bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Influencing Factors of English Majors’ Cross-Cultural Communicative Competence under Mobile Internet
Authors: Luo Hongwei
Published: May 2022

In order to study the influencing factors of cross-cultural communicative competence of English majors under mobile Internet, firstly, we made an empirical study on cross-cultural communication teaching for English majors. Then, we performed a comparative experiment between the mobile Internet-assisted high school English cross-cultural communication teaching model and the traditional teacher culture teaching-based teaching model. Finally, through questionnaires, informal interviews, and teachers’ observation of the teaching process, this paper aims to make an empirical study on the feasibility and effectiveness of mobile Internet assisted foreign language teachers in cultivating students’ awareness and ability of cross-cultural communication, so as to provide a basis for the follow-up research of cross-cultural communication teaching for English majors. The experimental results show that 93.4% of the students are satisfied with the mobile network-assisted English cross-cultural communication teaching model, and the integration of mobile network technology into English cross-cultural communication teaching has brought great changes to the teaching process. Compared with the traditional teaching mode of “chalk and blackboard,” multimedia organically integrates text, pictures, audio, video, and network, which greatly attracts the attention of teachers and students.
Visual Analysis of E-Commerce User Behavior Based on Log Mining
Authors: Wang Tingzhong , Li Nanjie , Wang Hailong , Xian Junhong , Guo Jiayi
Published: May 2022

With the continuous development of internet economy and e-commerce, the scale of data produced by users on e-commerce platform is increasing explosively. Mining the behavior of individual users and group users from massive user behavior data and analyzing the value and law behind the data are of great significance to the development of e-commerce. Taking the user behavior log data of an e-commerce website as the data source, this paper, firstly, processes and analyzes the original dataset through the data filtering and storage module, and it uses the combination of Kafka and Flume to store the user behavior log with reasonable structure and complete fields in HDFS. Secondly, a hierarchical system of data warehouse is constructed in Hive, and each layer of log data is effectively mined and multidimensionally analyzed with the help of log mining technology. Finally, based on the big data framework and Bi tools, a data warehouse system is designed and implemented, which could store and analyze massive data and visually display the results. The system uses dimensional modeling to build a data warehouse hierarchical system to mine and analyze user behavior data through log mining algorithm deeply. The K-means clustering algorithm and RFM model are used to divide the user behavior characteristics in detail, and AARRR funnel model is used to analyze the logs in a modular way. Through the effective mining and multidimensional visual analysis of user behavior data, the behavior analysis of group users and individual users, as well as the analysis of commodity sales flow and sales linkage are realized, which provides support for internal decision-making and precision marketing.
Balloon Fracturing Valve-in-Valve: How to Do It and a Case Report of TAVR in a Rapid Deployment Prosthesis
Authors: Saadi Rodrigo Petersen , Tagliari Ana Paula , Polanczyck Carisi Anne , Ferreira Leal João Carlos , Saadi Eduardo Keller
Published: May 2022

Transcatheter aortic valve replacement (TAVR) to treat degeneration of bioprosthetic heart valves (BHVs), called as valve-in-valve (ViV), is becoming a key feature since the number of BHVs requiring intervention is increasing and many patients are at high risk for a redo cardiac surgery. However, a TAVR inside a small previous cardiac valve may lead to prosthesis-patient mismatch (PPM) and not be as effective as we hoped for. An effective option to decrease the chance of PPM is to fracture the previous heart valve implanted using a high-pressure balloon. By performing a valve fracture, the inner valve ring of small BHVs can be opened up by a single fracture line, allowing subsequent implantation of a properly sized transcatheter heart valve, without increasing substantially the procedure risk. In this article, we provide a step-by-step procedure on how to safely and properly fracture a BHV and report a case of a TAVR in a degenerated rapid deployment valve.
Automated Registration of Multiangle SAR Images Using Artificial Intelligence
Authors: Chopra Pooja , Gollamandala Vijay Suresh , Ahmed Ahmed Najat , Babu S. B. G. Tilak , Kaur Chamandeep , Achyutha Prasad N. , Nuagah Stephen Jeswinde
Published: May 2022

Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.
A Smart Memory Controller for System on Chip‐Based Devices
Authors: Ahmed Mohammed Altaf , Aloufi Jaber
Published: May 2022

