bluuebunny/crossref_metadata_2025 huggingface dataset:
Secretory system in Cuphea calophylla (Lythraceae): the bi-compartmentalization of epidermal cells, reclassification of glandular appendages, and the first report of hydathodes
Authors: Seixas Diana Pacheco , Rodrigues Tatiane Maria
Published: September 2022

Cuphea calophylla Cham. & Schltdl. is a Lythraceae subshrub that occurs in different environmental conditions. The aerial portions of this plant have medicinal properties and are used for soap production. We assessed the structure, ultrastructure, and histochemistry of the secretory cells and tissues in C. calophylla leaves and stems to further understand its secretory system. Hydathodes with epithem, tracheary elements, and water pores occur at the leaf border and are here described for the first time for a Cuphea species. Glandular appendages occur in leaves and stems and are characterized by a wide basis and a narrower apical portion that exuded sticky material. Ontogenetical analysis showed that these appendages are glandular emergences; phenolic compounds, mucilage, and lipids were produced in their cells which exhibited Golgi bodies, vesicles, smooth endoplasmic reticulum, oil drops, and plastids. The epidermis of the leaf blade has bi-compartmentalized secretory cells divided by a cellulosic septum in their equatorial region. These cells contain phenolic compounds in the upper compartment and mucilage in the lower compartment. Our results shed light on the sites producing bioactive compounds in C. calophylla and expand the knowledge on the secretory system in Lythraceae, promoting a reclassification and an identification of new structures.
Frameworks for generalized anisotropic conformally flat polytropes in f(R) gravity
Authors: Mardan S. A. , Amjad Zargona , Noureen I.
Published: September 2022

The purpose of this paper is to analyze the conformally flat spherically symmetric fluid distribution with generalized polytropic equations of state. We have developed two different framework for two different definitions of generalized polytropes. The frameworks for development of modified Lane–Emden equation are presented for both cases. The conformally flat condition is used to calculate anisotropy factor which transform considered systems into consistent systems. Tolman mass function is used in the polytropic models to check their stability.
A quantum segmentation algorithm based on local adaptive threshold for NEQR image
Authors: Wang Lu , Liu Wenjie
Published: September 2022

The classical image segmentation algorithm based on local adaptive threshold can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem gradually emerges. In this paper, a quantum segmentation algorithm based on local adaptive threshold for NEQR image is proposed, which can use quantum mechanism to simultaneously compute local thresholds for all pixels in a gray-scale image and quickly segment the image into a binary image. In addition, several quantum circuit units, including median calculation, quantum binarization, etc. are designed in detail, and then a complete quantum circuit is designed to segment NEQR images by using fewer qubits and quantum gates. For a [Formula: see text] image with [Formula: see text] gray-scale levels, the complexity of our algorithm can be reduced to O[Formula: see text], which is an exponential speedup compared to the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Gait regulation using monopolar intraspinal micro-stimulation of spinal cord central pattern generator: New insights for improving Hodgkin–Huxley model
Authors: Wu Yan , Lou Xiongjie , Lu Song , Bian Xiongheng , Shen Xiaoyan
Published: September 2022

Restoring the motor function of paralyzed limbs has always been an important aim in the field of biomedical engineering. In view of the discovery of the repeatable experimental phenomenon that positive and negative stimulation pulses applied to specific points, identified as central pattern generator (CPG) sites, could induce switching of the movement patterns of bilateral hindlimbs, an improved Hodgkin–Huxley (HH) neuron model was established by introducing the electric field effect principle. A CPG neural network model comprising 12 neural units in six joints of the bilateral hindlimbs was modeled. The simulation results showed the alternating movement patterns of the bilateral hindlimbs through the action potential release of extensor and flexor neurons. The explosive electromyogram of the gastrocnemius (GM) and quadriceps femoris (QF) when stimulating the CPG sites with intraspinal micro-stimulation (ISMS) was consistent with the action potential diagram of the flexor and extensor neurons obtained via simulation. Our research considers the neural network model of electric field radiation, which can facilitate a deep understanding of the dynamic characteristics of neurons in the electric field environment, and verifies the correlation between the location of CPG sites, stimulus polarity and movement patterns to induce alternating left–right coordinated movements.
Multi-linear regression of triple diffusive convectively heated boundary layer flow with suction and injection: Lie group transformations
Authors: Mamatha S. U. , Devi R. L. V. Renuka , Ahammad N. Ameer , Shah Nehad Ali , Rao B. Madhusudhan , Raju C. S. K. , Khan M. Ijaz , Guedri Kamel
Published: September 2022

