bluuebunny/crossref_metadata_2025 huggingface dataset:
A Prospective Analysis of Vitamin D Levels in Pregnant Women Diagnosed with Gestational Hypertension after SARS-CoV-2 Infection
Authors: Dahma George , Craina Marius , Dumitru Catalin , Neamtu Radu , Popa Zoran Laurentiu , Gluhovschi Adrian , Citu Cosmin , Bratosin Felix , Bloanca Vlad , Alambaram Satish , Willie Anthony , Kodimala Shiva Charana , Negrean Rodica Anamaria , Bernad Elena
Published: February 2023

The great majority of existing studies suggests that the prognosis and outcomes of SARS-CoV-2 infections are improved with adequate vitamin D levels, with or without supplementation. Simultaneously, whether vitamin D supplementation during pregnancy lessens the chance of developing gestational hypertension is controversial. The objective of the present research was to evaluate whether vitamin D levels during pregnancy differ substantially among pregnant women who develop gestational hypertension following SARS-CoV-2 infection. The current research was designed as a prospective cohort following the pregnant women admitted to our clinic with COVID-19 until 36 weeks of gestation. Total vitamin D (25(OH)D) levels were measured in the three study groups in which pregnant women with COVID-19 during pregnancy and a diagnosis of hypertension after 20 weeks of gestation were considered the group of cases (GH-CoV). The second group (CoV) included those with COVID-19 and no hypertension, while the third group (GH) included those with hypertension and no COVID-19. It was observed that 64.4% of SARS-CoV-2 infections in the group of cases occurred during the first trimester, compared to 29.2% in the first trimester among the controls who did not develop GH. Normal vitamin D levels were measured at admission in a significantly higher proportion of pregnant women without GH (68.8% in the CoV group vs. 47.9% in the GH-CoV group and 45.8% in the GH group). At 36 weeks of gestation, the median values of 25(OH)D in the CoV group was 34.4 (26.9–39.7) ng/mL compared to 27.9 (16.2–32.4) ng/mL in the GH-CoV group and 29.5 ng/mL (18.4–33.2) in the GH group, while the blood pressure measurements remained over 140 mmHg among the groups who developed GH. There was a statistically significant negative association between serum 25(OH)D levels and systolic blood pressure (rho = −0.295; p-value = 0.031); however, the risk of developing GH was not significantly higher among pregnant women with COVID-19 if the vitamin D levels were insufficient (OR = 1.19; p-value = 0.092) or deficient (OR = 1.26; p-value = 0.057). Although insufficient or deficient vitamin D among pregnant women with COVID-19 was not an independent risk factor for the development of GH, it is likely that an association between first-trimester SARS-CoV-2 infection and low vitamin D plays a key role in developing gestational hypertension.
Metals and Trace Elements in Calcified Valves in Patients with Acquired Severe Aortic Valve Stenosis: Is There a Connection with the Degeneration Process?
Authors: Tomášek Aleš , Maňoušek Jan , Kuta Jan , Hlásenský Jiří , Křen Leoš , Šindler Martin , Zelený Michal , Kala Petr , Němec Petr
Published: February 2023

Background. Acquired calcified aortic valve stenosis is the most common valve disease in adulthood. In the etiopathogenesis of this complex pathology, the importance of inflammation is mentioned, in which non-infectious influences represented by the biological effects of metal pollutants may participate. The main goal of the study was to determine the concentration of 21 metals and trace elements—aluminium (Al), barium (Ba), cadmium (Cd), calcium (Ca), chrome (Cr), cobalt (Co), copper (Cu), gold (Au), lead (Pb), magnesium (Mg), mercury (Hg), molybdenum (Mo), nickel (Ni), phosphorus (P), selenium (Se), strontium (Sr), sulfur (S), tin (Sn), titanium (Ti), vanadium (V) and zinc (Zn)—in the tissue of calcified aortic valves and to compare them with the concentrations of the same elements in the tissue of healthy aortic valves in the control group. Material and methods. The study group consisted of 49 patients (25 men, mean age: 74) with acquired, severe, calcified aortic valve stenosis with indicated heart surgery. The control group included 34 deceased (20 men, median age: 53) with no evidence of heart disease. Calcified valves were explanted during cardiac surgery and deep frozen. Similarly, the valves of the control group were removed. All valves were lyophilized and analyzed by inductively coupled plasma mass spectrometry. The concentrations of selected elements were compared by means of standard statistical methods. Results. Calcified aortic valves contained significantly higher (p < 0.05) concentrations of Ba, Ca, Co, Cr, Mg, P, Pb, Se, Sn, Sr and Zn and—in contrast—lower concentrations of Cd, Cu, Mo, S and V than valves of the control group. Significant positive correlations of concentrations between the pairs Ca-P, Cu-S and Se-S and strong negative correlations between the elements Mg-Se, P-S and Ca-S were found in the affected valves. Conclusion. Aortic valve calcification is associated with increased tissue accumulation of the majority of the analyzed elements, including metal pollutants. Some exposure factors may increase their accumulation in the valve tissue. A relationship between exposure to environmental burden and the aortic valve calcification process cannot be ruled out. Advances in histochemical and imaging techniques allowing imaging of metal pollutants directly in valve tissue may represent an important future perspective.
On the Measurement of Hedging Effectiveness for Long-Term Investment Guarantees
Authors: Augustyniak Maciej , Badescu Alexandru , Boudreault Mathieu
Published: February 2023

