bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and study of fluorine-functionalized ZnTPPs
Authors: Zhang Yang , Viereck Jonathan , Rangan Sylvie , Bartynski Robert , Galoppini Elena
Published: February 2022

Five fluorinated zinc tetraphenyl porphyrins derivatives, fluorinated either on all ortho positions of the meso phenyl rings (Zn(II)-5,10,15,20-tetrakis(2,6-difluorophenyl)porphyrin) or in [Formula: see text]-positions with two, four, six or eight fluorine groups (2,3-difluoro-5,10,15,20-tetraphenylporphyrin, 7, 8,17,18-tetrafluoro-5,10,15,20-tetraphenylporphyrin, 7,8,12,13,17,18-hexafluoro-5,10,15,20-tetraphenyl- porphyrin and 2,3,7,8,12,13,17,18-octafluoro-5,10,15,20-tetraphenylporphyrin, respectively), were studied to probe the effect of fluorine in on-surface synthesis approaches. Additionally, bulkier substituents have been used in 7,8,17,18-tetrabromo-5,10,15,20-tetraphenylporphyrin and 7,8,17,18-tetramethyl-5,10,15,20-tetraphenylporphyrin, for a direct comparison with the corresponding fluorinated compounds. Reported are the synthesis and electronic structure characterization of the compounds, using UV-vis absorption and fluorescence spectroscopies as well as electronic structure calculations of the ground state molecular properties. Steric and electronic effects of fluorination are explored. For all molecules studied, substitution with fluorine maintains a planar tetrapyrrole macrocycle and shifts in the absorption spectra were consistent with calculated changes in the HOMOs and LUMOs energies of the molecules. In contrast, the bromo and methyl-substituted derivatives exhibit chemical instability and spectral shifts, compared to parent compound ZnTPP, consistent with structural distortions and accounted for calculations. The site-specific effects of fluorination on the electronic structure are discussed in detail.
Seismic Performance Evaluation Method of Underground Frame Structures Considering the Vertical Seismic Capacity of Structural Components
Authors: Wu Chunyu , Lu Dechun , Ma Chao , Du Xiuli
Published: February 2022

The seismic performance of large underground structures can be evaluated from two perspectives, namely the horizontal interlaminar deformation and the vertical loading-carrying capacity. The current seismic analysis methods focus on the horizontal seismic performance with the interlaminar drift ratio as the evaluation index. It is difficult to fully reflect the vertical failure characteristics of the overall structure. This paper aims to develop an evaluation method to comprehensively evaluate the seismic performance of underground frame structures. The quantitative evaluation indexes, i.e. the vertical loading-carrying risk factor of columns and the vertical deflection risk factor of slabs, were defined to evaluate the seismic performance of underground frame structures. Then, the seismic performance evaluation method was established and the evaluation processes were described. Seismic response analysis of an underground frame structure with different spans on the upper and lower stories was conducted, and the seismic performance of the structural components and overall structure was quantitatively evaluated by using the proposed evaluation method. The seismic response laws of columns and slabs were analyzed and the potential failure mode of the structure was clarified. Furthermore, the failure mechanism of the structure was explained in detail. Numerical results presented that the proposed evaluation method can effectively identify the seismic-induced weak components and failure modes of underground frame structures, and thus has guiding significance for the seismic design of the underground frame structures.
Blood glucose sensors and recent advances: A review
Authors: Peng Zhiqing , Xie Xiangyu , Tan Qilong , Kang Hu , Cui Ji , Zhang Xia , Li Wang , Feng Guoying
Published: February 2022

Blood glucose (BG) concentration of the human body serves as an important index for diagnosis of diabetes, and its detection methods need to be more efficient due to high mortality rates for diabetes. Typical household BG meters are invasive products based on electrochemistry and rely on finger pricks to procure blood, which might result in skin damage and bacterial infections. Moreover, such BG meters can only detect the BG concentration at a certain time while ignoring the BG level at other times. Recently, to achieve microinvasive, noninvasive and continuous glucose monitoring (CGM) by detecting body fluid, such as sweat or tears, the research direction has been gradually developing toward wearable devices. This review discusses the glucose detection mechanism of various electrochemical and optical glucose sensors, and briefly analyzes their advantages and challenges. Additionally, wearable products based on various principles that have appeared on the market are also summarized. Furthermore, based on the analysis, this review proposes a design concept for future research directions.
A hybrid elephant herding optimization and harmony search algorithm for potential load balancing in cloud environments
Authors: Ali Syed Muqthadar , Kumaran N. , Balaji G. N.
Published: February 2022

