bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding the “Pathway” Towards a Searcher’s Learning Objective
Authors: Urgo Kelsey , Arguello Jaime
Published: January 2022

Search systems are often used to support learning-oriented goals. This trend has given rise to the “search-as-learning” movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher’s
type
of learning objective (LO) influence their trajectory (or
pathway
) toward that objective? We report on a lab study (N = 36) in which participants gathered information to meet a specific type of LO. To characterize LOs
and pathways
, we leveraged Anderson and Krathwohl’s (A&K’s) taxonomy [
3
]. A&K’s taxonomy situates LOs at the intersection of two orthogonal dimensions: (1)
cognitive process
(CP) (remember, understand, apply, analyze, evaluate, and create) and (2)
knowledge type
(factual, conceptual, procedural, and metacognitive knowledge). Participants completed learning-oriented search tasks that varied along three CPs (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). A
pathway
is defined as a sequence of
learning instances
(e.g., subgoals) that were also each classified into cells from A&K’s taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants’ think-aloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the LO on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the LO on the types of A&K cells traversed along the pathway. Third, in RQ3, we study common and uncommon
transitions
between A&K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning.
Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning
Authors: Zhang Peng , Liu Baoxi , Lu Tun , Ding Xianghua , Gu Hansu , Gu Ning
Published: January 2022

User-generated contents (UGC) in social media are the direct expression of users’ interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person’s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method’s superiority on several classification metrics compared with existing related methods.
MiDTD: A Simple and Effective Distillation Framework for Distantly Supervised Relation Extraction
Authors: Li Rui , Yang Cheng , Li Tingwei , Su Sen
Published: January 2022

Relation extraction (RE), an important information extraction task, faced the great challenge brought by limited annotation data. To this end, distant supervision was proposed to automatically label RE data, and thus largely increased the number of annotated instances. Unfortunately, lots of noise relation annotations brought by automatic labeling become a new obstacle. Some recent studies have shown that the teacher-student framework of knowledge distillation can alleviate the interference of noise relation annotations via label softening. Nevertheless, we find that they still suffer from two problems:
propagation of inaccurate dark knowledge
and
constraint of a unified distillation temperature
. In this article, we propose a simple and effective Multi-instance Dynamic Temperature Distillation (MiDTD) framework, which is model-agnostic and mainly involves two modules: multi-instance target fusion (MiTF) and dynamic temperature regulation (DTR). MiTF combines the teacher’s predictions for multiple sentences with the same entity pair to amend the inaccurate dark knowledge in each student’s target. DTR allocates alterable distillation temperatures to different training instances to enable the softness of most student’s targets to be regulated to a moderate range. In experiments, we construct three concrete MiDTD instantiations with BERT, PCNN, and BiLSTM-based RE models, and the distilled students significantly outperform their teachers and the state-of-the-art (SOTA) methods.
Visualization of Anode Effect in Aluminum Electrolysis
Authors: Gao Bingliang , Niu Hongkun , Guan Yuechao , Wang Zhaowen , Liu Jingjing , Taylor Mark P. , Chen John J. J.
Published: January 2022

The aluminum smelting industry is a significant emitter of greenhouse gases. In addition to the emission of CO2 during normal operation, perfluorocarbons (PFCs) are generated when the Al2O3 concentration in the electrolyte falls below a certain critical level, a phenomenon referred to as anode effect. Understanding the mechanism of anode effect formation has become essential to mitigating or preventing its occurrence. Here, the anode effect occurring at an industrial carbon anode with an underside surface area of 50 cm2 (10 × 5 cm) was observed in a high-temperature see-through Hall-Héroult electrolytic cell operating at 940 °C. The visual observation of the phenomenon confirmed that the anode effect is caused by the formation of an intermediate, insulating CFx film. No gas layer or large bubbles were observed during the anode effect. The sharp increase of the cell voltage is due to the insulation of the continuous intermediate unbroken film, which has relatively much higher electrical resistance than the carbon anode and electrolyte.
In Situ Corrosion Studies
Authors: Davenport Alison J.
Published: January 2022

The corrosion of metals has a major economic impact, yet the underlying processes that control the phenomenon are very difficult to study. Removal of a corroded sample from its wet environment can change it, so the results of subsequent ex situ characterization may be artifacts. In recent years, there has been considerable interest in the development of in situ techniques that allow examination of metal surfaces during the corrosion process; a few examples of such studies are given here.
Robust Adaptive Beamforming Algorithm for Sparse Array Based on Covariance Matrix Reconstruction Technology
Authors: Du Yuxi , Cui Weijia , Wang Yinsheng , Jian Chunxiao , Zhang Jian
Published: January 2022

