bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive compensation visual odometry in dynamic scenarios
Authors: Wang Yaming , Huang Wenqing , Xu Zhengheng , Wang Meiliang
Published: March 2022

In dynamic scenarios, dynamic participants break the static assumptions of the visual odometry (VO) algorithm. Hence, dynamic participants are typically removed and only static participants are used as motion references. When semantic information is used to eliminate participants with dynamic semantic labels in the scene, the dynamic degree of the scene affects system robustness and poses estimation accuracy. In this paper, we propose a VO system that employs adaptive compensation for semantic information and utilizes an adaptive compensation model for sparse static feature map construction to make the compensation process a flexible, independent thread. Moreover, a layered extraction fusion framework is proposed to uniformly sample equi-probability feature points both globally and locally. This framework combines multi-level weights of scene mapping and spatial-temporal priority information of self-organization. Finally, in the candidate pixel extraction stage, system efficiency is improved through region matching candidate pixel detection, extraction and fusion semantic constraints. Experiments are done by using the TUM RGBD datasets. Comparisons with many conventional visual mileage calculation methods reveal that the absolute and relative trajectory errors of camera motion are significantly reduced in most scenes with different dynamic degrees. Thus, compared to previous algorithms, the proposed algorithm is more robust and precise in dynamic scenes.
Optimization-enabled deep convolutional network for the generation of normal speech from non-audible murmur based on multi-kernel-based features
Authors: Rajesh Kumar T. , Suresh G. R. , Kalaiselvi K.
Published: March 2022

Automatic Speech Recognition schemes are the alternating modes in which individuals interrelate with various mobile application applications. The user interactivity needs are a huge vocabulary identification system, high accuracy, energy-efficient solution and time. Although the automatic Speech Recognition system requires power budget and huge memory bandwidth, it is not applicable for many tiny forms of battery-controlled devices. Hence, an effective method is developed using the proposed Multi-kernel Perceptual linear predictive and Stochastic Biogeography-based whale optimization algorithm optimization to adapt non-audible mutter to regular speech. First, the input speech signal is initially given to the pre-processed module. Then, the features, such as spectral centroid, pitch chroma, Taylor amplitude modulation spectrogram (AMS), spectral skewness, and the developed Multi-kernel Perceptual linear predictive, are extracted to determine the appropriate features. After extracting features, the speech recognition is performed based on Deep Convolutional Neural Network, which is trained by the proposed Stochastic Biogeography whale optimization algorithm. The Stochastic Biogeography whale optimization algorithm combines the stochastic gradient descent method, whale optimization algorithm, and biogeography-based optimization. The developed model showed improved results with maximum accuracy of 0.985, minimal FPR of 0.001, maximal TPR of 1, respectively.
Software development processes in ocean system modeling
Authors: Jung Reiner , Gundlach Sven , Hasselbring Wilhelm
Published: March 2022

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical abstractions. Ocean science is a multidisciplinary discipline developing scientific models and simulations as ocean system models that are an essential research asset. In software engineering and information systems research, modeling is also an essential activity. In particular, business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise, so that the current process may be analyzed, improved and automated. In this paper, we employ process modeling for analyzing scientific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean science. We interviewed domain experts in semi-structured interviews, analyzed the results via thematic analysis, and modeled the results via the Business Process Modeling Notation (BPMN). The processes modeled as a result describe an aspired state of software development in the domain, which are often not (yet) implemented. This enables existing processes in simulation-based system engineering to be improved with the help of these process models.
Coyote–Wolf optimization-based deep neural network for cancer classification using gene expression profiles
Authors: Deshmukh Mandar Krishnarao , Vaze Vinod M. , Gaikwad Amit K.
Published: March 2022

Cancer is a deadly disease that affects millions of people all over the world, which stimulates unrestricted segmentation of the cell in the affected tissue. The prior determination of cancerous cells renders on-time healthcare service to the affected persons, which promotes easier life-saving. Hence, there is a need for automatic cancer classification methods. Though there are so many conventional classification methods to determine the cancer cells, these methods fail due to their training issues. In this research, a classification model is developed, named as Coyote–Wolf Optimization-based Deep Neural Network (CoWo-DNN), for the classification of cancerous cell from the normal tissues. The proposed CoWo-DNN for the classification of cancer cell utilizes gene expression data, which is log-transformed for effective processing. The comparative evaluation of the proposed CoWo-DNN with the conventional methods demonstrates the supremacy of the proposed method in terms of the performance parameters such as precision rate, accuracy, recall, [Formula: see text]-measure and TRP. The proposed CoWo-DNN achieves the maximum accuracy of 91.8%, precision of 93.7%, recall of 89.2% and [Formula: see text]-measure of 86.4% in the case of breast cancer, and the accuracy of 96.2%, precision of 93.33%, recall of 97.8% and [Formula: see text]-measure of 98.8% in the case of colon cancer.
Research on defect detection of improved target detection algorithm on the image surface of 5G communication ring
Authors: Zhu Hui
Published: March 2022