In cloud computing environment, load balancing delinquent arises when a large count of new IoT user requests are linked with specific fog nodes. So, a well-organized load balancing tactic is needed in cloud computing. Therefore, in this manuscript, a hybrid elephant herding optimization and harmony search algorithm (HSA) for potential load balancing in cloud environments (HEHO-HSA-PLB-CE) is effectively proposed for reducing task waiting time, load balancing rate, scheduling time, delay and energy consumption. The HEHO algorithm and HSA are mainly used for leveraging the allocation of virtual machine (VM) and incorporating an enhanced strategy of physical machine selection. The proposed HEHO-HSA-PLB-CE method aims at preventing the issue of premature convergence or the issue related to the solution falling at the point of local optimum. Finally, the proposed method potentially achieves load balance under the allocation of VM and enhancement of resource utilization in the cloud computing environment. The proposed approach is activated in CloudSim and the efficiency of the proposed method is assessed by evaluation metrics, such as response time, load balance rate, scheduling time, delay, energy consumption. Then, the simulation performance of the proposed method provides lower delay 32.82%, 25.32%, 29.34% and 34.18%, low energy consumption 38.22%, 25.46%, 42.12% and 15.34% compared with the existing methods, like Aquila optimizer for PLB in CE (AO-PLB-CE), arithmetic optimization algorithm for PLB in CE (AOA-PLB-CE), sine cosine algorithm for PLB in CE (SCA-PLB-CE), and enhanced krill herd algorithm for PLB in CE (EKHO-PLB-CE) respectively.
Identification of industrial air compression system using neural network
Authors: Khong Fan-Hao , Samad Md Fahmi Abd , Tamadaran Brahmataran
Published: February 2022

The existence of random variable in any industrial process is basically unavoidable. It occasionally creates nonlinearity behavior of a system and makes predictive control complicated. Such a random behavior must not be ignored as it may indicate any unknown event occurring during the process. System identification is an approach to construct the mathematical model of a dynamical system using the instrumentation signal of input and output of the system. This study performs system identification by using the NARX model as a base model with the nonlinear functions of a neural network for an industrial air compression system. The identification undergoes a series of analysis (number of neuron, delay and data division) to determine the most suitable NARX-NN model architecture configuration before coming up with a final model. Finally, the validation of model’s predictive performance is carried out through several analyses, namely, mean square error and regression value. The predicted data are compared to the industrial data to verify its accuracy which shows that the final model had successfully ruled out the suspicious random event data.
OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining
Authors: Wu Youxi , Wang Xiaohui , Li Yan , Guo Lei , Li Zhao , Zhang Ji , Wu Xindong
Published: February 2022

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid mining too many useless patterns. However, this method is difficult for users to set a suitable gap without prior knowledge and each character is considered to have the same effects. To tackle these issues, this article addresses a self-adaptive One-off Weak-gap Strong Pattern (OWSP) mining, which has three characteristics. First, it determines the gap constraint adaptively according to the sequence. Second, all characters are divided into two groups: strong and weak characters, and the pattern is composed of strong characters, while weak characters are allowed in the gaps. Third, each character can be used at most once in the process of support (the frequency of pattern) calculation. To handle this problem, this article presents OWSP-Miner, which equips with two key steps: support calculation and candidate pattern generation. A reverse-order filling strategy is employed to calculate the support of a candidate pattern, which reduces the time complexity. OWSP-Miner generates candidate patterns using pattern join strategy, which effectively reduces the candidate patterns. For clarification, time series is employed in the experiments and the results show that OWSP-Miner is not only more efficient but also is easier to mine valuable patterns. In the experiment of stock application, we also employ OWSP-Miner to mine OWSPs and the results show that OWSPs mining is more meaningful in real life. The algorithms and data can be downloaded at https://github.com/wuc567/Pattern-Mining/tree/master/OWSP-Miner.
Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach
Authors: Brik Bouziane , Messaadia Mourad , Sahnoun M’hammed , Bettayeb Belgacem , Benatia Mohamed Amin
Published: February 2022

Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems.

In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency.

Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.
Feature Extraction of High-dimensional Data Based on J-HOSVD for Cyber-Physical-Social Systems
Authors: Gao Yuan , Yang Laurence T. , Zhao Yaliang , Yang Jing
Published: February 2022

With the further integration of Cyber-Physical-Social systems (CPSSs), there is explosive growth of the data in CPSSs. How to discover effective information or knowledge from CPSSs big data and provide support for subsequent learning tasks has become a core issue. Moreover, modern applications in CPSSs increasingly rely on the processing and analysis of high-dimensional data; the correlation and internal structure of these high-dimensional data are gradually becoming more complex, which further makes traditional machine learning algorithms a little inadequate in processing these data. In this article, we propose two general dimension reduction and feature extraction methods for high-dimensional data based on joint tensor decomposition, namely core feature extraction methods and factor feature extraction methods, which can effectively mine out the common components and hidden patterns of high-dimensional data by joint analysis while maintaining the original data structure. We also verified the effectiveness of the methods from both theoretical and practical aspects. Furthermore, we extend the two feature extraction methods to the tensor distance scenario and illustrate that the compressed features extracted by our models can keep the global information of original data well. Finally, we evaluated proposed methods on two benchmark datasets through classification tasks, and experimental results show that the low-dimensional features extracted by the proposed models have higher classification accuracy than the direct classification of the original data, which further verifies the effectiveness and robustness of our methods.
A Hybrid Deep Learning Framework for Intelligent Predictive Maintenance of Cyber-physical Systems
Authors: Shcherbakov Maxim , Sai Cuong
Published: February 2022

The proliferation of cyber-physical systems (CPSs) and the advancement of the Internet of Things (IoT) technologies have led to explosive digitization of the industrial sector. It offers promising perspectives for high reliability, availability, maintainability, and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system’s health state. In this article, a practical and effective hybrid deep learning multi-task framework integrating the advantages of convolutional neural network (CNN) and long short-term memory (LSTM) neural network to reflect the relatedness of remaining useful life prediction with health status detection process for complex multi-object systems in CPS environment is developed. The CNN is used as a feature extractor to compress condition monitoring data and directly extract significant spatiotemporal features from raw multi-sensory input data. The LSTM is used to capture long-term temporary dependency features. The advantages of the proposed hybrid deep learning framework have been verified on the popular NASA’s C-MAPSS dataset. The experimental study compares this approach to the existing methods using the same dataset. The results suggest that the proposed hybrid CNN-LSTM model is superior to existing methods, including traditional machine learning and deep learning-based methods. The proposed framework can provide strong support for the health management and maintenance strategy development of complex multi-object systems.
Configurable Batch-Processing Discovery from Event Logs
Authors: Pika Anastasiia , Ouyang Chun , ter Hofstede Arthur H. M.
Published: February 2022

Batch processing is used in many production and service processes and can help achieve efficiencies of scale; however, it can also increase inventories and introduce process delays. Before organizations can develop good understanding about the effects of batch processing on process performance, they should be able to identify potential batch-processing behavior in business processes. However, in many cases such behavior may not be known; for example, batch processing may be occasionally performed during certain time frames, by specific employees, and/or for particular customers. This article presents a novel approach for the identification of batching behavior from process execution data recorded in event logs. The approach can discover different types of batch-processing behaviors and allows users to configure batch-processing characteristics they are interested in. The approach is implemented and evaluated through experiments with synthetic event logs and case studies with real-life event logs. The evaluation demonstrates that the approach can identify various batch-processing behaviors in the context of business processes.
Dynamic-Aware Federated Learning for Face Forgery Video Detection
Authors: Hu Ziheng , Xie Hongtao , Yu Lingyun , Gao Xingyu , Shang Zhihua , Zhang Yongdong
Published: February 2022

The spread of face forgery videos is a serious threat to information credibility, calling for effective detection algorithms to identify them. Most existing methods have assumed a shared or centralized training set. However, in practice, data may be distributed on devices of different enterprises that cannot be centralized to share due to security and privacy restrictions. In this article, we propose a Federated Learning face forgery detection framework to train a global model collaboratively while keeping data on local devices. In order to make the detection model more robust, we propose a novel Inconsistency-Capture module (ICM) to capture the dynamic inconsistencies between adjacent frames of face forgery videos. The ICM contains two parallel branches. The first branch takes the whole face of adjacent frames as input to calculate a global inconsistency representation. The second branch focuses only on the inter-frame variation of critical regions to capture the local inconsistency. To the best of our knowledge, this is the first work to apply federated learning to face forgery video detection, which is trained with decentralized data. Extensive experiments show that the proposed framework achieves competitive performance compared with existing methods that are trained with centralized data, with higher-level security and privacy guarantee.
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training
Authors: Kang Yan , Liu Yang , Liang Xinle
Published: February 2022

