bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering
Authors: Zhou Peng , Liu Xinwang , Du Liang , Li Xuejun
Published: September 2022

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.
Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference
Authors: Dai Shaojie , Wang Jinshuai , Huang Chao , Yu Yanwei , Dong Junyu
Published: September 2022

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and maintenance across the entire urban space. A more practical scenario to study the citywide traffic inference is effectively modeling the spatial and temporal traffic patterns with limited historical traffic observations. In this work, we propose a dynamic multi-view graph neural network for citywide traffic inference with the method CTVI+. Specifically, for the temporal dimension, we propose a temporal self-attention mechanism that is capable of learning the dynamics of traffic data with the time-evolving traffic volume variations. For spatial dimension, we build a multi-view graph neural network, employing the road-wise message passing scheme to capture the region dependencies. With the designed spatial-temporal learning paradigms, we enable our traffic inference model to encode the dynamism from both spatial and temporal traffic patterns, which is reflective of intra- and inter-road traffic correlations. In our evaluation, CTVI+ achieves consistent better performance compared with different baselines on real-world traffic volume datasets. Further ablation study validates the effectiveness of key components in CTVI+. We release the model implementation at https://github.com/dsj96/TKDD.
Hypergraph Transformer Neural Networks
Authors: Li Mengran , Zhang Yong , Li Xiaoyong , Zhang Yuchen , Yin Baocai
Published: September 2022

Graph neural networks (GNNs) have been widely used for graph structure learning and achieved excellent performance in tasks such as node classification and link prediction. Real-world graph networks imply complex and various semantic information and are often referred to as heterogeneous information networks (HINs). Previous GNNs have laboriously modeled heterogeneous graph networks with pairwise relations, in which the semantic information representation for learning is incomplete and severely hinders node embedded learning. Therefore, the conventional graph structure cannot satisfy the demand for information discovery in HINs. In this article, we propose an end-to-end hypergraph transformer neural network (HGTN) that exploits the communication abilities between different types of nodes and hyperedges to learn higher-order relations and discover semantic information. Specifically, attention mechanisms weigh the importance of semantic information hidden in original HINs to generate useful meta-paths. Meanwhile, our method develops a multi-scale attention module to aggregate node embeddings in higher-order neighborhoods. We evaluate the proposed model with node classification tasks on six datasets: DBLP, ACM, IBDM, Reuters, STUD-BJUT, and Citeseer. Experiments on a large number of benchmarks show the advantages of HGTN.
The Role of Functionalized Conducting Polymer Binders in Improving Power Density and Cycle Life of Lithium-Sulfur Batteries
Authors: Zayat Billal , Elizalde-Segovia Rodrigo , Das Pratyusha , Salamat Charlene Z. , Irshad Ahamed , Tolbert Sarah H. , Thompson Barry C. , Narayanan S. R.
Published: September 2022

Lithium-Sulfur batteries are promising as the next-generation of low-cost, high-energy rechargeable batteries. However, the commercialization of this battery has been limited by poor rate capability and cycle life. To improve the performance of Li-S cells, we have investigated the role of two n-dopable conducting polymers, N2200 and N2200-OE, as binders for the sulfur cathode. The electron and ion transport properties of these polymer binders, along with their strong affinity for polysulfides, results in significantly enhanced performance compared to cells with the traditional insulating PVDF binder. The role of these conducting polymer binders in enhancing the performance of the cells is analyzed using impedance spectroscopy, shuttle current measurements, conductivity measurements, UV–vis spectroscopy, GIWAXS studies and post-cycling analysis of the anodes. The conducting polymer binders reduce the cell impedance by a factor of four leading to a marked improvement in rate capability. The shuttling of the polysulfides and the formation of insoluble sulfides at the anode is curtailed by the interaction of the polysulfides with the backbone of the polymer binders, leading to an impressive capacity retention of 82% after 500 cycles. These studies demonstrate the benefit of tailored polymer binders at the sulfur electrode in addressing the limitations of lithium-sulfur batteries.
Towards Optimised Cell Design of Thin Film Silicon-Based Solid-State Batteries via Modelling and Experimental Characterisation
Authors: Vadhva Pooja , Boyce Adam M. , Hales Alastair , Pang Mei-Chin , Patel Anisha N. , Shearing Paul R. , Offer Gregory , Rettie Alexander J. E.
Published: September 2022

