bluuebunny/crossref_metadata_2025 huggingface dataset:
Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model
Authors: Shao Lei , Guo Quanjie , Li Chao , Li Ji , Yan Huilong
Published: August 2022

The purpose of this study was to better apply artificial intelligence algorithm to load forecasting and effectively improve the forecasting accuracy. Based on the long short-term memory neural networks, a combined model based on whale bionic optimization is proposed for short-term load forecasting. The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. The original signal is decomposed into multiple characteristic components by set empirical mode decomposition. Each feature component is input into the bionic optimized combination model for prediction. Finally, get the load forecasting results. Compared with the prediction results of EEMD-ARMA model, RNN model, LSTM model, and WOA-LSTM model, the combined prediction model optimized by whale bionics has less prediction error and higher prediction accuracy.
Epidemiology of Pediatric Tuberculosis and Factors Associated with Unsuccessful Treatment Outcomes in the Centre Region of Cameroon: A Three-Year Retrospective Cohort Study
Authors: Vukugah Thomas Achombwom , Akoku Derick Akompab , Tchoupa Micheline Mekemnang , Lambert Edward
Published: August 2022

Background. In Cameroon, there are limited data on treatment outcomes of pediatric tuberculosis (TB). We sought to identify the factors associated with unsuccessful treatment outcomes and the risk factors for mortality among children receiving TB treatment in the Centre Region of Cameroon. Methods. This was a multicentre facility-based retrospective cohort study using routinely collected programmatic data. All children <15 years old treated for TB between 2018 and 2020 in 21 health facilities were included. We assessed risk factors for experiencing an unsuccessful treatment outcome and mortality through multivariable logistic regression analysis. Results. Of the 610 children with TB, 307 (50.3%) were females and the median age was 6 years (IQR = 2–12). One hundred and fifty-three (25.1%) of the children were TB/HIV co-infected patients. TB treatment success (cases categorized as cured and completed treatment) was observed in 488 (80.0%) of the patients. Unsuccessful treatment outcomes were experienced by 122 (20.0%) children. Of these, 73 (12.0%) died, 4 (0.6%) had treatment failure, 25 (4.1%) were lost to follow-up, and the outcomes of 20 (3.3%) children were not evaluated. In multivariable analysis, HIV-positive status (adjusted odds ratio [AOR] = 2.43; 95% CI, 1.55–3.80,

p
<
0.001

) and clinical method of TB diagnosis (AOR = 2.46; 95% CI, 1.55–3.91,

p
<
0.001

] were associated with unsuccessful treatment outcomes. HIV-positive status (AOR = 4.23; 95% CI, 2.44–7.33,

p
<
0.001

) and clinical method of TB diagnosis (AOR = 2.22; 95% CI, 1.25–3.91,

p
=
0.006

) were the risk factors for mortality among children on TB treatment. Conclusion. The study found that HIV-TB co-infected children and those clinically diagnosed with TB were significantly more likely to have had unsuccessful TB treatment outcomes and mortality. Our findings underscore the need for healthcare workers to closely monitor and support HIV-TB co-infected children on TB treatment. TB/HIV collaborative activities should be strengthened by implementing TB preventive interventions among HIV-infected children.
Conducting Polymer Nanocomposite for Energy Storage and Energy Harvesting Systems
Authors: Sonika  , Verma Sushil Kumar , Samanta Siddhartha , Srivastava Ankit Kumar , Biswas Sonali , Alsharabi Rim M. , Rajput Shailendra
Published: August 2022

Conducting polymers (CPs) have received a lot of attention because of their unique advantages over popular materials, such as universal and tunable electrical conductivity, simple invention approach, high mechanical strength, low weight, low price, and ease of processing. Polymer nanocomposites have been enthusiastically explored as superlative energy generators for low-power-consuming electronic strategies and confirmed progressive surface area, electronic conductivity, and amazing electrochemical behaviour through expanding the opportunity of utilization. The hybridization of conducting polymer with inorganic hybrid and organic nanomaterials also resulted in multifunctional hybrid nanocomposites with better capabilities in a variety of devices, including sensors, energy storage, energy harvesting, and defensive devices. The capability and assistance of modern advancements for the development of multifunctional nanomaterials/nanocomposites have been presented, as well as the approaches for producing nanostructured CPs. The mechanisms underlying their electrical conductivity, and ways for modifying their properties, are investigated. The ongoing research towards generating superior CP-based nanomaterials is also discussed. This assessment focuses on the important schemes involved in the scientific and industrial use of polymeric materials and nanocomposites intended for the scheme and manufacture of energy strategies such as solar cells, rechargeable batteries, supercapacitors, and energy cells, as well as the waiting problems and their prospects.
Extraction of Microcrystalline Cellulose and Silica from Agriculture Waste and Its Application in Synthesis of Wheat Gluten and Fish Scales Derived Bioplastic
Authors: Samraj S. , Senthilkumar K. , Induja P. , Venkata Ratnam M. , Aatral G. V. , Ramakrishna G. V. S.
Published: August 2022

