bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultrasmall Superparamagnetic Particles of Iron Oxide and Cardiac Magnetic Resonance: Novel Imaging in Everyday Conditions
Authors: Tsampasian Vasiliki , Merinopoulos Ioannis , Cameron Donnie , Garg Pankaj , Vassiliou Vassilios S.
Published: July 2022

Myocardial inflammation has been hypothesised to be the common underlying mechanism through which several cardiovascular diseases develop and progress. Cardiac magnetic resonance (CMR) has become a powerful non-invasive tool that enables the direct visualisation of the myocardium. The emerging use of ultrasmall superparamagnetic particles of iron oxide (USPIO) and their magnetic properties is gaining a lot of research interest. USPIO-enhanced CMR can provide valuable information, as it allows for the identification of active inflammation in the myocardium, a process that has been hypothesised to be the substrate for adverse remodelling and, eventually, heart failure. In this review, we summarise the properties of USPIO and their role in cardiac magnetic resonance imaging as well as their clinical applications.
Complex Monitoring of Vertical Land Motions Corresponding to Geological Structure of Coastal and River Areas in Northwestern Poland
Authors: Zygmunt Marek , Garczyńska Ilona , Zalewski Paweł
Published: July 2022

This paper presents a study of vertical land movements conducted in northwestern Poland, covering the city of Szczecin. The land of Szczecin is made up of organic soils, covered in many places by water (lagoons, rivers, and lakes), which are susceptible to displacement; therefore, benchmarks in this area are susceptible to changes in height. The analysis of the geological structure of the near-surface layer of the Szczecin area, such as organic soils, sands, and clays, was carried out. The detailed recognition of geological structure in connection with height changes of benchmarks made it possible to locate stable benchmarks in the researched area. In the first step, the authors chose benchmarks in a stable geological structure based on archive data from the Polish Geodesy Documentation Center. In the second step, precise GNSS 3D measurements were performed in the direct vicinity of these benchmarks in order to verify their stability. Verified stations provided a reference for the reliable monitoring of the deformation of other surfaces resulting, for example, from changes in organic sediments. Existing and new engineering structures erected in the area of the Oder River Szczecin islands were selected for the final research of vertical land motions. Static GNSS observations were conducted in November 2020 and May 2021.
Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction
Authors: Safi Salah Al-Deen , Castillo Pedro A. , Faris Hossam
Published: July 2022

Financial distress prediction is crucial in the financial domain because of its implications for banks, businesses, and corporations. Serious financial losses may occur because of poor financial distress prediction. As a result, significant efforts have been made to develop prediction models that can assist decision-makers to anticipate events before they occur and avoid bankruptcy, thereby helping to improve the quality of such tasks. Because of the usual highly imbalanced distribution of data, financial distress prediction is a challenging task. Hence, a wide range of methods and algorithms have been developed over recent decades to address the classification of imbalanced datasets. Metaheuristic optimization-based artificial neural networks have shown exciting results in a variety of applications, as well as classification problems. However, less consideration has been paid to using a cost sensitivity fitness function in metaheuristic optimization-based artificial neural networks to solve the financial distress prediction problem. In this work, we propose ENS_PSONNcost and ENS_CSONNcost: metaheuristic optimization-based artificial neural networks that utilize a particle swarm optimizer and a competitive swarm optimizer and five cost sensitivity fitness functions as the base learners in a majority voting ensemble learning paradigm. Three extremely imbalanced datasets from Spanish, Taiwanese, and Polish companies were considered to avoid dataset bias. The results showed significant improvements in the g-mean (the geometric mean of sensitivity and specificity) metric and the F1 score (the harmonic mean of precision and sensitivity) while maintaining adequately high accuracy.
The Use of Hanseniaspora occidentalis in a Sequential Must Inoculation to Reduce the Malic Acid Content of Wine
Authors: van Wyk Niël , Scansani Stefano , Beisert Beata , Brezina Silvia , Fritsch Stefanie , Semmler Heike , Pretorius Isak S. , Rauhut Doris , von Wallbrunn Christian
Published: July 2022

In this study, the impact of the apiculate yeast Hanseniaspora occidentalis as a co-partner with Saccharomyces cerevisiae was investigated in a sequential-type mixed-culture fermentation of Muscaris grape must. As with other fermentation trials using Hanseniaspora strains, a significant increase in ethyl acetate was observed, but most intriguing was the almost complete abolition of malic acid (from 2.0 g/L to 0.1 g/L) in the wine. Compared to the pure S. cerevisiae inoculum, there was also a marked increase in the concentrations of the other acetate esters. Modulation of some of the varietal elements, such as rose oxide, was also observed. This work shows the promising use of H. occidentalis in a mixed-culture must fermentation, especially in the acid modulation of fruit juice matrices.
Design and Analysis: Servo-Tube-Powered Liquid Jet Injector for Drug Delivery Applications
Authors: Portaro Rocco , Ng Hoi Dick
Published: July 2022

