bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards a full general relativistic approach to galaxies
Authors: Astesiano Davide , Cacciatori Sergio L. , Gorini Vittorio , Re Federico
Published: June 2022

We analyse the dynamics of a single disk galaxy from a general relativistic viewpoint. We investigate dark matter (DM) effects in terms of a known family of stationary axially-symmetric solutions of Einstein equations coupled to a rotating dust. These effects are generated by the non-Newtonian features of such solutions and are ascribed to the essential role of frame dragging. Indeed, in such models, the off-diagonal elements of the metric are, in general, of the same order of magnitude of the diagonal ones. We generalize the results of Balasin and Grumiller (BG) to the physical case of differentially rotating dust. In particular, we find that for differential rotation the amount of energy density required to account for the flat rotation curves of disk galaxies is reduced with respect to the BG rigid rotation case. This stresses the discrepancy between Newtonian gravity and general relativity (GR), even at low velocities and low energy densities.
Pseudorapidity distribution of energy density and distribution of charged particles in different categories of the final state in pp collisions at s = 13 TeV
Authors: Ajaz M. , Khubrani A. M. , Waqas M. , Yasin Z. , Mughal B. J. , Hassan S. , Ali S. , Suleymanov M. K.
Published: June 2022

In this paper, we present here a detailed study of several observables as a function of pseudorapidity from several hadron production models commonly used for extended air shower simulations and compare these predictions with [Formula: see text] collisions measured by the CMS experiment at [Formula: see text] TeV. To facilitate the model/data comparisons, the pseudorapidity binning used in the energy density spectra data was also applied to the Monte Carlo analysis ([Formula: see text] and [Formula: see text]). Given that no model currently describes the full breadth of available hadronic collision data, four independent models were used in the comparison study, each with differences in the handling of soft quantum chromodynamics processes, in particular. The analysis results show that predictions from EPOS-LHC provide good agreement with the data, in contrast with the other models used in the study (QGSJETII-04, DPMJET-3.19, Sibyll2.3d). Significant modifications may be required in the rest of the model-based generators for them to provide a better description of the experimental observations.
4-Move distance of knots
Authors: Kanenobu Taizo , Takioka Hideo
Published: June 2022

A [Formula: see text]-move is a local change for knots and links which changes 4 half twists to 0 half twists or vice versa. In 1979, Yasutaka Nakanishi conjectured that every knot can be changed by [Formula: see text]-moves to the unknot. This is still an open problem. In this paper, we consider the [Formula: see text]-move distance of knots, which is the minimal number of [Formula: see text]-moves needed to deform one into the other. In particular, the [Formula: see text]-move unknotting number of a knot is the [Formula: see text]-move distance to the unknot. We give a table of the [Formula: see text]-move unknotting number of knots with up to nine crossings.
RISK CONTAGION DUE TO OVERLAPPING PORTFOLIOS WITH LEVERAGE DECISION
Authors: ZHU ZIYAN , LIU XIAOXING
Published: June 2022

Since the subprime mortgage crisis, it is urgent to quantify the systemic financial risk and the network externality from the vulnerable perspective. Based on the overlapping portfolio, this study constructs an optimal leverage decision-making model with deleveraging and the fire sales mechanism to describe the liquidity risk spillover effects. Through the empirical results, the institution of a larger size should be more systematically important. Higher leverages account for more vulnerability, while institutions with higher interest ratios of revenue or cost and capital adequacy ratios would be more invulnerable.
New Ant Colony Optimization Algorithms for Designing Two-Stage Supply Chain with Fixed Costs
Authors: Mostafa Ahmed , Elshaer Raafat
Published: June 2022

Supply Chain Networks Design (SCND) is a systematic approach for finding the best position and size for facilities to ensure optimal product flow using mathematical modeling. Three ant colony-based algorithms, ACO1, ACO2, and ACO3, are used to design supply chain networks in this paper. ACO2 and ACO3 are developed based on two new pheromone trails and one heuristic trail. To measure the effectiveness of the proposed algorithms, a numerical study is performed on generated problem cases, and the results are compared to those obtained using LINGO. The proposed algorithms provide a significantly better solution with a difference of about 3.04% for ACO1, 1.78% for ACO2, and 1.65% for ACO3 on average from the exact solution and in a very short time compared to Lingo. The computational analysis shows that ACO2 and ACO3 give better results than ACO1.
Prediction model for synergistic anti-tumor multi-compound combinations from traditional Chinese medicine based on extreme gradient boosting, targets and gene expression data
Authors: Sun Mengqiu , She Shengnan , Chen Hengwei , Cheng Jiaxi , Ji Wei , Wang Dan , Feng Chunlai
Published: June 2022

Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic compound combinations from complex components of TCM. In this study, a prediction model based on extreme gradient boosting (XGBoost) algorithm was constructed by integrating gene expression data of different cancer cell lines, targets information of natural compounds and drug response data. Radix Paeoniae Rubra (RPR) was selected as a model herbal sample to evaluate the reliability of the constructed model. The optimal XGBoost prediction model achieved a good performance with Mean Square Error (MSE) of 0.66, Mean Absolute Error (MAE) of 0.61, and the Root Mean Squared Error (RMSE) of 0.81 on test dataset. The superior synergistic anti-tumor combinations of D15 (Paeonol[Formula: see text][Formula: see text][Formula: see text]Ethyl gallate) and D13 (Paeoniflorin[Formula: see text][Formula: see text][Formula: see text]Paeonol) were successfully predicted from RPR and experimentally validated on MCF-7 cells. Moreover, the combination of D13 could work as a main contributor to a synergistic anti-proliferative activity in the compatibility of RPR and Cortex Moutan (CM). Our XGBoost model could be a reliable tool for the efficient prediction of synergistic anti-tumor multi-compound combinations from TCM.
Synthesis of porphyrin(2.1.2.1) with embedded naphthalene
Authors: Lv Xiaojuan , Liu Ningchao , Xiao Bentian , Morimoto Hirofumi , Kuzuhara Daiki , Aratani Naoki , Yamada Hiroko , Qiu Fengxian , Xue Songlin
Published: June 2022

Expanded nonaromatic stable porphyrins(2.1.2.1) were successfully synthesized by 2,3-di(1H-pyrrol-2-yl)naphthalene as buliding blocks and aromatic aldehydes under acid catalyzed condensation conditions in acceptable isolated yields. NMR, X-ray diffraction analysis, absorption, electrochemical, and density functional theory revealed these macrocycles to be non-aromatic due to highly saddle-shaped molecular structures of dinaphthoporphyrin(2.1.2.1). The saddle-shaped dinaphthoporphyrins(2.1.2.1) with embedded naphthalene units as [Formula: see text]-conjugation units show perturbations to molecular structure, optical and electronic properties. The dinaphthoporphyrins(2.1.2.1) are effective macrocyclic ligands giving copper(II) and nickel(II) complexes.
Absolute Cesàro summability via quasi-power increasing sequences
Authors: Özarslan Hi̇kmet Seyhan
Published: June 2022

In this paper, a general theorem dealing with [Formula: see text] summability of an infinite series is proved under weaker conditions by using a quasi-[Formula: see text]-power increasing sequence instead of an almost increasing sequence.
A framework for development of digital twin industrial robot production lines based on a mechatronics approach
Authors: Zhao Yuan , Liu Yongkui , Feng Jianyong , Guo Jinwei , Zhang Lin
Published: June 2022

During the past years, intelligent manufacturing has attracted enormous attention from both academia and industry. Smart factories/workshops/production lines are important carriers for realizing intelligent manufacturing. However, there are difficulties in smart factories in processing multi-source heterogeneous data, in integrating virtual and physical worlds, and in achieving continuous and iterative optimization with real-time feedback. Digital twin, which is able to deeply integrate physical and virtual worlds, provides an important technology for easing the difficulties mentioned above. The construction of a digital twin system can solve the problem of data fusion, analysis, processing, and applications in smart factory. Digital production lines are the foothold of a smart factory. Building a digital twin production line can facilitate rapid practice of digital twins and promote the development of intelligent manufacturing. To this end, this paper proposes a development framework for a digital twin production line with closed-loop control based on a mechatronics approach. The detailed development process is given based on the framework. The framework proposed may provide some inspiration and insights for design and development of digital twin production lines.
A Parkinson’s disease diagnosis approach for nonequilibrium gait data
Authors: Wang Sha
Published: June 2022

An important feature of Parkinson’s disease (PD) patients is dyskinesia, and gait signal analysis can provide a strong basis for the diagnosis and rehabilitation of Parkinson’s disease. Traditional machine learning methods are not suitable for directly classifying imbalanced data. In order to accurately distinguish healthy people from Parkinson’s disease patients, a cost-sensitive support vector machine (CS-SVM) method is designed in this paper, which is used to construct the model for classification of gait signals between Kinson’s disease patients and healthy individuals. Gait data for the entire subject was extracted from a real U-shaped electronic walkway. The extracted features are converted to a dimensionless form, the classification performance can be improved. The experimental results show that the prediction accuracy and F-measure obtained by the CS-SVM method are 94.16% and 87.08%, respectively.
Investigation of mechanical behavior of carbon nanotube-reinforced magnesium composite
Authors: Agarwal Avi , Garg Sajal , Wassan Harshit , Joshi Preeti , Tyagi R. K.
Published: June 2022