Although the finance literature has devoted a lot of research into the development of advanced models for improving the pricing and hedging performance, there has been much less emphasis on approaches to measure dynamic hedging effectiveness. This article discusses a statistical framework based on regression analysis to measure the effectiveness of dynamic hedges for long-term investment guarantees. The importance of taking model risk into account is emphasized. The difficulties in reducing hedging risk to an appropriately low level lead us to propose a new perspective on hedging, and recognize it as a tool to modify the risk–reward relationship of the unhedged position.
Threshold of Depression Measure in the Framework of Sentiment Analysis of Tweets: Managing Risk during a Crisis Period Like the COVID-19 Pandemic
Authors: Mba Jules Clement , Biyase Mduduzi
Published: February 2023

The COVID-19 pandemic has had a devastating impact on the world. The surge in the number of daily new cases and deaths around the world and in South Africa, in particular, has increased fear, psychological breakdown, and uncertainty among the population during the COVID-19 pandemic period, leading many to resort to prayer, meditation, and the consumption of religious media as coping measures. This study analyzes social media data to examine the perceptions and attitudes of the South African community toward religion as well as their well-being appreciation during the COVID-19 period. We extract four sets of tweets related to COVID-19, religion, life purpose, and life experience, respectively, by users within the geographical area of South Africa and compute their sentiment scores. Then, a Granger causality test is conducted to assess the causal relationship between the four time series. While the findings reveal that religious sentiment scores Granger-causes life experience, COVID-19 similarly Granger-causes life experience, illustrating some shifts experienced within the community during the crisis. This study further introduces for the first time a Threshold of Depression measure in the sentiment analysis framework to assist in managing the risk induced by extremely negative sentiment scores. Risk management during a period of crisis can be a hectic task, especially the level of distress or depression the community is experiencing in order to offer adequate mental support. This can be assessed through the Conditional Threshold of Depression which quantifies the threshold of depression of a community conditional on a given variable being at its Threshold of Depression. The findings indicate that the well-being indicators (life purpose and life experience) provide the highest values of this threshold and could be used to monitor the emotions of the population during periods of crisis to support the community in crisis management.
Scale Effect of Sloping Landscape Characteristics on River Water Quality in the Upper Reaches of the Si River in East-Central China
Authors: Liu Fang , Qin Tianling , Wang Hao , Liu Shanshan , Nie Hanjiang , Wang Jianwei
Published: February 2023

Landscape composition and configuration determine the source of pollutants. They also determine the interception and pollution-holding potential of the surface landscape. Using the upper reaches of the Si River Basin, a major grain-producing region in Shandong province in east-central China, as a case study, this study analyzed the influence of landscape characteristics on river water quality (RWQ) after superimposing topographic slope factors for 2017, and investigated which spatial scale had the strongest influence on RWQ. The landscape indices of three spatial scales (riparian zone, river reach and sub-catchment) and three slope scales (general land, flat ground and steep slope) were extracted. Correlation analysis and redundancy analysis were used to reveal the effects of landscape characteristics on RWQ at different scales. The results indicate that the landscape types were dominated by arable land and construction land in 2017. Landscape indices at different scales were significantly different. The RWQ generally met Class II or III surface water quality standard. Arable land and construction land had a negative impact on RWQ, both of which were “source” landscapes, while forest was a “sink” landscape that can effectively alleviate the deterioration of RWQ. The eight landscape indices which indicated heterogeneity, fragmentation level, landscape diversity, and shape information had different degrees of correlation with NO3−-N, NH4+-N, CODMn and BOD5. Different scales of landscape features had different correlations with RWQ, with the strongest correlation in the riparian zone, followed by the river reach, and the weakest in the sub-catchment. The influence of steep slope land was higher than that of flat ground land. The study confirmed that landscape structure and configuration had a scale effect on RWQ. It thus has great significance for water resources protection and land use management in the study area.
Enabling Conditions for Local Food Systems to Emerge in Predominately Rural Regions of Portugal—A Food Access Approach
Authors: Hernández Paola A.
Published: February 2023

Local food studies have stressed the importance of local food systems (LFS) in shortening the linkages between producers and consumers and in promoting resilient territories. Food consumption patterns are mostly studied around rural–urban dynamics, urban food security, and the revitalisation of rural communities, but little is known about the impact of LFS over rural residents and their capacity to access local foods. This paper explores the development of LFS in rural areas, from a food access approach, by characterising the rural landscapes promoting local food consumption. From a mapping of 74 predominately rural municipalities, statistical data of six socio-economic and political variables were collected to depict each municipality. A cluster analysis and Pearson’s correlation test informed us about the factors enabling these networks to emerge. Three clusters were identified: ‘meso-urban’, (N = 5) presenting urban-like characteristics (higher income and education levels, and reduced road infrastructure and small-scale farming); ‘dense’, (N = 26) characterised by high population density, road infrastructure and small-scale farming; and ‘castaway’ (N = 43) with low population density, income, post-secondary education, and expenditure in RD in agriculture. LFS emergence in rural Portugal was strongly determined by the levels of mean income and education levels in rural municipalities, which brought into question concerns regarding rural residents’ capacity to consume local foods. Low physical access, purchasing capacity, and awareness of food issues appeared to compromise the utilisation of these foods by the most socio-economically disfavoured groups. However, other territorial externalities and empirical work not included in this study could further complement our findings and provide a richer picture for the localisation of food systems in rural areas.
Analysis of Anthropogenic Disturbances of Green Spaces along an Urban–Rural Gradient of the City of Bujumbura (Burundi)
Authors: Kabanyegeye Henri , Sikuzani Yannick Useni , Sambieni Kouagou Raoul , Mbarushimana Didier , Masharabu Tatien , Bogaert Jan
Published: February 2023

