bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensitivity Analysis of a Hybrid MCDM Model for Sustainability Assessment—An Example from the Aviation Industry
Authors: Markatos Dionysios N. , Malefaki Sonia , Pantelakis Spiros G.
Published: April 2023

When it comes to achieving sustainability and circular economy objectives, multi-criteria decision-making (MCDM) tools can be of aid in supporting decision-makers to reach a satisfying solution, especially when conflicting criteria are present. In a previous work of the authors, a hybrid MCDM tool was introduced to support the selection of sustainable materials in aviation. The reliability of an MCDM tool depends decisively on its robustness. Hence, in the present work, the robustness of the aforementioned tool has been assessed by conducting an extensive sensitivity analysis. To this end, the extent to which the results are affected by the normalization method involved in the proposed MCDM tool is examined. In addition, the sensitivity of the final output to the weights’ variation as well as to the data values variation has been investigated towards monitoring the stability of the tool in terms of the final ranking obtained. In order to carry out the analysis, a case study from the aviation industry has been considered. In the current study, carbon fiber reinforced plastics (CFRP) components, both virgin and recycled, are assessed and compared with regard to their sustainability by accounting for metrics linked to their whole lifecycle. The latter assessment also accounts for the impact of the fuel type utilized during the use phase of the components. The results show that the proposed tool provides an effective and robust method for the evaluation of the sustainability of aircraft components. Moreover, the present work can provide answers to questions raised concerning the adequacy of the CFRP recycled parts performance and their expected contribution towards sustainability and circular economy goals in aviation.
Advanced Research of Rhizosphere Microbial Activity
Authors: Szili-Kovács Tibor , Takács Tünde
Published: April 2023

Soils are generally considered a complex and largely unexplored vital “black box” with thousands of microorganism taxa in their networks [...]
Visual Detection of Portunus Survival Based on YOLOV5 and RCN Multi-Parameter Fusion
Authors: Feng Rui , Zhang Gang , Yang Song , Chen Yuehua
Published: April 2023

Single-frame circulation aquaculture belongs to the important category of sustainable agriculture development. In light of the visual-detection problem related to survival rate of Portunus in single-frame three-dimensional aquaculture, a fusion recognition algorithm based on YOLOV5, RCN (RefineContourNet) image recognition of residual bait ratio, centroid moving distance, and rotation angle was put forward. Based on three-parameter identification and LWLR (Local Weighted Linear Regression), the survival rate model of each parameter of Portunus was established, respectively. Then, the softmax algorithm was used to obtain the classification and judgment fusion model of Portunus’ survival rate. In recognition of the YOLOV5 residual bait and Portunus centroid, the EIOU (Efficient IOU) loss function was used to improve the recognition accuracy of residual bait in target detection. In RCN, Portunus edge detection and recognition, the optimized binary cross-entropy loss function based on double thresholds successfully improved the edge clarity of the Portunus contour. The results showed that after optimization, the mAP (mean Average Precision) of YOLOV5 was improved, while the precision and mAP (threshold 0.5:0.95:0.05) of recognition between the residual bait and Portunus centroid were improved by 2% and 1.8%, respectively. The loss of the optimized RCN training set was reduced by 4%, and the rotation angle of Portunus was obtained using contour. The experiment shows that the recognition accuracy of the survival rate model was 0.920, 0.840, and 0.955 under the single parameters of centroid moving distance, residual bait ratio, and rotation angle, respectively; and the recognition accuracy of the survival rate model after multi-feature parameter fusion was 0.960. The accuracy of multi-parameter fusion was 5.5% higher than that of single-parameter (average accuracy). The fusion of multi-parameter relative to the single-parameter (average) accuracy was a higher percentage.
Antibiotic Consumption in a Cohort of Hospitalized Adults with Viral Respiratory Tract Infection
Authors: Debes Sara , Haug Jon Birger , De Blasio Birgitte Freiesleben , Lindstrøm Jonas Christoffer , Jonassen Christine Monceyron , Dudman Susanne Gjeruldsen
Published: April 2023

Development of antibiotic resistance, a threat to global health, is driven by inappropriate antibiotic usage. Respiratory tract infections (RTIs) are frequently treated empirically with antibiotics, despite the fact that a majority of the infections are caused by viruses. The purpose of this study was to determine the prevalence of antibiotic treatment in hospitalized adults with viral RTIs, and to investigate factors influencing the antibiotic decision-making. We conducted a retrospective observational study of patients ≥ 18 years, hospitalized in 2015–2018 with viral RTIs. Microbiological data were taken from the laboratory information system and information on antibiotic treatment drawn from the hospital records. To investigate decisions for prescribing antibiotic treatment, we evaluated relevant factors such as laboratory and radiological results, in addition to clinical signs. In 951 cases without secondary bacterial RTIs (median age 73 years, 53% female), 720 (76%) were prescribed antibiotic treatment, most frequently beta-lactamase-sensitive penicillins, but cephalosporins were prescribed as first-line in 16% of the cases. The median length of treatment (LOT) in the patients treated with antibiotics was seven days. Patients treated with antibiotics had an average of two days longer hospital stay compared to patients with no such treatment, but no difference in mortality was found. Our study revealed that there is still a role for antimicrobial stewardship to further improve antibiotic use in patients admitted for viral RTIs in a country with relatively low antibiotic consumption.
Equine Gram-Negative Oral Microbiota: An Antimicrobial Resistances Watcher?
Authors: Pimenta José , Pinto Ana Rita , Saavedra Maria José , Cotovio Mário
Published: April 2023

