bluuebunny/crossref_metadata_2025 huggingface dataset:
Strength damage mechanism and pore structure evolution of modified Pisha sandstone cement soil with metakaolin
Authors: Geng Kaiqiang , Chai Junrui , Qin Yuan , Li Xiaoli , Zhou Heng , Zhang Xianwei
Published: May 2022

To fully understand the solidification effect of metakaolin (MK) on Pisha sandstone cement soil, unconfined compressive strength (UCS), ultradepth 3D microscopy (U3DM), nuclear magnetic resonance (NMR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) tests were carried out on the solidified soil. The solidification mechanism of MK was comprehensively analysed from the perspectives of macromechanics, mesostructure and micromechanisms. From the perspective of macromechanics, the damage variable
V
tended to decrease with increase in MK content, which indicates that the addition of MK helps improve the ability of the solidified soil to resist damage. From a mesoscopic perspective, the addition of MK reduced the average height difference of the surface of the solidified soil. The complexity of the size, number, distribution and shape of pores in the solidified soil was comprehensively analysed based on NMR experiments. With increase in MK content, the proportion of pores with a pore radius >1 µm decreased, whereas the fractal dimensions
D
T
and
D
2
increased. XRD and SEM experiments show that the hydration products (calcium aluminate hydrate and calcium silicate hydrate) formed after the incorporation of MK were tightly combined with the soil particles and filled the pores. A grey entropy correlation analysis carried out to analyse the correlation effect of different pore structures on the strength of the solidified soil showed that pore sizes of 0–0.1 µm had a large influence on the UCS. This is a valuable finding on the link between the microstructure and macromechanics of solidified soils.
LSAB: Enhancing Spatio-temporal Efficiency of AoA Tracking Systems
Authors: Pan Qingrui , An Zhenlin , Yang Lei , Lin Qiongzheng
Published: May 2022

Estimating the angle-of-arrival (AoA) of an RF source by using a large-sized antenna array is a classical topic in wireless systems. However, AoA tracking systems are not yet used for Internet of Things (IoT) in the real world due to their unaffordable cost. Many efforts, such as a time-sharing array, emulated array, and sparse array, were recently made to cut the cost. This work introduces a log-spiral antenna belt (
LSAB
), a new novel sparse “planar array” that could estimate the AoA of an IoT device in 3D space by using a few antennas connected to a single timeshare channel. Unlike the conventional arrays,
LSAB
deploys antennas on a log-spiral-shaped belt in a non-linear manner, following the theory of minimum resolution redundancy newly discovered in this work. One physical 8 × 8 uniform planar array (UPA) and four logical sparse arrays, including
LSAB
, were prototyped to validate the theory and evaluate the performance of sparse arrays. The extensive benchmark demonstrates that the performance of
LSAB
was comparable to that of a UPA, with similar degree of resolution; and
LSAB
could provide over 40% performance improvement than existing sparse arrays. We also prototyped a second
LSAB
adapted to an RFID system for localizing RFID tags at centimeter-level accuracy.
T-cell counts in response to acute cardiorespiratory or resistance exercise in physically active or physically inactive older adults: a randomized crossover study
Authors: Graff Rachel M. , Jennings Kristofer , LaVoy Emily C. P. , Warren Victoria E. , Macdonald Brad W. , Park Yoonjung , Markofski Melissa M.
Published: May 2022

A bout of resistance exercise did not elicit the same T-cell responses as a bout of walking on a treadmill, and the response was also not the same for people who participate in regular exercise compared with those who do not. Although there were several similarities, these potential differences underscore the importance of careful selection of exercise protocol based on the population studied and the desired T-cell response to exercise outcome.
Kidney injury risk during prolonged exposure to current and projected wet bulb temperatures occurring during extreme heat events in healthy young men
Authors: Hess Hayden W. , Stooks Jocelyn J. , Baker Tyler B. , Chapman Christopher L. , Johnson Blair D. , Pryor Riana R. , Basile David P. , Monroe Jacob C. , Hostler David , Schlader Zachary J.
Published: May 2022

We have demonstrated that kidney injury risk is increased when men are exposed over an eight hour period to a wet bulb temperature of 31{degree sign}C and exacerbated at a wet bulb temperature of 34{degree sign}C. Importantly, these heat stress conditions parallel those that are encountered during current (31{degree sign}C) and future (34{degree sign}C) extreme heat events. The kidney injury biomarker analyses indicate both the proximal and distal tubules as the locations of potential renal injury, and that the injury is likely due to oxidative stress and inflammation.
Ameliorative Potential of Resveratrol in Dry Eye Disease by Restoring Mitochondrial Function
Authors: Chen Jingyao , Zhang Weijia , Zheng Yixin , Xu Yanze
Published: May 2022

