bluuebunny/crossref_metadata_2025 huggingface dataset:
TRILEMMA ASSOCIATION OF EDUCATION, INCOME AND POVERTY ALLEVIATION: MANAGERIAL IMPLICATIONS FOR INCLUSIVE ECONOMIC GROWTH
Authors: LIU ZHEN , HASAN MOHAMMAD MARUF , XUAN LI , SAYDALIEV HAYOT BERK , LAN JING , IQBAL WASIM
Published: June 2022

This study focused on the trilemma association of education, income and poverty alleviation: managerial implications for inclusive economic growth in developing countries in Asia to establish the proportion of the poor in the population and further identify its determinants. This research utilized secondary data from 1990 to 2016 by using econometric estimation. The results show that education decreases poverty when evaluated through the poverty gap and poverty headcount ratio and employment and increasing rate of economic development in the form of GDP to reducing poverty. GDP the Gini coefficient show the same signs while the magnitudes of the coefficients. Consequently, improvement in an independent variable will decrease poverty while the results have various levels of contributions through static and dynamic panel data methods, that education can reduce poverty. Results indicate that the level of poverty stood at 62.2%. The level of education, poverty headcount ratio, poverty gap and secondary school enrolment were significant in determining a household’s poverty status. However, land ownership and household head’s occupation were not statistically significant in explaining the probability of a household’s poverty status. From the results, this study recommends that all stakeholders work towards reducing poverty in the study to enhance education and family planning.
Deep Learning-Based Multi-Classification for Malware Detection in IoT
Authors: Wang Zhiqiang , Liu Qian , Wang Zhuoyue , Chi Yaping
Published: June 2022

Due to the open-source and versatility of the Android operating system, Android malware has exploded, and the malware detection of Android IoT devices has become a research hotspot in recent years. Static analysis technology cannot effectively analyze obfuscated malware. Without decomposing, the existing detection methods are mainly based on grayscale images and single files without analyzing and verifying their anti-obfuscation performance. In addition, the current detection of Android malware using deep learning is concentrated in the field of binary classification. This paper proposes a multi-classification method of the Android malware family based on multi-class feature files and RGB images to solve these problems. The method proposed in this paper does not need to decompile the Android APK installation package. However, it extracts the DEX file and XML file in batch from the APK installation package. Then, it converts the file into an RGB image using the conversion algorithm that converts Android software into images. Finally, the deep neural network automatically obtains the RGB image texture features to realize the multiple classifications of the Android malware family. Experimental data show that the proposed method has high detection performance, and the accuracy of multiple classifications of the Android malware family is as high as 99.84%. In addition, the method based on RGB image is better than the grayscale image in detection accuracy, and the effect of RGB image combined with DEX and XML is better than that of separate DEX file image and separate XML file image. Therefore, the method proposed in this paper can effectively detect the obfuscated Android malware, and the detection accuracy of 99.23% can be achieved for the obfuscated sample data. Furthermore, this method has good anti-obfuscation ability. The proposed method is compared with those based on Multi-Layer Perceptron, Long Short-Term Memory, bidirectional Long Short-Term Memory and Deep Belief Network. The experimental results show the proposed method’s effectiveness and high generalization performance.
Testing, Validation, and Verification of Robotic and Autonomous Systems: A Systematic Review
Authors: Araujo Hugo , Mousavi Mohammad Reza , Varshosaz Mahsa
Published: June 2022

We perform a systematic literature review on testing, validation, and verification of robotic and autonomous systems (RAS). The scope of this review covers peer-reviewed research papers proposing, improving, or evaluating testing techniques, processes, or tools that address the system-level qualities of RAS.

Our survey is performed based on a rigorous methodology structured in three phases. First, we made use of a set of 26 seed papers (selected by domain experts) and the SERP-TEST taxonomy to design our search query and (domain-specific) taxonomy. Second, we conducted a search in three academic search engines and applied our inclusion and exclusion criteria to the results. Respectively, we made use of related work and domain specialists (50 academics and 15 industry experts) to validate and refine the search query. As a result, we encountered 10,735 studies, out of which 195 were included, reviewed, and coded.

Our objective is to answer four research questions, pertaining to (1) the type of models, (2) measures for system performance and testing adequacy, (3) tools and their availability, and (4) evidence of applicability, particularly in industrial contexts. We analyse the results of our coding to identify strengths and gaps in the domain and present recommendations to researchers and practitioners.

Our findings show that variants of temporal logics are most widely used for modelling requirements and properties, while variants of state-machines and transition systems are used widely for modelling system behaviour. Other common models concern epistemic logics for specifying requirements and belief-desire-intention models for specifying system behaviour. Apart from time and epistemics, other aspects captured in models concern probabilities (e.g., for modelling uncertainty) and continuous trajectories (e.g., for modelling vehicle dynamics and kinematics).

Many papers lack any rigorous measure of efficiency, effectiveness, or adequacy for their proposed techniques, processes, or tools. Among those that provide a measure of efficiency, effectiveness, or adequacy, the majority use domain-agnostic generic measures such as number of failures, size of state-space, or verification time were most used. There is a trend in addressing the research gap in this respect by developing domain-specific notions of performance and adequacy. Defining widely accepted rigorous measures of performance and adequacy for each domain is an identified research gap.

