bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of the influence of gluten quality on highland barley dough sheet quality by different instruments
Authors: Wang Tianxiu , Jiang Yiyi , Liu Shuyi , Obadi Mohammed , Xu Bin , Jiang Song
Published: February 2022

This study was to compare the results of texture analyzer with those of farinograph and extensograph and determine whether texture analyzer could be used to evaluate the processing quality of highland barley flour (HBF) dough sheet. The farinograph and extensograph tests were used to determine the reconstituted flour properties, a texture analyzer was applied to measure the tensile strength (TS) of HBF dough sheet, and the content of glutenin macropolymer (GMP), free sulfhydryl (‐SH) and secondary structure of protein and microstructure in HBF dough sheet were investigated. Furthermore, correlations between these parameters were determined by regression analysis and Pearson correlation coefficient. It was suggested that the reconstituted flours with a higher gluten index showed a higher farinograph quality number (FQN) and greater maximum resistance to extension (Rm). HBF dough sheets with higher gluten index possessed higher GMP and lower free ‐SH contents, a more ordered secondary structure of protein, resulting in a more compact gluten network and a stronger TS. The regression and correlation analysis showed that TS was positively correlated with FQN and Rm. In addition, it was significantly correlated with the content of GMP, ‐SH, secondary structure of protein and gluten network. It was concluded that texture analyzer could be an alternative approach to evaluate the processing quality of HBF dough sheet. Moreover, the gluten index of flours could be used to predict the processing quality of HBF dough sheet.
Reducing errors in guided implant surgery to optimize treatment outcomes
Authors: Chackartchi Tali , Romanos Georgios E. , Parkanyi Laszlo , Schwarz Frank , Sculean Anton
Published: February 2022

Clinical considerations and treatment criteria in implant placement are constantly evolving. Prosthetically driven implant surgery has become the standard of care to improve short and long‐term functional and esthetic outcomes. Therefore, implant position and angulation are planned according to the available bone, anatomical structures, and the requirements of the future prosthetic superstructure. In parallel with these developments, significant progress has been made in data imaging and different software technologies to allow the integration of data within a digital file format. Digitalization in implant surgery enables optimal planning of implant position, as well as the ability to transfer this planning to the surgical field—a process defined as “computer‐supported implant planning and guided surgery.” The aims of the present review are as follows: (a) to critically appraise the indications and potential “added value” of guided implant surgery, elaborating the main differences between dynamic and static guidance; and (b) to discuss the most important clinical considerations relevant for the different steps of the workflow that might influence the surgical outcome and to offer recommendations on how to avoid or reduce process errors in order to optimize treatment outcomes.
Risk‐layering and optimal insurance uptake under ambiguity: With an application to farmers exposed to drought risk in Austria
Authors: Birghila Corina , Pflug Georg Ch. , Hochrainer‐Stigler Stefan
Published: February 2022

Many risks we face today will very likely not stay the same over time. For example, it is expected that climate change will alter future risks of natural disaster events considerably and, as a consequence, current risk management and governance strategies may not be effective anymore. Large ambiguities arise if future climate change impacts should be taken into account for analyzing risk management options today. Risk insurance, while albeit only one of many risk management actions possible, plays an important role in current societies for dealing with extremes. A natural starting point for our analysis is therefore the question of how ambiguity may be incorporated in a world with changing risks. To shed light on this question, we study how ambiguity can affect the uptake of insurance and risk mitigation within a risk‐layer approach where each layer is quantified using distortion risk measures that should reflect the risk aversion of a decisionmaker toward extreme losses. Importantly, we obtain a closed‐form solution for such a problem statement which allows an efficient numerical implementation. We apply this model to a case study of drought risk for Austrian farmers and address the question how ambiguity will affect the risk layers of different types of farmers and how subsidies may help to deal with current and future risks. We found that especially for small‐scale farmers the consequences of increasing risk and model ambiguity are pronounced and subsidies are especially needed in this case to cover the high‐risk layer.
Combined effects of maximal oxygen uptake and glucose status on mortality: The Prospective KIHD cohort study
Authors: Kurl Sudhir , Hakkarainen Pirjo , Voutilainen Ari , Lönnroos Eija
Published: February 2022

Objective

To examine the combined effects of cardiorespiratory fitness (CRF) and prediabetes or diabetes on cardiovascular and noncardiovascular mortality.

Patients and methods

This prospective study evaluated a population‐based cohort of 1562 men aged 42–60 years at baseline (1984–1989). We utilized maximal oxygen uptake (VO2max) for assessing aerobic capacity and CRF in the cohort and stratified participants into six groups according to both their glucose status (diabetes, prediabetes, or no diabetes) and whether they were below‐ or above‐median VO2max. Deaths in the cohort were recorded till December 31 2016. Cox regression was used to calculate hazard ratios (HR) with 95% confidence intervals (CI) for cardiovascular and noncardiovascular mortality. Smoking, alcohol consumption, BMI, blood pressure, cholesterol, diagnosis of ischemic heart disease, and socioeconomic status served as covariates in the mortality analyses.

