: March 2022

In this study, we prove the existence of a positive solution for a p‐Kirchhoff‐type problem with Sobolev exponent.
Data Source Analysis of Computerized Management Accounting Based on Data Warehouse and Mobile Edge Computing
Authors: Guo Yanping
Published: March 2022

Accounting work is more efficient when it is computerized, and the rate of errors is lower. The incentive mechanism should be improved before the development of management accounting practice in businesses. The emotion of employees in enterprises is a determinant of the spread of management accounting practice in businesses. Employees’ resistance to management accounting practice can be effectively addressed through incentive mechanisms. As a result, in order to encourage the growth of management accounting in businesses, they must first create a perfect management accounting incentive mechanism. The implementation of a management accounting information system is made more difficult by these new categories. The construction of a management accounting information system based on BI and mobile edge computing is systematically discussed in this study. BI has the advantage of being able to model in a variety of ways, process large amounts of data efficiently, and provide managers with actionable data quickly. To realize the quantitative analysis of cost, income, and input-output, the system employs advanced technologies such as data warehouse modeling, data acquisition, data display, and security design. Management accounting has emerged as the best choice for the innovation of enterprise management mode as an accounting service for the internal management of businesses. Big data in management accounting serves as a company’s “asset,” providing a wealth of information. It has become critical to understand how to mine and use big data information to provide a foundation for management accounting. This study explains how to collect data, prepare data, establish a model, mine data, and analyze data using a big data and mobile edge computing algorithm and identifies the key indicators affecting enterprise management accounting risk analysis through empirical analysis. The study of the diffusion mechanism and influencing factors of management accounting practice within enterprises and between enterprise clusters can help promote the development of management accounting practice and speed up the construction of China’s localized management accounting system.
Research on Online and Offline Mixed Teaching Practice Based on College Film and Television Literature Course
Authors: Liu Yajing
Published: March 2022

With the rapid development of new media technology, the demand for applied professional film and television talents in China’s film and television industry is getting higher and higher, especially the comprehensive practical ability. In the Internet age, information technology has been widely used. It is integrated into people’s lives with the unique characteristics of interaction and communication among all employees, and opens up a new mode of work, study and life with Internet as the medium. Under the new situation, all kinds of schools at all levels also make full and active use of the Internet for online teaching. In the past, the quality monitoring work focused on theoretical teaching. However, in the face of online and offline mixed practice teaching, there are bound to be many problems and deficiencies when using the theoretical teaching quality monitoring system. This paper mainly discusses the application of online and offline mixed tutorial mode in film and television literature course from the aspects of task-oriented teaching content, application of classroom teaching methods, diversification and dynamics of evaluation methods, etc., hoping to provide reference for college teachers who implement online and offline mixed teaching.
Recent Progress of Chronic Stress in the Development of Atherosclerosis
Authors: Gao Shang , Wang Xiang , Meng Ling-bing , Zhang Yuan-meng , Luo Yue , Gong Tao , Liu De-ping , Chen Zuo-guan , Li Yong-jun
Published: March 2022

With the development of the times, cardiovascular diseases have become the biggest cause of death in the global aging society, causing a serious social burden. Atherosclerosis is a chronic inflammatory disease, which can occur in large and medium-sized blood vessels in the whole body. It takes atherosclerotic plaque as the typical pathological change and endothelial injury as the core pathophysiological mechanism. It is the pathological basis of coronary heart disease, peripheral artery disease, cerebrovascular disease, and other diseases. Recent studies have shown that chronic stress plays an important role in the occurrence and development of atherosclerosis, endothelial injury, lipid metabolism, and chronic inflammation. This process involves a large number of molecular targets. It is usually the cause of atherosclerotic cardiovascular and cerebrovascular diseases. If chronic stress factors exist for a long time, patients have genetic susceptibility, and the combination of environmental factors triggers the pathogenesis, which may eventually lead to complete blockage of the blood vessels, unstable rupture of plaques, and serious adverse cardiovascular events. This paper reviews the role of chronic stress in the occurrence and development of atherosclerosis, focusing on the pathophysiological mechanism.
Physicochemical Modeling of the Adsorption of Pharmaceuticals on MIL-100-Fe and MIL-101-Fe MOFs
Authors: Quintero-Álvarez Fátima Gisela , Rojas-Mayorga Cintia Karina , Mendoza-Castillo Didilia Ileana , Aguayo-Villarreal Ismael Alejandro , Bonilla-Petriciolet Adrián
Published: March 2022