Horses are considered as reservoirs of multidrug resistant bacteria that can be spread through the environment and possibly to humans. The aim of this study was to characterize the oral Gram-negative microbiota of healthy horses and evaluate their antimicrobial susceptibility profile in a One Health approach. For this purpose, samples were collected from the gingival margin of healthy horses, free of antimicrobial therapy, cultured in selective mediums, identified, and tested for antimicrobial susceptibility. Fifty-five Gram-negative isolates were identified, with 89.5% being zoonotic and 62% affecting humans, which were also found commonly in the environment. Forty-eight isolates (96%) were MDR. The phenotypic resistance presented as higher to macrolides (81.8%), β-lactams (55.4%), and quinolones (50%), and lower to sulfonamides (27.3%), tetracyclines, and amphenicols (both with 30.9%). In total, 51.5% of the isolates presented resistance to carbapenems. In addition to being the first report on the commensal oral microbiota of horses and respective susceptibility profile, this study highlights the horse as a valuable sentinel that can control the evolution and transmission of multidrug-resistant bacteria between the “One Health triad” since it is in contact with humans, other animals, and the environment, in different geographic locations.
Epigallocatechin-3-Gallate Attenuates Leukocyte Infiltration in 67-kDa Laminin Receptor-Dependent and -Independent Pathways in the Rat Frontoparietal Cortex following Status Epilepticus
Authors: Kim Ji-Eun , Lee Duk-Shin , Kang Tae-Cheon
Published: April 2023

Status epilepticus (SE) evokes leukocyte infiltration in the frontoparietal cortex (FPC) without the blood-brain barrier disruption. Monocyte chemotactic protein-1 (MCP-1) and macrophage inflammatory protein-2 (MIP-2) regulate leukocyte recruitments into the brain parenchyma. Epigallocatechin-3-gallate (EGCG) is an antioxidant and a ligand for non-integrin 67-kDa laminin receptor (67LR). However, it is unknown whether EGCG and/or 67LR affect SE-induced leukocyte infiltrations in the FPC. In the present study, SE infiltrated myeloperoxidase (MPO)-positive neutrophils, as well as cluster of differentiation 68 (CD68)-positive monocytes in the FPC are investigated. Following SE, MCP-1 was upregulated in microglia, which was abrogated by EGCG treatment. The C–C motif chemokine receptor 2 (CCR2, MCP-1 receptor) and MIP-2 expressions were increased in astrocytes, which were attenuated by MCP-1 neutralization and EGCG treatment. SE reduced 67LR expression in astrocytes, but not endothelial cells. Under physiological conditions, 67LR neutralization did not lead to MCP-1 induction in microglia. However, it induced MIP-2 expression and extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation in astrocytes and leukocyte infiltration in the FPC. Co-treatment of EGCG or U0126 (an ERK1/2 inhibitor) attenuated these events induced by 67LR neutralization. These findings indicate that the EGCG may ameliorate leukocyte infiltration in the FPC by inhibiting microglial MCP-1 induction independent of 67LR, as well as 67LR-ERK1/2-MIP-2 signaling pathway in astrocytes.
Low-Dose Acetylsalicylic Acid and Mitochondria-Targeted Antioxidant Mitoquinone Attenuate Non-Alcoholic Steatohepatitis in Mice
Authors: Turkseven Saadet , Turato Cristian , Villano Gianmarco , Ruvoletto Mariagrazia , Guido Maria , Bolognesi Massimo , Pontisso Patrizia , Di Pascoli Marco
Published: April 2023