Results

During the follow‐up (mean 24.2 years), 341 men died from cardiovascular and 468 men from noncardiovascular causes. When compared to men with no diabetes and above‐median VO2max, the presence of either diabetes (HR = 4.10, 95% CI: 2.27–7.40) or prediabetes (HR = 2.10, 95% CI: 1.18–3.73) combined with below‐median VO2max increased the risk of cardiovascular death. Noncardiovascular mortality was increased by low oxygen uptake in men with prediabetes (HR = 2.24, 95% CI: 1.30–3.84), and among men with diabetes, the increase was not statistically significant (HR = 1.99, 95% CI: 0.91–4.32).

Conclusions

Cardiorespiratory fitness modifies the risk of death related to prediabetes and diabetes. This highlights the importance of CRF assessment and interventions to support the uptake of regular physical activity among aging men with disturbed glucose metabolism.
Editors' Introduction to Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition?
Authors: Kenett Yoed N. , Hills Thomas T.
Published: February 2022

Thinking is complex. Over the years, several types of methods and paradigms have developed across the psychological, cognitive, and neural sciences to study such complexity. A rapidly growing multidisciplinary quantitative field of network science offers quantitative methods to represent complex systems as networks, or graphs, and study the network properties of these systems. While the application of network science to study the brain has greatly advanced our understanding of the brains structure and function, the application of these tools to study cognition has been done to a much lesser account. This topic is a collection of papers that discuss the fruitfulness of applying network science to study cognition across a wide scope of research areas from generalist accounts of memory and encoding, to individual differences, to communities, and finally to cultural and individual change.
Risk management. A case study of a Colombian public sector company
Authors: Jurado-Zambrano Diego , Villanueva Eduart
Published: February 2022

The objective of this research is to analyze, in a Colombian public sector company, the key success factors for the implementation of a ERM initiative. To comply with the objective, an investigation with a qualitative approach and descriptive scope is developed. As instruments for collecting information, the following are used: The focus group technique; the documentary review specifically of the strategic plan, the operating policies, the risks previously identified, as well as the internal and external audit reports. The results show the existence of some key success factors, such as: Senior management leadership, resource allocation, methodological integration for risk management; which enabled the successful implementation of integrated risk management. Likewise, the main stages adopted in the implementation exercise are presented, which were based on the ISO 31000: 2018 standard.
Field emission arrays from graphite fabricated by laser micromachining
Authors: Ławrowski Robert , Bachmann Michael , Schreiner Rupert
Published: February 2022

Laser microstructuring allows the manufacturing of conical shaped emitters on a graphitized carbon substrate without any additional treatment. Integral field emission measurements were performed in a diode configuration with a 50 μm mica spacer and a metalized Si-grid in a vacuum chamber at pressures of about 10−9 mbar. Emission currents up to 10 μA at a voltage of 1000 V (11 MV/m) with a positive current drift were observed for an array of 16 emitters. A strong modification of the emitter tip is noticeable after individual measurements. A comparable alteration of the surface is also achieved by an electrochemical treatment of the samples with a potassium hydroxide solution. However, the IV-measurements of such samples show not only higher onset voltages but also higher integral emission currents. For all arrays, an ideal Fowler–Nordheim (FN) behavior is only noticeable at applied voltages below 625 V (7 MV/m). Numerical calculations indicate that the deviation in the FN-plot for higher voltage values is caused by the change of the effective work function.
Two Polyketides Intertwined in Complex Regulation: Posttranscriptional CsrA-Mediated Control of Colibactin and Yersiniabactin Synthesis in Escherichia coli
Authors: Rehm Nadine , Wallenstein Alexander , Keizers Marla , Homburg Stefan , Magistro Giuseppe , Chagneau Camille V. , Klimek Hanna , Revelles Olga , Helloin Emmanuelle , Putze Johannes , Nougayrède Jean-Philippe , Schubert Sören , Oswald Eric , Dobrindt Ulrich
Published: February 2022

Secondary metabolite expression is a widespread strategy among bacteria to improve their fitness in habitats where they constantly compete for resources with other bacteria. The production of secondary metabolites is associated with a metabolic and energetic burden.
Integrated care and co-production during a pandemic: responding to the needs of local populations using infographics
Authors: Gilmartin Chris , Soni Arpana , Rutherford Phoebe , Watson Mando
Published: February 2022

Connecting Care for Children, an integrated care collaborative in northwest London, responded to local child health needs during the start of the COVID-19 pandemic through the co-production of infographics. Here we describe the development and evaluation of co-produced infographics using quality improvement methodology, to highlight their effectiveness in swiftly responding to local community health concerns.
Generic analysis method to learn from serious adverse events in Dutch hospitals: a human factors perspective
Authors: Baartmans Mees Casper , Van Schoten Steffie Marijke , Wagner Cordula
Published: February 2022

Background

Hospitals in various countries such as the Netherlands investigate and analyse serious adverse events (SAEs) to learn from previous events and attempt to prevent recurrence. However, current methods for SAE analysis do not address the complexity of healthcare and investigations typically focus on single events on the hospital level. This hampers hospitals in their ambition to learn from SAEs. Integrating human factors thinking and using a holistic and more consistent method could improve learning from SAEs.