Bujumbura city has diversified but unevenly distributed green spaces. The typology and anthropogenic disturbances of these green spaces are still unknown. This study presents a typology of green spaces along the urban–rural gradient through a literature review. It assesses the presence of anthropogenic disturbances through inventories in 100 m × 100 m grids. Data reveal that Bujumbura’s green spaces are made up of green squares concentrated exclusively in urban areas, cemeteries present in peri-urban areas and sports green spaces observable all along the urbanization gradient. These green spaces are more exposed to trampling, which is more present in administrative entities with a peri-urban morphological status, as opposed to various constructions in administrative entities with an urban status. Finally, significative pairwise associations of anthropogenic disturbances were observed. The results show the need to protect these green spaces from all kinds of anthropogenic disturbances by raising the eco-responsible awareness of the population and the municipal authorities.
Influence of Acid Mine Drainage Leakage from Tailings Ponds on the Soil Quality of Desert Steppe in the Northwest Arid Region of China
Authors: Shi Jianfei , Qian Wenting , Zhou Zhibin , Jin Zhengzhong , Xu Xinwen
Published: February 2023

As decision-making tools helping to improve the understanding of soil quality, soil quality assessment and heavy metal pollution assessment are very important for the remediation of heavy metal soil pollution. In the past, soil quality and heavy metal pollution have been studied separately, and few studies have combined them. The desert steppe in the Northwest Arid Region is an important pasture resource in China, and its soil safety has always been the focus of attention. Therefore, to understand the impact of tailing stockpiles on the soil quality of desert steppe, this study analyzed 18 indicators in the sample and analyzed the soil quality status of desert steppe based on the soil quality index (SQI) and Nemerow pollution index (Pcom). The main conclusions are as follows. (1) The evaluation results of heavy metal soil pollution show that the heavy metals Cu, Ni, Cr and Cd are significant polluters, Mn is a moderate polluter and Zn is a slight polluter. The results of the positive matrix factorization model show that Cu and Ni come from industrial sources; Cr, Cd and Zn come from industrial and traffic sources; and Mn comes from natural sources. (2) Regarding the study area, the generated minimum data set contains clay, pH, soil organic matter, available phosphorus, urease and neutral phosphatase. (3) The results of the SQI show that the soil in the study area is grade V (SQI-TDSave (total data set) = 0.42; SQI-MDSave (minimum data set) = 0.39), and the soil condition is very poor. 4) The linear fitting results show that the SQI-MDS was positively correlated with the SQI-TDS (R2 = 0.79), and SQI-MDS and SQI-TDS were negatively correlated with the Pcom (R2 > 0.6). Therefore, the leakage of acid mine drainage from tailings pond accumulation has led to a significant decline in the soil quality of this desert steppe, and effective ecological restoration measures are urgently needed to ensure the sustainable stability of the steppe ecosystem.
Long COVID-19 Syndrome Severity According to Sex, Time from the Onset of the Disease, and Exercise Capacity—The Results of a Cross-Sectional Study
Authors: Paradowska-Nowakowska Elżbieta , Łoboda Danuta , Gołba Krzysztof S. , Sarecka-Hujar Beata
Published: February 2023

Symptoms of long COVID-19 syndrome (long COVID-19) are reported by 80% of convalescents up to several months after contracting the coronavirus-19 disease (COVID-19). The study aimed to assess the frequency and correlations of long COVID symptoms with sex, disease severity, time since the onset of the disease, and exercise capacity in a population of Polish convalescents hospitalized as a part of a rehabilitation program after COVID-19. The retrospective analysis was carried out based on medical records concerning reported symptoms, comorbidities, exercise capacity, fatigue and dyspnea on Borg’s scale, arterial oxygen saturation (SpO2), spirometric parameters, chest X-rays/computed tomography scans, systolic pulmonary artery pressure, and left ventricular ejection fraction. The study involved 471 patients aged 63.83 ± 9.93 years who had been hospitalized 191.32 ± 75.69 days from the onset of COVID-19, of which 269 (57.1%) were women. The most common symptoms were fatigue (99.57%), dyspnea (99.36%), and myalgia (97.03%). Women reported more symptoms than men (p < 0.001) and rated their fatigue as more severe (p = 0.021). Patients with depressed moods reported more physical symptoms than others (p < 0.001). Most long COVID symptoms, including dyspnea, fatigue, and depressive symptoms, were found with the same frequency in patients 12–24 weeks and >24 weeks after recovery (p = 0.874, p = 0.400, and p = 0.320, respectively), regardless of acute COVID-19 severity (p = 0.240, p = 0.826, and p = 0.108, respectively). Dyspnea severity correlated with forced vital capacity (FVC) (r = −0.153, p = 0.005), and forced expiratory volume in one second (FEV1) (r = −0.142, p = 0.008). Fatigue severity correlated with impaired FVC and FEV1 (both r = −0.162, p = 0.003). Fatigue and dyspnea inversely correlated with the distance in a six-minute walk test (r = −0.497, p < 0.001, and r = −0.327, p < 0.001). In conclusion, in our cohort, long COVID symptoms are more common in women. Dyspnea/fatigue and depressive symptoms do not tend to subside after an average six-month recovery period. The intensity of perceived fatigue may be exaggerated by the coexistence of neuropsychiatric disorders. Increased fatigue and dyspnea correlate with impaired spirometric parameters and significantly affects convalescents’ exercise capacity.
Advances in Systemic Sclerosis: From Pathogenetic Pathways toward Novel Therapeutic Targets
Authors: Romano Eloisa , Rosa Irene , Manetti Mirko
Published: February 2023