This research paper deals with a system‐on‐chip (SoC) architecture design where multiple processors are inbuilt with other blocks of memory and control logic developed by nanomaterials. The multiprocessing‐based SoC architecture is commonly used in the latest electronic devices such as smartphones, tablets, and smart wristwatches with large memory sizes. The data handing in these highly memory‐dense devices is a critical task, and it needs special attention for the smooth operation of the device. This research proposed a smart controller to exchange data between various processors and input‐output devices to tackle this challenge. A proposed controller block controls the data flow between memory and different SoC components and processors. A memory access controller (MAC) is presented in this research study to manage and accelerate data transmission speed and reduce the processors’ activity for SoC‐based devices. The proposed MAC will integrate into the SoC with multiprocessing units, including gaming processors, at minimum hardware overhead and low power consumption. It improves the memory accessing efficiency and reduces the processors’ activity of a system. As a result, the system’s performance and power consumption improve at an acceptable level compared with the other conventional methods. This research is aimed at enhancing the performance of any SoC‐based device where multiprocessing engines are inbuilt and flexible enough to serve various SoCs.
Production and Characterization of Maximum Liquid Oil Products through Individual and Copyrolysis of Pressed Neem Oil Cake and Waste Thermocol Mixture
Authors: Madhu P. , Sasireka M. , Samikannu Ravi , Vinoth K. , Kumar A. Udhaya , Patil Pravin P. , Kaliappan S. , Gebrekidan Atkilt Mulu
Published: May 2022

In this study, individual and copyrolysis experiments were performed with pressed neem oil cake (NOC) and waste thermocol (WT) to produce high grade liquid oil. The effects of reactor temperature, heating rate, feed ratio, and reaction time on product yields were investigated to identify the optimum parameters for maximum oil yield. The maximum oil yield of 49.3 wt%, 73.4 wt% and 88.5 wt% was obtained from NOC pyrolysis, copyrolysis, and WT pyrolysis under optimized conditions. During copyrolysis, the maximum oil product was obtained under NOC/WT ratio of 1 : 2 and at the temperature of 550°C. The liquid oils obtained from thermal and copyrolysis were subjected to detailed physicochemical analysis. When compared to biomass pyrolysis, the copyrolysis of WT and NOC had a substantial improvement in oil properties. The copyrolysis oil shows higher heating value of 40.3 MJ/kg with reduced water content. In addition to that, the copyrolysis oil obtained under optimized conditions is analyzed with Fourier transform infrared spectroscopy (FT-IR) and Gas chromatography–mass spectrometry (GC-MS) analysis to determine the chemical characterization. The analysis showed the presence of aliphatic and aromatic hydrocarbons in the oil.
Application of 3D Software Virtual Reality in Interior Designing
Authors: Wang Rui , Huang Yi
Published: May 2022

A 3D virtual reality expertise is a dynamic arena of research in current years. Virtual technology is a novel expertise participating in artificial intelligence, image processing, and multisensor knowledge. The formation of situation in 3D virtual reality scheme is the key foundation for the simulation of virtual world scene. Aiming at the main problems that the effect of traditional interior space design is not ideal and cannot achieve practicability. This article proposes an interior space virtual design method based on three-dimensional vision. Initially, the feature of the indoor scene is combined with the set of three points, and finally, the optimal combination of the main and subpoints is obtained through the iterative combination of the main and subpoints of the indoor scene. The information fusion of color background and vision of indoor space is used for indoor comprehensive design. It is proved that the method proposed in this paper can effectively improve the effect and practicability of indoor space design through the simulation experiment with virtual reality platform software.
Investigating the Effect of Keyboard Distance on the Posture and 3D Moments of Wrist and Elbow Joints among Males Using OpenSim
Authors: Gholami Milad , Choobineh Alireza , Abdoli-Eramaki Mohammad , Dehghan Azizallah , Karimi Mohammad Taghi
Published: May 2022

Musculoskeletal disorders (MSDs) of the upper extremities and computer use are common in modern societies, and both show a growing trend. This study was conducted to determine the posture and 3D moments of wrist and elbow joints at different keyboard distances on a desk. Twelve healthy right-handed male volunteers attended the motion analysis laboratory. A keyboard was placed at three different distances from the participants’ bodies while performing a standard computer task. The workstation was adjusted according to ANSI/HFES-100-2007 standard for each participant to maintain a comfortable ergonomic posture for controlling confounding variables. Qualisys motion capture system, OpenSim (Ver. 4.1), and visual analog scale were used to collect and analyze the data. The highest levels of wrist flexion and radial deviation as well as elbow flexion and pronation were observed when the keyboard was at the edge of the desk. When the keyboard was 8 cm away from the edge of the desk, the right wrist flexion and radial deviation decreased 83% and 89%, respectively. In the left wrist, flexion and radial deviation decreased 94%. With increasing the distance of the keyboard from the edge of the desk, the right elbow flexion, pronation, and left elbow flexion decreased, 95%, 76%, and 85%, respectively. No significant difference was found for the left elbow pronation, wrist, and elbow joint moments, in the studied keyboard distances. However, a cut-off point has to be specified because large keyboard distances cause high extension and flexion of the limbs. The keyboard position relative to the body is an important parameter in computer work and has a significant impact on the posture of the upper extremities. A keyboard should be located at a distance that allows the upper extremities to remain in a neutral position so that the risk of MSDs is reduced.
Brent Crude Oil Price Forecast Utilizing Deep Neural Network Architectures
Authors: Daneshvar Amir , Ebrahimi Maryam , Salahi Fariba , Rahmaty Maryam , Homayounfar Mahdi
Published: May 2022