Aim

This study aims to develop a novel generic analysis method (GAM) to: (1) facilitate a holistic event analysis using a human factors perspective and (2) ease aggregate analysis of events across hospitals.

Methods

Multiple steps of carefully evaluating, testing and continuously refining prototypes of the method were performed. Various Dutch stakeholders in the field of patient safety were involved in each step. Theoretical experts were consulted, and the prototype was pretested using information-rich SAE reports from Dutch hospitals. Expert panels, engaging quality and safety experts and medical specialists from various hospitals were consulted for face and content validity evaluation. User test sessions concluded the development of the method.

Results

The final version of the GAM consists of a framework and affiliated questionnaire. GAM combines elements of three methods for SAE analysis currently practised by Dutch hospitals. It is structured according to the Systems Engineering Initiative for Patient Safety model, which incorporates a human factors perspective into the analysis. These eases aggregated analysis of SAEs across hospitals and helps to consider the complexity of healthcare work systems.

Conclusion

The GAM is a valuable new tool for hospitals to learn from SAEs. The method can facilitate a holistic aggregate analysis of SAEs across hospitals using a human factors perspective, and is now ready for further extensive testing.
Gut microbiome and health: mechanistic insights
Authors: de Vos Willem M , Tilg Herbert , Van Hul Matthias , Cani Patrice D
Published: February 2022

The gut microbiota is now considered as one of the key elements contributing to the regulation of host health. Virtually all our body sites are colonised by microbes suggesting different types of crosstalk with our organs. Because of the development of molecular tools and techniques (ie, metagenomic, metabolomic, lipidomic, metatranscriptomic), the complex interactions occurring between the host and the different microorganisms are progressively being deciphered. Nowadays, gut microbiota deviations are linked with many diseases including obesity, type 2 diabetes, hepatic steatosis, intestinal bowel diseases (IBDs) and several types of cancer. Thus, suggesting that various pathways involved in immunity, energy, lipid and glucose metabolism are affected.

In this review, specific attention is given to provide a critical evaluation of the current understanding in this field. Numerous molecular mechanisms explaining how gut bacteria might be causally linked with the protection or the onset of diseases are discussed. We examine well-established metabolites (ie, short-chain fatty acids, bile acids, trimethylamine N-oxide) and extend this to more recently identified molecular actors (ie, endocannabinoids, bioactive lipids, phenolic-derived compounds, advanced glycation end products and enterosynes) and their specific receptors such as peroxisome proliferator-activated receptor alpha (PPARα) and gamma (PPARγ), aryl hydrocarbon receptor (AhR), and G protein-coupled receptors (ie, GPR41, GPR43, GPR119, Takeda G protein-coupled receptor 5).

Altogether, understanding the complexity and the molecular aspects linking gut microbes to health will help to set the basis for novel therapies that are already being developed.
Exosomes derived from γδ-T cells synergize with radiotherapy and preserve antitumor activities against nasopharyngeal carcinoma in immunosuppressive microenvironment
Authors: Wang Xiwei , Zhang Yanmei , Mu Xiaofeng , Tu Chloe Ran , Chung Yuet , Tsao Sai Wah , Chan Godfrey Chi-Fung , Leung Wing-Hang , Lau Yu-lung , Liu Yinping , Tu Wenwei
Published: February 2022

Background

Radiotherapy is the first-line treatment for patients nasopharyngeal carcinoma (NPC), but its therapeutic efficacy is poor in some patients due to radioresistance. Adoptive T cell-based immunotherapy has also shown promise to control NPC; however, its antitumor efficacy may be attenuated by an immunosuppressive tumor microenvironment. Exosomes derived from γδ-T cells (γδ-T-Exos) have potent antitumor potentials. However, it remains unknown whether γδ-T-Exos have synergistic effect with radiotherapy and preserve their antitumor activities against NPC in an immunosuppressive tumor microenvironment.

Methods

γδ-T-Exos were stained with fluorescent membrane dye, and their interactions with NPC were determined both in vitro and in vivo. NPC cell deaths were detected after treatment with γδ-T-Exos and/or irradiation. Moreover, effects of γδ-T-Exos on radioresistant cancer stem-like cells (CSCs) were determined. The therapeutic efficacy of combination therapy using γδ-T-Exos and irradiation on NPC tumor progression was also monitored in vivo. Finally, the tumor-killing and T cell-promoting activities of γδ-T-Exos were determined under the culture in immunosuppressive NPC supernatant.

