bluuebunny/crossref_metadata_2025 huggingface dataset:
The Construction Countermeasures of Shallow‐Buried Small Spacing Tunnel Undercrossing Cultural Relic Buildings: A Case Study
Authors: Wan Li , Shen Jiajia , Zhang Changan , Lin Zanquan , Zhang Hu
Published: December 2021

Based on the background of the reconstruction project from Changqing Chenzhuang‐Pingyin section of G220 east‐deep line in China, a special tunnel structure and construction plan was carried out according to the construction measures of the shallow‐buried small spacing tunnel passing underneath cultural relic buildings, and a comprehensive deformation control scheme of “CRD construction method single‐arm excavation + surface grouting prereinforcement + advanced large pipe shed presupport” was put forward. The results of numerical simulation and on‐site construction monitoring showed that the overall deformation of aqueduct foundation generally increases first, then decreases and increases again, and finally tends to be stable. The effects of surface grouting prereinforcement and advance large pipe shed presupport are obvious. The comprehensive deformation control scheme can ensure the safety of the existing construction and meet the safety prevention and control requirements.
Heart Failure Detection Using Quantum‐Enhanced Machine Learning and Traditional Machine Learning Techniques for Internet of Artificially Intelligent Medical Things
Authors: Kumar Yogesh , Koul Apeksha , Sisodia Pushpendra Singh , Shafi Jana , Verma Kavita , Gheisari Mehdi , Davoodi Mohamad Bagher
Published: December 2021

Quantum‐enhanced machine learning plays a vital role in healthcare because of its robust application concerning current research scenarios, the growth of novel medical trials, patient information and record management, procurement of chronic disease detection, and many more. Due to this reason, the healthcare industry is applying quantum computing to sustain patient‐oriented attention to healthcare patrons. The present work summarized the recent research progress in quantum‐enhanced machine learning and its significance in heart failure detection on a dataset of 14 attributes. In this paper, the number of qubits in terms of the features of heart failure data is normalized by using min‐max, PCA, and standard scalar, and further, has been optimized using the pipelining technique. The current work verifies that quantum‐enhanced machine learning algorithms such as quantum random forest (QRF), quantum K nearest neighbour (QKNN), quantum decision tree (QDT), and quantum Gaussian Naïve Bayes (QGNB) are better than traditional machine learning algorithms in heart failure detection. The best accuracy rate is (0.89), which the quantum random forest classifier attained. In addition to this, the quantum random forest classifier also incurred the best results in F1 score, recall and, precision by (0.88), (0.93), and (0.89), respectively. The computation time taken by traditional and quantum‐enhanced machine learning algorithms has also been compared where the quantum random forest has the least execution time by 150 microseconds. Hence, the work provides a way to quantify the differences between standard and quantum‐enhanced machine learning algorithms to select the optimal method for detecting heart failure.
A Personalized Eccentric Cyber-Physical System Architecture for Smart Healthcare
Authors: Balakrishnan Amutha , Kadiyala Ramana , Dhiman Gaurav , Ashok Gokul , Kautish Sandeep , Yadav Kusum , Maruthi Nagendra Prasad J.
Published: December 2021

The development and technological advancement of wireless sensor networks in different fields has been a revolution for mankind. To meet the high-end requirements, the support of the cloud that provides the resources for the application is very much essential. This paper presents an architecture called cloud sense to connect cyber and physical spaces for wireless body area networks with varying high-end workflow at different perspectives. The scalability issue in collecting patient data and processing the data is established using ganglia that is a scalable, distributed monitoring system to support high-performance computing in clusters for the set of input events such as electrocardiogram (ECG), blood pressure (BP), saturation of peripheral oxygen (SPO2), temperature, and skin conductance of the kind of human body parameters. Various parameter metrics have been analyzed based on the equivalent creation of instances. The connectivity mechanism behind the proposed cyber-physical system is unique of its kind; it is exhibited through wireless Internet on a small scale of three remote locations; the system works well with specific network parameter metrics; and the results proved that availability and scalability issues were addressed with numerical analysis.
Finite Element Model‐Based Weight‐Over Process Philosophy for Bridge Loading Capacity Evaluation and Rating Factor Estimation
Authors: Karimpour Ali , Rahmatalla Salam , Bolboli Ghadikolaee Hossein
Published: December 2021