Brent crude oil is considered as one of the most important sources of crude oil pricing in the worldwide market, and it is used to set the price of two-thirds of the traded crude oil supplies in the world. To predict the price of Brent crude oil, LSTM and Bi-LSTM methods are applied, which are the architecture of the recursive neural network. Initially, the database creates the appropriate data for the period January 2015 to March 2021 from Brent crude oil price signals and daily data from a financial market, and then, the modeling process is performed via the use of MATLAB software. Also, about 90% of the data are for training and the remaining for validation and comparison. Using LSTM and Bi-LSTM neural networks, the network architecture has been worked on, and by adding the number of layers and changing the solvers (SGDM, RMSProp, and Adam), the errors of different models are compared with each other. Nonlinear techniques of artificial neural networks and deep learning were used for modeling. Then, the network architecture was worked on and the model error rate was evaluated by comparing different layers and solvents such as SGDM, RMSProp, and Adam. The superiority of SGDM solvent over others was shown, and finally, it can be mentioned as the superior method of modeling of price forecasting in Brent crude oil field. The results show that the model with two layers of LSTM and SGDM solver has less error and better accuracy.
Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning
Authors: Yang Kaixin , Zhang Sujie , Yang Xinran , Wu Nan
Published: May 2022

Floods are one of the main natural disasters, which cause huge damage to property, infrastructure, and economic losses every year. There is a need to develop an approach that could instantly detect flooded extent. Satellite remote sensing has been useful in emergency responses; however, with significant weakness due to long revisit period and unavailability during rainy/cloudy weather conditions. In recent years, unmanned aerial vehicle (UAV) systems have been widely used, especially in the fields of disaster monitoring and complex environments. This study employs deep learning models to develop an automated detection of flooded buildings with UAV aerial images. The method was explored in a case study for the Kangshan levee of Poyang Lake. Experimental results show that the inundation for the focal buildings and vegetation can be detected from the images with 88% and 85% accuracy, respectively. And further, we can estimate the buildings’ inundation area according to the UAV images and flight parameters. The result of this study shows promising value of the accuracy and timely visualization of the spatial distribution of inundation at the object level for the end users from flood emergency response sector.
Prediction of Rockburst Intensity Grade in Deep Underground Excavation Using Adaptive Boosting Classifier
Authors: Ahmad Mahmood , Katman Herda Yati , Al-Mansob Ramez A. , Ahmad Feezan , Safdar Muhammad , Alguno Arnold C.
Published: May 2022

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety. The article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. The output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen's kappa index. The applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real‐world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction.
Knockdown of ILK Alleviates High Glucose-Induced Damage of H9C2 Cells through TLR4/MyD88/NF-κB Pathway
Authors: Yin Qiang , Li Zhendong , Lu Shaoying
Published: May 2022

The aim of this study was to explore the role of ILK in an in vitro model of diabetic cardiomyopathy. We used 30 mmol/L high glucose to treat H9C2 cells to construct an in vitro model, knocked down the ILK expression level of H9C2 cells by small interference technology, and detected the activity of antioxidant enzymes and inflammatory factors in the supernatant. The expression levels of SOD1 and IL-1β were detected by immunofluorescence staining. The expression levels of the TLR4/MyD88/NF-κB signaling pathway and its downstream factors were detected by quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Compared with the control group, after high-glucose culture of H9C2 cells, the cell activity decreased, while the apoptosis rate increased, with the TLR4/MyD88/NF-κB signaling pathway activated, thereby inducing oxidative stress and inflammation. Compared with the high-glucose group, the HG+si-ILK group increased cell activity, decreased the apoptosis rate, and inhibited the excessive activation of the TLR4/MyD88/NF-κB signaling pathway, thereby improving oxidative stress and inflammation. Knockdown of ILK expression can protect H9C2 cells from reducing high glucose-induced inflammation, oxidative stress, and apoptosis by inhibiting the TLR4/MyD88/NF-κB signaling pathway.
Majorization for Certain Classes of Analytic Functions Defined by Fournier–Ruscheweyh Integral Operator
Authors: Gour Murli Manohar , Goswami Pranay , Frasin Basem Aref , Althobaiti Saad
Published: May 2022

