bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions
Authors: Chuprov Sergei , Belyaev Pavel , Gataullin Ruslan , Reznik Leon , Neverov Evgenii , Viksnin Ilia
Published: May 2023

In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.
Study of Pavement Macro- and Micro-Texture Evolution Law during Compaction Using 3D Laser Scanning Technology
Authors: Lin Yuchao , Dong Chenyang , Wu Difei , Jiang Shengchuan , Xiang Hui , Weng Zihang
Published: May 2023

The pavement macro-texture and micro-texture are crucial factors for evaluating pavement performance as they have a significant correlation with friction, water film formation, and driving safety. During pavement construction, the macro-texture and micro-texture are significantly related to compaction operations. However, the current approach for evaluating pavement texture still relies on post-construction acceptance, with few considerations on the evolution patterns of pavement texture during the compaction process. Therefore, this study aimed to investigate the texture evolution law during compaction by implementing a laboratory compaction method. High-precision texture data from various asphalt mixtures were collected using 3D laser scanning during laboratory compaction. Macro-texture and micro-texture parameters were used to evaluate surface texture. Nineteen traditional geometric parameters were calculated at the macro-level to analyze macro-texture characteristics, while a 2D wavelet transform approach was applied at the micro-level to extract micro-texture, and the energy of each level and relative energy were calculated as indicators. This study analyzed the evolution law of parameters and found that certain parameters tend to converge. Moreover, geometric parameters and energy at lower levels of the samples could be utilized as supervising factors to regulate the compaction process.
Research on an Insulator Defect Detection Method Based on Improved YOLOv5
Authors: Qi Yifan , Li Yongming , Du Anyu
Published: May 2023

Insulators are widely used in various aspects of the power system and play a crucial role in ensuring the safety and stability of power transmission. Insulator detection is an important measure to guarantee the safety and stability of the transmission system, and accurate localization of insulators is a prerequisite for detection. In this paper, we propose an improved method based on the YOLOv5s model to address the issues of slow localization speed and low accuracy in insulator detection in power systems. In our approach, we first re-cluster the insulator image samples using the k-means algorithm to obtain different sizes of anchor box parameters. Then, we add the non-local attention module (NAM) to the feature extraction module of the YOLOv5s algorithm. The NAM improves the attention mechanism using the weights’ contribution factors and scaling factors. Finally, we recursively replace the ordinary convolution module in the neck network of the YOLOv5 model with the gated normalized convolution (gnConv). Through these improvements, the feature extraction capability of the network is enhanced, and the detection performance of YOLOv5s is improved, resulting in increased accuracy and speed in insulator defect localization. In this paper, we conducted training and evaluation on a publicly available dataset of insulator defects. Experimental results show that the proposed improved YOLOv5s model achieves a 1% improvement in localization accuracy compared to YOLOv5. The proposed method balances accuracy and speed, meeting the requirements of online insulator localization in power system inspection.
Food Classification and Meal Intake Amount Estimation through Deep Learning
Authors: Kim Ji-hwan , Lee Dong-seok , Kwon Soon-kak
Published: May 2023

This paper proposes a method to classify food types and to estimate meal intake amounts in pre- and post-meal images through a deep learning object detection network. The food types and the food regions are detected through Mask R-CNN. In order to make both pre- and post-meal images to a same capturing environment, the post-meal image is corrected through a homography transformation based on the meal plate regions in both images. The 3D shape of the food is determined as one of a spherical cap, a cone, and a cuboid depending on the food type. The meal intake amount is estimated as food volume differences between the pre-meal and post-meal images. As results of the simulation, the food classification accuracy and the food region detection accuracy are up to 97.57% and 93.6%, respectively.
Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
Authors: Nakamura Katsuya , Kobayashi Yoshikazu , Oda Kenichi , Shigemura Satoshi
Published: May 2023