Existing rating methods estimate bridge loading capacity and demand from secondary actions due to live loads in the primary structural components. In these methods, uniaxial yielding stress is traditionally used to detect component capacity using either stress quantities or shear‐moment actions to compute the capacity demand of the bridge. These approximations can lead to uncertainties in load capacity estimation. This article presents the weight‐over process (WOP), a novel computer‐aided approach to bridge loading capacity evaluation based on tonnage and rating factor estimation. WOP is expected to capture different forms of failure in a more general manner than existing methods. In WOP, a bridge finite element model (FEM) is discretized into many sections and element sets, each containing a single material type, and each assigned a suitable 3D failure criterion. Then, factored gross vehicle weights (GVWs) are incrementally imposed on the bridge FEM with those predefined ultimate unfavored loading scenarios in a manner similar to proof load testing. WOP code runs nonlinear analysis at each increment until a stopping criterion is met. Two representative bridges were selected to confirm WOP’s feasibility and efficacy. The results showed that WOP‐predicted values at the interior girders were between those of the conventional AASHTO and the nondestructive testing (NDT) strain measurement methods. That may put WOP in a favorable zone as a new method that is less conservative than AASHTO but more conservative than real NDT testing.
Ficus pandurata Hance Inhibits Ulcerative Colitis and Colitis‐Associated Secondary Liver Damage of Mice by Enhancing Antioxidation Activity
Authors: Dai Weibo , Zhan Xinyi , Peng Weijie , Liu Xin , Peng Weiwen , Mei Quanxi , Hu Xianjing
Published: December 2021

Inflammatory bowel disease (IBD), a global disease threatening human health, is commonly accompanied by secondary liver damage (SLD) mediated by the gut‐liver axis. Oxidative stress acts a critical role in the onset of IBD, during which excessive oxidation would destroy the tight junctions between intestinal cells, promote proinflammatory factors to penetrate, and thereby damage the intestinal mucosa. Ficus pandurata Hance (FPH) is widely used for daily health care in South China. Our previous study showed that FPH protected acute liver damage induced by alcohol. However, there is no study reporting FPH treating ulcerative colitis (UC). This study is designed to investigate whether FPH could inhibit UC and reveal its potential mechanism. The results showed that FPH significantly alleviated the UC disease symptoms including the body weight loss, disease activity index (DAI), stool consistency changing, rectal bleeding, and colon length loss of UC mice induced by dextran sulfate sodium (DSS) and reversed the influences of DSS on myeloperoxidase (MPO) and diamine oxidase activity (DAO). FPH suppressed UC via inhibiting the TLR4/MyD88/NF‐κB pathway and strengthened the gut barrier of mice via increasing the expressions of ZO‐1 and occludin and enhancing the colonic antioxidative stress property by increasing the levels of T‐SOD and GSH‐Px and the expressions of NRF2, HO‐1, and NQO1 and reducing MDA level and Keap1, p22‐phox, and NOX2 expressions. Furthermore, FPH significantly inhibited SLD related to colitis by reducing the abnormal levels of the liver index, ALT, AST, and cytokines including TNFα, LPS, LBP, sCD14, and IL‐18 in the livers, as well as decreasing the protein expressions of NLRP3, TNFα, LBP, CD14, TLR4, MyD88, NF‐κB, and p‐NF‐κB, suggesting that FPH alleviated UC‐related SLD via suppressing inflammation mediated by inhibiting the TLR4/MyD88/NF‐κB pathway. Our study firstly investigates the anticolitis pharmacological efficacy of FPH, suggesting that it can be enlarged to treat colitis and colitis‐associated liver diseases in humans.
Cross-Border Internet of Things E-Commerce Warehouse Control System Based on TRIZ Theory
Authors: Liu Tingting
Published: December 2021

Cross-border e-commerce of the Internet of Things is affected by international situations and political factors. Supply chain interruption and returns will cause violent fluctuations in commodity inventory, making the inventory control of cross-border e-commerce very difficult. The TRIZ principle is utilized to solve the problem of the difficulty to evaluate the suppliers comprehensively in e-commerce warehouse management. The Markov algorithm is used to describe the change of inventory level. The cyclic expected time and cost function are constructed by the horizontal crossing method, updating the process and Martingale theory. The effect of the correlation between the demand and supply interruption on the optimal inventory control strategy is studied by simulation. The change of the optimal control strategy under the different interrupt and return types is analyzed, and the validity of the management system is verified.
Projected Moisture Index (MI) for Tropical Sri Lanka
Authors: Wickramarachchi Charuni , Samarasinghe Jayanga T. , Alyousifi Yousif , Rathnayake Upaka
Published: December 2021