Results

γδ-T-Exos effectively interacted with NPC tumor cells in vitro and in vivo. γδ-T-Exos not only killed NPC cells in vitro, which was mainly mediated by Fas/Fas ligand (FasL) and death receptor 5 (DR5)/tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) pathways, but also controlled NPC tumor growth and prolonged tumor-bearing mice survival in vivo. Furthermore, γδ-T-Exos selectively targeted the radioresistant CD44+/highCSCs and induced profound cell apoptosis. The combination of γδ-T-Exos with radiotherapy overcame the radioresistance of CD44+/highNPC cells and significantly improved its therapeutic efficacy against NPC in vitro and in vivo. In addition, γδ-T-Exos promoted T-cell migration into NPC tumors by upregulating CCR5 on T cells that were chemoattracted by CCR5 ligands in the NPC tumor microenvironment. Although NPC tumor cells secreted abundant tumor growth factor beta to suppress T-cell responses, γδ-T-Exos preserved their direct antitumor activities and overcame the immunosuppressive NPC microenvironment to amplify T-cell antitumor immunity.

Conclusions

γδ-T-Exos synergized with radiotherapy to control NPC by overcoming the radioresistance of NPC CSCs. Moreover, γδ-T-Exos preserved their tumor-killing and T cell-promoting activities in the immunosuppressive NPC microenvironment. This study provides a proof of concept for a novel and potent strategy by combining γδ-T-Exos with radiotherapy in the control of NPC.
Leveraging Narrative to Generate Movie Script
Authors: Zhu Yutao , Song Ruihua , Nie Jian-Yun , Du Pan , Dou Zhicheng , Zhou Jin
Published: February 2022

Generating a text based on a predefined guideline is an interesting but challenging problem. A series of studies have been carried out in recent years. In dialogue systems, researchers have explored driving a dialogue based on a plan, while in story generation, a storyline has also been proved to be useful. In this article, we address a new task—generating movie scripts based on a predefined narrative. As an early exploration, we study this problem in a “retrieval-based” setting. We propose a model (ScriptWriter-CPre) to select the best response (i.e., next script line) among the candidates that fit the context (i.e., previous script lines) as well as the given narrative. Our model can keep track of what in the narrative has been said and what is to be said. Besides, it can also predict which part of the narrative should be paid more attention to when selecting the next line of script. In our study, we find the narrative plays a different role than the context. Therefore, different mechanisms are designed for deal with them. Due to the unavailability of data for this new application, we construct a new large-scale data collection
GraphMovie
from a movie website where end-users can upload their narratives freely when watching a movie. This new dataset is made available publicly to facilitate other studies in text generation under the guideline. Experimental results on the dataset show that our proposed approach based on narratives significantly outperforms the baselines that simply use the narrative as a kind of context.
Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling
Authors: Deng Yang , Li Yaliang , Zhang Wenxuan , Ding Bolin , Lam Wai
Published: February 2022

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same “completely summarized” answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences toward product aspects or information needs. To tackle this challenge, we propose a novel Personalized Answer GEneration method with multi-perspective preference modeling, which explores historical user-generated contents to model user preference for generating personalized answers in PQA. Specifically, we first retrieve question-related user history as external knowledge to model knowledge-level user preference. Then, we leverage the Gaussian Softmax distribution model to capture latent aspect-level user preference. Finally, we develop a persona-aware pointer network to generate personalized answers in terms of both content and style by utilizing personal user preference and dynamic user vocabulary. Experimental results on real-world E-Commerce QA datasets demonstrate that the proposed method outperforms existing methods by generating informative and customized answers and show that answer generation in E-Commerce can benefit from personalization.
SUMMER: Bias-aware Prediction of Graduate Employment Based on Educational Big Data
Authors: Xia Feng , Guo Teng , Bai Xiaomei , Shatte Adrian , Liu Zitao , Tang Jiliang
Published: February 2022

The failure of obtaining employment could lead to serious psychosocial outcomes such as depression and substance abuse, especially for college students who may be less cognitively and emotionally mature. In addition to academic performance, employers’ unconscious biases are a potential obstacle to graduating students in becoming employed. Thus, it is necessary to understand the nature of such unconscious biases to assist students at an early stage with personalized intervention. In this paper, we analyze the existing bias in college graduate employment through a large-scale education dataset and develop a framework called SUMMER (bia
S
-aware grad
U
ate e
M
ploy
ME
nt p
R
ediction) to predict students’ employment status and employment preference while considering biases. The framework consists of four major components. Firstly, we resolve the heterogeneity of student courses by embedding academic performance into a unified space. Next, we apply a
Wasserstein generative adversarial network with gradient penalty (WGAN-GP)
to overcome the label imbalance problem of employment data. Thirdly, we adopt a temporal convolutional network to comprehensively capture sequential information of academic performance across semesters. Finally, we design a bias-based regularization to smooth the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework.
Electroosmotic Flow in Skin
Authors: Bath Bradley D.
Published: February 2022

A Summary to The Electrochemical Society for the 1998 Edward G. Weston Summer Research Fellowship.
Low-Density Granulocytes in Immune-Mediated Inflammatory Diseases
Authors: Ning Xin , Wang Wen-Ming , Jin Hong-Zhong
Published: February 2022