Federated learning allows multiple parties to build machine learning models collaboratively without exposing data. In particular, vertical federated learning (VFL) enables participating parties to build a joint machine learning model based upon distributed features of aligned samples. However, VFL requires all parties to share a sufficient amount of aligned samples. In reality, the set of aligned samples may be small, leaving the majority of the non-aligned data unused. In this article, we propose Federated Cross-view Training (FedCVT), a semi-supervised learning approach that improves the performance of the VFL model with limited aligned samples. More specifically, FedCVT estimates representations for missing features, predicts pseudo-labels for unlabeled samples to expand the training set, and trains three classifiers jointly based upon different views of the expanded training set to improve the VFL model’s performance. FedCVT does not require parties to share their original data and model parameters, thus preserving data privacy. We conduct experiments on NUS-WIDE, Vehicle, and CIFAR10 datasets. The experimental results demonstrate that FedCVT significantly outperforms vanilla VFL that only utilizes aligned samples. Finally, we perform ablation studies to investigate the contribution of each component of FedCVT to the performance of FedCVT.
Race and Redistricting
Authors: Canon David T.
Published: February 2022

This review examines the role of race in the decennial process of redistricting. I review the scholarly literature on three related questions: What role should racial redistricting play in the representation of racial interests, how may racial redistricting be used, and what is the connection between racial redistricting and the substantive representation of racial minorities? The review briefly examines the normative question of racial representation and then focuses on the last two topics: empirical research on how racial interests are represented in legislatures and legal questions concerning the use of redistricting to produce descriptive representation. Racial redistricting enhances the representation of racial interests in legislatures, and the legal status of the districts is complex; therefore, litigation will proceed on a heavily fact-based, case-by-case basis in which political scientists will continue to play a vital role with their research on racially polarized voting and representation.
A Tradition of Voting with Your Feet for Highly Effective Meetings
Authors: Rounsavill Brian E.
Published: February 2022

The heart of the Society has been successful meetings. For nearly a century, you could set your watch by them; typically, meetings were held in the first week of May and October each year.
(Invited) Efficient Photocatalytic CO Production from CO2 and H2O By the Aid of Artificial Photosynthesis
Authors: Teramura Kentaro
Published: February 2022

The reduction in human-induced emissions of CO2 from automobiles, factories, power stations, etc., over the next 15 years is currently one of the most important issues facing the planet. We should therefore attempt to develop industrial processes using CO2 as a feedstock in order to build a sustainable society in the near future. Photocatlaytic conversion of CO2 has recently drawn increased attention due to allowing direct solar-to-chemical energy conversion. If H2O does function as the electron donor, then it is important to obtain a stoichiometric ratio between the amount of O2 evolved and the amount of CO2 reduced. Moreover, the reduction of H+, released from H2O molecules, usually competes with CO2 reduction when several heterogeneous materials are used as photocatalysts for the reduction of CO2 with H2O. Generally, the production of H2 via the reduction of H+ is the dominant pathway. Therefore, to achieve high selectivity in the photocatalytic conversion of CO2 with H2O, the electrons generated through the oxidation of H2O must be controlled to selectively. Moreover, we achieved highly selective photocatalytic reduction of CO2 by H2O by simultaneously using an inhibitor of H2 production and materials capable of CO2 capture and storage, e.g., Ag-modified ZnGa2O4/Ga2O3, La2Ti2O7, SrO/Ta2O5, ZnGa2O4, ZnTa2O6, Sr2KTa5O15, SrNb2O6, LDH/Ga2O3, and Pr2O3/Ga2O3.
SGLT2 inhibition potentiates the cardiovascular, renal, and metabolic effects of sGC stimulation in hypertensive rats with prolonged exposure to high-fat diet
Authors: Reverte Virginia , Rodriguez Francisca , Oltra Lidia , Moreno Juan M. , Llinás María T. , Shea Courtney M. , Schwartzkopf Chad D. , Buys Emmanuel S. , Masferrer Jaime L. , Salazar F. Javier
Published: February 2022

This is the first study, to our knowledge, showing that SGLT2 inhibition potentiates the beneficial cardiovascular, renal, and metabolic effects elicited by sGC stimulation in hypertensive rats with prolonged high-fat diet. The effects of the simultaneous administration of praliciguat and empagliflozin are greater than those elicited by either one alone. The effects of the simultaneous treatment may be related to a greater reduction in the inflammatory status.
Study on the Construction of Landscape Architecture in Residential District Based on Urban Greening Remote Sensing
Authors: Zhai Yucen , Li Wen
Published: February 2022