Systemic sclerosis (SSc, or scleroderma) is a multifaceted rare connective tissue disease [...]
No Difference in Sleep Desaturations Severity between Patients with Wake-Up and Non-Wake-Up Stroke: A PRESS Study Results
Authors: Klobučníková Katarína , Kollár Branislav , Jurík Matúš , Valovičová Katarína , Hardoňová Miroslava , Poddaný Michal , Tedla Miroslav , Riant Michal , Klail Pavel , Turčáni Peter , Šiarnik Pavel
Published: February 2023

Background: Wake-up stroke (WUS) is a certain type of ischemic stroke in which a patient wakes up with a new neurological deficit due to cerebral ischemia. Sleep-disordered breathing is an independent risk factor for stroke, but the role of nocturnal oxygen desaturation in the pathophysiology of WUS is still insufficiently explored. According to several studies, patients with WUS have a significantly more severe sleep apnea syndrome and lower mean blood oxygen saturation. This study aimed to assess the severity of nocturnal desaturations in acute WUS and non-WUS patients using nocturnal pulse oximetry. Material and Methods: The cohort of 225 consecutive patients with neuroimaging-verified acute cerebral ischemia was prospectively enrolled. For further analyses, 213 subjects with known WUS/non-WUS status were selected (111 males and 102 females, average age 70.4 ±12.9, median baseline NIHSS = 5, median baseline mRS = 3). Patients were divided into the WUS group (n = 45) and the non-WUS group (n = 168). Overnight pulse oximetry was performed within 7 days of the stroke onset and data of both of the studied groups were compared. Results: We found oxygen desaturation index (ODI) in the WUS group was 14.5 vs. 16.6 (p = 0.728) in the non-WUS group, basal O2 saturation was 92.2% vs. 92.5% (p = 0.475), average low O2 saturation was 90.3% vs. 89.6% (p = 0.375), minimal O2 saturation was 79.5% vs. 80.6% (p = 0.563), and time with O2 saturation <90% (T90) was 4.4% vs. 4.7% (p = 0.729). Conclusions: In the studied sample, monitored respiratory parameters including ODI, basal O2 saturation, average low O2 saturation, minimal O2 saturation, and T90 did not significantly differ between groups of WUS and non-WUS patients.
Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review
Authors: Ruan Diwang , Chen Xuran , Gühmann Clemens , Yan Jianping
Published: February 2023

A small sample size and unbalanced sample distribution are two main problems when data-driven methods are applied for fault diagnosis in practical engineering. Technically, sample generation and data augmentation have proven to be effective methods to solve this problem. The generative adversarial network (GAN) has been widely used in recent years as a representative generative model. Besides the general GAN, many variants have recently been reported to address its inherent problems such as mode collapse and slow convergence. In addition, many new techniques are being proposed to increase the sample generation quality. Therefore, a systematic review of GAN, especially its application in fault diagnosis, is necessary. In this paper, the theory and structure of GAN and variants such as ACGAN, VAEGAN, DCGAN, WGAN, et al. are presented first. Then, the literature on GANs is mainly categorized and analyzed from two aspects: improvements in GAN’s structure and loss function. Specifically, the improvements in the structure are classified into three types: information-based, input-based, and layer-based. Regarding the modification of the loss function, it is sorted into two aspects: metric-based and regularization-based. Afterwards, the evaluation metrics of the generated samples are summarized and compared. Finally, the typical applications of GAN in the bearing fault diagnosis field are listed, and the challenges for further research are also discussed.
Simulation Study on Bearing Lubrication Mechanism and Friction Characteristics of the Biomimetic Non-Smooth Surface of a Cross-Scale, Second-Order Compound Microstructure
Authors: Liang Yingna , Wang Cunyuan , Zhang Zongyi , Zhang Zhepeng , Wang Wei , Xing Hao , Guan Tianyuan , Gao Dianrong
Published: February 2023