In terms of tools, the most widely used tools are well-established model-checkers such as Prism and Uppaal, as well as simulation tools such as Gazebo; Matlab/Simulink is another widely used toolset in this domain.

Overall, there is very limited evidence of industrial applicability in the papers published in this domain. There is even a gap considering consolidated benchmarks for various types of autonomous systems.
Stability-Guided Strategies to Mitigate Dendritic Growth in Lithium-Metal Batteries
Authors: Li Weiyu , Tchelepi Hamdi A. , Ju Yiguang , Tartakovsky Daniel M.
Published: June 2022

Dendritic growth is a leading cause of degradation and catastrophic failure of lithium-metal batteries. Deep understanding of this phenomenon would facilitate the design of strategies to reduce, or completely suppress, the instabilities characterizing electrodeposition on the lithium anode. We present a linear-stability analysis, which utilizes the Poisson-Nernst-Planck equations to describe Li-ion transport and, crucially, accounts for the lack of electroneutrality. This allows us to investigate the impact of electric-field gradients near the electrode surface on both ion diffusion and its anisotropy. Our analysis indicates that the use of anisotropic electrolytes (i.e., electrolytes with anisotropic diffusion coefficients of the Li ions) and the control of the local electric field can suppress dendritic growth of lithium metal. Specifically, changes in the local electric field can be used to enhance the longitudinal (perpendicular to the electrode) component of the cation diffusion coefficient tensor, which decreases the maximum growth rate of the dendrites. Electrolytes with electric field-dependent diffusion coefficients would reduce dendritic growth in small batteries, while anisotropic electrolytes (or separators with anisotropic pore structures or columnized membranes) are appropriate for batteries of any size.
A Model of the Potassium-Oxygen Battery and its Application in Cathode Design
Authors: Küpper Jannis , Li Xianglin , Simon Ulrich
Published: June 2022

The potassium-oxygen battery (KOB) is a new type of metal-oxygen battery with high rechargeability and long cycle life. Currently, the energy density is rather limited and must be improved for KOB to become a viable energy storage technology for practical applications. In this study, a two-dimensional, multiphase KOB model is developed to design an optimized cathode structure. The model is validated and is used to study the influence of cathode porosity, surface area, and thickness on the discharge behavior. Higher cathode porosity and surface area are found to increase the discharge capacity and lower the discharge overpotential. However, using a microporous cathode may not be ideal for KOB. The electronic transport properties of the discharge product KO2 are assessed, suggesting an effectively higher conductivity of KO2 than previously predicted. In consequence, the formation of large KO2 deposits with several μm thickness may effectively inhibit oxygen transport in microporous materials. Thus, a hierarchical cathode porosity together with an optimized current collector design may be the key to significantly higher discharge performance.
Dynamic Analysis of International Swimming Research Using CITESPACE
Authors: Sun Xinyu , Li Fangye , Wu Congxing , Zhai Feng
Published: June 2022

These days, the visual analysis of the literature (using the Web of Science as the data source) in the field of international swimming has been used to clarify the current status and patterns of research in this field. The data source of web of science provides a reference for the dynamic update of international swimming research. In this paper, we have used CiteSpace software and applied it to analyze the core literature related to international swimming from 1998 to 2020 in terms of overall literature measurement, core authors, source institutions, country distribution, discipline distribution, research hotspots, frontiers, and so on. We performed numerous experimental works for dynamic analysis of international swimming research using this tool. The findings indicate that international swimming research is in the second (growth) stage of the “Lepus curve,” and that there is a “head effect” in the distribution of journals (primarily in sports medicine). We obtained these large core cooperative networks, forming some high-production institutions as the core of large-scale cooperative networks and comprehensive university high-production institutions. In the future, we plan to focus on sports injury and rehabilitation, physiological characteristics, biomechanical analysis, and reaction in the future.
Optimization of TCM Diagnosis Information Management System Based on Artificial Neural Networks
Authors: Jia Dian
Published: June 2022

As a treasure of Chinese medicine, TCM has gradually formed and developed into a complete medicine with a unique medical theory system and rich treatment experience after thousands of years of medical practice. It requires high diagnostic experience, which is not conducive to application promotion and management. Therefore, the concept of digital medicine has been recognized by more and more people, in which medical diagnosis is one of the core issues of digital medicine. The accuracy and efficiency of medical diagnosis are closely related to people’s life and health, which is an important problem that cannot be ignored. Use the growing case base as knowledge base to reason and realize the diagnosis function of traditional Chinese medicine. Based on the characteristics of traditional Chinese medicine and taking case reasoning as a model, an expert system of traditional Chinese medicine diagnosis is established. This paper combines the strong learning ability, strong adaptability, and large-scale parallel processing ability of artificial neural networks (ANN) to solve the nonlinear and unstructured complex problems in management information system. By improving BP algorithm to optimize the error of weight and repair or energy parameters, the overall error of the optimized system is reduced by about 75.3% after experimental analysis, and the average accuracy of prediction is 75%.
Indonesian Migrant Workers: The Migration Process and Vulnerability to COVID‐19
Authors: Anaf Aswatini , Ibnu Fitranita , Romdiati Haning , Noveria Mita
Published: June 2022