Atmospheric moisture loading can cause a great impact on the performance and integrity of building exteriors in a tropical climate. Buildings can be highly impacted due to the changing climate conditions over the world. Therefore, it is important to incorporate the projected changes of moisture loads in structural designs under changing climates. The moisture index (MI) is widely used in many countries as a climate‐based indicator to guide the building designs for their durability performance. However, this was hardly considered in structural designs in Sri Lanka, even though the country is one of the most affected countries under climate change. Therefore, this study investigates future climate change impacts on the environmental moisture in terms of MI, which can be used in climate zoning, investigating indoor air quality, understanding thermal comfort and energy consumption, etc. The moisture index was found as a function of the drying index (DI) and wetting index (WI) to the whole country for its four rainfall seasons. The temporal and spatial distributions were plotted as MI maps and showcased under two categories; including historical MI maps (1990–2004) and future projected MI maps (2021–2040, 2041–2070, and 2071–2100). Future projected MI maps were constructed using bias‐corrected climatic data for two RCP climatic scenarios (RCP4.5 and RCP8.5). Results showed that the temporal and spatial variations of MIs are justifiable to the country’s rainfall patterns and seasons. However, notable increases of MIs can be observed for future projected MIs in two seasons, and thus a careful investigation of their impacts should be assessed in terms of the construction of buildings and various agricultural activities. Therefore, the outcome of this research can be essentially used in policy implementation in adapting to the ongoing climate changes in Sri Lanka.
Microbial and Chemical Characteristics of Doogh (Iranian Fermented Milk Drink)
Authors: Mohamadi Sara , Mofid Vahid , Zeinali Tayebeh , Rahmani Anosheh , Sadighara Parisa , Peivasteh-Roudsari Leila
Published: December 2021

Background. Regarding the increasing public health concerns about the safety of foodstuffs, the current survey was designed to argue the presence of preservatives (e.g., sodium benzoate (SB), potassium sorbate (PS), and natamycin) and also the level of salt and fungi in 148 samples of yoghurt drink “Doogh.” Methods. The enumeration of fungi and determination of salt content of samples were performed according to the standard procedures. Preservative determination was performed by reverse phase high-performance liquid chromatography with UV detection (RV-HPLC-UV). Results. 0.1% of the total analyzed samples was above the permitted level of Iranian standard for SB (0%), while PS was not detected in any of them. Furthermore, natamycin in 0.11% of the analyzed samples had more than the permissible level of Iranian standard. Additionally, about 15% of the tested samples was higher than the Iranian standard level for fungi (<102 CFU/mL). The average amount of salt in the tested Doogh samples and also in the examined Kefir samples was significantly (

P
<
0.001

) lower than the standard amount of salt (<0.8 g/100). Conclusion. In conclusion, the quality of Doogh and Kefir samples were acceptable in terms of salt content. Kefir had a significantly (

P
≤
0.001

) lower amount of salt in comparison with Doogh. Taken together, underlining the results of the present study, no significant public health concern would exist respecting the mentioned additives.
A Target Detection Algorithm for Remote Sensing Images Based on Deep Learning
Authors: Lv Yi , Yin Zhengbo , Yu Zhezhou
Published: December 2021

In order to improve the accuracy of remote sensing image target detection, this paper proposes a remote sensing image target detection algorithm DFS based on deep learning. Firstly, dimension clustering module, loss function, and sliding window segmentation detection are designed. The data set used in the experiment comes from GoogleEarth, and there are 6 types of objects: airplanes, boats, warehouses, large ships, bridges, and ports. Training set, verification set, and test set contain 73490 images, 22722 images, and 2138 images, respectively. It is assumed that the number of detected positive samples and negative samples is A and B, respectively, and the number of undetected positive samples and negative samples is C and D, respectively. The experimental results show that the precision-recall curve of DFS for six types of targets shows that DFS has the best detection effect for bridges and the worst detection effect for boats. The main reason is that the size of the bridge is relatively large, and it is clearly distinguished from the background in the image, so the detection difficulty is low. However, the target of the boat is very small, and it is easy to be mixed with the background, so it is difficult to detect. The MAP of DFS is improved by 12.82%, the detection accuracy is improved by 13%, and the recall rate is slightly decreased by 1% compared with YOLOv2. According to the number of detection targets, the number of false positives (FPs) of DFS is much less than that of YOLOv2. The false positive rate is greatly reduced. In addition, the average IOU of DFS is 11.84% higher than that of YOLOv2. For small target detection efficiency and large remote sensing image detection, the DFS algorithm has obvious advantages.
On Statistical Development of Neutrosophic Gamma Distribution with Applications to Complex Data Analysis
Authors: Khan Zahid , Al-Bossly Afrah , Almazah Mohammed M. A. , Alduais Fuad S.
Published: December 2021