When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (NA), etc. will fail. To solve this problem, we propose two algorithms that are suitable for a variety of SA. In the first method, assuming that the desired signal is a non-Gaussian signal, the desired signal direction vector (DSDV) is estimated using the fourth-order cumulant, and then the interference plus noise covariance matrix (INCM) is reconstructed by integrating the area outside the desired signal. When the desired signal is a Gaussian signal, we propose the second method. The second method estimates the power and direction of arrival (DOA) of the signals by performing eigenvalue decomposition on the sampled covariance matrix (SCM) and finally calculates the weight vector. However, this method needs to estimate the DOA of the signals, so it has certain requirements for the SA structure design. The simulation results show that the proposed method has good performance and strong robustness under different SA.
A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks
Authors: Zhu Difeng , Shen Guojiang , Chen Jingjing , Zhou Wenfeng , Kong Xiangjie
Published: January 2022

Due to the incomplete coverage and failure of traffic data collectors during the collection, traffic data usually suffers from information missing. Achieving accurate imputation is critical to the operation of transportation networks. Existing approaches usually focus on the characteristic analysis of temporal variation and adjacent spatial representation, and the consideration of higher-order spatial correlations and continuous data missing attracts more attentions from the academia and industry. In this paper, by leveraging motif-based graph aggregation, we propose a spatiotemporal imputation approach to address the issue of traffic data missing. First, through motif discovery, the higher-order graph aggregation model was presented in traffic networks. It utilized graph convolution network (GCN) to polymerize the correlated segment attributes of the missing data segments. Then, the multitime dimension imputation model based on bidirectional long short-term memory (Bi-LSTM) incorporated the recent, daily-periodic, and weekly-periodic dependencies of the historical data. Finally, the spatial aggregated values and the temporal fusion values were integrated to obtain the results. We conducted comprehensive experiments based on the real-world dataset and discussed the case of random and continuous data missing by different time intervals, and the results showed that the proposed approach was feasible and accurate.
The Impact of Campus Culture on the Satisfaction Level of Engineering Students: A Metaheuristics Approach
Authors: Alsulami Hemaid , Abualsauod Emad H. , Alomar Madani Abdu , Serbaya Suhail H. , Othman Asem Majed , Rizwan Ali
Published: January 2022

This research aims to assess the impact of campus culture on the satisfaction level of engineering students. Five latent variables, namely, respect, creativity, support, extracurricular activity, and individual responsibility, were used to examine the impact of campus culture. The data were collected with the help of an online questionnaire, which was distributed among male and female students. Subsequently, 304 responses were collected, which were analyzed using descriptive statistics, multicollinearity, and structural equation modeling (SEM) via AMOS 23 software. Internal consistency and reliability of each construct were done through Cronbach’s alpha, while confirmatory factor analysis was used for the validation of results. The findings of the metaheuristics-based research indicate that the campus culture has a positive impact on the satisfaction level of students (r = 0.774,

p
<
0.001

). Improving these factors reciprocally increases the satisfaction level, besides raising the education quality. This research can provide a gateway for educational leaders in focusing their resources for the best satisfaction of their students.
Subclinical Diabetic Peripheral Vascular Disease and Epidemiology Using Logistic Regression Mathematical Model and Medical Image Registration Algorithm
Authors: Jia Nailong , Fan Long , Wang Chuizhi , Fu Qimao , Chen Yan , Lin Changkun , Zhang Yupeng
Published: January 2022

The study aims to explore the effect of subclinical diabetic peripheral vascular disease and an epidemiological investigation of colour Doppler ultrasound images based on a logistic regression mathematical model and a medical image registration algorithm. Subclinical diabetes patients were selected as subjects, and after ultrasound colour Doppler ultrasonography of peripheral blood vessels, ultrasound images were taken. The experimental results show that the area under the curve (AUC) predicted by the model was 0.748, the sensitivity was 94.12%, and the specificity was 67.93%. All Δ were smaller than a single pixel. The detection rate of colour Doppler ultrasonography was 82.6%, which was significantly better than that of clinical examination (

P
<
0.01

). The age, course of disease, SBP, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), and triglyceride (TG) of the peripheral vascular disease group were significantly different from those of the no peripheral vascular disease group (

P
<
0.05

). The incidence of peripheral vascular diseases and nonperipheral vascular diseases in male patients was remarkably higher than that in female patients (

P
<
0.05

). Moreover, with the increase of age, the incidence of peripheral vascular disease and nonperipheral vascular disease in diabetic patients showed a trend of gradual increase (

P
<
0.05

). In summary, the mathematical model and registration method have high accuracy for medical image registration of patients with the diabetes epidemic. In addition, the age, course of disease, SBP, LDL-C, TG, and TC of diabetic patients were significantly different from those of normal people, which can provide a reference for the development of later diabetes epidemiology.
Two Remarks on the Infinite Approximation of a Finite World in Economic Models
Authors: Luckraz Shravan
Published: January 2022

