bluuebunny/crossref_metadata_2025 huggingface dataset:
Noble Design for Highly Active and Stable PEM Electrocatalyst Using Synergistic Interaction between Pt, TiO2 and Carbons
Authors: Ji Yunseong , Jeon Yukwon , Cho Yongil , Lee Chan Min , Hwang Ho Jung , Jeon Oksung , Kwon Oh Chan , Kim Jeong Pil , Shul Yong Gun
Published: January 2022

As interest in climate change has increased in recent years, research on alternative energy sources is becoming more active. Fuel cells are one of the most promising electrochemical energy conversion devices that hold many advantages. In particular, polymer electrolyte fuel cells (PEMFCs) are attracting much attention due to their high power density and relatively good portability. In PEMFC systems, the main cost is due to the catalyst in terms of high cost of platinum group metals. This derives the need to understand the way how to get a highly active and stable electrocatalysts.

In general, Pt/C is most widely used catalyst because it has a large surface area for platinum anchoring, high electrical conductivity, and suitable pore structure for transferring reactant and product. However, carbon support undergoes carbon corrosion which is happened thermodynamically favored in fuel cell operation conditions. This phenomenon occurs at the surface of the carbon and it makes void places under the Pt nanoparticles. This mechanism causes losing electrochemical active sites. And there are also agglomeration and sintering of Pt particles on the carbon surface. Consequentially, carbon corrosion and platinum dissolution result in reduced electrochemical surface area (ECSA) and interferes long-term operations. Thus, electrochemically stable support materials need to be developed.

In various alternative material candidates, our group selected the TiO2 based support because it exhibits excellent chemical stability under severe acidic condition and offers the opportunity to modifying electronic structure of platinum particles. However, hindrances such as low electrical conductivity, catalytic activity, and surface area limit the direct use of TiO2 as a catalyst support. To remedy these drawbacks, we have designed carbon-titania composite support based electrocatalysts. These noble TiO2 – carbon hybrid support was synthesized from electrospun nanofibers and platinum nanoparticles are deposited on a support by microwave-assisted polyol process. Through our new approaches, we can obtain the electrocatalysts which have a unique 3D pore structure with higher surface area, modified electronic structure with increased intrinsic activity and extended cyclic durability.
Shining Light on the Nervous System: From Biomaterials to Bioelectronics
Authors: Tang Jing
Published: January 2022

The dichotomy between advanced materials and brain has driven the curiosity of scientists to explore the wonders of the brain, as well as motivated the continued innovations of novel technologies based on advances in materials science and engineering to understand the brain. To improve treatments of brain-related diseases will require new tools and methods to map and to repair the brain with precision and biocompatibility. Current treatments of pain heavily rely on opioids, resulting in significant side effects such as addiction, tolerance, leading to the Opioid Overdose Crisis as we know of today. Smart drug delivery systems may provide an effective solution. Here I present the development of polymer-based externally-triggerable drug delivery systems for on-demand, repeatable and adjustable local anesthesia, where the timing, duration, and intensity of nerve block can be controlled through external energy triggers such as light. In addition to the new pharmacological approaches, bioelectronic platforms to enhance our insights into the eye and will also be discussed.

The restoration of light response with complex spatiotemporal features in retinal degenerative diseases towards retinal prosthesis has proven to be a considerable challenge over the past decades. Herein, inspired by the structure and function of photoreceptors in retinas, I develop artificial retina based on gold nanoparticle-decorated titania nanowire arrays, for restoration of visual responses in the blind mice with degenerated photoreceptors. Green, blue and near UV light responses in the retinal ganglion cells (RGCs) are restored with a spatial resolution better than 100 µm. ON responses in RGCs are blocked by glutamatergic antagonists, suggesting functional preservation of the remaining retinal circuits. Moreover, neurons in the primary visual cortex respond to light after subretinal implant of nanowire arrays. Improvement in pupillary light reflex suggests the behavioral recovery of light sensitivity. My study will shed light on the development of a new generation of optoelectronic toolkits for subretinal prosthetic devices. Through pharmacological, optical, and electrical toolsets, I aim to develop effective therapeutic solutions to neurological disease states. These results, along with a discussion of future neural interfaces, aim to improve our understanding of the nervous system and to inform new therapeutic approaches for biomaterials and bioelectronics.
Effects of Chlorpyrifos Exposure on Acetylcholine Metabolism across a Model Blood-Brain Barrier
Authors: McClain Ethan S. , Miller Dusty R. , Brown Jacquelyn A , Wikswo John P , Cliffel David E.
Published: January 2022