In this paper, we introduce three new classes , and of analytic functions defined by Fournier–Ruscheweyh integral operator. For these classes, we investigate the majorization problem. Furthermore, a number of new results are shown to follow upon specializing the parameters involved in our main results.
An Improved Model for Evaluating the Brittleness of Shale Oil Reservoirs Based on Dynamic Elastic Properties: A Case Study of Lucaogou Formation, Jimusar Sag
Authors: Gao Yang , Dong Yan , Chen Lei , Li Yingyan , Qin Jianhua , Jiang Zhenxue
Published: May 2022

Rock brittleness is a critical factor affecting the stimulation of shale oil reservoirs. For efficient development of the shale oil in Lucaogou Formation, Jimusar Sag, the brittleness of the sweet spots needs to be evaluated. In this paper, the triaxial compression, acoustic wave measurements, and three-point bending tests were carried out on the reservoir cores. Based on the prepeak energy characteristics of the stress-strain curve, the brittleness of different horizons was calculated, with the largest difference of 24.89%. An improved model based on dynamic elastic properties was proposed to evaluate the brittleness along the vertical pay zones, by which the continuous brittleness in the upper sweet spot was found more changeful than that in the lower sweet spot. The linear correlation coefficient between the brittleness from the improved model and that from the laboratory tests is 0.85, improving the accuracy by 21% and 27% respectively, compared with the conventional elastic property methods. From the characteristics of compression fractures and the length of the fracture process zone, it was found that the compression fractures were more complex and the fracture process zone length was shorter in a more brittle rock, verifying the reliability of the improvement model. The improved method based on dynamic elastic properties proposed in this paper is expected to guide the brittleness evaluation in other regions.
Investigation and Analysis of the Status Quo of Sports Dance Based on Mobile Communication
Authors: Zhang Xiaochen , Li Ze
Published: May 2022

Dance sports is an emerging sport that can not only exercise the body but also please the mind and body. Under the theoretical guidance of “lifelong sports” and “health first,” in recent years, sports dance courses have not only been introduced in ordinary colleges and universities in China, but the overall quality development of students has been greatly improved, and the call for quality education for the whole people has also been answered. In recent years, the rapid development of mobile communication systems around the world has greatly enhanced the communication services of developed and developing countries to various places. This paper studies the current situation of sports dance based on mobile communication and links the century-old sports dance project with modern mobile communication to promote Chinese sports dance education and summarize more practical experience and methods with reference value. This paper introduces the development of mobile communication technology in detail, focusing on improving the user experience and service quality of sports dance, and introducing the mobile communication network into the geographic grid to solve the problem of the association between the data attributes of the base station and the geographic space. In addition, this paper systematically investigates the current situation of sports dance in A district and designs a targeted questionnaire. On the basis of the analysis of the current situation, corresponding countermeasures and measures are put forward, aiming to provide reference for the development of sports dance and make a modest contribution to promoting the widespread development of national fitness movements and promoting social harmony and civilization progress in the region. The results of the survey show that the residents’ sports dance programs have obvious age characteristics, and 77% of the students are 10-20 years old and over 50 years old. In the investigation of the main restrictive factors of sports dance development, sports dance coaches and students both put the restrictive factor of the teaching staff in the first place.
An Investigation of EFL Learners’ Vocabulary Retention and Recall in a Technology-Based Instructional Environment: Focusing on Digital Games
Authors: Patra Indrajit , Shanmugam Neelakandan , Ismail Sayed M. , Mandal Gurudas
Published: May 2022

Using digital games in language learning is beneficial, but their effectiveness in the Bangladeshi EFL context has been overlooked. Therefore, the purpose of this research was to evaluate the impact of playing digital games on Bangladeshi EFL learners’ ability to remember and retain the language. The Oxford Quick Placement Test was given to 78 Bangladeshi English as foreign language students. Based on the findings, 50 primary school pupils were selected based on the convenient sampling method as the study’s target group for further investigation. Afterward, they were separated into two groups: one for the experiment and another for the CG. After that, all subjects were subjected to pretesting, and then, the therapy was administered. The EG was educated by the researcher via the use of digital games. The participants in the EG were taught English vocabulary via the use of a computerized game, which they found entertaining. On the other hand, the students of the CG received a traditional vocabulary teaching. After teaching 90 vocabulary in nine sessions, an immediate vocabulary posttest was conducted on both groups to determine the treatment’s possible effects on their vocabulary recall. Three weeks later, a delayed posttest was given to the participants to examine the effect of digital games on their vocabulary retention. The results of paired samples and independent sample

t

-tests showed that there was a significant difference between the immediate and delayed posttests of the EG and CGs in favor of the EG. The EG outperformed the CG both on the immediate and delayed posttests. This study has some implications for teaching and learning vocabulary.
Research on the Construction of Accounting Informatics System and Risk Assessment Method in Big Data’s Era
Authors: Wang Yining , Wang Tian , Zhao Rui , Lun Xi
Published: May 2022

