bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Rainfall and Evaporization Wetting-Drying Cycles on the Slope Stability
Authors: Zhao Ya
Published: July 2022

The decay of soil strength and the change of soil infiltration characteristics caused by the dry and wet cycle effect generated by the rainfall-evaporation process are important factors that induce slope instability. How to consider the effect of soil strength decay and water-soil characteristic curve hysteresis effect on transient stability change of slope is the key to solve this problem. In this paper, transient stability analysis of slopes considering soil strength decay and water-soil characteristic curve hysteresis is carried out based on Geo-Studio. The results of the study showed that the change of transient safety factor of the slope caused by rainfall-evaporation dry and wet cycle process has an overall decreasing trend and the safety factor decreased by 43% compared to the initial state. The seepage characteristics of the rainfall-evaporation dry-wet cycle have certain regularity. The location of slope measurement points has a greater influence on the magnitude of the pore pressure change: foot of slope > middle of slope > top of slope. Also, there is a significant response hysteresis in the change of pore pressure with increasing depth at the same location. The rainfall intensity has a certain influence on the change of slope safety factor, but its influence is not obvious when the rainfall intensity exceeds a certain amount.
Evaluation of Axillary Lymph Node Marking with Magseed® before and after Neoadjuvant Systemic Therapy in Breast Cancer Patients: MAGNET Study
Authors: Martínez María , Jiménez Sara , Guzmán Florentina , Fernández Marta , Arizaga Elena , Sanz Consuelo
Published: July 2022

Background. Due to the high false negative rate (FNR) associated with sentinel lymph node biopsy (SLNB) after neoadjuvant systemic therapy (NAST), the standard surgical treatment for patients with an initially positive axilla and indicated for NAST is axillary lymph node dissection (ALND). To avoid unnecessary ALND, this multicenter, prospective, observational study aimed to determine the effectiveness and ease of using magnetic seeds (Magseed®) for targeted axillary dissection (TAD) when the seeds are placed before or after NAST. Materials and Methods. We recruited 81 patients diagnosed with T1-T3 breast cancer, with clinically/radiologically positive nodal involvement (cN1, 75 patients with 1–3 nodes suspected nodes and 6 patients with up to 4 suspected nodes) prior to NAST. Positive nodes detected by fine-needle aspiration biopsy or core needle biopsy were marked with a stainless steel marker coil and after NAST with Magseed® prior to surgery (Post-NAST group), or directly with Magseed® before NAST (Pre-NAST group). The correlation between lymph nodes marked with Magseed® (MLNs) and sentinel lymph nodes (SLNs) was calculated based on pathologic assessment with the OSNA assay (Sysmex Corporation, Kobe) or conventional sectioning and staining techniques according to the standard protocols of each center. Results. All magnetic seeds were successfully identified and retrieved in just over 10 minutes of surgery, guided by the Sentimag® magnetometer system. The overall concordance rate between MLNs and SLNs was 81.5%, and the concordance between MLNs and SLNs with metastasis was 93.8%. Metastasis was detected in 54.3% of the MLNs compared with 48.1% of SLNs. In cases that presented negative MLN and negative SLN (negative TAD), the FNR was 0%. No significant differences were found between the Post-NAST and Pre-NAST groups. Conclusions. Our results validate the use of Magseed® for long-term marking of axillary lymph nodes and show that when used in combination with SLNB for TAD, a FNR of 0% can be achieved, avoiding unnecessary ALND.
English Word Learning Using Intelligent Massive-Scale Big Data Mining Technique
Authors: Jin Lei
Published: July 2022

Big data technique has become a scientific force driving disruptive innovation and change in the education system and the entire field, including English word learning, which will undergo profound mechanism changes under the impetus of this trend. The reform of basic English education evaluation in the new era empowered by big data is an inevitable choice to get rid of the chronic problems of the “five-only” education evaluation and to implement the education that builds morality as well as learners’ satisfaction. Traditional classroom teaching is combined with educational information technology to build a smart campus for English education. And data mining hidden educational information and rules are obtained to meet the requirements of students’ personalized learning and other demands. However, the reality is faced with the alienation of the concept of big data in English word learning. Data mining is a very important thinking tool and learning tool, which can help students better learn basic English knowledge, memorize basic words, and improve learning effect. At the same time, it has the effect of cultivating divergent thinking and systematic learning. The foundation of English learning in high school is vocabulary. In order to help students better memorize English vocabulary, teachers can apply mind map associative memory method to implement teaching. In order to solve the problem, this paper proposes the English value concept, constructs a basic education evaluation concept empowered by big data, and eliminates teachers’ concerns about technical ability. We also improve the intellectual support for evaluation reform and reasonably build a data resource platform to meet students’ comprehensive quality evaluation demands. The method accurately conducts the big data evaluation to drive suggestions such as the reconstruction of educational evaluation system.
Long Noncoding RNA LINC01554 Inhibits the Progression of NSCLC Progression by Functioning as a ceRNA for miR‐1267 and Regulating ING3/Akt/mTOR Pathway
Authors: Wang Zizong , Yang Bin , Zhang Jin , Chu Xiangyang
Published: July 2022