This work analyzes the two-dimensional flow of an incompressible magneto-hydrodynamic fluid over linear stretching sheet in the presence of suction or injection and convective boundary conditions. A scaling group transformation method is applied to the flow governing equations. The system remains invariant due to the relation between the transformation parameters. Upon finding three absolute invariants, third-order ordinary differential equations (ODEs) corresponding to momentum equation and second-order ODEs corresponding to energy and diffusion equations are derived. Shooting technique (R-K fourth-order) is applied to work out the flow equations numerically. MATLAB is used for the simulation and the results are exhibited through graphs. The computational results are validated with the published research work and a modest concurrence was found. The main outcome of this study is found to be that raising values of [Formula: see text] and [Formula: see text] decline the friction, whereas [Formula: see text] and [Formula: see text] show the opposite (increasing). The rising values of [Formula: see text] and [Formula: see text] in addition to [Formula: see text] and [Formula: see text] show a decline in friction factor. The Nusselt number values are improved as raising values of [Formula: see text] versus [Formula: see text] and [Formula: see text] versus [Formula: see text]. It is very clear the monotonically increasing [Formula: see text] versus [Formula: see text] and strictly increasing [Formula: see text] versus [Formula: see text] cases. It is very clear the mass-transfer rate is smoothly improved [Formula: see text] versus [Formula: see text] and strictly increased [Formula: see text] versus [Formula: see text].
Dynamic Data-driven Microscopic Traffic Simulation using Jointly Trained Physics-guided Long Short-Term Memory
Authors: Naing Htet , Cai Wentong , Nan Hu , Tiantian Wu , Liang Yu
Published: September 2022

Symbiotic simulation systems that incorporate data-driven methods (such as machine/deep learning) are effective and efficient tools for
just-in-time (JIT)
operational decision making. With the growing interest on Digital Twin City, such systems are ideal for real-time microscopic traffic simulation. However, learning-based models are heavily biased towards the training data and could produce physically inconsistent outputs. In terms of microscopic traffic simulation, this could lead to unsafe driving behaviours causing vehicle collisions in the simulation. As for symbiotic simulation, this could severely affect the performance of real-time base simulation models resulting in inaccurate or unrealistic forecasts, which could, in turn, mislead JIT what-if analyses. To overcome this issue, a physics-guided data-driven modelling paradigm should be adopted so that the resulting model could capture both accurate and safe driving behaviours. However, very few works exist in the development of such a car-following model that can balance between simulation accuracy and physical consistency. Therefore, in this paper, a new
“jointly-trained physics-guided Long Short-Term Memory (JTPG-LSTM)”
neural network, is proposed and integrated to a dynamic data-driven simulation system to capture dynamic car-following behaviours. An extensive set of experiments was conducted to demonstrate the advantages of the proposed model from both modelling and simulation perspectives.
Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey
Authors: Issa Wael , Moustafa Nour , Turnbull Benjamin , Sohrabi Nasrin , Tari Zahir
Published: September 2022

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness, and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources. Deep learning (DL) algorithms are increasingly integrated into IoT applications to learn and infer patterns and make intelligent decisions. However, current IoT paradigms rely on centralized storage and computing to operate the DL algorithms. This key central component can potentially cause issues in scalability, security threats, and privacy breaches. Federated learning (FL) has emerged as a new paradigm for DL algorithms to preserve data privacy. Although FL helps reduce privacy leakage by avoiding transferring client data, it still has many challenges related to models’ vulnerabilities and attacks. With the emergence of blockchain and smart contracts, the utilization of these technologies has the potential to safeguard FL across IoT ecosystems. This study aims to review blockchain-based FL methods for securing IoT systems holistically. It presents the current state of research in blockchain, how it can be applied to FL approaches, current IoT security issues, and responses to outline the need to use emerging approaches toward the security and privacy of IoT ecosystems. It also focuses on IoT data analytics from a security perspective and the open research questions. It also provides a thorough literature review of blockchain-based FL approaches for IoT applications. Finally, the challenges and risks associated with integrating blockchain and FL in IoT are discussed to be considered in future works.
The packet number space debate in multipath QUIC
Authors: De Coninck Quentin
Published: September 2022

With a standardization process that attracted much interest, QUIC can been seen as the next general-purpose transport protocol. Still, it does not provide true multipath support yet, missing some use cases that Multipath TCP addresses. To fill that gap, the IETF recently adopted a Multipath proposal merging several proposed designs. While it focuses on its core components, there still remains one major design issue: the amount of packet number spaces that should be used. This paper provides experimental results with two different Multipath QUIC implementations based on NS3 simulations to understand the impact of using one packet number space per path or a single packet number space for the whole connection. Our results show that using one packet number space per path makes Multipath QUIC more resilient to the receiver's heuristics to acknowledge packets and detect duplicates.
Wavelet Analysis of the Interconnection between Atmospheric Aerosol Types and Direct Irradiation over Cameroon
Authors: Alotse Douanla Yaulande , Mamadou Ossénatou , Dembélé André , Koukoui Djidjoho Renaud Roméo , Akpoly Fifamè Edwige , Lenouo André
Published: September 2022