Acoustic emission (AE) source localization has been used to visualize progress failures generated in a wide variety of materials. In the conventional approaches, AE source localization algorithms assume that the AE signal is propagated as a straight line. However, it is supposed that progress failures form heterogeneity of elastic wave velocity distributions. Hence, with the conventional source localization, it is expected that the localization accuracy is reduced in heterogeneous materials since diffraction and refraction waves are generated. Thus, if the straight propagation waves are classified, conventional source localizations are performed in the heterogeneous materials. The self-organizing map (SOM) is one of the unsupervised learning methods, and the SOM has potential to classify straight propagation waves for the source localizations. However, the application of classified AE signals in source localization is not popular. If classified AE signals are applied to the time difference of arrival (TDOA) method, which is the popular localization method, it is expected that number of visualized sources are decreased because the algorithm does not consider the selection of the propagated wave. Although ray tracing has potential to localize a larger number of sources than the TDOA method, it is expected that the localized sources are less accurate in comparison with results of the TDOA method. In this study, classified waves were applied to two of the source localizations, and model tests based on pencil-lead breaks (PLBs) generating artificial AE sources were conducted to validate the performance of the source localizations with classified waves. The results of the validation confirmed that the maximum error in the TDOA method is larger in comparison with ray tracing conducted with 20 mm intervals of source candidates. Moreover, ray tracing localizes the same number of sources as the number of PLB tests. Therefore, ray tracing is expected to more practically localize AE sources than the TDOA method if classified waves are applied.
Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
Authors: Xie Cunxiang , Zhang Limin , Zhong Zhaogen
Published: May 2023

Entity alignment helps discover and link entities from different knowledge graphs (KGs) that refer to the same real-world entity, making it a critical technique for KG fusion. Most entity alignment methods are based on knowledge representation learning, which uses a mapping function to project entities from different KGs into a unified vector space and align them based on calculated similarities. However, this process requires sufficient pre-aligned entity pairs. To address this problem, this study proposes an entity alignment method based on joint learning of entity and attribute representations. Structural embeddings are learned using the triples modeling method based on TransE and PTransE and extracted from the embedding vector space utilizing semantic information from direct and multi-step relation paths. Simultaneously, attribute character embeddings are learned using the N-gram-based compositional function to encode a character sequence for the attribute values, followed by TransE to model attribute triples in the embedding vector space to obtain attribute character embedding vectors. By learning the structural and attribute character embeddings simultaneously, the structural embeddings of entities from different KGs can be transferred into a unified vector space. Lastly, the similarities in the structural embedding of different entities were calculated to perform entity alignment. The experimental results showed that the proposed method performed well on the DBP15K and DWK100K datasets, and it outperformed currently available entity alignment methods by 16.8, 27.5, and 24.0% in precision, recall, and F1 measure, respectively.
A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application
Authors: Chen Juan , Zhu Zhencai , Hu Haiying , Qiu Lin , Zheng Zhenzhen , Dong Lei
Published: May 2023

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.
Characterizing Internal Flow Field in Binary Solution Droplet Combustion with Micro-Particle Image Velocimetry
Authors: Huang Bingyao , Zhang Haodong , Liu Zundi , Yang Xiaoyuan , Li Wei , Li Yuyang
Published: May 2023

Droplet internal flow participates in liquid-phase mass transfer during multicomponent solution droplet combustion. In this work, internal flow fields in the binary droplet combustion of two polyoxymethylene dimethyl ethers (CH3O(CH2O)nCH3, n ≥ 1, abbreviated as PODEn), i.e., PODE2 and PODE4, are characterized using micro-particle image velocimetry (Micro-PIV). The buoyancy-driven upward vapor flow around the droplet is found to initiate two opposite radial flows in the droplet, which form two vortex cores near the surface, while the gravitational effect and Marangoni effect resulting from the content and temperature gradients in the binary droplets can induce disturbance to the two flows. The binary droplets have comparable spatially averaged flow velocities at the stable evaporation stage to those of pure droplets, which are around 3 mm/s. The velocity curves are more fluctuant and tend to slightly increase and reach the peak values at around 250 ms, and then decrease until droplet atomization. The flow velocities in the droplet interior are generally higher than those near the droplet surface, forming a parabolic velocity profile along the horizontal radial direction. The peak velocity first increases to 5–9 mm/s as the radial flow and vortex structure start to form and then decreases to around 3 mm/s until droplet atomization. The radial flow with a spatially averaged velocity of 3 mm/s can run around one lap during the stable evaporation stage, which implies that the convection-induced mass transfer is relatively weak, and consequently, the content gradient of the binary droplet is still mainly controlled by mass diffusion.
Assessing the Performance of Machine Learning Algorithms for Soil Classification Using Cone Penetration Test Data
Authors: Chala Ayele Tesema , Ray Richard
Published: May 2023