To realise the promise of solid-state batteries, negative electrode materials exhibiting large volumetric expansions, such as Li and Si, must be used. These volume changes can cause significant mechanical stresses and strains that affect cell performance and durability, however their role and nature in SSBs are poorly understood. Here, a 2D electro-chemo-mechanical model is constructed and experimentally validated using steady-state, transient and pulsed electrochemical methods. The model geometry is taken as a representative cross-section of a non-porous, thin-film solid-state battery with an amorphous Si (a-Si) negative electrode, lithium phosphorous oxynitride (LiPON) solid electrolyte and LiCoO2 (LCO) positive electrode. A viscoplastic model is used to predict the build-up of strains and plastic deformation of a-Si as a result of (de)lithiation during cycling. A suite of electrochemical tests, including electrochemical impedance spectroscopy, the galvanostatic intermittent titration technique and hybrid pulse power characterisation are carried out to establish key parameters for model validation. The validated model is used to explore the peak interfacial (a-Si∣LiPON) stress and strain as a function of the relative electrode thickness (up to a factor of 4), revealing a peak volumetric expansion from 69% to 104% during cycling at 1C. The validation of this electro-chemo-mechanical model under load and pulsed operating conditions will aid in the cell design and optimisation of solid-state battery technologies.
LiCoO2 Battery Electrode Fabricated by High Deposition-Rate Atmospheric Plasma Spraying for Lithium Battery
Authors: Hsueh Tien-Hsiang , Tsai Chun-Huang , Liu Shang-En , Wang Min-Chuan , Chang Shu-Mei , Shiue Angus , Chin Kai-Yen
Published: September 2022

Lithium cobalt oxide (LiCoO2, LCO) thin-film cathodes are usually prepared with slow deposition rates by sputtering techniques. Atmospheric plasma spraying (APS) is a possible technology for the rapid deposition method replacing sputter deposition to fabricate all-solid-state lithium batteries. This study employed the APS device to rapidly deposit LCO thick films as cathodes with deposition rates exceeding 5,400 nm min−1. The results show that the LCO cathodes have presented a specific capacity of 120 mAh g−1 after being annealed and assembled in a gel polymer electrolyte lithium battery. Furthermore, the LCO cathodes also demonstrated a high areal capacity of 5.65 mAh cm−2, one of the best areal capacity performances. Therefore, APS technology offers a highly competitive approach to commercializing energy storage products.
Exercise adaptations in COPD: the pulmonary perspective
Authors: Nymand Stine B. , Hartmann Jacob P. , Ryrsø Camilla Koch , Rossen Ninna Struck , Christensen Regitse Højgaard , Iepsen Ulrik Winning , Berg Ronan M. G.
Published: September 2022

In chronic obstructive pulmonary disease (COPD), the progressive loss of lung tissue is widely considered irreversible. Thus, various treatment and rehabilitation schemes, including exercise-based pulmonary rehabilitation (PR) are thought to slow down but not reverse or halt the disease. Nonetheless, the adult lung conceals the intrinsic capacity for de novo lung tissue formation in the form of abundant progenitor/stem cell populations. In COPD, these maintain their differentiation potential but appear to be halted by a state of cellular senescence in the mesenchyme, which normally functions to support and coordinate their function. We propose that notably high-intensity interval training may improve pulmonary gas exchange during exercise in patients with COPD by interrupting mesenchymal senescence, thus reestablishing adaptive angiogenesis. By means of this, the downward spiral of dyspnea, poor quality of life, physical inactivity, and early death often observed in COPD may be interrupted. If this is the case, the perception of the regenerative capacity of the lungs will be fundamentally changed, which will warrant future clinical trials on various exercise schemes and other treatments targeting the formation of new lung tissue in COPD.
Multistrategy Improved Sparrow Search Algorithm Optimized Deep Neural Network for Esophageal Cancer
Authors: Wang Yanfeng , Liu Qing , Sun Junwei , Wang Lidong , Song Xin , Zhao Xueke
Published: September 2022

Deep neural network is a complex pattern recognition network system. It is widely favored by scholars for its strong nonlinear fitting ability. However, training deep neural network models on small datasets typically realizes worse performance than shallow neural network. In this study, a strategy to improve the sparrow search algorithm based on the iterative map, iterative perturbation, and Gaussian mutation is developed. This optimized strategy improved the sparrow search algorithm validated by fourteen benchmark functions, and the algorithm has the best search accuracy and the fastest convergence speed. An algorithm based on the iterative map, iterative perturbation, and Gaussian mutation improved sparrow search algorithm is designed to optimize deep neural networks. The modified sparrow algorithm is exploited to search for the optimal connection weights of deep neural network. This algorithm is implemented for the esophageal cancer dataset along with the other six algorithms. The proposed model is able to achieve 0.92 under all the eight scoring criteria, which is better than the performance of the other six algorithms. Therefore, an optimized deep neural network based on an improved sparrow search algorithm with iterative map, iterative perturbation, and Gaussian mutation is an effective approach to predict the survival rate of esophageal cancer.
Application of LSTM Neural Network Technology Embedded in English Intelligent Translation
Authors: Yang Yifang
Published: September 2022