Organophosphate (OP) compounds, used throughout the agricultural industry as insecticides, are known to directly and irreparably alter brain function in humans. Exposure to OPs decreases acetylcholinesterase activity and leads to a buildup of acetylcholine, with chronic exposure to sub-lethal levels inducing neuropathy. This buildup of acetylcholine can be monitored through electrochemical methods to study the effects of OP toxicity. The microclinical analyzer (µCA), an in vitro microfluidic device allowing for electrochemical analysis using a screen-printed electrode, can be modified with enzymes to detect acetylcholine. Using the µCA in combination with the neurovascular unit (NVU), an organotypic model of the blood-brain barrier (BBB), can provide a better understanding of the BBB forms, functions, and responds to insults. The NVU supports all the cell types necessary for proper BBB formation (endothelial cells, astrocytes, pericytes, and neurons) and provides the flow-created shear forces for mature tight junction formation. The µCA and NVU were used study the effects of chlorpyrifos on acetylcholine concentrations present across the BBB. Understanding the effects of OP like chlorpyrifos on neurotoxicity can contributes to the assessment and treatment of chronic and acute exposure and inform policy decisions around the uses of OP pesticides in the agricultural industry.
Altered IGF-I activity and accelerated bone elongation in growth plates precede excess weight gain in a mouse model of juvenile obesity
Authors: Machnicki Allison L. , White Cassaundra A. , Meadows Chad A. , McCloud Darby , Evans Sarah , Thomas Dominic , Hurley John D. , Crow Daniel , Chirchir Habiba , Serrat Maria A.
Published: January 2022

This paper validates a mouse model of linear growth acceleration in juvenile obesity. We demonstrate that high-fat diet induces rapid increases in bone elongation rate that precede excess weight gain and parallel pediatric growth. By imaging IGF-I delivery to growth plates in vivo, we reveal novel diet-induced changes in IGF-I uptake and activity. These results are important for understanding the sequelae of musculoskeletal complications that accompany advanced bone age and obesity in children.
Task-dependent neural control of regions within human genioglossus
Authors: Yeung Jade , Burke Peter G. R. , Knapman Fiona L. , Patti Jessica , Brown Elizabeth C. , Gandevia Simon C. , Eckert Danny J. , Butler Jane E. , Bilston Lynne E.
Published: January 2022

During swallowing, we observed two distinct, stereotyped muscle activation patterns that define the horizontal (monophasic, maximal EMG) and oblique (biphasic, submaximal EMG) neuromuscular compartments of genioglossus. In contrast, volitional tongue protrusions produced uniform activation across compartments. This provides evidence for task-dependent, functionally discrete neuromuscular control of the oblique and horizontal compartments of genioglossus. The magnitude and temporal patterning of genioglossus EMG during swallowing may help guide electrode placement in tongue EMG studies.
Effects of time-controlled adaptive ventilation on cardiorespiratory parameters and inflammatory response in experimental emphysema
Authors: Oliveira Milena Vasconcellos de , Magalhães Raquel Ferreira de , Rocha Nazareth de Novaes , Fernandes Marcus Vinicius , Antunes Mariana Alves , Morales Marcelo Marcos , Capelozzi Vera Luiza , Satalin Joshua , Andrews Penny , Habashi Nader M. , Nieman Gary , Rocco Patricia Rieken Macedo , Silva Pedro Leme
Published: January 2022

The TCAV method reduces lung damage in ARDS. However, so far, no study has evaluated the impact of the TCAV method on ventilator-induced lung injury and cardiac function in experimental emphysema. The TCAV method at EFT/EPF ratio of 25%, compared with EFT/EPF of 75% (frequently used in ARDS), reduced lung inflammation, alveolar heterogeneity and hyperinflation, and pulmonary arterial hypertension in elastase-induced emphysema. TCAV may be a promising and personalized ventilation strategy for patients with emphysema.
Circulating long-chain acylcarnitine concentrations are not affected by exercise training in pregnant women with obesity
Authors: Allman Brittany R. , Spray Beverly J. , Lan Renny S. , Andres Aline , Børsheim Elisabet
Published: January 2022

This research showed that a moderate-intensity prenatal exercise program, consisting of aerobic and resistance training, did not negatively impact normal alterations in substrate supply and demand for the mother and the offspring throughout gestation. Findings provide support for metabolic safety of exercise during pregnancy.
Rationalizing Denominators Using Gröbner Bases
Authors: Li Dongmei , Wu Man , Liu Jinwang , Gao Yiman
Published: January 2022

The problem of rationalizing denominators for two types of fractions is discussed in the paper. By using the theory and algorithms of Gröbner bases, we first introduce a method to rationalize the denominators of fractions with square root and cube root, and then, for the denominators with higher radical of the general form, the problem of rationalizing denominators is converted into the related problem of finding the minimal polynomials. Some interesting results and an executable algorithm for rationalizing the denominator of these type fractions are presented. Furthermore, an example is also established to illustrate the effectiveness of the algorithm.
Excess Weight and Body Fat Percentage Associated with Waist Circumference as a Cardiometabolic Risk Factor in University Students
Authors: Nuñez-Leyva Roberto E. , Lozano-López Tabita E. , Calizaya-Milla Yaquelin E. , Calizaya-Milla Sergio E. , Saintila Jacksaint
Published: January 2022