The adsorption of naproxen (NAP), diclofenac (DFC), and acetaminophen (APAP) molecules from aqueous solutions using MIL-100-Fe and MIL-101-Fe metal organic frameworks (MOFs) has been analyzed and modeled. Adsorption isotherms of these pharmaceuticals were experimentally quantified at 30 and 40°C and pH 7. Textural parameters and surface chemistry of these MOFs were analyzed, and results were utilized to explain the pharmaceutical adsorption mechanism. Density Functional Theory (DFT) calculations were performed to understand the reactivity of pharmaceutical molecules, and a statistical physics model was employed to calculate the main physicochemical parameters related to the adsorption mechanism. Results showed that the adsorption of these pharmaceuticals on MOFs was multimolecular and exothermic. Both MOFs displayed the highest adsorption capacities, up to 2.19 and 1.71 mmol/g, for NAP and DFC molecules, respectively. MIL-101-Fe showed better pharmaceutical adsorption properties than MIL-100-Fe due to its highest content of Fe-O clusters and mesopore volume. Adsorption mechanism of these organic molecules could involve hydrogen bond, van der Waals forces, and electrostatic interactions with MOF surfaces. In particular, MIL-101-Fe MOF is a promising material to prepare composites with competitive adsorption capacities for facing the water pollution caused by pharmaceutical compounds.
Mental Health Effects of Entrepreneurial College Students from Different Backgrounds: The Mediating Role of Social Equity
Authors: He Dehui , Li Wangzhuo , He Xinghui
Published: March 2022

In this study, the impact of entrepreneurial college students from different backgrounds on mental health which is primarily based on the mediating role of social justice is studied. For this purpose, we have selected those start-up students who graduated from a university for one to two years as the research object, and 1890 questionnaires were distributed. The subjective family background, objective family background, social equity perception, anxiety, and depression of entrepreneurial college students were investigated, and the results were analyzed. The level of social equity and mental health of start-up college students with better birth was significantly higher than that of start-up college students with lower birth. Background, social equity, and positive emotions were significantly positively correlated, which was significantly negatively correlated with negative emotions, depression, and anxiety; Social equity played a partial mediating role in the relationship between birth and mental health indicators. Social equity helped to maintain the mental health of entrepreneurial college students from different backgrounds. This was of great significance to the entrepreneurial college students who come from a lower background.
Medical Cannabis for Gilles de la Tourette Syndrome: An Open-Label Prospective Study
Authors: Anis Saar , Zalomek Corinne , Korczyn Amos D. , Rosenberg Alina , Giladi Nir , Gurevich Tanya
Published: March 2022

Objectives. Assessing the effectiveness and tolerability of medical cannabis (MC) treatment on Gilles de la Tourette syndrome (GTS) patients. Methods. We report on an open-label, prospective study on the effect of MC on adult GTS patients. MC mode of use was decided by the treating neurologist and the patient. Δ9-Tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD) content within MC product and monthly dose were titrated during the study. Following treatment initiation, patients were assessed after 4 and 12 weeks for efficacy, tolerability, and side effects. Results. Eighteen patients entered the study. Baseline Yale Global Tic Severity Scale- (YGTSS) Total (range 0-100) was60.3±17.1. Three patients did not reach the end of follow-up period. The most common mode of administration was smoking (80%). Following twelve weeks of treatment, a significant 38% average reduction (p=0.002) of YGTSS-Total and a 20% reduction (p=0.043) of Premonitory Urge for Tic Scale (PUTS) were observed. Common side effects were dry mouth (66.7%), fatigue (53.3%), and dizziness (46.7%). Three patients suffered from psychiatric side effects including worsening of obsessive compulsive disorder (stopped treatment), panic attack, and anxiety (resolved with treatment modification). Six patients (40%) reported cognitive side effects regarding time perception, visuospatial disorientation, confusion, slow processing speed, and attention. Conclusions. MC treatment demonstrates good efficacy and tolerability in adult GTS patients. Predilection for smoking rather than using oil drops requires further comparative studies to evaluate the efficacy of each. Cognitive and psychiatric side effects have to be monitored and addressed.
Characterization of Various Subunit Combinations of ADP‐Glucose Pyrophosphorylase in Duckweed (Landoltia punctata)
Authors: Wang Mingxiu , Dai Ya , Li Xinyu , Ma Xinrong , Li Caixia , Tao Xiang
Published: March 2022