Plastics play a significant part in human life and the world we live in. The use of plastics results in detrimental effects on the natural world, which compels us to look for viable replacements. As a result of their enhanced capacity to biodegrade, bioplastics are becoming increasingly important materials. In recent years, there has been a rapid ascent in the utilization of biopolymers in various applications. The objective of this research is to investigate the impact that silica obtained from rice hull ash (RHA) and microcrystalline cellulose (MCC) obtained from groundnut husk have on the properties of bioplastic obtained from wheat gluten and fish scales. The usage of fish scales has been shown to have a positive effect on weight reduction and debasement rates. Microcrystalline cellulose (MCC) is utilized in a wide range of concentrations, and the influence of MCC on bioplastic is researched. The biodegradability tests of bioplastic revealed that the plastic lost 35% of its weight in just 14 days. The experiments that were done to evaluate the chemical stability and tensile strength of the bioplastic indicated that the MCC content has a significant effect in improving the characteristics of the material.
The Influence of Digital Transformation on Intelligent Design of Architecture
Authors: Zhang Zhentao
Published: August 2022

With the continuous progress of China’s social economy and the subsequent improvement of science and technology, intelligent architectural design has been gradually developed. At the present stage, the design of intelligent system in architecture has become a systematic design project, which is widely used in the field of architecture. The rapid development of artificial intelligence has not only brought new opportunities but also new challenges to the field of architectural design. On the one hand, digital architecture can share some of the functions of traditional architecture in its advantageous application areas, but inevitably new building function requirements and building design types will be formed. On the other hand, the depiction of building plans, elevations, and renderings, daylight adjustment, and volume calculation, as well as the transfer of text and graphic files via the Internet, are all closely related to digital architecture, regardless of which application software is used. In the current digital era, where real and virtual environments will coexist, digital transformation also has a lasting impact on intelligent building design, drawing out a new development model.
A Comprehensive Reliability Assessment Method for Existing Railway Bridges Based on Bayesian Theory
Authors: Li Zhonglong , Chen Xiaowei , Bing Haonan , Zhao Yangjun , Ye Zhifeng
Published: August 2022

The increase in traffic volume and train load poses new challenges to the reliability assessment of existing railway bridges. To construct a comprehensive assessment method for the safety and reliability of existing railway bridges, firstly, the risk factors of railway bridge structures are analyzed and the evaluation criteria are determined; secondly, based on the accident tree theory, a multilevel Bayesian network model with key points is established, and the ability of the Bayesian network bidirectional reasoning and sensitivity analysis is used to evaluate the structural safety; finally, the result was applied to the marina northern Songhua River extra-large bridge to verify the applicability of the comprehensive evaluation of the reliability of an existing railway bridge. This approach provides a theoretical basis for the maintenance and reinforcement of the Songhua River Bridge along the Bin‐North Line.
Machine Learning for Predictive Analytics in the Improvement of English Speech Feature Recognition
Authors: Chen Yan , Martinuzzi Bukhari
Published: August 2022

The use of deep learning to improve English speaking has seen tremendous development in recent years. This study evaluates the noise that is present in the English speech environment, employs a two-way search method to select the optimum feature set, and applies a quick correlation filter to remove redundant features in order to increase the accuracy of English voice feature identification. In addition, this article designs a low-pass filter in the complex cepstrum domain to filter the room impulse response in order to obtain the estimated value of the complex cepstrum of the original speech signal. After doing so, the authors transform this estimated value into the time domain in order to obtain the estimated value of the original speech signal. In addition, this paper proposes a corresponding noise elimination model for the purpose of eliminating noise from English speech in a reverberant environment. It also designs a complex cepstrum domain filter in order to conduct simulation research on the different characteristics of the reverberation signal and the pure speech signal in the complex cepstrum domain. In conclusion, this study develops an English voice feature recognition model that is founded on a deep neural network. Furthermore, this paper uses experimental research to validate the validity of the algorithm model that was developed in this study.
The Acceptability of Traditional Culture under the Background of Deep Learning
Authors: Sima Qian , Wu Shan
Published: August 2022

The cultural values of a country impact its national psychology and identity. Citizens’ values and public opinions are conveyed to state leaders over the media and other information channels, both directly and indirectly influencing decisions on foreign policy. The traditional cultural values that affect the psyche of the Chinese people are harmony, generosity, morality, courtesy, wisdom, honesty, loyalty, and filial piety. This study aims to analyze the attitudes of Chinese college students toward traditional culture. The reliability and effect factors of the traditional culture acceptability questionnaire are set in the context of deep learning. A questionnaire on traditional culture education of college students is compiled using the indicator evaluation methods, and the current situation of traditional culture education is investigated for college students. A total of 300 valid respondents are returned from five universities including Shanghai Jiaotong University, Fudan University, Yunnan University, Kunming University of Science and Technology, and Yunnan Normal University. Results show that 28% of college students believe that practical activities including visiting and learning and traditional festival commemoration are the most effective ways to educate traditional culture for them, which accounts for the largest percentage. Similarly, 19% of students suggest online publicity, while 16% believe that lecture reports are particularly important, and 12% of students advocate the teaching courses. In addition, about 23% of the students choose other methods, such as seminars, setting up Chinese culture festivals, and building cultural associations. The outcomes of this study provide data support for identifying the shortcomings in traditional cultural education and formulating strategies.
Type 1 Diabetes Mellitus‐Related circRNAs Regulate CD4+ T Cell Functions
Authors: Chen Jianni , Jia Guanfei , Lv Xue , Li Shufa
Published: August 2022