Background. Obesity is one of the most important public health problems for university students. The objective of the study was to evaluate the association between body mass index (BMI) and body fat percentage (%BF) with waist circumference (WC) as a cardiometabolic risk factor (CMR) among university students. Methods. A cross-sectional study was carried out in 2,048 students from a private university located in Lima, Peru. Anthropometric data (weight, height, %BF, and WC) were collected. Chi-square test was used. Association analysis was performed using multiple logistic regression. Results. The findings indicated that 36.9% and 61.1% of men were overweight and had higher %BF, respectively, compared to women. Women (OR, 0.22; 95% CI, 0.17, 0.29), Peruvian students (OR, 0.59; 95% CI, 0.39, 0.90), and students enrolled in the faculty of health sciences (OR = 0.76; 95% CI, 0.62, 0.94) are less likely to manifest CMR. Also, excess body weight (OR, 17.28; 95% CI, 13.21, 22.59) and a high %BF (OR, 4.55; 95% CI, 3.55, 5.84) were strongly associated with CMR. Conclusion. CMRs are a public health problem among university students. Therefore, it is important to carry out healthy lifestyle programs to promote better control and prevention, particularly among male students and those who have excess weight and body fat.
Calculation and Influencing Factors of the Operating Efficiency of Chinese Airports under High‐Quality Development
Authors: Shang Meng , Jia Chunjie , Li Hui , Liu Yu , Cao Junwei
Published: January 2022

Based on the cross‐sectional data of 40 large‐scale international airports in China in 2019, this paper introduced natural and social factors into the calculation of airport operating efficiency and constructed a BCC‐SFA‐BCC‐Tobit 4‐stage analysis framework. Combining with the connotation of the “green, smart, humane, and safe” airport high‐quality development, the paper applied the Tobit model to quantitatively analyze the influencing factors of the operating efficiency of airports, and the research shows that (1) the overall operating efficiency of the study airports is 0.734; in terms of the region, the operating efficiency of airports located in southwest and northwest regions is greatly affected by natural factors, while the operating efficiency of airports located in North China is mostly affected by social factors. (2) Under the perspective of human‐earth relationship, the impact of natural factors on the operating efficiency of airports is greater than that of social factors, wherein PM2.5 and relative humidity have negative impact, while the actual foreign capital utilization of the city and domestic and foreign tourists have positive impact. (3) The regression results of the Tobit model indicate that green, smart, and safe indicators can promote the high‐quality development of airports, while the humane indicator restrains the high‐quality development of airports.
Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges
Authors: Hassan Shahzad , Tariq Noshaba , Naqvi Rizwan Ali , Rehman Ateeq Ur , Kaabar Mohammed K. A.
Published: January 2022

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.
Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization
Authors: Liu Jing-Jing , Liu Jian-Chao
Published: January 2022

High-precision permeability prediction is of great significance to tight sandstone reservoirs. However, while considerable progress has recently been made in the machine learning based prediction of reservoir permeability, the generalization of this approach is limited by weak interpretability. Hence, an interpretable XGBoost model is proposed herein based on particle swarm optimization to predict the permeability of tight sandstone reservoirs with higher accuracy and robust interpretability. The porosity and permeability of 202 core plugs and 6 logging curves (namely, the gamma-ray (GR) curve, the acoustic curve (AC), the spontaneous potential (SP) curve, the caliper (CAL) curve, the deep lateral resistivity (RILD) curve, and eight lateral resistivity (RFOC) curve) are extracted along with three derived variables (i.e., the shale content, the AC slope, and the GR slope) as data sets. Based on the data preprocessing, global and local interpretations are performed according to the Shapley additive explanations (SHAP) analysis, and the redundant features in the data set are screened to identify the porosity, AC, CAL, and GR slope as the four most important features. The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. The prediction results of the PSO-XGBoost model indicate a superior performance compared with that of the benchmark XGBoost model. In addition, the reliable application of the interpretable PSO-XGBoost model in the prediction of tight sandstone reservoir permeability is examined by comparing the results with those of two traditional mathematical regression models, five machine learning models, and three deep learning models. Thus, the interpretable PSO-XGBoost model is shown to have more advantages in permeability prediction along with the lowest root mean square error, thereby confirming the effectiveness and practicability of this method.
circRNA LDLRAD3 Enhances the Malignant Behaviors of NSCLC Cells via the miR-20a-5p-SLC7A5 Axis Activating the mTORC1 Signaling Pathway
Authors: Li Yu , Qin Guangle , Du Jinyun , Yue Peng , Zhang Yanling , Hou Na
Published: January 2022