Metal matrix-based carbon nanotube (CNT) composites are being extensively explored because of their superior mechanical properties. Among metals, magnesium due to its low density and low melting point is widely used in making light weight composite material. Addition of small amount CNTs in magnesium matrix can produce high strength, light weight material. In this work, mechanical properties of CNT/Magnesium composites are being investigated. Three-dimensional representative volume element is extracted from the composite, for the analysis. Four different types of models are constructed, each made of square shape consisting of 16 CNTs. Adequate properties of single-walled CNT/Magnesium composite are examined with RVE undergoing stretch and transverse loading conditions. The results are obtained for different percentages of CNT volume fraction in magnesium matrix. For closely simulating the properties of composite material, the interphase is developed between CNT and matrix material and random orientation of carbon nanotubes in the matrix is considered. With the insertion of 2.4% of CNTs in the magnesium matrix material, 31.64% rise in Young’s modulus is observed. The results acquired from the present analysis are validated with the rule of mixture results. The present results are observed to be in line with the experimental results from the literature.
Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series
Authors: Tipirneni Sindhu , Reddy Chandan K.
Published: June 2022

Multivariate time-series data are frequently observed in critical care settings and are typically characterized by sparsity (missing information) and irregular time intervals. Existing approaches for learning representations in this domain handle these challenges by either aggregation or imputation of values, which in-turn suppresses the fine-grained information and adds undesirable noise/overhead into the machine learning model. To tackle this problem, we propose a
S
elf-supervised
Tra
nsformer for
T
ime-
S
eries (STraTS) model, which overcomes these pitfalls by treating time-series as a set of observation triplets instead of using the standard dense matrix representation. It employs a novel Continuous Value Embedding technique to encode continuous time and variable values without the need for discretization. It is composed of a Transformer component with multi-head attention layers, which enable it to learn contextual triplet embeddings while avoiding the problems of recurrence and vanishing gradients that occur in recurrent architectures. In addition, to tackle the problem of limited availability of labeled data (which is typically observed in many healthcare applications), STraTS utilizes self-supervision by leveraging unlabeled data to learn better representations by using time-series forecasting as an auxiliary proxy task. Experiments on real-world multivariate clinical time-series benchmark datasets demonstrate that STraTS has better prediction performance than state-of-the-art methods for mortality prediction, especially when labeled data is limited. Finally, we also present an interpretable version of STraTS, which can identify important measurements in the time-series data. Our data preprocessing and model implementation codes are available at
https://github.com/sindhura97/STraTS
.
Chipnet: Enabling Large-scale Backscatter Network with Processor-free Devices
Authors: Song Yihang , Song Chao , Lu Li , Yang Shen , Li Songfan , Zhang Chong , Meng Qianhe , Shao Xiandong , Wang Haili
Published: June 2022

Differing from tremendous existing works that mainly focus on optimizing backscatter communication, Radio-to-Bus (R2B) communication utilizes backscatter to offload processors from IoT devices to the gateway, achieving processor-free devices of significantly reduced power and hardware cost. However, R2B communication is not suitable for large-scale backscatter networks, since R2B cannot support parallel and long-range communication between the gateway and hundreds of R2B devices. In this article, we present Chipnet, a network that supports hundreds of long-range and concurrent connections between the gateway and multiple processor-free devices. The high-level design of Chipnet includes a parallel frequency-division uplink mechanism that can work on processor-free devices and a processor-free MAC layer protocol that supports gateway to broadcast downlink data and individually manage each processor-free device. This design addresses practical issues facing the processor-free device architecture, such as synchronizing hundreds of processor-free devices, assigning unique channel frequencies to every device, and realizing power-efficient processor-free signal conversion. The results demonstrate that a Chipnet network can achieve a task throughput of 2,400 tasks/s with a latency of 72.23 ms. Compared with the R2B network, Chipnet achieves 3×–5× improvements in network coverage range and two orders of magnitude improvement in both network throughput and network latency.
Learnable Graph-Regularization for Matrix Decomposition
Authors: Zhai Penglong , Zhang Shihua
Published: June 2022

Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields, including computer vision, text mining, bioinformatics, and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations. In order to make the learned representations inherit the structures in the original data, graph-regularization terms are often added to the loss function. However, the prior graph construction often fails to reflect the true network connectivity and the intrinsic relationships. In addition, many graph-regularized methods fail to take the dual spaces into account. Probabilistic models are often used to model the distribution of the representations, but most of previous methods often assume that the hidden variables are independent and identically distributed for simplicity. To this end, we propose a learnable graph-regularization model for matrix decomposition (LGMD), which builds a bridge between graph-regularized methods and probabilistic matrix decomposition models for the first time. LGMD incorporates two graphical structures (i.e., two precision matrices) learned in an iterative manner via sparse precision matrix estimation and is more robust to noise and missing entries. Extensive numerical results and comparison with competing methods demonstrate its effectiveness.
Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches
Authors: Rejiba Zeineb , Chamanara Javad
Published: June 2022

Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. To determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by academia or the industry to drive further research and development activities in the area of custom scheduling in Kubernetes.
Human Umbilical Cord Mesenchymal Stem Cell-Derived Extracellular Vesicles Carrying MicroRNA-181c-5p Promote BMP2-Induced Repair of Cartilage Injury through Inhibition of SMAD7 Expression
Authors: Zhang Qiang , Cao Le , Zou Shanqi , Feng Yuanling , Miao Xudong , Huang Lu , Wu Yongping
Published: June 2022