Circular RNAs (circRNAs) participate in development of malignancies through its active role as a “miRNA sponge.” Their roles in type 1 diabetes mellitus (T1DM) pathogenesis are elusive. Here, the important role of circRNAs in T1DM was explored. circRNA profiling was performed for isolated CD4+ T cells from blood of T1DM and healthy volunteers. There were 257 differentially expressed circRNAs. Only three upregulated DEcircRNAs (hsa_circ_0000324, hsa_circ_0001853, and hsa_circ_0068797) were consistent with the GEO database. Through KEGG analyses, it was found that the three DEcircRNAs were associated with 11 miRNAs and 8 immune‐related target genes (mRNA). Further analysis found that four miRNAs, two circRNAs, and four mRNAs were associated with nine circRNA‐miRNA‐mRNA networks. This confirmed the requirements of sponge mechanisms. The qRT‐PCR analysis revealed that circRNA000324/miRNA675‐5p/MAPK14 and circRNA000324/miRNA‐675‐5p/SYK may be potential mechanisms in regulation of differentiation and proliferation of CD4+ T cell in patients with T1DM. Therefore, targeting circRNA to regulate cellular immune responses by regulating CD4+ T cell differentiation may be a new strategy for the treatment of T1DM.
Information Service Frequency of Urban Rail Transit Based on Passenger Flow Dissipation Rate
Authors: Zhou Huijuan , Liu Yu , Li Bei , Zhang Zhe
Published: August 2022

The improvement of the quality of information services can speed up the dispersal of aggregated passenger flow. To alleviate the safety hazards of the outburst mass passenger flow gathering at a certain station, we took the information service frequency as the research object and the passenger flow dissipation rate as an index to study the best information service frequency interval that passengers can accept. Firstly, we analyzed the influencing factors of passenger flow dissipation rate from the perspective of subjective and objective and proposed a model of passenger flow dissipation rate. Then, a corresponding influence factor model was established to determine the influence factor and the corresponding frequency interval. Finally, the model solving impact factors and the passenger flow data of the Wukesong subway station were used to solve the model of passenger flow dissipation rate, combining the frequency interval under a single factor to determine the best information service frequency interval under the influence of multiple factors.
Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning
Authors: Xu Wei , Yan Zhipeng , Chen Nan , Luo Yuxin , Ji Yuke , Wang Minli , Zhang Zhe
Published: August 2022

This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes and patients with RVO were used for model training and testing to investigate an intelligent diagnosis system. The images were first classified into four categories by fundus disease specialists: (i) healthy fundus (group 0), (ii) branch RVO (BRVO) (group 1), (iii) central RVO (CRVO) (group 2), and (iv) macular branch RVO (MBRVO) (group 3), before being diagnosed using the ResNet18 network model. Intelligent diagnoses were compared with clinical diagnoses. The specificity of the intelligent diagnosis system under each attention mechanism was 100% in group 0 and also revealed a high sensitivity of over 95%,

F
1

score of over 97%, and an accuracy of over 97% in this group. For the other three groups, the specificities of diagnosis ranged from 0.45 to 0.91 with different attention mechanisms, in which the ResNet18+coordinate attention (CA) model had the highest specificities of 0.91, 0.88, and 0.83 for groups 1, 2, and 3, respectively. It also provided a high accuracy of over 94% with a coordinate attention mechanism in all four groups. The intelligent diagnosis and classifier system developed herein based on deep learning can determine the presence of RVO and classify disease according to the site of occlusion. This proposed system is expected to provide a new tool for RVO diagnosis and screening and will help solve the current challenges due to the shortage of medical resources.
lncRNA GAS5 Induces Cell Apoptosis in Acute Myeloid Leukemia by Targeting Nrf2
Authors: Qin Xiaohua , Jiang Yanling , Zhang Xiaojun , Li Dan
Published: August 2022

Objective. This study is aimed at investigating the molecular mechanism of lncRNA GAS5-induced cell apoptosis in acute myeloid leukemia (AML) by targeting Nrf2. Methods. The RNA interfering technique was utilized to silence THP-1 in AML cell line, and lncRNA GAS5 expression in cell line was determined by real-time PCR. EdU experiment and flow cytometry were used to detect the apoptosis and proliferation ability of cells in different groups. PD-L1, STAT3, AKT, and MMP9 expressions were determined by Western blot. Results. The si-RNA significantly inhibited the expression of lncRNA GAS5 in THP-1 cells. Compared with the si-NC group, the difference in cell apoptosis between lncRNA GAS5 and Nrf2 groups was significant (