Objectives. This study focused on the biological functions and mechanisms of action of LINC01554 in nonsmall cell lung cancer (NSCLC). Methods. The expression and prognostic values of LINC01554 in NSCLC were evaluated using The Cancer Genome Atlas datasets. MTT, colony formation, wound healing, transwell, and in vivo assays were performed to investigate the role of LINC01554 in NSCLC. The related protein expression levels were measured via western blotting. Bioinformatic analysis was conducted to predict targeted genes. The relationship between LINC01554, microRNA‐ (miR‐) 1267, miR‐1267, and inhibitor of growth family member 3 (ING3) was analysed via a dual‐luciferase reporter assay. Results. LINC01554 expression was downregulated in NSCLC and associated with NSCLC prognosis. LINC01554 overexpression suppressed NSCLC cell proliferation, migration, invasion, and epithelial‐mesenchymal transition (EMT). Bioinformatic and dual‐luciferase reporter assays demonstrated that LINC01554 expression directly targeted miR‐1267 expression, which in turn directly acted on ING3. An miR‐1267 mimic significantly reduced ING3 expression, whereas an miR‐1267 inhibitor observably elevated its expression. LINC01554 overexpression increased ING3 expression, whereas this effect was counteracted by the miR‐1267 mimic. LINC01554 overexpression also significantly suppressed the expression of phosphorylated protein kinase B (Akt) and phosphorylated mammalian target of rapamycin (mTOR) expression; this effect was abrogated by the miR‐1267 mimic. Mechanistically, LINC01554 overexpression repressed the growth, migration, invasion, and epithelial‐mesenchymal transition (EMT) of NSCLC cells through the regulation of the miR‐1267/ING3 axis via regulation of the Akt/mTOR signalling pathway. Conclusions. We provide the first evidence of the involvement of the LINC01554/miR‐1267 axis in NSCLC proliferation and metastasis through the ING3Akt/mTOR pathway. Thus, LINC01554 may serve as a novel therapeutic target for NSCLC.
Embedding Guided End-to-End Framework for Robust Image Watermarking
Authors: Zhang Beibei , Wu Yunqing , Chen Beijing
Published: July 2022

In recent years, deep learning-based watermarking algorithms have received extensive attention. However, the existing algorithms mainly use the autoencoder to insert watermark automatically and ignore using the prior knowledge to guide the watermark embedding. In this paper, an end-to-end framework based on embedding guidance is proposed for robust image watermarking. It contains four modules, i.e., prior knowledge extractor, encoder, attacking simulator, and decoder. To guide the watermark embedding, the prior knowledge extractor providing chrominance and edge information of cover images is used to modify cover images before inserting the watermark by the encoder. To enhance the robustness of watermark extraction, the attacking simulator applying various differentiable attacks on the encoded images is introduced before extracting the watermark by the decoder. Experimental results show that the proposed algorithm achieves a good balance between invisibility and robustness and is superior to state-of-the-art algorithms.
The Combination of Rhodosin and MMF Prolongs Cardiac Allograft Survival by Inhibiting DC Maturation by Promoting Mitochondrial Fusion
Authors: Che Yanjia , Chen Yuanyang , Wang Zhiwei , Zheng Sihao , Xing Kai , Yuan Shun , Zhong Xiaohan
Published: July 2022

Despite being the gold-standard treatment for end-stage heart disease, heart transplantation is associated with acute cardiac rejection within 1 year of transplantation. The continuous application of immunosuppressants may cause side effects such as hepatic and renal toxicity, infection, and malignancy. Developing new pharmaceutical strategies to alleviate acute rejection after heart transplantation effectively and safely is of critical importance. In this study, we performed a murine model of MHC-full mismatch cardiac transplantation and showed that the combination of Rhodosin (Rho) and mycophenolate mofetil (MMF) could prevent acute rejection and oxidative stress injury and prolong the survival time of murine heart transplants. The use of Rho plus MMF in allografts improved the balance of Tregs/Teff cells, which had a protective effect on allotransplantation. We also isolated bone marrow-derived dendritic cells (BMDCs) and determined that Rho inhibited DC maturation by promoting mitochondrial fusion mainly through the mitochondrial fusion-related protein MFN1. Herein, we demonstrated that Rho, an active ingredient isolated from the plant Rhodiola rosea with antioxidant and anti-inflammatory activities, could efficiently alleviate acute rejection and significantly prolong murine heart allograft survival when used with a low dose of MMF. More importantly, we found that Rho restrained DC maturation by promoting mitochondrial fusion and decreasing reactive oxygen species (ROS) levels, which then alleviated acute rejection in murine cardiac transplantation. Interestingly, as a novel immunosuppressant, Rho has almost no side effects compared with other traditional immunosuppressants. Taken together, these results suggest that Rho has good clinical auxiliary applications as an effective immunosuppressant and antioxidant, and this study provides an efficient strategy to overcome the side effects of immunosuppressive agents that are currently used in organ transplantation.
Novel Big Data Networking Framework Using Multihoming Optimization for Distributed Stream Computing
Authors: Rao G. Sanjiv , Armstrong Joseph J. , Dhiman Gaurav , Mohammed Hussien Sobahi , Degadwala Sheshang , Bhavani R.
Published: July 2022