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. NAFLD can evolve from simple fatty liver to non-alcoholic steatohepatitis (NASH), and ultimately, to cirrhosis. Inflammation and oxidative stress, promoted by mitochondrial dysfunction, play a crucial role in the onset and development of NASH. To date, no therapy has been approved for NAFLD and NASH. The aim of this study is to evaluate if the anti-inflammatory activity of acetylsalicylic acid (ASA) and the mitochondria-targeted antioxidant effect of mitoquinone could hinder the progression of non-alcoholic steatohepatitis. In mice, fatty liver was induced through the administration of a deficient in methionine and choline and rich in fat diet. Two experimental groups were treated orally with ASA or mitoquinone. Histopathologic evaluation of steatosis and inflammation was performed; the hepatic expression of genes associated with inflammation, oxidative stress, and fibrosis was evaluated; the protein expression of IL-10, cyclooxygenase 2, superoxide dismutase 1, and glutathione peroxidase 1 in the liver was analyzed; a quantitative analysis of 15-epi-lipoxin A4 in liver homogenates was performed. Mitoquinone and ASA significantly reduced liver steatosis and inflammation by decreasing the expression of TNFα, IL-6, Serpinb3, and cyclooxygenase 1 and 2 and restoring the anti-inflammatory IL-10. Treatment with mitoquinone and ASA increased the gene and protein expression of antioxidants, i.e., catalase, superoxide dismutase 1, and glutathione peroxidase 1, and decreased the expression of profibrogenic genes. ASA normalized the levels of 15-epi-Lipoxin A4. In mice fed with a deficient in methionine and choline and rich in fat diet, mitoquinone and ASA reduce steatosis and necroinflammation and may represent two effective novel strategies for the treatment of non-alcoholic steatohepatitis.
Synergistic Combination of Citrus Flavanones as Strong Antioxidant and COX-Inhibitor Agent
Authors: Smeriglio Antonella , Iraci Nunzio , Denaro Marcella , Mandalari Giuseppina , Giofrè Salvatore Vincenzo , Trombetta Domenico
Published: April 2023

Recently, we demonstrated that a Citrus flavanone mix (FM) shows antioxidant and anti-inflammatory activity, even after gastro-duodenal digestion (DFM). The aim of this study was to investigate the possible involvement of the cyclooxygenases (COXs) in the anti-inflammatory activity previously detected, using a human COX inhibitor screening assay, molecular modeling studies, and PGE2 release by Caco-2 cells stimulated with IL-1β and arachidonic acid. Furthermore, the ability to counteract pro-oxidative processes induced by IL-1β was evaluated by measuring four oxidative stress markers, namely, carbonylated proteins, thiobarbituric acid-reactive substances, reactive oxygen species, and reduced glutathione/oxidized glutathione ratio in Caco-2 cells. All flavonoids showed a strong inhibitory activity on COXs, confirmed by molecular modeling studies, with DFM, which showed the best and most synergistic activity on COX-2 (82.45% vs. 87.93% of nimesulide). These results were also corroborated by the cell-based assays. Indeed, DFM proves to be the most powerful anti-inflammatory and antioxidant agent reducing, synergistically and in a statistically significant manner (p < 0.05), PGE2 release than the oxidative stress markers, also with respect to the nimesulide and trolox used as reference compounds. This leads to the hypothesis that FM could be an excellent antioxidant and COX inhibitor candidate to counteract intestinal inflammation.
Interaction between Dietary Fibre and Bioactive Compounds in Plant By-Products: Impact on Bioaccessibility and Bioavailability
Authors: Núñez-Gómez Vanesa , González-Barrio Rocío , Periago María Jesús
Published: April 2023

In Europe, around 31 million tonnes of food by-products are generated during primary production and trade. The management of these by-products may cause a negative impact, both at the economic and environmental levels, for both industry and society. In this regard, taking into consideration that these by-products retain the dietary fibre compositions and the bioactive compounds of the starting materials, plant food agro-industries have an interest in taking advantage of them, from a nutritional point of view. Therefore, this review evaluates the role of dietary fibre and bioactive compounds in these by-products as well as the potential interactions of both components and their implications for health, since the bioactive compounds associated with fibre may reach the colon, where they can be metabolised into postbiotic compounds, providing important health benefits (prebiotic, antioxidant, anti-inflammatory, etc.). Consequently, this aspect, on which there are few studies, is very relevant and must be considered in the revaluation of by-products to obtain new ingredients for food processing with improved nutritional and technological properties.
The Conjunctive Compensation Method Based on Inertial Navigation System and Fluxgate Magnetometer
Authors: Chen Bingyang , Zhang Ke , Yan Bin , Zhu Wanhua
Published: April 2023

Eliminating the magnetic interference of the carrier platform is an important technical link and plays a vital role in aeromagnetic survey. The traditional compensation method is based on the Tolles–Lawson (T-L) model and establishes the linear relationship between the aircraft interference magnetic field and the aircraft attitude. The compensation coefficients are solved by designing the calibration flight. At present, almost all aeromagnetic systems use the fluxgate magnetometer fixed to the aircraft to realize the attitude measurement of the flight platform. However, the fluxgate magnetometer has problems, such as non-orthogonal error, zero drift error, and linearity error limited by the production process, and the fluxgate magnetometer is also very susceptible to external magnetic interference as a magnetic sensor. These lead to the aircraft attitude calculated by the fluxgate magnetometer being inaccurate, thus reducing the compensation effect. In this article, we analyze the influence of the fluxgate magnetometer noise on compensation and propose a new conjunctive compensation method based on inertial navigation systems (INS) and fluxgate magnetometer information to improve the compensation effect. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the traditional compensation method only based on fluxgate magnetometer information, the improved ratio is increased by 30–60%, and it is a real-time compensation method. It shows that the proposed method has a remarkable compensation effect for aeromagnetic interference.
A Technique for Multi-Parameter Signal Processing of an Eddy-Current Probe for Measuring the Thickness of Non-Conductive Coatings on Non-Magnetic Electrically Conductive Base Metals
Authors: Syasko Michael , Solomenchuk Pavel , Soloviev Igor’ , Ampilova Natalia
Published: April 2023