In the production process of the communication circulator, defects such as solder joints, missing pieces, and wool may appear due to the current processing technology. The current communication circulator detection relies on manual experience for identification and classification. This method seriously affects production efficiency; and visual fatigue, physical condition, and working environment will affect the accuracy of manual detection; therefore, the production line of communication circulators urgently needs to be online automatic detection system. This paper will design a set of the stable automatic detection system. The main contributions are: (1) In the sample collection stage, by enhancing the limited samples, it proposes the morphological transformation of the limited defects themselves to realize the diversification of the number of samples and the morphology; (2) By reusing a sample, a cascaded Faster-RCNN and You Only Look Once (YOLO) target detection method is proposed, and the Faster-RCNN and YOLO models are trained, respectively; the model training stage uses migration learning and optimization of hyperparameters to obtain higher accuracy high training model; (3) In the inference stage, by cascading the two excellent target detection algorithms, Faster-RCNN and YOLO, this paper proposes a new decision conflict resolution algorithm, and finally obtains a higher comprehensive detection accuracy; (4) In the deployment phase, in view of the increased performance and computing power overhead caused by algorithm cascading, this paper proposes a solution for graphics processing unit shard sharing. Experimental results explain: The communication circulator surface defect detection system based on the deep learning method can effectively locate and identify various defects. The final recognition accuracy rate is about 98.5%, and the recognition speed can reach about 0.45, which is compared with traditional detection methods, which greatly improves the accuracy and speed of recognition.
Transnational Corporations, Biosphere Stewardship, and Sustainable Futures
Authors: Österblom H. , Bebbington J. , Blasiak R. , Sobkowiak M. , Folke C.
Published: March 2022

Corporations are perceived as increasingly powerful and critically important to ensuring that irreversible climatological or ecological tipping points on Earth are not crossed. Environmental impacts of corporate activities include pollution of soils, freshwater and the ocean, depletion of ecosystems and species, unsustainable use of resources, changes to air quality, and alteration of the global climate. Negative social impacts include unacceptable working conditions, erosion of traditional practices, and increased inequalities. Multiple formal and informal mechanisms have been developed, and innovative examples of corporate biosphere stewardship have resulted in progress. However, the biosphere crisis underscores that such efforts have been insufficient and that transformative change is urgently needed. We provide suggestions for aligning corporate activities with the biosphere and argue that such corporate biosphere stewardship requires more ambitious approaches taken by corporations, combined with new and formalized public governance approaches by governments.
Forecasting Tennis Match Results Using the Bradley-Terry Model
Authors: Fayomi Aisha , Majeed Rizwana , Algarni Ali , Akhtar Sohail , Jamal Farrukh , Nasir Jamal Abdul
Published: March 2022

Forecasting has been playing an important role in different fields of life, i.e., in decision-making activities of management, to predict uncertain events within an organization, in weather forecasting, in flood forecasting, etc. Stakeholders involved in betting market take advantage of tennis forecasting directly or indirectly. Winning probability calculated using forecasting models helps the bettors in deciding whether to place a bet or not. Keeping in view the importance of tennis forecasting, the Bradley-Terry model is used to model men’s professional tennis for predicting match outcomes in tennis matches of men’s singles. Model coefficients are estimated using data from January 2019 to September 2020 of 3439 matches. Ratings for each player are calculated using model coefficients. Player rankings are then calculated using these ratings. Comparison of model rankings with ATP rankings has shown satisfactory results. Winning probability for each player is calculated using model coefficients and ratings. These probability predictions are evaluated against four measures of performance. The results reveal that surface on which a game is played on contributes significantly towards a player’s performance. Due to this impact of the surface, our model has produced superior player rankings for certain players who had been ranked very low in official ATP rankings. According to most of the performance measures, the model has shown good results for clay court data which are closely followed by hard court data. To calculate return on investment, model results are compared with the bookmakers’ average odds and best available odds. It has been found that return on investment for a fitted model is highest in the case of clay court data in comparison to bookmaker’s average odds and best odds.
A Traceable Blockchain-Based Vaccination Record Storage and Sharing System
Authors: Chen Jingshou , Chen Xiaofeng , Chen Chin-Ling
Published: March 2022

Blockchain technology is essentially a decentralized database maintained by relevant parties and has been widely used in various scenarios such as logistics and finance. In terms of applications in the medical field, it is becoming more and more important because the patient's symptoms may be related to a certain vaccine. Whether the patient has been vaccinated with this vaccine will lead to different diagnostic results by the doctor. However, in the current vaccination environment of many regions, the vaccination record (VR) can only be kept in the patient’s vaccination booklet, which is easy to lose or destroy. Therefore, the doctor needs to retrieve the patient’s VR through a centralized database maintained by the government, which is time-consuming and will increase the medical risk. This study proposes a traceable blockchain-based vaccination record storage and sharing system. In the proposed system, the patient gets the vaccination at any legal clinic and the VR can be saved accompanied by the signature into the blockchain center, which ensures traceability. When the patient visits the hospital for treatment, the doctor can obtain the detail of the VR from the blockchain center and then make a diagnosis. The security of the proposed system will be protected by the programmed smart contracts. Through mutual authentication, our system can also provide and guarantee data integrity and nonrepudiation. Moreover, the proposed system has resistance to replay and man-in-the-middle attacks, and the performance is good.
Image Real-Time Detection Using LSE-Yolo Neural Network in Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes
Authors: Zhi-Xian Zheng , Zhang Fuquan
Published: March 2022