P
<
0.05

). Compared with the lncRNA GAS5 group, the number of apoptotic cells in the lncRNA GAS5+Nrf2 group significantly reduced (

P
<
0.05

). Compared with the si-NC group, the differences in the levels of four proteins between lncRNA GAS5 and Nrf2 groups were significant (

P
<
0.05

). In lncRNA GAS5+Nrf2 and lncRNA GAS5 groups, PD-L1 expression increased, while the expression of STAT3, AKT, and MMP9 decreased. Conclusion. In AML cells, lncRNA GAS5 with Nrf2 could regulate the proliferation and apoptosis of AML cells. lncRNA GAS5 inhibited Nrf2 expression, regulated cell apoptosis and proliferation, and further inhibited the progression of AML disease.
Identification of a Hypoxia-Angiogenesis lncRNA Signature Participating in Immunosuppression in Gastric Cancer
Authors: Wang Zicheng , Liang Xisong , Zhang Hao , Wang Zeyu , Zhang Xun , Dai Ziyu , Liu Zaoqu , Zhang Jian , Luo Peng , Li Jiarong , Cheng Quan
Published: August 2022

Hypoxia and angiogenesis are the leading causes of tumor progression, and their strong correlation has been discovered in many cancers. However, their collective function’s prognostic and biological roles were not reported in gastric cancer. Hence, we aimed to investigate the effects of hypoxia and angiogenesis on gastric cancer via sequencing data. This study used weighted gene coexpression network analysis and random forest regression to build a hypoxia-angiogenesis-related model (HARM) via the TCGA-STAD lncRNA data. It estimated the HARM’s correlation with clinical features and its accuracy for survival prediction. Sequential functional analyses were conducted to investigate its biological role, and we next sought the immune landscape status and immunological function variation by ESTIMATE score calculation and GSVA, respectively. Seven different algorithms were conducted to assess the immunocyte infiltration, and TIDE score and immune checkpoint levels were compared between the high- and low-HARM groups. As a result, we found that HARM predicted patient survival with high accuracy and was correlated with higher stages of gastric cancer. Various cancer-associated pathways and macrophage-related regulations were upregulated in the high-HRAM group. The high-HARM group harbored higher immune levels, and M2 macrophages and cancer-associated fibroblasts were particularly highly unfiltered. Furthermore, globally upregulated immune checkpoints and higher TIDE scores were observed in the high-HARM group. Finally, we filtered eight drugs with lower IC50 in the high-HARM group as potential drugs for the HARM-targeted therapy. We believe this study opens up novel perspectives into the interaction between hypoxia-angiogenesis and immunosuppression and will provide novel insights for gastric cancer immunotherapy.
Research on Optimization of Steel Foreign Trade Financial Transaction Based on Blockchain Technology
Authors: Lin Yu
Published: August 2022

For many years, China’s steel exports have been winning with low prices. As the upstream iron ore price rises, the profit margin is compressed while many problems are exposed. The author has constructed a SWOT-AHP model for the cross-border financial transaction link of steel foreign trade and analyze the main factors affecting the financial transactions of steel foreign trade by calculating the weights within the SWOT group and among the factors and ranking them. The results show that transaction credit risk, long and inefficient settlement cycle, changes in steel tax rebate policy, high cross-border transaction costs, and rising upstream raw material prices are the main factors for the development of China’s foreign trade in steel. Relying on blockchain technology, it will be improved and its application path in cross-border financial transactions will be explored to achieve the purpose of avoiding credit risks on both sides of transactions and reducing time and costs of foreign trade transactions. At the same time, it breaks the monopoly of cross-border transactions of the SWIFT system and optimizes the financial transaction mode of foreign trade of steel, and with the background of the E-CNY R&D pilot, we construct the application path of E-CNY cross-border payment under blockchain technology and provide some theoretical support for the promotion of E-CNY internationalization.
Agricultural Economic Evidence and Policy Prospects under Agricultural Trade Shocks and Carbon Dioxide Emissions
Authors: Kang Jian , Zhao Minjuan
Published: August 2022

With the development of the market economy, agricultural trade has become more and more significant for the development of the agricultural economy, which has triggered people’s further thinking and exploration on the impact of agricultural trade on agricultural carbon emissions. This paper takes the measurement of trade implied carbon as the carbon dioxide emission index under the impact of agricultural trade and analyzes the impact of trade implied carbon and implied carbon balance on carbon emission. Taking the impact of Sino‐US agricultural trade as an empirical background, this paper measures the impact of environmental changes in agricultural trade opening on China’s agricultural development and its carbon emissions, so as to predict changes in China’s regional agricultural carbon emissions performance. After calculation, it is found that the scale of China’s exports has decreased by 0.089%, which is lower than the decline of 0.361% in the United States. The trade conflict has a significant impact on China’s import and export structure. Under the scenario of mutual tariffs on agricultural products, China’s exports to the United States are expected to decrease by 6.28%, while China’s imports from the United States decreased by 13.02%. The Sino‐US agricultural trade dispute will reduce China’s carbon emissions by 0.013% and the United States’ carbon emissions by 0.024%, which is related to the negative impact on the economy. Improving the performance of agricultural carbon emissions is not only the need for the green and sustainable development of the agricultural economy but also conducive to improving the international competitiveness of agricultural products.
The Effect of TNF-α on CHD and the Relationship between TNF-α Antagonist and CHD in Rheumatoid Arthritis: A Systematic Review
Authors: Qian Yezhou , Mao Menghui , Nian Feige
Published: August 2022