In the absence of a correct distribution theory for complex data, neutrosophic algebra can be very useful in quantifying uncertainty. In applied data analysis, implementation of existing gamma distribution becomes inadequate for some applications when dealing with an imprecise, uncertain, or vague dataset. Most existing works have explored distributional properties of the gamma distribution under the assumption that data do not have any kind of indeterminacy. Yet, analytical properties of the gamma model for the more realistic setting when data involved uncertainties remain largely underdeveloped. This paper fills such a gap and develops the notion of neutrosophic gamma distribution (NGD). The proposed distribution represents a generalized structure of the existing gamma distribution. The basic distributional properties, including moments, shape coefficients, and moment generating function (MGF), are established. Several examples are considered to emphasize the relevance of the proposed NGD for dealing with circumstances with inadequate or ambiguous knowledge about the distributional characteristics. The estimation framework for treating vague parameters of the NGD is developed. The Monte Carlo simulation is implemented to examine the performance of the proposed model. The proposed model is applied to a real dataset for the purpose of dealing with inaccurate and vague statistical data. Results show that the NGD has better flexibility in handling real data over the conventional gamma distribution.
Valuation of Insurance Products with Shout Options in a Jump-Diffusion Model
Authors: Liu Jun , Liang Zhian
Published: December 2021

The insurance product with shout options which permit the holders to modify the contract rules is one of the most popular products in European and American markets today. Therefore, it is of great significance to price more precisely. A new mathematical model consisting of a partial differential inequality and constraint conditions is derived for the price of insurance products in a jump-diffusion model. The numerical experiments are performed to analyze the impact of parameters on the insurance product with shout put options, especially for the jump times and the quantities of shout opportunities. The experiment results show that the value of the product is strongly affected by the quantities of shouting opportunities, especially for high values of the underlying asset, while it is only weakly affected for low values. Meanwhile, another meaningful discovery is that the valuation has changed little as the jump times are less than five, while it has shown a sharp increase once the jump times are more than five. Furthermore, the indicator results of course grid errors show that the values of shout put options in the jump-diffusion model are more accurate than those in a Brownian motion.
Model Research on In-Service Learning of Intellectualization of Aerospace Knowledge
Authors: Ren Haoli , Li Hailan , Peng Kongyang
Published: December 2021

With the development of vocational education, it is necessary to construct the pattern of lifelong learning. To push delivery learning resources and provide a learning environment, it is necessary to innovate in-service learning mode. According to the characteristics of the aerospace position, the capacity model was studied and proposed. Based on the ability model, the intelligent in-service learning model is studied and proposed to improve the precision service quality. From the angle of principle and learning process, this paper discusses the intelligent in-service learning mode of including the learning model based on knowledge map and the learning model based on seminar hall. The framework of the job knowledge map is constructed according to the post ability model which is based on professional knowledge, professional skills, and professional quality. The intelligent on-the-job learning model includes four elements: (i) learning platform, (ii) learning resources, (iii) learning methods, and (iv) learning evaluation. The learning portrait can record and visualize the information of learning, including content, activities, and effects.
[Retracted] Cluster‐Based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo‐Advisors
Authors: Chen Xiaofei , Ye Shujun , Huang Chao
Published: December 2021

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo‐advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning‐based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster‐based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision‐making of the next generation robo‐advisor assisted by artificial intelligence.
Transcriptome Analysis of Rat Lungs Exposed to Moxa Smoke after Acute Toxicity Testing
Authors: Xu Xiaoyu , Deng Wen , Zhang Wanqing , Zhang Junhua , Wang Muchen , Shan Si , Liu Hongning
Published: December 2021

The increasing use of moxibustion has led to a debate concerning the safety of this treatment in human patients. Inhalation of cigarette smoke induces lung inflammation and granulomas, the proliferation of alveolar epithelial cells, and other toxic effects; therefore, it is important to assess the influence of inhaled moxa smoke on the lungs. In the present study, a novel poisoning cabinet was designed and used to assess the acute toxicity of moxa smoke in rats. We evaluated pathological changes in rat lung tissue and analyzed differentially expressed genes (DEGs) using RNA-seq and transcriptomic analyses. Our results show that the maximum tolerable dose of moxa smoke was 290.036 g/m³ and LC50 was 537.65 g/m³. Compared with that of the control group, the degree of inflammatory cell infiltration in the lung tissues of group A rats (all dead group) was increased, while that in group E rats (all live group) remained unchanged. GO and KEGG enrichment analyses showed that the DEGs implicated in cell components, binding, and cancer were significantly enriched in the experimental groups compared with the profile of the control group. The expressions of MAFF, HSPA1B, HSPA1A, AOC1, and MX2 determined using quantitative real-time PCR were similar to those determined using RNA-seq, confirming the reliability of RNA-seq data. Overall, our results provide a basis for future evaluations of moxibustion safety and the development of moxibustion-based technology.
Changes of Endothelin-1 and Calcitonin Gene-Related Peptide Concentrations in Patients with Cervical Radiculopathy after Wrist-Ankle Acupuncture-Moxibustion and Hot Compression with Chinese Herbal Medicine
Authors: Li Wei , Yao Chi , Zhou Yanghong , Chen Sishi
Published: December 2021