The current state of commercially available needle-free liquid jet injectors for drug delivery offers no way of controlling the output pressure of the device in real time, as the driving mechanism for these injectors provides a fixed delivery pressure profile. In order to improve the delivery efficiency as well as the precision of the targeted tissue depth, it is necessary to develop a power source that can accurately control the plunger velocity. The duration of a liquid jet injection can vary from 10 to 100 ms, and it generate acceleration greater than 2 g (where g is the gravity); thus, a platform for real-time control must exhibit a response time greater than 1 kHz and good accuracy. Improving the pioneering work by Taberner and others whereby a Lorentz force actuator based upon a voice coil is designed, this study presents a prototype injector system with greater controllability based on the use of a fully closed-loop control system and a classical three-phase linear motor consisting of three fixed coils and multiple permanent magnets. Apart from being capable of generating jets with a required stagnation pressure of 15–16 MPa for skin penetration and liquid injection, as well as reproducing typical injection dynamics using commercially available injectors, the novelty of this proposed platform is that it is proven to be capable of shaping the real-time jet injection pressure profile, including pulsed injection, so that it can later be tailored for more efficient drug delivery.
Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI
Authors: Ismaila Lukman E. , Rasti Pejman , Bernard Florian , Labriffe Mathieu , Menei Philippe , Minassian Aram Ter , Rousseau David , Lemée Jean-Michel
Published: July 2022

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.
Soldering Data Classification with a Deep Clustering Approach: Case Study of an Academic-Industrial Cooperation
Authors: Faragó Kinga Bettina , Skaf Joul , Forgács Szabolcs , Hevesi Bence , Lőrincz András
Published: July 2022

Modern industries still commonly use traditional methods to visually inspect products, even though automation has many advantages over the skills of human labour. The automation of redundant tasks is one of the greatest successes of Artificial Intelligence (AI). It employs human annotation and finds possible relationships between features within a particular dataset. However, until recently, this has always been the responsibility of AI specialists with a specific type of knowledge that is not available to the industrial domain experts. We documented the joint research of AI and domain experts as a case study on processing a soldering-related industrial dataset. Our image classification approach relies on the latent space representations of neural networks already trained on other databases. We perform dimensionality reduction of the representations of the new data and cluster the outputs in the lower dimension. This method requires little to no knowledge of the underlying architecture of neural networks by the domain experts, meaning it is easily manageable by them, supporting generalization to other use cases that can be investigated in future work. We also suggest a misclassification detecting method. We were able to achieve near-perfect test accuracy with minimal annotation work.
Effect of Over-The-Counter Tooth-Whitening Products on Enamel Surface Roughness and Microhardness
Authors: AlShehri Abdullah , AlRefeai Mohammad H. , AlZamil Faisal , AlOtaibi Nawaf , AlKinani Yara
Published: July 2022

Background: To evaluate the efficacy of new over-the-counter tooth-whitening products on enamel surface roughness and microhardness. Methods: A total of 120 enamel specimens were prepared and randomly allocated into six groups. Group A was treated with 10% carbamide peroxide; Group B was immersed in distilled water; Group C was treated with hydrated silica, sodium hexametaphosphate toothpaste; Group D was treated with sodium bicarbonate; Group E was treated with 0.25% citric acid; and Group F was treated with hydrated silica, charcoal powder. Results: A, B, and D demonstrated decreased Ra, whereas Groups C, E, and F showed an increased Ra after whitening. The changes in Ra from T0 to T1 in each group was statistically significant (p < 0.001) except for Group B (p = 0.85). The groups showed decreased KHN after whitening, except for specimens in Group B (distilled water). The KHN from T0 to T1 decreased significantly for groups A, C, E, and F (p = 0.001). Significant difference was observed at T1 (p < 0.0001). Conclusion: Within the limitations of this study, it could be demonstrated that surface roughness and enamel microhardness changes were influenced by the type, composition, and exposure time of the whitening product.
Simulation Dataset Preparation and Hybrid Training for Deep Learning in Defect Detection Using Digital Shearography
Authors: Li Weixian , Wang Dandan , Wu Sijin
Published: July 2022

Since real experimental shearography images are usually few, the application of deep learning for defect detection in digital shearography is limited. A simulation dataset preparation method of shearography images is proposed in this paper. Firstly, deformation distributions are estimated by finite element analysis (FEA); secondly, phase maps are calculated according to the optical shearography system; finally, simulated shearography images are obtained after 2π modulus and gray transform. Various settings in the parameters of object, defect, load and shearing in those three steps could prepare a diverse simulation dataset for deep learning. Together with the real experimental images taken from a shearography setup, hybrid trainings of deep learning for defect detection are performed and discussed. The results show that a simulation dataset, generated without any real defective specimen, shearography system or manual experiment, can greatly improve the generalization of a deep learning network when the number of experimental training images is small.
Effect of Rotor-Stator Spacing on Compressor Performance at Variable Operating Conditions
Authors: Niu Han , Chen Jiang , Xiang Hang
Published: July 2022