Circular RNA LDLRAD3 behaved as an oncogene in several malignancies, but its effects in NSCLC and the involvement of downstream molecules and activation of signaling pathways had not been fully reported. We planned to explore how LDLRAD3 facilitated the malignancy of NSCLC. QRT-PCR was performed to evaluate the expression levels of LDLRAD3, miR-20a-5p, and SLC7A5 in NSCLC tissues and cells. si-LDLRAD3 was transfected to A549 and H1299 cells to knock down intrinsic LDLRAD3 to determine its oncogenic roles. CCK-8 assay and transwell assay were executed to assess cell proliferative, migrative, and invasive abilities. Dual-luciferase reporter (DLR) assay was manipulated to verify the ENCORI-predicted relationships between LDLRAD3 and miR-20a-5p and between miR-20a-5p and SLC7A5. Western blot, immunofluorescent assay, and immunohistochemistry were applied to explore the expression levels of SLC7A5, and the levels of mTORC1 pathway-related proteins were evaluated using western blot. Rescue experiments were conducted by transfecting si-LDLRAD3, miR-20a-5p inhibitor, and si-SLC7A5 to explore the influence of the LDLRAD3-miR-20a-5p-SLC7A5 axis on the malignant behaviors of NSCLC cells. The expression levels of LDLRAD3 and SLC7A5 were boosted, whereas miR-20a-5p was impeded in NSCLC tissues and cell lines. Knockdown of LDLRAD3 weakened the proliferation, migration, and invasion of A549 and H1299 cells. LDLRAD3 was verified to sponge miR-20a-5p and miR-20a-5p targeted SLC7A5. LDLRAD3 activated the mTORC1 singling pathway via the miR-20a-5p-SLC7A5 axis to strengthen the malignant properties of A549 and H1299 cells. We concluded that LDLRAD3 exerted oncogenic effects via the miR-20a-5p-SLC7A5 axis to activate the mTORC1 signaling pathway in NSCLC. Our findings enlightened that LDLRAD3 could become a potential therapeutic target in the treatment and management of NSCLC.
Cashew Apples in Ghana: Stakeholders’ Knowledge, Perception, and Utilization
Authors: Akyereko Yaw Gyau , Wireko-Manu Faustina Dufie , Alemawor Francis , Adzanyo Mary
Published: January 2022

Cashew (Anacardium occidentale), a crop projecting Ghana internationally beside cocoa, is usually cultivated for its nut, for both local and international markets. The cashew apple is underutilized in many African countries. This study is aimed at determining the knowledge, perception, and utilization of cashew apples in Ghana among stakeholders in the cashew value chain. Results from the study showed that the cashew industry consisted of farmers (89.1%), nut buyers (6.8%), extension officers (3.5%), and processors (0.6%) with uneven distribution of males (66.2%) and females (33.8%). Cashew apple utilization was low (<10%), though 84.37% had in-depth knowledge on the health benefits and value-added products made from the apples. Cashew apple is mainly utilized as fresh fruits or juice, with minor uses as an ingredient in food preparation, animal feed formulation, and production of mushroom, weedicide, ethanol, and manure. The cashew apple processors identified high cost of processing equipment, perishability of apples, lack of capital, market, technical know-how, and government support as challenges. Based on these findings, education of the stakeholders on value addition or processing of the apples at household and industrial levels and provision of stimulus packages to private processors are recommended to maximize cashew apple utilization in Ghana.
Influence of Lower Extremity Deep Venous Thrombosis in Cerebral Infarction on Coagulation Index and Thromboelastogram and Its Risk Factors
Authors: Jin Tao , Jiang Lingkai , Zhang Xiaolei
Published: January 2022

Cerebral infarction is a serious brain injury disease, which is mainly caused by the blockage of blood circulation in patients’ brains; thus, the patient’s brain appears ischemia and hypoxia state, and large-scale nerve cell death occurs immediately. The aim of this study was to explore the influence of lower extremity deep venous thrombosis (LEDVT) on coagulation indexes and thromboelastogram (TEG) after cerebral infarction. Altogether, 67 patients with cerebral infarction complicated with LEDVT in our hospital from April 2017 to August 2019 were collected as the observation group (OG) and 58 patients with cerebral infarction without lower extremity deep venous thrombosis as the control group (CG). The R, K, angle, and MA values in PT, APTT, TT, FIB, and TEG indexes were compared between the two groups. The ROC curve was applied to analyze the diagnostic value of R value, K value, angle value, and MA value in the occurrence of LEDVT in patients with cerebral infarction. Logistic regression analysis was applied to analyze the independent risk factors of lower extremity deep venous thrombosis in cerebral infarction. PT, APTT, and TT in the OG were evidently lower than those in the CG, while FIB in the OG was evidently higher than that in the CG, R value and K value of the OG were evidently lower than those of the CG, and angle and MA values were higher than those in the CG. The AUC of R value, K value, angle value, and MA value of the ROC curve of LEDVT in patients with cerebral infarction was 0.735, 0.713, 0.790, and 0.819. Multivariate analysis showed that high FIB, angle, and MA were risk factors, while R and K values were protective factors. PT, APTT, and TT are lower and FIB is higher in patients with cerebral infarction with LEDVT. TEG has a certain diagnostic value. FIB value, angle value, and MA value are independent risk factors of LEDVT in patients with cerebral infarction, while R value and K value are protective factors.
A New Transfer Learning Ensemble Model with New Training Methods for Gear Wear Particle Recognition
Authors: Zhao Chunhua , Lin zhangwen , Tan Jinling , Hu Hengxing , Li Qian
Published: January 2022