While the assumption of infinity is prevalent in almost every area of economics, for two well‐known frameworks in decision theory, we note some fundamental differences between the finite versions and their infinite counterpart. The first is on the usage of mixed strategies in finite games, while the second is on a characterization of the truth axiom in models of information and knowledge, where the properties of belief can be related to the properties of preference.
Study of Third-Grade Fluid under the Fuzzy Environment with Couette and Poiseuille Flows
Authors: Nadeem Muhammad , Siddique Imran , Ali Rifaqat , Alshammari Nawa , Jamil Raja Noshad , Hamadneh Nawaf , Khan Ilyas , Andualem Mulugeta
Published: January 2022

In this work, fundamental ﬂow problems, namely, Couette flow, fully developed plane Poiseuille flow, and plane Couette–Poiseuille flow of a third-grade non-Newtonian ﬂuid between two horizontal parallel plates separated by a finite distance in a fuzzy environment are considered. The governing nonlinear differential equations (DEs) are converted into fuzzy differential equations (FDEs) and explain our approach with the help of the membership function (MF) of triangular fuzzy numbers (TFNs). Adomian decomposition method (ADM) is used to solve fundamental ﬂow problems based on FDEs. In a crisp environment, the current findings are in good accord with their previous numerical and analytical results. Finally, the effect of the

α

-cut

α
∈

0,1

and other engineering constants on fuzzy velocity proﬁle are invested in graphically and tabular forms. Also, the variability of the uncertainty is studied through the triangular MF.
A Case Report of COVID-19 in a Living Kidney Transplant Recipient: A Challenging Case with Complete Rehabilitation
Authors: Poor Zamany Nejat Kermaney Mahtab , Hamidi Madani Mohammad , Bonakdar Hashemi Milad , Zadmehr Alireza , Dadpour Mehdi , Roodneshin Fatemeh
Published: January 2022

Adjustment of immunosuppressive and COVID-19 treatment in terms of drug interactions is still challenging. Herein, we report a 45-year-old woman with end-stage renal disease due to autosomal dominant polycystic diseases (ADPKD) with COVID-19 and pulmonary involvement following kidney transplantation. The patient was properly treated by discontinuation of immunosuppressive drugs, bronchoscopy, and high volume of blood transfusions. The fact that we quickly used early intubation and a new treatment regimen that suppressed immune systems may help physicians develop optimal treatment strategies for similar severe cases. However, this treatment method requires more detailed evaluations due to the contradictory results in reviewing other studies.
Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network
Authors: Zhang Nan , Ren Jie , Xu Qixiao , Wu Hao , Wang Meng
Published: January 2022

Human multipose motion behavior is similar; there are many actions. However, it is difficult to recognize abnormal behavior. The existing human motion behavior anomaly recognition methods have the problems of low accuracy and being time-consuming. Therefore, an anomaly recognition method of human multipose motion behavior using Generative Adversarial Network (GAN) is proposed. The Gauss model is used to segment the human multipose motion behavior image, and the image foreground of the segmentation result is the human motion target detection result. The Shi-Tomasi algorithm is selected to extract contour feature points from human motion object detection results. The extracted contour features are set as hidden space random variables and input into the GAN. The GAN uses the generator and discriminator to recognize the multipose human motion behavior and determine whether the multipose human motion behavior is abnormal. The results show that the proposed algorithm can accurately recognize abnormal human multipose motion behavior, the recognition accuracy is higher than 99%, and the average recognition time is less than 200 ms. The shadow removal effect of the foreground image obtained by the proposed algorithm can realize the accurate recognition of human multipose motion behavior abnormalities and provide a reliable basis for research in related fields.
Recent Progress in Nanobiosensors for Precise Detection of Blood Glucose Level
Authors: Khosravi Ardakani Haniye , Gerami Mitra , Chashmpoosh Mostafa , Omidifar Navid , Gholami Ahmad
Published: January 2022

Diabetes mellitus (DM) follows a series of metabolic diseases categorized by high blood sugar levels. Owing to the increasing diabetes disease in the world, early diagnosis of this disease is critical. New methods such as nanotechnology have made significant progress in many areas of medical science and physiology. Nanobiosensors are very sensible and can identify single virus particles or even low concentrations of a material that can be inherently harmful. One of the main factors for developing glucose sensors in the body is the diagnosis of hypoglycemia in individuals with insulin-dependent diabetes. Therefore, this study aimed to evaluate the most up-to-date and fastest glucose detection method by nanosensors and, as a result, faster and better treatment in medical sciences. In this review, we try to explore new ways to control blood glucose levels and treat diabetes. We begin with a definition of biosensors and their classification and basis, and then we examine the latest biosensors in glucose detection and new biosensors applications, including the artificial pancreas and updating quantum graphene data.
Optimal Transportation Scheduling of Prefabricated Components Based on Improved Hybrid Differential Firefly Algorithm
Authors: Zou Chao , Zhu Jiwei , Ma Shaoxiong , Lou Kailun , Lu Nan , Li Liang
Published: January 2022