In this paper, a novel visual image real-time detection LSE-Yolo neural network is presented, which is in artificial intelligence-based Internet of Things for smart cites and smart homes. Despite the great achievements that have been acquired in image detection, the issue of visual image real-time detection combined with privacy data protection to serve for smart cities and smart homes has been overlooked. The technique we applied in our study is referred to as visual object detection, which can contribute to more healthy and comfortable life. When several studies have been carried out to test the validity, it is suggested that our proposed LSE-Yolo neural network has better performance in image real-time detection based on AIoT for smart cities and smart homes. And it is similar to state-of-the-art. The fruitful work has made great contributions to our present understanding of the visual image detection serving for smart cities and smart homes.
A Protection Model of Citizen Personal Information Administrative Law Based on BD Analysis and Edge Computing
Authors: Yang Fang
Published: March 2022

With the continued advancement and popularization of the Internet and Edge Computing, the right to privacy of big data has taken on new meanings, and the tools available to protect citizens’ personal information under administrative law have grown in number. In the context of big data, infringers frequently use the Internet as a natural barrier and medium, making it difficult to collect evidence once personal information has been leaked. This paper investigates the administrative law protection of citizens’ personal information using big data analysis. In comparison to traditional society, information technology innovation has created massive information storage equipment, efficient information processing equipment, and rapid information dissemination means for human beings to protect their legitimate rights and interests. Sharing goes far beyond traditional societal competition. The study of administrative law protection of citizens’ personal information in the big data era has the potential to not only advance the study of administrative law protection of citizens’ personal information but also to reveal the characteristics of administrative law protection of citizens’ personal information in the big data era.
Computation of Metric Dimension of Certain Subdivided Convex Polytopes
Authors: Imran S. , Ali Z. , Nigar N. , Kirmani Syed Ajaz K. , Siddiqui M. K. , Fufa S. A.
Published: March 2022

The distance d(z1, z2) from vertex z1 ∈ V(G) to z2 ∈ V(G) is minimum length of (z1, z2)‐path in a given connected graph G having E(G) and V(G) edges and vertices’/nodes’ sets, respectively. Suppose Z = {z1, z2, z3, …, zm}⊆V(G) is an order set and c ∈ V(G), and the code of c with reference to Z is the m‐tuple {d(c, z1), d(c, z2), d(c, z13), …, d(c, zk)}. Then, Z is named as the locating set or resolving set if each node of G has unique code. A locating set of least cardinality is described as a basis set for the graph G, and its cardinal number is referred to as metric dimension symbolized by dim(G). Metric dimension of certain subdivided convex polytopes STn has been computed, and it is concluded that just four vertices are sufficient for unique coding of all nodes belonging to this family of convex polytopes.
Improved Performance of Raft Foundation Using Detached Pile Columns in Loose Subsoil Conditions
Authors: Melese Fekadu
Published: March 2022

Piles act as settlement reducers in case of connected piled‐raft foundation and hence decrease the settlements of the raft. The design concept of the connected piled‐raft foundations is to lessen the number of piles and utilize the bearing capacity of the system piled raft. Due to significant straining actions at the pile head‐raft connection, an alternative technique is proposed to disconnect the piles from the raft. A granular layer (cushion) beneath the raft is incorporated. The disconnection has a beneficial effect on reducing axial load compared to connected piles. For small piled rafts, nonconnected piled rafts show less stiffness than connected piled rafts, and the soil is highly stressed and shows greater raft settlement. In the case of the large piled raft, nonconnected piled rafts show greater settlement efficiency. Cushion stiffness was realized to be more substantial for a nonconnected piled raft with shorter piles than one with longer piles. The results show that the load transfer mechanism in a nonconnected piled raft is mainly governed by the thickness and stiffness of the cushion layer and by the stiffness of the subsoil.
Intelligent Algorithm-Based Electrocardiography to Predict Atrial Fibrillation after Coronary Artery Bypass Grafting in the Elderly
Authors: Feng Tao , Deng Zhihua
Published: March 2022

The objective of this study was to explore the predictive value of electrocardiogram (ECG) based on intelligent analysis algorithm for atrial fibrillation (AF) in elderly patients undergoing coronary artery bypass grafting (CABG). Specifically, 106 elderly patients with coronary heart disease who underwent CABG in the hospital were selected, including 52 patients with postoperative AF (AF group) and 54 patients without arrhythmia (control group). Within 1-3 weeks after operation, the dynamic ECG monitoring system based on Gentle AdaBoost algorithm constructed in this study was adopted. After the measurement of the 12-lead P wave duration, the maximum P wave duration (Pmax) and minimum P wave duration (Pmin) were recorded. As for simulation experiments, the same data was used as the back-propagation algorithm. The results showed that for the detection accuracy of the test samples, the Gentle AdaBoost algorithm showed 93.7% accuracy after the first iteration, and the Gentle AdaBoost algorithm was 16.1% higher than the back-propagation algorithm. Compared with the control group, the detection rate of arrhythmia in patients after CABG was significantly lower (

