bluuebunny/crossref_metadata_2025 huggingface dataset:
First Insight into the Neuroprotective and Antibacterial Effects of Phlorotannins Isolated from the Cell Walls of Brown Algae Fucus vesiculosus and Pelvetia canaliculata
Authors: Meshalkina Darya , Tsvetkova Elena , Orlova Anastasia , Islamova Renata , Grashina Maria , Gorbach Daria , Babakov Vladimir , Francioso Antonio , Birkemeyer Claudia , Mosca Luciana , Tarakhovskaya Elena , Frolov Andrej
Published: March 2023

Phaeophyceae (brown algae) essentially contribute to biotopes of cold and temperate seas. Their thalli are rich in biologically active natural products, which are strongly and universally dominated with phlorotannins—polyphenols of complex and diverse structure based on multiple differently arranged phloroglucinol units and well known as strong antioxidants with a broad spectrum of biological activities. In the algal cells, phlorotannins can either accumulate in the cytoplasm or can be secreted into the cell wall (CW). The biological activities of extractable intracellular phlorotannins have been comprehensively characterized, whereas the properties of the CW-bound polyphenol fraction are still mostly unknown. Recently, we identified dibenzodioxin bonding as the principal structural feature of the CW-bound phlorotannins in fucoid algae, whereas soluble intracellular phlorotannins rely on aryl and ether bonds. However, profiles of biological activity associated with these structural differences are still unknown. Therefore, to the best of our knowledge, for the first time we address the antioxidant, cytotoxic, neuroprotective, and antibacterial properties of the CW-bound phlorotannin fractions isolated from two representatives of the order Fucales—Fucus vesiculosus and Pelvetia canaliculata. The CW-bound phlorotannins appeared to be softer antioxidants, stronger antibacterial agents and were featured with essentially less cytotoxicity in comparison to the intracellular fraction. However, the neuroprotective effects of both sub-cellular phlorotannin fractions of F. vesiculosus and P. canaliculata were similar. Thus, due to their lower cytotoxicity, CW-bound phlorotannins can be considered as promising antioxidants and neuroprotectors.
Urinary Phenolic Metabolites Associated with Peanut Consumption May Have a Beneficial Impact on Vascular Health Biomarkers
Authors: Parilli-Moser Isabella , Domínguez-López Inés , Vallverdú-Queralt Anna , Hurtado-Barroso Sara , Lamuela-Raventós Rosa
Published: March 2023

Phenolic compounds in peanuts may moderate inflammation and endothelial function. Thus, the aim of this study was to evaluate the association of urinary phenolic metabolites (UPMs) with vascular biomarkers after peanut product consumption. A three-arm parallel-group randomized controlled trial was conducted in 63 healthy young adults who consumed 25 g/day of skin roasted peanuts (SRP), 32 g/day of peanut butter (PB), or 32 g/day of a control butter for six months. UPMs were analyzed by liquid chromatography coupled to mass spectrometry. Additionally, urinary eicosanoids, prostacyclin I2 (PGI2), and thromboxane A2 (TXA2) were determined using two competitive enzyme-linked immunosorbent assay kits. Consumers of SRP and PB presented significantly higher excretion of UPMs (enterodiol glucuronide (p = 0.018 and p = 0.031), 3-hydroxybenzoic acid (p = 0.002 and p < 0.001), vanillic acid sulfate (p = 0.048 and p = 0.006), p-coumaric acid (p = 0.046 and p = 0.016), coumaric acid glucuronide I (p = 0.001 and p = 0.030) and II (p = 0.003 and p = 0.036), and isoferulic acid (p = 0.013 and p = 0.015) in comparison with the control group. An improvement in PGI2 (p = 0.037) levels and the TXA2:PGI2 ratio (p = 0.008) was also observed after the peanut interventions compared to the control. Interestingly, UPMs with significantly higher post-intervention levels were correlated with an improvement in vascular biomarkers, lower TXA2 (r from −0.25 to −0.48, p < 0.050) and TXA2:PGI2 ratio (r from −0.25 to −0.43, p < 0.050) and higher PGI2 (r from 0.24 to 0.36, p < 0.050). These findings suggest that the UPMs with higher excretion after peanut product consumption could have a positive impact on vascular health.
Sorting of Fresh Tea Leaf Using Deep Learning and Air Blowing
Authors: Cao Jie , Wu Zhengmin , Zhang Xuechen , Luo Kun , Zhao Bo , Sun Changying
Published: March 2023