One of the main technologies for big data networking framework is online multihoming optimization that is large-scale dimension table association technology in a distributed environment. It is often used in applications like real-time suggestion and research. Big data is concerned with the quality of large datasets that are distributed. These datasets demand sophisticated network technologies to adequately transmit massive share files. Dimension table association is the process of integrating multihoming stream data with offline stored dimension table data and executing data processing using novel big data frameworks, as described in this study. The current technological options for dimension table connection are assessed first, followed by accompanying optimization technologies and the design route of mainstream distributed engines. The dimension table data query is the one that has been optimized with the greatest performance. Nonetheless, the typical optimization approach is influenced by the dimension, table size, and the design route of the mainstream distributed engine—limits on data flow rate. Second, due to the limitations of existing optimization technologies for the overall consideration of the cluster in a distributed environment, a computing model suited for hybrid computing of offline batch data and real-time streaming data is provided, followed by a single-point reading. Dimension table data, the dimension table associated data technique for distribution and calculation after segmentation, and optimization of the dimension table associated calculation logic adapt to a larger dimension table scale and are no longer restricted to data connections. Since optimizing the query of dimension table, data is employed to reduce the I/O overhead and delay caused by querying dimension table in big data. Finally, both the suggested and standard dimension table association technologies are implemented on the Apache Flink stream computing engine. Through trials, the throughput and latency on data created by Alibaba’s “Double Eleven” are compared, demonstrating the usefulness of dimension table association techniques for Distributed Stream Computing optimization by utilizing multihoming networks.
Analysis and Experimental Investigation of A356 Aluminium Alloy Hybrid Composites Reinforced with Gr‐FE3O4‐B4C Nanoparticles Synthesised by Selective Laser Melting (SLM)
Authors: Anoop J. , Suyamburajan Vijay Ananth , Babu P. Sekhar , Filketu Sisay Addis
Published: July 2022

Additive manufacturing techniques (AMTs) evolved quickly from simple prototype options to promising additive manufacturing techniques. The additive technologies, which include point‐by‐point material merging, full melting, and solidification of powder particles, provide potential unique braves and advantages with nanometal powders. In the fabrication of A356 aluminium alloy‐based hybrid metal matrix nanocomposites, graphite (Gr), iron oxide (Fe3O4), and boron carbide (B4C) are used as nanoreinforcement. Famous AMTs like selective laser melting have created A356 hybrid nanocomposites (SLM). The ingot was made out of a cylindrical slot measuring 14 × 100 mm. The percentages 2%, 4%, and 6% are added to the reinforcements Gr, Fe3O4, and B4C. Microtensile and microhardness tests were used to determine the outcome of the reinforcement. Microtensile and microhardness parameters are assessed using microtensile test equipment and a Vickers hardness tester, with the specimen prepared in accordance with ASTM standards. A356 with 2, 4, and 6% reinforcing has a Vickers Hardness Number (VHN) of 144, 163, and 188, respectively. As the boron carbide reinforcement is increased, the load value of graphite and iron oxide (2, 4, and 6%) rises. The greatest ultimate tensile strengths are 260.10, 290.06, and 325.43 N/mm2, respectively. The bonding structure of nanocomposites is assessed using an OM, and then, microtensile specimens are assessed using a SEM. As a result of the superior effect of the diverse reinforcements Gr, Fe3O4, and B4C, more increased tensile and hardness qualities have been attained.
A Study about Prevent Tampering with Encrypted Messages over the Network Using Digital Signature
Authors: Al-khairow Majed , Al-halabi Mohamad Firas
Published: July 2022

In this paper, we present a proposed model to increase the authenticity of messages sent over the network; our model went through two stages: First, It is the modification of the MD5 algorithm, which is the basic and comprehensive algorithm from which the Digital signature was launched, where we have modified the logical operations by changing the direction of the shift from left to right and by changing the order of the temporary storage spaces (a, b, c, d). We came up with a lower execution time, and we also compared the execution time of our modified algorithm with the original algorithm and some other Digital signature algorithms. Second, the integration of our modified algorithm with cipher algorithms to increase the authenticity of the sent messages. We applied an example to our proposed model where we used two encryption standards (AES, DES). Finally, we implemented the model using the C# programming language.
The Effect of Propofol plus Remifentanil for PostoperativePain and Heart Rate Management in Patients Undergoing Abdominal Hysterectomy
Authors: Zhang Li , Wang Yuping , Zhang Longxin , Chen Xiaofen , Zhou Min
Published: July 2022