The reasonable design of biomimetic non-smooth surfaces is a novel and effective way to solve problems such as the poor lubricity and serious friction and wear of friction pairs of seawater axial piston pumps. Inspired by cross-scale, second-order compound microstructures on the surfaces of some living organisms, a hydrodynamic lubrication model of a slipper pair with a surface featuring spherical pits containing spherical convex hulls was built. This study analyzed the bearing lubrication mechanism and friction characteristics of cross-scale, second-order compound microstructure from the microflow perspective via the CFD method and optimized the working and geometric parameters using a hybrid orthogonal test scheme. The study’s results show that the cross-scale, second-order compound microstructure can produce a superimposed hydrodynamic pressure effect to improve the bearing capacity of the lubrication film of a slipper pair, reducing the friction coefficient. The orders of factors (the working parameter and geometric parameters) under multiple indices (the total pressure-bearing capacity and the friction coefficient) were found. The optimal combination is a spherical pit with a first order diameter of 0.7 mm, a first order depth-to-diameter ratio of 0.1, an area rate of 20%, an arrangement angle of α/3 and a spherical convex hull with a second order diameter of 0.13 mm, and a second order depth-to-diameter ratio of 0.3. Compared to a smooth surface and a first-order, non-smooth microstructure, the cross-scale, second-order compound microstructure has an 11.0% and 8.9% higher total pressure-bearing capacity, respectively, and the friction coefficient decreased by 9.5% and 5.4%, respectively.
Solution Precursor Plasma Spraying of TiO2 Coatings Using a Catalyst-Free Precursor
Authors: Simfroso Key , Cabo Shena Ramyr , Unabia Romnick , Britos Angelito , Sokołowski Paweł , Candidato Rolando
Published: February 2023

The microstructural characteristics and phase composition of solution precursor plasma-sprayed (SPPS) titania-based coatings using a catalyst-free precursor are reported in this work. An ethanol-based solution containing titanium isopropoxide was used to deposit TiO2 coatings. The thermal behavior of the solution precursor changed as its phase transformation temperature increased when the molar concentration was increased from 0.3 M to 0.6 M. Scanning electron micrographs showed that the surface of the coatings was composed of nano- and submicron-sized spherical particles (<1 μm) with sintered and melted particles. The cross-sections showed a porous structure using lower concentrations and dense coating formation with micropores using higher concentrations, with thicknesses of about 5 µm–8 µm. Moreover, the coatings when the number of spray passes was increased were 16 µm–20 µm thick, giving an average layer thickness of 0.6 µm deposited per spray pass in all cases. Phase analysis revealed the presence of both the anatase and rutile phases of TiO2 in coatings sprayed with various concentrations at various stand-off distances. More detailed discussion is presented with respect to the effects of the solution concentration, stand-off distance, and number of spray passes on the coating’s phase composition and microstructure.
Surface-Modified In2O3 for High-Throughput Screening of Volatile Gas Sensors in Diesel and Gasoline
Authors: Zhang Deqi , Guo Shenghui , Gao Jiyun , Yang Li , Zhu Ye , Ma Yanjia , Hou Ming
Published: February 2023

In this paper, with the help of the method of composite materials science, parallel synthesis and high-throughput screening were used to prepare gas sensors with different molar ratios of rare earths and precious metals modified In2O3, which could be used to monitor and warn the early leakage of gasoline and diesel. Through high-throughput screening, it is found that the effect of rare earth metal modification on gas sensitivity improvement is better than other metals, especially 0.5 mol% Gd modified In2O3 (Gd0.5In) gas sensor has a high response to 100 ppm gasoline (Ra/Rg = 6.1) and diesel (Ra/Rg = 5) volatiles at 250 °C. Compared with the existing literature, the sensor has low detection concentration and suitable stability. This is mainly due to the alteration of surface chemisorption oxygen caused by the catalysis and modification of rare earth itself.
The d Orbital Multi Pattern Occupancy in a Partially Filled d Shell: The KFeF3 Perovskite as a Test Case
Authors: Pascale Fabien , Mustapha Sami , D’Arco Philippe , Dovesi Roberto
Published: February 2023

The occupancy of the d shell in KFeF3 is t2g4eg2, with five α and one β electrons. The Jahn–Teller lift of degeneracy in the t2g sub-shell produces a tetragonal relaxation of the unit cell (4.09 vs. 4.22 Å, B3LYP result) not observed experimentally. In order to understand the origin of this apparent contradiction, we explored, with a 2 × 2 × 2 supercell (40 atoms per cell), all possible local structures in which contiguous Fe atoms have a different occupancy of the t2g orbitals with the minority spin electron. A total of 6561 configurations (with occupancies from (8,0,0) to (3,2,2) of the 3 t2g orbitals of the 8 Fe atoms) have been explored, with energies in many cases lower (by up to 1550 μEh per 2 Fe atoms) than the one of the fully ordered case, both for the ferromagnetic and the anti-ferromagnetic solutions. The results confirm that the orientation of the β d electron of Fe influences the electrostatics (more efficient relative orientation of the Fe quadrupoles of the d shell) of the system, but not the magnetic interactions. Three hybrid functionals, B3LYP, PBE0, and HSE06, provide very similar results.
Structural Performance of Infilled Steel–Concrete Composite Thin-Walled Columns Combined with FRP and CFRP: A Comprehensive Review
Authors: Rashid S. M. Priok , Bahrami Alireza
Published: February 2023