Indonesia is one of the largest sources of migrant workers in Southeast Asia. Presently, these workers are vulnerable to COVID‐19 due to the prolonged migration process, which requires them to relocate from their villages to another country and back to Indonesia on completion of their working contract. Therefore, this study describes and discusses the vulnerability of Indonesian migrant workers (IMWs) to the pandemic at various phases of the migration process. It is related to the implementation and practice of health protocols, ignorance and indifference to the dangers and transmission of the virus, and also to the national vaccination program. The analysis is based on the review of literature studies, such as studies related to the topic, international and national regulations on migrant workers, and official data and statistics published by the Indonesian government. The materials and data were collected from search engines such as Google Search and Google Scholar and also relevant published reports available. Several policies have been implemented by the government of Indonesia and other destination countries where the prospective IMWs intend to work, to protect and prevent the transmission of COVID‐19. However, there is still a contagion among IMWs willing to leave abroad and those returning home after completing their employment contract. Therefore, both countries need to be responsible for each migration process, specifically related to providing health protection, increasing awareness of the danger and transmission of the virus, and applying polymerase chain reaction (PCR) tests and COVID‐19 vaccination for migrant workers.
Swimming Attitude for Tracking Error Correction Based on Mahony Algorithm
Authors: Chang Zheng , Zhao Yu
Published: June 2022

Swimming is predominantly a long-distance endurance sport. In this sport, like in many others, monitoring and tracking swimming attitude error correction and how it changes over time is critical. Besides, in swimming posture measurement, due to the absorption and refraction of various signals of water, sensors relying on external information cannot provide accurate information. In addition, the inertial technology is not dependent on external information, suitable for the field of swimming posture measurement. Inertial attitude measurement requires initial alignment technology to provide initial values to calculate the attitude in the swimming movement. However, the swimmer’s jump time is uncertain and the traditional initial alignment algorithm needs a long time to get a high-precision result. To solve this problem, in this paper, we proposed a fast alignment method by the Mahony algorithm. In our work, we have used nine-axis inertial measurement unit of micro-electro-mechanical systems (MEMS) to collect information. Furthermore, we calculated the attitude angle by angular velocity information, horizontal attitude angle, and yaw angle, and these were corrected by acceleration information and magnetic field intensity information, respectively, and multisource information was integrated by a complementary filtering method. It is simple to acquire the starting value of inertial attitude measurement. Laboratory experiments verify that the horizontal accuracy of attitude angle can reach the angle classification within 3 s, which meets the requirements of swimming sports. The algorithm’s viability is further confirmed through experiments in real-world sporting scenarios.
AS-tDR-007872: A Novel tRNA-Derived Small RNA Acts an Important Role in Non-Small-Cell Lung Cancer
Authors: Fan Hang , Liu Hongbing , Lv Yanling , Song Yong
Published: June 2022

tRNA-derived small RNAs (also known as tsRNAs) are novel kinds of non-coding RNAs. Although tsRNAs are aberrantly expressed in different tumor types, there is scanty of research investigating their expression profiling and functions in pulmonary adenocarcinoma (PADC). We identified the expression of AS-tDR-007872 in 30 non-small-cell lung carcinoma (NSCLC) patients’ carcinoma tissues and conducted biological function evaluation. We also test the expression levels of AS-tDR-007872 in plasma samples obtained from 35 healthy people and 79 NSCLC cases. The results identified downregulated AS-tDR-007872 in both cancer tissues and plasma samples versus adjacent normal counterparts (

p
<
0.05

) and healthy controls (

p
<
0.001

). The area under the curve of AS-tDR-007872 was identified by receiver operating characteristic curve analysis to be 0.756 (95% CI, 0.663-0.849;

p
<
0.001

). Furthermore, overexpression of AS-tDR-007872 in vitro inhibited tumor cell proliferation, invasion, and migration and promoted apoptosis. The knockdown of AS-tDR-007872 showed the opposite results. Meanwhile, we found significantly downregulated BCL2L11 after overexpressing AS-tDR-007872. From the above, our research suggests that AS-tDR-007872 can be a tumor suppressor and a promising biomarker for diagnosing lung cancer.
Interaction Design System for Artificial Intelligence User Interfaces Based on UML Extension Mechanisms
Authors: Zhao Yiyi
Published: June 2022