Purpose. To explore the effect of propofol plus remifentanil for postoperative pain and heart rate management in patients undergoing abdominal hysterectomy. Methods. In this prospective randomized controlled study, 96 patients who underwent abdominal hysterectomy in an affiliated hospital of Fujian Medical University from April 2016 to April 2017 were recruited and randomized into the study group (n = 48) and control group (n = 48) via the random number table method. The control group received remifentanil for anesthesia, and the study group was given propofol plus remifentanil. The postoperative pain and heart rates of patients were compared between the two groups. Results. No significant difference was observed in the heart rate and adrenaline values between the two groups before anesthesia, and the study group had significantly lower adrenaline values and heart rates intraoperatively and 15 min after operation than the control group. Patients in the study group showed shorter time-lapse before independent breathing recovery, extubation, and resuscitation compared to those in the control group. The study group received less patient-controlled intravenous analgesia (PCIA) as compared to the study group within 48 h after operation. In the study group, the numeric rating scale (NRS) scores within 1 h, 2 h, 6 h, 8 h, and 12 h after operation were significantly lower than those in the control group (

P
<
0.001

). Propofol plus remifentanil offer a viable alternative for postoperative pain management and stress alleviation after abdominal hysterectomy with a high safety profile. Further clinical trials are, however, required prior to clinical promotion.
Personalized Recommendation System Design for Library Resources through Deep Belief Networks
Authors: Liu Minyu
Published: July 2022

In recent years, with the continuous development of science and Internet technology, people’s lifestyles are changing dramatically, especially with the development of information technology, which has contributed to the transformation of digital libraries. As an essential information infrastructure and a new source of knowledge, digital libraries have brought great convenience to users. To be specific, with the widespread use of smart devices and internet of things technology, users are eager to be intelligent in their information needs while enjoying services, which makes the resource recommendation service of digital libraries increasingly important. In addition, as a provider of knowledge and information services, libraries should organically combine advanced information technology with existing resources to promote the construction of libraries in the information age. However, in the era of big data, users can only passively receive a large amount of information and services in the face of the ever-expanding mass of resources in digital libraries. In this context, libraries might only provide a single set of information resources and services, which cannot meet the individual needs of users and ultimately leads to inefficient allocation of resources and information. After all, users of digital libraries want to be better able to receive personalized recommendations for library resources through relevant technologies. At the same time, libraries are increasing their research and development efforts on algorithms and technologies for personalized recommendations. Also, with the explosive growth of the total amount of information worldwide, people are entering the information age. Massive amounts of data are constantly being generated, and the problem of information overload is becoming more and more serious. The sheer volume of this data and information increases the degree of difficulty in accessing the information people need. In this situation, it is necessary for digital libraries to dynamically analyze user behavior and interests while responding to user requests in a timely manner and accordingly take the initiative to recommend information resources and knowledge services that meet users’ individual needs. As a result, this study uses a deep belief network model for multimodal feature learning and designs a personalized recommendation system for library resources by fusing features from multiple modalities. Furthermore, this research implements the construction of a semantic user interest model and the design of a personalized recommendation algorithm to achieve an accurate description of user interest preferences and semantic personalized recommendation functions.
Designing an Automatic Piano Accompaniment System using Artificial Intelligence and Sound Pattern Database
Authors: Mo Ying
Published: July 2022

The rapid development of artificial intelligence has made various fields to have corresponding connections with it, and music plays an important role in daily life. One of the applications of artificial intelligence in the field of art is to use music generation algorithms to endow machines with the function of generating melody. This ability can provide certain assistance to musicians when composing music, so that music creators can inspire inspiration in the creative process. Researchers have done a lot of work in the automatic generation of music. The piano is widely used in the field of automatic accompaniment and has strong versatility. The main purpose of this paper is to design a piano-based automatic accompaniment system, to think of melody and harmony as a machine learning-like task. By training on a selected series of samples, a database of phonomorphic metastructures is constructed, to systematically collect the original piano accompaniment patterns by building a sound pattern database, and convert the collected original sound patterns into the original sound pattern structure and store in the database. Then, by establishing two Hidden Markov Model (HMM) systems to simulate the thinking mode of the composer’s piano accompaniment process, a melody style related to a certain collection of samples is formed. Finally, the Viterbi algorithm is used to select the appropriate piano accompaniment metastructure in the database to generate the piano accompaniment of the melody section. The experimental results of this paper show that, as far as the accompaniment generation effect is concerned, although the generation effect of this paper is slightly different, the overall difference does not change much. It shows that the effect generated by the method in this paper is relatively stable.
Performance of the Colloidal Gold Immunochromatography of Cryptococcal Antigen on Bronchoalveolar Lavage Fluid for the Diagnosis of Pulmonary Cryptococcosis
Authors: Zhu Ning , Lin Shanhong , Weng Xingbei , Sun Weijie , Chen Xueqin
Published: July 2022