The therapy role of mesenchymal stem cell- (MSC-) derived extracellular vesicles (EVs) in cartilage regeneration has been well studied. Herein, we tried to analyze the role of human umbilical cord MSC- (hUCMSC-) EVs carrying microRNA- (miR-) 181c-5p in repair of cartilage injury. After successful isolation of hUCMSCs, the multidirectional differentiation abilities were analyzed. Then, the EVs were isolated and identified. After coculture of PKH26-labled EVs with bone marrow MSCs (BMSCs), the biological behaviors of which were detected. The relationship between the predicted early posttraumatic osteoarthritis-associated miRNA, miR-181c-5p, and SMAD7 was verified. Gain- and loss-of functions were performed for investing the role of miR-181c-5p and SMAD7 in BMP-induced chondrogenesis in vitro and in vivo. hUCMSC-EVs could be internalized by BMSCs and promote the proliferative, migratory, and chondrogenic differentiation potentials of BMSCs. Additionally, miR-181c-5p could target and inhibit SMAD7 expression to promote the bone morphogenic protein 2- (BMP2-) induced proliferative, migratory, and chondrogenic differentiation potentials of BMSCs. Also, overexpression of SMAD7 inhibited the repairing effect of BMP2, and overexpression of BMP2 and miR-181c-5p further promoted the repair of cartilage injury in vivo. Our present study highlighted the repairing effect of hUCMSC-EVs carrying miR-181c-5p on cartilage injury.
Large Eddy Simulation Research on the Evolution Mechanism of Aircraft Wake Influenced by Cubic Obstacle
Authors: Pan Weijun , Luo Yuming , Han Shuai , Wang Hao
Published: June 2022

Aircraft wake is a kind of intense air movement, and the study of its generation, development, and dissipation law is of great significance to the flight safety. There are abundant researches on the evolution of aircraft wakes affected by weather and ground effects; however, there are few studies on the influence of a single obstacle on the evolution of aircraft wake. In this article, in order to explore the influence of a single obstacle on the evolution of aircraft wake, firstly, we develop a computational fluid dynamics-based method of simulation of aircraft wake affected by cubic obstacle of different heights in order to obtain the wake intensity changes and position changes before and after being affected by the obstacle. Then, the result data are visualized and analyzed, and we obtain the results of velocity and

Q

criterion contours, circulation, and data related to wake vortex structure. CFD simulations are conducted, including the cases of the vertical distance between wake vortex and obstacle which is 20 m, 60 m, 100 m, and no obstacle. The quantitative results indicate that a single obstacle also has a great influence on the evolution of the wake vortex. Obstacle will shorten the time for the wake vortex to enter the fast decay stage, and the smaller the distance the wake vortex is above the obstacle, the faster it enters the fast decay stage. In the same time, the circulation will reduce 20% more under the same calculation time when the wake is 20 m above the obstacle than when the wake is 100 m above the obstacle, and the circulation will reduce 45% more than when there is no obstacle. Single obstacle also leads to the generation of multiple secondary vortices and rotates around the wake vortex, resulting in the increase of wake vortex core radius, wake vortex core spacing, and wake vortex height.
Adaptive Neural Network Iterative Sliding Mode Course Tracking Control for Unmanned Surface Vessels
Authors: Zhao Chunbo , Yan Huaran , Gao Deyi , Wang Renqiang , Li Qinrong
Published: June 2022

In view of the problem of course tracking control of under‐driven USV under complex external environment, the adaptive control law is designed by constructing an iterative sliding mode function and using Lyapunov stability theory on the basis of the kinetic model of ship motion. The RBF neural network control technology and adaptive control technology are integrated into the control algorithm, and the iterative sliding mode heading tracking controller of the unmanned surface ship adaptive‐neural network is designed. The online reinforcement learning of the RBF neural network is carried out by the reinforcement learning algorithm, which enhances the approximation performance of the network. Besides, the particle swarm optimization with shrinkage factor is applied to the optimization for control parameters to enhance the adaptability and robustness of the control system. The performance experiment verifies the effectiveness and practicability of the adaptive iterative sliding mode control algorithm for unmanned surface ships based on reinforcement learning and particle swarm optimization algorithm; meanwhile, comparative tracking experiments verify that the comprehensive performance of the proposed USV’s course tracking control system is better than that of the genetically optimized neural network sliding mode control system. Therefore, the fusion of multiple algorithms can be applied to improve the performance of the control system.
Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm
Authors: Ma Lili , Liu Jiangping
Published: June 2022