The sorting of machine-picked fresh tea leaves after mechanized harvesting remains a challenge because of the complex morphological characteristics and physicochemical properties of fresh tea leaves. First, the recognition results of four types of models, namely, YOLOv5, YOLOv3, Fast RCNN, and SSD, were compared. It was found that YOLOv5, with guaranteed recognition accuracy, had a recognition speed of 4.7 ms/frame (about four times that of the second ranked YOLOv3). Therefore, this study presents a novel fresh tea leaf sorting system that provides rapid and high-precision multi-channel sorting for four grades of tea leaves using a tea leaf recognition model based on the You Only Look Once (YOLOv5) deep learning model. Subsequently, a raw dataset, consisting of 6400 target images of different grades and different moisture contents, was used to evaluate three different optimization methods. Among these, the Stochastic Gradient Descent (SGD) optimization method was found to provide the best model training results with an average recognition accuracy of 98.2%. In addition, the recognition efficacy of the recognition model was found to be positively correlated with the gradient coverage of tea’s moisture content in the training set. Theoretical analysis was then conducted, along with the experimental investigation of the air-blowing force on the fresh tea leaves in the sorting process, with 30° determined to be the optimal air-blowing angle. Finally, the overall results showed that the construction of the full moisture content training set enabled a model recognition accuracy of up to 88.8%, a recall of 88.4%, a recognition speed of 4.7 ms/frame, and an overall sorting accuracy of 85.4%. This result is promising for multi-channel sorting of fresh tea leaf grades in complex situations, and as such provides a strong basis for the application of tea leaf sorting equipment.
Structural and Vibro-Acoustics Optimization of a Car Body Rear Part
Authors: Citarella Roberto , Landi Tommaso , Caivano Luca , D’Errico Giuseppe , Raffa Francesca , Romano Mario , Armentani Enrico
Published: March 2023

The perceived vibro-acoustic comfort, inside the passenger compartment, under driving conditions, is strictly related to the car body torsional behavior. The aim of this work was to identify which parts of a car body most influence the first torsional mode, in order to modify them and acquire an increase in such car body natural frequency. It was also intended to exploit the great potential of 3D printing that allows an increase in the complexity of component shapes, with an acceptable compromise with respect to production costs. A design and material (from steel to aluminum) change of a car body rear part, which was identified as the structural part of the car body with the most relevant impact on the frequency of the first torsional mode, was assessed in terms of structural and vibro-acoustic performances. In particular, with the constraint of increasing the structural and vibro-acoustic performances and, at the same time, minimize the weight of the structure itself, geometric, structural (e.g., type of connections), and material changes of the car body rear part were assessed. Working on a car model dating back to 2008, which was already compliant with structural and vibro-acoustic regulatory norms, an increase of 2 Hz on the first torsional mode frequency of the Trimmed Body model was obtained. In parallel, a weight reduction in the optimized components was also gained. It was also requested to lower the cabin sound pressure levels, optimizing the vibro-acoustic transfer functions from the accelerations at engine mounts and suspension attachment points to the cabin inside. It was shown how the combined use of advanced topological and structural optimization tools, with the capabilities of an unconventional manufacturing technology, such as 3D printing in aluminum, could guarantee an increase in the vibro-acoustics and structural car performances, also gaining a weight reduction.
Quaternary Evolution of Ischia: A Review of Volcanology and Geology
Authors: Aiello Gemma , Caccavale Mauro
Published: March 2023

Ischia shows intriguing and complex geology, which has been deeply investigated. In this paper, a reappraisal of the Quaternary geologic evolution of Ischia based on literature data is advised, concentrating on the volcanology of the island, based on field data and geochemistry, due to the happening of active fumarolic systems on the island and the marine geology and geophysics, which are intensively studied in the frame of the CARG Project. The literature studies have been incorporated with the geological interpretation of high-resolution seismic profiles, partly previously published and herein reorganized with the aim to highlight the geologic evolution of the different sectors of the island (northern Ischia, southern Ischia). The outcrop data have shown the deposits of ten explosive eruptions: among them, we focused on the S. Angelo Tephra. The laccolith model has been described in order to explain the resurgence of Ischia starting from 55 ky B.P. Geochemical information has been synthesized to reconstruct the volcano-tectonic development of Ischia during the last 55 ky B.P. Different models of block resurgence of Ischia have been discussed, based on literature studies. These aspects have supplemented the Quaternary geologic evolution of Ischia. While the northern Ischia offshore shows complex stratigraphic relationships between buried volcanic edifices, the southern Ischia offshore has been mainly commanded by erosional activities, progressive next to a dense system of submarine channels, and by the volcano-tectonic activities, which have triggered off the location of the Ischia Debris Avalanche.
Study on Interface Mechanical Properties of Graphene/Copper Matrix Composites
Authors: Li Dongbo , Liu Yongkun , Liu Qinlong
Published: March 2023

