bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of pre‐transplant bloodstream infection on clinical outcomes after heart transplantation
Authors: Chen Jeng‐Wei , Chou Heng‐Wen , Chou Nai‐Kuan , Wang Chih‐Hsien , Chi Nai‐Hsin , Huang Shu‐Chien , Yu Hsi‐Yu , Chen Yih‐Sharng , Hsu Ron‐Bin
Published: April 2022

Background

Active bloodstream infection (BSI) is a contraindication for heart transplantation (HT). However, some critical patients with BSI may undergo HT as a life‐saving procedure. We aimed to investigate the impact of pre‐transplant BSI on the clinical outcomes after HT.

Methods

We enrolled 511 consecutive patients who underwent HT between 1999 and 2019. Patients were divided into two groups based on the presence of BSI within 30 days preoperatively. Forty‐three patients (8.4%) with BSI who were clinically stable and had no metastatic infection were considered for HT on an individual basis. In‐hospital mortality, incidence of early postoperative BSI, length of postoperative hospital stays, and long‐term survival were compared between the groups. Logistic and Cox regression analyses were performed to identify risk factors for in‐hospital and 1‐year mortality.

Results

Patients with pre‐transplant BSI had a high incidence of previous cardiopulmonary resuscitation, pre‐transplant ventilator use, mechanical circulatory support use, renal replacement therapy, United Network for Organ Sharing status 1A, and a prolonged preoperative hospital waiting period. The in‐hospital mortality rate was higher in patients with pre‐transplant BSI (21% vs. 12%, p = .081), and the mortality rate was very high (33.3%) for those with BSI 0–15 days before HT. In addition, patients with pre‐transplant BSI had a significantly longer postoperative hospital stay than patients in the control group. However, long‐term survival was similar in both groups.

Conclusions

Although pre‐transplant BSI was associated with higher in‐hospital mortality and prolonged postoperative hospital stay, patients who survived the early period had a similar long‐term prognosis.
Diverse and mobile:eccDNA‐based identification of carrot low‐copy‐numberLTRretrotransposons active in callus cultures
Authors: Kwolek Kornelia , Kędzierska Patrycja , Hankiewicz Magdalena , Mirouze Marie , Panaud Olivier , Grzebelus Dariusz , Macko‐Podgórni Alicja
Published: April 2022

Long terminal repeat retrotransposons (LTR‐RTs) are mobilized via an RNA intermediate using a ‘copy and paste’ mechanism, and account for the majority of repetitive DNA in plant genomes. As a side effect of mobilization, the formation of LTR‐RT‐derived extrachromosomal circular DNAs (eccDNAs) occurs. Thus, high‐throughput sequencing of eccDNA can be used to identify active LTR‐RTs in plant genomes. Despite the release of a reference genome assembly, carrot LTR‐RTs have not yet been thoroughly characterized. LTR‐RTs are abundant and diverse in the carrot genome. We identified 5976 carrot LTR‐RTs, 2053 and 1660 of which were attributed toCopiaandGypsysuperfamilies, respectively. They were further classified into lineages, families and subfamilies. More diverse LTR‐RT lineages, i.e. lineages comprising many low‐copy‐number subfamilies, were more frequently associated with genic regions. Certain LTR‐RT lineages have been recently active inDaucus carota. In particular, low‐copy‐number LTR‐RT subfamilies, e.g. those belonging to theDcAlelineage, have significantly contributed to carrot genome diversity as a result of continuing activity. We utilized eccDNA sequencing to identify and characterize twoDcAlesubfamilies,Alex1andAlex3, active in carrot callus. We documented 14 and 32de novoinsertions ofAlex1andAlex3, respectively, which were positioned in non‐repetitive regions.
Multi‐objective optimization of root phenotypes for nutrient capture using evolutionary algorithms
Authors: Rangarajan Harini , Hadka David , Reed Patrick , Lynch Jonathan P.
Published: April 2022

Root phenotypes are avenues to the development of crop cultivars with improved nutrient capture, which is an important goal for global agriculture. The fitness landscape of root phenotypes is highly complex and multidimensional. It is difficult to predict which combinations of traits (phene states) will create the best performing integrated phenotypes in various environments. Brute force methods to map the fitness landscape by simulating millions of phenotypes in multiple environments are computationally challenging. Evolutionary optimization algorithms may provide more efficient avenues to explore high dimensional domains such as the root phenotypic space. We coupled the three‐dimensional functional–structural plant model, SimRoot, to the Borg Multi‐Objective Evolutionary Algorithm (MOEA) and the evolutionary search over several generations facilitated the identification of optimal root phenotypes balancing trade‐offs across nutrient uptake, biomass accumulation, and root carbon costs in environments varying in nutrient availability. Our results show that several combinations of root phenes generate optimal integrated phenotypes where performance in one objective comes at the cost of reduced performance in one or more of the remaining objectives, and such combinations differed for mobile and non‐mobile nutrients and for maize (a monocot) and bean (a dicot). Functional–structural plant models can be used with multi‐objective optimization to identify optimal root phenotypes under various environments, including future climate scenarios, which will be useful in developing the more resilient, efficient crops urgently needed in global agriculture.
Novel Correlations Between Process Forces and Void Morphology for Effective Detection and Minimization of Voids During Friction Stir Welding
Authors: Ansari Mohammad Ali , Agiwal Hemant , Zinn Mike , Pfefferkorn Frank , Rudraraju Shiva
Published: April 2022