With the rapid development of computer technology, the loss of long-distance information in the transmission process is a prominent problem faced by English machine translation. The self-attention mechanism is combined with convolutional neural network (CNN) and long-term and short-term memory network (LSTM). An English intelligent translation model based on LSTM-SA is proposed, and the performance of this model is compared with other deep neural network models. The study adds SA to the LSTM neural network model and constructs the English translation model of LSTM-SA attention embedding. Compared with other deep learning algorithms such as 3RNN and GRU, the LSTM-SA neural network algorithm has faster convergence speed and lower loss value, and the loss value is finally stable at about 8.6. Under the three values of adaptability, the accuracy of LSTM-SA neural network structure is higher than that of LSTM, and when the adaptability is 1, the accuracy of LSTM-SA neural network improved the fastest, with an accuracy of nearly 20%. Compared with other deep learning algorithms, the LSTM-SA neural network algorithm has a better translation level map under the three hidden layers. The proposed LSTM-SA model can better carry out English intelligent translation, enhance the representation of source language context information, and improve the performance and quality of English machine translation model.
Nuclear Factor I-C Regulates Stemness Genes and Proliferation of Stem Cells in Various Mineralized Tissue through Epithelial-Mesenchymal Interactions in Dental Epithelial Stem Cells
Authors: Lee Dong-Seol , Song Yeo Joon , Gug Hye Ri , Lee Ji-Hyun , Bae Hyun Sook , Park Joo-Cheol
Published: September 2022

Tooth development includes numerous cell divisions and cell-cell interactions generating the stem cell niche. After an indefinite number of divisions, pluripotent cells differentiate into various types of cells. Nuclear factor I (NFI) transcription factors are known as crucial regulators in various organ development and stem cell biology. Among its members, nuclear factor I-C (NFI-C) has been reported to play an essential role in odontogenesis. Nfic knockout mice show malformation in all mineralized tissues, but it remains unclear which stage of development Nfic is involved in. We previously reported that Nfic induces the differentiation of ameloblast, odontoblast, and osteoblast. However, the question remains whether Nfic participates in the late stage of development, perpetuating the proliferation of stem cells. This study aimed to elucidate the underlying mechanism of NFI-C function in stem cells capable of forming hard tissues. Here, we demonstrate that Nfic regulates Sox2 and cell proliferation in diverse mineralized tissue stem cells such as dental epithelial stem cells (DESCs), dental pulp stem cells, and bone marrow stem cells, but not in fibroblasts. It was also involved in the expression of pluripotency genes Lin28 and NANOG. Especially in DESCs, Nfic regulates the proliferation of epithelial cells via epithelial-mesenchymal interactions, which are the Fgf8-Nfic-Sox2 pathway in epithelium and Nfic-Fgf10 in the mesenchyme. Moreover, Nfic slightly increased reprogramming efficiency in induced pluripotent stem cells of mineralized tissues, but not in soft tissues. In conclusion, these results suggest that Nfic is a crucial factor for maintaining the stem cell niche of mineralized tissues and provides a possibility for Nfic as an additional factor in improving reprogramming efficiency.
Weakly Supervised Real-Time Object Detection Based on Salient Map Extraction and the Improved YOLOv5 Model
Authors: Ma Yue , Zhi Zhuangzhi
Published: September 2022

In order to improve the accuracy and processing speed of object detection in weakly supervised learning environment, a weakly supervised real-time object detection method based on saliency map extraction and improved YOLOv5 is proposed. For the case where only image-level annotations are available, class-specific saliency maps are generated from the backpropagation process using a VGG-16-based classification network. After obtaining the position information of the target in the image, the pseudobounding box of the target is generated, and the pseudobounding box is used as the ground-truth bounding box to optimize the real-time target detection network. An improved YOLOv5 model is proposed to transfer clear target features to deeper network layers by designing a jump connection operation, thereby solving the problem of feature ambiguity. At the same time, the convolutional attention mechanism module is introduced to solve the problem that the recognition accuracy is affected by invalid features. Experiments on the PASCAL VOC 2007+2012 datasets show that when only image-level annotations are available in the training data, the proposed method can effectively improve the processing speed and maintain a good target detection accuracy, realizing real-time object detection under weakly supervised conditions.
[Retracted] Balance Harvest from the Forest as a Renewable Resource Using Game Theory
Authors: Acwin Dwijendra Ngakan Ketut , Sivaraman R. , Kaliyaperumal Karthikeyan , Romero-Parra Rosario Mireya
Published: September 2022

Forests are among the most important renewable natural resources. While having a great range of benefits, the crucial advantage of forests is the production of oxygen and the absorption of pollutants. Thus, the current article attempts to examine forest exploitation as a source of wood to prevent forest overexploitation. Three smugglers, residents, and timber companies exploit this resource. There is a competition for the perception of this common source between these factors. In this article, these interactions are considered a game in order to obtain the most probable solution in the real competition between these factors by examining this game. This answer is the balance of the game. In order to use this answer, the behavior of the players can be predicted and used in management planning. Since the amount of withdrawals of operating companies depends on the current inventory (t), the player’s behavior is determined by the system’s current state and then Markov equilibrium was obtained for them.
[Retracted] Beliefs and Practice Evaluation Based on Artificial Intelligence Models under the IP Environment
Authors: Zhou Yang
Published: September 2022