Conventional soil classification methods are expensive and demand extensive field and laboratory work. This research evaluates the efficiency of various machine learning (ML) algorithms in classifying soils based on Robertson’s soil behavioral types. This study employs 4 ML algorithms, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), and decision trees (DT), to classify soils from 232 cone penetration test (CPT) datasets. The datasets were randomly split into training and testing datasets to train and test the ML models. Metrics such as overall accuracy, sensitivity, precision, F1_score, and confusion matrices provided quantitative evaluations of each model. Our analysis showed that all the ML models accurately classified most soils. The SVM model achieved the highest accuracy of 99.84%, while the ANN model achieved an overall accuracy of 98.82%. The RF and DT models achieved overall accuracy scores of 99.23% and 95.67%, respectively. Additionally, most of the evaluation metrics indicated high scores, demonstrating that the ML models performed well. The SVM and RF models exhibited outstanding performance on both majority and minority soil classes, while the ANN model achieved lower sensitivity and F1_score for minority soil class. Based on these results, we conclude that the SVM and RF algorithms can be integrated into software programs for rapid and accurate soil classification.
A Comparison of the Fuel Consumption and Truck Models in Different Production Scenarios
Authors: Vera-Burau Alejandra , Álvarez-Ramírez Daniel , Sanmiquel Lluís , Bascompta Marc
Published: May 2023

Mine planning and mine design are crucial stages of a project in the mining industry. This study aimed to determine the impact of different constraints on the design and planning of a mine. Some of the deposit characteristics and parameters that influence the economic feasibility of a project were studied. Using economic criteria such as NPV, scenarios were established based on the best conditions to obtain higher profitability. Production was identified as the most relevant variable. Subsequently, the mining design was evaluated through technical parameters such as slope gradients and ramp widths, and it was identified that they have lower sensitivity in the final design and higher sensitivity in terms of economic feasibility, performance, and environmental implications. Using operational production, the performance of the loading and haulage equipment fleets was evaluated for each of the techno-economic scenarios. Additionally, the environmental cost was compared using equipment fuel consumption and CO2 emissions. This study presents a practical methodology for analyzing relevant variables in mining projects, their interactions, and identifying potential scenarios.
Using Multiple Monolingual Models for Efficiently Embedding Korean and English Conversational Sentences
Authors: Park Youngki , Shin Youhyun
Published: May 2023

This paper presents a novel approach for finding the most semantically similar conversational sentences in Korean and English. Our method involves training separate embedding models for each language and using a hybrid algorithm that selects the appropriate model based on the language of the query. For the Korean model, we fine-tuned the KLUE-RoBERTa-small model using publicly available semantic textual similarity datasets and used Principal Component Analysis (PCA) to reduce the resulting embedding vectors. We also selected a highly-performing English embedding model from available SBERT models. We compared our approach to existing multilingual models using both human-generated and large language model-generated conversational datasets. Our experimental results demonstrate that our hybrid approach outperforms state-of-the-art multilingual models in terms of accuracy, elapsed time for sentence embedding, and elapsed time for finding the nearest neighbor, regardless of whether a GPU is used. These findings highlight the potential benefits of training separate embedding models for different languages, particularly for tasks involving finding the most semantically similar conversational sentences. We expect that our approach will be used for diverse natural language processing-related fields, including machine learning education.
Implementation of an Accurate Measurement Method for the Spatial Distribution of the Electromagnetic Field in a WPT System
Authors: Petrascu Calin , Tulbure Adrian , Topa Vasile
Published: May 2023

In an inductive wireless power transfer (WPT) system, the transferred power depends on the coupling between the transmitter coil and the receiver coil and, consequently, on their spatial positioning with respect to each other. Since this positioning needs to be as flexible as possible, one can design various construction shapes for the coils, in terms of both winding geometry and core geometry. The system efficiency for different positionings and configurations can be assessed if the structure and distribution of the electromagnetic field (EMF) in the space close to the system are determined. In this context, this paper proposes an investigation method based on measuring the intensity of the electromagnetic field generated by the emitting coil in the WPT space in terms of both modulus and direction. The measurements needed to be performed at a sufficient number of points, so that the entire field was determined. The EMF value was estimated by linear spatial interpolation between the measured points. Moreover, this paper presents the method and the results of investigations carried out only in the presence of the emitting coil. Calibration or correction solutions were also proposed to obtain sufficient accuracy, and the viability of the method was therefore demonstrated.
Soil Deformation Investigation of a Piled-Raft Foundation Pit Under-Crossed by a Super-Large Diameter Shield Tunnel
Authors: Jiang Yue , Li Yanlin , Yao Aijun , Gao Xiangzhi , Li Hui
Published: May 2023