The garden landscape of urban residential areas has significant environmental benefits, allowing people to get closer to nature and integrate with nature to relax and have some social benefits at the same time. This article is aimed at studying the effect of urban greening remote sensing on residential landscape construction. This paper uses the greening remote sensing image processing algorithm combined with the analysis method of SURF color remote sensing image and conducts the experiment based on the urban greening remote sensing on the residential area landscape architecture research. This article combines sustainable development strategies with innovative design methods to deeply study the role of urban greening remote sensing on the construction of residential landscape architecture. At the same time, the experiment in this article also proposes that the garden landscape of urban residential areas can provide entertainment for people, soothe residents’ mood, and achieve the best use of residential buildings. However, there are still some constraints in the development process of residential industry: insufficient government support, relatively backward technology of residential industrialization, lack of support and input from enterprises, and prejudice of people’s traditional concept towards it. The experimental results of this paper show that the remote sensing image features of buildings in residential areas and public areas are obvious. In the recognition of 46 training features and 34 detection features, the number of correct remote sensing recognition features is 32, and the green building image remote sensing recognition is good. The accuracy is 94%, which can meet the identification requirements of greening in the building. Combined with the specific conditions of urban greening and garden construction in my country, it can meet people’s entertainment needs and, to a certain extent, can also improve people’s life and cultural taste.
Disturbance-Observer-Based Adaptive Fuzzy Control for Islanded Distributed Energy Resource Systems
Authors: Yang Chengshun , Hua Tao , Dai Yuchen , Liu Guofu , Huang Xiaoning , Zhang Dongdong
Published: February 2022

With the aim to improve the antidisturbance ability of the islanded distributed energy resource (DER) systems, a disturbance-observer-based adaptive fuzzy sliding mode control (DAFSC) voltage controller is designed based on indirect vector control, which implements the voltage tracking and improves the self-regulation ability of the islanded DER systems. Firstly, the circuit diagram and the mathematical model of the DER system are presented. Then, the second-order sliding mode differentiator is designed to solve the problem of calculation expansion in the backstepping control method. To solve the influence of lumped disturbance on the system, a disturbance-observer is proposed to observe the unknown disturbance and compensate the controller feed-forward. Moreover, fuzzy control is proposed to reduce the dependence of the control effect on model accuracy. Finally, the stability of the controller is verified by Lyapunov stability theory, and the hardware in the loop results is given to verify that the control effect of the proposed DAFC controller has better dynamic performance compared with proportion-integral (PI) and the backstepping control strategy.
Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model
Authors: Rahman Zia Ur , Ullah Syed Irfan , Salam Abdus , Rahman Taj , Khan Inayat , Niazi Badam
Published: February 2022

According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function, paralysis of body parts, and severe back pain for which a physiotherapist employs many therapies to restore the mobility needs of everyday life. This research article presents an automated approach to detect different therapy exercises performed by stroke patients during rehabilitation. The detection of rehabilitation exercise is a complex area of human activity recognition (HAR). Due to numerous achievements and increasing popularity of deep learning (DL) techniques, in this research article a DL model that combines convolutional neural network (CNN) and long short-term memory (LSTM) is proposed and is named as 3-Layer CNN-LSTM model. The dataset is collected through RGB (red, green, and blue) camera under the supervision of a physiotherapist, which is resized in the preprocessing stage. The 3-layer CNN-LSTM model takes preprocessed data at the convolutional layer. The convolutional layer extracts useful features from input data. The extracted features are then processed by adjusting weights through fully connected (FC) layers. The FC layers are followed by the LSTM layer. The LSTM layer further processes this data to learn its spatial and temporal dynamics. For comparison, we trained CNN model over the prescribed dataset and achieved 89.9% accuracy. The conducted experimental examination shows that the 3-Layer CNN-LSTM outperforms CNN and KNN algorithm and achieved 91.3% accuracy.
Identification of Key Pathways and Establishment of a Seven-Gene Prognostic Signature in Cervical Cancer
Authors: An Ran , Meng Silu , Qian Hua
Published: February 2022

Cervical cancer (CC) remains high morbidity and mortality. We aimed to identify critical pathways underlying cervical carcinogenesis and establish a prognostic signature. Six datasets from the gene expression omnibus (GEO) database were used to screen the differentially expressed genes (DEGs) between CC and normal tissues. We used the unions of the DEGs to perform functional analysis. The 108 overlapped DEGs were analyzed to determine a prognostic signature by Cox regression and Lasso analysis based on The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) and Immune Cell Abundance Identifier (ImmuCellAI) were used to determine the relationships between the signature and biological functions. The PI3K-Akt signaling pathway, the Ras signaling pathway, and the viral carcinogenesis pathway may be critical for CC development. We identified seven genes (PLOD2, DSG2, SPP1, CXCL8, MCM5, HLTF, and KLF4) to construct a risk score formula. Survival analysis showed that the high-risk group indicated a worse prognosis than the low-risk group