P
<
0.05

). Bivariate logistic regression analysis on Pmax and Pmin showed as follows: Pmax: 95% confidential interval (CI): 1.024-1.081,

P
<
0.05

; Pmin: 95% CI: 1.036-1.117,

P
<
0.05

. The sensitivity of Pmax and Pmin in predicting paroxysmal AF was 78.2% and 73.4%, respectively; the specificity of them was 80.1% and 85.6%, respectively; the positive predictive value was 81.2% and 83.4%, respectively; and the negative predictive value was 79.5% and 75.3%, respectively. In conclusion, the generalization ability of Gentle AdaBoost algorithm was better than that of back-propagation algorithm, and it can identify arrhythmia better. Pmax and Pmin were important indicators of AF after CABG.
Upper Limb Ischemia Due to Arterial Thrombosis after COVID-19 Vaccination
Authors: Alsmady Moaath M. , Al-Qaryouti Rahaf A. , Sultan Nesrin G. , Khrais Oweis I. , Khrais Huthaifah
Published: March 2022

This report describes a case of a 60-year-old male patient who received the first dose of the AstraZeneca vaccine and presented to the emergency department complaining of left hand pain and paresthesia. Investigations revealed upper limb ischemia; he was hospitalized for further management.
Design of Online Learning System of College Sports Gymnastics Based on BP Neural Network
Authors: Dong YingHui
Published: March 2022

With the rapid development of artificial intelligence technology and big data analysis technology, the use of online learning has become more technical premises. It has the advantages of convenient data update, simple real-time communication, and high stability and flexibility. These have laid a good technical cornerstone for the development of online learning platforms. In order to solve the problems such as the lack of teaching materials for physical gymnastics in colleges and the lack of technical support for teaching, this study aims to study the design of college sports gymnastics online learning system based on BP neural network. On the basis of analyzing the introduction of BP neural network, the status quo of gymnastics teaching in Chinese colleges and universities and the principles of system design and adopting the principles of reliability, stability, and maintainability as the method of system design, the gymnastics online learning system based on BP neural network is carried out. Finally, the performance of the constructed system is tested. The test results show that, as the number of concurrent users continues to increase, the response time is gradually increasing. When the number of concurrent users is 3000, the maximum response time is 1.832 s, the minimum response time is 1.473 s, and the average response time is 1.512 s, which can meet the needs of users when the amount of concurrency is large.
Effects of Dioctyl Phthalate on Performance of Asphalt Sealant
Authors: Luo Yang Ming , Guo Hao Yan , Wang Zhen Jun , Liu Zhuo Ran
Published: March 2022

Dioctyl phthalate (DOP) was introduced into asphalt-based sealant as a plasticizer in this work. Four kinds of sealants with different DOP content were prepared, and thermal stability and low-temperature performance of five samples with asphalt as a control group were evaluated by fluidity test, softening point test, thermogravimetric-differential thermal analysis (TG-DTA), low-temperature tensile test, and differential scanning calorimeter (DSC), respectively. Furthermore, the mechanism of DOP on sealant properties was analyzed by Fourier transform infrared spectroscopy (FTIR) and fluorescence microscope. Results show that DOP can significantly improve the low-temperature performance of sealant, but slightly deteriorate the high-temperature performance. This is because DOP can supplement the light component of sealant and enhance the fluidity of the internal components. On the one hand, DOP reduces the apparent activation energy of sealant and lowers the barrier to chemical reactions, indicating that DOP is slightly detrimental to the thermal stability of sealant. On the other hand, DOP greatly reduces the glass transition temperature of sealant and improves its low-temperature performance. However, there is no evident chemical reaction between DOP and sealant, and physical effects play a major role in the effects of DOP on sealant properties. In addition, DOP can promote the expansion and cross-linking of the polymer in the sealant and ultimately enhance the compatibility between asphalt and polymer molecules. In short, DOP is recommended as a plasticizer to prepare asphalt sealants with excellent properties.
A Secured and Efficient Anonymous Roaming Scheme of Mobile Internet
Authors: Cheng Yao , Yue Li , Duan Naixia , Li Chenglong
Published: March 2022

An anonymous roaming scheme of mobile Internet was discussed in this paper aiming to improve the traditional authentication protocol that cannot satisfy the demand of user’s identity authentication when the mobile terminal is roaming in mobile Internet. The authentication server of remote network will complete the identity legitimacy verification of mobile terminal with the help of the home network authentication server. A temporary identity is used to prevent user’s anonymity protection from being tracked and eavesdropped, as well as other attacks, which can improve the confidential of user’s identity and location considerably. This anonymous roaming scheme can achieve a high-level of safety efficiently. This will also satisfy the development of the network technology.
Existence of Solutions for p-Kirchhoff Problem of Brézis-Nirenberg Type with Singular Terms
Authors: Matallah Atika , Benchira Hayat , El Mokhtar M. El Mokhtar Ould
Published: March 2022