Graphene/copper matrix composites have a wide range of application prospects, but the mechanical properties of the interface have been one of the key problems restricting their wide application. In this paper, the mechanical behaviors at the interface of graphene/copper matrix composites, such as pulling up, pulling out, and cohesion, and the effects of temperature and graphene content on them were studied by the molecular dynamics method. The results show that the pull-up force and cohesiveness show two stages in the whole process. The pulling force increases rapidly and then decreases to 0 slowly. The pull-out force shows three stages: it rises rapidly at first, then fluctuates continuously, and finally drops to 0. The mechanical properties of the interface deteriorated with the increase in temperature. When the temperature increased from 0 K to 1100 K, the interface normal strength, shear strength, and cohesion strength of the interface decreased by 26.3%, 32.9%, and 24.8%, respectively. In addition, with the increase in graphene content, the normal strength of the interface increases, the shear strength decreases, and the cohesion strength almost stays the same. When the graphene content increases from 6.71 at% to 11.75 at%, the normal strength increases by 6.8%, while the shear strength decreases by 37.4%. The influence mechanism of temperature and content is explained from the aspects of the influence of atomic thermal motion and the hindering effect of graphene on the dislocation motion of the copper matrix. The relevant results have certain reference values for the engineering application and theoretical research of graphene/copper composites.
Multi-Objective Hybrid Flow-Shop Scheduling in Parallel Sequential Mode While Considering Handling Time and Setup Time
Authors: Feng Yingjie , Kong Jili
Published: March 2023

Hybrid flow-shop scheduling based on the parallel sequential movement mode (HFSP-PSMM) is an extended application of hybrid flow-shop scheduling that ensures that the equipment works continuously during the processing cycle. However, current research has only investigated the flow-shop scheduling of single-equipment processing, and ignores the effect of auxiliary time. Therefore, this paper investigates a multi-equipment hybrid flow-shop scheduling problem based on the parallel sequential movement mode and considers the setup time and handling time. The mathematical model of the HFSP-PSMM was developed with the handling and makespan numbers as the optimization objectives. The NSGA-II-V based on the NSGA-II was designed by combining the problem characteristics. New crossover, mutation, and selection strategies were proposed and variable neighborhood search operations were implemented for the optimal set of Pareto solutions. Finally, through an algorithm comparison, performance testing, and an example simulation, the effectiveness of the NSGA-II-V for solving the HFSP-PSMM was verified.
An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection
Authors: Lee Ji-Na , Lee Ji-Yeoun
Published: March 2023

The Saarbruecken Voice Database (SVD) is a public database used by voice pathology detection systems. However, the distributions of the pathological and normal voice samples show a clear class imbalance. This study aims to develop a system for the classification of pathological and normal voices that uses efficient deep learning models based on various oversampling methods, such as the adaptive synthetic sampling (ADASYN), synthetic minority oversampling technique (SMOTE), and Borderline-SMOTE directly applied to feature parameters. The suggested combinations of oversampled linear predictive coefficients (LPCs), mel-frequency cepstral coefficients (MFCCs), and deep learning methods can efficiently classify pathological and normal voices. The balanced datasets from ADASYN, SMOTE, and Borderline-SMOTE are used to validate and evaluate the various deep learning models. The experiments are conducted using model evaluation metrics such as the recall, specificity, G, and F1 value. The experimental results suggest that the proposed voice pathology detection (VPD) system integrating the LPCs oversampled by the SMOTE and a convolutional neural network (CNN) can effectively yield the highest accuracy at 98.89% when classifying pathological and normal voices. Finally, the performances of oversampling algorithms such as the ADASYN, SMOTE, and Borderline-SMOTE are discussed. Furthermore, the performance of SMOTE is superior to conventional imbalanced data oversampling algorithms, and it can be used to diagnose pathological signals in real-world applications.
The Influence of Disclosing the AI Potential Error to the User on the Efficiency of User–AI Collaboration
Authors: Lukashova-Sanz Olga , Dechant Martin , Wahl Siegfried
Published: March 2023

User–AI collaboration is an increasingly common paradigm in assistive technologies. However, designers of such systems do not know whether communicating the AI’s accuracy is beneficial. Disclosing the accuracy could lead to more informed decision making or reduced trust in the AI. In the context of assistive technologies, understanding how design decisions affect User–AI collaboration is critical because less efficient User–AI collaboration may drastically lower the quality of life. To address this knowledge gap, we conducted a VR study in which a simulated AI predicted the user’s intended action in a selection task. Fifteen participants had to either intervene or delegate the decision to the AI. We compared participants’ behaviors with and without the disclosure of details on the AI’s accuracy prior to the system’s deployment while also varying the risk level in terms of decision consequences. The results showed that communicating potential errors shortened the decision-making time and allowed the users to develop a more efficient strategy for intervening in the decision. This work enables more effective designs of the interfaces for assistive technologies using AI.
Simultaneous Viscosity Measurement of Suspended Blood and Plasma Separated by an Ultrasonic Transducer
Authors: Kang Yang Jun
Published: March 2023