The comparative analysis of the intra- and interannual dynamics between the Direct Normal Irradiation (DNI) under clear sky conditions and five aerosol types (Dust, Sea Salt, Black Carbon, Organic Carbon, and Sulfate) is the purpose of this study. To achieve this aim, we used fifteen-year DNI and aerosols data downloaded at 3-hour time intervals in nine defined zones throughout Cameroon. The wavelet transform is a powerful tool for studying local variability of amplitudes in a temporal dataset and constitutes our principal tool. The results show unequal distribution of aerosol types according to zones, but the Desert Dusts (DU) and Organic Carbon (OM) aerosols have been found as dominant particles in the studied region. The wavelet coherence analysis between DNI and each aerosol type reveals three bands of periodicity:

∼

4-month band, 8–16-month band, and sometimes after-32-month band, with the most important frequency at 8–16-month band period. However, the intensity of coherence across bands varies with respect to aerosol type as well as each of the nine climate zones. A significant anticorrelation relationship was obtained between DNI and each type of aerosol, emphasizing that the presence of such atmospheric particles could dampen the renewable energy utilized by power systems. Also, the analysis shows that scattering aerosols such as Sulfate and Sea Salt (SU and SS, respectively) lead DNI in phase while absorbing aerosols such as Organic Carbon, Black Carbon, and Dust (OM, BC, and DU, respectively) give phase lag with DNI.
Numerical Analysis and Optimization of English Reading Corpus for Feature Extraction
Authors: Juan Wu , Wei Xiong , Hongyi Liao
Published: September 2022

In order to solve the problem of comparative analysis ability of English reading corpus, meet the needs of the construction of language feature analysis system of English reading corpus, make up for the shortcomings of English reading corpus, and improve the wide application of English, the construction method of English reading corpus is proposed. For a long time, English has been an important communication tool in terms of national politics and final economic communication. Especially with the in-depth development of economic globalization, English plays a vital role in international communication. Therefore, the study of the semantics and features of English texts can provide important information materials for English learning and communication in nonnative English speaking countries. Taking this as the starting point, this paper focuses on the numerical analysis and optimization of English reading corpus based on feature extraction and puts forward a language feature analysis system of English reading corpus. At the same time, feature extraction and text classification are used to improve the comparative analysis ability of English reading corpus. The research shows that the feature extraction oriented English reading corpus can solve the problem of English reading learning and improve the ability of reading and analyzing English articles.
Impact of Climate Change on Cereal Crops Production in Ethiopia
Authors: Asfew Milkessa , Bedemo Amsalu
Published: September 2022

Climate change adversely affected agricultural productivity in developing countries. This study aimed to explore the effects of this climate change, particularly on cereal crops production in Ethiopia. The study employed Autoregressive Distributed Lag (ARDL) model approach to the co-integration with an error correction term. ARDL technique was selected due to its stationarity assumption and unbiased estimates of its long-run coefficients. The estimated model justifies the existence of a long-run relationship between cereal crops production, climate change variables (temperature and precipitation), and other explanatory variables. Precipitation has a positive and significant effect on cereal crops production both in the long and short runs, while temperature change has a significant negative effect. In the long run, cereal crops production was positively and significantly affected by arable land, fertilizer consumption, and carbon dioxide emissions, while in the short run, labor force participation has a positive and significant effect on cereal crops production. The study results confirmed that there is a long-run relationship between cereal crops production and climate change variables. In agriculture, research and development should focus on varieties of cereal crops that can tolerate high temperatures. Climate Resilient Green Economy should have to strengthen in the country. All countries should have to work hand-in-hand to mitigate the effect of climate change.
Risk Constraints in the Context of Big Data and Optimization of a Virtuous Interaction Mechanism for Insurance Management Based on Distorted Risk Metrics
Authors: Zhao Yuanyuan
Published: September 2022

A relevant definition of big data technology has been released by relevant research institutes in China. Big data technology is defined as follows: big data technology is a digitally important productivity factor in the network era; big data technology is based on network technology, statistical technology, and mathematical technology; big data technology, as a carrier of network technology, should be integrated with various industries and technologies other than contemporary ones in order to achieve better informatization and industrialization. In the context of the new crown epidemic in 2020, China’s big data technology continues to grow steadily, with a total value of 39.2 trillion yuan, accounting for 38.6% of GDP. This paper investigates the risk constraint in the context of big data and optimizes the benign interaction mechanism of insurance management based on distorted risk metrics in this context. The current situation of insurance risk is investigated through a profound discussion of theories in this paper and through research methods such as distortion risk metrics and SQL Server database management. It is found that the overall situation of insurance industry development in the era of digital economy is not optimistic and so on. And relevant suggestions are made in the conclusion.
Integration and Modeling of Multi‐Energy Network Based on Energy Hub
Authors: Mou Min , Zhou Yuhao , Zheng Wenguang , Xie Yurong
Published: September 2022