In this paper, we prove the existence of positive solution for a p-Kirchhoff problem of Brézis-Nirenberg type with singular terms, nonlocal term, and the Caffarelli-Kohn-Nirenberg exponent by using variational methods, concentration compactness, and maximum principle.
Intelligent Online Monitoring and Remote Verification of Gateway Meters under the Embedded Sensor and Clustering Algorithm
Authors: Li Chong , Wang Hao , Li Chuan , Zhou Zhongliang , Yang Ziye , Gu Kuixian , Xu Jianyun
Published: March 2022

Since gateway meters cannot simultaneously realize online monitoring and remote verification, the study is aimed at exploring the intelligent online monitoring and remote verification of the gateway meter. First, the similarity and related evaluation indexes of embedded sensors are analyzed based on the relevant theories, like the theories of the embedded sensor and clustering algorithm. Second, the gateway meter is tested on the standard vibration test bed, and the accuracy, timeliness, and environmental adaptability of its intelligent online system are tested. Finally, the remote verification of the gateway meter is carried out from three aspects: the error/load value, secondary voltage drop, and the admittance test. The results show that when the intelligent online monitoring ability of the gateway meter is tested on the standard vibration test bed, the error of the same pilot is controlled between 0.1 mm after multiple peak tests, and the error is within the allowable range. In the intelligent online monitoring system based on the embedded sensor and clustering algorithm, the vibration acceleration is -0.6 cm/s~0.3 cm/s, the speed is -1 cm/s~1 cm/s, and the displacement fluctuates between -0.8 cm/s~0.8 cm/s. This shows that the intelligent online monitoring system can meet the performance requirements of online monitoring. In the process of remote verification of gateway meters, the active error and reactive error are 0.2% mm. The results of the secondary voltage drop and the admittance test show that the relevant technical indexes of the system meet the expected requirements. Therefore, the intelligent online monitoring and remote verification of gateway meters are discussed based on the embedded sensor and clustering algorithm, which provides a reference for the rapid development of gateway meters.
Effect of Integrated Nursing Care Based on Medical Alliance Mode on the Prevention and Treatment of Complications and Self-Efficacy of Patients with Coronary Heart Disease after PCI
Authors: Zhao Yujie , Wang Xiaonan
Published: March 2022

Objective. To explore the effect of integrated nursing care based on the medical alliance model on prevention and treatment of complications and self-efficacy in patients with coronary heart disease (CHD) after PCI. Methods. The data of CHD patients treated in our hospital from January 2019 to January 2021 were analyzed in this retrospective study. One hundred and twenty patients were selected as the research subjects according to the inclusion and exclusion criteria and equally split into the observation group and reference group according to the order of admission. Both groups received routine nursing care, while the observation group was additionally given integrated nursing care based on the medical alliance model to compare the self-efficacy scores, scores of self-management abilities, and incidence of postoperative complications between the two groups before and after nursing. Both groups were nursed for 6 months. Results. Compared with the reference group, the observation group after nursing achieved a notably higher GSES score (26.10 ± 1.30 vs 22.18 ± 1.30, t = 16.516,

P
<
0.001

), higher scores of self-management ability

P
<
0.001

, and a lower incidence of postoperative complications

P
<
0.05

. Conclusion. Integrated nursing care based on the medical alliance model can improve the self-efficacy of CHD patients undergoing PCI, enhance their self-management ability, and reduce the incidence of postoperative complications, which is conducive to improving the prognosis of patients.
A New Schiff Base Organically Modified Silica Aerogel-Like Material for Metal Ion Adsorption with Ni Selectivity
Authors: Vareda João P. , Matos Paulo D. , Valente Artur J. M. , Durães Luisa
Published: March 2022

Nickel has several industrial uses and is a valuable metal, making its selective separation and recycling a priority goal. A novel adsorbent, a Schiff base organically modified silica (ORMOSIL) aerogel, was prepared, for selective nickel removal from wastewater with other metal ions, by including a salen ionophore in the silica-based network. The newly developed adsorbent takes advantage of the salen’s selectivity and of the high porosity of silica aerogels. The aerogel-like adsorbent was prepared via sol-gel chemistry, using a coprecursor approach and ambient pressure drying. The inclusion of the Schiff base in the silica network was accomplished by reacting an amine-containing silica precursor with an aldehyde and confirmed by nuclear magnetic resonance (NMR) analysis. The adsorbent shrunk only 10% after evaporative drying, which resulted in a highly porous material (85% porosity, 4 cm3 g−1 specific pore volume). The low surface area of 28 m2 g-1 was due to the predominantly macroporous structure of the material (mean pore diameter of 563 nm). Adsorption isotherms and kinetic curves with single and binary mixtures of cations at room temperature were used to assess the selectivity of the adsorbent. The adsorption follows a BET (Brunauer-Emmett-Teller) trend. Due to the proximity of the oxygen and nitrogen atoms in the salen and steric hindrance from their neighboring atoms, it is likely that only the smallest hydrated cations can act as a coordination center and interact with both donor atoms. Thus, nickel was fairly removed (50 mg g-1), while other cations barely interacted with the adsorbent (cadmium adsorption maximum of 5 mg g-1). The estimated selectivity coefficient for nickel ranges from 1.8, in relation to copper, to 9.4 relatively to cadmium, which can be relevant for the separation of nickel in several industrial contexts, for instance, from electroplating sludge.
A Novel Deep Learning-Enabled Physical Education Mechanism
Authors: Wang Weiqi , Jiang Jianan
Published: March 2022