The traditional financial informatics administration system risk assessment method in the evaluation of system risk can deal with less data throughput, and the system risk assessment effect is not good, so the study of the financial informatics administration system of the risk assessment method under the background of big data is a necessity. The method sets logical nodes according to system modules and builds risk estimation models by finding logical relationships between data. Based on the big data background, according to the utility theory and the risk preference function estimation, choose the big data batch calculation and streaming calculation methods to calculate the system programme risk index and financial data faithless risk index. According to the risk residual value, the probability of a system function failure in the main module and submodule of the system is adjusted, and the risk level is defined by the exponential function to realize the system risk estimation under the background of big data. Experimental results show that compared with the two traditional system risk estimation methods, the proposed method has a larger data throughput and a wider range of risk indicators. It can be seen that the proposed method meets the risk estimation requirements of the enterprise financial informatics administration system.
Pseudomonas aeruginosa Scleritis following Pterygium Surgery with Mitomycin C or Beta Irradiation: Three-Case Report
Authors: Chaidaroon Winai , Ausayakhun Somsanguan , Tananuvat Napaporn , Upaphong Phit , Thabsuwan Kittipong
Published: May 2022

Purpose. To report three cases of culture-positive Pseudomonas aeruginosa scleritis following pterygium surgery. Patients and Methods. A retrospective study of all patients of Pseudomonas aeruginosa scleritis after pterygium surgery presented from May 2017 to May 2020 was performed. Patient demographics and clinical characteristics included age, gender, time between prior surgery and onset, adjunctive therapy, risk factors, initial visual acuity, final visual acuity, clinical features, medical treatment, and surgical intervention were analyzed. Results. Three eyes of three patients with clinical characteristics and laboratory-confirmed Pseudomonas aeruginosa scleritis were identified. Two patients were related with mitomycin C application after pterygium surgery, and only one had beta irradiation. Antibiotic administration and scleral debridement were required in 3 patients. One eye was enucleated. Final visual outcomes of two patients were improved. Conclusions. Pseudomonas aeruginosa scleritis after pterygium surgery is a crucial ophthalmic disease. An early diagnosis with a prompt intensive antibiotic treatment in combination with surgical interventions may improve visual outcome.
Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm
Authors: Xiang Wujun
Published: May 2022

The proportion of network video traffic in the network is increasing as Internet and streaming media technology continues to advance. The current network environment is becoming more complicated as new application services emerge. ISPs are grappling with how to effectively allocate limited network bandwidth resources while maintaining network service quality (Internet service provider). Currently, in the process of physical coaching, trainers use the video replay and analysis management method with less difficulty to explain the essentials of movements to athletes, but it is not intuitive enough to identify and classify network video services and can guide Internet service providers (ISPs) to optimise network resource management and rationally configure traffic engineering, in order to ensure the quality of service (QOS) of video services. Finally, using the shot as the unit, find similar video clips in the video database based on the video examples submitted by the user, and return retrieval results based on similarity. This paper uses streaming video as the research object and examines the transmission control of sports video using the particle swarm optimization algorithm in detail, based on a comprehensive introduction to the analysis and recognition of sports video in the process of physical coaching.
Stability Analysis and Optimal Control of a Limnothrissa Miodon Model with Harvesting
Authors: Mutasa Farikayi K. , Jones Brian , Hove-Musekwa Senelani D. , Tendaupenyu Itai H. , Nhiwatiwa Tamuka , Ndebele-Murisa Mzime R.
Published: May 2022

We construct a theoretical, deterministic mathematical model of the dynamics of Limnothrissa miodon with nutrients, phytoplankton, and zooplankton and investigate the effect of harvesting on the population density of Limnothrissa miodon in a lake. For the autonomous model, results from local stability analysis are in agreement with numerical simulations in that the coexistence equilibrium is locally stable, provided certain conditions are satisfied. The coexistence equilibrium is globally unstable if it is feasible. Numerical results show that a stable limit cycle exists for the nonautonomous model. Optimal control results show an optimal harvesting monthly effort of 15394 boat nights which corresponds to 505 fishing units, showing that there is overcapacity in Lake Kariba. A maximum sustainable annual catch of 34669 tonnes is obtained and simulation results show that Limnothrissa miodon abundance is more closely related to nutrient inflow than to harvesting.
Research on the Sound Quality Evaluation Method Based on Artificial Neural Network
Authors: Song Xiedong , Yang Wei
Published: May 2022