This paper investigates the effect of rotor-stator spacing and operating conditions on compressor performance and the rotor-stator interaction mechanism. We focus on the interaction between the rotor wake and the stator’s flow field through several unsteady numerical simulations aiming at a transonic compressor stage in the equal radius flow path with two-axial spacing at three operating conditions. The simulation results indicate that the time-averaged efficiency almost declined by 0.6 points due to mixing loss in the spacing ranging from close to far at nominal and low load operating conditions. The deep upstream wake leads to corner separation at high load conditions due to close spacing, imposing a fluctuating efficiency at the frequency where the stator wake near the shroud oscillates, as found by the dominant frequency screening method. Under all conditions, the wake transport can reduce the static pressure on the pressure surface and weaken the hub leakage flow afterward.
A Multiscale Study of Moisture Influence on the Crumb Rubber Asphalt Mixture Interface
Authors: Wang Lan , Liu Yang , Zhang Le
Published: July 2022

In order to study the influence of moisture on the interface of crumb rubber–asphalt (CR) mixture, the interface bonding performance and crack resistance of a crumb rubber–asphalt mixture under dry and wet conditions were studied at three scales. At the macroscale, the characteristics of medium temperature fatigue cracking and low temperature fracture were studied by semi-circular bending tensile test (SCB) on the example of digital image correlation (DIC) technique. At the microscale, the surface energy of CR with basalt and limestone was measured using the contact angle measurement test, and then the adhesion work was calculated and analyzed. At the molecular scale, the model of CR, the model of basalt representative mineral (augite) and limestone representative mineral (calcite) were studied by molecular dynamics simulation. The relationship between these three scales was further explored to reveal the mechanism of the damage of moisture on the interface deterioration of the CR mixture. The results show that moisture has a certain effect on the interface of the CR mixture, which is characterized by macroscopically reducing the crack resistance of the asphalt mixture, microscopically reducing the adhesion ability between the asphalt and the aggregate and weakening the interaction between the asphalt and aggregate molecules at the molecular scale. Molecular dynamics can accurately simulate the deterioration of micro asphalt-aggregate adhesion under the damage of moisture. The decrease in microadhesion leads to the decrease in the crack resistance of the macro-CR mixture.
A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
Authors: Thiruvenkadam Srinivasan , Sujitha Venkatapathy , Jo Han-Gue , Ra In-Ho
Published: July 2022

Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructure that matches their needs. In order to maximize the use of shared resources, it is critical to provide an efficient virtual network embedding strategy for mapping each user’s requests to a physical infrastructure. Virtual network embedding primarily deals with the two most important network parameters—node mapping and link mapping. This paper proposes the heuristic fuzzy algorithm for node mapping and Dijikstra’s algorithm for link mapping. The proposed fuzzy based multi-criteria decision making technique uses membership functions for node parameters to prepare node mapping. By determining the shortest path, Dijikstra’s algorithm is used to provide link mapping. The proposed strategy is tested under dynamic physical infrastructure conditions for validation. The average acceptance ratio, costrevenue ratio, and average utilization of node capacity and link bandwidth are used to evaluate the performance of the proposed strategy. In addition, the obtained results are compared to the literature to show that the proposed strategy is effective.
The Ameliorating Effect of Lizhong-Tang on Antibiotic-Associated Imbalance in the Gut Microbiota in Mouse
Authors: Ahn Hye-Ri , Park Do Hwi , Shin Myoung-Sook , Nguyen Quynh Nhu , Park Jun Yeon , Kim Dong-Wook , Kang Ki Sung , Lee Hye Lim
Published: July 2022

Some herbal medicines have anti-inflammatory and anti-diarrheal effects. This study analyzed the modulating effect of gut microbiota of anti-inflammatory herbal medicines on antibiotic-associated diarrhea (AAD). The anti-inflammatory effects of 10 herbal medicines and Lizhong-tang active compounds were studied by measuring the nitric oxide production in an in vitro experiment. This was followed by an in vivo experiment in which the anti-diarrheal effects of Lizhong-tang and Magnolia officinalis in a lincomycin-induced AAD mouse model were measured. Changes in the intestinal microflora were observed using terminal restriction fragment length polymorphism analysis. Both Lizhong-tang and M. officinalis were effective against AAD, with Lizhong-tang’s anti-diarrheal effects being particularly effective. In addition, the active compounds of Lizhong-tang, liquiritin and 6-gingerol, inhibited the expression of inducible nitric oxide synthase and cyclooxygenase-2, thus showing an anti-inflammatory effect. Gut microbiota analysis showed that Lizhong-tang could alter the composition of the gut microbiota and ameliorated imbalance in the gut microbiota in a lincomycin-induced AAD mouse model.
Portable X-ray Fluorescence Analysis of Organic Amendments: A Review
Authors: López-Núñez Rafael
Published: July 2022