In recent years, there has been a rise in the construction of expansive underground structures and shield tunnels with exceptionally large diameters. These projects introduce unique challenges regarding their impact on the surrounding soil and structures, which differ from those typically encountered in conventional shield tunnels. However, the existing body of research in this specific domain remains insufficient. When such tunnels intersect deep foundation pits supported by piled-raft foundations, the discrepancies in soil deformation can become even more pronounced. At present, there is a dearth of research on the underlying principles governing these differences, and theoretical investigations have not kept pace with practical engineering applications. Consequently, the existing settlement prediction methods employed for diverse projects need to be reevaluated and adjusted to accommodate the distinctive characteristics of each individual project. Regarding the engineering focus of this paper, it is crucial to recognize that soil subsidence in the pit bottom has a significant influence on the mechanical response of the piles. Consequently, the implementation of targeted correction measures remains consistently important. Based on this concept, this paper focuses on a super-large diameter shield tunnel project that under-crossed a deep foundation pit with a piled-raft foundation. The influence of different construction methods on the settlement law of the soil at the bottom of the deep foundation pit is discussed after verification of the accuracy of the model through numerical simulation and field monitoring data. Additionally, two correction coefficients that consider the project’s load characteristics are proposed in this research. These coefficients were used to correct the surface settlement curve. The corrected soil settlement curve at the pit’s bottom can successfully reflect the numerical simulation results, which in turn can reflect the mechanical response of the pile under the influence of tunnel excavation.
Inversion of Interlayer Pressure in High-Vacuum Multilayer Insulation Structures for Cryogen Storage Using Extreme Learning Machine
Authors: Wu Hao , Tan Hongbo
Published: May 2023

Revealing the interlayer pressure distribution in multilayer insulation (MLI) for cryogen (e.g., liquid hydrogen) containers is very important to improve the insulation-performance-predicting quality. This paper proposed an inversion method to reconstruct the interlayer pressure of multilayer insulations on the basis of experimentally measuring the reflectors’ temperatures. The layer-by-layer (LBL) model was modified by considering the interlayer pressure distribution in MLIs to calculate the reflectors’ temperatures. Groups of pre-given interlayer pressure distributions and the corresponding temperature distributions calculated by the LBL model were used to train an extreme learning machine (ELM) algorithm. Finally, the interlayer pressure distribution of the MLI was reconstructed by the trained ELM algorithm based on the measured reflectors’ temperatures. The method was validated by four additional testing cases. The results showed that the proposed algorithm was accurate in reconstructing the interlayer pressures. Published experimentally measured temperature distributions of a 60-layer MLI were used as input data. The abovementioned inversion method was adopted, and a reasonable interlayer pressure distribution was obtained. Moreover, the thermal insulation performance of the MLI was calculated by the LBL model considering the reconstructed interlayer pressure distribution. We found that the predicted heat flux of the MLI deviated from the experimental results by only 2.77%, while the error of the classical LBL model ignoring the non-ideal vacuum condition was as high as 89%. Meanwhile, the predicted corresponding temperature distribution deviated from the tested value by less than 1.13 K. The proposed method can be applied to assess the interlayer pressure distribution of industrial cryogen containers and precisely predict the thermal insulation performance of a practical multilayer insulation structure.
Physiological Study of Visual and Non-Visual Effects of Light Exposure
Authors: Morioka Haruki , Ozawa Haruki , Kato Takeo
Published: May 2023

Light simultaneously induces visual and non-visual effects. Although the differences in the spectral sensitivity of intrinsic photosensitive retinal ganglion cells induce opposing influences on physiological responses, it is difficult to independently measure only non-visual effects. Therefore, the reported effects of light color on physiological responses are inconsistent. This study aimed to clarify the visual and non-visual effects of light color on physiological responses. Three different conditions were employed to construct a lighting environment in which light colors were difficult to perceive due to chromatic adaptation and change blindness: constant white light (baseline condition), a gradual transition from white to blue light, and a gradual transition from white to red light. The physiological responses (brain activity, heart rate variability, and electrodermal activity) of 21 participants were measured with and without light color perception. The results suggested that blue light causes more non-visual effects compared to red light as blue light induces brain activation in some regions of the PFC (p < 0.05) and increases sweating, although the differences were not statistically significant. A mean comparison suggested that the visual effects of blue light showed tendencies toward a calming role for the prefrontal cortex and inhibition of sweating, but the differences were not statistically significant. Another mean comparison suggested that the visual effects of red light tended to enhance sweating, but the differences were not statistically significant. Visual and non-visual effects did not cause significant differences in heart rate variability. Additionally, a mean comparison did not reveal any significant tendencies.
Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers’ Disobedience in ITS
Authors: Hu Zhengyan , Labadie Nacima , Khoukhi Lyes
Published: May 2023