The energy conversion units and energy storage equipment connected to the multi‐energy system are becoming diversified, and the uncertain factors brought by distributed wind power and photovoltaic power generation make the system energy flow structure more complex, which brings great difficulties to the modeling and application of traditional energy hub modeling methods. This study deeply analyzes the multi‐energy flow coupling structure and operation mechanism of multi‐energy systems, and carries out the power flow calculation and analysis of multi‐energy systems based on an energy hub, so as to ensure the safe and stable operation of regional energy. Based on the physical characteristics of energy systems such as power systems, thermal systems, and gas systems, this article studies the comprehensive power flow model including the electric‐gas‐thermal multi‐energy coupling network and proposes the power flow decomposition of the energy supply subsystem and its applicable equation based on Newton–Raphson method. The effectiveness of the proposed method under different operation modes is verified by case studies. The calculation results show that under constant load, the energy hub running in fixing thermal by electricity (FEL) and fixing electricity by thermal (FTL) mode has little influence on the voltage of each node in the power sub‐network. Within the constraint range, the natural gas flow obtained from the natural gas subsystem is coupled with the power subsystem to meet the load demand. The influence on the power flow at each node of the heat network is not obvious.
The Occurrence and Distribution of Plasmodium Species in Woyn Wuha Health Center, Ethiopia
Authors: Assemie Anmut
Published: September 2022

Plasmodium species is an important causative agent of malaria in the world including Ethiopia, and the majority of people were at risk of infection. The study’s general objective was to determine the occurrence and distribution of Plasmodium species in the study area through microscopic examination of blood films. A cross‐sectional study was carried out in the study sites from September 2016 to February 2017. Out of 512 participants, 32 (6.25%) were malaria positive. Of these, 17 (53.12%, 95% CI: 0.358, 0.704) were P. vivax, 12 (37.5%, 95% CI: 0.207, 0.543) P. falciparum, and 3 (9.375%, 95% CI: ‐0.007, 0.195) mixed out of the total positive case. The occurrence of parasites was greater in rural villages (59.375%) than in urban villages (40.625%) but not significant (χ2 = 1.2917, df = 1, p = 0.2557). More males were infected compared to females but not significant (χ2 = 0.0005665, df = 1, p = 0.981). The monthly distribution of Plasmodium species was higher in September and October but there was no significant variation in each month (χ2 = 10.142, p = 0.4281). Due to the high occurrence of Plasmodium vivax in the study area, the result contrasts with the national figure of the Plasmodium species report. The result of the current study may be useful to those individuals who work in Plasmodium species control and prevention program.
Integrated Sensory Throughput and Traffic-Aware Arbiter for High Productive Multicore Architectures
Authors: Venkata Sridhar T. , Krishnaiah G. Chenchu
Published: September 2022

The increasing demand for network and high-performance devices requires large data throughputs with minimal loss or repetition. Network on chips (NoC) provides excellent connectivity among multiple on-chip communicating devices with minimal loss compared with old bus systems. The motivation is to improve the throughput of the NoC that integrated on multicores for communication among cores by reducing the communication latency. The design of the arbiter in the crossbars switch of an NoC’s router has a vital role in judging the system’s speed and performance. Low latency and high-speed switching are possible with high performance and good switching equipment at the network level. One of the significant components in NoC under SoC design is the arbiter, which governs the system’s performance. Proper arbitrations can avoid network or traffic congestions like livelock and buffer waiting. The proposed work in this paper is to design an efficient and high productive arbiter for multicore chips, especially SoCs and CMPs. The proposed arbiter is showing good improvement in the throughput at higher data rates; an average of more than 10% throughput improvement is noticed at higher flit injection rates independent of the VCs implemented. Further, the critical delays are reduced to 15.84% with greater throughputs.
An Effective Frequency Drift and Phase Offset Estimation Based on Two-Way Message Exchange for WSNs Time Synchronization
Authors: Wang Yijun , Wang Jie , Luo Qi , Liu Dakun
Published: September 2022

Time synchronization is one of the key technologies of wireless sensor networks. At present, most studies assume that the transmission delay is a fixed value, and there is no effective processing of historical clock data. Therefore, this paper proposes a clock frequency drift and phase offset estimation scheme which is considering variable transmission delay and historical clock data. This paper has clarified the effectiveness of the Gaussian delay model in the calculation of time synchronization error. On this basis, this paper calculated the maximum likelihood estimations of frequency drift and phase offset and the corresponding Cramer-Rao lower bound. Meanwhile, the transmission delay is optimized by comparing the chi-square distance between measured and standard data. Also, this paper conducts autonomous training on historical data to provide services for improving the accuracy of fitting estimation. Simulation results validate the performance of the proposed scheme.
[Retracted] Academic Map and Hotspot Analysis of China’s Legal Construction Research since the Eighteenth National Congress of the Communist Party of China: Visual Analysis Based on CiteSpace
Authors: Peng Hongwei
Published: September 2022