Sub-surface voids and material heterogeneities resulting from the friction stir welding (FSW) process often necessitate post-weld inspection to ensure the quality of weld obtained from this solid-state welding process. In this context, in-process void detection techniques can potentially help in optimizing the process conditions and thereby reduce expensive and time-consuming post-process inspection of welds. Current in-process void detection techniques rely on approaches that try to directly correlate the part-scale welding quality to void formation, without a fundamental understanding of the underlying mechanics and materials physics that modulate void evolution. In this work, we demonstrate an effective in-process numerical technique that uses process force signals to detect volumetric void formation and connect the variations in the force signals to interactions between the tool probe and the underlying material voids. Our approach relies on a high-fidelity finite element analysis simulation of the FSW process and on correlation of numerically obtained process force signals with the corresponding void structures. This correlation is obtained in the phase-space relating in-plane reaction forces on the tool to the tool rotation angle. We focus on the interactions of the tool geometry and tool motion with the surrounding material undergoing plastic deformation and deduce novel insights into various correlations of tool motion and void formation. Through this approach, we can identify tool-related process conditions that can be optimized to minimize void formation and demonstrate a potential in situ force-based void monitoring method that links to the underlying plastic flow and void structures during the FSW process.
Description of three new species and a new record of eriophyid mites (Acari: Eriophyoidea) from Egypt
Authors: Elhalawany Ashraf S. , Ueckermann Edward A.
Published: April 2022

Four eriophyid mite species were collected on plants during field survey in Egypt. Abacarus sacchari ChannaBasavanna is recorded for the first time from Egypt and a supplementary morphological description and illustrations are given. A new species, Aculodes sacchari sp. nov., is vagrant in grooves on the inner surface of the leaves of Saccharum officinarum L. (Poaceae) and no apparent symptoms were observed. Two other new species are described: Aculus glinus sp. nov., causes deformations on stems and leaves of Glinus lotoides L. (Molluginaceae), and Aceria nodiflorae sp. nov., is vagrant on leaves and flowers of Phyla nodiflora (L.) Greene (Verbenaceae) and no apparent symptoms were observed. Both adult sexes and immature stages are described and illustrated for all species.
Improving the efficiency of Rubisco by resurrecting its ancestors in the family Solanaceae
Authors: Lin Myat T. , Salihovic Heidi , Clark Frances K. , Hanson Maureen R.
Published: April 2022

Plants and photosynthetic organisms have a remarkably inefficient enzyme named Rubisco that fixes atmospheric CO
2
into organic compounds. Understanding how Rubisco has evolved in response to past climate change is important for attempts to adjust plants to future conditions. In this study, we developed a computational workflow to assemble de novo both large and small subunits of Rubisco enzymes from transcriptomics data. Next, we predicted sequences for ancestral Rubiscos of the (nightshade) family Solanaceae and characterized their kinetics after coexpressing them in
Escherichia coli
. Predicted ancestors of C
3
Rubiscos were identified that have superior kinetics and excellent potential to help plants adapt to anthropogenic climate change. Our findings also advance understanding of the evolution of Rubisco’s catalytic traits.
Chip-scale atomic wave-meter enabled by machine learning
Authors: Edrei Eitan , Cohen Niv , Gerstel Elam , Gamzu-Letova Shani , Mazurski Noa , Levy Uriel
Published: April 2022

The quest for miniaturized optical wave-meters and spectrometers has accelerated the design of novel approaches in the field. Particularly, random spectrometers (RS) using the one-to-one correlation between the wavelength and an output random interference pattern emerged as a promising tool combining high spectral resolution and cost-effectiveness. Recently, a chip-scale platform for RS has been demonstrated with a markedly reduced footprint. Yet, despite the evident advantages of such modalities, they are very susceptible to environmental fluctuations and require an external calibration process. To address these challenges, we demonstrate a paradigm shift in the field, enabled by the integration of atomic vapor with a photonic chip and the use of a machine learning classification algorithm. Our approach provides a random wave-meter on chip device with accurate calibration and enhanced robustness against environmental fluctuations. The demonstrated device is expected to pave the way toward fully integrated spectrometers advancing the field of silicon photonics.
Dental management of a patient with systemic mastocytosis
Authors: Legge Alexander Richard , Whyte Andrew , Heywood Simon
Published: April 2022