The digitization of thought theory is not yet sufficient to meet the needs of the students. It is very necessary to strengthen the construction of ideological and political (IP) courses, strengthen the education of mainstream ideology, and occupy the initiative of discourse. There are effective ways and means to study the deep integration of information technologies into the new age of philosophy and philosophy education of students, which can greatly improve the quality of teaching and the effectiveness of humanities courses. The intuitive development of intellectual and political education through artificial intelligence is both a real prerequisite for modern development and technological innovation and for new ideas confronting the specific problems of thought‐politics education, and it is a necessary prerequisite to ensure the quality and efficiency to improve the teaching of thought politics. AI in ways that embed technology provides a powerful impetus for contradictory movements in thought‐politics discourse that lead the effective coordination of its internal elements to high‐quality developments. In a practical way, we should take full advantage of the technical advantages of artificial intelligence, through intelligent mehrfachanalyse corresponding algorithms, artificial intelligenzbilder, and artificial roboterbilder photos and profiles that are used to write and accurately provide more precise, leading, stacking, and accurate estimates to maximize and to improve the accuracy of thought and policy education. Philosophy and political theory are the keys to guiding people to accomplish basic human tasks. With the passage of time and with continuous innovation, ideology and philosophical principles are both prerequisites for the self‐development of the emotions of the age and necessary for improving the outcomes and effectiveness of scientific education. Advances in the field of artificial intelligence have fundamentally changed human life and have also impacted traditional school and university education systems. This brings new opportunities and challenges to students. This includes the use of smart technology to improve the learning process. Ideological teachers and policymakers must keep up with the all‐round trend of the times, make full use of the benefits brought by intelligent new technologies and platforms, effectively improve the effect of ideological education, and further increase the attractiveness, attraction, persuasion, and contagion of ideological education.
Synthesis, Characterization, and Antiproliferative Effect of CuO‐TiO2‐Chitosan‐Amygdalin Nanocomposites in Human Leukemic MOLT4 Cells
Authors: Elderdery Abozer Y. , Alzahrani Badr , Hamza Siddiqa M. A. , Mostafa-Hedeab Gomaa , Mok Pooi Ling , Subbiah Suresh Kumar
Published: September 2022

The main aim of this study was to synthesize copper oxide‐ (CuO‐) titanium oxide‐ (TiO2‐) chitosan‐amygdalin nanocomposites (CTCANc) and to characterize them physically and biologically (antimicrobial and anticancer activity using MOLT4 blood cancer cell line) to endorse their useful applications as potential drug candidates in anticancer avenues. CuO‐TiO2‐chitosan‐amygdalin nanocomposites were synthesized according to standard, reported methods. Physical characterization of the nanocomposites was performed using methods like X‐ray diffractometer (XRD), and morphological and ultrastructural analysis of nanocomposites were done using electron microscope scanning and transmission. FTIR was recorded using a Perkin‐Elmer spectrometer, and photoluminescence (PL) spectra were done using the spectrometer. Further, antibacterial activities were assessed using standard bacterial cultures. To demonstrate the nanocomposite’s anticancer effects, MTT assay, morphological analysis, apoptosis studies using acridine orange/ethidium bromide (AO/EtBr) dual staining, reactive oxygen species (ROS) analysis, and levels of antioxidant enzymes were analyzed using the MOLT4 blood cancer cell line. Synthesized nanocomposites were characterized using XRD and showed various peaks, respectively, for CuO‐TiO2, amygdalin, and chitosan. MTT assay indicated an IC50 value of 38.41 μg/ml concentration of CTCANc. Hence, 30 and 40 μg/ml were used for the subsequent experiments. Morphological analysis, staining for apoptosis using AO/EtBr, mitochondrial membrane potential (MMP or ΔΨm) analysis, ROS analysis, and determination of the SOD, CAT, MDA, and GSH levels were performed. Observations like a significant loss of morphology, induction of apoptosis, elevated ROS, and decreased MMP were significant in 30 and 40 μg/ml nanocomposite‐treated cells when compared to control cells. The bimetallic nanocomposites exhibited typical nanocomposites characteristics and significant antibacterial and anticancer effects. The study results endorse the antibacterial, anticancer activity of CuO‐TiO2‐chitosan‐amygdalin nanocomposites and strongly suggest that further in‐depth research using CuO‐TiO2‐chitosan‐amygdalin nanocomposites could reveal their efficacy in the clinical scenario.
Tool Cutting Force Prediction Model Based on ALO-ELM Algorithm
Authors: Zhang Hongna
Published: September 2022