With the rapid development of computer network technology in recent years, more and more demands have been placed on the functionality and attributes of the user interface. In the development of many computer projects, the variability and flexibility of user interface requirements have greatly increased the complexity of program development for researchers. In addition, the poor reusability of page access control writing has created a pressing need for a highly standardized and flexible way of developing software. Thus, the development and design of user interfaces for application software systems occupy an important position and have been a hot topic of research in the field of human-computer interaction. The traditional methods of describing user interaction, such as state transitions and data flow diagrams, are not based on global and intuitive concepts. Moreover, there is little support for the design of user interface interaction behavior, resulting in user interfaces being ignored at design time and left to implementers to grasp at coding time. It is therefore an issue that needs to be addressed in order to integrate traditional methods and intuitive descriptions from the user’s perspective into a new interface development model and methodology. This research creates a user interface framework based on interaction behavior from the user’s perspective. Furthermore, UML extension mechanisms are used to enable the user interface framework to better support UML-based modelling environments. In addition, the UML is structured and extended to include structural elements that support interface generation, and a structured use case model is proposed, which drives the analysis and design of the individual submodels. The extracted abstract interface elements and their mapping to concrete interface elements are documented in a way that explores the generation of different target languages under different platforms. This study incorporates user requirements and provides a scientific reference for the development and design of user interfaces.
Exploration on the Construction of Cross-Border E-Commerce Logistics System Using Deep Learning
Authors: Wang Zhifeng
Published: June 2022

The intention of this article is to solve the disadvantages of the current logistics model and promote the healthy development of modern cross-border e-commerce (CBEC) Logistics. First, this paper expounds on and compares the traditional and CBEC logistics models. Then, the CBEC logistics system is constructed and adjusted according to system construction requirements. Further, two key subsystems are designed: the logistics object distribution subsystem and risk detection subsystem, based on the deep learning backpropagation neural network (BPNN) algorithm. The relevant parameters of the object distribution subsystem are calculated and sent to the risk detection subsystem model and tested. It is concluded that the sorting completion rate before 18:00 can reach 95.2%, indicating that the proposed CBEC logistic system can meet the needs of CBEC logistics enterprises. Logistics risk detection’s expected and actual outputs can fit 99%, indicating a tiny deviation. The research has certain reference significance for clarifying the logistics system and service mode of CBEC.
The Role of Small Extracellular Vesicles and MicroRNAs in the Diagnosis and Treatment of Allergic Rhinitis and Nasal Polyps
Authors: Liu Yiting , Sha Jichao , Meng Cuida , Zhu Dongdong
Published: June 2022

Allergic rhinitis and nasal polyps are common otorhinolaryngological diseases. Small extracellular vesicles and microRNAs have recently become major research topics of interest due to their key regulatory roles in cancer, inflammation, and various diseases. Although very detailed and in-depth studies on the pathogenesis and pathophysiology of allergic rhinitis and nasal polyps have been conducted, few studies have assessed the regulatory effects of exosomes and microRNAs on allergic rhinitis and nasal polyps. This paper reviews the studies on small extracellular vesicles and microRNAs in allergic rhinitis and nasal polyps conducted in recent years and focuses on the regulation of small extracellular vesicles and microRNAs in allergic rhinitis and nasal polyps with the aim of providing insights for the future diagnosis and treatment of allergic rhinitis and nasal polyps.
A Machine Learning Approach to Optimize, Model, and Predict the Machining Factors in Dry Drilling of Nimonic C263
Authors: Lakshmana Kumar S. , Jacintha V. , Mahendran A. , Bommi R. M. , Nagaraj M. , Kandasamy Umamahesawari
Published: June 2022

In this present paper, the machine learning approach is used to optimize, model, and predict the factors during drilling Nimonic C263 under dry mode. Nimonic C263 is tough to machine aero alloys, and it is required to find a predictive model and to optimize the factors in drilling this alloy before the actual machining process. It helps to avoid the actual machining cost and material cost. Experimental trails are planned based on Taguchi analysis, and L27 orthogonal array was chosen. Speed, feed, and approach angle of drill were considered as controlling factors, and cutting force and surface roughness were considered as responses. The feed forward neural network (FFNN) was used to develop a predictive model. The prediction capability was validated with a predictive model developed by Taguchi analysis. Furthermore, ANOVA (analysis of variance) analysis was done to find out the most influence factor on the responses.
A New Approach to Evaluate Regular Semirings in terms of Bipolar Fuzzy k‐Ideals Using k‐Products
Authors: Bashir Shahida , Al-Kenani Ahmad N. , Kausar Tanzila , Mazhar Rabia
Published: June 2022

In this paper, we provide a generalized form of ideals that is k‐ideals of semirings with the combination of a bipolar fuzzy set (BFS). The BFS is a generalization of fuzzy set (FS) that deals with uncertain problems in both positive and negative aspects. The main theme of this paper is to present the idea of (α, β)‐bipolar fuzzy k‐subsemiring (k‐BFSS), (α, β)‐bipolar fuzzy k‐ideals (k‐BFIs), and (α, β)‐bipolar fuzzy k‐bi‐ideals (k‐BFbIs) in semirings by applying belongingness (∈) and quasi‐coincidence (q) of the bipolar fuzzy (BF) point. After that, upper and lower parts of k‐product of BF subsets of semirings are introduced. Lastly, the notions of k‐regular and k‐intraregular semirings in terms of (∈, ∈∨q)‐k–BFIs and (∈, ∈∨q)‐k–BFbIs are characterized.
A Secure Dynamic Content Delivery Scheme in Named Data Networking
Authors: Zhang Jiancong , Li Shining , Wang Changhao
Published: June 2022