Low-density granulocytes (LDGs), a distinct subset of neutrophils that colocalize with peripheral blood mononuclear cells after density gradient centrifugation, have been observed in many immune-mediated diseases. LDGs are considered highly proinflammatory because of enhanced spontaneous formation of neutrophil extracellular traps, endothelial toxicity, and cytokine production. Concomitantly, increased numbers of LDGs are associated with the severity of many immune-mediated inflammatory diseases. Recent studies, with the help of advanced transcriptomic technologies, demonstrated that LDGs were a mixed cell population composed of immature subset and mature subset, and these two subsets showed different pathogenic features. In this review, we summarize the current knowledge on the composition, origin, and pathogenic properties of LDGs in several immune-mediated inflammatory diseases and discuss potential medical interventions targeting LDGs.
Thermal Analysis of the Solar Dish Array Concentrator System
Authors: Mahmood Yaseen H. , Al-Salih R. Y. J. , Amer Mahdi Saif , Mohamed Ibrahim Abdelrahman
Published: February 2022

This research aimed at fabricating solar array concentrator. It is one of the best options for solar energy concentration. Compared with other systems, because of the higher concentration ratios and high control focal length, good accept angles, higher efficiency, rigid with high wind, and low cost, the system has been locally designed and fabricated to produce steam. The dimensions of the active aperture area of 3.25 m2 were studied. The absorber receiver, which conveyed water as heat in a closed cycle with an appropriate pump, produced steam at a higher temperature and with more efficiency. The best characterizations of the system are the focal length 1.25 m, concentration ratio 103, optical power 1.2 kW, losses 0.2 kW, and efficiency 60–70%.
Effect of Joint Roughness and Infill Thickness on Shear Characteristics of Rock Mass
Authors: Wu Xuezhen , Zheng Hanfang , Wang Gang , Zhou Yuanjin , Guan Zhenchang
Published: February 2022

The joint planes in fractured rock mass will reduce the tensile strength and shear strength of rock mass, which has a decisive influence on the deformation and failure mode of engineering rock mass. Furthermore, infill also exists between the joint surfaces, which will also affect the shear characteristics of the joint. A method of importing standard joint profile into PFC2D was proposed, and a series of numerical simulation tests were carried out to study the effect of joint roughness and infill thickness on the shear characteristics of joints. The numerical results revealed that rock bolts profoundly improved the shear strength of the infilled rock joints, enhanced the toughness of the joint surface, increased the number of micro‐cracks, and made the dilatation more obvious. The shear stress and the normal displacement of unbolted or bolted infilled rock joints increased with increasing the joint roughness and decreasing the infill thickness. The maximum horizontal compression stress in the middle of the bolt gradually increased with the increase of joint roughness coefficient. Different roughness has different effects on the number of micro‐cracks in the sample. The number of total cracks and tensile cracks of the bolted and unbolted specimens increased with the increase of joint roughness coefficient, while the shear cracks remained almost the same. Through the study of the coupling effect of joint roughness and infill thickness on peak shear stress, results can be obtained as follows. The unbolted samples are highly sensitive to JRC changes. The greater the infill thickness, the greater the sensitivity of unbolted samples to JRC changes. The reinforcement effect of the bolt will strengthen the meshing strength between the joint surface and the filling material; that is, the meshing strength is positively correlated with joint roughness and negatively correlated with filling thickness.
Modeling and Optimization of Water Distribution in Mineral Processing considering Water Cost and Recycled Water
Authors: Wang He , Cui Qiuyu , Du Hualong
Published: February 2022

Reducing mineral processing water costs and freshwater consumption is a challenging task in the mineral processing water distribution (MPWD). The work presented in this paper focuses on two aspects of the MPWD optimization model and the MPWD optimization method. To achieve MPWD optimization effectively, a nonlinear constrained multiobjective model is built. The problem is formulated with two objectives of minimizing the mineral processing water costs and maximizing the amount of recycled water. In this paper, an optimization method named enhancing the multiobjective artificial bee colony (EMOABC) algorithm is proposed to solve this model. The EMOABC algorithm uses four strategies to obtain the Pareto-optimal solutions and to achieve the MPWD optimal solutions. With the three benchmark functions, the EMOABC algorithm outperforms the other two widely used algorithms in solving complex multiobjective optimization problems. The EMOABC algorithm is then applied to two cases. Results have shown that the proposed algorithm has the ability to solve the MPWD optimization model. The developed model and the proposed algorithm provide decision support for the actual MPWD problem.
Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)
Authors: Abdan Masoud , Seno Seyed Amin Hosseini
Published: February 2022

A wormhole attack is a type of attack on the network layer that reflects routing protocols. The classification is performed with several methods of machine learning consisting of

K

-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), naive Bayes (NB), and convolutional neural network (CNN). Moreover, we used nodes’ properties for feature extraction, especially nodes’ speed, in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious nodes. The classification results show that the accuracy of the KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method’s accuracy is 98.9% and higher than other ways. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively.
A Method of the Active and Passive Event Service Based on the Sensor Web
Authors: Liu Lan , Fan Jingjing , Li Chengfan , Liu Xuefeng
Published: February 2022