Portable XRF spectrometry (pXRF) has recently undergone significant technological improvements and is being applied in a wide range of studies. Despite pXRF advantages, this technique has rarely been used to characterize organic amendments and residues. This article reviews those studies undertaken to date in which pXRF is used to characterize these products. Published studies show that pXRF correctly measures elements such as Fe, Pb, Zn, Mn, Ca, and K but gives conflicting results for elements such as Cr, Ni, and As. Among the reasons that may cause the low performance of the technique with certain elements or under certain measurement conditions would be the inadequacy of the analytical comparison procedures used (i.e., digestion with aqua regia), the lack of knowledge of the interfering effects of organic matter, and sample moisture on the XRF signals and the need for a standardized protocol for performing the measurements. However, the speed and low cost of the procedure forecast a greater future use of this technique, especially in cooperation with other fast spectroscopic techniques based on near-infrared (NIRS) or mid-infrared (MIR) spectroscopies. Chemometric procedures based on one or more of these techniques will allow the prediction of elements below the detection limit of pXRF instruments (Cd, Hg), or other properties of organic amendments (organic matter, N, electrical conductivity, cation exchange capacity).
Solving Aggregate Production Planning Problems: An Extended TOPSIS Approach
Authors: Yu Vincent F. , Kao Hsuan-Chih , Chiang Fu-Yuan , Lin Shih-Wei
Published: July 2022

Aggregate production planning (APP) was developed for solving the problem of determining production, inventory, and workforce levels to meet fluctuating demand requirements over a planning horizon. In this work, multiple objectives were considered to determine the most effective means of satisfying forecasted demand by adjusting production rates, hiring and layoffs, inventory levels, overtime work, back orders, and other controllable variables. An extended technique for order preference via the similarity ideal solution (TOPSIS) approach was developed. It was formulated to solve this complicated, multi-objective APP decision problem. Compromise (ideal solution) control minimized the measure of distance, providing which of the closest solutions has the shortest distance from a positive ideal solution (PIS) and the longest distance from a negative ideal solution (NIS). The proposed method can transform multiple objectives into two objectives. The bi-objective problem can then be solved by balancing satisfaction using a max–min operator for resolving the conflict between the new criteria based on PIS and NIS. Finally, an application example demonstrated the proposed model’s applicability to practical APP decision problems.
Performance Evolution of Recycled Aggregate Concrete under the Coupled Effect of Freeze–Thaw Cycles and Sulfate Attack
Authors: Jia Pu , Li Lang , Zhou Jin , Zhang Di , Guan Zhongwei , Dong Jiangfeng , Wang Qingyuan
Published: July 2022

The high porosity of recycled coarse aggregate, which results in recycled aggregate concrete (RAC) more vulnerable to freeze–thaw (FT) damage and chemical attack, is a dominant factor that limits the industrialization of recycled aggregate concrete in civil engineering. This paper presents an experimental study on the combined effects of FT damage and sulfate attack on mechanical properties of high-performance RAC. The influence of the combined damage on the mass, solution-filled pore volume, dynamic elastic modulus, compressive strength, splitting tensile strength and fracture energy of RAC was studied. Results showed that the water-exposed FT cycles would result in more severe deterioration in the mass loss, elastic modulus and compressive strength, while for the sulfate-exposed FT cycles, the splitting tensile strength and fracture energy have more significant degradation. Moreover, compared with compressive strength, deterioration in splitting tensile strength is more severe. The maximum losses in compressive and splitting tensile strength were 28.7% and 35%, respectively. The fracture energy showed an increasing trend to 60 FT cycles, followed by an overall decrease to 180 FT cycles. The fracture energy exhibits a maximum increment of about 45% and 39% for water- and sulfate-exposed samples, respectively, after being subjected to 60 FT cycles. The analysis of failure modes of coarse aggregate has revealed that FT damage results in a significant deterioration in the binding force of mortar. After being subjected to 180 FT cycles, the area percentage of pulled-out failure was increased from 7.3% to larger than 17.3%.
Validation of MIGHTI/ICON Atmospheric Wind Observations over China Region Based on Meteor Radar and Horizontal Wind Model (HWM14)
Authors: Chen Zhou , Liu Yi , Du Zhitao , Fan Zhiqiang , Sun Haiyang , Zhou Chen
Published: July 2022

The Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) on board the ICON satellite provides effective measurement of horizontal winds in the mesosphere and lower thermosphere (MLT) region. In order to verify the measurement accuracy of the horizontal wind, this study uses the measurements of the meteor radar in Wuhan and the simulation results of a horizontal wind field model (HWM14) to compare and analyze the measurement results of MIGHTI/ICON in the whole year of 2020. The comparative analysis indicated that two datasets from MIGHTI/ICON and meteor radar are strongly correlated (r = 0.65, 0.76) with an RMS difference of 39.21 m/s (30.31 m/s). The consistency for meridional wind from MIGHTI/ICON, meteor radar, and HWM14 is worse than that of zonal wind. The accuracy of horizontal wind observations is influenced by altitude, diurnal, and seasonal patterns.
A Spatio-Temporal Weighted Filling Method for Missing AOD Values
Authors: Gao Rongfeng , Rui Xiaoping , Tang Jiakui
Published: July 2022