For the improvement of the traditional evaluation effect of the automobile sound quality, an evaluation model of automobile sound quality is constructed based on BP neural network. The first is to introduce the basic principle of the BP neural network in detail. The second is to use the MGC parameters to establish the vehicle interior sound conversion model. The converted sound characteristic parameters are taken into the WORLD model to synthesize the new sound signals. Furthermore, the wavelet decomposition method is used to remove noise from the synthesized sound signals. Finally, a sound evaluation model based on BP neural network is established. The sound quality of automobiles can be better evaluated by carrying out the ABX test and MOS test in the field of sound conversion. For the newly synthesized sound signal and the target sound signal, it can be seen that the newly synthesized sound signal is more inclined to the target sound signal, and the sound quality is better. In addition, the sound quality is tested through loudness, roughness, sharpness, and level A in the field of sound quality evaluation. The final results show that the quality of newly synthesized sound is better, and the average errors of sound signals meet the sound standard. Therefore, the constructed sound conversion model and the sound evaluation model are feasible and effective.
Efficiency of Carbon Dioxide (CO2) Emission Control Target Allocations in China
Authors: Chen Liyun , Li Zhiwei
Published: May 2022

Effectively allocating emission control targets is critical for China to achieve its emission reduction goals. This study researched the efficiency of total carbon dioxide (CO2) emission control target allocations during the 13th Five-Year Plan period (2015–2020). The efficiency of carbon intensity reduction targets, allocated by the National Development and Reform Commission in 30 regions, was assessed using a Directional Distance Function (DDF) model. Then, a Zero-Sum Gains (ZSG)-DDF model was constructed to determine how to optimally allocate total CO2 emission, under the premise of maximizing economic benefits and minimizing CO2 emissions. The results showed that in the case of fixed total CO2 emissions, to improve the resource allocation efficiency, the quota should be increased in the regions with high efficiencies, and the quota should be reduced in the regions with low efficiencies. The results in this paper can help guide the future allocations of total CO2 emission in China.
Investigation of the Effect of Physical Factors on Exergy Efficiency of a Photovoltaic Thermal (PV/T) with Air Cooling
Authors: Alayi Reza , Jahanbin Farnaz , Aybar Hikmet Ş. , Sharifpur Mohsen , Khalilpoor Nima
Published: May 2022

Thermal photovoltaic systems are used to harness solar energy to generate electricity and thermal at the same time. In this technology, electrical efficiency is very low compared to thermal efficiency; as the cell surface temperature rises, the electrical efficiency decreases, so one of the ways to achieve high efficiency is exergy analysis. Exergy analysis of a process or system shows how much of the ability to perform the work or input exergy has been consumed by that process or system. In this research, an ordinary thermal photovoltaic panel with air cooling has been examined for exergy. To do this, it has identified the effective performance variables from a mechanical point of view, which are inlet air temperature, inlet air flow, and length (number of modules that are connected in series). The effect of changing each of the variables based on Saveh weather conditions has been simulated using MATLAB software. The results show that the exergy efficiency of the panel decreases with the inlet air temperature increasing. It was also observed that the optimal airflow is 0012 (kg/s) and will have the highest efficiency per 8.8 m length.
Body Mass Index and Mammographic Density in a Multiracial and Multiethnic Population-Based Study
Authors: Barnard Mollie E. , Martheswaran Tarun , Van Meter Margaret , Buys Saundra S. , Curtin Karen , Doherty Jennifer Anne
Published: May 2022

Background:

Mammographic density (MD) is strongly associated with breast cancer risk. We examined whether body mass index (BMI) partially explains racial and ethnic variation in MD.

Methods:

We used multivariable Poisson regression to estimate associations between BMI and binary MD [Breast Imaging Reporting and Database System (BI-RADS) A&B versus BI-RADS C&D] among 160,804 women in the Utah mammography cohort. We estimated associations overall and within racial and ethnic subgroups and calculated population attributable risk percents (PAR%).