The intelligent information system constructed by the sensor web can monitor all kinds of sudden abnormal events, improve the ability of event discovery and rapid disposal, and promote the development of smart city and the construction of the Internet of Things (IoT). In this paper, we consider the problem of complex integration and poor expansibility in the large-scale full-network operation and maintenance system construction and propose an active and passive event service (APES) method based on the sensor web. In the APES, a system framework with the perception layer, data service layer, event service layer, and user layer is firstly defined and constructed. Secondly, a middleware with the ability to active and passive event service (APES) is designed and implemented based on the system framework. Finally, taking abnormal weather and fire warning as examples, the performance of the proposed event service middleware is tested, respectively. Experimental results show that the proposed APES model in this paper has the advantages of high precision, stable operation, and strong practicability and solves “Information Island” and low reusability in the whole network operation and maintenance system. This is an attempt at the structural design of a similar intelligent information system.
Analysis of Poetry Style Based on Text Classification Algorithm
Authors: Wang Can
Published: February 2022

In order to realize the effect of intelligent poetry style analysis, this paper applies the text classification algorithm to the poetry style analysis, combines the knowledge representation algorithm to perform text classification and recognition, improves the algorithm, and applies it to the poetry style analysis model. Moreover, this paper combines intelligent algorithms to construct a poetry style analysis system, constructs the system’s functional modules, preprocesses the poetry documents in the corpus, and maps them to the vector space that can be directly processed by the computer. In addition, after constructing the system model, this paper verifies the poetry style analysis system based on the text classification algorithm through simulation experiments. From the research results, the effect of the poetry style analysis method based on the text classification algorithm proposed in this paper is very good, which meets the actual needs of poetry style analysis.
Effects of Antarctic Krill Products on Feed Intake, Growth Performance, Fillet Quality, and Health in Salmonids
Authors: Kaur Kiranpreet , Kortner Trond M. , Benitez-Santana Tibiabin , Burri Lena
Published: February 2022

The interest for krill-based ingredients for aquaculture feed applications has increased steadily in recent years. For decades, there has been a heavy reliance on the limited sources of fishmeal and fish oil in the salmonid aquaculture industry. Further growth in farming of carnivorous fish is dependent on new feed resources becoming available. The only unexploited marine resources of significant biomass are found at lower trophic levels, of which the Antarctic krill (Euphausia superba) has a high potential. Apart from being the biggest single species biomass, Antarctic krill is also rich in nutrients, such as omega-3 polyunsaturated fatty acids, phospholipids, astaxanthin, vitamins, and minerals. This makes Antarctic krill a high-quality source of health-beneficial lipids and proteins. The present article provides an overview on the documented benefits of feeding salmonids (Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss)) with krill products (krill meal, krill oil, and krill hydrolysate), focusing on growth performance (feed intake, growth rate, and feed conversion), fillet quality, slaughter yield, and health benefits in terms of reducing fat accumulation in liver and intestinal tissues. Besides, the article discusses possible future studies, to widen the knowledge on krill benefits in salmonids and to unravel the underlying mechanisms.
Novel Decision-Making Techniques in Tripolar Fuzzy Environment with Application: A Case Study of ERP Systems
Authors: Afridi Minhaj , Gumaei Abdu H. , AlSalman Hussain , Khan Asghar , Mizanur Rahman Sk. Md.
Published: February 2022

The intuitionistic fuzzy set (IFS) and bipolar fuzzy set (BFS) are all effective models to describe ambiguous and incomplete cognitive knowledge with membership, non-membership, negative membership, and hesitancy sections. But in daily life problems, there are some situations where we cannot apply the ordinary models of IFS and BFS, separately. Hence, there is a need to combine both the models of IFS and BFS into a single one. A tripolar fuzzy set (TFS) is a generalization of IFS and BFS. In circumstances where BFS and IFS models cannot be used individually, a tripolar fuzzy model is more dependable and efficient. Further, the IFS and BFS models are reduced to corollaries due to the proposed model of TFS. For this purpose in this article, we first consider some novel operations on tripolar fuzzy information. These operations are formulated on the basis of well-known Dombi T-norm and T-conorm, and the desirable properties are discussed. By applying the Dombi operations, arithmetic and geometric aggregation operators of TFS are proposed, and we introduce the concepts of a TF-Dombi weighted average (TFDWA) operator, a TF-Dombi ordered weighted average (TFDOWA) operator, and a TF-Dombi hybrid weighted (TFDHW) operator and explore their fundamental features including idempotency, boundedness, monotonicity, and others. In the second part, we propose TF-Dombi weighted geometric (TFDWG) operator, TF-Dombi ordered weighted geometric (TFDOWG) operator, and TF-Dombi hybrid geometric (TFDHG) operator. The features and specific cases of the mentioned operators are examined. Enterprise resource planning (ERP) is a management and integration approach that organizations employ to manage and develop many aspects of their operations. The study’s primary contribution is to employ TFS to create certain decision-making strategies for the selection of optimal ERP systems. The proposed operators are then used to build several techniques for solving multiattribute decision-making (MADM) issues with TF information. Finally, an example of ERP system selection is investigated to demonstrate that the techniques suggested are trustworthy and realistic.
Agronomic Performance, Variance Components, and Clustering in Vernonia galamensis Germplasm from Ethiopia
Authors: Mideksa Alemneh , Tesfaye Kassahun
Published: February 2022