Blood viscosity is influenced by several factors, including red blood cell (RBC) deformability, hematocrit (Hct), and plasma protein levels. To effectively isolate the individual contributions of several factors, it is necessary to simultaneously measure the viscosities of the blood and plasma. In this study, the viscosities of suspended blood and plasma were obtained sequentially by adopting an ultrasonic transducer for plasma separation and a co-flowing microfluidic channel for viscosity measurement. To improve the measurement accuracy of viscosity, the correction factor was obtained through experiments and numerical simulations, which was then inserted into the analytical expression for viscosity. To stabilize the pulsatile blood flow resulting from a micropump, the frequency (f) and voltage (v) were set to f = 300 Hz and v = 140 au, respectively. Flexible polyethylene tubing (i.d. = 500 µm, length = 40 mm) was connected to the microfluidic device as an air damper. Consequently, the coefficient of variance of the blood velocity decreased by up to 1%. As a demonstration, suspended blood (Hct = 20%, 30%, and 40%) was prepared by adding normal RBCs to autologous plasma. Compared with the previous method, the present method overestimates the viscosity values of both the fluids (i.e., suspended blood: 14–25% and plasma: 7–21%). The present method has the ability to sequentially measure the viscosities of suspended blood and plasma. The integrated system contributes to reducing blood-handling procedures (i.e., blood collection, blood loading into the syringe, and syringe installation into the syringe pump).
Development of a Prediction Model for Daily PM2.5 in Republic of Korea by Using an Artificial Neutral Network
Authors: Huh Jin-Woo , Youn Jong-Sang , Park Poong-Mo , Jeon Ki-Joon , Park Sejoon
Published: March 2023

This study aims to develop PM2.5 prediction models using air pollutant data (PM10, NO2, SO2, O3, CO, and PM2.5) and meteorological data (temperature, humidity, wind speed, atmospheric pressure, precipitation, and snowfall) measured in South Korea from 2015 to 2019. Two prediction models were developed using an artificial neural network (ANN): a nationwide (NW) model and administrative districts (AD) model. To develop the prediction models, the independent variables daily averages and variances of air pollutant data and meteorological data (independent variables) were used as independent variables, and daily average PM2.5 concentration set as a dependent variable. First, the correlations between independent and dependent variables were analyzed. Second, prediction models were developed using an ANN to predict next-day PM2.5 daily average concentration, both NW and in 16 AD. The ANN models were optimized using a factorial design to determine the hidden layer layout and threshold, and a seasonal (monthly) factor was also considered. In the optimal prediction model, the absolute error in 1 σ was 91% (in-sample 91%, out-of-sample 91%) for the NW model, and the absolute error in 1 σ was 86% (in-sample 88%, out-of-sample 84%) for AD model. The accuracy of these prediction models increases further when they are developed using the next-day weather data, assuming that the weather prediction is accurate.
Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
Authors: Hu Lihe , Zhang Yi , Wang Yang , Yang Huan , Tan Shuyi
Published: March 2023

Semantic mapping can help robots better understand the environment and is extensively studied in robotics. However, it is a challenge for semantic mapping that calibrates all the obstacles with semantics. We propose integrating two network models to realize the salient semantic segmentation used for mobile robot mapping, which differs from traditional segmentation methods. Firstly, we detected salient objects. The detection result was the grayscale image form, which was recognized and annotated by our trained model. Then, we projected the salient objects’ contour with semantics to the corresponding RGB image, which realized the salient objects’ semantic segmentation. We treated the salient objects instead of all the obstacles as semantic segmentation objects that could reduce the background consideration. The neural network model trained based on the salient object’s shape information was stable for object recognition and easy for model training. We only used the shape feature for training, which could reduce the calculation amount of feature details. Experiments demonstrated that the algorithm could quickly realize the model’s training and provide a semantic landmark in the point cloud map as the relative position reference for robot repositioning when the map needs to be used again and exist in a similar environment.
Design and Implementation of an IPT Charger with Minimum Number of Elements for Battery Charging Applications
Authors: Zhou Xuebin , Shao Shuai , Tan Yonghong , Chen Aiwu , Wang Zhitang , Yang Lin
Published: March 2023