Fiber addition enhances the composite action between the steel tube and concrete core, increasing the strength of the concrete core. To better understand how fiber-reinforced infilled steel–concrete composite thin-walled columns (SCTWCs) behave, multiple investigations have been conducted using both experimental and analytical methods. This article provides a comprehensive review of SCTWCs’ confinement approaches using fiber-reinforced polymer (FRP) and carbon fiber-reinforced polymer (CFRP). In this research, the behavior and formation of FRP and CFRP wrappings of the SCTWCs are reviewed and discussed. The ability of the FRP to serve as a confining material and reinforcement for the columns has increased its use in columns applications. The FRP can be applied to reinforce the structures from the exterior. By applying the CFRP strips, the columns’ load-carrying capacity is improved up to 30% when compared with their corresponding un-strengthened columns. External bonding of the CFRP strips efficiently creates external confinement pressure, prevents local buckling of the steel tubes, and enhances the load-carrying capacity of the SCTWCs. The primary goal is to facilitate a clear understanding of the SCTWCs. This article helps structural researchers and engineers better understand the behavior of the SCTWCs that include the FRP and CFRP composites as external reinforcement. Future research directions are also suggested, which utilize previous research works.
A Multi-Information Fusion ViT Model and Its Application to the Fault Diagnosis of Bearing with Small Data Samples
Authors: Xu Zengbing , Tang Xinyu , Wang Zhigang
Published: February 2023

To solve the fault diagnosis difficulty of bearings with small data samples, a novel multi-information fusion vision transformer (ViT) model based on time–frequency representation (TFR) maps is proposed in this paper. The original vibration signal is decomposed into different scale sub-signals by the discrete wavelet transforms (DWTs), and the continuous wavelet transforms (CWTs) are used to transform these different scale sub-signals into time–frequency representation (TFR) maps, which are concatenated to input to the ViT model to diagnose the bearing fault. Through the multifaceted experiment analysis on the fault diagnosis of bearings with small data samples, the diagnosis results demonstrate that the proposed multi-information fusion ViT model can diagnose the fault of bearings with small data samples, with strong generalization and robustness; its average diagnosis accuracy achieved 99.85%, and it was superior to the other fault diagnosis methods, such as the multi-information fusion CNN, ViT model based on one-dimensional vibration signal, and ViT model based on the TFR of the original vibration signal.
A Novel Rolling Bearing Fault Diagnosis Method Based on BLS and CNN with Attention Mechanism
Authors: Wang Xiaojia , Hua Tong , Xu Sheng , Zhao Xibin
Published: February 2023

In actual industrial application scenarios, noise pollution makes it difficult to extract fault features accurately via conventional methods. A novel method for rolling bearing fault diagnosis combining a convolutional neural network (CNN), an attention mechanism for a squeeze-and-excitation network (SENet) module and a broad learning system (BLS) is proposed (SECNN–BLS). The one-dimensional bearing vibration signal is processed by using multiple short-time Fourier transforms (STFT); the two-dimensional image in the time-frequency domain is used as the model input. In the CNN fault feature extraction process, SENet is introduced to replace the CNN convolution layer, and global information is obtained through the squeeze operation. Excitation operation captures the importance of image channels, assigns weights adaptively to improve the attention on important information and eliminates the interference of irrelevant features without increasing the spatial and temporal complexity. The weighted feature representation is then transferred to the BLS input. The BLS has the characteristics of a flat structure and ridge regression to quickly solve the weights; as a fault classifier, it can save more computing resources and improve the accuracy of the fault classification. The SECNN–BLS fault diagnosis has achieved more than 98% accuracy on the Society for Machinery Failure Prevention Technology (MFPT) dataset. We also demonstrate the excellent performance of SECNN–BLS in a noisy environment.
Comparative Study of the Photocatalytic Degradation of Crystal Violet Using Ferromagnetic Magnesium Oxide Nanoparticles and MgO-Bentonite Nanocomposite
Authors: Elashery Sally E. A. , Ibrahim Islam , Gomaa Hassanien , El-Bouraie Mohamed M. , Moneam Ihab A. , Fekry Shimaa S. , Mohamed Gehad G.
Published: February 2023

In this work, the exploitation of the synthesized magnesium oxide nanoparticles and MgO-bentonite nanocomposite as an effective photocatalyst has been reported. They were utilized to study their applicability for the photocatalytic degradation of crystal violet in wastewater. Fourier-transform infrared (FTIR) spectra, X-ray powder diffraction (XRPD), energy-dispersive X-ray spectroscopy (EDX), and transmission electron microscope (TEM) were used for characterization. The photocatalytic efficiency of the synthesized photocatalysts for CV decomposition has been optimized in terms of several factors such as pH, contact time, the dose of the catalyst, and the dye concentration. The maximum degradation efficiency of CV was found to be 99.19% at the optimum state of pH value of 7, using 0.2 g of MgO NPs, while in the case of MgO-bentonite nanocomposite, the maximum degradation efficiency was decreased to 83.38%. The photocatalytic reaction mechanism was investigated using the scavenging reaction process, revealing that holes were majorly responsible for the degradation of CV. The kinetic data were suitable and best fitted by the pseudo-first-order kinetic model.
Dynamic Volatility Spillover Effects and Portfolio Strategies among Crude Oil, Gold, and Chinese Electricity Companies
Authors: Wang Guannan , Meng Juan , Mo Bin
Published: February 2023