In order to realize the real-time and accurate prediction of dissolved oxygen concentration in the sewage treatment process, a prediction model of dissolved oxygen concentration in the sewage treatment process based on a data identification algorithm was proposed. Combined with the data characteristics of the sewage treatment process, a new sample similarity measure is defined to extract more representative modeling data. In the improved algorithm, in order to improve the quality of the initial members of the basic fireworks algorithm, the chaos algorithm is integrated. The search mechanism of the basic fireworks algorithm is improved, and the optimization process is divided into two stages based on the set criteria, and two groups are used simultaneously. The results show that compared with the basic FWA algorithm, the CFWA algorithm makes better use of the chaotic search mechanism. On the one hand, it avoids the excessive random or blind selection of the initial weight threshold of the neural network in the initial stage; on the other hand, in the optimization process of the weight threshold, two types of search mechanisms, FWA and COA, are used to give full play to their respective strengths and to continuously conduct information exchange and mutual cooperation between groups and individuals. The number of times is better than the basic FWA algorithm, and the training error and generalization error of the CFWA model in the simulation results of the soft sensor model are also better than those of the FWA model, which fully verifies the effectiveness of the CFWA algorithm. It is proved that the data recognition algorithm can effectively predict sewage treatment. It is proved that the data recognition algorithm can effectively predict the dissolved oxygen concentration in wastewater treatment process. It provides a new measurement method for some key process variables that cannot be measured or are difficult to measure in complex chemical processes.
Osimertinib Improves the Immune Microenvironment of Lung Cancer by Downregulating PD-L1 Expression of Vascular Endothelial Cells and Enhances the Antitumor Effect of Bevacizumab
Authors: Xiao Xuejun , Wu Yang , Shen Fang , MuLaTiAize Yusufu , Xinhua Nabi
Published: June 2022

Objective. To investigate the effect and mechanism of osimertinib combined with bevacizumab on lung cancer through cell and transplanted tumor animal experiments and to provide theoretical basis for further clinical trials. Methods. Immunohistochemistry was used to detect the expression of PD-L1 in tumor vessels of nonmetastatic lung adenocarcinoma and metastatic lung adenocarcinoma. At the same time, the expression of CD8 and FoxP3 in tumor tissue was detected. qRT-PCR was used to detect the effect of osimertinib on PD-L1 expression in HUVECs. The expression levels of p-Akt and p-ERK in HUVECs treated with osimertinib were analyzed by Western blot. AKT was blocked by AKT specific inhibitor Ly294002 to analyze the expression of PD-L1 in HUVECs. An animal model of transplanted tumor was constructed to analyze whether osimertinib could enhance the antitumor effect of bevacizumab. Results. PD-L1 was highly expressed in vascular endothelial cells of metastatic lung cancer. FoxP3 was highly expressed in metastatic lung adenocarcinoma, while CD8 expression was low. Osimertinib inhibits PD-L1 expression in endothelial cells. Mechanism studies have shown that osimertinib inhibits PD-L1 expression in endothelial cells through the AKT/ERK pathway. Osimertinib inhibited endothelial cell PD-L1 expression, increased CD8+T cell infiltration, inhibited tumor growth, and enhanced the tumor effect of bevacizumab. Conclusion. Osimertinib can significantly increase the killing ability of bevacizumab against tumor. Osimertinib can improve the tumor microenvironment and enhance the antitumor effect of bevacizumab by reducing the expression of PD-L1 in tumor blood vessels.
Quality of Life in University Students with Diabetes Distress: Type 1 and Type 2 of Diabetes Differences
Authors: Moawd Samah A.
Published: June 2022

Background. This study constitutes a preliminary trial to clarify the relationship between quality of life (QoL) and diabetes distress (DD in patients with diabetes mellitus (DM) by comparing patients with type 1 and type 2 diabetes. Methods. A cross-sectional study of university students with diabetes (type 1 and type 2) diabetes. One hundred sixty-six students were assigned to participate in this study. A self-report questionnaire on demographic and clinical parameters was taken. Rating worries and anxieties related to diabetes were evaluated using the diabetes distress scale-17, and quality of life was tested using SF-36 v2. Results. No significant differences were observed in the level of DD according to sociodemographics in type 1 DM (T1DM) and type 2 DM (T2DM) (

p
>
0.05

). The mean scores for Physical Component Summary (PCS) and Mental Component Summary (MCS) and six subscales of the SF-36 v2 demonstrated no significant differences between T1DM and T2DM (

p
>
0.05

). High scores of diabetes distress were independently associated with lower glycemic control for students with both types of diabetes. Likewise, high scores of distress were associated with lower PCS (

p
<
0.05

). Additionally, the results showed that high scores of diabetes distress were associated with lower MCS (

p
<
0.05

). Conclusions. University students with diabetes showed a high level of DD with no significant differences between both types of diabetes; this consequently affects all components of QoL. Psychological support is the better choice for those students for better health and future career.
Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business
Authors: Aggarwal Shruti , Suchithra M. , Chandramouli N. , Sarada Macha , Verma Amit , Vetrithangam D. , Pant Bhaskar , Ambachew Adugna Biruk
Published: June 2022

Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain.
[Retracted] Gait Improvement in Patients with Knee Osteoarthritis after Proximal Fibular Osteotomy
Authors: Li Xiaotong , Cao Yuqing , Cao Zhenguo , Zheng Pengfei , Merryweather Andrew , Wang Hui , Chen Ding , Xu Hang
Published: June 2022

Proximal fibula osteotomy (PFO) is a relatively new surgery to treat medial compartment knee osteoarthritis (KOA), which can improve varum deformity and relieve knee joint pain. However, the gait alterations in KOA patients after PFO are still poorly understood. The purpose of this study was to evaluate the gait patterns change in patients of medial compartment KOA after PFO. Gait data were collected for 9 females with unilateral medial compartment KOA before and at 6 months after PFO and also for 9 healthy age‐matched females. Paired t‐test was used to determine the effect of PFO within the KOA group, and independent t‐test were performed to compare between KOA and control groups for spatiotemporal, kinematic, and kinetic variables. The results showed that patients with KOA had significantly increased knee peak flexion angle, knee sagittal range of motion, and peak external hip adduction moment but decreased knee frontal range of motion in the affected limb after PFO. The gait symmetry was improved postoperatively confirmed by single support and swing phases, knee peak flexion angle and sagittal range of motion, peak external hip and knee adduction moments, and peak anterior and peak posterior ground reaction forces. These findings provided evidence of a biomechanical benefit and gait improvement following PFO to treat medial compartment KOA.
The Management of Educational Talents in Vocational Colleges Based on Wireless Network in the Artificial Intelligence Era
Authors: Zhang Yang , Wang Ce
Published: June 2022

Faced with the AI (artificial intelligence) era, it is both theoretically and practically important to examine the challenges and opportunities that vocational college education in China faces, as well as to actively explore how vocational college education can overcome the challenges and achieve a realistic path. This paper proposes and implements a wireless network-based vocational college talent management system. The main personnel management system primarily completes business operations related to daily personnel file management, while the data mining subsystem mines talent data using the DT (decision tree) classification algorithm to aid talent selection. At the same time, a new topology optimization algorithm based on the principle of minimum rigidity graph is proposed for talent management system wireless network optimization. The maximum-minimum balance criterion and the user-by-user optimization mechanism are designed to obtain the optimal relay selection and subchannel allocation strategy, ensuring the system’s reliability and fairness. The optimized algorithm has a user security satisfaction of 0.93 in the range of 250 m, which is higher than other algorithms. It demonstrates that this algorithm’s communication link is short, and it has good network connectivity and structural stability.
CNN-Based Attack Defense for Device-Free Localization
Authors: Han Zhaoyang , Lin Liang , Wang Ziyue , Lian Zhuotao , Qiu Chen , Huang Huakun , Zhao Lingjun , Su Chunhua
Published: June 2022

Device-free localization technology aims to find a target by analyzing the signal strength difference between transmitter and receiver deployed in the target area in advance. Up to now, device-free localization technology has been applied to a wide range of applications and scenarios, such as intrusion detection, environment modeling, and activity recognition. However, some sensors remain at potential risk that signal strength values of sensors have been tampered, or even devices sensors are physically damaged, which leads to inaccurate location results or a whole system crash. To solve the abovementioned problems, we design a CNN-based attack defense method for device-free localization, which can discover falsified signal strength values and error-prone devices. Firstly, we simulate a partial sensor attack or dropout in the device-free localization scenario. Then, we transform the localization problem into an image classification problem and use the convolutional neural networks (CNN) technique for abnormal detection. The experiment result shows that our algorithm can maintain high localization accuracy even under most sensor compromised and disconnected circumstances.
Quantitative Diagnosis of TCM Syndrome Types Based on Adaptive Resonant Neural Network
Authors: Zhao Yue , Huang Yuandi
Published: June 2022

Artificial intelligence has become one of the most rapidly developing disciplines in the application field of pattern recognition. In target recognition, sometimes, there are multiple identical or similar copies of the target to be recognized in the image, and it is difficult to classify and estimate by traditional methods. In this case, it is necessary to use the SOM network to separate multiple targets and use the multiple order parameters in the improved SNN to pair the target. The change of its thickness can intuitively reflect the abnormality of its tissue. Therefore, the choroidal thickness of the central fovea can be measured to study the relationship between the choroidal structure and BRVO and arteriosclerosis. The purpose of this study is to further study the correlation between branch retinal vein occlusion and arteriosclerosis by quantitatively measuring retinal vessel diameter and choroidal thickness, to analyze the correlation between different TCM syndrome types of nonischemic BRVO and retinal arteriosclerosis, and to provide theoretical basis for clinical nonischemic BRVO TCM syndrome types and traditional Chinese medicine treatment, so as to reflect its clinical application value. In order to solve the single fixed structure of traditional SNN and poor scalability, combined with the Kohonen layer structure in the self-organizing mapping network, an improved collaborative neural network model is proposed. This paper studies the network training method and operation convergence and analyzes the converged network and the pattern classification results obtained by the network. In order to solve the single fixed structure of traditional SNN and poor scalability, combined with the Kohonen layer structure in the self-organizing mapping network, an improved collaborative neural network model is proposed. The results of our proposed improved model on the MNIST dataset can achieve the same level of current state-of-the-art machine learning classifiers in recognition accuracy with a smaller network size and network complexity.
Analysis and Prediction of Corporate Finance and Exchange Rate Correlation Based on Machine Learning Algorithms
Authors: Zhang Ke , Wang Xiaofei , Wang Junjie , Wang Sinan , Hui Feng
Published: June 2022