Since the 18th National Congress of the Communist Party of China, the research on the construction of the rule of law in China has received keen attention from scholars. In this study, CSSCI literature published from 2012 to 2022 included in CNKI, Beijing Wanfang, and Chongqing VIP Chinese databases were used as data templates. NoteExpress software was used to deduplicate and organize, and 4,603 literature related to the construction of the rule of law in China were obtained. The citespace5.8 software has carried out a visual analysis, and the results show that: (1) The research on the “rule of law China” as the overall concept in the academic circle first appeared in 2013, and the overall stability was stable, and the local volatility increased. (2) The relationship between researchers and research institutions in this field is relatively loose, and the high‐yield author group has not yet been formed. (3) Through the visualization of keyword co‐occurrence map and keyword clustering map and the analysis of high‐frequency words and centrality, four academic maps and research hotspots are mainly proposed: core cognition of the construction of the rule of law in China, internal theory, external Practice orientation and construction path. (4) Future research on the construction of the rule of law in China strengthens the legal response to scientific and technological information, pays attention to the research on foreign‐related fields of the construction of the rule of law in China, and insists on promoting the combination of “domestic rule of law” and “foreign rule of law.”
Mathematical Control of Space-Based Kinetic Energy Weapons Based on Partial Differential Equations and Evaluation of Their Destructive Effects
Authors: Lian Zhixin , Ma Tiehua
Published: September 2022

This paper presents an in-depth study and analysis of the mathematical control of space-based kinetic energy weapons and the evaluation of the damaging effect by partial differential equations. The spectral element discrete format of the optimal control problem is constructed, the a priori error estimate of the control problem solution is proved theoretically, the posteriori error estimator is constructed, and the adaptive solution algorithm is designed. The posteriori error estimator is used as the encryption criterion to guide the local encryption of the grid so that the distribution of the dissection nodes is denser where the function regularity is poor. In the case of a compartment subjected to a shell attack, the effect of different factors on the structural damage of a single compartment under two explosions is investigated by varying the explosive mass, the location of the blast point, and the interval between the two explosions. In this paper, the problem of implosion in the cabin is studied, and the main factors affecting the response of the implosion structure are analyzed using dimensionality. The data of various simulation conditions are counted, and the dimensionless damage number is fitted with the deformation results of the bulkhead under each damage mode. It is still difficult to obtain the acceleration information of the target. Experimental studies were conducted on the composite honeycomb sandwich structure penetrated by fragmentation at different velocities and angles, and the accuracy of the theoretical model and the fragmentation residual formula was verified based on the test results. The analysis found that the antidamage performance of the composite honeycomb sandwich structure is better than the existing honeycomb sandwich body structure, and the energy absorption per unit volume of the structure is 27.2%–84.2% higher than that of the existing structure in the range of different penetration velocities. The average error between the theoretical calculation results and experimental results of the remaining velocity of the broken piece is within 6%, the error with the numerical simulation is about 8%, and the composite honeycomb sandwich structure with the best energy absorption characteristics and structural parameters is obtained.
Icon Art Design with Generative Adversarial Network under Deep Learning
Authors: Meng Nan , Yang Jia , Wang Haibo
Published: September 2022

With the rapid development of the Internet, application interface design has undergone rapid changes. Numerous new design styles and resources have appeared; thus, a large number of interface icon design needs have been generated. Icons are quite different from ordinary photographed images, because they are all drawn by designers and have certain schematic and artistic features. Moreover, artistic icons can convey their drawn characteristics and meanings faster and better than captured images. The ideation process in icon design is time-consuming, and its design style and method of drawing are influenced by the device and the environment in which it is used. To simplify the process of icon design and enrich the creativity of icon conception, this study proposes to use the generative adversarial network technology in deep learning to train computers to generate artistic icons. This paper completes the construction of the icon generation model with generative adversarial network (GAN) model combined with the actual icon design process. For the problem of automatic icon generation, this paper does the following research work: (1) based on the conditional classification generative adversarial network, a multifeature icon generation model (MFIGM) is proposed. In the discriminator, a multifeature identification module is added to optimize the structure of the conditional feature to ensure that the icon generated by the model meets the given conditional feature. (2) Experiments on the icon dataset show that the MFIGM-based icon generation model proposed in this paper has better performance in designing various feature expressions of icons.
ZFP36 Inhibits Tumor Progression of Human Prostate Cancer by Targeting CDK6 and Oxidative Stress
Authors: Yuan Dongbo , Fang Yinyi , Chen Weiming , Jiang Kehua , Zhu Guohua , Wang Wei , Zhang Wei , You Ganhua , Jia Zhenyu , Zhu Jianguo
Published: September 2022

Background. The expression of ZFP36 in previous study was reduced in prostate cancer (PCa) tissues as compared to benign prostate tissues, indicating the potential of ZFP36 as an auxiliary marker for PCa. Further evaluation was conducted in clinical samples for in vitro and in vivo experiments, to prove the potential possibility that ZFP36 dysregulation participated in the malignant phenotype of PCa, to determine its potential mechanism for tumor regulation, and to provide a new theoretical basis for gene therapy of PCa. Methods. First, the expression of ZFP36 in prostate tissue and PCa tissue was explored, and the relationship between ZFP36 and clinical features of PCa patients was illustrated. Subsequently, the impact of ZFP36 on the biology of PCa cells and relevant downstream pathways of ZFP36’s biological impact on PCa were elucidated. Finally, whether oxidative stress mediated the regulation of ZFP36 in PCa was verified by the determination of oxidative stress-related indicators and bioinformatics analysis. Results. The downregulation of ZFP36 in PCa tissue had a positive correlation with high Gleason scores, advanced pathological stage, and biochemical recurrence. ZFP36 was identified as an independent prognostic factor for PCa patients’ BCR-free survival (