This study was conducted to assess the agronomic performance, variance components, and clustering analysis of 80 accessions of Vernonia galamensis, in alpha-lattice design with two replications at Melka Werer and Wondo Genet agricultural research centers in 2018/19. The one-way ANOVA showed that the high significant variation (

p
<
0.05

) for most of the quantitative traits among accessions except for days to emergence, and the mean performance of seed yield per hectare ranged from 348.9 to 624.3 kg ha−1, with an average of 474.4 kg ha−1. The result of principal component analysis indicated that those exhibited more than one eigenvalue were about 71.0% of variability. While the cluster analysis based on 80% of similarity into eight clusters from the eighty accessions, the first cluster had the largest number of accessions. Overall, the study confirmed that the agromorphological characters such as the number of heads per plant, number of seeds per head, seed yield per plant, and seed weight per plot were the main contributors for seed yield per hectare in V. galamensis.
A Dioxidovanadium Complex cis-[VO2 (obz) py] Attenuates Hyperglycemia in Streptozotocin (STZ)-Induced Diabetic Male Sprague-Dawley Rats via Increased GLUT4 and Glycogen Synthase Expression in the Skeletal Muscle
Authors: Mbatha Bonisiwe , Khathi Andile , Sibiya Ntethelelo , Booysen Irvin , Ngubane Phikelelani
Published: February 2022

Vanadium has demonstrated antihyperglycemic effects in diabetes mellitus (DM) but is, however, associated with toxicity. Therefore, new vanadium complexes envisaged to possess heightened therapeutic potency while rendering less toxicity are being explored. Accordingly, the aim of the study was to investigate the effects of a dioxidovanadium (V) complex, cis-[VO2 (obz) py], on selected glucose metabolism markers in streptozotocin (STZ)-induced diabetic rats. STZ-induced diabetic rats were treated orally with cis-[VO2 (obz) py] (10, 20, and 40 mg/kg) twice every 3rd day for 5 weeks. Blood glucose concentrations, body weight, and food and water intake were monitored weekly, for 5 weeks. Rats were then euthanized after which blood, liver, and muscle tissues were collected for biochemical analysis. The administration of dioxidovanadium complex significantly decreased blood glucose concentrations throughout the 5-week period in comparison with the diabetic control (DC). The attenuation of hyperglycemia was accompanied by an increased glycogen concentration in both liver and muscle tissues in the treated groups. Furthermore, a significant increase was observed in the expression of glucose transporter type 4 (GLUT4) in the skeletal muscle tissues and glycogen synthase in the liver tissues. These findings indicate that our vanadium complex cis-[VO2 (obz) py] may exert antihyperglycemic effects through increased glucose uptake, glycogen synthesis, and increased GLUT4 and glycogen synthase expression.
ox-LDL-Induced Endothelial Progenitor Cell Oxidative Stress via p38/Keap1/Nrf2 Pathway
Authors: Jiang Qijun , Chen Qiao , Li Chengpeng , Gong Zhigang , Li Zhigang , Ding Shifang
Published: February 2022

Background. Nrf2 which was recently reported to regulate the antioxidant genes and cellular redox regulators was highly expressed in EPCs. However, its role in ox-LDL-induced EPC oxidative stress and apoptosis has not been fully illustrated. Methods. EPCs isolated from human peripheral blood mononuclear cells were treated with different concentrations of ox-LDL, Keap1 siRNA, and a specific p38 MAPK inhibitor SB203580 and then used to assay the cytoplasmic Nrf2, nuclear Nrf2, NAD(P) H:quinone oxidoreductase 1 (NQO1) and Bax/Bcl-2 levels with Western blot, NQO1 mRNA levels with RT-PCR, ROS levels with H2DCF-DA, loss/disruption of mitochondrial membrane potential with JC-1, apoptosis with Annexin V and PI, migration with transwell chambers, and tube formation with Matrigel. Results. ox-LDL decreased the nuclear Nrf2/Histone H3 to cytoplasmic Nrf2/GAPDH ratio, NQO1 mRNA, and protein levels. ox-LDL enhanced ROS production, induced the loss of membrane potential, and increased the cell shrinkage, pyknotic nuclei, and apoptosis of EPCs. Keap1 siRNA increased Nrf2 nuclear translocation, NQO1 mRNA transcription, and protein expression and prevented ROS generation and formation of JC-1 monomers. ox-LDL increased the activation of p38. SB203580 significantly eliminated ox-LDL induced inhibition of Nrf2 nuclear translocation, depression of NQO1 mRNA transcription, generation of ROS, and formation of JC-1 monomers in EPCs. Keap1 siRNA decreased the Bax/Bcl-2 ratio which was increased by ox-LDL in EPCs. ox-LDL decreased EPC migration and tube formation. Keap1 siRNA preserved the migration and tube formation of EPCs. Conclusion. ox-LDL activated EPCs p38/Keap1/Nrf2 pathway and induced oxidative stress, dysfunction, and apoptosis of EPCs.
Simulation and Experimental Investigations on the Sugarcane Cutting Mechanism and Effects of Influence Factors on the Cutting Quality of Small Sugarcane Harvesters under Vibration Excitations
Authors: Mo Hanning , Li Shangping , Zhou Jinghui , Zeng Bang , He Guiqing , Qiu Chen
Published: February 2022