Named data networking (NDN) is a promising alternative data dissemination technology of TCP/IP communication networks, which can bring out much more cost-effective and resilient communication in a highly mobile environment. However, due to the feature of NDN, content poisoning comes out as a potential threat. Hence, state-of-the-art studies introduce network layer approaches based on name-key binding, in which the producer notifies routers of the bindings of names and key values. Key values include publisher public key digest or content digest. Routers check key values to determine whether incoming data packets have been poisoned. Unfortunately, the approaches lead to more vulnerabilities in dynamic content poisoning because attackers can impersonate the producer to alter or fabricate the bindings. Thus, we introduce a consumer-oriented two-phased lightweight security scheme, which consists of an end-to-end authentication and a packet-level name-key query mechanism. Specifically, the name-key bindings are authenticated via an additional verification by the consumer. Furthermore, we also introduce a novel trust model to help routers to determine and disconnect from the malicious nodes. Finally, our extensive experimental results demonstrate that the scheme can work effectively in improving the vulnerability of existing studies on dynamic content poisoning and lowering the system overhead simultaneously.
Statistical Inference of Reliability Parameter for the Skew‐Normal Distribution under Progressive Type‐II Censored Samples
Authors: Babayi Salman , Seidpisheh Mohammad , Gholami Gholamhossein
Published: June 2022

This study investigates the inference of the reliability parameter R = P[Y < X] for the skew‐normal distribution based on progressively Type‐II censored samples, where Y and X are two independent random variables. The maximum likelihood estimator and the asymptotic confidence interval for S = (2,2, …,2,5)1×20 are extracted via an iterative algorithm. Moreover, the Bayes estimator and the corresponding credible interval are computed using Gibbs sampling. Also, the MLE, UMVUE, Bayes estimator, and confidence interval are acquired when the scale parameter is known. We compare the performances of the different estimation methods using Monte Carlo simulations. Finally, the real datasets are analyzed for illustrative purposes.
Multimedia Based Medical Big Data Analysis of Leg Swing Strike Effect in Wushu Sanda Sports
Authors: Zhang Ning , Shan Zhaolin , Yao Qiang
Published: June 2022

In order to explore the effect of leg swing in Wushu Sanda, a method based on multimedia medical big data is proposed. The joint torque of hip joint is less than that of healthy subjects. The peak values of the horizontal and vertical components of the joint reaction force are approximately the same as those of the sound subject, and a comparison is made between the two kinds of leg swing strike effects of Wushu Sanda to select the leg swing strike which is more suitable for Wushu Sanda. These results show that the turning strategy of Wushu Sanda on the stairs can be used to swing the legs after turning with similar or smaller hip joint dynamics. Multimedia medical big data analysis has the greatest impact on the variability of simulation dynamic parameters. Six test students turned their knees and turned their heels forward, which is better than the technology without turning their knees and turned to swing their legs in the air. At the moment of striking, the running track of the front striking feet in the air is generally shorter. Therefore, the technology of turning their knees and turning their heels forward has higher concealment and better striking effect. It is proved that the research results may improve the inertia and usability of Wushu Sanda process design.
Fire Risk Assessment and Experimental Study of Transformer Insulating Oil
Authors: Jun Ji , Xin Chen , Lin Li , Hui Zhu , Jingkai Nie , Yu Han
Published: June 2022

Most previous works concentrated on burning characteristics of pool fire using common fuels such as heptane, propane, biodiesel, and diesel, whereas burning characteristics for transformer oil are barely involved, although transformer oil is arguable of more practical importance in power system. This study performed a series of pool fire experiments using transformer oil to study the burning characteristics in open and confined spaces. Three fuel thicknesses and five initial temperatures are changed in open space. The essential parameters of mass loss rate, flame height, and fire plume temperature are obtained and analyzed. Moreover, three oil pool diameters are varied in a chamber. The main conclusions are summarized as follows: the variations of mass loss rate, flame height, and fire plume temperature not only obviously increase as the fuel thickness increases from 0.5 cm to 1.0 cm, but also insignificantly increase as the fuel thickness changes from 1.0 cm to 2.0 cm. The mass loss rate is less sensitive to the initial temperature of transformer oil, but the flame height and fire plume temperature significantly rise with the initial temperature. Moreover, the modified models to predict the flame height and fire plume temperature for 25°C initial temperature conditions are proposed, but the fitting coefficients are obviously different from that for common liquid fuels. The flame height in confined space is higher and will rapidly increase to the maximum, then decreases, and tends to be stable, which is obviously different from the oil pool fire burning in open space. In addition, the phenomenon of burning blast and the ignition of the adjacent oil pool will be observed with a high‐temperature ignition source and a certain high temperature in a chamber under 30 cm oil pool diameter, which will not be recorded in 15 cm and 20 cm.
Missed at Birth: A Rare Case of Acute Pancreatitis Secondary to Congenital Diaphragmatic Hernia
Authors: Khalyfa Ahamed A. , Randhawa Navkiran , Gabbert David , Al-Ghanoudi Ashirf
Published: June 2022