Mastocytosis is a term encompassing a group of clinical disorders characterised by clonal proliferation of abnormal mast cells (MCs) in organ systems of the body. Mastocytosis can be systemic (with or without skin involvement) or cutaneous, and can affect organs including bone marrow, liver, spleen, lymph nodes and mucosal surfaces. Patients with systemic mastocytosis (SM) are susceptible to triggers that could cause activation of abnormal MCs, resulting in multiorgan dysfunction and life-threatening anaphylactic reactions. Mastocytosis has a number of ramifications for the dental management of a patient with the condition. Patients are at increased risk of complications due to a number of risk factors for MC activation present within the dental context, including stress, certain prescribed drugs, oral hygiene products and dental materials. This report presents the oral management of an adult with SM, discussing the implications of the condition within the context of the limited existing literature on the subject.
Dual-specificity phosphatase 8 (DUSP8) induces drug resistance in breast cancer by regulating MAPK pathways
Authors: Zhang Hanchao , Wang Meng , Chen Di , Luo Chengyu
Published: April 2022

The aim of the study was to explore the role and molecular mechanism of dual-specificity phosphatase 8 (DUSP8) in the drug resistance of trastuzumab in breast cancer. Real-time PCR and western blot detected the difference in expression of DUSP8 between breast cancer tissue/cells and trastuzumab-resistant tissues/cells. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of DUSP8 in breast cancer. si-DUSP8 or dusp8 overexpression vector was transiently transfected, and the effects of si-DUSP8 on apoptosis, cell viability and cell migration of drug-resistant cell lines were investigated by flow cytometry, MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide) and Transwell assays, and its regulation mechanism finally explored. The results showed that the expression of DUSP8 in breast cancer tissues and cells was significantly higher than in matched non-tumor tissues and cells. DUSP8 was significantly upregulated in non-responsive patients compared with patients who responded to trastuzumab. ROC analysis showed that the area under the curve was 0.732, and the diagnostic sensitivity and specificity were 64.86% and 75.76%. DUSP8 knockdown promotes apoptosis and reduces trastuzumab resistance in BT474/TR and SKBR3/TR cells by inhibiting cell migration and cell viability. Knockdown of DUSP8 increased the expression of p-p38 and p-ERK, and the regulation of DUSP8 in chemotherapy resistance of breast cancer cells may be realized by mediating mitogen-activated protein kinase (MAPK)-related signaling pathways. In conclusion, knockdown of DUSP8 expression in trastuzumab-resistant cells can inhibit cell migration and proliferation, and leads to decreased drug resistance by activating MAPK signaling pathway in trastuzumab-resistant cells.
Tumor-associated macrophages in multiple myeloma: advances in biology and therapy
Authors: Sun Jennifer , Park Chaelee , Guenthner Nicole , Gurley Shannon , Zhang Luna , Lubben Berit , Adebayo Ola , Bash Hannah , Chen Yixuan , Maksimos Mina , Muz Barbara , Azab Abdel Kareem
Published: April 2022

Multiple myeloma (MM) is a cancer of plasma cells in the bone marrow (BM) and represents the second most common hematological malignancy in the world. The MM tumor microenvironment (TME) within the BM niche consists of a wide range of elements which play important roles in supporting MM disease progression, survival, proliferation, angiogenesis, as well as drug resistance. Together, the TME fosters an immunosuppressive environment in which immune recognition and response are repressed. Macrophages are a central player in the immune system with diverse functions, and it has been long established that macrophages play a critical role in both inducing direct and indirect immune responses in cancer. Tumor-associated macrophages (TAMs) are a major population of cells in the tumor site. Rather than contributing to the immune response against tumor cells, TAMs in many cancers are found to exhibit protumor properties including supporting chemoresistance, tumor proliferation and survival, angiogenesis, immunosuppression, and metastasis. Targeting TAM represents a novel strategy for cancer immunotherapy, which has potential to indirectly stimulate cytotoxic T cell activation and recruitment, and synergize with checkpoint inhibitors and chemotherapies. In this review, we will provide an updated and comprehensive overview into the current knowledge on the roles of TAMs in MM, as well as the therapeutic targets that are being explored as macrophage-targeted immunotherapy, which may hold key to future therapeutics against MM.
Predicting Lattice Constants of Half-Heusler Alloys Through Deep Neural Network Models Using Symbols of Elements and/or Ionic Radii
Authors: Mehmood Nasir , Ahmad Rashid , Gul Aqsa , Zaman Anwar , Murtaza Ghulam , Ahmad Jamil , Khattak Fida Younus
Published: April 2022