Background and Significance. Dry eye disease (DED) is a prevalent optic surface illness with a high incidence worldwide that is caused by a variety of factors, including mitochondrial dysfunction. Resveratrol has been confirmed to protect the eye surface in DED, and as an antioxidant, resveratrol can maintain mitochondrial function. Therefore, we investigated whether resveratrol can improve DED by restoring mitochondrial function. Methods. The mitochondrial dysfunction of HCE-2 human corneal epithelial cells was induced by high osmotic pressure exposure and treated with resveratrol (50 μM). Western blotting was used to detect the expression of the antioxidant proteins SOD2, GPx, and SIRT1, and flow cytometry was used to detect cell apoptosis and ROS production. The DED mouse model was induced by 0.2% benzalkonium chloride (BAC) and treated with resveratrol. The tear yield was measured by the phenol cotton thread test, the density of cup cells in the conjunctiva was measured by periodic acid-Schiff (PAS) staining, and the expression levels of SIRT1, GPx, and SOD2 in lacrimal glands were detected by Western blotting. Results. In hypertonic conditions, the apoptosis of HCE-2 cells increased, the expression of the antioxidant proteins SOD2 and GPx decreased, ROS production increased, and the expression of SIRT1 protein, an essential regulator of mitochondrial function, was downregulated. Treatment with resveratrol reversed the mitochondrial dysfunction mediated by high osmotic pressure. In the DED mouse model, resveratrol treatment promoted tear production and goblet cell number in DED mice, decreased corneal fluorescein staining, upregulated SIRT1 expression, and induced SOD2 and GPx expression in DED mice. Conclusion. Resveratrol alleviates mitochondrial dysfunction by promoting SIRT1 expression, thus reducing ocular surface injury in mice with dry eye. This study suggests a new path against DED.
A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology
Authors: Liu Li , Ji Yunfeng , Gao Yun , Li Tao , Xu Wei
Published: May 2022

With the increasing pressure on college students in terms of study, work, emotion, and life, the emotional changes of college students are becoming more and more obvious. For college student management workers, if they can accurately grasp the emotional state of each college student in all aspects of the whole process, it will be of great help to student management work. The traditional way to understand students’ emotions at a certain stage is mostly through chats, questionnaires, and other methods. However, data collection in this way is time-consuming and labor-intensive, and the authenticity of the collected data cannot be guaranteed because students will lie out of impatience or unwillingness to reveal their true emotions. In order to explore an accurate and efficient emotion recognition method for college students, more objective physiological data are used for emotion recognition research. Since emotion is generated by the central nervous system of the human brain, EEG signals directly reflect the electrophysiological activity of the brain. Therefore, in the field of emotion recognition based on physiological signals, EEG signals are favored due to their ability to intuitively respond to emotions. Therefore, a deep neural network (DNN) is used to classify the collected emotional EEG data and obtain the emotional state of college students according to the classification results. Considering that different features can represent different information of the original data, in order to express the original EEG data information as comprehensively as possible, various features of the EEG are first extracted. Second, feature fusion is performed on multiple features using the autosklearn model integration technique. Third, the fused features are input to the DNN, resulting in the final classification result. The experimental results show that the method has certain advantages in public datasets, and the accuracy of emotion recognition exceeds 88%. This proves the used emotion recognition is feasible to be applied in real life.
Effects of Fagonia indica on Letrozole-Induced Polycystic Ovarian Syndrome (PCOS) in Young Adult Female Rats
Authors: Younas Anam , Hussain Liaqat , Shabbir Arham , Asif Muhammad , Hussain Musaddique , Manzoor Faiza
Published: May 2022

Polycystic ovarian syndrome is a multidisciplinary endocrinopathy of reproductive-aged women that provokes insulin resistance, hyperandrogenism, cardiovascular problems, obesity, and menstrual complications. The present study was designed to investigate the effectiveness of ethanolic extract of Fagonia indica in letrozole-induced PCOS young adult female rats. HPLC was carried out to find the phenolic and flavonoid content of the ethanolic extract of Fagonia indica. Twenty-five female rats were taken and initially divided into two groups: group I (control group) and group II (PCOS group). PCOS was induced by letrozole given orally by gavage. Body weight was recorded weekly and vaginal cytology was analyzed daily. After induction of disease, the PCOS group is further divided into four groups (n = 5): group II (positive control with PCOS), group III (metformin 20 mg/kg treated group), group IV (ethanolic extract of Fagonia indica 500 mg/kg treated group), and group V (metformin plus Fagonia extract). At the end of experimental period, the blood sample of each rat was collected and serum was separated by centrifugation. Afterwards hormonal analysis, lipid profile and liver functioning tests were performed. Ovaries were removed and preserved for histopathological findings while the liver of each rat was stored for the determination of antioxidant potential assessment. Fagonia indica was found to possess quercetin as one of the major flavonoid phytoconstituents. The plant extract exhibited its beneficial effects by restoring hormonal balance, lipid profile, and liver functioning markers. Treatment with F. indica reduced body weight, resolved ovarian cysts, and showed positive effects on follicular growth. Treatment with plant also increased the levels of antioxidant enzymes. This study validates the potential of Fagonia indica for the amelioration of metabolic, as well as, hormonal disturbances that occurred in PCOS.
Development of Economic Evaluation System for Building Project Based on Computer Technology
Authors: Chen Siyu , Cui Yuyang , Zhu Yun , Song Guolin , Shi Yanghua
Published: May 2022