Inductive power transfer (IPT) systems have been extensively studied and incorporated into various industrial applications. An IPT system with the capability to provide constant current output (CCO) and constant voltage output (CVO) is necessary to ensure optimal battery performance. However, the resistance of the battery will continue to increase during the charging process, making it difficult for the system to realize load-independent CCO and CVO with zero phase angle (ZPA) operation. Therefore, this paper proposes an LC/S compensated IPT system, which can provide CCO and CVO at fixed frequencies. ZPA operation can be maintained in both CC and CV modes for low reactive power loss. Moreover, the total number of compensation elements of the system is minimized, with only one compensation capacitor on the receiving side, which ensures not only the low cost of the system but also a compact receiver. Finally, the correctness and feasibility of the proposed system are verified with simulations and experiments.
Spectroscopic and Imaging Analyses on Easel Paintings by Giovanni Santi
Authors: Amadori Maria Letizia , Poldi Gianluca , Germinario Giulia , Arduini Jgor , Mengacci Valeria
Published: March 2023

The most important painter from Urbino in the last decades of the 15th century (1439 ab.–1494) was Giovanni Santi, the father of Raphael. The lack of scientific literature about Santi’s practice and the possible peculiar role of Urbino in the development of painting techniques in northern Italy suggested in-depth investigations of the entire corpus of his paintings. A well-established sequence of multispectral imaging, spectroscopic and microscopic investigations was performed on 24 wood panel paintings and 2 canvases attributed by most scholars to Giovanni Santi (1439 ab.–1494) to collect a large set of significant data. This systematic research allowed his painting technique to be defined, starting from the type of supports he used and from the features of the underdrawing, which quite frequently included characteristic regular hatching. The pigments used were widely investigated by means of ED-XRF and reflectance spectroscopy (vis-RS); a meaningful multivariate statistical method (PCA and HCA analysis) was also applied to the ED-XRF dataset acquired for representative hues. In particular, the vis-RS technique proved to be a simple and effective diagnostic tool to detect many pigments, including indigo, and to distinguish between two different types of red lakes, avoiding sampling and more complex analyses. Santi used lead white, Fe-Mn-based pigments, vermilion, red lake, natural blue ultramarine, azurite, copper-based green pigments (particularly verdigris), lead-tin yellow, scarcely ever orpiment and, in a few green mixtures, also indigo. Despite the palette being linked to tradition, the master appeared to introduce some peculiarities, such as the addition of glass powder, and mixing pigments both in a traditional way and using them to create chromatic effects unusual for his time. This research confirmed that the systematic use of the integrated non-invasive methods is highly representative, and the results of this wide diagnostic campaign provided a significant dataset which allowed the implementation of a scientific database related to central Italy Renaissance paintings and materials.
Risk-Based Hybrid Light-Weight Ship Structural Design Accounting for Carbon Footprint
Authors: Garbatov Yordan , Palomba Giulia , Crupi Vincenzo
Published: March 2023

The study aims to develop an integrating risk-based formulation and cost-benefit analysis for identifying an optimal ship hull structural design solution where the steel cargo holds aluminium honeycomb sandwich panels to replace inner side shells. The risk of progressive structural failure includes hazards related to environmental pollution due to accidental fuel and oil spills, possible loss of cargo, crew members and ship during operations, and air pollution during shipyard construction and ship voyages. The structural failure incorporates progressive time-dependent structural degradation coupled with ship hull load-carrying capacity in predicting structural integrity during the service life. The ship hull structural failure and associated risk are estimated over the ship’s service life as a function of the design solution. The carbon footprint and cost to mitigate the impact for the entire steel and hybrid ship hull structural solution implemented as a sustainable life cycle solution are analysed where the steel ship hull structure is built through primary construction. The cost of structural measures accounts for redesigning the ship structure and implementing aluminium honeycomb composite panels instead of steel plates, reducing steel weight, environmental pollution and cost and increasing the transported cargo and corrosion degradation resistance. It has been found that design solutions AHS1 and AHS2, in which aluminium honeycomb panels replace the inner steel shell plates, enhance the corrosion degradation resistance, and reduce the ship hull’s lightweight, reflecting a better beta-reliability index at the time of the first repair with a lower repair cost and more transported cargo. The cost of the ship associated with the design solutions AHS1 and AHS2 is about 11% lower than the steel solutions.
A Systematic Review of Spatial Differences of the Ball Impact within the Serve Type at Professional and Junior Tennis Players
Authors: Vacek Jan , Vagner Michal , Cleather Daniel John , Stastny Petr
Published: March 2023

Since the flat serve (FS) minimizes the ball spin and kick serve (KS) combined topspin and sidespin, this systematic review aimed to explore the ball impact location (BI) within the FS and KS at the professional men, junior men, and women tennis players. The PRISMA guideline was used, and the original articles were searched in Scopus, Web of Science, and PubMed. The means and standard deviations computed from the distance of BI from the origin within the FS and KS on the x, y, and z axes (global coordinate system) were normalized by the participants’ height and weighted by the number of participants in one-way ANOVA. Ten articles with a pooled sample of 133 males and 51 females aged 11–25 were included. The professional men had more stable BI on the x-axis within the FS by 56% (p < 0.001), within the KS by 58% (p < 0.001), and on the y-axis within the KS by 90% (p < 0.001) than junior men. The professional and junior men had the BI more leftwards from the origin on the x-axis within the KS by 188% (p < 0.001) and 88% (p < 0.001), respectively than within the FS.
Shaking Table Test of the Negative Skin Friction of a Single Pile Induced by Seismic Settlement of Model Soft Soil
Authors: Li Ping , Lin Mingyuan , Liu Yingci
Published: March 2023