P
=
0.022

) by survival analysis. Following a subsequent experiment of function gain and loss, ZFP36 inhibited the proliferation, invasion, and migration in DU145 and 22RV1 cells and inhibits tumor growth in the mouse model. Additionally, high-throughput sequencing screened out CDK6 as the downstream target gene of ZFP36. Western blot/Q-PCR demonstrated that overexpression of ZFP36 could reduce the expression of CDK6 at both cellular and animal levels, and the dual-luciferase experiment and RIP experiment proved that CDK6 was the downstream target of ZFP36, indicating that CDK6 was a downstream target of ZFP36, which mediated tumor cell growth by blocking cell cycle at the G1 stage. Furthermore, ZFP36 inhibited oxidative stress in PCa cells. Conclusions. In PCa, ZFP36 might be a tumor suppressor that regulated growth, invasion, and migration of PCa cells. The lately discovered ZFP36-CDK6 axis demonstrated the molecular mechanism of PCa progression to a certain extent which might act as a new possible therapeutic target of PCa therapy.
Fractional Fourier Transform and Ulam Stability of Fractional Differential Equation with Fractional Caputo-Type Derivative
Authors: Selvam Arunachalam , Sabarinathan Sriramulu , Noeiaghdam Samad , Govindan Vediyappan
Published: September 2022

In this paper, we study the Ulam-Hyers-Mittag-Leffler stability for a linear fractional order differential equation with a fractional Caputo-type derivative using the fractional Fourier transform. Finally, we provide an enumeration of the chemical reactions of the differential equation.
[Retracted] Educational Reward and Punishment and the Effect of Psychological Intervention on Adolescent Depression
Authors: Wu Wensheng , Chen Hao
Published: September 2022

Teenage depression, also known as TD, is a common mental illness that is characterized by symptoms such as hopelessness, helplessness, pessimism, depression, and decreased energy. It has always been a hot topic to discuss how rewards and punishments work in education. In order to prevent and treat adolescent depression, this study examines the mechanisms of educational reinforcement and punishment as well as psychological interventions. In this study, the activated brain regions are analyzed using data mining (DM) technology to determine whether they are significantly more or less active than the rest of the brain of students who are not experiencing negative emotions. When the word vector has 90 dimensions, the results demonstrate that the average F1 value of the weighted word vector method is 81.3 percent. It has been established that the approach taken in this work offers a reliable way to diagnose TD.
Accuracy Analysis of Sports Performance Prediction Based on BP Neural Network Intelligent Algorithm
Authors: Yang Hong , Luo Changdi
Published: September 2022

With the increasing attention and popularity of competitive sports, the continuous progress of artificial intelligence, and deep learning theory, people’s sports performance prediction technology for professional athletes or sports students is also developing. Accurate and effective prediction can help athletes and students to carry out more targeted training, so as to further improve their performance. BP neural network (BPNN) is a multilayer feedforward neural network (NN). Therefore, based on the BPNN algorithm, this work conducts a deep research on the prediction of sports performance. First of all, this work uses the three-layer structure of BPNN to design the algorithm and then selects the weight, oxygen saturation, systolic and diastolic blood pressure, the previous best score, the worst score in the past period, and the average score of one week before the examination as the feature vector of the input sample. The students’ scores of two classes of physical education major in a university are selected as the prediction objects, and the time period of students’ relevant information data is selected from September 2018 to December 2018. The quantity of hidden units is calculated to be 15 by training. After the successful construction of BPNN sports performance estimation method, the PSO search approach is utilized to enhance the BPNN sports performance prediction model. Finally, the relationship between the two classes’ performance and the students’ performance is analyzed. 49% of the total number of times the error of class A was predicted to be 0, and the number of times that the error of class B was 0 was 50%, 58%, and 75%, respectively. There is no strong linear correlation between sports performance and body weight, but a high correlation systolic, pulse pressure, and plasma oxygen levels. This shows that the BPNN sports prediction model established in this work has high accuracy in predicting sports performance.
Analysis the Innovation Path on Psychological Ideological with Political Teaching in Universities by Big Data in New Era
Authors: Luo Xiufen
Published: September 2022