Aiming at the problems of low learning efficiency, slow convergence speed, and low prediction accuracy of traditional data-driven model applied to tool cutting force prediction, a prediction method of tool cutting force based on ant lion optimizer (ALO) extreme learning machine (ELM) is proposed. ALO was used to improve the weights of input layer and hidden layer of ELM, so as to improve its prediction accuracy. The tool cutting force prediction models were established by using ALO-ELM, ELM, BP (backpropagation) neural network, and support vector machine, respectively. The experimental results show that the mean square error, mean absolute percentage error, and mean absolute error of ALO-ELM prediction model are 0.9911%, 0.0011%, and 1.0863%, respectively, which are far lower than the other three prediction models. ALO-ELM prediction model has stronger prediction accuracy and generalization ability, which can be effectively applied to the prediction of cutting force.
Detonation Self‐Lubricating Antifriction Glass Composition
Authors: Babak V. P. , Shchepetov V. V. , Kharchenko S. D. , Kruchinin S. P. , Bellucci Stefano
Published: September 2022

Antifriction self‐lubricating glass composite coatings of the Sic‐Ni‐Cu‐Al‐Si‐C type additionally contain an aluminoborosilicate glass phase and structurally free MgC2, which forms α‐graphite during thermolysis, the synergistic effect of which causes modification of the friction surface due to the formation antifriction layer. The influence of the structural components of the coating on their contribution to the antifriction properties is considered. It was clarified that an increase in the adhesive strength of the coatings was achieved by preliminary application of a sublayer of vitreous sodium silicate. The developed coatings showed high performance properties, while the means of minimizing and stabilizing wear characteristics was the presence of a thin‐film antifriction layer based on α‐graphite, which shields unacceptable processes of molecular‐adhesive interaction.
Research on the Linkage Mechanism between Migrant Workers Returning Home to Start Businesses and Rural Industry Revitalization Based on the Combination Prediction and Dynamic Simulation Model
Authors: Wang Xiaogang
Published: September 2022

Township and rural migrant workers are returning to the business personnel’s main force. Party 18 proposed a rural revitalization strategy, and the central committee of the State Council issued a series of encouraging policy measures to bring them back. The study found that migrant workers’ return-home entrepreneurship and the connection between the rural industries on the basis of factor resources flow are related. As a result, this study analyzed the practical situation of migrant workers’ return-home entrepreneurship. Next, it used a combination forecast method and dynamic simulation model of the migrant workers’ return-home entrepreneurship and revitalization of the relationship with the rural industry. The resuscitation of rural industries can be efficiently facilitated by the return of rural migrant workers to launch firms. Their success can attract more migrant workers to launch businesses. The total regeneration of rural areas and the prosperity of farmers can be achieved by effectively linking the return of migrant workers to launch businesses and the revival of rural industries.
Composition and Bioactivity of a Modified Huang-Lian-Jie-Du Decoction
Authors: Lin Mei-Yi , Chen Lih-Geeng , Siao Ying-Yu , Lin Tao-Hsuan , Huang I-An , Liu Yi-Wen , Huang Chin-Chin
Published: September 2022

Background. Epidermal growth factor receptor inhibitors (EGFRIs) and tyrosine kinase inhibitors (TKIs) are key drugs in targeted cancer therapy. However, they may cause skin toxicity. We previously prepared a modified Huang-Lian-Jie-Du (mHLJD) decoction cream using 10 herbs, which effectively alleviated EGFRI/TKI-induced skin toxicity. In the present study, we identified the reference markers of the mHLJD decoction and investigated the anti-inflammatory and antibacterial effects of the mHLJD decoction extract. Methods. We performed high-performance liquid chromatography (HPLC) to determine the composition of the mHLJD decoction. Human epidermoid A431 cells were treated with tumor necrosis factor (TNF)-α to induce inflammation; then, the effects of the mHLJD decoction extract on the cytokine expression were determined using a cytokine array and by performing real-time quantitative polymerase chain reaction (qPCR). The antibacterial effects of the extract were examined using disk diffusion and microdilution assays. Results. HPLC results revealed that the mHLJD decoction primarily consisted of geniposide, berberine chloride, baicalin, coptisine, and palmatine. TNF-α treatment increased the expression of certain cytokines, including IL-1β, IL-8, M-CSF, and TGF-β2; however, pretreatment with the mHLJD decoction extract reduced their expression. The qPCR results demonstrated a decreased mRNA expression of IL-8, M-CSF, and TGF-β2. The antibacterial assay revealed that the extract exerted inhibitory effects on Staphylococcus aureus, forming an inhibition zone at the minimum inhibitory concentrations of 3.125 and 6.25 mg/mL; however, the extract exerted no effects on Escherichia coli and Pseudomonas aeruginosa. Conclusions. We developed an HPLC method to quantify the reference markers of the mHLJD decoction. The bioactivity analysis provided the potential mechanisms underlying the effects of the mHLJD decoction on EGFRI/TKI-induced skin toxicity.
Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques
Authors: Arivazhagan N. , Mukunthan M. A. , Sundaranarayana D. , Shankar A. , Vinoth Kumar S. , Kesavan R. , Chandrasekaran Saravanan , Shyamala Devi M. , Maithili K. , Barakkath Nisha U , Abebe Tewodros Getinet
Published: September 2022