Some research claims that the cultural features of people may have a major impact on driving behavior and can play a serious role in the driving safety. Indeed, unlike machines that follow the instructions strictly, drivers have their own will and may disobey; they sometimes do things completely opposite to the instructions given by the central vehicle system. On the other side, it is proven that the traffic flow guidance feedback strategy has a significant effect on improving road traffic flow capacity. This paper deals with traffic flow dynamics while real-time information is integrated. Drivers’ obedience rate to the instructions provided by an intelligent transportation system (ITS) is also considered. In addition, we implemented the process of pre-filtering the drivers for better managing the traffic, to the best of our knowledge; thus this process is not considered in the related literature. We introduced a dynamic weighted vehicle density feedback strategy (DWVDFS) with SUMO software 19.386-16 for determining whether traffic congestion happens or not. The simulation model considers three-route scenarios with one entrance and one exit. The added value of the proposed pre-filtering process and DWVDFS is also evaluated.
Τopology Optimization under a Single Displacement Constraint Using a Strain Energy Criterion
Authors: Provatidis Christopher G.
Published: May 2023

Based on a previous concept that has been successfully applied to the sizing optimization of truss and frame structures, this work extends and improves the strain energy criterion in the topology optimization of 2D continuum structures under a single displacement constraint. To make the proposed methodology transparent to other researchers and at the same time meaningful, the numerical value of the displacement constraint was taken to be equal to that obtained through the well-known Solid Isotropic Material with Penalization (SIMP) method under the same boundary conditions and the same external forces. The proposed method is more efficient than the SIMP method while leading to topologies very close to those obtained by the latter.
Born in Translation and Iteration: On the Poetics of João Delgado
Authors: Mauas Lea , Rotman Diego
Published: May 2023

João Delgado’s poetry first appeared as an anthology of translated poetry in He’arat Shulaym Issue 1, published in November 2001 in Jerusalem by the artist collective Sala-Manca. The entire issue was devoted to João Delgado. Delgado was a Portuguese-Argentinean poet, born in Lisbon circa 1920 (or not), who left Portugal as a political refugee for Buenos Aires. He disappeared in 1976 during the military dictatorship in Argentina (1976–1983). Since 1976, there has been no trace of his fate, although new fragments of his work are constantly being discovered, translated, and published by the Sala-Manca group. There is no evidence of any originals of his work. Literary critics in Israel praised Delgado’s poetry and art and even identified previously unknown relationships to poets and artists from the European avantgarde. In Sala-Manca’s artistic work, dating back two decades, João Delgado and his heteronyms would have a central role and focus, blurring the boundaries of the group, blurring the fictional with the “real”, and proposing a subjectivity that embraces multiplicity and dispersion. Translating his poetry into Hebrew, a foreign language (to Delgado), and bringing it into print for the first time in neither its original language nor the cultural context in which it was created may be seen as a kind of de-contextualization of the poet’s poetry, since Delgado always kept himself and his oeuvre connected to the immediate surroundings where it was produced. On the other hand, perhaps it is actually this possibility—to “become only in translation”—that is one of the outstanding characteristics of Delgado’s poetry, emblematic of its linguistic and conceptual elasticity. This paper examines João Delgado’s poetic work in relation to Sala-Manca’s artistic work and the way in which both Sala-Manca and Delgado create a “system of life” by being heteronyms of one another, allowing for a multiplicity of identities, and stressing the relation to Others.
Turbulent Convection at Very High Rayleigh Numbers and the Weakly Nonlinear Theory
Authors: Sreenivasan Katepalli R. , Niemela Joseph J.
Published: May 2023

To provide insights into the challenging problem of turbulent convection, Jack Herring used a greatly truncated version of the complete Boussinesq equations containing only one horizontal wavenumber. In light of later observations of a robust large-scale circulation sweeping through convecting enclosures at high Rayleigh numbers, it is perhaps not an implausible point of view from which to reexamine high-Rayleigh-number data. Here we compare past experimental data on convective heat transport at high Rayleigh numbers with predictions from Herring’s model and, in fact, find excellent agreement. The model has only one unknown parameter compared to the two free parameters present in the lowest-order least-squares power-law fit. We discuss why the underlying simplistic physical picture, meant to work at Rayleigh numbers slightly past the critical value of a few thousand, is consistent with the data when the single free parameter in it is revised, over some eleven decades of the Rayleigh number—stretching from about a million to about 1017.
Impacts and Identification of Hearing Aid Refurbishing Programs for People with Hearing Loss: A Scoping Review
Authors: Hotton Mathieu , Prud’Homme Virginie , Richard Léa , Cormier Laurie , Simoneau Katherine , Lefebvre-Demers Mathilde , Vincent Claude , Boucher Normand
Published: May 2023