In order to study the sugarcane cutting mechanism and the effects of influence factors on the sugarcane cutting quality (SCQ) of small sugarcane harvesters in hilly areas, sugarcane cutting experiments and simulations were done through a self-developed sugarcane harvester experiment platform (SHEP) and the finite element analysis (FEA) method. The comprehensive cutting quality evaluating value (CCQEV) was calculated through the number of sugarcane cracks, the crack thickness, and the crack length to evaluate the SCQ. Effects of the amplitude and the frequency of the axial cutter vibration, the cutter rotation velocity, the sugarcane feeding velocity, and the cutter installing angle on the CCQEV were studied. Effects of interaction between the amplitude and the frequency of the axial cutter vibration and that between the axial cutter vibration amplitude and the cutter rotation velocity on the CCQEV were also studied. The sugarcane cutting mechanism was studied through analysis of cutting force signals, the high-speed photographing result in the sugarcane cutting process, and FEA simulations of the sugarcane cutting process, which verified discoveries obtained through sugarcane cutting experiments. This research laid the foundation for the development of small sugarcane harvesters with a good SCQ in hilly areas.
[Retracted] Dance Evaluation Based on Movement and Neural Network
Authors: Lei Yan , Li Xin , Chen Yi Jiao
Published: February 2022

In terms of music‐driven dance movement generation, the music movement matching model and the statistical mapping model have poor fit between the dance generated by the model and the music self. The generated dance movement is incomplete, and the smoothness and rationality of long‐term dance sequences are low. The new dance moves and other related issues cannot be generated by the traditional model. In order to address these issues, we design a dance generation algorithm based on movements and neural networks that will extract the mapping between voice and movement features. In the first stage, where the prosody features and audio beat features extracted from music are used as music features, and the coordinates of key points of the human body extracted from dance videos are used as motion features for training. In the second stage, the basic mapping of music and dance movements are realized through the generator module of the model to generate a smooth dance posture; the consistency of dance and music is realized through the discriminator module; the audio characteristics are more possessed through the Autoencoder module representative. In the third and final stage, the modified version of the model transforms the dance posture sequence into a realistic version of the dance. Finally, a realistic version of the dance that fits the music is obtained. The experimental data is obtained from dance videos on the Internet, and the experimental results are analyzed from five aspects: loss function value, comparison of different baselines, evaluation of sequence generation effect, user research, and quality evaluation of real‐life dance videos. The results show that the proposed dance generation model has a good effect in transforming into realistic dance videos.
Application of Artificial Intelligence Nuclear Medicine Automated Images Based on Deep Learning in Tumor Diagnosis
Authors: Sun Jian , Yuan Xin
Published: February 2022

In order to correctly obtain normal tissues and organs and tumor lesions, the research on multimodal medical image segmentation based on deep learning fully automatic segmentation algorithm is more meaningful. This article aims to study the application of deep learning-based artificial intelligence nuclear medicine automated images in tumor diagnosis. This paper studies the methods to improve the accuracy of the segmentation algorithm from the perspective of boundary recognition and shape changeable adaptive capabilities, studies the active contour model based on boundary constraints, and proposes a superpixel boundary-aware convolution network to realize the automatic CT cutting algorithm. In this way, the tumor image can be cut more accurately. The experimental results in this paper show that the improved algorithm in this paper is more robust than the traditional CT algorithm in terms of accuracy and sensitivity, an increase of about 12%, and a slight increase in the negative prediction rate of 3%. In the comparison of cutting images of malignant tumors, the cutting effect of the algorithm in this paper is about 34% higher than that of the traditional algorithm.
Subjective Employment Obstacle of College Students and Its Predictor Model Based on Deep Learning
Authors: Wang Yinyin
Published: February 2022

With the development of higher education in full swing, the number of college students in China is increasing, the employment pressure of college students is increasing, and the employment situation in universities is not optimistic. Subjective career obstacles are obstacles that individuals may encounter when they perceive themselves according to their own conditions and surrounding environmental factors based on their future career pursuit and goals. In this paper, it is of practical significance to use the employment confidence index of college students to analyze and predict their employment confidence. Based on DL (deep learning), a model GM-BPNN (Gray model-BP neural network) of subjective employment obstacles of college students and its predictive factors is proposed. Initially, the employment data of a university are collected and normalized. Then, GM and BPNN are used to model and predict the number of college students’ employment from different angles. Finally, the weights of the prediction results of GM and BPNN are determined, and the final prediction results of the number of college students’ employment are obtained by weighting. The results show that the relative error of the combined model is smaller and the accuracy is higher.