The known amplitude-sensitive eddy-current method for measuring the thickness of non-conductive coatings on conductive non-magnetic base metals does not satisfy the accuracy requirements. A primary consideration is the significant influence of a change in the specific electrical conductivity of the base metals on results of measurements. In this study, we developed a technique for measuring the thickness of non-conductive coatings on non-magnetic conductive base metals by using the eddy-current amplitude-phase method and implemented algorithms to process obtained information. Our method considered the influence of the specific electrical conductivity of the base metals by forming a two-dimensional graduation characteristic of the thickness gauge by using several base metals with different specific electrical conductivity. The algorithm for point-in-polygon determination was applied, which allowed us to measure the thickness of the coatings and the specific electrical conductivity of the base metals as independent values. The equipment necessary to construct the two-dimensional graduation characteristic and the algorithm for calculation of the thickness are described in detail.
A Lightweight Deep Learning Model for Automatic Modulation Classification Using Residual Learning and Squeeze–Excitation Blocks
Authors: Nisar Malik Zohaib , Ibrahim Muhammad Sohail , Usman Muhammad , Lee Jeong-A
Published: April 2023

Automatic modulation classification (AMC) is a vital process in wireless communication systems that is fundamentally a classification problem. It is employed to automatically determine the type of modulation of a received signal. Deep learning (DL) methods have gained popularity in addressing the problem of modulation classification, as they automatically learn the features without needing technical expertise. However, their efficacy depends on the complexity of the algorithm, which can be characterized by the number of parameters. In this research, we presented a deep learning algorithm for AMC, inspired by residual learning, which has remarkable accuracy and great representational ability. We also employed a squeeze-and-excitation network that is capable of exploiting modeling interconnections between channels and adaptively re-calibrates the channel-wise feature response to improve performance. The proposed network was designed to meet the accuracy requirements with a reduced number of parameters for efficiency. The proposed model was evaluated on two benchmark datasets and compared with existing methods. The results show that the proposed model outperforms existing methods in terms of accuracy and has up to 72.5% fewer parameters than convolutional neural network designs.
Improving Landslide Susceptibility Assessment through Frequency Ratio and Classification Methods—Case Study of Valencia Region (Spain)
Authors: Cantarino Isidro , Carrion Miguel Angel , Martínez-Ibáñez Víctor , Gielen Eric
Published: April 2023

Landslide susceptibility maps are widely used in land management and urban planning to delimit potentially problematic areas. In this article we improve their reliability by acting on the frequency ratio method and map classification systems. For the frequency ratio method, we have worked with continuous variables and established intervals grouped by probability according to the landslide inventory and based on the characteristics of the data rather than on standard divisions. For map classification systems, we have compared the efficacy of conventional classifications and those based on the concepts of sensitivity and specificity, with the specificity classifications being supported by the information offered by available comparative data. Both strategies make it possible to avoid subjective and repetitive procedures that are alien to the nature of the data being assessed. We present a case study in the 23,000 km2 Region of Valencia where a total of 48 different susceptibility maps were generated. We demonstrate that the methods applied in this study to calculate the frequency ratio provide an improvement in specificity in areas of high susceptibility while maintaining good sensitivity. In particular, the Area Under Curve (AUC) values increase from 0.67 for the conventional methods to 0.76 with the methods proposed in this work. This improvement is transferred to susceptibility mapping much more clearly when classifications that incorporate sensitivity, and especially specificity parameters, are used.
Poly(lactic acid)-Based Blends: A Comprehensive Review
Authors: Vayshbeyn Leonid Ilyich , Mastalygina Elena Evgenyevna , Olkhov Anatoly Aleksandrovich , Podzorova Maria Victorovna
Published: April 2023

Aliphatic and aromatic polyesters of hydroxycarboxylic acids are characterized not only by biodegradability, but also by biocompatibility and inertness, which makes them suitable for use in different applications. Polyesters with high enzymatic hydrolysis capacity include poly(lactic acid), poly(ε-caprolactone), poly(butylene succinate) and poly(butylene adipate-co-terephthalate), poly(butylene succinate-co-adipate). At the same time, poly(lactic acid) is the most durable, widespread, and cheap polyester from this series. However, it has a number of drawbacks, such as high brittleness, narrow temperature-viscosity processing range, and limited biodegradability. Three main approaches are known for poly(lactic acid) modification: incorporation of dispersed particles or low molecular weight and oligomeric substances, copolymerization with other polymers, and blending with other polymers. The review includes an analysis of experimental works devoted to developing mixtures based on poly(lactic acid) and other polymers. Regularities in the formation of the structure of such systems and the possibility of controlling the properties of poly(lactic acid) are considered.
Seismic Response Analysis of Reinforced Concrete Frame Structures Considering Slope Effects
Authors: Song Pengyan , Guo Shuang , Zhao Wenao , Xin Qin
Published: April 2023