In this paper, we have developed models based on Deep-Learning Neural Network (DNN) for accurately predicting lattice constants of half-Heusler alloys. A commercial software WIEN2k employing the Density Functional Theory (DFT) is first used to generate data of the lattice constants of 377 half-Heusler alloys for the training, testing and validation of the models. These models use elemental symbols or/and ionic radii as input parameters. The model that uses only symbols of the constituent element and the model that uses symbols in combination with the radii of the ions predict lattice constants of half-Heusler alloys with an average error of less than 1% to the data obtained from the WIEN2k calculations. The average error stays below 2% in the prediction by the model that uses radii of the ions alone. These results show a great promise for these models to be extended for the prediction of structural and elastic properties of not only new half-Heusler alloys but also other new materials such as full-Heusler, spinels, perovskites, etc. with greater convenience, saving hours of computation time.
Novel Method for Monitoring the Electrochemical Capacitance by In Situ Impedance Spectroscopy as Indicator for Particle Cracking of Nickel-Rich NCMs: Part III. Development of a Simplified Measurement Setup
Authors: Oswald Stefan , Riewald Felix , Gasteiger Hubert A.
Published: April 2022

As the optimization of the electrochemical performance of lithium-ion batteries by the adjustment of the composition of the cathode active materials (CAMs) has come to a limit, the focus has shifted to the modification of the morphological aspects. However, new methodologies for the quantification of characteristics such as particle size, particle cracking, and surface area change are needed. A previously reported impedance-based method allows for monitoring the capacitance of CAMs in the positive electrodes as indicator for their surface area but relies on a sophisticated cell setup. In this study, we deduce a stepwise simplification of the capacitance measurements from the setup using a gold-wire reference electrode to a conventional coin half-cell setup, which is commonly used in industry as testing platform for the initial benchmarking of newly developed CAMs. Additionally, it is shown that the CAM capacitance does not have to be extracted from a full impedance spectrum that requires an impedance analyzer, but that it can be obtained solely from a low-frequency single-point impedance measurement, which can be performed with a simple battery cycler. The working principle of this approach is validated using four different cell and electrochemical test hardware configurations (potentiostat, battery cycler) over several charge/discharge cycles.
Tuning Electrocatalytic Activity of Gold Silver Nanoparticles on Reduced Graphene Oxide for Oxygen Reduction Reaction
Authors: Radinović Kristina , Mladenović Dušan , Milikić Jadranka , Alsaiari Mabkhoot , Harraz Farid A. , Santos Diogo M. F. , Šljukić Biljana
Published: April 2022

Gold-silver (AuAg) nanoparticles were deposited on reduced graphene oxide (rGO), with Au wt% varying from 34.6 to 46.2 and Ag wt% ranging from 31.7 to 35.3. Three AuAg/rGO samples were characterized by inductively coupled plasma optical emission spectroscopy, transmission and scanning electron microscopy with energy-dispersive X-ray spectroscopy, and X-ray diffraction analysis. Subsequently, AuAg/rGO materials were studied using the rotating disk electrode as electrocatalysts for oxygen reduction reaction (ORR) for potential application in alkaline fuel cells. AuAg/rGO-3 (with ca. 34.6 wt% Au and 35.3 wt% Ag) showed the most promising performance, with high ORR activity and good stability, as evidenced by the highest current density (−4.45 mA cm−2 at 0.5 V), the lowest Tafel slope (103 mV dec−1), and the highest number of electrons exchanged (4.1).
A Color Spot Extraction Method Based on the Multifused Enhancement Algorithm
Authors: Zhao Xiuzhi
Published: April 2022

The color spot can reflect the skin status and the physiological change. It has been used in the evaluation of skin quality and medical diagnosis. However, the gray level of the color spot is very similar to that of normal skin. Because the shape, size, and position of the color spots are irregular. It is difficult to extract the accurate region of the color spot with the traditional methods. In order to extract the region of the color spot, we propose a multifused enhancement algorithm to enhance the feature of the color spot and extract the accurate region. The multifused enhancement algorithm mainly includes three components: the polarized image acquirement, the color model transformation, and the wavelet transform and enhancement. The color spot, which is in the intraepidermal basal cell, is captured by the polarized camera with the polarized light. The acquired method can remove the reflective light, covering the color spot. To further enhance the feature of the color spot, we use saturation instead of the gray level to characterize the color spot. The color spot is more conspicuous in the saturation image. Then, the wavelet transform and enhancement are used to enhance the feature of the color spot and reduce the effect of uneven illumination. The color spot is extracted by the proposed self-adaptive segmentation method. In addition, we discuss the performance with the different wavelets to choose the optimal wavelet. The actual reason for removing uneven illumination in the proposed algorithm is also analyzed in this work. The experimental results indicate that the proposed algorithm achieves the best accuracy among all the algorithms compared.
Prediction of Soil Heavy Metal Content Based on Deep Reinforcement Learning
Authors: Zhao Yongqi , Wei Zhangdong , Wen Jing
Published: April 2022