p
<
0.0001

. The AUC of the prognostic signature was 0.7449, 0.7641, and 0.8146 at 1, 3, and 5 years. We also identified that the signature is an independent prognostic factor. GSEA showed five pathways were relevant to the signature, such as the adherens junction pathway. The signature also affected the abundances of various types of immune cells, such as B cell, CD4+ T cell, and CD8+ T cell. Further, we found that SPP1 was co-expressed with HK3, CD163, CCL3, CLEC5A, MMP8, TREM1, OLR1, and TREM2. The results of Gene Ontology analysis showed that SPP1 and its co-expressed related proteins mainly affected metabolic process, multicellular organismal process, cell communication, cell proliferation, protein binding, and transporter activity. In conclusion, the present study explored the key pathways for CC development and the seven-gene signature can effectively make the prognosis evaluation of CC patients.
Game-Based Channel Selection for UAV Services in Mobile Edge Computing
Authors: Chen Y. , Xing H. , Chen S. , Zhang N. , Chen X. , Huang J.
Published: February 2022

Computation offloading is a hot research topic in mobile edge computing (MEC). Computation offloading among multiedge nodes in heterogeneous networks can help reduce offloading cost. In addition, the unmanned aerial vehicles (UAVs) play a key role in MEC, where UAVs in the air communicate with ground base stations to improve the network performance. However, limited channel resources can lead to the increase of transmission delay and the decline of communication quality. Effective channel selection mechanisms can help address those issues by improving transmission rate and ensuring communication quality. In this paper, we study channel selection during communication between multiple UAVs and base stations in an MEC system with heterogeneous networks. To maximize the transmission rate of each UAV user, we formulate a channel selection problem and model it as a noncooperative game. Then, we prove the existence of Nash equilibrium (NE). In addition, we design a multiple UAV-enabled transmission channel selection (UTCS) algorithm to obtain the equilibrium strategy profile of all the UAV users. Experimental results validate that UTCS algorithm can converge after a finite number of iterations and it outperforms random transmission algorithm (RTA) and sequential transmission algorithm (STA).
The Interaction between Public Environmental Art Sculpture and Environment Based on the Analysis of Spatial Environment Characteristics
Authors: Wang Yuhong
Published: February 2022

The interaction with the public and the coordinated change with the environment are more important in public environmental art sculpture. To be more accessible to the public, public environmental art sculptures should be integrated with the environment and interact with the public. Paying close attention to the scale and volume design of sculpture is one of the elements that embody and express the connotation of works, as well as one of the objects that sculpture, as a member of environmental elements, must study and analyze thoroughly. Based on this, this paper investigates the relationship between sculpture and environment in public environmental art through an examination of space environment characteristics, with the goal of creating a public space environment in which public art sculpture, the environment, and people are all in sync. The improved AFSA (artificial fish swarms algorithm) model is used to optimize the landscape ecological pattern. The landscape index can be predicted and analyzed more accurately using a dynamic model of landscape pattern from remote sensing and a geographic information system. Experiments show that this algorithm's prediction accuracy has significantly improved, providing a solid foundation for landscape ecological management and planning.
A Decision‐Making Framework Using q‐Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information for the Drug Selection to Treat COVID‐19
Authors: Attaullah  , Ashraf Shahzaib , Rehman Noor , AlSalman Hussain , Gumaei Abdu H.
Published: February 2022

In our current era, a new rapidly spreading pandemic disease called coronavirus disease (COVID‐19), caused by a virus identified as a novel coronavirus (SARS‐CoV‐2), is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID‐19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID‐19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID‐19 is still a complicated and uncertain decision. This study’s key objective is to develop a novel approach called q‐rung orthopair probabilistic hesitant fuzzy rough set (q‐ROPHFRS), which incorporates the q‐rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set structures. New q‐ROPHFR aggregation operators have been established: the q‐ROPHFR Einstein weighted averaging (q‐ROPHFREWA) operator and the q‐ROPHFR Einstein weighted geometric (q‐ROPHFREWG) operator. In this study, we explored some basic features of the developed operators. Afterward, to demonstrate the viability and feasibility of the established decision‐making approach in real‐world applications, a case study related to selecting drugs for COVID‐19 pandemic is addressed. Furthermore, a comprehensive comparison with the q‐rung orthopair probabilistic hesitant fuzzy rough TOPSIS technique is also presented to illustrate the benefits of the new framework. The obtained results confirm the reliability and effectiveness of the proposed approach for finding uncertainty in real‐world decision‐making.
New Media Marketing Strategy Optimization in the Catering Industry Based on Deep Machine Learning Algorithms
Authors: Peng Zikang
Published: February 2022