According to the seismic damage due to past events, buildings located on slopes can present a worse seismic performance. To explore this, this study established a finite element model based on a 6-story RC frame structure and soil models based on a practical slope using OpenSees software. Combining the superstructure model with the soil model through soil spring elements, three soil-structure interaction systems with different slope rates were set up. Twenty near-field seismic actions were used as input loads for dynamic time–history analysis. The analysis shows that in the process of seismic action, the deformation tendency of the structure is affected by the slope. There is a clear tendency for lateral displacement towards the slope, and it is more obvious with a greater slope ratio. Meanwhile, the slope has no impact on the shear force at the base of the structure or at the bottom of the column. In addition, there is no correlation between the degree of impact and the slope gradient on the peak value of internal forces and deformations of structure.
Model for Semi-Automatic Serious Games Generation
Authors: Silva-Vásquez Pedro Omar , Rosales-Morales Viviana Yarel , Benítez-Guerrero Edgard , Alor-Hernández Giner , Mezura-Godoy Carmen , Montané-Jiménez Luis Gerardo
Published: April 2023

Serious games (SG), (video games with an educational purpose), provide teachers with tools to strengthen their students’ knowledge. Developing a SG requires knowledge, time, and effort. As a result, specialized tools to aid in the development process are needed. This work presents a model for the development of SG in the platformer genre. A tool implementing the model is introduced as a proof of concept. A SG was generated using this tool, which in turn was evaluated in terms of gameplay, mechanics, story, and usability. The evaluation results show that the SG has the minimum elements requested by an audience of students, who were expecting a game with both entertaining and educational value. Furthermore, the results are satisfactory in three out of four areas, showing that there are opportunities for improvement regarding the game’s story. Our work intends to improve the development times of new SG, as well as to make them easier to develop by both software engineers and teachers who wish to implement them in their classrooms.
Considering the Consequences of Cybersickness in Immersive Virtual Reality Rehabilitation: A Systematic Review and Meta-Analysis
Authors: Li Xin , Luh Ding-Bang , Xu Ruo-Hui , An Yi
Published: April 2023

VR rehabilitation is a rapidly evolving field, with increasing research and development aimed at improving its effectiveness, accessibility, and integration into mainstream healthcare systems. While there are some commercially available VR rehabilitation programs, their adoption and use in clinical practice are still limited. One of the limitations is defined as cybersickness, which is dependent on human contact with virtual reality products. The purpose of this essay is to raise awareness of the associated elements that contribute to cybersickness in rehabilitation using immersive VR. The common factors that influence the amount of cybersickness are user characteristics and device software and hardware. The Simulator Sickness Questionnaire (SSQ) was used as one of the formal models for determining the variables related to virtual reality sickness. The systematic review of the literature and the meta-analysis were chosen by whether the Simulator Sickness Questionnaire in the articles matched the research criteria. Based on PRISMA guidelines, a systematic review of the literature was conducted. Twenty-six publications from the recent past were totaled, comprising 862 individuals with ages ranging from 19 to 95, and 49% were female. The highest overall SSQ mean score for different kinds of symptoms was determined to be 21.058 for brain injuries, with a 95% confidence interval (CI) of 15.357 to 26.760. Time, content, locomotion, control, and display types were other elements that contributed to cybersickness and had significant p-values in the SNK Q-test. The future direction of immersive VR rehabilitation involves the development of immersive and interactive environments that simulate real-world situations, providing patients with a safe and controlled environment in which to practice new skills and movements.
Fast Point Cloud Registration Method with Incorporation of RGB Image Information
Authors: Cao Haiyuan , Chen Deng , Zheng Zhaohui , Zhang Yanduo , Zhou Huabing , Ju Jianping
Published: April 2023

Point cloud registration has a wide range of applications in 3D reconstruction, pose estimation, intelligent driving, heritage conservation, and digital cities. The traditional iterative closest point (ICP) algorithm has strong dependence on the initial position, poor robustness, and low timeliness. To address the above issues, a fast point cloud registration method that incorporates RGB image information is proposed. The SIFT algorithm is used to detect feature points of point clouds corresponding to the RGB image, followed by feature point matching. The RANSAC algorithm is applied to remove erroneous point pairs in order to calculate the initial transformation matrix. After applying a pass-through filter for noise reduction and transiting down with a voxel grid, the point cloud is subjected to rotation and translation transformation for initial registration. On the basis of initial alignment, the FR-ICP algorithm is utilized for achieving precise registration. This method not only avoids the problem of ICP easily getting stuck in local optima, but also has higher registration accuracy and efficiency. Experimental studies were conducted based on point clouds of automotive parts collected in real scenes, and the results showed that the proposed method has a registration error of only 0.487 mm. Among the same group of experimental point clouds with comparable registration error, the proposed method showed a speed improvement of 69%/48% compared to ICP/FR-ICP with regard to registration speed.
Use of Foundry Sands in the Production of Ceramic and Geopolymers for Sustainable Construction Materials
Authors: Sgarlata Caterina , Ariza-Tarazona Maria Camila , Paradisi Enrico , Siligardi Cristina , Lancellotti Isabella
Published: April 2023