This article consists of a scoping review completed to describe the impacts of refurbished hearing aids (HAs) for people with hearing loss, and to identify existing HA refurbishing programs around the world. In this review, JBI methodological guidance for scoping reviews was followed. All types of sources of evidence were considered. Thirty-six sources of evidence were included, 11 articles and 25 web pages. Results suggest that refurbished HAs may improve communication and social participation for individuals with hearing loss and provide monetary savings to them and to governmental agencies. Twenty-five HA refurbishing programs were identified, all based in developed countries and distributing refurbished HAs mostly locally, but also in developing countries. Issues related to refurbished HAs were highlighted, such as potential cross-contamination, quick obsolescence, and repairing problems. Some facilitators for the success of this intervention are to offer accessible and affordable follow-up services, repairs, and batteries, and to ensure awareness and participation of hearing healthcare professionals and citizens with hearing loss. In conclusion, the use of refurbished HAs appears to be a valuable option for low-income people with hearing loss, but it should be included in a more global intervention program to ensure its sustainability.
Dynamic Charge Acceptance Compared to Electrochemical Impedance Spectroscopy Parameters: Dependencies on Additives, State of Charge, and Prior Usage
Authors: Bauknecht Sophia , Kowal Julia , Settelein Jochen , Föhlisch Markus , Karden Eckhard
Published: May 2023

The goal of this work was to predict the dynamic charge acceptance (DCA) for cells using different additives on the negative electrode from the evaluation of small-signal measurements by electrochemical impedance spectroscopy (EIS). Thereby, various operating points were evaluated, such as state of charge (SoC) and prior usage (charge or discharge). The 2V test cells under investigation utilized plates of enhanced flooded 3P2N battery cells (EFB). They contained three positive and two negative electrodes. The latter varied in their additive composition. In total, eight different negative electrodes were investigated, five including specially synthesized amorphous carbon as an additive, two with unknown additive mixes, and one including a commercially available carbon black. The best parameters for predicting the DCA were found within the first semicircle of the negative half-cell spectra measured during a superimposed charging current.
The Interaction of Factor Xa and IXa with Non-Activated Antithrombin in Michaelis Complex: Insights from Enhanced-Sampling Molecular Dynamics Simulations
Authors: Balogh Gábor , Bereczky Zsuzsanna
Published: May 2023

The interaction between coagulation factors Xa and IXa and the activated state of their inhibitor, antithrombin (AT),have been investigated using X-ray diffraction studies. However, only mutagenesis data are available for non-activated AT. Our aim was to propose a model based on docking and advanced-sampling molecular dynamics simulations that can reveal the conformational behavior of the systems when AT is not binding a pentasaccharide. We built the initial structure for non-activated AT-FXa and AT-FIXa complexes using HADDOCK 2.4. The conformational behavior was studied using Gaussian accelerated molecular dynamics simulations. In addition to the docked complexes, two systems based on the X-ray structures were also simulated, with and without the ligand. The simulations revealed large variability in conformation for both factors. In the docking-based complex of AT-FIXa, conformations with stable Arg150–AT interactions can exist for longer time periods but the system also has a higher tendency for reaching states with very limited interaction with the “exosite” of AT. By comparing simulations with or without the pentasaccharide, we were able to gain insights into the effects of conformational activation on the Michaelis complexes. RMSF analysis and correlation calculations for the alpha-carbon atoms revealed important details of the allosteric mechanisms. Our simulations provide atomistic models for better understanding the conformational activation mechanism of AT against its target factors.
Therapeutic Advances in the Treatment of Gastroesophageal Cancers
Authors: Li Jenny J. , Rogers Jane E. , Yamashita Kohei , Waters Rebecca E. , Blum Murphy Mariela , Ajani Jaffer A.
Published: May 2023

Gastroesophageal cancers are a group of aggressive malignancies that are inherently heterogeneous with poor prognosis. Esophageal squamous cell carcinoma, esophageal adenocarcinoma, gastroesophageal junction adenocarcinoma, and gastric adenocarcinoma all have distinct underlying molecular biology, which can impact available targets and treatment response. Multimodality therapy is needed in the localized setting and treatment decisions require multidisciplinary discussions. Systemic therapies for treatment of advanced/metastatic disease should be biomarker-driven, when appropriate. Current FDA approved treatments include HER2-targeted therapy, immunotherapy, and chemotherapy. However, novel therapeutic targets are under development and future treatments will be personalized based on molecular profiling. Herein, we review the current treatment approaches and discuss promising advances in targeted therapies for gastroesophageal cancers.
Phospholipases in Gliomas: Current Knowledge and Future Perspectives from Bench to Bedside
Authors: Marvi Maria Vittoria , Neri Irene , Evangelisti Camilla , Ramazzotti Giulia , Asioli Sofia , Zoli Matteo , Mazzatenta Diego , Neri Niccolò , Morandi Luca , Tonon Caterina , Lodi Raffaele , Franceschi Enrico , McCubrey James A. , Suh Pann-Ghill , Manzoli Lucia , Ratti Stefano
Published: May 2023