Objective. This study aimed to investigate the efficacy of the colloidal gold immunochromatography method in the detection of Cryptococcus antigen in bronchoalveolar lavage fluid (BALF) for pulmonary cryptococcosis (PC) diagnosis. Methods. A total of 111 patients with clinically suspected PC who were finally diagnosed with nonhuman immunodeficiency virus infection and hospitalized in the Ningbo First Hospital from March 2017 to December 2021 were retrospectively analyzed. All the confirmed cases were divided into two groups as follows: the PC group (33 cases) and the non-PC group (78 cases). All the patients were subjected to serum and BALF cryptococcal capsular polysaccharide antigen-lateral flow immunochromatographic assay (CrAg-LFA) and etiological culturing. Results. In the PC group, serum CrAg-LFA was positive for 24 and negative for 9 cases, serum Cryptococcus culture was positive for 1 and negative for 32 cases, BALF CrAg-LFA was positive for 31 and negative for 2 cases, and BALF Cryptococcus culture was positive for 9 and negative for 24 cases. In the non-PC group, serum CrAg-LFA was positive for 1 and negative for 77 cases, serum culture was negative in all the cases, and both BALF CrAg-LFA and culture were negative in all the cases. The sensitivity, specificity, and accuracy of BALF CrAg-LFA for PC diagnosis were 93.9%, 100%, and 98.2%, respectively, whereas those of BALF culture were 27.3%, 100%, and 78.4%, respectively. The sensitivity and accuracy of BALF CrAg-LFA were higher than that of serum CrAg-LFA and BALF etiological culture with statistically significant differences (

p
<
0.05

). Conclusion. The diagnostic value of BALF CrAg-LFA for PC is superior to that of serum CrAg-LFA and BALF etiological culture.
Rangeland Degradation and Its Impacts Post-1992: Constructing the Perceptions of Boorana Pastoralist, Southern Ethiopia
Authors: Dika Galgalo , Tolossa Degefa , Eyana Shiferaw Muleta
Published: July 2022

The rangelands of Africa have been subject to substantial changes. The changes in the savanna rangelands ecology were often assessed from different points of view. This study constructs the perceptions of Boorana pastoralists on historic changes in rangelands post-1992 based on the Gadaa timelines. The construction of pastoralist perceptions can make an immense contribution to comprehending the rate, trends, indicators, and impacts of rangeland degradation in Boorana rangelands. This study thus points out that pastoralists can better understand and describe rangeland conditions and factors associated with such changes in the Gadaa periods. The study was based on the data collected from 332 pastoralists using a survey questionnaire and qualitative data, and the results were analyzed using descriptions of responses and narrations of cases. The study revealed that the conditions of the Boorana rangelands have been deteriorating over time and rangeland degradation showed unprecedented growth trends, mainly attributed to vegetation, rangeland production, human, environmental, and climate change-related factors. The study also shows that the progressive degradation of rangeland resources has jeopardized rangeland production, livestock productivity, and human well-being in the Boorana rangeland system. The study suggests that the recognition and promotion of indigenous ecological knowledge of pastoralists could sustain the pastoralist community in arid and semiarid rangelands. It is, therefore, imperative to understand pastoralists’ knowledge of rangeland ecology for sustainable management of rangeland resources.
Microstructure and Morphology Analysis of a Novel Coating MgO–B2O3–SiO2–Zn
Authors: Yu Zhan , Song Bo , Ma Ping , Fan Wenhui , Gong Enzhong , Deng Chuntao , Ding Yunlong , Ju Dongying
Published: July 2022

This article focuses on the microstructure and mechanism analysis of the novel coating MgO– B2O3–SiO2–Zn prepared by plasma spraying and flame spraying on the SUS304 alloy substrate. Firstly, the crystal compositions of the powders were analyzed by XRD (X-ray diffraction). Secondly, the surface and cross section microstructures of the coating were observed by SEM (Scanning Electron Microscope) and EPMA (electron probe microanalysis) in order to study the mechanism of the thermal insulation performance. It is concluded that, the microstructure, different thickness and compositions have various effect on heat insulation.
A Review on Characteristics of Experimental Research on Acupuncture Treatment for Alzheimer’s Disease: Study Design
Authors: Ke Chao , Shan Shengtao , Fang Chuang , Xia Yewan , Zhang Wei
Published: July 2022