The negative skin friction (NSF) of pile foundations is one of the most important factors in triggering building subsidence. To study the mechanism and development of the NSF over time, four methods have been used: theoretical calculations, field tests, model tests, and numerical modeling. To address the difficulty of sampling prototype soft soils in model tests, this study uses a dynamic triaxial instrument to select a similar material ratio that can effectively simulate the dynamic properties of the prototype soft soil as a soft soil layer, and input sine waves with different frequencies and peak ground acceleration (PGA) for shaking table tests to investigate the effects of frequency and PGA on the development of the distribution of NSF and the location of the neutral point of the pile foundation. The test results show that the pile NSF is mainly distributed in the upper part of the pile under horizontal sine wave loading, increases rapidly from 0 to 5 s, and stabilizes after 5 s. Its magnitude is influenced by the sine wave frequency and PGA, and the effect of PGA on the NSF is more significant. In addition, the pile neutral point position shifts downwards with increasing sine wave frequency and PGA, but the downwards shift is not significant.
Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
Authors: Wang Xiaoli , Piao Yan , Jin Yuanshang , Li Jie , Lin Zechuan , Cui Jie , Xu Tingfa
Published: March 2023

Fourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by a reconstruction algorithm. It is a kind of microscopy that can achieve both a large field of view and high resolution. Here in this article, a Fourier ptychographic reconstruction method applied to a self-training physical model is proposed. The SwinIR network in the field of super-resolution is introduced into the reconstruction method for the first time. The input of the SwinIR physical model is modified to a two-channel input, and a data set is established to train the network. Finally, the results of high-quality Fourier stack microscopic reconstruction are realized. The SwinIR network is used as the physical model, and the network hyperparameters and processes such as the loss function and optimizer of the custom network are reconstructed. The experimental results show that by using multiple different types of data sets, the two evaluation index values of the proposed method perform best, and the image reconstruction quality is the best after model training. Two different evaluation indexes are used to quantitatively analyze the reconstruction results through numerical results. The reconstruction results of the fine-tuning data set with some real captured images are qualitatively analyzed from the visual effect. The results show that the proposed method is effective, the network model is stable and feasible, the image reconstruction is realized in a short time, and the reconstruction effect is good.
A Blind Image Quality Index for Synthetic and Authentic Distortions with Hierarchical Feature Fusion
Authors: Hu Lingbi , Peng Juan , Zhao Tuoxun , Yu Wei , Hu Bo
Published: March 2023

Blind Image Quality Assessment (BIQA) for synthetic and authentic distortions has attracted much attention in the community, and it is still a great challenge. The existing quality metrics are mildly consistent with subjective perception. Traditional handcrafted quality metrics can easily and directly extract low-level features, which mainly account for the outline, edge, color, texture, and shape features, while ignoring the important deep semantics of the distorted image. In the field of popular deep learning, multilevel features can be acquired easily. However, most of them either use only high-level features, ignoring the shallow features, or they simply combine features at different levels, resulting in limited prediction performance. Motivated by these, this paper presents a novel BIQA for synthetic and authentic distortions with hierarchical feature fusion in a flexible vision-Transformer framework. First, multiscale features are extracted from a strong vision-Transformer backbone. Second, an effective hierarchical feature fusion module is proposed to incorporate the features at different levels progressively. To eliminate redundant information, a simple but effective attention mechanism is employed after each fusion. Third, inspired by the human visual system, local and global features from the fusion features are extracted to represent different granularity distortions. Finally, these local and global features are mapped to the final quality score. Extensive experiments on three authentic image databases and two synthetic image datasets show that the proposed method is superior to the state-of-the-art quality metrics for both single-database testing and cross-database testing.
A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare
Authors: Minopoulos Georgios M. , Memos Vasileios A. , Stergiou Konstantinos D. , Stergiou Christos L. , Psannis Konstantinos E.
Published: March 2023

A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff.
Investigating and Measuring Usability in Wearable Systems: A Structured Methodology and Related Protocol
Authors: Andreoni Giuseppe
Published: March 2023