Phospholipases are essential intermediaries that work as hydrolyzing enzymes of phospholipids (PLs), which represent the most abundant species contributing to the biological membranes of nervous cells of the healthy human brain. They generate different lipid mediators, such as diacylglycerol, phosphatidic acid, lysophosphatidic acid, and arachidonic acid, representing key elements of intra- and inter-cellular signaling and being involved in the regulation of several cellular mechanisms that can promote tumor progression and aggressiveness. In this review, it is summarized the current knowledge about the role of phospholipases in brain tumor progression, focusing on low- and high-grade gliomas, representing promising prognostic or therapeutic targets in cancer therapies due to their influential roles in cell proliferation, migration, growth, and survival. A deeper understanding of the phospholipases-related signaling pathways could be necessary to pave the way for new targeted therapeutic strategies.
Transcriptome Profiling of Rhipicephalus annulatus Reveals Differential Gene Expression of Metabolic Detoxifying Enzymes in Response to Acaricide Treatment
Authors: Achuthkumar Amritha , Uchamballi Shamjana , Arvind Kumar , Vasu Deepa Azhchath , Varghese Sincy , Ravindran Reghu , Grace Tony
Published: May 2023

Ticks are hematophagous ectoparasites of economic consequence by virtue of being carriers of infectious diseases that affect livestock and other sectors of the agricultural industry. A widely prevalent tick species, Rhipicephalus (Boophilus) annulatus, has been recognized as a prime vector of tick-borne diseases in South Indian regions. Over time, the use of chemical acaricides for tick control has promoted the evolution of resistance to these widely used compounds through metabolic detoxification. Identifying the genes related to this detoxification is extremely important, as it could help detect valid insecticide targets and develop novel strategies for effective insect control. We performed an RNA-sequencing analysis of acaricide-treated and untreated R. (B.) annulatus and mapped the detoxification genes expressed due to acaricide exposure. Our results provided high-quality RNA-sequenced data of untreated and amitraz-treated R. (B.) annulatus, and then the data were assembled into contigs and clustered into 50,591 and 71,711 uni-gene sequences, respectively. The expression levels of the detoxification genes across different developmental stages of R. (B.) annulatu identified 16,635 transcripts as upregulated and 15,539 transcripts as downregulated. The annotations of the differentially expressed genes (DEGs) revealed the significant expression of 70 detoxification genes in response to the amitraz treatment. The qRT-PCR revealed significant differences in the gene expression levels across different life stages of R. (B.) annulatus.
The Role of NETosis and Complement Activation in COVID-19-Associated Coagulopathies
Authors: Ghanbari Emily Parissa , Jakobs Kai , Puccini Marianna , Reinshagen Leander , Friebel Julian , Haghikia Arash , Kränkel Nicolle , Landmesser Ulf , Rauch-Kröhnert Ursula
Published: May 2023

Inflammation-induced coagulopathy is a common complication associated with coronavirus disease 2019 (COVID-19). We aim to evaluate the association of NETosis and complement markers with each other as well as their association with thrombogenicity and disease severity in COVID-19. The study included hospitalized patients with an acute respiratory infection: patients with SARS-CoV2 infection (COVpos, n = 47) or either pneumonia or infection-triggered acute exacerbated COPD (COVneg, n = 36). Our results show that NETosis, coagulation, and platelets, as well as complement markers, were significantly increased in COVpos patients, especially in severely ill COVpos patients. NETosis marker MPO/DNA complexes correlated with coagulation, platelet, and complement markers only in COVpos. Severely ill COVpos patients showed an association between complement C3 and SOFA (R = 0.48; p ≤ 0.028), C5 and SOFA (R = 0.46; p ≤ 0.038), and C5b-9 and SOFA (R = 0.44; p ≤ 0.046). This study provides further evidence that NETosis and the complement system are key players in COVID-19 inflammation and clinical severity. Unlike previous studies that found NETosis and complement markers to be elevated in COVID-19 patients compared to healthy controls, our findings show that this characteristic distinguishes COVID-19 from other pulmonary infectious diseases. Based on our results, we propose that COVID-19 patients at high risk for immunothrombosis could be identified via elevated complement markers such as C5.
The Very Low IgE Producer: Allergology, Genetics, Immunodeficiencies, and Oncology
Authors: Matricardi Paolo Maria
Published: May 2023