The aim of this research was to evaluate the possibility of reusing waste foundry sands derived from the production of cast iron as a secondary raw material for the production of building materials obtained both by high-temperature (ceramic tiles and bricks) and room-temperature (binders such as geopolymers) consolidation. This approach can reduce the current demand for quarry sand and/or aluminosilicate precursors from the construction materials industries. Samples for porcelain stoneware and bricks were produced, replacing the standard sand contained in the mixtures with waste foundry sand in percentages of 10%, 50%, and 100% by weight. For geopolymers, the sand was used as a substitution for metakaolin (30, 50, 70 wt%) as an aluminosilicate precursor rather than as an aggregate to obtain geopolymer pastes. Ceramic samples obtained using waste foundry sand were characterized by tests for linear shrinkage, water absorption, and colorimetry. Geopolymers formulations, produced with a Si/Al ratio of 1.8 and Na/Al = 1, were characterized to evaluate their chemical stability through measurements of pH and ionic conductivity, integrity in water, compressive strength, and microstructural analysis. The results show that the addition of foundry sand up to 50% did not significantly affect the chemical-physical properties of the ceramic materials. However, for geopolymers, acceptable levels of chemical stability and mechanical strength were only achieved when using samples made with 30% foundry sand as a replacement for metakaolin.
Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA
Authors: Koroulis Christos , Makris Evangelos , Kolaitis Angelos , Tsanakas Panayiotis , Pavlatos Christos
Published: April 2023

The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction.
Electromagnetic Scattering by Bianisotropic Spheres
Authors: Durach Maxim
Published: April 2023

Electromagnetic fields in bulk bianisotropic media contain plane waves whose k-vectors can be found using the method of the index of refraction’s operator and belong to the Fresnel wave surfaces that fall into one of the five hyperbolic classes of the Durach et al. taxonomy of bianisotropic media. Linear combinations of vector spherical harmonics can be used as a set of solutions of vector Helmholtz equations in gyrotropic media to develop Mie’s theory of scattering by anisotropic spheres as accomplished by Lin et al. and Li et al. In this study, we introduced electromagnetic orbitals for bianisotropic media as linear combinations of vector spherical harmonics, which represent solutions of Maxwell’s equations in bianisotropic media. Using these bianisotropic orbitals, we developed a theory of the scattering of electromagnetic radiation by bianisotropic spheres with arbitrary effective material parameters and sizes. As a by-product, we obtained a simple expression for the expansion of a vector plane wave over vector spherical harmonics in a more compact form than the frequently used by Sarkar et al. We obtained the polarizability expressions in the Rayleigh limit in agreement with the results of the electrostatic approximation of Lakhtahia and Sihvola.
Dense-HR-GAN: A High-Resolution GAN Model with Dense Connection for Image Dehazing in Icing Wind Tunnel Environment
Authors: Zhou Wenjun , Yang Xinling , Zuo Chenglin , Wang Yifan , Peng Bo
Published: April 2023

To address the issue of blurred images generated during ice wind tunnel tests, we propose a high-resolution dense-connection GAN model, named Dense-HR-GAN. This issue is caused by attenuation due to scattering and absorption when light passes through cloud and fog droplets. Dense-HR-GAN is specifically designed for this environment. The model utilizes an atmospheric scattering model to dehaze images with a dense network structure for training. First, sub-pixel convolution is added to the network structure to remove image artifacts and generate high-resolution images. Secondly, we introduce instance normalization to eliminate the influence of batch size on the model and improve its generalization performance. Finally, PatchGAN is used in the discriminator to capture image details and local information, and then drive the generator to generate a clear and high-resolution dehazed image. Moreover, the model is jointly constrained by multiple loss functions during training to restore the texture information of the hazy image and reduce color distortion. Experimental results show that the proposed method can achieve the state-of-the-art performance on image dehazing the in icing wind tunnel environment.
A Financial Time-Series Prediction Model Based on Multiplex Attention and Linear Transformer Structure
Authors: Xu Caosen , Li Jingyuan , Feng Bing , Lu Baoli
Published: April 2023

Financial time-series prediction has been an important topic in deep learning, and the prediction of financial time series is of great importance to investors, commercial banks and regulators. This paper proposes a model based on multiplexed attention mechanisms and linear transformers to predict financial time series. The linear transformer model has a faster model training efficiency and a long-time forecasting capability. Using a linear transformer reduces the original transformer’s complexity and preserves the decoder’s multiplexed attention mechanism. The results show that the proposed method can effectively improve the prediction accuracy of the model, increase the inference speed of the model and reduce the number of operations, which has new implications for the prediction of financial time series.
On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
Authors: Moura André , Lima Pedro , Mendonça Fábio , Mostafa Sheikh Shanawaz , Morgado-Dias Fernando
Published: April 2023