Wearable systems are pervading our lives in several applications: from fitness to sport, from health monitoring to rehabilitation, up to prosthetics and empowering human functions through exoskeletons. If the technological requirements are mainly quantitative and easy to measure, their usability, acceptance, and user experience are generally poorly studied. There is a lack of a structured methodological approach to develop a comprehensive protocol. This paper aimed at providing these methodological bases and at defining some of the related tools. The first action was to clearly define the objectives of the study: (a) to identify design inconsistencies and usability problems or errors; (b) to validate the use of wearable systems under controlled test conditions with representative users; and (c) to establish a baseline in terms of user performance and user satisfaction levels. A five-step approach should be adopted: (1) define the target users; (2) conduct a task analysis for identifying the context, the parameters to be measured, and the methodology to collect data; (3) prepare a protocol and the investigation tools; (4) execute the usability experiments; and (5) analyze and report the data. This segmentation of the complex task of usability measurement into single steps can help in elaborating a proper protocol where users, usability factors and parameters, and their recording tools (questionnaires or measurement methods) are correctly identified and prepared for the experimental activity. The application of this methodology can support researchers, developers, and users in improving the deployment of these devices in our lives and the exploitation of these systems for increasing our quality of life.
An Improvement of DV-Hop Localization Algorithm Based on Cyclotomic Method in Wireless Sensor Networks
Authors: Zhao Qing , Xu Zhen , Yang Lei
Published: March 2023

Location information is one of the crucial and essential elements for monitoring data in wireless sensor networks. The distance vector-hop (DV-Hop) localization algorithm is of practical importance in improving its localization performance. To achieve global optimization, a DV-Hop algorithm based on the cyclotomic method and weighted normalization, also known as CMWN-DV-Hop, is nominated in this paper. Therefore, the segmentation and weighting factors are introduced and normalized. The weighted recursive least-squares (WRLS) algorithm is chosen to compute the coordinates of the unknown nodes. The effects of various factors on this algorithm are tested, including the number of nodes, the anchor node ratio, and the communication radius. The simulation results show that the proposed algorithm has a super performance in reducing the localization error.
Classification of Interpretation Differences in String Quartets Based on the Origin of Performers
Authors: Istvanek Matej , Miklanek Stepan , Spurny Lubomir
Published: March 2023

Music Information Retrieval aims at extracting relevant features from music material, while Music Performance Analysis uses these features to perform semi-automated music analysis. Examples of interdisciplinary cooperation are, for example, various classification tasks—from recognizing specific performances, musical structures, and composers to identifying music genres. However, some classification problems have not been addressed yet. In this paper, we focus on classifying string quartet music interpretations based on the origin of performers. Our dataset consists of string quartets from composers A. Dvořák, L. Janáček, and B. Smetana. After transferring timing information from reference recordings to all target recordings, we apply feature selection methods to rank the significance of features. As the main contribution, we show that there are indeed origin-based tempo differences, distinguishable by measure durations, by which performances may be identified. Furthermore, we train a machine learning classifier to predict the performers’ origin. We evaluate three different experimental scenarios and achieve higher classification accuracy compared to the baseline using synchronized measure positions.
The Effect of Plyometric Training on the Speed, Agility, and Explosive Strength Performance in Elite Athletes
Authors: Huang Hsuan , Huang Wei-Yang , Wu Cheng-En
Published: March 2023

The purpose of this study was to evaluate the speed, agility, and explosive strength performance of elite basketball players over an 8-week plyometric training program. Fifteen elite male college basketball players in Taiwan (average age 22.16 ± 0.85 years old) were publicly recruited. All participants received 24 plyometric training courses three times per week for 8 weeks, and the courses were implemented pre- and post-test. The speed and agility test items were divided into a 20 m sprint and a T-shaped run. In the explosive strength test, a force plate was used to measure countermovement jump to understand the pre- and post-test differences in all the test indicators, including the rate of force development, time of the rate of force development, ground reaction forces for the moment of jumping, duration of passage, and jump height. It was found that, after the participants underwent the plyometric training program, the body mass index and body fat percentage were significantly reduced, the skeletal muscle mass was significantly increased, and the post-test scores for speed and agility improved significantly. All the participants exhibited a steeper gradient for the rate of force development (r = −0.816~−0.963) and a shorter time for the rate of force development (0.107~0.232 s). The ground reaction forces reached 1509.61~2387.11 Newtons. The duration of passage reached 0.643 s, and the jump height reached 0.624 m. The conclusion was that the plyometric training program can increase muscle volume in the lower limbs and legs, increase the rate of force development, and shorten the jumping time, thereby enhancing explosive strength.
Finite Element Analysis of the Influence of Chamfer Hub Geometry on the Stress Concentrations of Shrink Fits
Authors: Izard Eulalia , García-Martín Roberto , Rodríguez-Martín Manuel , Lorenzo Miguel
Published: March 2023