With the in‐depth development of new‐generation network technologies such as the Internet, big data, and cloud intelligence, people can obtain massive amounts of information on mobile phones or mobile platforms. The era of unreachable big data has arrived, which raises questions for the development of corporate marketing. With the development of Internet technology, people use mobile terminals for longer and longer periods of time. New media has gradually become the mainstream of the media arena. It has distinctive features such as freedom to find audiences, diverse content forms, and timeliness of information release, which has changed the traditional. The marketing model has a profound impact on the development of the market. This article uses relevant theories, such as new media, marketing, and catering industry marketing strategies, studies the related concepts and characteristics of new media, clarifies the impact of the development of new media on the catering industry and audience groups, and studies the impact of the catering industry from multiple dimensions. Based on the development factors in the new media environment, combined with marketing theory, it puts forward suggestions for catering companies to use new media to carry out marketing planning in product innovation, improving information channels, creating network events and topics, and promoting innovation and health in the catering industry. And a marketing strategy is proposed based on deep machine learning algorithms; including a cloud server, the cloud server communicates with the e‐commerce software platform and the input of physical sales is recorded. The adopted cloud server is connected with data collection, data processing, and communication module. The communication module is connected with a deep machine learning algorithm system; that is, deep machine learning algorithm system is connected with a sales platform in communication. The sales platform is connected with advertising settings and advertising, and the advertising is electrically connected with an algorithm of advertising delivery methods. Advertisement delivery method algorithm communication is connected to the cloud server. This article uses deep machine learning algorithms to process the data information to make the data information easy to view and clear. The advertisement delivery method algorithm calculates the best way of advertising and then calculates the advertisement to deliver.
Cross-Talking Pathways of Forkhead Box O1 (FOXO1) Are Involved in the Pathogenesis of Alzheimer’s Disease and Huntington’s Disease
Authors: Liu Liyang , Bai Jun , Liu Fangxi , Xu Ying , Zhao Mei , Zhao Chuansheng , Zhou Zhike
Published: February 2022

Alzheimer’s disease (AD) and Huntington’s disease (HD) are destructive worldwide diseases. Efforts have been made to elucidate the process of these two diseases, yet the pathogenesis remains elusive as it involves a combination of multiple factors, including genetic and environmental ones. To explore the potential role of forkhead box O1 (FOXO1) in the development of AD and HD, we identified 1,853 differentially expressed genes (DEGs) from 19,414 background genes in both the AD&HD/control and FOXO1-low/high groups. Four coexpression modules were predicted by the weighted gene coexpression network analysis (WGCNA), among which blue and turquoise modules had the strongest correlation with AD&HD and high expression of FOXO1. Functional enrichment analysis showed that DEGs in these modules were enriched in phagosome, cytokine-cytokine receptor interaction, cellular senescence, FOXO signaling pathway, pathways of neurodegeneration, GABAergic synapse, and AGE-RAGE signaling pathway in diabetic complications. Furthermore, the cross-talking pathways of FOXO1 in AD and HD were jointly determined in a global regulatory network, such as the FOXO signaling pathway, cellular senescence, and AGE-RAGE signaling pathway in diabetic complications. Based on the performance evaluation of the area under the curve of 85.6%, FOXO1 could accurately predict the onset of AD and HD. We then identified the cross-talking pathways of FOXO1 in AD and HD, respectively. More specifically, FOXO1 was involved in the FOXO signaling pathway and cellular senescence in AD; correspondingly, FOXO1 participated in insulin resistance, insulin, and the FOXO signaling pathways in HD. Next, we use GSEA to validate the biological processes in AD&HD and FOXO1 expression. In GSEA analysis, regulation of protein maturation and regulation of protein processing were both enriched in the AD&HD and FOXO1-high groups, suggesting that FOXO1 may have implications in onset and progression of these two diseases through protein synthesis. Consequently, a high expression of FOXO1 is a potential pathogenic factor in both AD and HD involving mechanisms of the FOXO signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and cellular senescence. Our findings provide a comprehensive perspective on the molecular function of FOXO1 in the pathogenesis of AD and HD.
Clinical Manifestations of Supra-Large Range Nonperfusion Area in Diabetic Retinopathy
Authors: Tong Nianting , Wang Liangyu , Gong Huimin , Pan Lin , Yuan Fuxiang , Zhou Zhanyu
Published: February 2022