Chatbots are becoming increasingly popular and require the ability to interpret natural language to provide clear communication with humans. To achieve this, intent detection is crucial. However, current applications typically need a significant amount of annotated data, which is time-consuming and expensive to acquire. This article assesses the effectiveness of different text representations for annotating unlabeled dialog data through a pipeline that examines both classical approaches and pre-trained transformer models for word embedding. The resulting embeddings were then used to create sentence embeddings through pooling, followed by dimensionality reduction, before being fed into a clustering algorithm to determine the user’s intents. Therefore, various pooling, dimension reduction, and clustering algorithms were evaluated to determine the most appropriate approach. The evaluation dataset contains a variety of user intents across different domains, with varying intent taxonomies within the same domain. Results demonstrate that transformer-based models perform better text representation than classical approaches. However, combining several clustering algorithms and embeddings from dissimilar origins through ensemble clustering considerably improves the final clustering solution. Additionally, applying the uniform manifold approximation and projection algorithm for dimension reduction can substantially improve performance (up to 20%) while using a much smaller representation.
Deep Learning Methods for Bug Bite Classification: An End-to-End System
Authors: Ilijoski Bojan , Trojachanec Dineva Katarina , Tojtovska Ribarski Biljana , Petrov Petar , Mladenovska Teodora , Trajanoska Milena , Gjorshoska Ivana , Lameski Petre
Published: April 2023

A bite from a bug may expose the affected person to serious, life-threatening conditions, which may require immediate medical attention. The identification of the bug bite may be challenging even for experienced medical personnel due to the different manifestations of the bites and similarity to other skin conditions. This motivated our work on a computer-aided system that offers information on the bug bite based on the classification of bug bite images. Recently, there have been significant advances of methods for image classification for the detection of various skin conditions. However, there are very few sources that discuss the classification of bug bites. The goal of our research is to fill in this gap in the literature and offer a comprehensive approach for the analysis of this topic. This includes (1) the creation of a dataset that is larger than those considered in the related sources; (2) the exploration and analysis of the application of pre-trained state-of-the-art deep learning architectures with transfer learning, used in this study to overcome the challenges of low-size datasets and computational burden; (3) the further improvement of the classification performance of the individual CNNs by proposing an ensemble of models, and finally, (4) the implementation and description of an end-to-end system for bug bite classification from images taken with mobile phones, which should be beneficial to the medical personnel in the diagnostic process. In this paper, we give a detailed discussion of the models’ architecture, back-end architecture, and performance. According to the general evaluation metrics, DenseNet169 with an accuracy of 78% outperformed the other individual CNN models. However, the overall best performance (accuracy of 86%) was achieved by the proposed stacking ensemble model. These results are better than the results in the limited related work. Additionally, they show that deep CNNs and transfer learning can be successfully applied to the problem of the classification of bug bites.
Assessment of Photosynthetic Carbon Capture versus Carbon Footprint of an Industrial Microalgal Process
Authors: Oliver Arthur , Camarena-Bernard Cristobal , Lagirarde Jules , Pozzobon Victor
Published: April 2023

It is often read that industrial microalgal biotechnology could contribute to carbon capture through photosynthesis. While technically accurate, this claim is rarely supported by sound figures nor put in regard to the carbon emissions associated with said processes. In this view, this work provides a quantitative assessment of the extent microalgal processes compensation for their carbon dioxide emissions. To do so, microalgae were cultivated under photolimited conditions. Their growth dynamic and photosynthetic apparatus status were monitored by daily cell density measurement and fluorescence assays. Ultimate analyses were used to determine microalgal carbon content. Simultaneously, the power consumption of the process was recorded, and the associated carbon dioxide emissions were computed using European electrical production carbon intensity. All in all, the recorded values confirmed microalgae growth under good physiological conditions and allowed computing the carbon capture rate, the energy storing rate, and the carbon dioxide emissions of the process. The process captured 0.72 ± 0.19 gCO2/day while emitting 182 gCO2/day, on average (over 15 days). The photoconversion efficiency was 4.34 ± 0.68%. Even if it were highly optimized (red/blue LED instead of white, for example), the process could only capture 1.02 ± 0.40% of its emissions. From these figures, the claim stating that a biotechnological microalgal production process could partly compensate for its emission seems rather bold. Authors should, therefore, emphasize other ecosystemic benefits of microalgal cultivation, such as phosphorous intake. Finally, we were also able to evaluate Chlorella vulgaris light and dark respiration (0.0377 ± 0.042 day−1 and 7.42 × 10−3 ± 3.33 × 10−3 day−1), which could help to assess carbon emission by biomass respiratory activity.
Relationship between HLB Number and Predominant Destabilization Process in Microfluidized Nanoemulsions Formulated with Lemon Essential Oil
Authors: Santos Jenifer , Alfaro-Rodríguez Maria-Carmen , Vega Lili , Muñoz José
Published: April 2023