In order to effectively reduce the risk of construction project (CEP) and maximize the benefit of CEP, this study establishes the construction project economic evaluation (PEE) system by using computer technologies such as transfinite learning machine, artificial intelligence algorithm, and related fuzzy neural network and compares the coupling and sensitivity of different algorithms for PEE indicators, Through the demonstration of the comprehensive efficiency of the PEE system under the condition of computer technology, The results show that the index coupling and early warning sensitivity of the project economic evaluation system using the artificial intelligence algorithm of overlimit learning machine under computer technology are better than the previous traditional algorithms, which can realize the artificial intelligence of the economic evaluation system, reduce the investment cost of the project and improve the delivery time of the project, make the best balance between the technical application and economic operation of the project, and promote the sustainable development of the construction project.
Application of Virtual Reality Technology in Visual Optimization of Product Appearance Design
Authors: Yan Xiaocong , Liu Feng , Mojtahe Kaza
Published: May 2022

Virtual reality technology is used in this research to perform an in-depth investigation and the analysis of visual optimization of product appearance design, as well as to adapt evolving technology to practical design. The breadth of virtual display technology may be further investigated, and software abilities can be enhanced by studying the virtual display of intelligent goods. The performance indicators of the product may be exhibited in an all-around manner via the virtual display, giving customers an intuitive experience and thereby stimulating consumers’ desire to purchase. To achieve the research objectives of this paper, we first researched the professional positions of designers, operators, and product planners in related industries, and used the survey analysis method to compile valid data from the research results. And we conducted research and analysis on consumers of different age levels to derive different functional requirements of wearable products for different age levels, and compare and analyze the advantages and shortcomings of various types of smart wearable products in terms of their product presentation. The method refers to the product appearance design, making the product’s functional attributes, user emotions, and other factors as the starting point, and taking the visual effect of color as the center of the design scheme conception, the designer first conceives the visual effect of color to meet the requirements of function and structure, constructs a three-dimensional form that echoes it, and then explores the comprehensive effect of color and form, and further considers and designs the color, material, and so on from the perspective of users, product function, and structure. Then, we explore the comprehensive effect of color and form, and further think and design color and form from the perspective of users, product function, structure, and material, and finally get the color and form matching and reasonable product appearance. This design method can break through the limitation of “filling colors with shapes” and design a new, individual, and emotional product image.
Three Prognostic Biomarkers Correlate with Immune Checkpoint Blockade Response in Bladder Urothelial Carcinoma
Authors: Guo Ya , Zhang Yin Bin , Li Yi , Su Wang Hui , He Shan , Pan Shu Pei , Xu Kun , Kou Wei Hua
Published: May 2022

Aim. We aim to develop a signature that could accurately predict prognosis and evaluate the response to immune checkpoint blockade (ICB) in bladder urothelial carcinoma (BLCA). Methods. Based on comprehensive analysis of public database, we identified prognosis-related hub genes and investigated their predictive values for the ICB response in BLCA. Results. Among 69 common DEGs, three genes (AURKA, BIRC5, and CKS1B) were associated with poor prognosis, and which were related to histological subtypes, TP53 mutation status, and the C2 (IFN-gamma dominant) subtype. Three genes and their related risk model can effectively predict the response of immunotherapy. Their related drugs were identified through analysis of drug bank database. Conclusions. Three genes could predict prognosis and evaluate the response to ICB in BLCA.
Surgical Treatment of Radiation‐Induced Late‐Onset Scalp Wound in Patients Who Underwent Brain Tumor Surgery: Lessons from a Case Series
Authors: Kim Jinhyun , Ahn Kyung Chan , Chang Hak , Jeong Jae Hoon , Pak Changsik John , Kim Byung Jun
Published: May 2022