The prefabricated construction industry has entered a new stage of development with the support of national policies. In order to solve the optimal scheduling problem of transportation vehicles in urban multi-prefabricated component factories, in this study, the aggregation optimization algorithm is introduced, and a new hybrid optimization algorithm is designed and improved, that is, the improved hybrid difference firefly algorithm (IHDFA). On the basis of fully considering the transportation cost and road traffic impedance, the prefabricated component factory constructs a linear programming model with the shortest transportation path and the lowest comprehensive cost as the main goal to reasonably plan the driving arrangement of transportation vehicles. Finally, the IHDFA algorithm is applied to a specific example and compared with the genetic algorithm, firefly algorithm, differential evolution algorithm, particle swarm algorithm, and hybrid differential firefly algorithm. The results show that the IHDFA proposed in this study effectively reduces the comprehensive transportation cost of prefabricated components, which verifies the practicability and effectiveness of the optimization model and algorithm.
A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning
Authors: Yang Weiwei , Song Haifeng , Du Lei , Dai Songsong , Xu Yingying
Published: January 2022

With the rapid development of remote sensing technology, change detection (CD) methods based on remote sensing images have been widely used in land resource planning, disaster monitoring, and urban expansion, among other fields. The purpose of CD is to accurately identify changes on the Earth’s surface. However, most CD methods focus on changes between the pixels of multitemporal remote sensing image pairs while ignoring the coupled relationships between them. This often leads to uncertainty about edge pixels with regard to changing objects and misclassification of small objects. To solve these problems, we propose a CD method for remote sensing images that uses a coupled dictionary and deep learning. The proposed method realizes the spatial-temporal modeling and correlation of multitemporal remote sensing images through a coupled dictionary learning module and ensures the transferability of reconstruction coefficients between multisource image blocks. In addition, we constructed a differential feature discriminant network to calculate the dissimilarity probability for the change area. A new loss function that considers true/false discrimination loss, classification loss, and cross-entropy loss is proposed. The most discriminating features can be extracted and used for CD. The performance of the proposed method was verified on two well-known CD datasets. Extensive experimental results show that the proposed method is superior to other methods in terms of precision, recall, F1-score,

IoU

, and

OA

.
Machine Learning-Aided Energy Efficiency Strategy for Multiuser Cooperative Networks
Authors: Shan Lin , Zhao Ou , Temma Katsuhiro , Kojima Fumihide , Adachi Fumiyuki , Matsumura Takeshi
Published: January 2022

Cooperative communication is widely seen as a promising key technology for improving the energy efficiency of battery-driven multiple mobile terminals (MTs). In this study, we investigate the use of machine learning (ML) in multiuser cooperative access networks. Because MT cooperation and bandwidth allocation are considered two main issues in such networks, we design an ML-aided method to solve the bandwidth issues so that the proposed method can maximize the network’s energy efficiency. Specifically, we use machine learning with artificial neural network (ANN) trained at base station (BS) (a) to decide whether MTs in the heterogeneous access network should cooperatively communicate and (b) to determine the optimal bandwidth allocation for this communication by distributing the trained ANN to all MTs. The computer simulation results show that under the described communication environment in this paper, the proposed method can provide 99.8% correct prediction for MT cooperation and output the optimal bandwidth allocation with at least 88% accuracy, which demonstrates the effectiveness of the proposed method. Besides, the simulations also show that the proposed method can provide about 14%–25% power consumption reduction, which validates the EE performance of the proposed method.
A Stage-Structured Fishery Model for African Catfish and Nile Tilapia Feeding on Two Food Resources with Harvesting
Authors: Nankinga L. , Luboobi L. S. , Mugisha J. Y. T. , Nannyonga B. , Carlsson L.
Published: January 2022

In this paper, a fishery model for African catfish and Nile tilapia is formulated. This model is used to compare financial profit and biomass outtakes in a two-species system versus single species systems. We consider a stage-structured fish population model consisting of the aforementioned fish species together with two food resources. The model dynamics include cannibalism, predator-prey, feeding, reproduction, maturation, development, mortality, and harvesting. We prove consistency of the model in the sense that the solutions will stay bounded and nonnegative over time. Conditions for local stability of fish-free equilibrium point are established. The simulation results reveal asymptotically stable solutions with coexistence of African catfish, Nile tilapia, and two food resources. The major conclusion from our findings is that fisheries should culture both species to maximize the biomass outtake and financial profit.
Design of the Secure Smart Home System Based on the Blockchain and Cloud Service
Authors: Liao Kun
Published: January 2022