Aerosol Optical Depth (AOD) is a key parameter in defining the characteristics of atmospheric aerosols, evaluating atmospheric pollution, and studying aerosol radiative climate effects. However, a large amount of the AOD data obtained by satellite remote sensing are missing due to cloud cover and other factors. To obtain AOD data with continuous distribution in space, this study considers the spatial and temporal correlation of AOD and proposes a spatio-temporal weighted filling method based on a sliding window to supply the missing AOD data blocks. The method uses the semivariogram and autocorrelation function to judge the spatial and temporal correlation of AOD and uses the AOD spatial autocorrelation threshold as the sliding window size, and then it builds a spatio-temporal weighted model for each window to fill in the missing values. We selected the area with full values for simulation. The results show that the accuracy of this method has been significantly improved compared with the mean filling method. The R2 reaches 0.751, the RMSE is 0.021, and the filling effect is smoother. Finally, this method was used to fill in the missing values of the MultiAngle Implementation of Atmospheric Correction (MAIAC) AOD in the Beijing–Tianjin–Hebei region in 2019, and AErosol RObotic NETwork (AERONET) AOD was used as the true value for testing. The results show that the filled AOD has a high correlation with AERONET AOD, the R2 is 0.785, and the RMSE is 0.120. A summary of the AOD values of the 13 cities in the Beijing–Tianjin–Hebei region shows that the values in the first and third quarters are higher than those in the second and fourth quarters, with the highest AOD value in March and the second highest in August; among the 13 cities, the AOD values in Chengde and Zhangjiakou are lower than those in the other cities.
Microphysical Analysis of Precipitation in the Central and Eastern Margins of the Tibetan Plateau
Authors: Gong Ming , Li Maoshan , Shu Lei , Chang Na , Xu Pei , Ma Yaoming , Sun Fanglin , Yang Yaoxian
Published: July 2022

Through the observation and study of the raindrop spectrum, we can not only explore the evolution law of precipitation but also understand the microphysical characteristics of different types of precipitation clouds. This paper uses the raindrop spectrum data observed at Naqu Station, Yushu Station, Linzhi Station, and Emei Mount Station, as well as cloud radar data in the Yushu region in the Tibetan Plateau. Raindrop spectral characteristics are studied, and the raindrop size distribution (DSD) characteristics of the four stations are analyzed. The results are as follows: (1) The overall raindrop spectral concentration of the four stations decreases with the increase in particle size after the peak value. The downtrend is most gentle at the peak. (2) All rain intensity levels show the characteristics of multiple vertices. At Linzhi Station, Naqu Station and Yushu Station, the particles with a diameter of <1 mm contributed the most to the precipitation rate, while the particles with a diameter of <1 mm at Emei Mount Station contributed the least to the precipitation rate. (3) The precipitation in the central and eastern margins of the Tibetan Plateau is dominated by small and medium-sized particles, accounting for 95–99% of the precipitation particle number concentration. (4) The raindrop spectra of the three types of precipitation clouds at the four sites are all cumulonimbus precipitation Cumulonimbus in the uppermost layer, stratiform cloud precipitation stratiform cloud in the lowermost layer, and mixed cloud precipitation stratiform cloud in between. (5) Cloud radar analysis of precipitation structure shows that cumulonimbus and mixed clouds develop vigorously, and the highest cloud height can reach 13 km, but the average precipitation duration of mixed clouds is shorter than that of cumulonimbus; stratiform clouds develop relatively smoothly, with cloud heights of 6–7 km, the average precipitation duration is the shortest.
Using a Constructivist Multi-Criteria Decision Aid Model (MCDA-C) to Develop a Novel Approach to Self-Manage Motivation in Organizations
Authors: Ensslin Leonardo , Dutra Ademar , Ensslin Sandra Rolim , Graziano Luiz Antonio Giardino , Longaray André Andrade
Published: July 2022

The Brazilian Federal Highway Patrol is an organ of public administration with the constitutional mission to carry out the extensive patrolling of federal highways. Stress and physical and emotional exhaustion are constant, causing illnesses along with frustration, demotivation, and depression. The objective of this research is the development of a self-evaluation model of performance for the Federal Highway Patrols, using their perceptions to enhance their motivation at work considering the factors under their control. The constructivist multi-criteria decision aid (MCDA-C) methodology was used to elaborate the model. The proposed model identifies, establishes, and structures a process to measure the performance of patrol officers on the relevant aspects that they can manage, to guide their decisions toward the desired consequences, thus regulating their stress levels.
Design and Development of Cathode Materials for Rechargeable Batteries
Authors: Kim Byunghoon
Published: July 2022