Objective. The management of late‐onset scalp wounds following irradiation is troublesome, especially in patients with a surgical history of intracranial neoplasms. It, insidiously, starts with wound dehiscence or discharge and never heals spontaneously without appropriate surgical treatment. Nevertheless, definite treatment guidelines have not yet been established. Here, we present our clinical experience with radiation‐induced scalp wounds and suggest a surgical principle for their treatment. Patients and Methods. The medical records of 13 patients with brain tumors, who were treated for intractable scalp wounds after irradiation between January 2000 and August 2015, were retrospectively reviewed. All the patients underwent a craniotomy for brain tumor resection. Surgical treatment for a late‐onset scalp wound was decided based on the “reconstructive ladder” and according to the status of bone flap and scalp tissue. The patients’ clinical characteristics and information regarding irradiation, surgery, and postoperative complications were recorded. Results. Scalp wounds developed 4.4 years, on average, after the completion of irradiation. Revision operations were performed an average of 2.3 times, and 6 patients (46%) required more than 2 operations. The bone flap was removed in 11 patients (84.6%) to achieve complete wound healing. Among them, 3 patients underwent a cranioplasty using artificial materials, but 2 patients underwent removal due to recurrent wound problems. Conclusions. Postirradiation scalp wounds are difficult to treat and have a high risk of recurrence. If osteoradionecrosis is suspected, the bone flap should be removed. It is important to debride unhealthy tissues aggressively and cover defects with robust tissue.
Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation
Authors: Faisal Muhammad , Chaudhury Sushovan , Sankaran K. Sakthidasan , Raghavendra S. , Chitra R. Jothi , Eswaran Malathi , Boddu Rajasekhar
Published: May 2022

Turtles are one of the ancient marine animals that live today. However, the population is threatened with extinction, so its existence needs to be protected and preserved because turtles often eat plastic waste in the ocean whose shape, texture, and color are similar to jellyfish. The technology in the computer vision area can be used to find the solution related to the case of reducing plastics and bottles trash in the ocean by implementing robotics. The region-based Convolutional Neural Network (CNN) is the latest image segmentation and has good detection accuracy based on the Faster R-CNN algorithm. In this study, the training image was built based on two different objects, namely plastic bottles and plastic bags. The target is that the two objects can be recognized even if there are other objects in the vicinity, or the image quality will be affected by the color of the seawater. The results obtained are that plastic objects and bottles can be recognized correctly in the picture. Of the five-color hues tested, the results show that the object detection process is valid on the average color hue, sepia, bandicoot, and grayscale. In contrast, the object detection process is invalid in black-and-white tones. The test results shown in the table explain that the object detection that gets the highest results is an image with normal coloring, while the lowest value is on bandicoot. The average accuracy of all types of images tested is 96.50. However, the accuracy value still needs to be improved to apply feasibility permanently to hardware such as diving robots.
Research on the Application of DM Technology with RF in Enterprise Financial Audit
Authors: Hao Yinhui , Qiu Fuqiang
Published: May 2022

Data mining (DM), as a new technology in the information age, is applied to modern audit work, which is more effective than traditional audit methods. In view of the problems existing in traditional tax audit methods, such as the huge amount of audit data, limited knowledge and experience of auditors, and difficult tracking of audit data, this paper uses computer-aided audit technology to collect, clean up, convert, and analyze data, comprehensively uses data warehouse technology, pattern recognition method, data analysis method, and anomaly detection theory as research methods, and makes a comprehensive study on tax affairs. Then, a random forest (RF) algorithm is used to establish the classification and identification model of audit risk. Second, based on the RF algorithm, the audit early warning framework of accounts receivable and payable in enterprise financial sharing mode is constructed, and the financial data and business data in enterprise financial sharing mode are extracted by using big data technology. The comparison of the results shows that the RF model has higher prediction accuracy and better robustness, which can better improve the antirisk ability of listed companies in China.
Clustering Ensemble Technology Based on Granular Computing to Extract Cervical Cancer Predictors
Authors: Wang Ye-Cheng , Tang Xu-Qing , Xu Honglin
Published: May 2022

Background. Cervical cancer is the most common gynecological malignancy, and its incidence has tended to be younger in recent years. Through the analysis of high-throughput expression data, the identification of key genes in cancer and healthy individuals as predictors of cervical cancer is of great significance for the early detection and early treatment of cervical cancer. Method. Granular computing is a concept and computing paradigm to deal with problems through information granulation, and the process of granulation can be realized by means of clustering. Based on this, this paper proposes an AB method to obtain representative elements in a multiattribute data system. First, the evaluation index FHEI of the clustering structure is introduced, and Algorithm 1 is designed to obtain the optimal clustering structure of each attribute of the data system and use it as the base cluster. Secondly, based on the clustering ensemble technology of granular computing, Algorithm 2 is designed with the help of the concept of information entropy. The algorithm takes the base cluster as the input to obtain the optimal ensemble clustering structure. Finally, using the nearest center principle, the representative elements of each class in the optimal ensemble clustering structure are obtained. Results. In this paper, the differentially expressed genes (DEGs) are screened out by using the gene expression data of cervical cancer, and the scores of the four interaction relationships among the DEGs are used as a multiattribute data system and input into the AB method. The five representative elements obtained are RTTN, SAMD10, ZNF207, WAC, and METTL14, which are the predictors of cervical cancer. The classification accuracy of these predictors is as high as 98.82%. This paper also conducts a comparative study between the AB method and other classical methods on six independent gene expression datasets. The results show that the number of predictors obtained by the AB method is small but has a high classification accuracy in the classification of patient samples.
Implementation and Analysis of AI-Based Gesticulation Control for Impaired People
Authors: Nivash S. , Ganesh E. N. , Manikandan T. , Dhaka Arvind , Nandal Amita , Hoang Vinh Truong , Kumar Adarsh , Belay Assaye
Published: May 2022