Aiming at solving the acquisition problems of wear particle data of large-modulus gear teeth and few training datasets, an integrated model of LCNNE based on transfer learning is proposed in this paper. Firstly, the wear particles are diagnosed and classified by connecting a new joint loss function and two pretrained models VGG19 and GoogLeNet. Subsequently, the wear particles in gearbox lubricating oil are chosen as the experimental object to make a comparison. Compared with the other four models’ experimental results, the model superiority in wear particle identification and classification is verified. Taking five models as feature extractors and support vector machines as classifiers, the experimental results and comparative analysis reveal that the LCNNE model is better than the other four models because its feature expression ability is stronger than that of the other four models.
Mechanical Properties and Deterioration Instability of Filling Body with Gangue-Cement Material Based on AE Experiment
Authors: Yu Guofeng , Han Yunchun , Yu Xiaoyang , Bao Ren , Guo Jiaxing , Feng Qingbo
Published: January 2022

Gangue materials have been used to solve mine disasters with a support tunnel along the goaf and filling mining. Mastering the properties and damage characteristics of filling materials is an important basis for effective implementation. Based on the conventional uniaxial compression acoustic emission (AE) test, the effects of cementitious materials, ratio between water and cementitious material, gangue particle size, and grading parameters on the mechanical properties of gangue-cement samples were analyzed. The stage characteristics of compression deformation were studied. The fracture propagation characteristics and rock mass failure types induced by different graded gangues were revealed. The fracture forming mechanism from clustered damage and failure was interpreted. The results show that the compressive strength of the backfill increases with the increase of cementitious material; however, it decreases with the increase of water binder ratio. Controlling the proportion and dosage of materials was the key factor to realizing pumpability and stability. Combined with the deformation and AE characteristics, the failure stage of the backfill body is divided into three stages: linear deformation-low energy changing, block compression-high energy changing, and gentle stability-stable energy changing. Affected by the gangue distribution, the load in each stage will induce fracture to produce five distribution modes of single, turning, breakthrough, bifurcated, and collapsed surrounding gangue. In the process of loading failure, different gradation and particle sizes will also change its stress concentration characteristics, resulting in the transformation of rock failure types. The surface structure and roughness of gangue play an important role in the compressive performance of cement paste. The research results try to provide some guidance for efficient filling mining.
Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network
Authors: Ma Baoliang , Zhang Yuzhu , Ma Lixing
Published: January 2022

Calcium complex ferrate is an ideal binder phase in the sintered ore phase, and a detailed study of the whole process of calcium complex ferrate generation is of great significance to improve the quality of sintered ore. In this paper, we first investigated calcium ferrate containing aluminum (CFA), which is an important precursor compound for the generation of complex calcium ferrate (SFCA), followed by a series of composite calcium ferrate generation process phase XRD detections and data preprocessing of data. Data correlation and data fitting analysis were combined with composite calcium ferrite phase diagram energy spectrum analysis to obtain the effect of MgO and Al2O3 on the formation of composite calcium ferrite. Then a modified RBF neural network model using the resource allocation network algorithm (RAN) was used to predict the generation trend of complex calcium ferrate. The parameters of the neural network are optimized with the Dragonfly algorithm, compared with the traditional RBF neural network. The prediction accuracy of the improved algorithm was found to be higher, with a prediction result of 97.6%. Finally, the predicted results were based on comparative metallurgical experimental results and data analysis. The validity and accuracy of the findings in this paper were verified.
Durability Prediction Method of Concrete Soil Based on Deep Belief Network
Authors: Tian Xiao , Zhu Niankun
Published: January 2022