Opposite to other immunoglobulin (Ig) classes and subclasses, there is no consensus on the definition of normal levels of serum total IgE. However, longitudinal studies on birth cohorts produced growth charts of total IgE levels in helminth-free and never atopic children and defining the normal ranges of total serum IgE concentration at the individual, rather than population, level. Accordingly, very ‘low IgE producers’ (i.e., children whose tIgE level belong to the lowest percentiles) became atopic while keeping their total IgE levels in a range considered ‘normal’ if compared to the general age-matched population but ‘abnormally high’ if projected on the tIgE growth chart against the trajectory of that child’s own percentile levels. In ‘low IgE producers’, the IgE-specific activity, i.e., the ratio between allergen-specific and total IgE, is more important than the absolute specific IgE levels to confirm causality between allergen exposure and allergic symptoms. Patients with allergic rhinitis or peanut anaphylaxis but low or undetectable allergen-specific IgE levels must therefore be reconsidered considering their total IgE levels. Low IgE producers have been also associated with common variable immunodeficiency, lung diseases, and malignancies. A few epidemiological studies have shown a higher risk of malignancies in very low IgE producers, leading to a debated hypothesis proposing a novel, evolutionistic-relevant function for IgE antibodies for antitumor immune surveillance.
Understanding the Phage–Host Interaction Mechanism toward Improving the Efficacy of Current Antibiotics in Mycobacterium abscessus
Authors: Gorzynski Mylene , De Ville Katalla , Week Tiana , Jaramillo Tiana , Danelishvili Lia
Published: May 2023

Pulmonary infections caused by Mycobacterium abscessus (MAB) have been increasing in incidence in recent years, leading to chronic and many times fatal infections due to MAB’s natural resistance to most available antimicrobials. The use of bacteriophages (phages) in clinics is emerging as a novel treatment strategy to save the lives of patients suffering from drug-resistant, chronic, and disseminated infections. The substantial research indicates that phage–antibiotic combination therapy can display synergy and be clinically more effective than phage therapy alone. However, there is limited knowledge in the understanding of the molecular mechanisms in phage–mycobacteria interaction and the synergism of phage–antibiotic combinations. We generated the lytic mycobacteriophage library and studied phage specificity and the host range in MAB clinical isolates and characterized the phage’s ability to lyse the pathogen under various environmental and mammalian host stress conditions. Our results indicate that phage lytic efficiency is altered by environmental conditions, especially in conditions of biofilm and intracellular states of MAB. By utilizing the MAB gene knockout mutants of the MAB_0937c/MmpL10 drug efflux pump and MAB_0939/pks polyketide synthase enzyme, we discovered the surface glycolipid diacyltrehalose/polyacyltrehalose (DAT/PAT) as one of the major primary phage receptors in mycobacteria. We also established a set of phages that alter the MmpL10 multidrug efflux pump function in MAB through an evolutionary trade-off mechanism. The combination of these phages with antibiotics significantly decreases the number of viable bacteria when compared to phage or antibiotic-alone treatments. This study deepens our understanding of phage–mycobacteria interaction mechanisms and identifies therapeutic phages that can lower bacterial fitness by impairing an antibiotic efflux function and attenuating the MAB intrinsic resistance mechanism via targeted therapy.
Osteopontin in Pulmonary Hypertension
Authors: Mamazhakypov Argen , Maripov Abdirashit , Sarybaev Akpay S. , Schermuly Ralph Theo , Sydykov Akylbek
Published: May 2023

Pulmonary hypertension (PH) is a pathological condition with multifactorial etiology, which is characterized by elevated pulmonary arterial pressure and pulmonary vascular remodeling. The underlying pathogenetic mechanisms remain poorly understood. Accumulating clinical evidence suggests that circulating osteopontin may serve as a biomarker of PH progression, severity, and prognosis, as well as an indicator of maladaptive right ventricular remodeling and dysfunction. Moreover, preclinical studies in rodent models have implicated osteopontin in PH pathogenesis. Osteopontin modulates a plethora of cellular processes within the pulmonary vasculature, including cell proliferation, migration, apoptosis, extracellular matrix synthesis, and inflammation via binding to various receptors such as integrins and CD44. In this article, we provide a comprehensive overview of the current understanding of osteopontin regulation and its impact on pulmonary vascular remodeling, as well as consider research issues required for the development of therapeutics targeting osteopontin as a potential strategy for the management of PH.