Skin cancer is the uncontrolled growth of irregular cancer cells in the human-skin's outer layer. Skin cells commonly grow in an uneven pattern on exposed skin surfaces. The majority of melanomas, aside from this variety, form in areas that are rarely exposed to sunlight. Harmful sunlight, which results in a mutation in the DNA and irreparable DNA damage, is the primary cause of skin cancer. This demonstrates a close connection between skin cancer and molecular biology and genetics. Males and females both experience the same incidence rate. Avoiding revelation to ultraviolet (UV) emissions can lower the risk rate. This needed to be known about in order to be prevented from happening. To identify skin cancer, an improved image analysis technique was put forth in this work. The skin alterations are routinely monitored by this proposed skin cancer categorization approach. Therefore, early detection of suspicious skin changes can aid in the early discovery of skin cancer, increasing the likelihood of a favourable outcome. Due to the blessing of diagnostic technology and recent advancements in cancer treatment, the survival rate of patients with skin cancer has grown. The strategy for detecting skin cancer using image processing technologies is presented in this paper. The system receives the image of the skin lesion as an input and analyses it using cutting-edge image processing methods to determine whether skin cancer is present. The Lesion Image Analysis Tools use texture, size, and shape assessment for image segmentation and feature phases to check for various cancer criteria including asymmetries, borders, pigment, and diameter. The image is classified as Normal skin and a lesion caused by skin cancer using the derived feature parameters.
Offloading Cost Optimization in Multiserver Mobile Edge Computing Systems with Energy Harvesting Devices
Authors: Liu Zheng , Jiang Kun , Wu Xiuqiang , Zeng Xianxiong
Published: September 2022

In mobile edge computing (MEC) systems with energy harvesting, edge devices are powered by unstable energy harvested from the environment. To prolong the lifetime of edge devices, some computing tasks should be offloaded to MEC servers. However, computing services offered by MEC servers may be very costly. In this work, we aim to minimize total costs caused by computing services and dropping tasks while avoiding the devices running out energy. With the consideration of the unpredictability of the harvestable energy, we adopt the stochastic Lyapunov optimization framework to jointly manage energy and make task execution decisions (i.e., local executing, offloading, or dropping tasks) and develop an online algorithm which could help asymptotically obtain the optimal results for the whole system. The algorithm does not require any knowledge of the harvestable energy and the statistics of task arriving processes and can be easily implemented in a distributed manner. Numerical results corroborate that the proposed algorithm can try its best to push battery energy of edge devices to a preset parameter and effectively reduce the service costs and task drops.
Network Toxicology Guided Mechanism Study on the Association between Thyroid Function and Exposures to Polychlorinated Biphenyls Mixture
Authors: Li Chunxia , Xing Hong , He Qiaoyu , Liu Jing , Liu Hong , Li Yue , Chen Xiaopeng
Published: September 2022

Polychlorinated biphenyls (PCBs) are persistent and highly toxic pollutants, which can accumulate in organisms and produce toxic effects, especially damaging the function of thyroid hormones. So far, the molecular mechanism of PCBs mixture and their metabolites interfering with thyroid hormones has not been studied thoroughly except for individual compounds. In this study, PubMed, Web of Science, and STITCH databases were used to search PCBs and their corresponding target proteins. The intersection of PCBs and thyroid hormone dysfunction target proteins was obtained from GeneCards. The “compounds‐targets‐pathways” network was constructed by Cytoscape software. And KEGG and Go analyses were performed for key targets. Finally, molecular docking was used to verify the binding effect. Four major active components, five key targets, and 10 kernel pathways were successfully screened by constructing the network. Functional enrichment analysis showed that the interference was mediated by cancer, proteoglycans, PI3K‐Akt, thyroid hormone, and FoxO signaling pathways. The molecular docking results showed that the binding energies were less than ‐5 kcal·mol-1. PCBs and their metabolites may act on the key targets of MAPK3, MAPK1, RXRA, PIK3R1, and TP53. The toxic effect of sulfated and methyl sulfone PCBs is greater. The method of screening targets based on the simultaneous action of multiple PCBs can provide a reference for other research. The targets were not found in previous metabolite toxicity studies. It also provides a bridge for the toxic effects and experimental research of PCBs and their metabolites in the future.
The Systematic Analyses of RING Finger Gene Signature for Predicting the Prognosis of Patients with Hepatocellular Carcinoma
Authors: Zhang Chunfeng , Yang Yang , Wang Kun , Chen Muhua , Lu Min , Hu Chenyu , Du Xiaojuan , Xing Baocai , Liu Xiaofeng
Published: September 2022