To truly reflect the durability characteristics of concrete subjected to multiple factors under complex environmental conditions, it is necessary to discuss the prediction of its durability. In response to the problem of durability prediction of traditional concrete structures, there is a low prediction accuracy, and the predicted time is long, and a concrete structural durability prediction method based on the deep belief network is proposed. The influencing factors of the concrete structural durability parameters are analyzed by two major categories of concrete material and external environmental conditions, and the transmission of chloride ions in the concrete structure is described. According to the disconnection of the steel bars, the durability of the concrete structure is started, and the determination is determined. The concrete structural antiflexural strength, using a deep belief network training concrete structural antiflexural strength judgment data, constructs a concrete structural durability predictive model and completes the durability prediction of the concrete structure based on the deep belief network. The proposed prediction method based on the deep belief network has a high prediction accuracy of 98% for the durability of concrete column structures. The simulation results show that the concrete structural durability’s prediction accuracy is high and the prediction time is short. The prediction of concrete durability discussed here has important guiding significance for the improvement of concrete durability test methods and the improvement of concrete durability evaluation standards in China.
Whole-Genome Sequencing Revealed a Late-Maturing Isogenic Rice Koshihikari Integrated with Hd16 Gene Derived from an Ise Shrine Mutant
Authors: Tomita Motonori , Tokuyama Ryotaro , Matsumoto Shosuke , Ishii Kazuo
Published: January 2022

We identified the key genes controlling the late maturation of the Japonica cultivar Isehikari, which was found at Ise Jingu Shrine and matures 6 days later than Koshihikari. We conducted a genetics-based approach through this study. First, the latest mature plants, which flowered later than Isehikari, were segregated in the F2 and F3 generations of Koshihikari×Isehikari. Next, the linkage relationship of a single late-maturing gene with the SSR markers on the long arm of chromosome 3 was inferred by using late-maturing homozygous F2 segregants. Moreover, genetic analyses of late maturity were conducted through the process of six times of continuous backcross with Koshihikari as a recurrent parent by using the late-maturing homozygous F3 line as a nonrecurrent parent, thus developing a late-maturing isogenic Koshihikari (BC6F2). As a result, we elucidated a single late-maturing gene with incomplete dominance that caused the 14-day maturation delay of Koshihikari. The whole-genome sequencing was conducted on both of Koshihikari and the late-maturing isogenic Koshihikari. Then, the SNP call was conducted as the reference genome of Koshihikari. Finally, a single SNP was identified in the key gene Hd16 of the late-maturing isogenic Koshihikari.
Antibacterial and Antioxidant Effects of Magnesium Alloy on Titanium Dental Implants
Authors: Bai Yang , Wang Lin , Zhao Lisheng , Lingling E. , Yang Shuo , Jia Shunyi , Wen Ning
Published: January 2022

Objectives. In this study, a new type of dental implant by covering the surface of the titanium (Ti) implant with zinc-magnesium (Zn-Mg) alloy was designed, to study the antibacterial and antioxidant effects of Mg alloy on titanium (Ti) implants in oral implant restoration. Methods. Human gingival fibroblasts (HGFs), S. sanguinis, and F. nucleatum bacteria were used to detect the bioactivity and antibacterial properties of Mg alloy-coated Ti implants. In addition, B6/J mice implanted with different materials were used to further detect their antibacterial and antioxidant properties. Results. The results showed that Mg alloy could better promote the adhesion and proliferation and improve the alkaline phosphatase (ALP) activity of HGFs, which contributed to better improved stability of implant osseointegration. In addition, Mg alloy could better inhibit the proliferation of S. sanguinis, while no significant difference was found in the proliferation of F. nucleatum between the two implants. In the mouse model, the peripheral inflammatory reaction and oxidative stress of the Mg alloy implant were significantly lower than those of the Ti alloy implant. Conclusions. Zn-Mg alloy-coated Ti implants could better inhibit the growth of Gram-positive bacteria in the oral cavity, inhibit oxidative stress, and facilitate the proliferation activity of HGFs and the potential of osteoblast differentiation, thus, better increasing the stability of implant osseointegration.
MiR-223 Promotes Tumor Progression via Targeting RhoB in Gastric Cancer
Authors: Hu You , Yi Bin , Chen Xin , Xu Lu , Zhou Xiaojun , Zhu Xinguo
Published: January 2022

Gastric cancer (GC) is among the most prevalent causes of cancer-related death globally. MiR-223 has been implicated in a variety of cellular mechanisms linked to cancer progression. However, the miR-223 expressions and its function in GC are unknown. We discovered that miR-223 expression was raised in GC tissues in comparison with nearby normal tissues in this investigation. Additionally, multiplied miR-223 expression was strongly linked with TNM stage (

p
=
0.022

), live metastasis (

p
=
0.004

),lymph node metastasis (

p
=
0.004

),and Borrmann type and was associated with an unfavorable prognostic for patients with GC. Furthermore, suppressing miR-223 significantly increased cell death and prevented cell migration and invasion in vitro. Additionally, miR-223 silencing decreased tumor development in vivo. Additionally, we discovered that miR-223 enhanced GC development by specifically targeting RhoB. In summary, our findings reveal that miR-223 increases tumor progression in GC by targeting RhoB, suggesting that it could serve to be a potential biomarker for the prediction of the disease.
Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning
Authors: Zeng Yan , Wang Xin , Yuan Junfeng , Zhang Jilin , Wan Jian
Published: January 2022