Race walking is one of the key events in the Tokyo Olympic Games, and also one of the strengths of China in athletics events. In recent years, China has made remarkable achievements in various race-walking competitions. However, with the improvement of the performance of race walkers, more and more technical problems have emerged, and the number of fouls due to nonstandard movements has increased significantly. It is a pity that athletes are disqualified for technical fouls in long-distance race-walking competitions. Therefore, it is necessary to introduce scientific training methods to help coaches strictly monitor the training process of athletes and accurately detect their standard degree of action in real-time. This paper mainly proposes a novel mechanism for foul recognition in race walking based on deep learning. Firstly, the image frames in the video are preprocessed by the Yolo algorithm to obtain the athletes' separated images. The U-Net network mixed with the attention mechanism is used to detect the athletes’ actions to identify fouls and nonstandard actions, so as to assist the coach to identify the athletes’ nonstandard actions in training and adjust them in time. Experiments show that the above method can identify the foul actions and nonstandard actions of multiple athletes in training at the same time quickly, and the recognition accuracy is higher than human eyes. It is more conducive to assist the coach to monitor and standardize the athletes’ actions in the long-term training process, so as to reduce the error rate and improve the performance.
Cost Control Analysis of Construction Projects Based on Wireless Communication and Artificial Intelligence Decisions
Authors: Xie Huadong , Ge Yungang , Yi Jingyi
Published: March 2022

In decades of development, the country has been vigorously developing infrastructure projects and providing a steady stream of help, so our infrastructure strength has always been world’s top part. But now, because of the vigorous development of environmental protection enterprises, a variety of raw materials and environmental treatment costs are getting higher and higher, in order to maintain the profitability of enterprises, so we need to carry out cost control of construction projects to ensure a certain profit. In recent years, various emerging terms such as artificial intelligence and big data have become more and more popular, so in order to ensure the stability of the industry, it is imperative for the construction industry to introduce new technologies for the technological revolution. Wireless communication is also widely used with artificial intelligence to build systems because of the fast speed of interaction. In order to ensure the steady development of the construction industry in the future, we need to control the cost of the project and the introduction of high-tech. The purpose of this paper is to control the cost of construction projects by means of wireless communication and artificial intelligence decision-making. After reviewing the history of the construction industry in recent decades, as well as the price fluctuations of building materials and the relevant laws and regulations on environmental protection and construction in recent years, this paper uses the emerging artificial intelligence and traditional wireless communication methods to carry out the traditional project cost control experiments. After a period of experiments, people found that the use of wireless communication with artificial intelligence decision-making ability can be very good control of construction project costs in a certain range, to ensure the profitability of the project and the maximum use of materials.
A New Variant of Symmetric Distance Spaces and an Extension of the Banach Fixed‐Point Theorem
Authors: Roy Kushal , Saha Mantu , Parvaneh Vahid , Khorshidi Maryam
Published: March 2022

The notion of Δ‐metric spaces has been proposed in this study as a generalization of b‐metric spaces, extended b‐metric spaces, and p‐metric spaces. A number of topological characteristics of such spaces have been investigated in this paper. On such spaces, a noncompactness measure has been established, and some results in the framework of noncompactness measure have been achieved. We prove an analogous of the Banach contraction principle in such spaces based on this approach. In order to investigate the validity of the underlying space and our proven fixed‐point theorems, supporting examples have been presented. Furthermore, the well‐posedness of the fixed‐point problem has been tested using our fixed‐point result.
Aducanumab Therapy to Treat Alzheimer’s Disease: A Narrative Review
Authors: Beshir Semira Abdi , Aadithsoorya A. M. , Parveen Affana , Goh Sheron Sir Loon , Hussain Nadia , Menon Vineetha Bharathan
Published: March 2022