Background. Landoltia punctata can be used as renewable and sustainable biofuel feedstock because it can quickly accumulate high starch levels. ADP‐glucose pyrophosphorylase (AGPase) catalyzes the first committed step during starch biosynthesis in higher plants. The heterotetrameric structure of plant AGPases comprises pairs of large subunits (LSs) and small subunits (SSs). Although several studies have reported on the high starch accumulation capacity of duckweed, no study has explored the underlying molecular accumulation mechanisms and their linkage with AGPase. Therefore, this study focused on characterizing the roles of different L. punctate AGPases. Methodology. Expression patterns of LpAGPs were determined through comparative transcriptome analyses, followed by coexpressing their coding sequences in Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Nicotiana tabacum. Results. Comparative transcriptome analyses showed that there are five AGPase subunits encoding cDNAs in L. punctata (LpAGPS1, LpAGPS2, LpAGPL1, LpAGPL2, and LpAGPL3). Nutrient starvation (distilled water treatment) significantly upregulated the expression of LpAGPS1, LpAGPL2, and LpAGPL3. Coexpression of LpAGPSs and LpAGPLs in Escherichia coli generated six heterotetramers, but only four (LpAGPS1/LpAGPL3, LpAGPS2/LpAGPL1, LpAGPS2/LpAGPL2, and LpAGPS2/LpAGPL3) exhibited AGPase activities and displayed a brownish coloration upon exposure to iodine staining. Yeast two‐hybrid and bimolecular fluorescence complementation (BiFC) assays validated the interactions between LpAGPS1/LpAGPL2, LpAGPS1/LpAGPL3, LpAGPS2/LpAGPL1, LpAGPS2/LpAGPL2, and LpAGPS2/LpAGPL3. All the five LpAGPs were fusion‐expressed with hGFP in Arabidopsis protoplasts, and their green fluorescence signals were uniformly localized in the chloroplast, indicating that they are plastid proteins. Conclusions. This study uncovered the cDNA sequences, structures, subunit interactions, expression patterns, and subcellular localization of AGPase. Collectively, these findings provide new insights into the molecular mechanism of fast starch accumulation in L. punctata.
Machine Vision and Intelligent Algorithm Based on Neural Network
Authors: Li Meng , Sun Tiebo
Published: March 2022

Neural network algorithms and intelligent algorithms are hot topics in the field of deep learning. In this study, the neural network algorithm and intelligence are optimized, and it is used in simulation experiments to improve the target image recognition ability of the algorithm in the machine vision environment. First, this paper introduces the application of neural networks in the field of machine vision. Second, in the experiment, the improved VGG-16 convolutional neural network (CNN) model is applied to metal block defect detection. Experimental results show that the optimized network can classify metal block defects with the maximum accuracy of 99.28%. Then, the intelligent algorithm based on neural network is studied, and the CIFAR-10 data set is taken as the experimental target for training test and verification test. Using BP algorithm, particle swarm optimization algorithm (PSO-BP), and improved neural network algorithm, respectively, the convergence speed of ICS algorithm based on BP neural network is compared. In contrast, ICS-BP algorithm has the fastest convergence speed and converges when the number of iterations is 32, followed by PSO-BP algorithm.
Design of the Mixed Oral English Teaching Method Based on the Hierarchical Aggregation Algorithm
Authors: Xiao Xiaofei , Huang Yao
Published: March 2022

As a key skill for language learners, oral communication ability is one of the most important factors to measure one person’s overall quality. Being a widely used language, English has become an important communicative medium worldwide. Oral English teaching is an important part of the whole process of English learning and is generally an emerging topic for the education circle. However, the traditional oral English teaching model has no significant effect on improving oral English performance. Therefore, this paper studies the mixed oral English teaching model under the guidance of core qualities. The connotation of core literacy of oral English is analyzed and the general framework of the core literacy system of oral English is constructed. Then, the relationship between the four elements of English subject core literacy is examined based on the classification of the spoken English teaching resources. Next, the integration of spoken English teaching resources is realized and the oral English knowledge ontology and the cultural differences between Chinese and Western analysis are explored. Finally, a task-based oral English teaching hybrid model is developed. The experimental results show that there is a significant difference in the oral English level of the students before and after the experiment. The model can improve the overall quality and overall performance of English learning for our society.
Set-Theoretic Inequalities Based on Convex Multi-Argument Approximate Functions via Set Inclusion
Authors: Rahman Atiqe Ur , Saeed Muhammad , Khan Khuram Ali , Matendo Mabela Rostin
Published: March 2022

Hypersoft set is a novel area of study which is established as an extension of soft set to handle its limitations. It employs a new approximate mapping called multi-argument approximate function which considers the Cartesian product of attribute-valued disjoint sets as its domain and the power set of universe as its co-domain. The domain of this function is broader as compared to the domain of soft approximate function. It is capable of handling the scenario where sub-attribute-valued sets are considered more significant than taking merely single set of attributes. In this study, notions of set inclusion, convex (concave) sets, strongly convex (concave) sets, strictly convex (concave) sets, convex hull, and convex cone are conceptualized for the multi-argument approximate function. Based on these characterized notions, some set-theoretic inequalities are established with generalized properties and results. The set-theoretic version of classical Jensen’s type inequalities is also discussed with the help of proposed notions.