Acute pancreatitis is a common gastrointestinal cause of hospitalizations across the world. The most common etiologies of acute pancreatitis include gallstones, excessive alcohol use, hypertriglyceridemia, or, rarely, trauma. Traction-induced pancreatitis is an uncommon but previously reported cause of acute pancreatitis. We present a 60-year-old male with a past medical history of cerebral palsy who presented to our facility with acute pancreatitis secondary to a congenital diaphragmatic hernia.
English Translation Recognition Model Based on Optimized GLR Algorithm
Authors: Guangming Zuo
Published: June 2022

In the current era of information technology, people’s requirements for English translation are gradually increasing, and the need for a computer to understand and translate English language is becoming more urgent. In order to accurately identify phrases, this paper proposes an English translation recognition model based on optimized GLR algorithm, which can improve the accuracy of recognition by locating phrases in the text.
The Effect of Drilling Fluid on Coal’s Gas-Water Two-Phase Seepage
Authors: Zhang Xianyong , Zheng Shuangjin , Bai Kai
Published: June 2022

In the process of coal bed methane (CBM) production, the output of CBM is mainly related to the relative permeability of gas and water in coal seams. However, during the drilling process, the invasion of drilling fluid into CBM reservoirs changes the wettability, which may cause the gas and water’s redistribution through the pores and cracks, further changing their two-phase seepage characteristics and influencing CBM production. Therefore, studying the effect of drilling fluid on coal’s gas-water two-phase seepage has practical implications. Using a steady-state method, the influence of changing wettability and reducing the solution’s interfacial tension on relative permeability is investigated by adding different surfactants. The increase in coal’s hydrophilicity exhibits an impact on its relative gas-water permeability. At the same water saturation level, the relative gas permeability decreases and the relative water permeability increases. The hydrophilicity of coal was enhanced after adding anionic surfactants, which reduced gas permeability. Cationic surfactants are difficult to adsorb to the surface of coal due to the fact that the interfacial tension of the water and coal surface is reduced when the coal seam water is added to the cationic surfactants. After adding cationic surfactants, the gas permeability increased in favor of CBM production. The findings of this study could help to better understand the influence of drilling fluid intrusion on coal’s gas-water two-phase seepage and provide technical guidance for selecting better surfactants during the preparation of drilling fluids and help to increase CBM production.
Fiberoptic Bronchoscopic Findings in Patients Suffering from Emerging Pulmonary Lophomoniasis: A First Registry-Based Clinical Study
Authors: Aliyali Masoud , Taheri Amirmasoud , Fakhar Mahdi , Sharifpour Ali , Nakhaei Maryam , Abedi Siavash , Mehravaran Hossein , Safanavaei Sepideh
Published: June 2022

Background. Lophomonas blattarum is an emerging protozoan agent that mainly infects the lower respiratory system, causing pulmonary lophomoniasis. The bronchoscopic findings in patients with pulmonary lophomoniasis have not been investigated yet. Accordingly, we assess the bronchoscopic findings of lophomoniasis in patients suffering from pulmonary lophomoniasis through a registry-based clinical study. Methods. In this retrospective study, of 480 patient candidates for bronchoscopy, 50 Lophomonas-positive patients were enrolled. Demographic data, relevant characteristics, and bronchoscopy findings of the patients were recorded and analyzed. Results. Overall, 50 (male = 32, female = 18) patients with an average age of 61.8 ± 13.3 years were examined. Nineteen patients (38%) had normal bronchoscopic findings, and 31 patients (62%) had abnormal bronchoscopic findings. According to the severity index, most (52%) of patients had mild severity, followed by moderate (30%) and severe (18%) cases. The highest involvement was in the right lung bronchus (46%), and the lowest was in the carina (8%). Furthermore, purulent and mucosal secretions in the right and left lung bronchus were the most abnormalities found in different anatomical locations. Conclusion. For the first time, the current study demonstrated that pulmonary lophomoniasis does not have pathognomonic bronchoscopic findings. However, each suspected patient must be checked for lophomoniasis, even with normal bronchoscopic findings, particularly in endemic areas.
Estimation of Bearing Capacity of Strip Footing Rested on Bilayered Soil Profile Using FEM‐AI‐Coupled Techniques
Authors: Ebid Ahmed M. , Onyelowe Kennedy C. , Salah M.
Published: June 2022

In this research work, finite element method (FEM) and 2D Plaxis were employed to generate numerical values for bilayered soils bearing a strip footing of width B and depths, h and H, in order to predict the ultimate bearing capacity (UBC) of the strip footing underlain by layered soil profile. Several research works have tried to solve bearing capacity problems using limit equilibrium (LE) techniques. But, the LE techniques have limitations in terms of soil properties and profile arrangement. The need, however, for using constitutive models or numerical methods powered by FEM and discrete element method (DEM) has been on the rise due to the versatility and robustness of these techniques to accommodate erratic soil behaviors. Multiple numerical data were generated for the case under study and artificial intelligence (AI)‐based techniques; generalized reduced gradient (GRG), genetic programming (GP), artificial neural network (ANN), and evolutionary polynomial regression (EPR) were used to predict the UBC. In order to conduct the parametric analysis to investigate the effect of different soil layers, footing width and overburden pressure at foundation level on the ultimate bearing capacity sets of finite element models were prepared. This was executed using the following soil properties: soil type of top layer (from S1 to S6), soil type of bottom layer (from S1 to S6), strip footing width (B) (from 1.0 to 5.0 m), thickness of the top layer (h) (from 0.5 B to 1.0 B), overburden pressure (σ′v) (from 1.0 m to 3.0 m multiplied by the γ′t), and the parameters combination of each finite model in the set is randomly selected. The results of the FEM/Plaxis parametric study produced the 2D‐model, deformed shape, stress distribution, and plastic point (failure point) models. The loading produced appreciable deformation on both x‐ and y‐axes of the soil profile with the y‐axis showing a scattered failure configuration. The AI‐based prediction produced UBC equations which performed at over 90% accuracy with ANN (99.9%; 6.3%) outperforming other techniques followed by GRG, GP, and EPR.
Research on Lane Change Motion Planning Steering Input Based on Optimal Control Theory
Authors: Liu Yongsheng , Liu Yingjie
Published: June 2022