Lemon essential oil (LEO) is associated with a multitude of health benefits due to its anticancer, antioxidant, antiviral, anti-inflammatory and bactericidal properties. Its drawback is that it is very sensitive to oxidation by heat. For this reason, researchers are increasingly investigating the use of LEO in nanoemulsions. In this work, we used laser diffraction, rheology and multiple light scattering techniques to study the effects of different HLB numbers (indicating different mixtures of Tween 80 and Span 20) on the physical stability of nanoemulsions formulated with LEO. We found that different HLB numbers induced different destabilization mechanisms in these emulsions. An HLB number lower than 12 resulted in an Ostwald ripening effect; an HLB number higher than 12 resulted in coalescence. In addition, all the developed nanoemulsions exhibited Newtonian behavior, which could favor the mechanism of creaming. All emulsions exhibited not only a growth in droplet size, but also a creaming with aging time. These findings highlight the importance of selecting the right surfactant to stabilize nanoemulsions, with potential applications in the food industry.
On the Fixed Circle Problem on Metric Spaces and Related Results
Authors: Mlaiki Nabil , Özgür Nihal , Taş Nihal , Santina Dania
Published: April 2023

The fixed-circle issue is a geometric technique that is connected to the study of geometric characteristics of certain points, and that are fixed by the self-mapping of either the metric space or of the generalized space. The fixed-disc problem is a natural resultant that arises as a direct outcome of this problem. In this study, our goal is to examine new classes of self-mappings that meet a new particular sort of contraction in a metric space. The common geometrical characteristic of the set of fixed points of any element of these classes is that a circle or even a disc, that is either termed the fixed circle or even the fixed disc of the appropriate self-map, is included within that set. In order to accomplish this, we establish two new classifications of contraction mapping: Fc-contractive mapping and Fc-expanding mapping. In the investigation of neural networks, activation functions with either fixed circles (or even fixed discs) are observed frequently. This demonstrates how successful our results with the fixed-circle (respectively, the fixed-disc) model were.
Aperiodic Sampled-Data Control for Anti-Synchronization of Chaotic Nonlinear Systems Subject to Input Saturation
Authors: Li Meixuan , Fan Yingjie
Published: April 2023

This paper studies the aperiodic sampled-data (SD) control anti-synchronization issue of chaotic nonlinear systems under the effects of input saturation. At first, to describe the simultaneous existence of the aperiodic SD pattern and the input saturation, a nonlinear closed-loop system model is established. Then, to make the anti-synchronization analysis, a relaxed sampling-interval-dependent Lyapunov functional (RSIDLF) is constructed for the resulting closed-loop system. Thereinto, the positive definiteness requirement of the RSIDLF is abandoned. Due to the indefiniteness of RSIDLF, the discrete-time Lyapunov method (DTLM) then is used to guarantee the local stability of the trivial solutions of the modeled nonlinear system. Furthermore, two convex optimization schemes are proposed to expand the allowable initial area (AIA) and maximize the upper bound of the sampling period (UBSP). Finally, two examples of nonlinear systems are provided to illustrate the superiority of the RSIDLF method over the previous methods in expanding the AIA and enlarging the UBSP.
Involvement of Dmp1 in the Precise Regulation of Hair Bundle Formation in the Developing Cochlea
Authors: Wang Yanmei , Lyu Jihan , Qian Xiaoqing , Chen Binjun , Sun Haojie , Luo Wenwei , Chi Fanglu , Li Hongzhe , Ren Dongdong
Published: April 2023

Dentin matrix protein 1 (Dmp1) is a highly phosphorylated, extracellular matrix protein that is extensively expressed in bone and teeth but also found in soft tissues, including brain and muscle. However, the functions of Dmp1 in the mice cochlea are unknown. Our study showed that Dmp1 was expressed in auditory hair cells (HCs), with the role of Dmp1 in those cells identified using Dmp1 cKD mice. Immunostaining and scanning electron microscopy of the cochlea at P1 revealed that Dmp1 deficiency in mice resulted in an abnormal stereociliary bundle morphology and the mispositioning of the kinocilium. The following experiments further demonstrated that the cell-intrinsic polarity of HCs was affected without apparent effect on the tissue planer polarity, based on the observation that the asymmetric distribution of Vangl2 was unchanged whereas the Gαi3 expression domain was enlarged and Par6b expression was slightly altered. Then, the possible molecular mechanisms of Dmp1 involvement in inner ear development were explored via RNA-seq analysis. The study suggested that the Fgf23–Klotho endocrine axis may play a novel role in the inner ear and Dmp1 may regulate the kinocilium–stereocilia interaction via Fgf23–Klotho signaling. Together, our results proved the critical role of Dmp1 in the precise regulation of hair bundle morphogenesis in the early development of HCs.