Since the prediction accuracy of heavy metal content in soil by common spatial prediction algorithms is not ideal, a prediction model based on the improved deep Q network is proposed. The state value reuse is used to accelerate the learning speed of training samples for agents in deep Q network, and the convergence speed of model is improved. At the same time, adaptive fuzzy membership factor is introduced to change the sensitivity of agent to environmental feedback value in different training periods and improve the stability of the model after convergence. Finally, an adaptive inverse distance interpolation method is adopted to predict observed values of interpolation points, which improves the prediction accuracy of the model. The simulation results show that, compared with random forest regression model (RFR) and inverse distance weighted prediction model (IDW), the prediction accuracy of soil heavy metal content of proposed model is higher by 13.03% and 7.47%, respectively.
Neurostimulation as an Efficacious Nonpharmacologic Analgesic following Arthroscopic Rotator Cuff Repair
Authors: Juncker Ryan B. , Gagnier Joel J. , Mirza Faisal M.
Published: April 2022

This case highlights the importance of pursuing nonpharmacologic analgesic modalities in orthopedic surgery to combat the current opioid epidemic. Presented is a patient who underwent an arthroscopic rotator cuff repair and biceps tenodesis operation and through the use of neurostimulation (in the form of auricular electrostimulation), fully recovered from surgery without the usage of any opioid or nonsteroidal anti-inflammatory medications. The patient was fitted with a novel auricular electrostimulation device (DyAnsys Primary Relief) in the immediate postoperative period that provided constant neurostimulation for 10 days, this neurostimulator was the only analgesic modality used in this case, and the patient reported minimal postoperative pain. The utility of this case centers around the lack of postoperative opioid use, presenting the idea that postsurgical orthopedic pain can be managed in a nonpharmacologic capacity, combatting the fields’ ongoing opioid epidemic.
The Internet of Things Term Feature Extraction Method and Its Application in English-Chinese Translation
Authors: Jian Lili
Published: April 2022

From the emergence of the concept of the Internet of Things to today, China has always been very concerned about the development of the Internet of Things technology. The research and application of Internet of Things technology has become an important measure for the country to promote supply-side reform. The Internet of Things technology is the core innovation content of the country and an important measure to realize the smart city. This paper analyzes the cutting-edge research results in the field of Internet of Things abroad. This paper focuses on the application of IoT technology in English terminology research. This paper specifically analyzes the feature extraction methods of Internet of Things terms and studies the application content of Internet of Things technology in English-Chinese translation. We specifically conduct translation research on the content in “The Internet of Things: Enabling Technologies, Platforms, and Use Cases,” focusing on the translation of scientific and technological terms. As a kind of technical English terminology, IoT English terminology has the characteristics of strong professionalism and rigorous semantics. This article specifically translates the original part of the practice report and classifies the terms that appear in it, including IoT English terms for which canonical translations have been made and IoT English terms that do not have standardized translations. The research will further carry out the investigation and analysis work. The research is to identify the terms that have standardized translation, especially the detailed research of the terms of the industry field. This paper selects standard translations and summarizes the translation methods of terms from three aspects: abbreviations, compound words, and semi-technical words. The research results provide guidance for terminology translation. This article translates terms that do not yet have a canonical translation. Based on terminology characteristics and existing terminology translation methods, this paper proposes a variety of translation methods such as literal translation, decomposing and combining, and non-translation. The study described in detail with examples. The research results of this paper are expected to provide some reference for translators engaged in the translation of scientific and technological documents.
Residual Oil Distribution Pattern in a Fault-Solution Carbonate Reservoir and Countermeasures to Improve Oil Development Effectiveness
Authors: Liu Peiliang , Jiang Lin , Tang Bochao , Ren Ke , Huang Mina , Geng Chunying
Published: April 2022

Because of the strong random distribution of fractures and caves, fault-solution carbonate reservoirs exhibit significantly different flow mechanisms and development methods from conventional carbonate reservoirs. Natural elastic and edge-bottom water flooding are the main processes for developing fault-solution reservoirs. The rapid decline in production and the complex residual oil distribution are major challenges for oil production. This study is aimed at (1) assessing the residual oil migration law, (2) determining the residual oil distribution characteristics, and (3) identifying the main controlling factors using numerical simulation, to provide suggestions for enhancing oil recovery. The results showed that fractures are the main channel for oil flow and the main path of bottom water coning in the fault-solution reservoirs. The channeling of bottom water along high-angle fractures is the main reason for the decline in oil production. In addition, bottom water coning and gas/water injection are the key factors affecting the distribution of residual oil, while the irregular development of fractures and caves is the main factor causing diversified distribution patterns of the residual oil. The residual oil distribution patterns of fault-solution reservoirs include 4 types, namely, attic, bottom water rising and blocking, separated fracture-cavity, and pores near high-conductivity channel types. For tapping the potential of residual oil, several approaches can be used, namely, deploying new wells or using sidetracking of old wells in the loft and the separated fracture-cavity reservoirs. In addition, the attic residual oil type can also be developed using drainage oil recovery or gas injection for oil replacement. Liquid lift pump, water shutoff, and water cone restrain can also be used to tap residual oil from rising bottom water. Optimizing profile control and water shutoff measures and adjusting the injection and production relationship can be effective approaches for developing residual oil in the pores and cracks beside the high diversion channel.
Influence of Different Excavation Sequence of Double‐Side Heading Method on Supporting Structure
Authors: Ling Chengkun , Ruan Yanmei , Wu Pengbo , Li Jin , Zhao Jin , Yuan Bingxiang
Published: April 2022