Aiming at traditional control methods are not suitable for solving the multiconstraint problem of the vehicle steering and lane-change maneuvering process, a hybrid optimization scheme based on dynamic adaptive salp swarm algorithm and Gaussian pseudospectral method (DASSA-GPM) is proposed for its strong global search capability. Based on a steering inverse dynamics model, the lane change motion planning steering problem is converted into an optimal control problem which is then converted into a nonlinear programming problem by applying the adaptive salp group algorithm which is solved through the sequential quadratic programming (SQP) method finally. The simulation results verify the effectiveness and feasibility of the proposed dynamic adaptive salp swarm algorithm and hybrid optimization scheme. In addition, compared with GPM which the absolute error of steering wheel angle is 8.2 × 10−4 degree, the proposed method has higher computational accuracy, which absolute error of steering wheel angle is 5.5 × 10−4 degree. At the same time, under the same calculation accuracy (10−3), compared with GPM which CPU time is 4.81 s, the proposed method has higher calculation efficiency which CPU time is 3.86 s when solving non-smooth problems. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.
Activator Protein Accelerates Dihydropyrimidine Dehydrogenase Gene Transcription in Cancer Cells
Authors: Ukon Kei , Tanimoto Keiji , Shimokuni Tatsushi , Noguchi Takuya , Hiyama Keiko , Tsujimoto Hiroaki , Fukushima Masakazu , Toge Tetsuya , Nishiyama Masahiko
Published: June 2022

Dihydropyrimidine dehydrogenase is the most extensively investigated predictive marker for individual response to 5-fluorouracil. Clinical responses to the anticancer agent, along with various reports, have clearly shown that dihydropyrimidine dehydrogenase activity is closely correlated to its mRNA levels, but the regulatory mechanisms of its expression have remained unclear. We attempted to clarify the mechanisms and found that activator protein (AP-1) is probably one of the key factors in the transcriptional regulation of DPYD in cancer cells, and that phorbol 12-myristate 13-acetate (PMA) plus ionomycin treatment enhances transcription of DPYD via AP-1 activation. In this study, we characterized our previously subcloned 5′ region of human DPYD, an ∼3.0-kb fragment (accession no. AB162145). Luciferase reporter assay showed that the clone showed strong promoter activities in 293T and HSC42 cells, and comparative analysis using 5′ deletion mutants suggested the existence of several positive and negative regulatory regions, including putative binding sites for AP-1, SP-1, and nuclear factor-κB. PMA/ionomycin treatment increased the mRNA level of DPYD in HSC42 cells, and electrophoretic gel mobility shift assay showed that the complex on the putative AP-1 binding site was drastically induced by PMA/ionomycin treatment. The complexes formed were competed out by preincubation with the cold-consensus AP-1 binding site, and the DNA binding complex formed on the site contained c-Jun and c-Fos, which are components of AP-1 transcription factor. We further identified the functional AP-1 binding site (nucleotide positions from −290 to −280), whose nucleotide mutations abolished PMA/ionomycin-induced DPYD promoter activation.
Diverse CD8+ T-Cell Responses to Renal Cell Carcinoma Antigens in Patients Treated with an Autologous Granulocyte-Macrophage Colony-Stimulating Factor Gene-Transduced Renal Tumor Cell Vaccine
Authors: Zhou Xianzheng , Jun Do Youn , Thomas Amy Morck , Huang Xin , Huang Lan-Qing , Mautner Josef , Mo Wa , Robbins Paul F. , Pardoll Drew M. , Jaffee Elizabeth M.
Published: June 2022