Background. Aducanumab, a new monoclonal antibody that targets β-amyloid aggregates, has been granted conditional approval by the U.S. FDA for treatment of mild Alzheimer’s disease (AD). The approval of this drug without a confirmed significant clinical impact has resulted in several debates. Objective. In this narrative review, aducanumab approval-related controversy, the drug’s pharmacokinetics and pharmacodynamic characteristics, evidence from the efficacy and safety trials of aducanumab, implications of the drug approval, and the future directions in the management of patients with AD are summarized. Methods. Using relevant keywords, Google Scholar, Web of Science, and MEDLINE databases and manufacturer’s website were searched. Results. Infusion of aducanumab at a higher dose resulted in a modest slowing of cognitive decline among patients with mild cognitive impairment or early-onset AD dementia. The drug however can cause amyloid-related imaging abnormalities. Due to modest impact on cognition, the use of this drug by patients with AD will most likely be limited. The manufacturer is required to run an extended phase IIIb trial to verify the benefit of this drug. Access to therapy requires a careful selection of patients and periodic monitoring to ensure the optimal use of the drug. Conclusion. Despite the limitations, aducanumab is the first disease-modifying therapy approved for the treatment of AD. Aducanumab addresses a part of the pathogenesis of AD; therefore, drugs that can act on multiple targets are needed. In addition, the search for preventive strategies, validated plasma-based assays, and newer drugs for AD, which are effective, safe, convenient, and affordable, is vital.
Effect of WeChat-Based Health Preaching Combined with an Enhanced Recovery after Surgery Model on Perioperative Limb Motor Function and Complications in Orthopaedic Patients
Authors: Pang Zhijuan , Hu Bin , Chai Dejun , Li Ping , Ma Le , Liu Lei , Li Wei
Published: March 2022

Objective. To evaluate the effect of WeChat-based health preaching combined with an enhanced recovery after surgery (ERAS) model on perioperative limb motor function and complications in orthopaedic patients.Methods. By means of retrospective analysis, the medical data of 68 orthopaedic patients who received surgical treatment in our hospital (from 01, 2020–12, 2021) were collected, and the patients were equally divided into the study group (SG) and control group (CG) according to their admission order, with 34 cases each. From 7 d before surgery to the time of hospital discharge, WeChat-based health preaching combined with ERAS perioperative nursing was performed to patients in the SG, and routine orthopaedic perioperative nursing was performed to those in the CG. Before and after nursing, patients’ Visual Analog Scale for Fatigue (VAS-F) scores, Houston Pain Outcome Instrument (HPOI) scores, and brief Fu-gI-Meyer (FMA) motor scores were investigated and the incidence rates of postoperative complications and nursing satisfaction of patients in the two groups were recorded. Results. After nursing, SG obtained a significantly better VAS-F score and HPOI score (

P

 < 0.001), significantly higher postoperative 7 d and predischarge lower limb FMA scores (20.06 ± 2.13 vs 18.38 ± 2.36, 27.50 ± 1.90 vs 24.09 ± 2.25,

P

 < 0.05), and significantly lower annual incidence rate of complications compared with those of the CG (

P

 < 0.05); and the nursing satisfaction scores of the SG and CG were 9.18 ± 0.82 points and 6.76 ± 0.91 points, respectively, indicating significantly higher nursing satisfaction in the SG than in the CG (

P

 < 0.001). Conclusion. The nursing model of WeChat-based health preaching combined with ERAS can effectively improve the knowledge level of orthopaedic patients, thereby improving their abilities of pain management and self-management, accelerating the recovery of their limb function, and reducing the incidence rate of postoperative complications. The patients are more satisfied with such nursing model, indicating its better promotion value.
Prevalence and associated risk factors of type 2 diabetes mellitus in Nilufer District, Bursa, Turkey
Authors: Aksu Hurol , Pala Kayıhan , Aksu Huseyin
Published: March 2022

The objectives of this study were to determine the prevalence of diabetes mellitus in Nilufer District, Bursa, Turkey using
standardized diagnostic criteria to investigate associated factors, and to determine the risk groups for a preventive public
health program. This cross-sectional study was conducted in Nilufer Public Health Training and Research Area, Bursa
Province in Turkey. The study population of 727 persons (382 women and 345 men) were selected by a random sampling
method. The response rate was 80.6 %. According to the fasting blood glucose levels of the 727 subjects, 44 (6.1 %) were
diabetic. In this study, the prevalence of known diabetes was 5.0 %, and the prevalence of previously undiagnosed diabetes
was 1.1 %. The ratio of previously undiagnosed to known diabetes was 1:5. Multivariate logistic analyses showed that age
(50 and over), family history of diabetes, hypertension and overweight (BMI > 25.0 kg/m2) were risk factors for type 2
diabetes (P < 0.05) in Nilufer District. A public health program in the district should be aimed primarily at these risk groups.
The people in the risk groups should be registered and followed-up in a program aimed at preventing overt diabetes. The
program can also provide efficient therapy if needed. (Int J Diabetes Metab 14: 98-102, 2006 )
Impact of Glucose Tolerance and Its Change on Incident Proteinuria: Analysis of a Nationwide Population-Based Dataset
Authors: Suzuki Yuta , Kaneko Hidehiro , Okada Akira , Itoh Hidetaka , Fujiu Katsuhito , Michihata Nobuaki , Jo Taisuke , Takeda Norifumi , Morita Hiroyuki , Yamaguchi Satoko , Kamiya Kentaro , Matsunaga Atsuhiko , Ako Junya , Node Koichi , Yamauchi Toshimasa , Nangaku Masaomi , Yasunaga Hideo , Komuro Issei
Published: March 2022