The double‐side heading method is often used in the construction of large‐span and large‐section tunnels. The excavation of the pilot tunnel is complex, so the construction efficiency is low. Based on the underground excavation tunnel project of a subway in Guangzhou, the section excavation sequence of the traditional double‐side heading method is optimized according to the actual situation. Midas/GTS software is used for finite element analysis, the displacement and internal force of ground settlement and support structure under two different section excavation sequences are calculated, and the calculation results are compared with the field monitoring data. The calculation results show that the influence of the two excavation sequences on the displacement of the supporting structure is not much different, but the influence on the internal force of the supporting structure is obviously different. The stress value of the supporting structure caused by the optimized excavation sequence is larger, especially the temporary inverted arch, but it is within the controllable range. The optimized excavation sequence increases the construction work surface, greatly improves the construction efficiency, and reduces the project cost, which can provide a reference for the construction of urban subway tunnels under similar engineering conditions in the future.
Compression and Storage Algorithm of Key Information of Communication Data Based on Backpropagation Neural Network
Authors: Wei Dahuan , Feng Gang
Published: April 2022

This paper presents a backpropagation neural network algorithm for data compression and data storage. Data compression or establishing model ten coding is the most basic idea of traditional data compression. The traditionally designed ideas are mainly based on reducing the redundancy in the information and focus on the coding design, and its compression ratio has been hovering around dozens of percent. After the traditional coding compression of information, it is difficult to further compress by similar methods. In order to solve the above problems, the information that takes up less signal space can be used to represent the information that takes up more signal space to realize data compression. This new design idea of data compression breaks through the traditional limitation of relying only on coding to reduce data redundancy and achieves a higher compression ratio. At the same time, the information after such compression can be repeatedly compressed, and it has a very good performance. This is the basic idea of the combination of neural network and data compression introduced in this paper. According to the theory of multiobjective function optimization, this paper puts forward the theoretical model of multiobjective optimization neural network and studies a multiobjective data compression method based on neural network. According to the change of data characteristics, this method automatically adjusts the structural parameters (connection weight and bias value) of neural network to obtain the largest amount of data compression at the cost of small information loss. This method has the characteristics of strong adaptability, parallel processing, knowledge distributed storage, and anti-interference. Experimental results show that, compared with other methods, the proposed method has significant advantages in performance index, compression time and compression effect, high efficiency. and high-quality robustness.
Information Diffusion between Users in Open Data Ecosystem: Modelling and Simulation Analysis
Authors: Tang Junjie , Zhu Hongxian , Guo Jiaxin
Published: April 2022

Exploring the information interaction between users can provide a new way to solve open data ecosystem (ODE) sustainable development. In this paper, an information diffusion model, ILSOC, is proposed to analyze the information spreading among users within the ODE, and we use this model to inspect the impact on the sustainability of the ODE. First, we establish an information diffusion model that considers the public swing mentality. As a result, the relevant parameter variables are defined, and their influence is evaluated. Finally, this article proposes countermeasures to solve the sustainability problem within ODEs from the perspectives of users. Results demonstrate that (1) simply increasing the probability of interaction between users cannot effectively improve the sustainability of the ODE. (2) Parameters, such as contact rate, conversation rate, silent rate, and reversal rate, have constant effects on the ODE. (3) To maintain this ecosystem stability, some incentives and punishments for users in the public event are necessary.
Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition
Authors: Tharewal Sumegh , Malche Timothy , Tiwari Pradeep Kumar , Jabarulla Mohamed Yaseen , Alnuaim Abeer Ali , Mostafa Almetwally M. , Ullah Mohammad Aman
Published: April 2022

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.
An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development
Authors: Guo Zhen
Published: April 2022