Tumor necrosis factor-alpha (TNF-α) plays an important role in coronary heart disease (CHD), a chronic inflammatory process. Meanwhile, this pro-inflammatory factor is also involved in the pathogenesis of autoimmune diseases such as rheumatoid arthritis (RA). Patients with RA correspond to a higher risk of CHD. TNF-α antagonist, one of the main treatments for RA, may reduce the risk of CHD in patients with RA. This review summarizes the pathogenesis of TNF-α in CHD and discusses the relationship between TNF-α antagonist and CHD in patients with RA.
Research on Fast Face Recognition Method Based on Double Decision Subspace
Authors: Wang Lei , Zhang Liming , Huang zhiqiu
Published: August 2022

Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.
Effect of Irrigation Regime and Fertilization on Recovery of Dicamba Injured Soybean
Authors: France Wesley , Norsworthy Jason , Roberts Trent , Ross Jeremy , Barber Tom , Gbur Edward
Published: August 2022

With the release of the dicamba-resistant crop technology and subsequent increase in dicamba off-target movement to non-dicamba-resistant crops, discovering means of mitigating yield loss through studying dicamba injury to soybean and interactions with factors such as irrigation regime and fertilization would prove beneficial. Field experiments were conducted in 2019 in Fayetteville and Colt, Arkansas, to evaluate the effect of irrigation regime to non-dicamba-resistant soybean that was injured by dicamba at a low dose at multiple timings. Another experiment was conducted in Fayetteville in 2019 and 2020 evaluating the impact of nitrogen (N) and potassium (K) fertilization on soybean recovery following injury by dicamba at multiple reproductive stages. Visible injury in both experiments was affected by application timing. Soybean yield components were impacted by dicamba applications within the irrigation regime experiment, and yields were decreased by dicamba applications; however, soybean yield was higher from branches than from the mainstem in dicamba-treated compared to nontreated plants. In the fertilization experiment, soybean treated with a low dose of dicamba that received N fertilization tended to have reduced biomass compared to treatments receiving no fertilizer or K alone, with greatest biomass reduction tending to occur among treatments receiving both N and K. Total grain yield was not affected by either irrigation regime or fertilization. While an increase in yield due to neither irrigation nor fertilization was observed, these results may help improve understanding of the effect of low-dose dicamba on soybean and aid producers making management decisions.
SPTSSA Is a Prognostic Marker for Glioblastoma Associated with Tumor-Infiltrating Immune Cells and Oxidative Stress
Authors: Wang Ziheng , Ge Xinqi , Shi Jinlong , Lu Bing , Zhang Xiaojin , Huang Jianfei
Published: August 2022

Background. SPTSSA encodes the small subunit A of serine palmitoyltransferase. It catalyzes the formation of sphingoid long-chain base backbone of sphingolipids. Its role in glioma prognosis and tumor-infiltrating immune cells remains unclear. Methods. We analyzed SPTSSA expression and association with clinical prognosis using GEPIA and CGGA database. Then, GSEA was performed to identify relevant biological functions of SPTSSA. The correlations between SPTSSA expression and tumor immune infiltrates were investigated using CIBERSORT and TIMER. Finally, IHC and IF were performed to confirm the value of prognosis and the correlation with immune infiltration. Results. SPTSSA expression was significantly upregulated in diffuse glioma compared to normal tissues and associated with poor survival in GEPIA and CGGA database. Then, we identified biological processes and signaling pathways associated with SPTSSA expression. The result showed that SPTSSA enriched in the GO term like oxidative stress. Finally, we showed that SPTSSA expression was significantly associated with tumor-infiltrating immune cells and overall survival via IHC. Conclusion. These findings suggest that SPTSSA expression might be used as a prognostic biomarker for glioma and potential target for novel glioma therapy.
Circularly Polarized Leaky-Wave Antenna Based on Low-Loss Transmission Line
Authors: Huo Xingying , Li Zheng
Published: August 2022

A leaky-wave antenna with circular polarization property is proposed in millimeter-wave band, which can be applied in the wireless communication systems and remote sensing. The radiation is produced by the periodic patches on both sides of the dielectric slab of the antenna based on a low-loss planar Goubau line structure. The circular polarization unit cell of this antenna is made of two pairs of H-shaped patches, orthogonally set on each side of the dielectric substrate, which can efficiently reduce the cross polarization. This antenna is excited by a central strip line on the upper surface of the slab, removing the ground of the traditional microstrip structure, to reduce the loss of the antenna. The circular polarization property is achieved by optimizing the size and the distribution of the radiation unit cell and the structural periodicity. The radiation performance of this antenna is analyzed by CST simulation, which shows that the overlapping bandwidth of this antenna for 3 dB axial ratio with S11 < −10 dB is about 5.3%, with cross polarization 23 dB lower than copolarization. The simulated maximum directivity is l5.5 dBi with efficiency over 90% during the whole band.
An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities
Authors: Refaai Mohamad Reda A. , Dattu Vinjamuri S. N. C. H. , Niranjana Murthy H. S. , Pramod Kumar P. , Kannadasan B. , Diriba Abdi
Published: August 2022