This paper presents an intelligent human PC intuitive framework. In this proposed work, artificial intelligence is utilized for home mechanization, which perceives human motions with the assistance of a camera and performs tasks appropriately. The idea of perceiving the motions depends on three layers: detection, tracking, and recognition. We use a camera-PC interface that can catch the developments, and later use PC vision innovation and AI calculations to comprehend the fundamental example and match the information with a preprepared dataset. When it comes to safes, an extra layer of security is provided by using face recognition, and the safe is opened if the individual is recognized from the dataset.
Research on the Evaluation Method of Elderly Wheelchair Comfort Based on Body Pressure Images
Authors: Li Bingzhe
Published: May 2022

With the continuous deepening of the global aging process, more and more users demand wheelchair. In the interaction between users and wheelchairs, comfort is a crucial factor. In order to provide a set of effective evaluation of wheelchair comfort, this study makes use of the back propagation neural network (BPNN) and analyzes the body pressure index by combining with the subjective evaluation in the wheelchair comfort evaluation. As shown by results, the neural network simulation values and 13 groups of wheelchair comfort levels have been obtained from the analysis of the BPNN on the wheelchair of the elderly, and the relative error of the actual comfort value is basically within 5%. At the same time, the three sets of network verification results reveal that the error between the actual value and the simulated value is 1.9%, -1.5%, and 2.5%, respectively, which basically reflects that this comfort evaluation system can quickly, conveniently, effectively, and scientifically provide wheelchair tactile comfort evaluation.
Effect of English Reading on the Cultivation of College Students’ Humanistic Spirit Based on Embedded Sensor System
Authors: Wang Lingtuo , Li Xiaobin , Jiao Yufang
Published: May 2022

With the development of information technology in recent years, people have integrated English reading with information technology, which is conducive to improving the ability of English reading and cultivating the humanistic spirit of college students. This paper aims to study how to play the role of English reading in the cultivation of college students’ humanistic spirit based on the embedded sensor system. This paper proposes an embedded speech recognition technology algorithm and a speech recognition technology algorithm based on dynamic time warping. Speech recognition technology is conducive to the improvement of English reading efficiency, as well as improving the reading interest of college students. The experimental results of this paper show that the speed of English development has become faster and faster in recent years, from 30% in 2011 to 55% in 2020. Although it fluctuates in the middle, the overall trend is upward. People pay more and more attention to English, and the proportion of English reading is gradually increasing, from about 20% in 2011 to about 41%. Therefore, it can be known that the importance of English reading is getting higher and higher, and it is necessary to cultivate the humanistic spirit of college students through English reading.
Liquidity, Asset Price Volatility, and Monetary Policy Choices: Empirical Evidence from China
Authors: Zhu Qing , Bai Shuyu , Wang Jia
Published: May 2022

This article effectively identifies the high and low volatility state of asset prices in China by constructing the MS‐AR model, and further investigates the relationship between different dimensions of liquidity and asset price volatility. Moreover, we try to incorporate liquidity into the analytical framework and adopt the TVP‐SV‐VAR model to study the time‐varying characteristics between monetary policy, liquidity, asset price volatility and macroeconomy. The results are as follows: firstly, it shows that the high or low volatility state of China’s stock market and real estate market can be clearly divided, and display the consistency with the trend of asset price volatility. Secondly, liquidity has a strong ability to explain the high and low volatility state of asset prices, but it shows some hysteresis effects. Thirdly, the time‐varying results reveal that monetary policy has a regulating effect on liquidity, and the response cycle of quantitative monetary policy is relatively short, which reflects the effects of macroeconomy precisely. However, price‐based monetary policy has a longer response cycle and plays a vital role in the anticipatory adjustment and fine‐tuning of asset price volatility. These conclusions can provide an explanation for the attention to asset price bubbles and potential financial risks, and offer decision‐making references for the central bank to implement differentiated and dynamic monetary policy choices.
miRNA-432 and SLC38A1 as Predictors of Hepatocellular Carcinoma Complicated with Alcoholic Steatohepatitis
Authors: Cai Chao , Lin Jing , Li Ji , Wang Xiao-Dong , Xu Lan-Man , Chen Da-Zhi , Chen Yong-Ping
Published: May 2022