Background. This review aims to systematically summarize and analyze recent high-quality animal research results about the use of acupuncture in Alzheimer’s disease (AD) patients. This information will be useful in providing a reference for future experimental research and an experimental basis for the clinical use of acupuncture in the treatment of AD. Methods. We utilized and referenced various electronic libraries from their inception to November 2021. Relevant information was reviewed and information such as the journal names, publication records, animal model selections and preparations, intervention measures, acupoint selections, detection methods, and detection indicators was extracted. Results. A total of 75 eligible studies were selected for additional review. Male SAMP8 mice, APP/PS1 double transgenic mice, Sprague–Dawley (SD) rats, and Wistar rats were the four commonly used animal strains in the experiments. The animals were categorized as transgenic and surgical mouse models. Experimental interventions included manual acupuncture (MA), Electro-acupuncture (EA), Moxibustion, and EA combined with Moxibustion. A retention time of 20 min was the optimal timing for experimental studies, with 14 sessions chosen as the most common treatment time. EA was the most prescribed acupuncture treatment type with continuous wave, 2 Hz frequency, and 1 mA electric current selected as frequently used parameters. A total of 78 acupoint prescriptions were analyzed involving 21 acupoints. The top 3 combinations of common acupoints were GV20 ⟶ EX-HN3, GV20 ⟶ BL23, and GV20 ⟶ GV26. A total of 39 articles had positive drug control groups, sham acupuncture, and/or nonacupoint control groups. Furthermore, 10 types of behavioral tests, 29 detection methods, 178 evaluation indicators, and 18 tissue samples were included in the analysis. Conclusions. By collating these high-quality research studies systematically and comprehensively, acupuncture was found to be a viable and effective treatment in AD animal models. In addition, when designing experiments, researchers could refer to the detailed data provided here to make better schemes and maybe conduct more investigations in unresearched areas.
Research on Management Accounting Framework and Quantitative Correlation Based on Hall Three-Dimensional Structure
Authors: Sun Yanyong , Yu Guang , Zeng Ceng
Published: July 2022

The management accounting framework aims to improve the operation control ability. It is the integration of the comprehensive use of management accounting tools. Given the lack of an internal relationship between management methods and operation control in the previous research on management accounting framework systems, this paper puts forward the purpose of this research. We use Hall's three-dimensional structure model for reference to construct the management accounting framework based on operation control and study the characteristics of operation control with the contingency elements such as organizational structure, control methods, and ability improvement as the logical dimension; taking eight tools such as comprehensive budget, performance evaluation, and responsibility body as the knowledge dimension to promote the application of management accounting, the enterprise operation matrix is defined. DSM method is used to study the quantitative correlation between operation control and management accounting tools and reveal the quantitative relationship between management accounting tools. Finally, the case of China Unicom verifies the core role of a comprehensive budget in enterprise operation management and reveals the significant correlation and hierarchical characteristics between management accounting tools. The research methods proposed in this paper can enrich the framework theory of management accounting and have enlightenment in guiding the practice of enterprise operation and management.
Characterization and Optical Studies of Hydroxyethyl Cellulose-Copper Oxide Nanocomposites
Authors: Alsubaie A. S. A.
Published: July 2022

In this study, monometallic copper oxide nanoparticles (CuONPs) were synthesized by chemical reduction of copper sulfate (CuSO4) salt through sugar glucose. X-ray diffraction profiles approved the formation of metallic oxide nanoparticles. TEM images showed spherical nanoparticles with an average particle size of 60 nm. The interaction of HEC and copper oxide nanoparticles was investigated by FTIR spectroscopy. The UV-visible absorption spectrum showed a surface plasmon resonance peak at 270 nm. The effect of doping of copper oxide nanoparticles (CuONPs) on the optical and thermal properties of HEC was studied. The results showed that the concentration of CuO nanoparticles has a prominent influence on the optical, structural, and thermal properties of hydroxyethyl cellulose.
An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia
Authors: Ahmad Nazir , Rehman Mohammed Burhanur , El Hassan Hatim Mohammed , Ahmad Iqrar , Rashid Mamoon
Published: July 2022

Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection.
Application of Multiobjective Optimization Integrating Numerical and Scientific Computing in Graph Theory Coloring Algorithm
Authors: Li Shuxia
Published: July 2022

In the current era of increasingly complex social life, as people’s demand quality is getting higher and higher, the solution of the problem often has multiple indicators to achieve the optimum. This forces the graph coloring problem (GCP) to become complicated, and it is difficult to directly obtain the optimal solution, which brings new challenges to the solution of the problem. In response to this problem, the GCP field is of great research significance. With the in-depth study of GCP, the research on multiobjective optimization (MOO) in graph coloring algorithm (GCA) is gradually carried out. Its performance advantage is of great significance for solving multicondition constraint problems. This paper aims to study the application of Genetic Algorithm (GA) in GCA. Through the analysis and research of GA, and the fusion of numerical analysis and scientific computing, it can be applied to the construction of the neighbor distinguishable uniform V-full coloring algorithm (AVDEVTCA) to solve the AVDEVTC problem. This paper describes the basic theory of MOO and graph coloring. It conducts an experimental analysis of the algorithm performance and uses the relevant theoretical formulas to explain it. The results show that the algorithm takes 5754.142 S seconds to test 21325415 images and can color a large number of results that cannot be done manually in less time. It has greatly improved in terms of time and manpower saving and greatly improved practicability and work efficiency.
Left Ventricle Segmentation in Cardiac MR Images via an Improved ResUnet
Authors: Xu Shengzhou , Lu Haoran , Cheng Shiyu , Pei Chengdan
Published: July 2022