Based on the risk management of exposure to foreign exchange assets and liabilities and the application of financial derivatives, this paper provides an in-depth analysis of the financial and exchange rate risks of foreign-funded enterprises. Therefore, a method of evaluating the financial performance of listed financial enterprises based on principal component analysis and neural network model is proposed. First, principal components of alternative financial performance input-output indicators are extracted using principal component analysis. Subsequently, these principal components are used as input-output data for the DEA model to derive the relative validity evaluation results of the financial performance of individual financial enterprises and to provide a reference for decision making to improve the financial performance level of financial enterprises. Combined with the economic business data of the enterprises, an empirical test on exchange rate risk management is conducted and relevant suggestions are made on how foreign enterprises can reduce exchange rate risk losses. It has important theoretical value and practical significance for enterprise finance and exchange rate management.
Design Method of Product Concept Model Based on CAD Technology
Authors: Meng Xiangbin
Published: June 2022

With the comprehensive development of information technology, the continuous optimization of products can make enterprises stand in the market. Various industries are involved in the upsurge of computer-enabling products; computer-aided (CAD) technology has also been rapid development. CAD technology is composed of problem analysis, innovative method, problem transformation and problem-solving invention principle, standard solution, and so on. In this paper, the theoretical system, basic scheme, and solving algorithm of CAD technology are described in detail. Meanwhile, the content of value evaluation and root cause analysis is analyzed and solved to further illustrate the application process of CAD technology optimization products and gradually form a more perfect application method of CAD innovation technology so that its solution process is more scientific, operable, and universal.
Inhibition of PLK3 Attenuates Tubular Epithelial Cell Apoptosis after Renal Ischemia–Reperfusion Injury by Blocking the ATM/P53-Mediated DNA Damage Response
Authors: Deng Weiming , Wei Xiangling , Xie Zhenwei , Zhang Rui , Dong Zhanwen , Zhang Jinhua , Luo You , Cheng Qingdi , Wang Ruojiao , Li Heng , Na Ning
Published: June 2022

Objective. Renal ischemia–reperfusion (I/R) injury is a major cause of acute kidney injury (AKI) in transplanted kidneys. This study was aimed at exploring the role of PLK3 (polo-like kinase 3) in renal I/R injury, focusing on its relationship with oxidative stress-induced DNA damage and renal tubular epithelial cell (TEC) apoptosis. Methods. TRAP-seq data from the development dataset GSE52004 and the validation dataset GSE121191 were analyzed using GEO2R. PLK3 overexpression plasmids and targeted silencing siRNAs were used in a model of hypoxia/reoxygenation (H/R) injury, and rAAV-9-PLK3-KD were administered to C57BL/6J mice exposed to I/R injury. The ATM-specific inhibitor KU-60019 was used to block the DNA damage response (DDR). Western blotting was performed to measure DDR- and apoptosis-associated protein expression. Cell viability was measured by CCK-8 reagent, and apoptosis was examined by flow cytometry and TUNEL assay. Furthermore, the fluorescent probes H2DCFH-DA and DHE were used to measure ROS production in vitro. The MDA level and SOD activity were measured to assess oxidative stress in vivo. KIM-1 staining and Scr and BUN were used to evaluate kidney injury. Results. The mRNA and protein levels of PLK3 were markedly increased in the H/R injury and I/R injury models. GO terms showed that PLK3 was mainly involved in oxidative stress and DNA damage after renal I/R injury. Overexpression of PLK3 decreased cell viability and increased apoptosis. In contrast, targeted silencing of PLK3 expression decreased the Bax/Bcl-2 ratio by decreasing P53 phosphorylation, thereby reducing TEC apoptosis. Furthermore, KU-60019 reduced PLK3 activation and DDR-induced apoptosis, while overexpression of PLK3 reversed the mitigating effect of KU-60019 on TEC apoptosis. Similarly, rAAV-9-PLK3 KD mice exhibited a lower rate of TEC apoptosis and milder renal damage after I/R injury. Conclusion. We demonstrate for the first time that PLK3 is involved in oxidative stress-induced DNA damage and TEC apoptosis in renal I/R injury. Inhibition of PLK3 attenuates TEC apoptosis after I/R injury by blocking the ATM/P53-mediated DDR. Therefore, PLK3 may serve as a potential therapeutic target for ischemic AKI.
Contamination and Ecological Risk Assessment of Heavy Metals in Surface Sediments of Huangshui River, Northwest China
Authors: Chen Liwei , Wei Qi , Xu Guangsu , Wei Meng , Chen Hao
Published: June 2022