Drones, the Internet of Things (IoT), and Artificial Intelligence (AI) could be used to create extraordinary responses to today’s difficulties in smart city challenges. A drone, which would be effectively a data-gathering device, could approach regions that become complicated, dangerous, or even impossible to achieve for individuals. In addition to interacting with one another, drones must maintain touch with some other ground-based entities, including IoT sensors, robotics, and people. Throughout this study, an intelligent approach for predicting the signal power from a drone to IoT applications in smart cities is presented in terms of maintaining internet connectivity, offering the necessary quality of service (QoS), and determining the drone’s transmission range offered. Predicting signal power and fading channel circumstances enables the adaptable transmission of data, which improves QoS for endpoint users/devices while lowering transmitting data power usage. Depending on many relevant criteria, an artificial neural network (ANN)-centered precise and effective method is provided to forecast the signal strength from such drones. The signal strength estimations are also utilized to forecast the drone’s flight patterns. The results demonstrate that the proposed ANN approach has an excellent correlation with the verification data collected through computations, with the determination of coefficient R2 values of 0.97 and 0.98, correspondingly, for changes in drone height and distances from a drone. Furthermore, the finding shows that signal distortions could be considerably decreased and strengthened.
Simulation of Multisensor Energy Data Fusion Transformer Acquisition System Based on FPGA
Authors: Luan Lan , Hu Dan
Published: August 2022

In order to solve the problems of small signal acquisition range and poor acquisition accuracy of the existing multichannel acquisition system, a multisensor energy data fusion transformer acquisition system simulation method based on FPGA is proposed, and key hardware functions are designed and implemented. The system uses FPGA to control the core logic, synchronously collects and controls the energy data of the CCD camera and the laser rangefinder, organizes and uses an external large-capacity SDRAM group for buffering, and uses a dedicated PCI interface chip PLX9656 to achieve high-speed data transmission. Two pieces of sensor energy data and PCI bus energy data are stored in real time using a large-capacity disk array composed of multiple SATA hard disks. The function and performance of the energy data acquisition and storage system were tested. After the actual system test, the experimental results show that the transmission speed of the system through the PCI bus exceeds 200 MB/s, the writing speed of the continuous disk array is 240 MB/s, and the real-time acquisition and recording speed is 100 MB/ss. Conclusion. The system effectively solves the problems of high-speed data acquisition and storage and large capacity data transmission of key sensor nodes.
Regular Flow Field Sparse Processing and Blue Finance of Ocean City Research Based on GIS System
Authors: Sun Ying , Anurag Kalra
Published: August 2022

In order to explore the development path of blue finance in ocean cities, this paper combines GIS to conduct blue finance research in ocean cities, draws on the traditional direct visualization method of point icons, and uses arrow symbols to render vector flow field data. Moreover, this paper uses GIS means to perform statistical analysis on flow field data, studies the adaptive sparse algorithm of flow field data spatial structure based on GIS, and proposes a program implementation method for regular flow field sparse processing suitable for the general flow of the sparse algorithm. The experimental research shows that the GIS technology proposed in this paper can play an important role in the research of blue finance in ocean cities and has a certain role in promoting the development of ocean blue finance.
Analytical Facts of Product Designing, Branding, Packaging, and Distributing through Internet of Things Applications
Authors: Zhang Wen , Xie Juan
Published: August 2022

The unmistakable parts of a decided product can be crucial for imparting the situation of the brand, since these viewpoints are projected as perceived components of the product. Hence, the point of this exploration is to survey the connection between the course of shopper buys right now of securing individual consideration products and the unmistakable parts of packaging as per their generational accomplice. These substantial parts of packaging are the visual perspectives that a product contains, recognized as realistic components and educational components. Deviations in notable improvements stand out. Moreover, deviations in the presence of upgrades influence the manner by which purchasers classify improvements. Our trial examines the impact of the level of deviation of espresso bundles on buyers’ consideration and classification. The boosts are altered bundles of the most popular Dutch ground espresso brand.
Designing Landscape of Urban Gardening Based on Optimized Artificial Intelligence Model
Authors: Liu Long
Published: August 2022