Over the past two decades, rechargeable Li-ion batteries (LIBs) have been the de facto standard power source for electronic devices [...]
A Map of 3′ DNA Transduction Variants Mediated by Non-LTR Retroelements on 3202 Human Genomes
Authors: Halabian Reza , Makałowski Wojciech
Published: July 2022

As one of the major structural constituents, mobile elements comprise more than half of the human genome, among which Alu, L1, and SVA elements are still active and continue to generate new offspring. One of the major characteristics of L1 and SVA elements is their ability to co-mobilize adjacent downstream sequences to new loci in a process called 3′ DNA transduction. Transductions influence the structure and content of the genome in different ways, such as increasing genome variation, exon shuffling, and gene duplication. Moreover, given their mutagenicity capability, 3′ transductions are often involved in tumorigenesis or in the development of some diseases. In this study, we analyzed 3202 genomes sequenced at high coverage by the New York Genome Center to catalog and characterize putative 3′ transduced segments mediated by L1s and SVAs. Here, we present a genome-wide map of inter/intrachromosomal 3′ transduction variants, including their genomic and functional location, length, progenitor location, and allelic frequency across 26 populations. In total, we identified 7103 polymorphic L1s and 3040 polymorphic SVAs. Of these, 268 and 162 variants were annotated as high-confidence L1 and SVA 3′ transductions, respectively, with lengths that ranged from 7 to 997 nucleotides. We found specific loci within chromosomes X, 6, 7, and 6_GL000253v2_alt as master L1s and SVAs that had yielded more transductions, among others. Together, our results demonstrate the dynamic nature of transduction events within the genome and among individuals and their contribution to the structural variations of the human genome.
Kinetic Changes in B7 Costimulatory Molecules and IRF4 Expression in Human Dendritic Cells during LPS Exposure
Authors: Velazquez-Soto Henry , Real-San Miguel Fernanda , Pérez-Tapia Sonia Mayra , Jiménez-Martínez María C.
Published: July 2022

A key aspect of the inflammatory phenomenon is the involvement of costimulatory molecules expressed by antigen-presenting cells (APCs) and their ability to secrete cytokines to set instructions for an adaptive immune response and to generate tolerance or inflammation. In a novel integrative approach, we aimed to evaluate the kinetic expression of the membrane and soluble B7 costimulatory molecules CD86, ICOS-L, PDL1, PDL2, the transcription factor Interferon Regulatory Factor 4 (IRF4), and the cytokines produced by monocyte-derived dendritic cells (Mo-DCs) after challenging them with different concentrations of stimulation with E. coli lipopolysaccharide (LPS) for different lengths of time. Our results showed that the stimuli concentration and time of exposure to an antigen are key factors in modulating the dynamic expression pattern of membrane and soluble B7 molecules and cytokines.
Therapeutic Potential of a Novel Vitamin D3 Oxime Analogue, VD1-6, with CYP24A1 Enzyme Inhibitory Activity and Negligible Vitamin D Receptor Binding
Authors: Alshabrawy Ali K. , Cui Yingjie , Sylvester Cyan , Yang Dongqing , Petito Emilio S. , Barratt Kate R. , Sawyer Rebecca K. , Heatlie Jessica K. , Polara Ruhi , Sykes Matthew J. , Atkins Gerald J. , Hickey Shane M. , Wiese Michael D. , Stringer Andrea M. , Liu Zhaopeng , Anderson Paul H.
Published: July 2022

The regulation of vitamin D3 actions in humans occurs mainly through the Cytochrome P450 24-hydroxylase (CYP24A1) enzyme activity. CYP24A1 hydroxylates both 25-hydroxycholecalciferol (25(OH)D3) and 1,25-dihydroxycholecalciferol (1,25(OH)2D3), which is the first step of vitamin D catabolism. An abnormal status of the upregulation of CYP24A1 occurs in many diseases, including chronic kidney disease (CKD). CYP24A1 upregulation in CKD and diminished activation of vitamin D3 contribute to secondary hyperparathyroidism (SHPT), progressive bone deterioration, and soft tissue and cardiovascular calcification. Previous studies have indicated that CYP24A1 inhibition may be an effective strategy to increase endogenous vitamin D activity and decrease SHPT. This study has designed and synthesized a novel C-24 O-methyloxime analogue of vitamin D3 (VD1-6) to have specific CYP24A1 inhibitory properties. VD1-6 did not bind to the vitamin D receptor (VDR) in concentrations up to 10−7 M, assessed by a VDR binding assay. The absence of VDR binding by VD1-6 was confirmed in human embryonic kidney HEK293T cultures through the lack of CYP24A1 induction. However, in silico docking experiments demonstrated that VD1-6 was predicted to have superior binding to CYP24A1, when compared to that of 1,25(OH)2D3. The inhibition of CYP24A1 by VD1-6 was also evident by the synergistic potentiation of 1,25(OH)2D3-mediated transcription and reduced 1,25(OH)2D3 catabolism over 24 h. A further indication of CYP24A1 inhibition by VD1-6 was the reduced accumulation of the 24,25(OH)D3, the first metabolite of 25(OH)D catabolism by CYP24A1. Our findings suggest the potent CYP24A1 inhibitory properties of VD1-6 and its potential for testing as an alternative therapeutic candidate for treating SHPT.
Effect of Additional Loads on Joint Kinetics and Joint Work Contribution in Males and Females Performing Vertical Countermovement Jumps
Authors: Fessl Isabella , Harbour Eric , Kröll Josef , Schwameder Hermann
Published: July 2022