Alcoholic steatohepatitis (ASH) is asymptomatic in the early stages and is typically advanced at the time of diagnosis. With the global rise in alcohol abuse, ASH is currently among the most detrimental diseases around the world. Hepatocellular carcinoma (HCC) is one of the final outcomes of numerous liver diseases. However, at present, HCC screening is mostly focused on liver cancer development. Moreover, there is no effective biomarker to predict the prognosis and recurrence of liver cancer. Meanwhile, there are limited studies on the prognosis and recurrence of HCC patients complicated with ASH. In this study, using bioinformatic analysis as well as cellular and animal models, we screened the differentially expressed (DE) miRNA-432 and SLC38A1 gene in ASH. Based on our analysis, miRNA-432 targeted SLC38A1, and the levels of miRNA-432 and SLC38A1 could accurately predict the overall survival (OS) and relapse free survival (RFS) in patients with liver cancer. Hence, these two genetic elements have the potential to synergistically predict the prognosis and recurrence of HCC complicated with ASH.
Recent Advances and Perspectives on the Sources and Detection of Antibiotics in Aquatic Environments
Authors: Zeng Yanbo , Chang Fengqin , Liu Qi , Duan Lizeng , Li Donglin , Zhang Hucai
Published: May 2022

Water quality and safety are vital to the ecological environment, social development, and ecological susceptibility. The extensive use and continuous discharge of antibiotics have caused serious water pollution; antibiotics are widely found in freshwater, drinking water, and reservoirs; and this pollution has become a common phenomenon and challenge in global water ecosystems, as water polluted by antibiotics poses serious risks to human health and the ecological environment. Therefore, the antibiotic content in water should be identified, monitored, and eliminated. Nevertheless, there is no single method that can detect all different types of antibiotics, so various techniques are often combined to produce reliable results. This review summarizes the sources of antibiotic pollution in water, covering three main aspects: (1) wastewater discharges from domestic sewage, (2) medical wastewater, and (3) animal physiology and aquaculture. The existing analytical techniques, including extraction techniques, conventional detection methods, and biosensors, are reviewed. The electrochemical biosensors have become a research hotspot in recent years because of their rapid detection, high efficiency, and portability, and the use of nanoparticles contributes to these outstanding qualities. Additionally, the comprehensive quality evaluation of various detection methods, including the linear detection range, detection limit (LOD), and recovery rate, is discussed, and the future of this research field is also prospected.
Diagnostic Value of Emission Computed Tomography Combined with Computed Tomography for Metastatic Malignant Tumor of Spine
Authors: Qin Feng , Feng Yapei , Zhang Panpan , Li Yuemei , Fan Weiqiang
Published: May 2022

Objective. To explore the diagnostic value of emission computed tomography (ECT) combined with computed tomography (CT) for metastatic malignant tumor of spine. Methods. By means of retrospective study, a total of 102 patients with extraskeletal primary malignant tumor treated in our hospital from February 2019 to February 2021 were selected as the subjects. All patients had single lesion of the spine, of which 72 were malignant and 30 were benign according to the results of pathological examination. ECT and CT examinations were performed to all patients, and by taking the pathological findings as the gold standard, the sensitivity, specificity, positive predictive value and negative predictive value of ECT, CT, and their combination were calculated, and their efficacy in diagnosing metastatic malignant tumor of spine was analyzed. Results. A total of 68 (94.4%) metastatic malignant spinal tumors were detected by ECT combined with CT, with a detection rate of 100% in breast cancer and lung cancer, 94.1% in liver cancer, and 78.6% in prostate cancer, respectively; the combined diagnosis had a diagnostic sensitivity of 94.4%, specificity of 73.3%, positive predictive value of 89.5%, negative predictive value of 84.6%, and diagnostic accuracy rate of 88.2%, and AUC (95% CI) = 0.839 (0.739–0.939). Conclusion. Combining ECT with CT has a good diagnostic efficacy for metastatic malignant spinal tumors.
Improvement and Application of Fractional Particle Swarm Optimization Algorithm
Authors: Li Jing , Zhao Chunna
Published: May 2022

The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes.
Correlation Modeling between Ocean Spatial Distribution and Economic Sustainability
Authors: Wang Cong
Published: May 2022

The ocean is the second most vital space for human production and life, and it is a backbone of the ecosystem of the earth. There is a strong correlation between the ocean’s spatial distribution and environment; similarly, there is a strong correlation between spatial distribution and economic sustainability. This study proposes a strategy for analyzing the relationship between marine spatial layout and economic sustainability based on a correlation combination weighting model. The essential conditions of marine industry development are examined utilizing soft computing methodologies to analyze important theories of marine industry layout. The entropy method is used to compute the weight of the marine economic evaluation index. By maximizing the difference, we can get the upper and lower boundaries of the weight of the marine industry development combination. To study the association between ocean spatial arrangement and economic sustainability, the correlation assessment results are obtained using the combination weighting model. The correlation analysis results of the experimental data are used to determine the appropriate spatial layout of the marine industry, which has some practical implications for the long-term development of the marine industry layout economy and the coordination and interaction of land-sea systems.
Design and Bulk Sensitivity Analysis of a Silicon Nitride Photonic Biosensor for Cancer Cell Detection
Authors: S. Prasanna Kumaar , A. Sivabramanian
Published: May 2022