A phase I clinical trial with granulocyte-macrophage colony-stimulating factor tumor cell vaccines in patients with metastatic renal cell carcinoma (RCC) showed immune cell infiltration at vaccine sites and delayed-type hypersensitivity (DTH) responses to autologous tumor cells indicative of T-cell immunity. To further characterize RCC T-cell responses and identify relevant RCC-associated antigens, we did a detailed analysis of CD8+ T-cell responses in two vaccinated RCC patients who generated the greatest magnitude of DTH response and also displayed a strong clinical response to vaccination (>90% reduction in metastatic tumor volume). Three separate CD8+ T-cell lines (and subsequent derived clones) derived from patient 24 recognized distinct RCC-associated antigens. One recognized a shared HLA-A*0201-restricted antigen expressed by both renal cancer cells and normal kidney cells. This recognition pattern correlated with a positive DTH test to normal kidney cells despite no evidence of impairment of renal function by the patient's remaining kidney after vaccination. A second line recognized a shared HLA-C7-restricted antigen that was IFN-γ inducible. A third line recognized a unique HLA-A*0101-restricted RCC antigen derived from a mutated KIAA1440 gene specific to the tumor. In addition, two independent CTL lines and three clones were also generated from patient 26 and they recognized autologous tumor cells restricted through HLA-A*0205, HLA-A/B/C, and HLA-B/C. These results show that paracrine granulocyte-macrophage colony-stimulating factor tumor vaccines may generate a diverse repertoire of tumor-reactive CD8+ T-cell responses and emphasize the importance of polyvalency in the design of cancer immunotherapies.
Flat Dysplastic Aberrant Crypt Foci Are Related to Tumorigenesis in the Colon of Azoxymethane-Treated Rat
Authors: Paulsen Jan Erik , Løberg Else Marit , Ølstørn Hege Benedikte , Knutsen Helle , Steffensen Inger-Lise , Alexander Jan
Published: June 2022

We evaluated the role of aberrant crypt foci (ACF) as biomarkers of colon cancer by studying the sequential development (6-28 weeks) from early lesion to tumor in the colon of azoxymethane-exposed F344 rats (15 mg/kg bw × 2). Surface examination of unsectioned methylene blue–stained colon preparations, transilluminated in the inverse light microscope, revealed two types of early lesions: classic elevated ACF and small flat lesions, which we denoted flat ACF and which were characterized by bright blue staining, compressed crypt openings, and crypts not elevated above the surrounding mucosa. At a later stage, the crypts surrounding large flat ACF became enlarged, a change that slightly raised the structure; principally, large flat ACF and nascent tumors displayed the same surface morphology. Furthermore, flat ACF with 18.6 ± 10.6 crypt/focus and tumors showed a uniform picture of severe dysplasia with frequent presence of Paneth cells, compressed crypts, cytoplasmic/nuclear overexpression of β-catenin, and nuclear overexpression of cyclin D1. In contrast, classic elevated ACF with 5.3 ± 2.5 crypts/focus did not display such changes: they showed mainly hyperplasia, mild or moderate dysplasia but never severe dysplasia. Along the time course, the number of flat ACF + tumors, including microscopic and macroscopic, was virtually constant, ∼2.5 lesions/rat. The number of classic elevated ACF was initially ∼180 lesions/rat and terminally ∼80 lesions/rat. Flat ACF grew significantly faster than classic elevated ACF. In conclusion, our data indicate a continuous developmental growth from small flat dysplastic ACF to the stage of a tumor. In contrast, classic elevated ACF do not seem to be as closely related to tumorigenesis.
Glycolytic Enzymes Can Modulate Cellular Life Span
Authors: Kondoh Hiroshi , Lleonart Matilde E. , Gil Jesus , Wang Jing , Degan Paolo , Peters Gordon , Martinez Dolores , Carnero Amancio , Beach David
Published: June 2022

An unbiased screen for genes that can immortalize mouse embryonic fibroblasts identified the glycolytic enzyme phosphoglycerate mutase (PGM). A 2-fold increase in PGM activity enhances glycolytic flux, allows indefinite proliferation, and renders cells resistant to ras-induced arrest. Glucosephosphate isomerase, another glycolytic enzyme, displays similar activity and, conversely, depletion of PGM or glucosephosphate isomerase with short interfering RNA triggers premature senescence. Immortalized mouse embryonic fibroblasts and mouse embryonic stem cells display higher glycolytic flux and more resistance to oxidative damage than senescent cells. Because wild-type p53 down-regulates PGM, mutation of p53 can facilitate immortalization via effects on PGM levels and glycolysis.
A Mutation in the SDHC Gene of Complex II Increases Oxidative Stress, Resulting in Apoptosis and Tumorigenesis
Authors: Ishii Takamasa , Yasuda Kayo , Akatsuka Akira , Hino Okio , Hartman Philip S. , Ishii Naoaki
Published: June 2022

Intracellular oxidative stress from mitochondria is thought to be important in carcinogenesis and tumorigenesis, but direct experimental proof is limited. In this study, a transgenic mouse cell line (SDHC E69) with a mutated SDHC gene (a subunit of complex II in the electron transport chain) was constructed to test this question. The SDHC E69 cells overproduced superoxide anion (O2−) from mitochondria, had elevated cytoplasmic carbonyl proteins and 8-OH-deoxyguanine in their DNA as well as significantly higher mutation frequencies than wild type. There were many apoptotic cells in this cell line, as predicted by the observed increase in caspase 3 activity, decrease in mitochondrial membrane potential, and structural changes in their mitochondria. In addition, some cells that escaped from apoptosis underwent transformation, as evidenced by the fact that SDHC E69 cells caused benign tumors when injected under the epithelium of nude mice. These results underscore the notion that mitochondrially generated oxidative stress can contribute to nuclear DNA damage, mutagenesis, and ultimately, tumorigenesis.