In this paper, a variety of cloud service combinations is used to form the control core of a smart home, realize data forwarding, storage, and analysis in the cloud, and complete the remote management of smart home devices. This paper studies the secure access control of smart home data combined with the blockchain technology and password technology to realize the secure and efficient access control of smart home data. The system can achieve the goal of independent research and development. Aiming at the problem that the access control of a smart home is generally managed by third-party authorized institutions and there is an unauthorized access, a blockchain-based smart home (BSH) access control scheme is proposed. The scheme extends the attribute-based access control model (ABAC) and applies the blockchain technology to the smart home ABAC model to realize fine-grained access control. In the scheme, the resource provider first publishes the access control policy of the resource to the blockchain. If the resource visitor wants to access the resource, he needs to submit an access request to the blockchain and use an SM2 threshold signature to process the transaction proposal. The endorsement peer node in the blockchain runs the access control policy smart contract to decide whether to grant access rights. This scheme can ensure that resource providers can participate in all the processes of access control and avoid the risk of unauthorized access caused by the centralized management of the third party. Finally, the simulation experiment is carried out on the Hyperledger Fabric alliance chain development platform. The results show that the BSH access control scheme has good applicability in the smart home scenario.
Endostatin Inhibits Blood-Retinal Barrier Breakdown in Diabetic Rats by Increasing the Expression of ICAM-1 and VCAM-1 and Decreasing the Expression of VEGF
Authors: Zhou Jinhua , Xie Zhenggao
Published: January 2022

Objective. Endostatin has become the strongest endogenous angiogenesis inhibitor due to suppressing VEGF expression. The purpose of this study was to assess the impact of endostatin on the blood-retinal barrier (BRB) in diabetic rats. Methods. SD rats were induced to develop diabetes by streptozotocin, and endostatin was administrated by intravitreal injection. The body weight, the level of blood glucose, the expressions of C-reactive protein (CRP), adhesion molecules intercellular cell adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1), junction proteins (occludin, claudin-5, and zonula occluden-1), and VEGF were measured in rats’ retinas of diabetes. The BRB breakdown was evaluated using Evans blue. Results. The level of CRP and adhesion molecules (ICAM-1 and VCAM-1) was increased in retinas of diabetic rats, while endostatin significantly inhibited the upregulation of these. Diabetes increased the BRB permeability and retinal thickness. Diabetes also decreased the levels of occludin, claudin-5, and ZO-1 in retinals. These changes were inhibited by endostatin treatment. Upregulation of vascular endothelial growth factor (VEGF), transforming growth factor-β (TGF-β), and protein kinase C- (PKC-) β2 was also reversed by endostatin in retinas of diabetic rats. Conclusions. Endostatin provides protection against diabetic retinopathy, which may involve its barrier-enhancing effects.
Trust-Region Method for Load Flow Solution of Three-Phase Unbalanced Electric Power Distribution System
Authors: Gianto Rudy , Purwoharjono
Published: January 2022

At present, the electric power system is getting bigger and more complex, and its loading is also increasing. As a consequence, planning, operation, and control of the power system also become more complicated. It is known that system planning and operation are mostly based on the steady-state condition of the power system, and the system steady-state condition can only be determined from the load flow study. Thus, the development of a reliable and efficient method to solve the load flow problem is necessary so that the system steady-state condition can properly be evaluated. Since the characteristics of the electric distribution system are different from those of the transmission system, special treatments are usually required in the distribution system load flow (DSLF) analysis. In this context, several interesting techniques have been proposed in the analysis. In this paper, the application and extension of the trust-region method to solve the three-phase DSLF problem are proposed and investigated. Case studies using 19-node, 25-node, and 123-node distribution systems are also given in this paper. Results of the studies show that the output values obtained by the proposed method are in excellent agreement with those obtained by previously published methods. These results confirm the validity of the proposed method. Case study results also indicate that the proposed method has better computational performances than the forward/backward sweeping (FBS) method.
Comparison of Three Energy-Saving Strategies Based on Models Established for In Situ Gas Thermal Remediation of Contaminated Soil
Authors: Zhang Haijing , Zhang Xiang , Yin Yao , Liu Gaojie , Zhu Yu , Tan Xuejun , Huang Yuan
Published: January 2022

Soil pollution has become an issue of concern with the development of industrialization. In situ gas thermal remediation is a suitable remediation technology for heavily organic contaminated soil, yet its high energy consumption limits the application. In this study, three energy-saving strategies, off-gas burn-back mode, heat-returning mode, and air-preheating mode, were proposed, and their natural gas consumption and energy utilization ratio were analysed. A mathematical model was established for the heat and mass transfer in unsaturated soil by employing conjugate heat transfer. The temperature of the soil and flue gas, and the concentration of components in the soil and off-gas were simulated, which indicated the thermal behavior of the contaminated soil and the timing for operation control. Models of burners were also developed under different energy-saving modes for energy analysis. By comparing the basic method, adopting the off-gas burn-back mode obtained an energy-saving effect of 3.37%, which relied on the content and calorific value of the pollutant in the off-gas. Under combustion air-preheating ratios of 0.5 and 1.0, natural gas could only be saved by 13.56% and 18.88%, respectively. In general, the most effective energy-saving measure was the heat-returning mode, by which 21.44% of natural gas could be saved when the reflux ratio of the flue gas was 0.5.
Study on the Relation between Tailing Particle Size and Tailing Slurry Underflow Concentration
Authors: Zou Yi , Liu Qi , Liu Weijun
Published: January 2022