This paper examines the dynamic relationships and the volatility spillover effects among crude oil, gold, and Chinese electricity companies’ stock prices, from 2 December 2008 to 25 July 2022. By estimating the dynamic conditional correlation (DCC) model, we identify the time-varying correlation between crude oil, gold, and Chinese electricity stocks. Then, we use the time-varying parameter VAR model (TVP-VAR) to analyze the total and net volatility spillover effects. In addition, we compare the hedge ratio strategy and the portfolio weights strategy, as well as the corresponding hedging effectiveness among the crude oil, gold, and Chinese electricity companies. Considering the impact of the extreme events, we also extend the examination to the special period analysis of two crises, the Chinese stock market crash in 2015 and the COVID-19 pandemic in 2020. The results indicate that significant volatility spillover effects exist among crude oil, gold, and Chinese electricity companies’ stock volatility, and the total spillover effects show a sharp increase under the impact of the crisis. On average, gold is a much cheaper hedging tool than crude oil, whereas these two commodity assets remain net volatility receivers during the whole period and the crisis. However, it is worth noting that for specific assets, the impact of the crisis on spillover effects depends on the characteristics of crisis events and the assets analyzed. Additionally, most optimal weight strategies provide better hedging effectiveness than hedging strategies from the perspective of hedging effectiveness.
Multidigraph Autocatalytic Set for Modelling Complex Systems
Authors: Kasmin Nor Kamariah , Ahmad Tahir , Idris Amidora , Awang Siti Rahmah , Abdullahi Mujahid
Published: February 2023

The motion of solid objects or even fluids can be described using mathematics. Wind movements, turbulence in the oceans, migration of birds, pandemic of diseases and all other phenomena or systems can be understood using mathematics, i.e., mathematical modelling. Some of the most common techniques used for mathematical modelling are Ordinary Differential Equation (ODE), Partial Differential Equation (PDE), Statistical Methods and Neural Network (NN). However, most of them require substantial amounts of data or an initial governing equation. Furthermore, if a system increases its complexity, namely, if the number and relation between its components increase, then the amount of data required and governing equations increase too. A graph is another well-established concept that is widely used in numerous applications in modelling some phenomena. It seldom requires data and closed form of relations. The advancement in the theory has led to the development of a new concept called autocatalytic set (ACS). In this paper, a new form of ACS, namely, multidigraph autocatalytic set (MACS) is introduced. It offers the freedom to model multi relations between components of a system once needed. The concept has produced some results in the form of theorems and in particular, its relation to the Perron–Frobenius theorem. The MACS Graph Algorithm (MACSGA) is then coded for dynamic modelling purposes. Finally, the MACSGA is implemented on the vector borne disease network system to exhibit MACS’s effectiveness and reliability. It successfully identified the two districts that were the main sources of the outbreak based on their reproduction number, R0.
Finite-Time Command Filtered Control for Oxygen-Excess Ratio of Proton Exchange Membrane Fuel Cell Systems with Prescribed Performance
Authors: Phan Van Du , Trinh Hoai-An , Ahn Kyoung Kwan
Published: February 2023

In recent years, proton exchange membrane fuel cell (PEMFC) has received growing attention as a new sustainable energy source because of its high-power density and zero-emission. In the PEMFC system, the air supply control has a significant impact on the efficiency and lifetime of the PEMFC stack. However, external disturbances and output constraints regularly have negative effects on air supply control. This paper aims to investigate a novel system analysis and advanced strategy control for the oxygen-excess ratio of a PEMFC system under the variant load current disturbance. The air-supply dynamic model is established which takes into account the supply manifolds, compressor, and the PEMFC stack. The proposed control method is designed based on finite-time command-filter control (FTCFC) to improve the tracking performance and ensure the finite-time convergence. Moreover, owing to the suggested prescribed performance function, the oxygen-excess ratio output remains in the pre-boundedness. Theoretical analysis exhibits that the closed-loop system stability is guaranteed by the Lyapunov theory. Finally, the simulation and hardware-in-loop (HIL) experiments are carried out on MATLAB environment and a 100 W power PEMFC system to validate the effectiveness of the suggested methodology.
Cost-Effectiveness Mathematical Model to Evaluate the Impact of Improved Cardiac Ablation Strategies for Atrial Fibrillation Treatment
Authors: Sánchez-Périz Ixeia , Barrachina-Martínez Isabel , Díaz-Carnicero Javier , Climent Andreu M. , Vivas-Consuelo David
Published: February 2023