The increasing pressure of life and the rapid development of the economy have caused huge mental health situation problems for people. Mental health situation has an important relationship with one’s own life values. The cognition Ideological with political is to improve people’s optimistic attitude and values. The students of college will face enormous pressure from their studies, which can easily causes the mental health situation problems. However, the psychological and ideological education in universities still adopts the traditional teaching method, which reduces students’ learning hobbies and learning efficiency. This also reduces students’ understanding of the content of psychological ideological with political teaching. Big data theory can process complex research object data and relationships. It can help researchers discover characteristic factors related to psychological ideological with political teaching. This study uses the hole convolution in big data theory and GRU technology to analyze the three factors of student behavior characteristics, mental health situation content characteristics, and the cognition Ideological with political content characteristics in college psychological the cognition Ideological with political. The research results show that the atrous convolution and GRU methods can more accurately predict the three characteristics of psychological ideological with political teaching in universities. This is helpful for educators to discover more appropriate psychological and ideological with political teaching methods.
Qishen Granule Protects against Doxorubicin-Induced Cardiotoxicity by Coordinating MDM2-p53-Mediated Mitophagy and Mitochondrial Biogenesis
Authors: Li Weili , Zhang Yawen , Wang Xiaoping , Cao Jing , Qian Weina , Ling Guanjing , Tan Nannan , Jiang Jinchi , Sun Qianbin , Li Chun , Wang Wei , Wang Yong
Published: September 2022

Doxorubicin (DOX), the anthracycline chemotherapeutic agent, is widely used for the treatment of various cancers. However, its clinical application is compromised by dose-dependent and fatal cardiotoxicity. This study is aimed at investigating the cardioprotective effects of Qishen granule (QSG) and the specific mechanism by which QSG alleviates DOX-induced cardiotoxicity (DIC) and providing an alternative for the treatment of DIC. We first evaluated the cardioprotective effects of QSG in a DIC mouse model, and the obtained results showed that QSG significantly protected against DOX-induced myocardial structural and functional damage, mitochondrial oxidative damage, and apoptosis. Subsequently, after a comprehensive understanding of the specific roles and recent developments of p53-mediated mitochondrial quality control mechanisms in DIC, we investigated whether QSG acted on MDM2 to regulate the activity of p53 and downstream mitophagy and mitochondrial biogenesis. The in vivo results showed that DOX inhibited mitochondrial biogenesis and blocked mitophagy in the mouse myocardium, while QSG reversed these effects. Mechanistically, we combined nutlin-3, which inhibits the binding of p53 and MDM2, with DOX and QSG and evaluated their influence on mitophagy and mitochondrial biogenesis in H9C2 cardiomyocytes. The obtained results showed that both DOX and nutlin-3 substantially inhibited mitophagy and mitochondrial biogenesis and induced mitochondrial oxidative damage and apoptosis, which could be partially recovered by QSG. Importantly, the immunoprecipitation results showed that QSG promoted the binding of MDM2 to p53, thus decreasing the level of p53 protein and the binding of p53 to Parkin. Collectively, QSG could promote the degradation of p53 by enhancing the binding of MDM2 to the p53 protein, resulting in the reduced binding of p53 to the Parkin protein, thus improving Parkin-mediated mitophagy. Increased degradation of p53 protein by QSG simultaneously enhanced mitochondrial biogenesis mediated by PGC-1α. Ultimately, QSG relieved DOX-induced mitochondrial oxidative damage and apoptosis by coordinating mitophagy and mitochondrial biogenesis.
Using Pitch Feature Matching to Design a Music Tutoring System Based on Deep Learning
Authors: Jiang Jing
Published: September 2022

It is a challenge for the current music teaching system to carry out teaching according to the difference of score difficulty and realize automatic grading. Therefore, identifying the difficulty of music score according to pitch is the key to individualize music teaching resources. This paper summarizes and analyzes the problem of pitch feature extraction in music teaching. In the pitch extraction, the audio signal is divided into frames, and the feature matching of high-pitched content in music teaching resources is realized by smoothing the pitch sequence. In addition, the pitch feature extraction algorithm in MIDI music score files is proposed, and the pitch feature matching model is constructed. Finally, a music tutoring system based on pitch feature matching is designed, including a music score learning tool, overall structure of system, and interaction between teachers and students. Tutoring strategies include three main functions: learning suggestions of knowledge points, skills in practice and training, and learning path adjustment. This study is helpful to further improve the music teaching model and realize intelligent and personalized music learning.
Osteogenic Differentiation of Human Umbilical Cord Blood Mesenchymal Stem Cells Induced by Liu’s Zhenggudan No. 2 Formula
Authors: Deng Qiaosong , Wang Shengru , Dai Zheyi , Li Xiaojun , Li Guanshen , Wang Ziheng , Chen Jianfeng
Published: September 2022