RING finger (RNF) proteins are frequently dysregulated in human malignancies and are tightly associated with tumorigenesis. However, the expression profiles of RNF genes in hepatocellular carcinoma (HCC) and their relations with prognosis remain undetermined. Here, we aimed at constructing a prognostic model according to RNF genes for forecasting the outcomes of HCC patients using the data from The Cancer Genome Atlas (TCGA) program. We collected HCC datasets to validate the values of our model in predicting prognosis of HCC patients from International Cancer Genome Consortium (ICGC) platform. Then, functional experiments were carried out to explore the roles of the representative RNF in HCC progression. A total of 107 differentially expressed RNFs were obtained between TCGA-HCC tumor and normal tissues. After comprehensive evaluation, a prognostic signature composed of 11 RNFs (RNF220, RNF25, TRIM25, BMI1, RNF216P1, RNF115, RNF2, TRAIP, RNF157, RNF145, and RNF19B) was constructed based on TCGA cohort. Then, the Kaplan-Meier (KM) curves and the receiver operating characteristic curve (ROC) were employed to evaluate predictive power of the prognostic model in testing cohort (TCGA) and validation cohort (ICGC). The KM and ROC curves illustrated the good predictive power in testing and validation cohort. The areas under the ROC curve are 0.77 and 0.76 in these two cohorts, respectively. Among the prognostic signature genes, BMI1 was selected as a representative for functional study. We found that BMI1 protein level was significantly upregulated in HCC tissues. Moreover, the inhibitor of BMI1, PTC-209, displayed an excellent anti-HCC effect in vitro. Enrichment analysis of BMI1 downstream targets showed that BMI1 might be involved in tumor immunotherapy. Together, our overall analyses revealed that the 11-RNFs prognostic signature might provide us latent chances for evaluating HCC prognosis and developing novel HCC therapy.
Impact of the Belt and Road Initiative on Poverty Reduction in Countries along the Route
Authors: Ma Wei , Bo Na , Song Yang , Qiao Fuwei
Published: September 2022

Poverty reduction is an important driving force for the global realization of the United Nations 2030 Sustainable Development Goals and the building of a global community with a shared future for mankind. This paper takes the Belt and Road Initiative (BRI) as a quasi‐natural experiment. Based on the panel data of 134 countries in the world from 2000 to 2018, this paper uses the difference‐in‐difference (DID) model to examine the impact of the BRI on poverty reduction in countries along the route and to study its internal mechanism from the dual perspectives of trade openness and investment openness. The results show that (1) the BRI can significantly reduce the incidence of poverty with the impact coefficient stable at −0.26, and the empirical conclusion has passed the parallel test, placebo test, and instrumental variable test; (2) the mediating effect test model shows that the BRI can achieve poverty reduction through the dual openness of trade and investment, and the intermediary effect of trade openness is greater than investment openness; and (3) from the perspective of heterogeneity analysis, geographically, the BRI has a slightly higher role in promoting poverty reduction in landlocked countries than in coastal countries. In terms of economic location, the effect of poverty reduction has a certain “pro‐poverty” characteristic, that is, the BRI promotes poverty reduction in low‐ and middle‐income countries far more than other types of income countries. Therefore, we believe that the continuous deepening of the BRI high‐quality construction and the strengthening of cooperation among countries along the route will play a key role in promoting the international cycle of trade, investment, and other factors, as well as the cause of poverty reduction in the region and the world.
Screening of the Key Genes for the Progression of Liver Cirrhosis to Hepatocellular Carcinoma Based on Bioinformatics
Authors: Chen Yuanbin , Qian Hongyan , He Xiao , Zhang Jing , Xue Song , Wu Yumeng , Chen Jian , Wu Xuming , Zhang Suqing
Published: September 2022

Hepatocellular carcinoma (HCC), which is among the most globally prevalent cancers, is strongly associated with liver cirrhosis. Using a bioinformatics approach, we have identified and investigated the hub genes responsible for the progression of cirrhosis into HCC. We analyzed the Gene Expression Omnibus (GEO) microarray datasets, GSE25097 and GSE17549, to identify differentially expressed genes (DEGs) in these two conditions and also performed protein-protein interaction (PPI) network analysis. STRING database and Cytoscape software were used to analyze the modules and locate hub genes following which the connections between hub genes and the transition from cirrhosis to HCC, progression of HCC, and prognosis of HCC were investigated. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to detect the molecular mechanisms underlying the action of the primary hub genes. In all, 239 DEGs were obtained, with 94 of them showing evidence of upregulation and 145 showing evidence of downregulation in HCC tissues as compared to cirrhotic liver tissues. We identified six hub genes, namely, BUB1B, NUSAP1, TTK, HMMR, CCNA2, and KIF2C, which were upregulated and had a high diagnostic value for HCC. Besides, these six hub genes were positively related to immune cell infiltration. Since these genes may play a direct role in the progression of cirrhosis to HCC, they can be considered as potential novel molecular indicators for the onset and development of HCC.
Evaluation of Heavy Metals in Soil Wastewater Stream
Authors: Alnuwaiser Maha Abdallah
Published: September 2022