Cardiovascular diseases are reported as the leading cause of death around the world. Automatic segmentation of the left ventricle (LV) from magnetic resonance (MR) images is essential for an early diagnosis. An enhanced ResUnet is proposed in this paper to improve the performance of extracting LV endocardium and epicardium from MR images, improving the accuracy of the model by introducing a medium skip connection for the contracting path and a short skip connection for the residual unit. Also, a depth-wise separable convolution replaces the typical convolution operation to improve training efficiency. In the MICCAI 2009 LV segmentation challenge test dataset, the percentages of “good” contours, dice metric, and average perpendicular distance of endocardium (epicardium) are

99.12
%
±
2.29
%

100
%
±
0
%

,
0.93
±
0.02
 

0.96
±
0.01

,
and
 
1.60
±
0.42
 
mm
 

1.37
±
0.23
 
mm

, respectively. Experimental results demonstrate that the proposed model obtains promising performance and outperforms state-of-the-art methods. By incorporating these various skip connections, the segmentation accuracy of the model is significantly improved, while the depth-wise separable convolution also improves the model efficiency.
Biogas Production and Applications in the Sustainable Energy Transition
Authors: Kabeyi Moses Jeremiah Barasa , Olanrewaju Oludolapo Akanni
Published: July 2022

Biogas is competitive, viable, and generally a sustainable energy resource due to abundant supply of cheap feedstocks and availability of a wide range of biogas applications in heating, power generation, fuel, and raw materials for further processing and production of sustainable chemicals including hydrogen, and carbon dioxide and biofuels. The capacity of biogas based power has been growing rapidly for the past decade with global biogas based electricity generation capacity increasing from 65 GW in 2010 to 120 GW in 2019 representing a 90% growth. This study presents the pathways for use of biogas in the energy transition by application in power generation and production of fuels. Diesel engines, petrol or gasoline engines, turbines, microturbines, and Stirling engines offer feasible options for biogas to electricity production as prme movers. Biogas fuel can be used in both spark ignition (petrol) and compression ignition engines (diesel) with varying degrees of modifications on conventional internal combustion engines. In internal combustion engines, the dual-fuel mode can be used with little or no modification compared to full engine conversion to gas engines which may require major modifications. Biogas can also be used in fuel cells for direct conversion to electricity and raw material for hydrogen and transport fuel production which is a significant pathway to sustainable energy development. Enriched biogas or biomethane can be containerized or injected to gas supply mains for use as renewable natural gas. Biogas can be used directly for cooking and lighting as well as for power generation and for production of Fischer-Tropsch (FT) fuels. Upgraded biogas/biomethane which can also be used to process methanol fuel. Compressed biogas (CBG) and liquid biogas (LBG) can be reversibly made from biomethane for various direct and indirect applications as fuels for transport and power generation. Biogas can be used in processes like combined heat and power generation from biogas (CHP), trigeneration, and compression to Bio-CNG and bio-LPG for cleaned biogas/biomethane. Fuels are manufactured from biogas by cleaning, and purification before reforming to syngas, and partial oxidation to produce methanol which can be used to make gasoline. Syngas is used in production of alcohols, jet fuels, diesel, and gasoline through the Fischer-Tropsch process.
Construction of a Knowledge Map of Speech Emotion Features Based on Impulse-Coupled Neural Networks
Authors: Song Yan
Published: July 2022

This paper constructs knowledge graphs of speech emotion feature to support the expression of rich semantic information due to their unique graphical structure and to provide new ideas for studying speech emotion recognition recommendations. Impulse-coupled neural networks, as a mathematical abstraction of the visual properties of the human eye, have been widely used in various fields of speech processing. In this paper, we take the classical impulse-coupled neural network model as the research object, aiming to explore, analyze, and study the method of constructing knowledge maps of speech emotion features based on the impulse-coupled neural network model, and propose several improved impulse-coupled neural network models, which are used in the fields of target detection, speech segmentation, and quantization compression. This paper further constructs various domain semantic concept maps and performs multidimensional semantic enhancement understanding of search text based on the concept maps and the constructed entity-relationship knowledge maps and proposes a knowledge-based interpretable recommendation method for cloud services and a generalized recommendation and sample enhancement method for cloud scenarios. The fusion algorithm based on multiscale analysis can decompose the source speech into subspeech at different scales and then fuse each subspeech separately, while typical objects in the real world are also composed of many components at different scales. Based on the factors of different degrees in the speech context, the imagery work ruler evaluation model is refined with the basic principles of risk perception and risk resolution in the context, and an exploratory design is carried out separately for the verbal description, graphics, text, color, animation, and sound signals in the audiovisual signal using the workshop format to obtain a large number of design samples and evaluate the important units in turn, finally integrating the context involved in the study. The perceptual design system is given as a specific contextual design method.
Evaluation of Blended Learning Effect of Engineering Geology Based on Online Surveys
Authors: Yang Binbin , Zhao Xiaoming , Tang Dongqi
Published: July 2022