Aim. This study aimed to investigate the potential of Liu’s Zhenggudan No. 2 Formula (LZF2) in inducing osteogenic differentiation of human umbilical cord blood mesenchymal stem cells (hUCB-MSCs) and treating osteoporosis (OP), thereby providing new methods and ideas for the treatment of OP by traditional Chinese medicine. Methods. Forty sample rats were equally divided into five groups: high-concentration LZF2, low-concentration LZF2, the Eucommia ulmoides (EU) group, the classical osteogenesis induction (COI) group, and the blank control group. Eight rats in each group were routinely housed for 7 days. Subsequently, to induce hUCB-MSCs, drug-containing serum was extracted from the abdominal aorta of rats to prepare the osteogenic induction solution. In addition, alkaline phosphatase (ALP) activity and osteocalcin (OCN) content assays, and alizarin red staining were performed on days 3, 6, 9, and 12 after culture. Results. After induction of hUCB-MSCs, ALP activity and OCN content increased significantly in the high-concentration LZF2 group. Alizarin red staining also depicted numerous orange-red calcified nodules in rats in the high-concentration LZF2 group. Conclusion. High concentration of LZF2 can facilitate the differentiation of hUCB-MSCs to promote osteogenesis.
Little-YOLOv4: A Lightweight Pedestrian Detection Network Based on YOLOv4 and GhostNet
Authors: Zheng Hongtao , Liu Hong , Qi Wei , Xie Hao
Published: September 2022

Nowadays, the areas such as intelligent-assisted driving, intelligent monitoring, pedestrian analysis, and attitude detection are developing rapidly. Inseparable from these fields is pedestrian detection technology. In order to deploy pedestrian detection algorithms on devices with limited hardware resources, a recognition algorithm that takes into account both performance and speed is required. Thus, a Little-YOLOv4 network structure is proposed, where the GhostNet is used to extract image features and the PANet is ameliorated by adding BiFPN path fusion which can integrate richer semantic features and preserve spatial information. The DO-DConv and DSC take place of standard convolution and the ReLU6 replaces the Leaky ReLU, which reduce the computational cost. The squeeze-and-excitation network is added to YOLOv4 head network, which could greatly reduce the interference information. The pedestrian detection results show that the mAP is 90.11% and the FPS is 79 by using Little-YOLOv4. The Little-YOLOv4 network structure could achieve a good compromise between algorithmic accuracy and speed.
Multidrug‐Resistant Bacteria: Their Mechanism of Action and Prophylaxis
Authors: Bharadwaj Alok , Rastogi Amisha , Pandey Swadha , Gupta Saurabh , Sohal Jagdip Singh
Published: September 2022

In the present scenario, resistance to antibiotics is one of the crucial issues related to public health. Earlier, such resistance to antibiotics was limited to nosocomial infections, but it has now become a common phenomenon. Several factors, like extensive development, overexploitation of antibiotics, excessive application of broad‐spectrum drugs, and a shortage of target‐oriented antimicrobial drugs, could be attributed to this condition. Nowadays, there is a rise in the occurrence of these drug‐resistant pathogens due to the availability of a small number of effective antimicrobial agents. It has been estimated that if new novel drugs are not discovered or formulated, there would be no effective antibiotic available to treat these deadly resistant pathogens by 2050. For this reason, we have to look for the formulation of some new novel drugs or other options or substitutes to treat such multidrug‐resistant microorganisms (MDR). The current review focuses on the evolution of the most common multidrug‐resistant bacteria and discusses how these bacteria escape the effects of targeted antibiotics and become multidrug resistant. In addition, we also discuss some alternative mechanisms to prevent their infection as well.
Methyl Gallate Suppresses Tumor Development by Increasing Activation of Caspase3 and Disrupting Tumor Angiogenesis in Melanoma
Authors: Park Jeong-Ki , Yoo Min-Jae , Jang Hyuk , Park Sang-Youel , Choi Jawun , Seol Jae-Won
Published: September 2022

Methyl gallate is a phenolic compound mainly found in medicinal plants. It has been reported to its anticancer activity in various tumors. In this study, we aimed to demonstrate the antitumor effect of methyl gallate in the melanoma mouse model and B16F10 cells. Our results showed that methyl gallate decreased cell viability and induced apoptosis by increasing the expression of cleaved caspase3 in B16F10 cells and prevented cell migration and tube formation in human umbilical vein endothelial cells. In B16F10 cell-inoculated mice, methyl gallate not only decreased tumor volume by 30% but also significantly reduced tumor vessel density and pericyte coverage. Moreover, methyl gallate diminished by close to 50% the expression of cytokeratin and LYVE-1 in mouse right inguinal lymph nodes, indicating that methyl gallate could suppress metastasis. In conclusion, this study suggests that methyl gallate inhibits tumor development by inducing apoptosis and blocking tumor angiogenesis and metastasis and might be considered a therapeutic agent for melanoma.
Sports Training Management Model Based on Big Data Digital Technology under Complex Human Environment
Authors: Xu Jingni , Xu Li , Wu Xiu
Published: September 2022

Data physical education process is also the practical process of information technology and college physical education curriculum integration. In order to improve the accuracy of college sports training management evaluation, we propose a golden sine algorithm (GoldenSA) based on back propagation neural network (BPNN) model whose performance is affected by its parameter selection to optimize BPNN (GoldSA-BPNN). The quality evaluation model of physical education in higher education institutions is based on GoldenSA. Firstly, a multi-indicator evaluation system of university sports training management based on hierarchical analysis was constructed from four dimensions, including teaching contents, teaching methods, teaching attitudes, and teaching effects; secondly, the scores of 12 secondary indicators of university sports training management evaluation were taken as the input vector of BPNN.