In order to study the effect of the physical properties of tailings on the concentration of tailing slurry underflow, the median particle size, control particle size, and density of the tailings of 9 typical mines were measured. Besides, static settlement test and dynamic thickening test of the tailing slurry of these 9 mines were separately conducted. The test results show that the underflow concentration of the tailing slurry is unrelated to the tailing density but is related to the tailing particle size. The finer the tailing particle size, the lower the underflow concentration. Through the nondimensional regression analysis, 3 relation equations were obtained: number 1 relation equation between the median particle size of tailings and the underflow concentration in the static settlement test, number 2 relation equation between the control particle size of tailings and the change in concentration in the static settlement test, number 3 relation equation between the underflow concentration in the static settlement test and the underflow concentration in the dynamic thickening test under different overflow water solid content conditions. Comparing the calculation results with the test results, it is found that the relative error between the predicted value and the test value does not exceed 6.1%, which has proved that the relation equations derived are convenient and feasible. The underflow concentration of the deep cone thickener can be predicted by merely measuring the particle size of the tailings, which means that the relation equations are highly recommended for wider use and application.
Changes of Physical Function and Quality of Life in Patients with Type 2 Diabetes after Exercise Training in a Municipality or a Hospital Setting
Authors: Molsted Stig , Jensen Trine Munk , Larsen Jane Sedum , Olesen Louise Bockhoff , Eriksen Sofie Bjerre Milling , Rehling Thomas , Rasmussen Signe Sætre , Aadahl Mette
Published: January 2022

Introduction. The aim was to compare changes in physical function and quality of life (QOL) after an exercise training programme to patients with type 2 diabetes mellitus (T2DM) in a municipality and a hospital setting and to compare the patients’ physical function and QOL with an age- and sex-matched general population. Methods. Patients with T2DM were stratified to exercise training in a municipality (

n
=
26

) or a hospital (

n
=
46

), respectively. The training was one hour twice weekly for 12 weeks. The outcomes were physical function (30 sec chair stand test (CST)) and QOL (using the SF-36). The data for the general population were collected from previous reference studies. Results. Fifty-one (71%) participants completed the intervention. The CST results improved in both groups with no difference between the municipality and hospital groups (1.6 [0.1; 3.1] vs. 3.5 [2.3; 4.8] no., respectively,

p
=
0.062

). The QOL scales physical function and general health increased more in the municipality group than in the hospital group (10.5 [2.8; 18.2] vs. -1.2 [-7.9; 5.5], respectively,

p
=
0.031

, and 8.3 [2.3; 14.4] vs. -0.2 [-5.6; 5.1], respectively,

p
=
0.042

). Dropout (

n
=
21

) during the intervention was associated with reduced QOL at baseline. The patients’ CST results at baseline were reduced compared to the general population (

11.8
±
3.5

vs.

18.9
±
3.3

, respectively,

p
<
0.001

). All QOL scales apart from social function were reduced in the patients compared to the general population. Conclusion. Patients in a 12-week exercise training programme in a hospital or a municipality setting had significantly lower QOL compared to an age- and sex-matched population sample. Similar improvements in physical function were observed in patients after completion of the exercise programme irrespective of exercise setting, whereas patient exercising in a municipality setting had higher positive changes in QOL than patients undergoing the same exercise programme in a hospital setting.
Adsorption Characteristics of Chitosan-Modified Bamboo Biochar in Cd(II) Contaminated Water
Authors: Huang Anxiang , Bai Wenlian , Yang Shoulu , Wang Zhongwei , Wu Nengying , Zhang Yanxiong , Ji Ning , Li Dan
Published: January 2022

The purpose of this study was to fabricate a low-cost and eco-friendly adsorbent using bamboo biochar (BB), a kind of charcoal composed of high Brunauer–Emmett–Teller surface area and variety of functional groups, and chitosan as substrates for remediation of Cd(II) in Cd(II) contaminated water and characterized the functional group characteristics, surface morphology, and Cd(II) adsorption effect using the Fourier transform infrared (FT-IR), scanning electron microscope (SEM), and energy-dispersive X-ray spectrometer (EDS). Results showed that chitosan-modified bamboo biochar (CBB) provided more active adsorption sites (such as –NH2, –COOH, –OH, and C=O) on the surface to enhance the Cd(II) removal efficiency in Cd(II) contaminated wastewater. Meanwhile, the optimal pH, contact time, and dose of CBB on the Cd(II) removal efficiency are 7, 120 min, and 600 mg, respectively. In addition, the adsorption isotherm results revealed that the possible adsorption mechanisms might include surface adsorption, electrostatic adsorption, and ion exchanges. Furthermore, the maximum adsorption capacity (Qm) values predicted from the Langmuir model were 37.74 and 93.46 mg/g for BB and CBB, respectively, also indicating a potential application of CBB in practical wastewater. Desorption and regeneration of CBB were attained simultaneously and the results showed that even after five cycles of adsorption-elution, the adsorption and desorption of CBB exhibited a slight decline and still reached at 71.70% and 65.92%. Results from this study would provide a reference to functionalized CBB for Cd(II) adsorption in contaminated water.
Evaluating the Effects of Heat-Clearing Traditional Chinese Medicine in Stable Bronchiectasis by a Series of N-of-1 Trials
Authors: Zi Suna , Huang Haiyin , Yang Peilan , Xu Minhua , Wu Yingen , Wang Zhenwei , Ge Fei , Chen Xinlin
Published: January 2022