Objective. This study aimed at investigating the effects of wrist-ankle acupuncture-moxibustion and hot compression with Chinese herbal medicine on pain symptoms, endothelin-1 (ET-1), and calcitonin gene-related peptide (CGRP) concentrations of patients with cervical radiculopathy (CR). Methods. A total of 82 patients with CR were randomly divided into the study group and control group, with 41 cases in each group. The control group was treated with standard treatment. In addition to standard treatment, the study group was additionally treated with wrist-ankle acupuncture-moxibustion and hot compression with Chinese herbal medicine. The ET-1 and CGRP concentrations in the plasma were measured by the radioimmunoassay method. Results. The total response rate in the study group and the control group was 97.55% and 82.93%, respectively. The study group showed lower scores of the visual analogue scale (VAS), Northwick Park Neck Pain Questionnaire (NPQ), McGill Pain Questionnaire (MPQ), numbness intensity assessment, and neck disability index (NDI) but higher scores of the Short Form-36 (SF-36) health survey questionnaire than the control group after treatment. Besides, the study group exhibited reduced ET-1 and substance P (SP) concentrations concomitant with increased CGRP and β-endorphin (β-EP) concentrations compared with the control group. Conclusion. Wrist-ankle acupuncture-moxibustion and hot compression with Chinese herbal medicine could effectively alleviate the pain of CR patients, affect ET-1 and CGRP concentrations, promote the recovery of cervical function, and improve the quality of life.
Electroacupuncture Alleviates Hyperalgesia by Regulating CB1 Receptor of Spinal Cord in Incisional Neck Pain Rats
Authors: Wang Junying , Zhang Jinling , Gao Yonghui , Chen Yu , Duanmu Chenglin , Liu Junling
Published: December 2021

Acupuncture therapy is effective in relieving postoperative pain of neck surgery, but its underlying mechanisms remain largely unknown. This study, in the incisional neck pain rat model, was designed to explore whether the endocannabinoid receptor 1 (CB1) in the cervical spinal cord is involved in the analgesic effect of electroacupuncture (EA) or not.The incisional neck pain model was established by making a longitudinal incision and applied EA treatment of Futu (LI18), Hegu-Neiguan (LI4-PC6), or Zusanli-Yanglingquan (ST36-GB34) for pain relief. The results showed that EA LI18 and EA LI4-PC6 effectively relieve allodynia caused by neck incision, which was obviously better than EA ST34-GB34 (

P

 < 0.05). After EA, the expression levels of CB1 mRNA at 4h in the EALI18 group, and 24 and 48h in both EALI18 and EALI4-PC6 groups, and those of CB1 protein at 4, 24, and 48h in the EALI18 group, and the immunoactivity of CB1 in both EALI18 and EALI4-PC6 groups at 4h were significantly upregulated in contrast to those of the model group (

P

 < 0.05). EA of either acupoint group had no effect on the expression of CB2 protein (

P

 > 0.05). Moreover, the antinociceptive effect of EA was reversed by AM251 (CB1 antagonist). Immunofluorescence dual staining showed that CB1 expressed in astrocytes in the superficial layer (laminae I and II) of dorsal horns of the cervical spinal cord. Therefore, the findings of this study revealed that upregulation of CB1 expression in the cervical spinal cord contributes to the analgesic effect of EA in incisional neck pain rats. The CB1 receptor expresses on astrocytes.
Naphthenic Acids: Formation, Role in Emulsion Stability, and Recent Advances in Mass Spectrometry-Based Analytical Methods
Authors: Facanali Roselaine , Porto Nathália de A. , Crucello Juliana , Carvalho Rogerio M. , Vaz Boniek G. , Hantao Leandro W.
Published: December 2021

Naphthenic acids (NAs) are compounds naturally present in most petroleum sources comprised of complex mixtures with a highly variable composition depending on their origin. Their occurrence in crude oil can cause severe corrosion problems and catalysts deactivation, decreasing oil quality and consequently impacting its productivity and economic value. NAs structures also allow them to behave as surfactants, causing the formation and stabilization of emulsions. In face of the ongoing challenge of treatment of water-in-oil (W/O) or oil-in-water (O/W) emulsions in the oil and gas industry, it is important to understand how NAs act in emulsified systems and which acids are present in the interface. Considering that, this review describes the properties of NAs, their role in the formation and stability of oil emulsions, and the modern analytical methods used for the qualitative analysis of such acids.
[Retracted] Novel Prediction Model for COVID‐19 in Saudi Arabia Based on an LSTM Algorithm
Authors: Alkhammash Eman H. , Algethami Haneen , Alshahrani Reem
Published: December 2021