Federated learning is a new framework of machine learning, it trains models locally on multiple clients and then uploads local models to the server for model aggregation iteratively until the model converges. In most cases, the local epochs of all clients are set to the same value in federated learning. In practice, the clients are usually heterogeneous, which leads to the inconsistent training speed of clients. The faster clients will remain idle for a long time to wait for the slower clients, which prolongs the model training time. As the time cost of clients’ local training can reflect the clients’ training speed, and it can be used to guide the dynamic setting of local epochs, we propose a method based on deep learning to predict the training time of models on heterogeneous clients. First, a neural network is designed to extract the influence of different model features on training time. Second, we propose a dimensionality reduction rule to extract the key features which have a great impact on training time based on the influence of model features. Finally, we use the key features extracted by the dimensionality reduction rule to train the time prediction model. Our experiments show that, compared with the current prediction method, our method reduces 30% of model features and 25% of training data for the convolutional layer, 20% of model features and 20% of training data for the dense layer, while maintaining the same level of prediction error.
Deviation Detection in Clinical Pathways Based on Business Alignment
Authors: Tian Yinhua , Li Xinran , Qi Man , Han Dong , Du Yuyue
Published: January 2022

Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work. To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways. Firstly, the noise in diagnosis and treatment logs of clinical pathways will be removed. Then, the synchronous composition model is constructed to embody the deviations between the actual process and the theoretical model. Finally,

A
∗

algorithm is selected to search for optimal alignment. A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments.
Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning
Authors: Ma Shaobin , Li Lan , Zhang Chengwen
Published: January 2022

Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.
Workforce Participation, Ageing, and Economic Welfare: New Empirical Evidence on Complex Patterns across the European Union
Authors: Cristea Mirela S. , Pirtea Marilen G. , Suciu Marta C. , Noja Gratiela G.
Published: January 2022

The ageing population has become one of the major issues, with manifold consequences upon the economic welfare and elderly living standards satisfaction. This paper grasps an in‐depth assessment framework of the ageing phenomenon in connection with the labor market, with significant implications upon economic welfare, across the European Union (EU–27). We configure our research on four distinctive groups of the EU–27 countries based on the Active Ageing Index mapping, during 1995–2018, by acknowledging the different intensities of ageing implications on economic well‐being from one group of countries to another. The methodological endeavor is based on Structural Equation Modelling. Empirical results highlight that the ageing dimensions and labor market productivity notably shape the socioeconomic development of EU countries, visibly distinguished across the four panels. The economic development induced remarkable positive spillover effects on the welfare of older people, under the influence of the ageing credentials and dynamic shaping factors. Our research advances the literature underpinnings on this multifaceted topic by investigation made on specific groups of the EU countries and distinctive strategies proposed for each group of countries, as effective results for improving the well‐being of older people. Constant policy rethinking and adequate strategies should be a top priority for each specific group of EU countries, to further sustain the ageing phenomenon, with positive implications mostly on elderly welfare.
Influence of Fasting Plasma Glucose Level on Admission of COVID-19 Patients: A Retrospective Study
Authors: Zhao Yingying , Xing Huichun
Published: January 2022

Background. The coronavirus disease 2019 (COVID-19) is a serious global health threat and has spread dramatically worldwide. Prolonged viral shedding is associated with a more severe disease course and inflammatory reaction. Blood glucose levels were significantly associated with an increased hazard ratio (HR) for poor outcomes in COVID-19 patients. Objective. Previous studies focused primarily on the relationship between blood glucose and mortality or severe outcomes, but there were few research studies on the relationship between fasting plasma glucose (FPG) and duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA positive status. To explore the relationship between FPG levels and prolonged duration of SARS-CoV-2 viral positivity, the clinical data of COVID-19 patients were analyzed. Method. In this retrospective study, 99 cases of COVID-19 patients in Beijing Ditan Hospital were recruited, and their clinical and laboratory findings at admission were collected and analyzed. Furthermore, the risk factors for prolonged duration of SARS-CoV-2 RNA shedding were identified, and the relationship between FPG levels and the prolonged presence of SARS-CoV-2 RNA was evaluated. Result. We found that elevated FPG levels were correlated with longer duration of SARS-CoV-2 RNA positivity, classification of COVID-19, imaging changes of chest CT, inflammation-related biomarkers, and CD8+ T cell number in COVID-19 patients. In a logistic regression model, after adjusting for gender and age, COVID-19 patients with elevated FPG were more likely to had longer duration of SARS-CoV-2 RNA positivity than those with normal FPG levels (OR 3.053 [95% CI 1.343, 6.936]). Conclusion. Higher FPG levels (≥6.1 mmol/l) at admission was an independent predictor for prolonged SARS-CoV-2 shedding, regardless of a known history of diabetes. It suggests that intensive monitoring and control of blood glucose are important for all COVID-19 patients.
A Novel Rollover Warning Approach for Commercial Vehicles Using Unscented Kalman Filter
Authors: Hou Junjian , Lei Haizhu , Fu Zhijun , Yuan Peixin , Yin Yuming , Feng Heyang , Li Zihao , Zhang Mingxu , Cui Minghui , Xu Yuqing
Published: January 2022