Purpose. The purpose of this study is to study the effects of heat-clearing Traditional Chinese Medicine (TCM) in the stable stage of bronchiectasis via N-of-1 trials. Methods. The N-of-1 trials in this study were randomized and double-blinded with crossover comparisons consisting of three pairs. Each pair was of two 4-week periods. Each patient took the individualized decoction in the experimental period and the individualized decoction was removed of heat-clearing drugs, mainly including heat-clearing and detoxifying drugs, in the control period for three weeks. After three weeks, the patients stopped taking the decoction for one week. The primary outcome was from patients’ self-reporting symptoms scores on a 1–7-point Likert scale. Mixed-effects models were used to conduct statistical analysis on these N-of-1 trials. Results. Of the 21 patients enrolled, 15 completed three pairs of N-of-1 trials (71.43%). (1) Seen from the individual level, no statistical difference between the experimental decoction and the control (

P

 > 0.05) was observed. However, 5 patients found better decoctions according to the clinical criteria. (2) As revealed by the group data of all the N-of-1 trials, the control was better than the individualized decoction in terms of symptom scores on the Likert scale (1.94 ± 0.69 versus 2.08 ± 0.68,

P

 = 0.04, mean difference, and 95% CI: 0.19 (0.01, 0.37)) and on CAT scores (13.66 ± 6.57 versus 13.95 ± 6.97,

P

 = 0.04, mean difference, and 95% CI: 0.86 (0.042, 1.67)), but such differences were not clinically significant. The other outcomes, such as Likert scale score of respiratory symptoms and 24-hour sputum volume, showed no statistical difference. Conclusion. The experimental design of this study can make the TCM individualized treatment fully play its role and can detect the individualized tendencies according to the severity of phlegm and heat in some subjects. With the intermittent use or reduced use of heat-clearing drugs, most of the subjects, at the group level, enrolled in the series of N-of-1 trials may improve the symptoms and quality of life while saving the cost of TCM and reducing the potential side effects of heat-clearing TCM. This trial is registered with clinicaltrials.goc (NCT03147443).
Applications of Big Data in Economic Information Analysis and Decision-Making under the Background of Wireless Communication Networks
Authors: Deng Yaotian , Zheng Han , Yan Jingshi
Published: January 2022

Owing to the growing volumes of mobile telecommunications customers, Internet websites, and digital services, there are more and more big data styles and types around the world. With the help of big data technology with high semantic information, this paper focuses on exploring the value and corresponding application of big data in finance. By comparing with the existing methods in terms of search speed and data volume, we can effectively see the effectiveness and superiority of the algorithm proposed in this paper. Furthermore, the algorithm proposed in this paper can provide some reference ideas for the follow-up-related research work.
Dynamics of 2SIH2R Rumor-Spreading Model in a Heterogeneous Network
Authors: Wang Yan , Qing Feng , Yan Ming
Published: January 2022

Due to the development of social media, the threshold for information dissemination has become lower than ever before. As a special kind of information, rumors are usually harmful and are usually accompanied by a high degree of ambiguity that makes them difficult to immediately identify, but “rumors stop at wise men.” When someone identifies a rumor as false and begins spreading the truth instead, a confrontational relationship obtains between the rumor and the truth that leads to the stifling of the former. Given this, we developed a 2SIH2R model in this study that contains mechanisms of discernment and confrontation in a heterogeneous network to examine the dissemination of the rumor and the truth. By using mean-field equations of the 2SIH2R model, the threshold of the spreading of each can be determined separately in three cases. The results of a numerical simulation show that under the same conditions, the greater is the mechanism of discernment or confrontation, the smaller is the instantaneous maximum influence and the final range of influence of the rumor. It can be also concluded that the earlier release of the truth about the event by the government can significantly control the rumor. Secondly, it is more effective to publish the truth in advance than after the rumor has appeared. Thirdly, it is more important for the government to increase education and improve the ability of citizens to reveal the rumor than to increase the spread of the truth after the rumor occurs. These results can be used to help reduce the harmful effects of rumors.
lncRNA-NEAT1 Sponges miR-128 to Promote Inflammatory Reaction and Phenotypic Transformation of Airway Smooth Muscle Cells
Authors: Song Danyang , Jiang Yajing , Zhao Qiuju , Li Jinling , Zhao Yuqi
Published: January 2022