The rapid emergence of the novel SARS‐CoV‐2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning‐based time‐series techniques are used to predict worldwide COVID‐19 cases for short‐term and medium‐term dependencies using adaptive learning. This study aimed to predict daily COVID‐19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID‐19 prevention measures. A fully connected deep neural network, long short‐term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID‐19 cases. Initially, data preprocessing and feature extraction were performed using COVID‐19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID‐19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID‐19 containment. This study also provides insights into the prevention of future outbreaks.
Significance of Hyperreflective Foci as an Optical Coherence Tomography Biomarker in Retinal Diseases: Characterization and Clinical Implications
Authors: Fragiotta Serena , Abdolrahimzadeh Solmaz , Dolz-Marco Rosa , Sakurada Yoichi , Gal-Or Orly , Scuderi Gianluca
Published: December 2021

Hyperreflective foci (HRF) is a term coined to depict hyperreflective dots or roundish lesions within retinal layers visualized through optical coherence tomography (OCT). Histopathological correlates of HRF are not univocal, spacing from migrating retinal pigment epithelium cells, lipid-laden macrophages, microglial cells, and extravasated proteinaceous or lipid material. Despite this, HRF can be considered OCT biomarkers for disease progression, treatment response, and prognosis in several retinal diseases, including diabetic macular edema, age-related macular degeneration (AMD), retinal vascular occlusions, and inherited retinal dystrophies. The structural features and topographic location of HRF guide the interpretation of their significance in different pathological conditions. The presence of HRF less than 30 μm with reflectivity comparable to the retinal nerve fiber layer in the absence of posterior shadowing in diabetic macular edema indicates an inflammatory phenotype with a better response to steroidal treatment. In AMD, HRF overlying drusen are associated with the development of macular neovascularization, while parafoveal drusen and HRF predispose to macular atrophy. Thus, HRF can be considered a key biomarker in several common retinal diseases. Their recognition and critical interpretation via multimodal imaging are vital to support clinical strategies and management.
Color Zero-Watermarking Algorithm for Medical Images Based on BEMD-Schur Decomposition and Color Visual Cryptography
Authors: Wu Deyang , Wang Miaomiao , Zhao Jing , Wang Jiayan , Zhong Meiyu , Zheng Haichun , Hu Sen , Tang Yong , Qu Changbo
Published: December 2021

With the widespread use of medical images in telemedicine, personal information may be leaked. The traditional zero-watermarking technology has poor robustness under large-scale attacks. At the same time, most of the zero-watermarking information generated is a binary sequence with a single information structure. In order to effectively solve the poor robustness problem of traditional zero-watermarking under large-scale attacks, a color zero-watermarking algorithm for medical images based on bidimensional empirical mode decomposition (BEMD)-Schur decomposition and color visual cryptography is proposed. Firstly, the color carrier image and the color copyright logo are decomposed into R, G, and B three color components, respectively, and the feature value of each sub-block are extracted by wavelet transform, BEMD decomposition, block operation, and Schur decomposition. Then, the R, G, and B components of the copyright logo are scrambled by Arnold scramble and converted into binary watermark information. Finally, a color visual cryptography scheme is proposed to generate two color shared images based on the carrier characteristics and copyright information. One shared image is used to generate a color zero-watermark, and the other is used for copyright authentication phase. Experimental results show that this algorithm has strong robustness and stability in resisting large-scale noise attacks, filtering attacks, JPEG compression, cropping attacks, and translation attacks at different positions. Compared with similar zero-watermarking algorithms, the robust performance is improved by about 10%, and it can adapt to more complex network environments.
Effect of Intermittent Fasting (18/6) on Energy Expenditure, Nutritional Status, and Body Composition in Healthy Adults
Authors: Ağagündüz Duygu , Acar-Tek Nilüfer , Bozkurt Osman
Published: December 2021

Objective. This study was aimed at evaluating the effect of intermittent fasting of Ramadan on resting energy expenditure (REE), body composition, and nutritional status. Methods. The study was conducted on a total of 27 adults (16 females, 11 males) who were fasting (18 h) in the Ramadan month (May 6–June 3) of 2019. REE was measured using the indirect calorimeter. Dietary energy and nutrient intakes were evaluated by 3-day food records in baseline and post-Ramadan. Body composition and some metabolic parameters were analyzed simultaneously with REE measurements. All measurements were performed two times at baseline, and post-Ramadan. Results. Body weight (−2.9% vs. −1.4%), body mass index (BMI) (−3.1% vs. −2.1%), fat-free mass (−2.7% vs. −1.4%), and hydration status were decreased in both males and females after the Ramadan fasting (p<0.05). REEs (kcal/d) of the participants were 1708.1 ± 262.50 kcal/d and 1596.5 ± 302.27 kcal/d at baseline and post-Ramadan, respectively (6.5%) (p<0.05). This decrease in REE (kcal/d) in females was greater than that in males (−8.1% vs. −4.6%). However, no statistically significant difference was found in sleep duration (h), physical activity levels, dietary energy and nutrient intakes, and blood pressures (mm Hg) of both genders compared to baseline (p>0.05). Conclusion. Intermittent circadian fasting may lead to a decreased energy expenditure and a change in fat-free mass in healthy individuals, and this effect is interpreted as gender-dependent.
Quality Evaluation of Online Mental Health Education Based on Reinforcement Learning in the Pandemic
Authors: Zhang Weifeng
Published: December 2021