Results:

We observed the lowest BMI and highest MD among Asian women, the highest BMI among Native Hawaiian and Pacific Islander women, and the lowest MD among American Indian and Alaska Native (AIAN) and Black women. BMI was inversely associated with MD [RRBMI≥30 vs. BMI<25 = 0.43; 95% confidence interval (CI), 0.42–0.44] in the full cohort, and estimates in all racial and ethnic subgroups were consistent with this strong inverse association. For women less than 45 years of age, although there was statistical evidence of heterogeneity in associations between BMI and MD by race and ethnicity (P = 0.009), magnitudes of association were similar across groups. PAR%s for BMI and MD among women less than 45 years were considerably higher in White women (PAR% = 29.2, 95% CI = 28.4–29.9) compared with all other groups with estimates ranging from PAR%Asain = 17.2%; 95% CI, 8.5 to 25.8 to PAR%Hispanic = 21.5%; 95% CI, 19.4 to 23.6. For women ≥55 years, PAR%s for BMI and MD were highest among AIAN women (PAR% = 37.5; 95% CI, 28.1–46.9).

Conclusions:

While we observed substantial differences in the distributions of BMI and MD by race and ethnicity, associations between BMI and MD were generally similar across groups.

Impact:

Distributions of BMI and MD may be important contributors to breast cancer disparities.
The Impact of Morbidity and Disability on Attendance at Organized Breast Cancer–Screening Programs: A Systematic Review and Meta-Analysis
Authors: McWilliams Lorna , Groves Samantha , Howell Sacha J. , French David P.
Published: May 2022

Individuals with morbidity experience worse breast cancer outcomes compared with those without. This meta-analysis assessed the impact of morbidity on breast cancer–screening attendance and subsequent early detection (PROSPERO pre-registration CRD42020204918). MEDLINE, PsychInfo, and CINAHL were searched. Included articles published from 1988 measured organized breast-screening mammography attendance using medical records by women with morbidity compared with those without. Morbidities were assigned to nine diagnostic clusters. Data were pooled using random-effects inverse meta-analyses to produce odds ratios (OR) for attendance. 25 study samples (28 articles) were included. Data were available from 17,755,075 individuals, including at least 1,408,246 participants with one or more conditions;16,250,556 had none. Individuals with any morbidity had lower odds of attending breast screening compared with controls [k = 25; OR, 0.76; 95% confidence interval (CI), 0.70–0.81; P = <0.001; I2 = 99%]. Six morbidity clusters had lower odds of attendance. The lowest were for neurological, psychiatric, and disability conditions; ORs ranged from 0.45 to 0.59 compared with those without. Morbidity presents a clear barrier for breast-screening attendance, exacerbating health inequalities and, includes a larger number of conditions than previously identified. Consensus is required to determine a standardized approach on how best to identify those with morbidity and determine solutions for overcoming barriers to screening participation based on specific morbidity profiles.
SNPs atSMG7Associated with Time from Biochemical Recurrence to Prostate Cancer Death
Authors: Song Xiaoyu , Ru Meng , Steinsnyder Zoe , Tkachuk Kaitlyn , Kopp Ryan P. , Sullivan John , Gümüş Zeynep H. , Offit Kenneth , Joseph Vijai , Klein Robert J.
Published: May 2022

Background:

A previous genome-wide association study identified several loci with genetic variants associated with prostate cancer survival time in two cohorts from Sweden. Whether these variants have an effect in other populations or if their effect is homogenous across the course of disease is unknown.

Methods:

These variants were genotyped in a cohort of 1,298 patients. Samples were linked with age, PSA level, Gleason score, cancer stage at surgery, and times from surgery to biochemical recurrence to death from prostate cancer. SNPs rs2702185 and rs73055188 were tested for association with prostate cancer–specific survival time using a multivariate Cox proportional hazard model. SNP rs2702185 was further tested for association with time to biochemical recurrence and time from biochemical recurrence to death with a multi-state model.

Results:

SNP rs2702185 at SMG7 was associated with prostate cancer–specific survival time, specifically the time from biochemical recurrence to prostate cancer death (HR, 2.5; 95% confidence interval, 1.4–4.5; P = 0.0014). Nine variants were in linkage disequilibrium (LD) with rs2702185; one, rs10737246, was found to be most likely to be functional based on LD patterns and overlap with open chromatin. Patterns of open chromatin and correlation with gene expression suggest that this SNP may affect expression of SMG7 in T cells.

Conclusions:

The SNP rs2702185 at the SMG7 locus is associated with time from biochemical recurrence to prostate cancer death, and its LD partner rs10737246 is predicted to be functional.