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia. Despite the frequency of the disease, the treatment strategies for AF are inefficient. We developed a cost-effectiveness model to evaluate potential improvements in the application of cardiac ablations to treat AF. These are surgical procedures to terminate the arrhythmia and restore Sinus Rhythm. A Markov Model with a time horizon of five years was built to represent the management of patients in AF. A Montecarlo simulation was developed as a sensitivity analysis when the effectiveness increases the estimate of the potential impact of an improvement on the efficacy of cardiac ablation. The result of the analysis showed 44% of patients were untreated in any way. The base case ends up with 45% of patients having sinus rhythm restored after five years. The Montecarlo simulation estimates that in 58% of cases, the alternative of increasing ablation effectiveness by 25% would be cost-effective. If the number of performed ablations is doubled, the robustness increases to 86%. In conclusion, the model of management of AF highlights the importance of not only increasing effectiveness, but also treating more patients. Our study shows that investing in new screening technology to increase the effectiveness of ablations would be cost-effective.
A Methodology for Analysis and Prediction of Volume Fraction of Two-Phase Flow Using Particle Swarm Optimization and Group Method of Data Handling Neural Network
Authors: Iliyasu Abdullah M. , Bagaudinovna Dakhkilgova Kamila , Salama Ahmed S. , Roshani Gholam Hossein , Hirota Kaoru
Published: February 2023

Determining the volume percentages of flows passing through the oil transmission lines is one of the most essential problems in the oil, gas, and petrochemical industries. This article proposes a detecting system made of a Pyrex-glass pipe between an X-ray tube and a NaI detector to record the photons. This geometry was modeled using the MCNP version X algorithm. Three liquid-gas two-phase flow regimes named annular, homogeneous, and stratified were simulated in percentages ranging from 5 to 95%. Five time characteristics, three frequency characteristics, and five wavelet characteristics were extracted from the signals obtained from the simulation. X-ray radiation-based two-phase flowmeters’ accuracy has been improved by PSO to choose the best case among thirteen characteristics. The proposed feature selection method introduced seven features as the best combination. The void fraction inside the pipe could be predicted using the GMDH neural network, with the given characteristics as inputs to the network. The novel aspect of the current study is the application of a PSO-based feature selection method to calculate volume percentages, which yields outcomes such as the following: (1) presenting seven suitable time, frequency, and wavelet characteristics for calculating volume percentages; (2) the presented method accurately predicted the volume fraction of the two-phase flow components with RMSE and MSE of less than 0.30 and 0.09, respectively; (3) dramatically reducing the amount of calculations applied to the detection system. This research shows that the simultaneous use of time, frequency, and wavelet characteristics, as well as the use of the PSO method as a feature selection system, can significantly help to improve the accuracy of the detection system.
Unified Theory of Zeta-Functions Allied to Epstein Zeta-Functions and Associated with Maass Forms
Authors: Wang Nianliang , Kuzumaki Takako , Kanemitsu Shigeru
Published: February 2023

In this paper, we shall establish a hierarchy of functional equations (as a G-function hierarchy) by unifying zeta-functions that satisfy the Hecke functional equation and those corresponding to Maass forms in the framework of the ramified functional equation with (essentially) two gamma factors through the Fourier–Whittaker expansion. This unifies the theory of Epstein zeta-functions and zeta-functions associated to Maass forms and in a sense gives a method of construction of Maass forms. In the long term, this is a remote consequence of generalizing to an arithmetic progression through perturbed Dirichlet series.
TKRM: Learning a Transfer Kernel Regression Model for Cross-Database Micro-Expression Recognition
Authors: Chen Zixuan , Lu Cheng , Zhou Feng , Zong Yuan
Published: February 2023

Cross-database micro-expression recognition (MER) is a more challenging task than the conventional one because its labeled training (source) and unlabeled testing (target) micro-expression (ME) samples are from different databases. In this circumstance, a large feature-distribution gap may exist between the source and target ME samples due to the different sample sources, which decreases the recognition performance of existing MER methods. In this paper, we focus on this challenging task by proposing a simple yet effective method called the transfer kernel regression model (TKRM). The basic idea of TKRM is to find an ME-discriminative, database-invariant and common reproduced kernel Hilbert space (RKHS) to bridge MEs belonging to different databases. For this purpose, TKRM has the ME discriminative ability of learning a kernel mapping operator to generate an RKHS and build the relationship between the kernelized ME features and labels in such RKHS. Meanwhile, an additional novel regularization term called target sample reconstruction (TSR) is also designed to benefit kernel mapping operator learning by improving the database-invariant ability of TKRM while preserving the ME-discriminative one. To evaluate the proposed TKRM method, we carried out extensive cross-database MER experiments on widely used micro-expression databases, including CASME II and SMIC. Experimental results obtained proved that the proposed TKRM method is indeed superior to recent state-of-the-art domain adaptation methods for cross-database MER.
Consensus in Networks of Agents with Cooperative and Antagonistic Interactions
Authors: Gao Yanping , Kou Kaixuan , Zhang Weijing , Dai Yishu , Ma Jingwei
Published: February 2023

This paper studies the consensus of first-order discrete-time multi-agent systems with fixed and switching topology, and there exists cooperative and antagonistic interactions among agents. A signed graph is used to model the interactions among agents, and some sufficient conditions for consensus are obtained by analyzing the eigenvalues of a Laplacian matrix in the case of fixed topology. The results indicate that having a spanning tree is only a necessary condition for the consensus of multi-agent systems with signed graphs, which is also affected by edge weights. Consensus is further discussed in the case of switching topology, and the results reveal that consensus can be reached if the controller gain and the union graphs among some consecutive time intervals satisfy some conditions. Finally, several simulation examples further confirm the theoretical results.