The COVID-19 pandemic has become one of the biggest major health crises reported due its massive impact on many countries. From mental health experts, we know that we cannot lose sight of an equally alarming issue which is the long-term mental health impact the pandemic is going to leave on the society. The rapid spread of the pandemic gives little chance to prepare for or even process all that has happened in terms of job losses and the complete uprooting of everyday life and relationships. It is understandable that students may feel irritable, frustrated, or sad sometimes. Loneliness, confusion, and anxiety are also common, but the issue is how we can know if students’ emotions are a normal reaction to an abnormal situation. Therefore, online mental health education has become pretty important for students during the pandemic. Furthermore, it is important to evaluate the quality of online mental health education through microlessons. In this paper, based on Q-learning algorithm, the real-time adaptive bitrate (ABR) configuration parameters mechanism is proposed to detect the changes of network state constantly and select the optimal precalculated configuration according to the current network state. The simulation results show that the proposed algorithm based on Q-learning outperforms other baselines in average latency, average bitrate, and Mean Opinion Score (MOS) on Chrome DevTools and Clumsy. Meanwhile, the experimental results also reveal that the average number of identified mental health problems of the proposed mechanism has always been the best with the bandwidth from 10 Mbit/s to 500 Mbit/s.
Hydromechanical Simulation of Tunnel Excavation in Rock Considering a Nearby Karst Cave
Authors: Zhao Huiling , Zhang Fan , Yao Xupeng
Published: December 2021

Tunnel excavation tends to be affected by karst cavities in karst areas. Some cavities that are at low risk of causing safety issues without treatment tend to be ignored in the design and construction of tunnels to reduce costs. It is necessary to gain a better understanding of the effect of such a cavity on the seepage around a tunnel, the deformation of the surrounding rock, and the stress of the tunnel lining. In this paper, a two-dimensional rock-tunnel hydromechanical model with a karst cave was established with FLAC3D finite difference software to simulate the tunnel excavation with the consideration of seepage. Numerical simulations were performed to analyze the deformation of the surrounding rock, the seepage field of the surrounding rock, and the stress of the tunnel lining, and the results were compared for scenarios when the karst cave is at different locations relative to the tunnel. These results can provide a reference for the design and construction of tunnel engineering in rock with karst caves.
The Orchestrating Role of Carbon Subsidies in a Capital-Constrained Supply Chain
Authors: Song Wen , Ren Ai , Li Xiaodong , Li Qi
Published: December 2021

In this paper, we investigate the role of carbon subsidies in a capital-constrained supply chain. We analyze two green technology investment structures in such supply chains: one where the manufacturer determines the optimal carbon emission abatement level (MI-structure) and one where the retailer determines the optimal carbon emission abatement level (RI-structure). As the leader (the powerful participant or the first mover in a supply chain), the manufacturer may choose the investment structure that is most favorable to them. Our major findings are as follows: (1) carbon subsidies can improve the performance of a centralized green supply chain; (2) there exists a threshold value of carbon subsidy that determines the manufacturer’s choice of the best carbon emission abatement investment structure, but the retailer always benefits from RI-structure; and (3) the traditional cost-sharing contract fails to achieve green supply chain coordination. However, as an orchestrator, the carbon subsidy plays a crucial role in achieving quantity coordination when implemented alongside traditional cost-sharing contracts. Furthermore, using a parameter of side-payment, we propose a new contract design that facilitates win-win coordination.
Wireless Resource Management and Resilience Optimization of the M2M‐Oriented Mobile Communication System
Authors: Liao Li , Ji Chengjun
Published: December 2021

Machine‐to‐machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low‐energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results.
Desafíos de la educación en odontología
Authors: Isbej Esposito Lorena , Cantarutti Martínez Cynthia , Carrasco-Labra Alonso , Hassi Thumala José , García-Huidobro Kirberg Rosario , Ortuño Borroto Duniel , Oyarzo Paredes Natacha , Naranjo Camilla Claudia , Véliz Paiva Claudia
Published: December 2021