Bulk sensitivity is an important parameter to validate the efficiency of the photonic waveguide sensor. Due to recent advancements in point-of-care silicon photonic biosensing, the focus is to identify the effective way to improve sensitivity. Integrating polydimethylsiloxane (PDMS) microfluidic channel in sensor architecture decreases the sensitivity due to leakage of molecules at edges. The silicon nitride (SiN4) Mach–Zehnder interferometer utilizes the refractive index of different cancer cells (1.39–1.401) to determine the bulk sensitivity. The proposed gradient step rib-slot structure of 970 nm wide and 400 nm thickness is designed to hold the liquid sample without any PDMS material. This novel waveguide exhibits high waveguide bulk sensitivity

S

w
,
bulk

and device bulk sensitivity

S

d

compared with the gradient rib waveguide. We achieved a waveguide bulk sensitivity

S

w
,
bulk

of 2.0699 RIU/RIU and device sensitivity

S

d

of 568 nm/RIU through finite-difference time-domain (FDTD) analysis.
Disposal of Unused and Expired Medicines within the Sunyani Municipality of Ghana: A Cross‐Sectional Survey
Authors: Amoabeng Ivy Anima , Otoo Bernice Araba , Darko Godfred , Borquaye Lawrence Sheringham
Published: May 2022

The occurrence of pharmaceuticals in the environment is a global challenge. Pharmaceuticals such as antibiotics and analgesics have been reported in various environmental matrices at varying concentrations. The major disposal route for unused and expired pharmaceuticals in Ghana is throwing them into dustbins. Although there are laws on the proper disposal patterns of drugs, these laws are poorly implemented. Sunyani is a fast‐growing community with several health facilities that dispense medications daily. The purchase and use of medications among households are also high. However, no data exist on the disposal patterns of pharmaceuticals within the Sunyani Municipality. This study aims to identify the disposal patterns for unused and expired medications by households and pharmacies within the Sunyani Municipality, Ghana. A descriptive cross‐sectional study was conducted among 400 persons in homes and 35 persons from randomly selected pharmacies and over‐the‐counter medication shops (OTCMs) within the Sunyani Municipality. A face‐to‐face interview approach using structured questionnaires for each respondent was employed. Household respondents disposed of unused and expired medications mainly through dumping in garbage cans (70.8%), incineration (11.5%), and flushing down the sink (9%). Pharmaceutical shop respondents also disposed of unused and expired medications into dump cans, by incineration, through the Food and Drugs Authority of Ghana, and took back to pharmaceutical wholesalers. Disposal practices observed in this study by households and pharmacy respondents were largely inappropriate. This could be due to the lack of education on the proper disposal patterns available to these respondents. It is recommended that guidelines on safe disposal be put in place, and a structured procedure for collecting unused and expired pharmaceuticals should be introduced.
[Retracted] Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network
Authors: Zhong Jun , Xu Jian
Published: May 2022

The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. As a typical classification task in computer vision, human behavior recognition has immeasurable potential value in medical, family, transportation, and other scenarios. At the same time, in the field of competitive sports, the integration of artificial intelligence technology and sports technical and tactical analysis is undoubtedly an important way to innovate and improve the technical and tactical level. Taking karate as an example, the study of athletes’ training and competition videos is an important means and method for technical and tactical analysis in competitive sports. Traditional tactical intelligence analysis methods have many shortcomings, such as high labor cost, serious data loss, long delay, and low accuracy. Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. The technology effectively makes up for the disadvantages of traditional tactical intelligence analysis methods. The research results show that the new topology map construction method has a significant effect on improving the accuracy of behavior recognition and also lays a foundation for technical and tactical analysis.
Application of Adsorptive Blood Purification Techniques during Cardiopulmonary Bypass in Cardiac Surgery
Authors: Liu Meng-Han , Yu Hong , Zhou Rong-Hua
Published: May 2022