Roll responses of the semitrailer and the tractor provide higher lead time and characterise the roll instability of the commercial vehicles subjected to directional manoeuvres at highway speeds. This paper proposes a novel rollover index based on the synthesized roll angles of the tractor and trailer. Owing to the poor measurability, the unscented Kalman filter (UKF) algorithm is used to estimate the roll angle of the track and trailer, respectively. Meanwhile, different weight coefficients are considered in the rollover index to eliminate the influence of mutual coupling between the tractor and the trailer and improve the accuracy of the warning. For the practical implementation of the algorithm, a two-stage rollover warning method triggered by the video and audio is finally proposed to reduce the possibilities of false warnings. Co-simulation is presented to prove the validity of the proposed rollover warning approach.
The Design Blueprint for a Large-Scale Telehealth Platform
Authors: Poonsuph Rattakorn
Published: January 2022

Technological innovation plays a crucial role in digital healthcare services. A growing number of telehealth platforms are concentrating on using digital tools to improve the quality and availability of care. Virtual care solutions employ not only advanced telehealth technology but also a comprehensive range of healthcare services. As a result, these can reduce patient healthcare costs as well as increase accessibility and convenience. At the same time, the healthcare service provider can leverage healthcare professionals to get a better perspective into the needs of their patients. The objective of this research is to provide a comprehensive design blueprint for a large-scale telehealth platform. Telehealth is the digital healthcare service combining online services and offline access for healthcare facilities to offer various healthcare services directly to patients. This design blueprint covers the digital healthcare ecosystem, new patient journey design for digital health services, telehealth functionality design, and an outline of the platform infrastructure and security design. Ultimately, telehealth platforms establish a completed digital healthcare service and new ecosystem that provides better care for every patient worldwide.
Immune Checkpoint Inhibitor-Induced Limbic Encephalitis during Treatment with Atezolizumab in a Patient with Small-Cell Lung Cancer: A Case Report and Review of the Literature
Authors: Nakashima Koki , Demura Yoshiki , Kurokawa Kosuke , Takeda Toshihiro , Jikuya Norihiro , Oi Masahiro , Tada Toshihiko , Akai Masaya , Ishizuka Tamotsu
Published: January 2022

Paraneoplastic neurological syndrome (PNS) is associated with malignancies, including small-cell lung cancer. Recently, PNS cases among patients with small-cell lung cancer (SCLC) induced by immune checkpoint inhibitors have increased. We herein report a 66-year-old man with SCLC who developed disorientation, dysphagia, and gait disturbance after three courses of treatment with atezolizumab. Brain magnetic resonance imaging revealed a high-intensity area in the bilateral temporal lobes. Blood test results were positive for anti-Hu and anti-Zic4 antibodies, which led to the diagnosis of limbic encephalitis as PNS. Some symptoms improved with intravenous administration of steroids and immunoglobulins.
Production and Optimization of Bio-Based Silica Nanoparticle from Teff Straw (Eragrostis tef) Using RSM-Based Modeling, Characterization Aspects, and Adsorption Efficacy of Methyl Orange Dye
Authors: Amibo Temesgen Abeto , Beyan Surafel Mustefa , Damite Tsegaye Markos
Published: January 2022

The brown teff straw was utilized in this study to produce silica using the sol-gel technique. After pretreatment, the raw material of brown teff straw was characterized. The data were analyzed using the central composite design and response surface technique, and four independent parameters, namely, temperature, NaOH concentration, rotational speed, and extraction time, were evaluated for process optimization. Before extracting silica with an alkaline solution, the silica content in the ash was determined using an AAS spectrometer. The silica content of teff straw ash is around 92.89%. The ash was treated with NaOH solution in the concentrations range of 1 M to 3 M (0.5 M interval). The extraction time varied at intervals of 55, 70, 85, 100, and 115 minutes. Temperatures were changed using magnetic stirrer equipment in the range of 80°C to 100°C (5°C interval). At 350 rpm, 400 rpm, 450 rpm, 500 rpm, and 550 rpm, the rotating speed was adjusted. The best extraction conditions for amorphous silica were 1.50 M NaOH, 109.99 min, 94.98°C, and a rotating speed of 499.57 rpm, with a maximum yield of 85.85%. XRD and FTIR analyses were used to assess the physicochemical characteristics of the extracted silica. The aqueous solutions of methyl orange were used to test the adsorption efficiency of silica. The percent of removal efficiency for methyl orange was 90.48%.