Introducción: los procesos de enseñanza aprendizaje en odontología deben adaptarse permanentemente a los cambios sociales, demográficos, científicos y tecnológicos, con el fin de formar profesionales con un perfil de egreso basado en conocimientos, habilidades y actitudes integrales, conectados con la realidad y necesidades de la sociedad, que impacten positivamente en el sistema de salud. El propósito de esta revisión narrativa fue analizar los principales desafíos de la educación en odontología reportados en la literatura y proponer acciones concretas para abordarlos. Métodos: a través de un método de consenso, un panel de nueve expertos seleccionó las principales áreas donde se presentan estos desafíos, para luego realizar una búsqueda de información científica en inglés y español, consultando las bases de datos MEDLINE vía PubMed,  ERIC (EBSCO), Scopus,  SciELO y Google Scholar. Resultados: los cuatro desafíos para la educación en odontología analizados fueron: responsabilidad social de la educación, perfil de egreso de en diferentes contextos, metodologías de enseñanza aprendizaje y sistemas de evaluación, procesos de titulación y certificación. Conclusiones: las acciones que sustentan los principales cambios requeridos a nivel nacional e internacional en la educación en odontología necesitan el apoyo transversal de organizaciones gubernamentales, científicas, sociales y de las propias instituciones de educación superior, a través de un trabajo colaborativo. Este camino es vital para lograr la formación de cirujanos dentistas que serán reales agentes de cambio, preparados integralmente para insertarse en un sistema que garantice la calidad, seguridad, efectividad y equidad en salud bucal.
Challenges of implementing competency-based medical education postgraduate training programs: the issue of context
Authors: Gomes Marcio , Snell Linda
Published: December 2021

Introduction: Competency-based medical education (CBME) is being adopted worldwide. The aim of this paper is to discuss the evolution of CBME and address some perceived challenges in CBME curriculum development and implementation in postgraduate (residency) medical education. Methods: This is an opinion paper based on lived experiences and personal beliefs. The authors have professional training in medical education and are actively involved in CBME research, curriculum development and implementation around the world. Results: The issue of local and system-wide context seems to be of particular importance to individuals, programs, institutions, governing bodies and other stakeholders involved in the development and implementation of CBME programs. CBME has evolved differently at different places, and there are concerns regarding the fidelity of implementation. Stakeholders have been dealing with challenging questions in their CBME journeys, which reflect the varied, complex and dynamic nature of health and education systems. Recently, scholars have established core components of any CBME program. Discussion and conclusions: CBME design should benefit from ground-up strategies that consider the local context. It is essential to approach implementation with a quality improvement lens and pay special attention to the fidelity and integrity of the core CBME components.
Perspective and the Past: Modeling Historical Representation from Camera Obscura to Virtual Reality
Authors: Kazlauskaitė Rūta
Published: December 2021

This article examines the concept of “perspective” as an embodied metaphor with ontological and epistemological implications for the modeling of historical understanding of contested pasts. The metaphors employed in modeling past reality shape how we make sense of the controversial past. In particular, I explore how perspectival metaphorical models conjure the notions of presence/proximity/engagement and absence/distance/detachment. To open this up, the paper juxtaposes two distinct models of seeing and knowing as sources of embodied metaphor: 1) static and distancing optical metaphors of cognition, and 2) a “post-cognitive” (i.e., enactive, embodied and dynamic) process of interaction. I argue that the shift towards the affective, experiential and immersive forms of engagement in historical representation is indicative of the growing importance of dynamic, embodied and interactive features in mediated models of the past. The paper concludes by discussing the implications of virtual reality as an emergent medium that offers a new way of modeling the past. Despite its novelty, however, virtual reality raises age-old questions about the dynamics of engagement and detachment in historical understanding.
Records of Anthicidae (Insecta: Coleoptera) from Xinjiang Uyghur Autonomous Region, China, with new descriptions and records
Authors: Telnov Dmitry
Published: December 2021

New data on the Anthicidae (ant-like flower beetles) from Xinjiang Uyghur Autonomous Region are presented. Three new species are described and illustrated, namely Anthicus (s. str.) lobanovi sp. n., Nitorus uyghur sp. n. and Stricticomus kucha sp. n. Anthicus laetus L.Medvedev, 1974 is redescribed. Four new regional records are presented.
Revolutionary Christian Attitudes toward Women and Family in Late Qing and Republican-Era China
Authors: Stasson Anneke H.
Published: December 2021

In the late 1800s and early 1900s, Chinese Christian familial ideals were traditional and revolutionary at the same time. They were traditional in wanting to preserve some role for parents in forming the marriages of their children and in seeing wives as primarily responsible for the care of children. But Christians were revolutionary in encouraging women to develop their personalities and work outside the home. They advocated women’s education and associated education with women’s empowerment and independence. Christians taught that marriage should be based on love and that daughters were just as important as sons, even if they chose to be single. Singleness, spouse self-selection, prioritizing the husband-wife relationship over the parent-child relationship, and pursuing a companionate model of marriage were all ways that Christians helped revolutionize familial ideals in China.