This study aimed to investigate the effect of additional loads and sex on countermovement jump (CMJ) joint kinetics during the entire take-off impulse in males and females. Twelve female and 13 male sport students performed vertical countermovement jumps without and with additional loads up to +80% of body mass using a straight barbell. Ground reaction forces and body kinematics were collected simultaneously. A significant increase was found for peak ankle power, whereas knee and hip peak power decreased significantly as additional load increased in both males and females. Joint work increased in each joint as additional load increased, although significance was observed only in the hip joint. Peak power of each joint (22–47%) and total hip work (61%) were significantly higher for males than females. Relative joint contributions to total joint work (“joint work contribution”) remained stable as additional loads increased, whereas meaningful differences were found in the magnitudes of joint work contribution between males and females. CMJ joint kinetics and joint work contributions were distinctly influenced by additional load and sex. Hence, these differences should be considered when prescribing loaded jumps for training or testing.
Advances in Waveguide Bragg Grating Structures, Platforms, and Applications: An Up-to-Date Appraisal
Authors: Butt Muhammad A. , Kazanskiy Nikolay L. , Khonina Svetlana N.
Published: July 2022

A Bragg grating (BG) is a one-dimensional optical device that may reflect a specific wavelength of light while transmitting all others. It is created by the periodic fluctuation of the refractive index in the waveguide (WG). The reflectivity of a BG is specified by the index modulation profile. A Bragg grating is a flexible optical filter that has found broad use in several scientific and industrial domains due to its straightforward construction and distinctive filtering capacity. WG BGs are also widely utilized in sensing applications due to their easy integration and high sensitivity. Sensors that utilize optical signals for sensing have several benefits over conventional sensors that use electric signals to achieve detection, including being lighter, having a strong ability to resist electromagnetic interference, consuming less power, operating over a wider frequency range, performing consistently, operating at a high speed, and experiencing less loss and crosstalk. WG BGs are simple to include in chips and are compatible with complementary metal-oxide-semiconductor (CMOS) manufacturing processes. In this review, WG BG structures based on three major optical platforms including semiconductors, polymers, and plasmonics are discussed for filtering and sensing applications. Based on the desired application and available fabrication facilities, the optical platform is selected, which mainly regulates the device performance and footprint.
Emerging Biosensors for Oral Cancer Detection and Diagnosis—A Review Unravelling Their Role in Past and Present Advancements in the Field of Early Diagnosis
Authors: Umapathy Vidhya Rekha , Natarajan Prabhu Manickam , Swamikannu Bhuminathan , Moses Johnson , Jones Sumathi , Chandran Manoj Prathap , Anbumozhi Madurai Kannan
Published: July 2022

Oral cancer is a serious concern to people all over the world because of its high mortality rate and metastatic spread to other areas of the body. Despite recent advancements in biomedical research, OC detection at an early stage remains a challenge and is complex and inaccurate with conventional diagnostics procedures. It is critical to study innovative approaches that can enable a faster, easier, non-invasive, and more precise diagnosis of OC in order to increase the survival rate of patients. In this paper, we conducted a review on how biosensors might be an excellent tool for detecting OC. This review covers the strategies that use different biosensors to target various types of biomarkers and focuses on biosensors that function at the molecular level viz. DNA biosensors, RNA biosensors, and protein biosensors. In addition, we reviewed non-invasive electrochemical methods, optical methods, and nano biosensors to analyze the OC biomarkers present in body fluids such as saliva and serum. As a result, this review sheds light on the development of ground-breaking biosensors for the early detection and diagnosis of OC.
Validation of the Glasgow Antipsychotic Side-Effect Scale (GASS) in an Italian Sample of Patients with Stable Schizophrenia and Bipolar Spectrum Disorders
Authors: Rodolico Alessandro , Concerto Carmen , Ciancio Alessia , Siafis Spyridon , Fusar-Poli Laura , Romano Carla , Scavo Elisa , Petralia Antonino , Salomone Salvatore , Signorelli Maria , Leucht Stefan , Aguglia Eugenio
Published: July 2022