By reason of surgical demand, the majority of cardiovascular procedures still depend on the use of cardiopulmonary bypass (CPB). Due to the nonphysiological state of CPB, it can cause complex and unpredictable inflammatory response, which may lead to significant morbidity and mortality. Unfortunately, the pharmacological and mechanical strategies that currently exist do not offer significant advantages in controlling inflammatory response and improving patient outcomes. The best strategy to reduce inflammation in CPB is still uncertain. In recent years, adsorptive blood purification techniques (BPTs) have emerged, among which CytoSorb is the latest representative device. Currently, the primary application area of adsorptive BPTs is in the control and treatment of systemic hyperinflammatory states, such as refractory septic shock patients. However, the evidences on efficacy and safety of adsorptive BPTs application during CPB surgery are still inconclusive, so we summarize the relevant evidences here and suggest future potential research areas.
Practical Undeniable Multiparty Drawing-Straw Protocol in Asynchronous Networks for Resource-Constrained Information Systems
Authors: Hsu Ching-Fang , Harn Lein , Xia Zhe , Xu Hang
Published: May 2022

The next generation of mobile networks and communications (5G networks) has a very strong ability to compute, store, and so on. Group-oriented applications demonstrate their potential ability in resource-constrained information systems (RISs) towards 5G. The security issues in RIS towards 5G have attracted great attention. For example, how to conduct fair and orderly multiparty communication in an intelligent transportation system (ITS). One of the main challenges for secure group-oriented applications in RIS towards 5G is how to manage RIS communications fairly in multiparty applications. In other words, when the users cannot transmit their messages simultaneously, the order of their communication can cause security concerns in multiparty applications. A feasible solution to the problem is for the group of users to follow a specific order to transmit their messages. Otherwise, some users may take advantage over other users if there has no agreeable order to be followed. In this paper, we propose a novel cryptographic primitive, called multiparty drawing-straw (MDS) protocol, which can be used by a group of users to determine the order of the group to participate in the multiparty applications. Our scheme is based on Pedersen’s verifiable secret sharing (VSS), which is a well-known scheme. Our proposed protocol is fair since the output is uniformly distributed, and this is an attractive feature for secure multiparty applications in RIS towards 5G.
Countermeasure of Telecom Network Fraud Investigation Based on Big Data
Authors: Wang Tianyu , Yang Bo
Published: May 2022

With the diversification of information data in the information age of big data and the integration of network technology and the development of different industries, criminals who carry out telecommunication fraud are also using the technical loopholes existing in the process of integration of big data with different industries as an opportunity to commit crimes. This paper studies the investigation process and countermeasures of telecom network fraud through big data technology. This paper first introduces the characteristics of big data, analyzes the challenge of personal information security under the background of big data, warns people to protect their personal information in the era of big data, puts forward the clustering algorithm based on big data, introduces the concrete steps based on big data clustering algorithm, and then puts forward the specific steps of big data clustering algorithm. The current situation of telecom network fraud is analyzed, and the telecommunication network fraud is clustered based on big data. The experimental results show that, based on the clustering analysis of telecommunication network fraud based on big data, it is found that through the information age of big data, as long as big data are used rationally, it can effectively suppress telecommunications fraud and reduce it by 80%.
Potentiation of the Activity of Antibiotics against ATCC and MDR Bacterial Strains with (+)‐α‐Pinene and (‐)‐Borneol
Authors: Leite-Sampaio Nadghia F. , Gondim Cicera N. F. L. , Martins Rachel A. A. , Siyadatpanah Abolghasem , Norouzi Roghayeh , Kim Bonglee , Sobral-Souza Celestina E. , Gondim Gonçalo E. C. , Ribeiro-Filho Jaime , Coutinho Henrique D. M.
Published: May 2022

The increasing rates of antimicrobial resistance have demanded the development of new drugs as conventional antibiotics have become significantly less effective. Evidence has identified a variety of phytocompounds with the potential to be used in the combat of infections caused by multidrug‐resistant (MDR) bacteria. Considering the verification that terpenes are promising antibacterial compounds, the present research aimed to evaluate the antibacterial and antibiotic‐modulating activity of (+)‐α‐pinene and (‐)‐borneol against MDR bacterial strains. The broth microdilution method was used to determine the minimum inhibitory concentration (MIC) of the compounds and antibiotics and further evaluate the intrinsic and associated antibiotic activity. These analyses revealed that (+)‐α‐pinene showed significant antibacterial activity only against E. coli (MIC = 512 μg.mL−1), while no significant inhibition of S. aureus and P. aeruginosa growth was observed (MIC ≥ 1024 μg mL−1). However, when combined with antibiotics, this compound induced a significant improvement in the activity of conventional antibiotics, as observed for ciprofloxacin, amikacin, and gentamicin against Staphylococcus aureus, as well as for amikacin and gentamicin against Escherichia coli, and amikacin against Pseudomonas aeruginosa. On the other hand, (‐)‐borneol was found to inhibit the growth of E. coli and enhance the antibiotic activity of ciprofloxacin and gentamicin against S. aureus. The present findings indicate that (+)‐α‐pinene and (‐)‐borneol are phytocompounds with the potential to be used in the combat of antibacterial resistance.