The state vigorously develops the application of community sports and takes it as an important unit of social and economic development. With the development of the economy, residents’ living standards are getting better, which also greatly promotes the development of community public sports. Community public sports have made great progress in terms of scale, hardware, and services, and their impact on society is getting worse. Come bigger. However, there are still some deficiencies in the development of community sports. How to accurately evaluate and predict its facilities, construction, and service levels and put forward corresponding development strategies is an urgent problem to be solved in the field of community public sports. In this paper, an iterative simulated annealing algorithm is proposed to evaluate the level of public sports services and help community public sports institutions identify their own development deficiencies. Through the simulation analysis of the community sports organizations, community members, venue facilities and funds, venue facilities and funds, administrators, and the community sports activities, it is found that the identification accuracy of the evaluation results of the five indicators is above 95%, while the initial threshold of community public sports and social and economic development and the prediction accuracy required by the standard of development status are both 10%. At the same time, the recognition time of the evaluation results in the five indicators is less than 7 seconds. In community public sports development trend prediction and service level judgment, the identification time of the evaluation result is less than 4 seconds, and the overall judgment result is better.
Woven Coronary Artery Anomaly: An Incidental Finding and Literature Review
Authors: Bamousa Bdoor , Sbitli Taher , Mohamed Tahir , Al Johani Khalid , Almasood Ali
Published: April 2022

Woven coronary artery anomaly is a rare description of an epicardial vessel segment that divides into multiple intertwining segments with eventual convergence of the distal vessel. We present our case, a 57-year-old male with an incidental woven coronary artery anomaly found during work-up investigations for a possible lung transplant, and we conduct a literature review on woven anomaly cases reported from 1988 to 2021 and provide a thorough analysis of its diversified clinical presentation. Imaging identification and various treatment modalities are also discussed.
The Incidence of Septic Pulmonary Embolism in Patients with Klebsiella pneumoniae Liver Abscess: A Systematic Review and Meta-analysis
Authors: Wang Yunan , Wang Hairui , Liu Zhaoyu , Chang Zhihui
Published: April 2022

Background: Septic pulmonary embolism (SPE) is an associated complication of Klebsiella pneumoniae liver abscess (KPLA). However, previous studies have reported that its incidence varies widely. We conducted a systematic review and meta-analysis to investigate the incidence of SPE in patients with KPLA. We further analyzed their clinical and computed tomography (CT) features. Methods: Two researchers reviewed PubMed, EMBASE, Web of Science, and Cochrane Library databases to identify the articles that reported SPE in patients with KPLA. The search was conducted from the date of establishment of each database up to January 2021. After screening the articles and extracting the data, we used Review Manager 5.3 for analysis and processing. Results: We selected six articles that included 1,158 patients with KPLA. Of these, 70 patients had SPE. The pooled incidence of SPE was 6% (95% confidence interval, 3%–9%). Among patients with SPE, 85% were men, 72% had diabetes, and 52% displayed the feeding vessel sign on the chest CT. The mortality rate was 12%. Quality assessment revealed that half of the included studies had a high quality. Conclusion: The pooled incidence of SPE in patients with KPLA was 6%. Men and patients with diabetes were more prone to SPE. For patients with KPLA who had SPE as an associated complication, the mortality rate was approximately 12%.
TLR4 Agonist and Hypoxia Synergistically Promote the Formation of TLR4/NF-κB/HIF-1α Loop in Human Epithelial Ovarian Cancer
Authors: Zhao Bin , Niu Xiulong , Huang Suhui , Yang Jing , Wei Yiyi , Wang Xiujuan , Wang Junhong , Wang Yue , Guo Xiaoqin
Published: April 2022

Inflammation and hypoxia are involved in numerous cancer progressions. Reportedly, the toll-like receptor 4 (TLR4)/nuclear factor kappa B (NF-κB) pathway and hypoxia-inducible factor-1α (HIF-1α) are activated and closely related to the chemoresistance and poor prognosis of epithelial ovarian cancer (EOC). However, the potential correlation between TLR4/NF-κB and HIF-1α remains largely unknown in EOC. In our study, the possible positive correlation among TLR4, NF-κB, and HIF-1α proteins was investigated in the EOC tissues. Our in vitro results demonstrated that LPS can induce and activate HIF-1α through the TLR4/NF-κB signaling in A2780 and SKOV3 cells. Moreover, hypoxia-induced TLR4 expression and the downstream transcriptional activity of NF-κB were HIF-1α-dependent. The cross talk between the TLR4/NF-κB signaling pathway and HIF-1α was also confirmed in the nude mice xenograft model. Therefore, we first proposed the formation of a TLR4/NF-κB/HIF-1α loop in EOC. The positive feedback loop enhanced the susceptibility and responsiveness to inflammation and hypoxia, which synergistically promote the initiation and progression of EOC. The novel mechanism may act as a future therapeutic candidate for the treatment of EOC.
Algorithm of Ethnic Sports Wellness and Health Promotion in the Perspective of Body-Medicine Integration
Authors: Zhong Yuanyuan , Shao Zhe , Yu Zhida , Liu Cong , Chu Xingping
Published: April 2022