Antipsychotics are a class of psychotropic drugs that improve psychotic symptoms and reduce relapse risk. However, they may cause side effects (SE) that impact patients’ quality of life and psychosocial functioning. Therefore, there is a need for practical tools to identify them and possibly intervene. The objective of the present study was to translate into Italian the Glasgow Antipsychotic Side Effect Scale (GASS), which is suggested as the questionnaire of choice to collect SE reported by patients treated with antipsychotics. We administered the GASS and the Udvalg for Kliniske Undersøgelser (UKU) SE scale—which is considered the gold standard—to 100 stable patients with schizophrenia and bipolar spectrum disorders. We measured the structural validity, internal consistency, concurrent criterion validity, construct validity, and clinical feasibility. GASS was characterized by modest structural validity and good internal consistency. The binary correlations concerning the presence of specific symptoms investigated with the GASS and the UKU were strong or relatively strong for only half of them. The GASS total scale score was inversely related to patients’ quality of life and psychosocial functioning. The GASS is useful to briefly assess the burden of antipsychotic SE (~5 min) but is not optimal in identifying them.
Feature-Based Deep Learning Classification for Pipeline Component Extraction from 3D Point Clouds
Authors: Xu Zhao , Kang Rui , Li Heng
Published: July 2022

This paper proposes a novel method for construction component classification by designing a feature-based deep learning network to tackle the automation problem in construction digitization. Although scholars have proposed a variety of ways to achieve the use of deep learning to classify point clouds, there are few practical engineering applications in the construction industry. However, in the process of building digitization, the level of manual participation has significantly reduced the efficiency of digitization and increased the application restrictions. To address this problem, we propose a robust classification method using deep learning networks, which is combined with traditional shape features for the point cloud of construction components. The proposed method starts with local and global feature extraction, where global features processed by the neural network and the traditional shape features are processed separately. Then, we generate a feature map and perform deep convolution to achieve feature fusion. Finally, experiments are designed to prove the efficiency of the proposed method based on the construction dataset we establish. This paper fills in the lack of deep learning applications of point clouds in construction component classification. Additionally, this paper provides a feasible solution to improve the construction digitization efficiency and provides an available dataset for future work.
Sustainable Building Design Development Knowledge Map: A Visual Analysis Using CiteSpace
Authors: Guo Yanlong , Geng Xinlei , Chen Denghang , Chen Yufei
Published: July 2022

Based on the Web of Science (WoS) core collection database, this article compares the research results in this subject area since 2000 with the literature data on the theme of sustainable architectural design and conducts an in-depth investigation into the research themes, basic literature, development trends, and research frontiers. Qualitative and quantitative analyses were conducted through the CiteSpace scientific visualization software, and the degree of collaboration between authors, institutions, and countries was analysed through research power. The topical research hotspots and their evolution were explored through a word frequency analysis, cluster analysis, and timeline analysis; the origins and development of a particular issue in sustainable building design were explored in conjunction with mutation analysis; and the frontier hotspots were explored. The analysis of co-citations was used to identify important knowledge bases in the field; the flow of knowledge between disciplines was explored through biplot overlay analysis. By interpreting the scientific visualization knowledge map, it was concluded that the research trends in sustainable building design are mainly in the areas of resource control, energy consumption, renewable building materials, evaluation systems, and computer-aided tools, and so on. The major topics of future research related to sustainable building design are discussed and summarized.
Development and Application of Smart SPIN Model: Measuring the Spectrum, Penetration, Impact and Network of Smart City Industries in South Korea
Authors: Jo Sungsu , Lee Sangho
Published: July 2022

The research agenda on smart cities has increasingly extended not only on perspectives of social–economic relations between technologies and cities but also on the industrial economic ecosystem. The purpose of this study is to focus on an analytical method for the characteristics of a smart city’s ecology and industry. With that thought, we have developed a smart SPIN (Spectrum, Penetration, Impact and Network) model and applied it to analyze the ecology of the Korean smart city industry in general. This model consists of smart spectrum model, smart penetration model, smart impact path model and smart network clustering model. The smart SPIN model shows great potential as an analytical method for the smart city industry ecosystem. As a source of data for analyses from 1960, 1985 and 2015 via input–output table, we revised these data into 25 and 8 industries related to the smart city ecosystem. Additionally, we applied the 2015 GDP deflator. The results of analysis are as follows: First, spectrum, the number of smart industries is increasing. This means that the smart city industry scope and area are expanding. Second, analysis of the smart penetration model and smart ecological industry can be applied into other industries. In other words, traditional industries can crossover and utilize smart technology. Third, with the results of our analysis of the smart impact path model, production paths are increasing while parameter paths did not show a triple parameter path. This means the value chain of the smart city industry is highly divested, but the structure of the industry is weakening. Fourth, smart network analysis shows important clusters to be centered on traditional industries: the clusters do not appear in smart industry centers. This means the impact of the smart city is not strong. Our analysis shows that, today, the Korean industrial ecosystem of smart cities is interacting with existing industries and raising it to a more intelligent and smarter level. Thus, there is a need for this kind of analysis study in order to find optimized smart city industry ecosystem.