The landscape is driven by innovative design, adhering to the concept of “poetic residence, inheritance, and innovation,” which has long served China’s urban and rural development and the construction of ecological civilization and provided high-quality planning and design services for governments at all levels throughout the country; we construct a particle swarm optimization (PSO) landscape pattern spatial optimization model and solution algorithm to optimize the spatial layout of the landscape for economic development, ecological protection, and integrated scenarios in a city in southwest China. The results show that the PSO-based landscape pattern spatial optimization model and algorithm can use particle position to simulate landscape distribution for spatial pattern optimization. In the development of landscape pattern optimization methods, the landscape pattern optimization model with landscape simulation evolution as the core has shown its advantages. In the target year, the dominant landscape of economic development scenario is urban and orchards, and the landscape pattern shows the distribution characteristics of urban, farmland in the western dam area, and orchards in the eastern mountainous area; the dominant landscape of ecological protection scenario is forest, urban, and rural residential and industrial mining; and the landscape pattern shows the distribution characteristics of urban and rural residential and industrial mining, orchards, farmland in the western dam area, and forest in the eastern mountainous area. The landscape pattern shows that the western dam area is dominated by urban and rural residential and industrial, while the eastern mountainous area is dominated by forests and orchards. The integrated scenario has the highest potential for the future, and its economic, ecological, and comprehensive benefits can be optimized, which is the best spatial layout of the landscape pattern in the study area in the target year.
Auxiliary Teaching System of Higher Mathematics Based on Random Matrix Model
Authors: Xiao Yabin , Zhou Bin , He Dan , Liu Jingzhong
Published: August 2022

With the development of computer technology, computers have become a part of people’s lives and the Internet has connected the world’s networks as a whole. Computer technology is changing people’s study, life, and work. People’s traditional education mode, thinking, content, method, and talent training program have a significant impact. The development from traditional to computer technology-based teaching methods has brought new developments and leaps in educational technology. This paper analyzes the research background, significance, and research status of the advanced mathematics auxiliary teaching system, introduces the related technologies and development modes used in the development of the system, and especially discusses the access database technology by ADO and the mathematical expression based on MathML language. Secondly, starting from the actual teaching, we analyze the functional requirements and performance requirements of the system in detail and make detailed planning and design for the system architecture, database selection, functional modules, etc. The design and implementation process of this teaching system are summarized. The teaching strategy inference engine is the key to the personalization and intelligence of the ICAI system. According to the learning models provided by different students, the system designs a corresponding teaching sequence for the learners by controlling the meta-knowledge of the domain knowledge base. The teaching strategy inference engine cuts the domain knowledge tree, selects the knowledge points suitable for the student, and sorts the selected knowledge points reasonably to generate an optimal teaching sequence. According to the students’ learning situation, combined with the teaching rules in the teaching rule library, the students’ grades are dynamically adjusted, so as to select new learning content for students and provide teaching suggestions in time. The student model is the premise of the ICAI system to achieve individualization and intelligence. The system makes a comprehensive evaluation and diagnosis of students through fuzzy comprehensive evaluation and fuzzy reasoning. On this basis, a cognitive student model is established, which is the teaching strategy that provided the basis for the formulation.
Association between Ultraprocessed Food Intake and Overweight, Obesity, and Malnutrition among Children in Tehran, Iran
Authors: Asgari Elaheh , Askari Mohammadreza , Bellissimo Nick , Azadbakht Leila
Published: August 2022

Childhood is a critical period for susceptibility to malnutrition. The consumption of ultraprocessed foods (UPFs) has been increasing among children. The objective of this study was to evaluate the relationship between UPF intake and overweight/obesity and malnutrition in children. 788 children aged 6 years were included in a population-based cross-sectional study in Tehran. A 168-item semiquantitative food frequency questionnaire was used to evaluate dietary intake. UPFs were detected using the NOVA classiﬁcation system. Logistic regression analyses were used, and results were reported as odds ratios (ORs) and 95% confidence interval (CI) of obesity and malnutrition across the tertiles of UPFs adjusted for energy intake, socioeconomic status, and physical activity. The mean weight, height, BMI, and total energy intake of participants were 20.85 ± 2.35 kg, 113.75 ± 2.00 cm, 16.12 + 1.84 kg/m2, and 1014.74 ± 259.16 (kcal/d), respectively. There were no significant associations between UPF intake and obesity (OR = 0.97; 95% CI 0.31 to 3.01; P-trend = 0.98), wasting (OR = 0.94; 95% CI 0.30 to 2.87; P-trend = 0.87), overweight/obesity (OR = 0.86; 95% CI 0.59 to 1.25; P-trend = 0.45), underweight/wasting (OR = 0.69; 95% CI 0.40 to 1.17; P-trend = 0.17), marginal-stunting (OR = 1.16; 95% CI 0.71 to 1.89; P-trend = 0.53), or marginal-stunting/overweight/obesity (OR = 1.25; 95% CI 0.62 to 2.54; P-trend = 0.47). There was no evidence of an association between intake of UPFs and risk of overweight, obesity, and malnutrition in children.
Diagnostic Accuracy of 18F‐FDG‐PET/CT and 18F‐FDG‐PET/MRI in Detecting Locoregional Recurrence of HNSCC 12 Weeks after the End of Chemoradiotherapy: Single‐Center Experience with PET/MRI
Authors: Murtojärvi Sarita , Malaspina Simona , Kinnunen Ilpo , Tuokkola Terhi , Honka Miikka-Juhani , Saunavaara Virva , Tolvanen Tuula , Schrey Aleksi , Kemppainen Jukka
Published: August 2022