Introduction: Although diabetes mellitus (DM) increases the risk of proteinuria, the relationship between prediabetes and proteinuria remains not fully understood. Further, whether the change in glucose is associated with the risk for proteinuria is unknown. Methods: This was a retrospective cohort study that included 1,849,074 participants (median age, 45 years; 59.3% men). No participants were taking glucose-lowering medications, and none had positive proteinuria at the initial health check-up. Each participant was categorized into three groups: normal (hemoglobin A1c [HbA1c] of <5.7%, n = 1,563,121), prediabetes (HbA1c of 5.7–6.4%, n = 253,490), and DM (HbA1c of ≥6.5%, n = 32,463) groups. We investigated the association between each HbA1c category and incident proteinuria using Cox proportional hazards models. We analyzed the association between the annual change in HbA1c and the risk for proteinuria. Results: A total of 65,954 participants developed proteinuria during the observation period. Not only DM (hazard ratio [HR]: 2.15, 95% confidence interval [CI]: 2.07–2.24) but also prediabetes (HR: 1.14, 95% CI: 1.12–1.17) was associated with a greater risk for proteinuria. The relative risk reduction for proteinuria that was associated with prediabetes and DM was 12.3% and 53.5%, respectively. An annual increase in HbA1c was associated with a greater risk for proteinuria. This association was more pronounced in participants having prediabetes. Conclusion: Not only DM but also prediabetes increased the risk for proteinuria. The influence of change in HbA1c on incident proteinuria was pronounced in people with prediabetes. Optimizing glucose would provide more benefit to individuals having prediabetes for proteinuria prevention.
R848/TLR7-Mediated Stronger CD8+ T Immunity Is Dependent on DC-NK Cell Interactions
Authors: Zhou Jianchun , Nouri-Shirazi Mahyar , Tang Hua , Shen Yuan , Zeng Menghua
Published: March 2022

Background: Toll-like receptor (TLR) 7 agonists are effective candidates for Th1 immune adjuvants, which compensate for the insufficient Th1 immune responses induced by traditional adjuvants. This effect is currently dependent on TLR7-mediated induction of dendritic cell (DC) maturation and increased IL-12 production. Methods: In vivo, we intraperitoneally injected TLR agonists with OVA, and LNs were collected for detection. In vitro, Activated DCs, natural killer (NK) cells, and CD8+ T cells were tested using flow cytometry for surface expression and enzyme-linked immunosorbent assay for cytokine production. NK cell migration was evaluated using transwell system. All experiments were performed in both C57BL/6 and BALB/C backgrounds. Results: Our findings revealed that the enhanced CD8+ T immunity characterized by CD8+ T accumulation, proliferation, and IFN-γ+CD8+ T induction induced by R848 was attributed to DC-dependent NK cell migration and DC-NK interactions. Our results demonstrated that R848 induced CD8+ T cell accumulation and IFN-γ+CD8+ T cells in lymph nodes (LNs) to a greater degree in vivo than TLR4 agonists (lipopolysaccharide) and TLR9 agonists (Class C CPG). R848-activated DCs enhanced CD8+ T cell proliferation and increased IFN-γ+CD8+ T cells with the assistance of NK cells. In contrast, depletion of NK cell decreased IFN-γ+CD8+ T cell production. Greater NK cell migration to LNs occurred in R848-immunized mice. A similar effect of R848 on NK cell migration was observed in an in vitro transwell study. When co-cultured, NK cells plus R848 could promote DCs maturation, and in turn, DCs in combination of R848 augmented NK cells activation. Further studies demonstrated that among several TLR agonists, R848 produced the largest amount of the chemokine CXCL9 from activated DCs, which is relevant to NK cell migration. CXCL9 blockade reduced the number of migrated NK cells, and the addition of CXCL9 increased the number of NK cells. Discussion: Taken together, R848-mediated stronger CD8+ T cell immunity does not depend on DC activation alone, rather that NK cells must also be considered. By increasing our immunological understanding of the effect of R848/TLR7, these findings provide a new perspective for applying R848 in future clinical studies.
Treatment of Fingernail Onychomycosis with Efinaconazole 10% Solution in a Patient with Scleroderma: A Case Report
Authors: Miller Rhiannon C. , Lipner Shari R.
Published: March 2022

Introduction: Oral antifungals are typically preferred over topicals for moderate to severe onychomycosis due to efficacy and shorter treatment courses. However, systemics are contraindicated or cautioned in patients with liver dysfunction and with some autoimmune diseases, and in those taking interacting medications. Efinaconazole 10% solution is a topical antifungal therapy, but application for fingernail onychomycosis has not been adequately studied. Case Presentation: We present a case of a 78-year-old female with scleroderma and moderate onychomycosis of the right 4th fingernail successfully treated with topical efinaconazole 10% solution. Conclusion: We review the literature on contraindications to oral antifungals for onychomycosis, precautions with terbinafine in patients with some autoimmune diseases, and topical onychomycosis therapies. Topical efinaconazole may represent an effective alternative for patients with fingernail onychomycosis who have contraindications to oral medications.