Algorithms are ubiquitous in nature and human society, and algorithms in national sports are the internal mechanisms for the creation and development of national sports. Algorithms from nature, society, and culture act as the external driving force for the development of ethnic sports. Different ethnic sports are based on physical behaviors, including physical recreation, social interaction, and life-shaping behaviors. In this paper, we suggest an algorithm for health and wellness elevation of ethnic sports in the context of body-medicine integration, examine the fall situation in sports life, and propose a bidirectional LSTM fall detection model, which can automatically extract deeper data features within the fall behavior for the input data (taken from inertial sensors) and realize the processing of data from preprocessing to detection results. Medical disciplines provide scientific ideas and pathways that are founded on a rigorous medical way of thinking and knowledge system to summarize sports, so that they can be prescribed to explore new pathways of exercise for health, to carry out deliberate, planned, and scientific exercise. Finally, the superiority of the proposed algorithm in this paper is verified on a relevant dataset.
Weighted Composition Operators from

H

∞

to

α
,
m

-Bloch Space on Cartan-Hartogs Domain of the First Type
Authors: Su Jianbing , Zhang Ziyi
Published: April 2022

Let

Y

I

be nonhomogeneous Cartan-Hartogs domain of the first type,

ϕ

a holomorphic self-map, and

ψ

a fixed holomorphic function on

Y

I

. We study the weighted composition operator

ψ

C

ϕ

f
=
ψ

f
∘
ϕ

for a function

f

holomorphic on

Y

I

. Our main results generalize both cases of the unit ploydisc and the unit ball obtained by Li and Stević (Li 2007 and Li 2008). Firstly, we obtain two crucial inequalities on

Y

I

; furthermore, the boundedness and compactness of operator

ψ

C

ϕ

from the space

H

∞

of all bounded holomorphic functions to the

α
,
m

-Bloch space

B

α
,
m

on

Y

I

are investigated.
DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification
Authors: Liu Yujian , Hu Yaowen , Cai Weiwei , Zhou Guoxiong , Zhan Jialei , Li Liujun
Published: April 2022

Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network's perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network's accuracy while also reducing its size. The DCCAM-classification MRNet's accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy.
On the Dynamics of Cournot Duopoly Game with Governmental Taxes
Authors: Askar S. S.
Published: April 2022

A quadratic utility function is introduced in this paper to study the dynamic characteristics of Cournot duopoly game. Based on the bounded rationality mechanism, a discrete dynamical map that describes the game’s dynamic is obtained. The map possesses only one equilibrium point which is Nash point. The stability conditions for this point are analyzed. These conditions show that the point becomes unstable due to two bifurcation types that are flip and Neimark–Sacker. The synchronization property for that map is studied. Through local and global analysis, some dynamics of attracting sets are investigated. This analysis gives some insights on the basis of those sets and the shape of the critical curves. It also shows some lobes found in those attracting sets which are constructed due to the origin focal point.
Emission Reduction Benefits and Economic Benefits of China’s Pilot Policy on Carbon Emission Trading System
Authors: Wang Zhijia , Liang Lijuan , Cheng Dong , Li Hujun , Zhang Yongheng
Published: April 2022

As a market means to control nongreen behaviors of firms, the most expected incentive effect of the carbon emission trading system (CETS) is to achieve the dual economic and environmental effects. As a typical developing country, whether China’s CETS has a positive incentive effect is significant to controlling greenhouse gas. Based on the quasinatural experiment of China’s pilot policy on CETS in 2013, this study investigates its emission reduction and economic benefits using the difference-in-difference (DID) method. Then, the realization mechanism of CETS’s incentive benefits is reversely studied with the idea that goals generate behavior. The results show that the following: (a) China’s CETS has produced positive incentive effects of promoting both economic and emission reduction benefits. Furthermore, the results are still valid after using the instrumental variable to overcome the endogenous problem, placebo tests to eliminate sampling bias, and a series of robustness tests. (b) Further analysis shows that firms can choose to improve technology innovation and energy efficiency to get the positive incentive effects of CETS. (c) The incentive effects of CETS also have regional heterogeneity. The emission reduction and economic benefits are greater in provinces with deficient resource endowments and strict environmental law enforcement.
A Novel Anchorless Node Positioning Method for Wireless Sensor Network
Authors: He Wenxiu , Cheng Ran , Mao Keji , Yan Ke , Wei Jianliang , Xu Yingying
Published: April 2022

Wireless sensor network sensing data is meaningless without any location information, and a large number of anchor node-based localization methods have been proposed to obtain the location information of nodes. However, this kind of method requires a certain number of anchor nodes deployed in the network and complex information interaction between nodes. Therefore, it is not ideal in positioning cost and effect. A WSN positioning method based on the steering antenna of the base station is proposed to address this problem. By rotating the orientation information of the antenna, accurate positioning can be achieved without the assistance of anchor nodes, and no signal interaction between nodes is required. Theoretically, the method is easier to be deployed in the actual scene to realize the positioning of sensor nodes. Experimental results show that the proposed method is better than most positioning methods assisted by anchor node in positioning accuracy, positioning time, and energy consumption.