Purpose. In head and neck squamous cell carcinoma (HNSCC), the early diagnosis and efficient detection of recurrences and/or residual tumor after treatment play a very important role in patient’s prognosis. Positron emission tomography (PET) using 2‐deoxy‐2‐18F‐fluoro‐D‐glucose (18F‐FDG) has become an established method for the diagnosis of suspected recurrence in head and neck carcinomas. In particular, integrated PET/MRI imaging that provides optimal soft tissue contrast and less dental implant artifacts compared to PET/CT is an intriguing technique for the follow‐up imaging of HNSCC patients. The aim of this study was to evaluate the benefit of PET/MRI compared to PET/CT in post‐treatment follow‐up imaging of HNSCC patients. Methods. This retrospective observational cohort study consists of 104 patients from our center with histologically confirmed HNSCC. All patients received chemoradiotherapy (CRT) and underwent 18F‐FDG‐PET/CT (n = 52) or 18F‐FDG‐PET/MRI (n = 52) scan 12 weeks after the end of treatment. Image analysis was performed by two independent readers according to a five‐point Likert scale analysis. Results. PET/MRI was more sensitive (1.00 vs. 0.77) than PET/CT in the detection of locoregional recurrence. PET/MRI also had better negative (1.00 vs. 0.87) predictive values. AUCs for PET/MRI and PET/CT on patient‐based analysis were 0.997 (95% CI 0.989–1.000) and 0.890 (95% CI 0.806–0.974), respectively. The comparison of sensitivity, AUCs, and negative predictive values revealed a statistically significant difference, p < 0.05. In PET/CT, false‐negative and positive findings were observed in the more advanced disease stages, where PET/MRI performed better. Also, false‐negative findings were located in the oropharyngeal, laryngeal, and nasopharyngeal regions, where PET/MRI made no false‐negative interpretations. Conclusion. Based on these results, PET/MRI might be considered the modality of choice in detecting locoregional recurrence in HNSCC patients, especially in the more advanced stages in the oral cavity, larynx, or nasopharynx.
Characteristics and Challenges of Primary Adrenal Insufficiency in Africa: A Review of the Literature
Authors: Mofokeng Thabiso R. P. , Beshyah Salem A. , Ross Ian L.
Published: August 2022

Backgrounds. Africa comprises 54 countries with varying degrees of economic development. As with other healthcare systems, rare diseases such as adrenal insufficiency are neglected and poorly documented. Objectives. We wished to explore primary adrenal insufficiency (PAI) in Africa, its prevalence, aetiology, genetics, presentation, diagnosis, and treatment and to determine the unmet needs in clinical care, education, and research. Materials and Methods. A narrative nonsystematic review of the literature was undertaken. We searched two online databases (PubMed and Google scholar) using the search terms “Addison’s disease/PAI, primary adrenal insufficiency coupled with “Africa,” “country names,” and “genetic disorders.” A total of 184 PAI records were reviewed. The exclusion of abstracts, conference proceedings, single case reports, and duplicate studies covering the same subject matter yielded 124 articles, of which 97 informed the final manuscript. Results. A wide range of aetiology of PAI was encountered, but their true prevalence is unknown. Aetiology varied with region and age of presentation as reflected by predominantly TB, HIV, and infective causes occurring in sub-Saharan Africa and more congenital forms described in North Africa associated with consanguinity. In Africa, the diagnostic criteria for PAI do not universally accord with conventional criteria, and there is a heavy reliance on clinical suspicion and biochemistry, including random cortisol of <400 nmol/L, rather than the tetracosactide test where stimulated cortisol <500–550 nmol/L confirms the diagnosis. Conclusions. A high index of suspicion is warranted to diagnose PAI in resource-limited settings, especially where tetracosactide tests are not universally available.
Modeling and Solving the Flow‐Shop Scheduling Problem with Sequence‐Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry)
Authors: Mohammadi Mustafa , Shayannia Seyed Ahmad , Lotfi Mohamadreza , Rezaeian Zaidi Javad
Published: August 2022

Progress in today’s modern industry requires a lot of knowledge, one of which is scheduling. Flow‐shop scheduling is one of the most widely used optimization problems. In this research, considering the importance of simultaneously order in different stages of production in the automotive industry, and also in order to make the problem more practical, we have investigated the problem of scheduling the flow‐shop, taking into account the lead time and the costs of each order. Due to the fact that in most research studies, the lead time and costs of an order have been ignored because they made it difficult to find the initial solution to the problem. In this research, using the firefly meta‐heuristic method, a suitable solution is provided to overcome this problem. Therefore, considered objective function is to minimize the total completion time. Absolute relative error (ARE) has been used to validate the model in a deterministic and meta‐heuristic mode. According to the ARE result, the difference in the results between the two algorithms is negligible. Then, the sequence results are determined according to the desired algorithm for 5 tasks considered for automobile parts. The results show that the completion time of job 1 is 1397.85; job 2 is 771.44; job 3 is 608.65; job 4 is 1163.87; and job 5 is 479.45.