Engineering geology applies geological theories and methods to the practice of engineering activities, evaluates the engineering geological conditions of the engineering site through engineering geological investigation and comprehensive research of theories, solves the engineering geological problems related to engineering activities, predicts and demonstrates the occurrence and development laws of engineering geological problems in the engineering activity area, and puts forward technical measures for prevention and control, so as to contribute to the planning, design, and construction and provide necessary geological and technical data for use and maintenance. Based on the theory of multiattribute decision-making, this paper puts forward the evaluation index system of engineering geology teaching quality in applied universities. A curriculum teaching quality evaluation index system is constructed, which is composed of four main indexes: online resources and teaching, offline teaching process, classroom teaching quality evaluation, and classroom teaching quality evaluation. Furthermore, 11 second-class indices and 28 third-class indices are considered. The judgment matrices and membership degree of each attribute are obtained through use of a questionnaire, and the total weight of each subitem is calculated using the analytic hierarchy process. The calculation results show that the classroom instructional quality of the engineering geology course can be considered excellent; furthermore, other courses could also be evaluated using the same method, where those with low evaluation results can be further improved. Using the fuzzy comprehensive evaluation method based on the analytic hierarchy process, we evaluate the instructional quality, comprehensively consider the nonlinear and fuzzy characteristics of evaluation factors, can scientifically calculate the weight value of each evaluation index, and combine qualitative and quantitative aspects, in order to objectively provide an evaluation of the classroom instructional quality of teachers, which is conducive to the high-quality construction of university courses.
Activation of SIRT1 Alleviates Ferroptosis in the Early Brain Injury after Subarachnoid Hemorrhage
Authors: Yuan Bin , Zhao Xu-Dong , Shen Jun-Da , Chen Shu-Juan , Huang Han-Yu , Zhou Xiao-Ming , Han Yan-Ling , Zhou Long-Jiang , Lu Xiao-Jie , Wu Qi
Published: July 2022

Ferroptosis is a regulated cell death that characterizes the lethal lipid peroxidation and iron overload, which may contribute to early brain injury (EBI) pathogenesis after subarachnoid hemorrhage (SAH). Although Sirtuin 1 (SIRT1), a class III histone deacetylase, has been proved to have endogenous neuroprotective effects on the EBI following SAH, the role of SIRT1 in ferroptosis has not been studied. Hence, we designed the current study to determine the role of ferroptosis in the EBI and explore the correlation between SIRT1 and ferroptosis after SAH. The pathways of ferroptosis were examined after experimental SAH in vivo (prechiasmatic cistern injection mouse model) and in HT-22 cells stimulated by oxyhemoglobin (oxyHb) in vitro. Then, ferrostatin-1 (Fer-1) was used further to determine the role of ferroptosis in EBI. Finally, we explored the correlation between SIRT1 and ferroptosis via regulating the expression of SIRT1 by resveratrol (RSV) and selisistat (SEL). Our results showed that ferroptosis was involved in the pathogenesis of EBI after SAH through multiple pathways, including acyl-CoA synthetase long-chain family member 4 (ACSL4) activation, iron metabolism disturbance, and the downregulation of glutathione peroxidase 4 (GPX4) and ferroptosis suppressor protein 1 (FSP1). Inhibition of ferroptosis by Fer-1 significantly alleviated oxidative stress-mediated brain injury. SIRT1 activation could suppress SAH-induced ferroptosis by upregulating the expression of GPX4 and FSP1. Therefore, ferroptosis could be a potential therapeutic target for SAH, and SIRT1 activation is a promising method to inhibit ferroptosis.
Flash Pyrolysis Experiment on Albizia odoratissima Biomass under Different Operating Conditions: A Comparative Study on Bio-Oil, Biochar, and Noncondensable Gas Products
Authors: Sowmya Dhanalakshmi C. , Kaliappan S. , Mohammed Ali H. , Sekar S. , Depoures Melvin Victor , Patil Pravin P. , Subbaiah Baskara Sethupathy , Socrates S. , Birhanu Habtewolde Ababu
Published: July 2022

This study deals with the flash pyrolysis of Albizia odoratissima biomass wastes at different temperature, sweep gas flow rate, and heating rate in a fluidized bed reactor. In the first phase of the experimental work, the effect of temperature (350–550°C) on product yield was analyzed, the second and third phases of the work were to analyze the effect of sweeping gas (N2), flow rate (1.25–2.25 m3/hr), and heating rate (20–40°C/min). The experimental works were carried out to get maximum bio-oil production. The experimental results demonstrated that the maximum yield of bio-oil was obtained at a temperature of 450°C, N2 flow rate of 1.75 m3/hr, and heating rate of 30°C/min. Temperature was found to be the crucial factor rather than sweep gas flow rate in the product distribution. Fourier transform infrared spectroscopy (FT-IR), gas chromatography mass spectroscopy (GC-MS), and elemental analysis were done on the obtained bio-oil, biochar, and noncondensable gas products. The heating value of the bio-oil and biochar was identified as 18.15 and 23.47 MJ/kg, respectively. The chemical analysis of the bio-oil showed that the oil is a mixture of phenol and oxygenated elements. The gas analyses showed that hydrogen and carbon dioxide were dominant, followed by carbon monoxide and methane.