Objective. Pediatric asthma is still a health threat to the children. Long noncoding RNA-NEAT1 (lncRNA-NEAT1) was reported to be positively correlated with the severity of asthma. We aimed to study the effects and mechanism of lncRNA-NEAT1on inflammatory reaction and phenotypic transformation of airway smooth muscle cells (ASMCs) in the bronchial asthma. Method. The degree of lncRNA-NEAT1 and miR-128 mRNA in children with bronchial asthma and healthy individuals was tested by qRT-PCR. After the inflammatory reaction and phenotypic transformation of PDGF-BB-induced ASMCs, the expression of lncRNA-NEAT1 or miR-128 in the AMSC was disturbed in the AMSC. Subsequently, the expression of lncRNA-NEAT1 and miR-128 was detected by the way of qRT-PCR, and western blot was applied to measure the expression of MMP-2, MMP-9, α-SMA, calponin, NF-κB, and so on in the cells. The content of TNF-α, IL-1β, IL-6, and IL-8 in the cell culture supernatant was checked by ELISA. MTT, Transwell, and flow cytometry were used to detect cell proliferation, migration, and apoptosis. Further, the targeting relations between lncRNA-NEAT1 and miR-128 were evaluated by the dual-luciferase reporter assay. Result. In the sputum of children with bronchial asthma, lncRNA-NEAT1 was significantly upregulated while miR-128 was rapidly downregulated. Besides, lncRNA-NEAT1 and miR-128 were competitively combined and, for their expression, negatively correlated. Conclusion. lncRNA-NEAT1 sponges miR-128 to boost PDGF-BB-induced inflammatory reaction and phenotypic transformation of ASMCs to aggravate the occurrence and development of childhood bronchial asthma.
An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model
Authors: Cao Jinyang
Published: January 2022

As an important component of family business management, human resource allocation greatly affects all aspects of the company’s strategic direction and organizational structure. Therefore, the optimal allocation of core human resources in family businesses has become a topic of great practical significance. Based on the Markov model, this article is based on the B/S architecture, designed the three‐tier architecture of the presentation layer, business logic layer, and data layer adopted by the system, forming the basis of quality management, job management, performance management, and salary management‐oriented human resources optimization configuration system. Taking family business A as an example, the Markov chain is used to predict the flow of personnel in the company in the next three years. The results show that with the development of normalization and the improvement of the management level, the demand for personnel in the family business will decrease year by year. Competency analysis of human resources positions found that grassroots employees have higher requirements for competency, but at the same time, lower performance requirements, while company seniors need relatively low requirements for competence indicators and higher requirements for performance. In addition, through data calculation and prediction, it is found that the prediction accuracy of the model algorithm is 81.37% on average. Compared with other model algorithms, the Markov model algorithm has the highest accuracy in predicting the state, and the accurate prediction results make the recruitment work predictable. The system makes up for the shortcomings of traditional human resource management data, such as insufficient data, low accuracy, and inability to achieve homogeneity. It uses a centralized database to comprehensively and organically link human resource management‐related information to provide a basis for future family business human resource optimization.
Evaluation of Amine Functionalized Thermal Power Plant Solid Waste for Industrial Wastewater Remediation
Authors: Kozbek Ali Y. , Şahin Kubilay , Sarı Ertuğrul , Bedir Esra , Yüce Fatma G. , Çitoğlu Senem , Duran Hatice
Published: January 2022

Micro/nanoparticles generated after the combustion of coal/lignite in the thermal power plants were modified with amino groups of (3-aminopropyl) triethoxysilane (APTES). These silane-based functional particles were applied in textile dye (xylenol orange, XO and methyl orange, MO) removal process to deal with an industrial wastewater problem. The maximum adsorption efficiencies of APTES coated micro/nanoparticles for MO and XO dye molecules were calculated to be around 98% and 75%, respectively. The adsorption behavior of the LCFA against dyes is also assessed by investigating the effect of adsorbent dosage, contact time, pH, and temperature. The optimum dye removal was observed at a pH of 4.0, and the equilibrium was achieved within 5 min. The maximum uptake capacities of LCFA-APTES for MO and XO dye molecules were calculated to be around 17.91 and 14.72 mg g−1, respectively. This value is approximately 3 − 5 times higher than the similar adsorbent in the literature. The uptake mechanism of MO and XO dyes onto LCFA-APTES is governed by electrostatic interaction and hydrogen bonding between dye molecules and APTES. The surface chemical modifications and the nature of functional groups were ascertained by scanning electron microscopy (SEM), thermogravimetric analysis (TGA), X-ray fluorescence (XRF), and X-ray photoelectron spectroscopy (XPS). The application of recovered micro/nanoparticles from solid wastes and their utilization for wastewater treatment is important not only for economy of developing countries but also for protecting the environment.