Objective. We describe the clinical manifestations of supra-large range nonperfusion area (SLRNPA) in diabetic retinopathy (DR). Methods. This was a retrospective case-control study. A total of 260 eyes of 236 patients with DR who underwent pars plana vitrectomy in the Department of Ophthalmology of Qingdao Municipal Hospital from February 2016 to June 2019 were enrolled. Fundus fluorescein angiography was performed after surgery to determine whether SLRNPA or non-SLRNPA in DR was present. All demographic and clinical data were carefully collected. Results. Forty-one eyes of 22 patients were diagnosed with SLRNPA in DR (15.77% of all eyes). Compared to non-SLRNPA, SLRNPA patients were more likely to be male and younger with earlier DR onset, a smoking history, other comorbidities, and a higher HbA1c level. SLRNPA in DR eyes exhibited more neovascular glaucoma (NVG) and diabetic keratopathy (DK) than did other eyes. Such eyes were more likely to require anti-VEGF therapy before surgery or a silicone oil or a gas tamponade during surgery and to suffer from persistent corneal epithelial erosion and NVG recurrence after surgery. Conclusions. SLRNPA in DR is a severe status of DR. Treatment for DR patients with SLRNPA is difficult, and the prognosis is poor, so clinicians must thus pay more attention to SLRNPA in DR.
Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions
Authors: Javeed Ashir , Khan Shafqat Ullah , Ali Liaqat , Ali Sardar , Imrana Yakubu , Rahman Atiqur
Published: February 2022

One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
Stress Mediates the Relationship between Personality and the Affordance of Socially Distanced Online Education
Authors: Rivers Damian J.
Published: February 2022

The novel coronavirus pandemic has made life significantly more stressful for large populations of people. As one such demographic, university students worldwide have experienced a sudden shift toward the provision of socially distanced online education, often in the absence of a coherent institutional plan. The mechanisms of stress appraisal and response differ between individuals in part determined by personality. With a sample of 293 undergraduate students at a Japanese university operating under prohibitions relating to face-to-face education, this article examines the impact of personality on the affordance of socially distanced online education mediated through generalized life stress and online learning stress appraisal. A retrimmed structural model returned an acceptable goodness of fit accounting for 31.6% of the criterion variance. The model indicates that conscientiousness (positive) and neuroticism (negative) hold a significant mediated impact on the affordance of socially distanced online education through generalized life stress and online learning stress appraisal. Moreover, and in the absence of face-to-face social interaction, the model shows that extroverted students experience greater online learning stress appraisals than neurotic students. Neurotic students were, however, negatively impacted by appraisals of generalized life stress but not online learning stress. Informed by personality characteristics and stress appraisals, the outcomes are discussed in relation to educational improvements and appropriate pedagogies for the delivery of socially distanced online education.
Neonatal Screening for Sickle Cell Disease in Congo
Authors: Dokekias Alexis Elira , Ocko Gokaba Lethso Thibaut , Louokdom Josué Simo , Ocini Lydie Ngolet , Galiba Atipo Tsiba Firmine Olivia , Ondzotto Ibatta Coreillia Irène , Kouandzi Quentin Ngoma , Tamekue Serge Talomg , Bango Jayne Chelsea , Nziengui Mboumba Jade Vanessa , Kobawila Simon Charles
Published: February 2022

Introduction. Sickle cell disease is an autosomal recessive inherited disorder due to the mutation of a gene coding for the globin beta chain. The aim of this study is to update the epidemiological data on hemoglobinoses, in particular sickle cell disease in newborns in Congo. Materials and Methods. This was a descriptive cross-sectional study, conducted from October 1, 2019, to March 31, 2020, throughout the Congolese national territory. It involved all full-term newborns, without distinction of nationality, aged 5 days or less, and whose parents consented to participate in the study. The blood samples, taken at the heel and collected on Whatman blotting paper, were analyzed using the HPLC Variant NBS machine. Results. In 2897 newborns (NN) screened, hemoglobin abnormalities were found in 603 NN (20.81%). The mean age of these newborns was 1 day (extremes 0 and 5 days). The male-to-female ratio was 1.03. Abnormal hemoglobins were mainly Hb S (n = 597 (97.71%)); Hb C (n = 5 (0.82%)); and variants (n = 7 (1.15%)). The national prevalence of major sickle cell (MSC) syndromes and sickle cell trait was 1.35% and 19.43%, respectively. The prevalence ranged from 1.77% to 2.56% for MSS in four departments and from 20.5% to 25.8% for the sickle cell trait in six other departments. Conclusion. Data on homozygous sickle cell disease remain consistent with previous studies. However, further studies should clarify the molecular anomalies of the variants observed in our samples.