Environmental pollution is one of the main factors that significantly affect biological systems and human health. Soil pollution with heavy metals is an increasingly pressing problem worldwide. In general, heavy metals are stable and do not decompose, unlike other organic pollutants. The quantity of them is natural components of soil crust, the remaining come from human activities, which may result from the extensive use of sewage. In the present study, a methodology aimed at simultaneous quantification of 16 heavy elements in soil of 3 different regions was developed. The concentration of 16 soil heavy metals (Se, Cd, V, Be, As, Mn, Co, Zn, Fe, Cr, Pb, Ni, Cu, Mo, Hg, and Ti) was measured in 11 sampling along Riyadh, Qassim, and Medina, Kingdom of Saudi Arabia from 3 sites soil treated with sewage water. These chemical minerals were identified in the samples using an ICPE-9000 spectrometer. The assessment of heavy metal contamination was derived using enrichment factors (EF), the pollution load index (PLI), and geoaccumulation index (Igeo). This study revealed that the soil is predominantly polluted by Cd, As, and Mo of Riyadh and Medina and As, Mo, and Cd of Qassim region at site B and site C, respectively. As recorded, the highest concentration value of 5000 mg/kg for Fe at site (B) followed by Cu. The Igeo value of Cd is 1.1520 in Medina region. The Igeo value of Se is 3.2395 in Medina region, while its cumulative geographical index decreased in the regions of Riyadh and Qassim, which amounted to 2.6114 and 2.1699, respectively. The Igeo values of the rest of the minerals in the three regions studied indicated that the soil is unpolluted, while it was slightly to moderately polluted for both Mo and Hg in most regions studied. The minerals in the soil at all sites studied were less than the general average concentration. With the exception of mercury, molybdenum, arsenic, cadmium, and selenium, whose concentration exceeded the permissible and recommended limits. The increasing order of concentration of minerals was Be < V < Cd < Hg < Mo < Co < Zn < Pb < Cr < Se < As < Ni < Ti < Mn < Cu < Fe at all sites, respectively.
[Retracted] Mental Health Study from the Perspective of Behavior Preference under the Environment of Internet Media: Based on China’s Data Analysis
Authors: Chen Guangjun , Zhong Yubo , Yu Wangyang , Yan Jin
Published: September 2022

To prevent the mental health problems of college preference students effectively, this paper analyzes the influence of college students’ internet media use behaviors on their mental health in the information age. We make an empirical study on the above problems by using metrology models under the condition of controlling individual factors. The result shows that the mental health of college students is significantly affected by different internet media use behaviors and shows obvious heterogeneity. Preference for games and soap operas or films has a significant negative relationship with the mental health of college students, while the preference for science, education programs, and the preference to obtain current political news comments through official media have a positive impact on the psychological security of college students. Meanwhile, the mental health of college students is also significantly influenced by individual factors. The education level, family income, and social satisfaction show a significant positive correlation with the mental health of college students.
Exploring the Influence of Local Government Debt on Enterprise Investment through the Empirical Evidence at the Municipal Level
Authors: Cao Mingyao , Duan Keyi , Ibrahim Haslindar
Published: September 2022

The data of new urban investment bonds in cities from 2011 to 2021 to represent the growth of local government debt is used in this article. This paper studies the relationship between the increase of local government debt and the underinvestment of A-share listed enterprises, which are nonfinancial companies in China. The research finds that the increase in local government debt will crowd out the debt financing of enterprises and then significantly the degree of underinvestment of enterprises, which is represented non-state-owned enterprises, enterprises with high financing constraints, and enterprises with nonlocal debt invested in the industry. As for listed enterprises with relatively low fixed assets, the increase in local debt will further lead to the degree of underinvestment. The above conclusions remain unchanged after a series of robustness tests in this paper. Further research shows that the increase in local government debt will weaken the stimulus effect of loose monetary policy and thus affect the underinvestment of enterprises. This paper expands the research on the factors affecting the underinvestment of enterprises and deepens the research on the consequences of the influence of local government debt on the economic activities of microsubjects.
Risks and Strategies of China’s Direct Investment in Central Asia from the Perspective of Big Data
Authors: Huang Jiong
Published: September 2022

With the growth of Chinese economy and the improvement of its international status, Chinese enterprises foreign direct investment has ushered in new development opportunities. Although scale foreign investment continues to expand and has broad development prospects, it also faces huge risks that domestic investment has never had. China and Central Asia are highly complementary economically. Strengthening direct investment in the Central Asian countries can effectually drive China’s economic development. Based on the quantitative evaluation and analysis from the perspective of big data, this paper uses a variety of methods to evaluate and explore the risks of China’s direct investment in Central Asian countries, and gives some policy suggestions. The research shows that the motivation of Chinese foreign investment cooperation is different from the marginal industry transfer in the traditional international investment theory. The problem of country risks in Central Asia is prominent. Chinese government must rely on the build of the Belt and Road to strengthen intergovernmental communication and exchanges, and establish a good cooperation mechanism to deal with the increasingly prominent problem of country risks. Further we will improve trade facilitation and expand the trade openness of Central Asian countries to China. To avoid risks, Chinese companies should invest under the guidance of the government and establish a complete investment chain for large projects. At the same time, we also need to seize the opportunity of the development of digital economy, and gradually establish a scientific and efficient investment model to effectively avoid the risk of direct investment.