In the Xining area of Huangshui River, China, the concentrations of globally alarming toxic heavy metals such as chromium (Cr), arsenic (As), lead (Pb), cadmium (Cd), and zinc (Zn) in the surface sediments were measured to determine the ecological risks to the riverine ecosystem. Overall, the concentrations of heavy metals in the surface sediments were relatively high. The results indicated that the concentrations of Pb/Zn/Cr were lower than 120/250/200 mg·kg−1 which was the risk screening value of each heavy metal for soil contamination of agricultural land (GB15618-2018). The concentrations of As/Cd were higher than 120/3.0 mg·kg−1 which was the risk intervention value of As/Cd for soil contamination of agricultural land (GB15618-2018). Their values were arranged in the following decreasing order: As (151.23–818.55 mg·kg−1) > Cr (44.18–201.70 mg·kg−1) > Pb (29.10–121.95 mg·kg−1) > Zn (1.45–86.18 mg·kg−1) > Cd (4.36–6.21 mg·kg−1). The concentrations of As, Cr Pb, and Cd greatly exceeded the background contents of elements in Qinghai soils. While Zn was lower than background. The contamination factor (CF), the geoaccumulation index (Igeo), and the potential ecological risk index (RI) of As, Cr, Cd, and Pb followed the descending order of Cd > As > Pb > Cr > Zn. The order of the enrichment factor (EF) was as follows: As > Cd > Pb > Cr > Zn. The contamination level of heavy metals implied that the condition is frightening and probably severely affecting the aquatic ecology. Cd and As were the main contributing elements to the ecological risk of the sediments in the Xining area of the Huangshui River, which should be mainly studied and prevented.
The Prognostic Value of lncRNA MCM3AP-AS1 on Clinical Outcomes in Various Cancers: A Meta- and Bioinformatics Analysis
Authors: Fu Liangyin , Zhang Guangming , Wang Yongfeng , Lu Tingting , Liu Bin , Jiao Yajun , Ma Haizhong , Ma Shixun , Yang Kehu , Cai Hui
Published: June 2022

Background. MCM3AP antisense RNA 1 (MCM3AP-AS1) is a newly identified potential tumor biomarker. Nevertheless, the prognostic value of MCM3AP-AS1 in cancer has been inconsistent in the available studies. We performed this meta-analysis to identify the prognostic role of MCM3AP-AS1 in various cancers. Methods. We searched PubMed, Web of Science, EMBASE, and the Cochrane Library databases to screen relevant studies. Hazard ratios (HR) or odds ratios (OR) and corresponding 95% confidence intervals (CI) were used to evaluate the relationship between aberrant MCM3AP-AS1 expression and survival and clinicopathological features (CFS) of cancer patients. A meta-analysis was performed using STATA 12.0 software. Additionally, results were validated by an online database based on The Cancer Genome Atlas (TCGA). Subsequently, we analyzed the MCM3AP-AS1-related genes and molecular mechanisms based on the MEM database. Results. Our results showed that overexpression of MCM3AP-AS1 was related to poor overall survival (OS) (

HR
=
2.00

, 95% CI, 1.52–2.64,

P
<
0.001

) and relapse-free survival (RFS) (

HR
=
3.28

, 95% CI 1.56–6.88,

P
=
0.002

). In addition, MCM3AP-AS1 overexpression was associated with TNM stage, differentiation grade, and lymph node metastasis, but not significantly with age, gender, and tumor size. In addition, MCM3AP-AS1 overexpression was verified by the GEPIA online database to be associated with poorer survival. The further functional investigation suggested that MCM3AP-AS1 may be involved in several cancer-related pathways. Conclusions. The overexpression of MCM3AP-AS1 was related to poor survival and CFS. MCM3AP-AS1 may be considered a novel prognostic marker and therapeutic target in various cancers.
Brand Preference Prediction Method of Cross-Border E-Commerce Consumers Based on Potential Tag Mining
Authors: Qin Xujie
Published: June 2022

There are many brands in cross-border e-commerce platforms. Obtaining consumers’ preference for brands will help promote the development of cross-border e-commerce industry. A brand preference prediction method of cross-border e-commerce consumers based on potential tag mining is proposed. Preprocess the cross-border e-commerce brand comment information obtained, build a HowNet emotion dictionary, and calculate consumers’ emotional tendency towards the brand on this basis. The projection pursuit regression model is optimized by differential evolution algorithm to reduce the dimension of the obtained consumer brand emotion information. Mining the potential labels of the information after dimensionality reduction, combined with Bayesian personalized sorting method and paired interaction tensor decomposition method, this paper constructs a brand preference’s prediction model to predict the brand preference of cross-border e-commerce consumers. The experimental results show that the proposed method has high accuracy of brand tendency calculation results, small average absolute error of prediction results, and high model accuracy.