Impact:

These results suggest that future association studies of prostate cancer survival should consider various intervals over the course of disease.
All About That Ras: Novel Fusion Drives Ras Pathway Activation in Lung Cancer
Authors: Moorthi Sitapriya , Berger Alice H.
Published: May 2022

Lung cancers in never- and light-smokers often harbor targetable oncogenic mutations in Ras pathway genes. Here, a novel OCLN-RASGRF1 fusion is identified in an otherwise Ras wild-type lung tumor. Studying this and other RASGRF1 fusions, the authors show that these fusions lead to malignant phenotypes that can be reversed by MEK inhibition.

See related article by Hunihan et al., p. 3091
Preclinical Evaluation of IMGC936, a Next-Generation Maytansinoid-based Antibody–drug Conjugate Targeting ADAM9-expressing Tumors
Authors: Scribner Juniper A. , Hicks Stuart W. , Sinkevicius Kerstin W. , Yoder Nicholas C. , Diedrich Gundo , Brown Jennifer G. , Lucas Jacquelynn , Fuller Megan E. , Son Thomas , Dastur Anahita , Hooley Jeff , Espelin Christopher , Themeles Marian , Chen Francine Z. , Li Ying , Chiechi Michael , Lee Jenny , Barat Bhaswati , Widjaja Lusiana , Gorlatov Sergey , Tamura James , Ciccarone Valentina , Ab Olga , McEachem Kerry A. , Koenig Scott , Westin Eric H. , Moore Paul A. , Chittenden Thomas , Gregory Richard J. , Bonvini Ezio , Loo Deryk
Published: May 2022

ADAM metallopeptidase domain 9 (ADAM9) is a member of the ADAM family of multifunctional, multidomain type 1 transmembrane proteins. ADAM9 is overexpressed in many cancers, including non–small cell lung, pancreatic, gastric, breast, ovarian, and colorectal cancer, but exhibits limited expression in normal tissues. A target-unbiased discovery platform based on intact tumor and progenitor cell immunizations, followed by an IHC screen, led to the identification of anti-ADAM9 antibodies with selective tumor-versus-normal tissue binding. Subsequent analysis revealed anti-ADAM9 antibodies were efficiently internalized and processed by tumor cells making ADAM9 an attractive target for antibody–drug conjugate (ADC) development. Here, we describe the preclinical evaluation of IMGC936, a novel ADC targeted against ADAM9. IMGC936 is comprised of a high-affinity humanized antibody site-specifically conjugated to DM21-C, a next-generation linker-payload that combines a maytansinoid microtubule-disrupting payload with a stable tripeptide linker, at a drug antibody ratio of approximately 2.0. In addition, the YTE mutation (M252Y/S254T/T256E) was introduced into the CH2 domain of the antibody Fc to maximize in vivo plasma half-life and exposure. IMGC936 exhibited cytotoxicity toward ADAM9-positive human tumor cell lines, as well as bystander killing, potent antitumor activity in human cell line-derived xenograft and patient-derived xenograft tumor models, and an acceptable safety profile in cynomolgus monkeys with favorable pharmacokinetic properties. Our preclinical data provide a strong scientific rationale for the further development of IMGC936 as a therapeutic candidate for the treatment of ADAM9-positive cancers. A first-in-human study of IMGC936 in patients with advanced solid tumors has been initiated (NCT04622774).
Nail Clipping with PAS Stain and Correlation with Fungi Isolated in Culture: A Valuable Exchange in the Diagnosis of Onychomycosis
Authors: Trevisan Flávia , Veasey John Verrinder , Werner Betina
Published: May 2022

Introduction: The etiological agents of onychomycosis are dermatophyte fungi, nondermatophyte filamentous fungi (NDFF), and yeasts. Nail clippings are an accurate tool for diagnosing onychomycosis. The objective of this study was to correlate the characteristics of fungi observed in nail clipping examinations with the results for agents isolated in culture, indicating whether the fungus found on microscopy is a dermatophyte, NDFF, or yeast. Methods: An observational descriptive study of onychomycosis nail clippings stained with PAS comparing culture results with microscopic criteria of fungi morphology, arrangement/orientation, and location of organisms in the nail and presence of neutrophils, serous lakes, and bacterial colonies was conducted. Results: Thirty-three nail clippings from 29 participants were analyzed. A statistically significant correlation (p < 0.05) was found between smooth/thin/long hyphae with a “combed” pattern and dermatophyte agents (sensitivity 89.4%, specificity 91.5%), and between irregular/thick/short anarchic distributed hyphae and nondermatophytes (sensitivity 61.5%, specificity 89.4%). No statistical association with region where fungi were in the nail or with the presence of neutrophils, serous lakes or bacteria was found. Small, rounded, yeast-like organisms correlated with Candida infection. Conclusions: Although not a substitute for culture, microscopic analysis of fungal structures offers good accuracy for differentiating agents of onychomycosis.