The theoretical expressions commonly used in the design of interference fits do not take into account the huge stress concentrations located at the edges of the hub. This underestimation of the real stress state can induce the incorrect performance of the shaft–hub assembly. Among the different methods to address this problem is the use of chamfer hubs, which are used for reducing such stress concentrations. In this paper, an analysis, performed via finite element method, of the influence of the geometric parameters of a shrink fit with chamfer hubs was carried out with the aim of determining the optimal dimensions for the design of this type of mechanical assembly. To achieve this goal, different chamfer hub geometries were considered: (i) full–chamfer hubs defined by the chamfer angle and (ii) partial–chamfer hubs defined by the chamfer angle and the chamfer height. According to the obtained results, stress concentrations can be reduced by using a full–chamfer hub with chamfer angles within the range 13°–15° depending on the hub thickness. In addition, similar results can be obtained by using partial–chamfer hubs with a chamfer height of half of the hub thickness and chamfer angles within the range 13°–15°. By using these design recommendations, the theoretical equations can be used without underestimating the real stress state.
Characterization of the Aroma Profile of Commercial Prosecco Sparkling Wines
Authors: Slaghenaufi Davide , Luzzini Giovanni , Borgato Matteo , Boscaini Anita , Dal Cin Andrea , Zandonà Vittorio , Ugliano Maurizio
Published: March 2023

In this work, the aromatic characterization of commercially available Prosecco wines with a price range between EUR 7 and 13 was carried out. These wines came from three different areas of origin: Valdobbiadene, Asolo and Treviso. Seventy volatile compounds were identified and quantified in the wines. Quantitatively, the wines were mainly characterized by compounds of fermentation origin (alcohols, acids, esters), and C6-alcohols, and to a lesser extent, terpenes, low molecular weight volatile sulfur compounds (VSC), and benzenoids. To determine their impact on the aroma of Prosecco wine, the respective OAVs were calculated. The molecules with higher OAV were ethyl hexanoate, isoamyl acetate, and β-damascenone. More generally, esters, responsible for fruity notes, seemed to play a major role in the aroma of Prosecco wine. Investigation into the possible effect of different production zones indicated 16 significantly different compounds accounting for differences between the various areas of origin of the wines, being mostly VSC, esters and C6-alcohols. A sensory evaluation through a sorting task highlighted the formation of clusters; wine samples were divided into two main groups partially attributable to the areas of origin. From a chemical point of view, cluster A was richer in esters, while cluster B had, on average, higher concentrations of compounds associated with wine aging such as cyclic terpenes, norisoprenoids (TDN and vitispirane), and VSC.
Prophylactic Coronoidectomy Approach during Stable Bone Osteosynthesis after Major Cranio-Facial Injury in the Temporal Region with Muscle Contusion—Should It Be Used?
Authors: Nelke Kamil , Bujak Krzysztof , Szczepański Wojciech , Łukaszewski Marceli , Janeczek Maciej , Pasicka Edyta , Blicharski Tomasz , Morawska-Kochman Monika , Dobrzyński Maciej
Published: March 2023

The coronoidectomy approach is a known but still quite underestimated procedure focused on the removal of the coronoid process of the mandible. Most cases related to outer-joint-related factors causing limited mouth opening (LMO) might be related to a great variety of factors. Quite often, patients suffer a lot from the inability to fully open their mouths. During this time, they are scheduled for limitless conservative, pharmacological, and diagnostic steps to estimate the cause of LMO and increase patients’ mouth opening. In those cases, a diagnostic coronoidectomy might be useful to estimate the potential cause of such a problem. On the other hand, a prophylactic coronoidectomy is still quite randomly described in the world literature and mostly selected for a limited number of special cases. Herein, we would like to present a special issue of the major trauma in the temporal bone, middle cranial fossa, and zygomatico-malar area that resulted in temporal muscle contusion, scarring, and later contraction. A hemicoronal approach was performed to stabilize the fractures of the left zygomatico-orbital area with titanium osteosynthesis plates and screws. Over time, the LMO increased. All temporomandibular joint (TMJ)-related pathologies have been excluded. In the second week, the post-op patient was only able to open his mouth at 12 mm. Temporal muscle injury over time caused major contractions, greatly influencing LMO. The following paper describes the influence of temporal muscle trauma on limited mouth opening and indications for simultaneously performing coronoidectomy during stable bone osteosynthesis.
Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
Authors: Li Xin , Guo Yuxin , Pan Weiguo , Liu Hongzhe , Xu Bingxin
Published: March 2023

Heatmap-based traditional approaches for estimating human pose usually suffer from drawbacks such as high network complexity or suboptimal accuracy. Focusing on the issue of multi-person pose estimation without heatmaps, this paper proposes an end-to-end, lightweight human pose estimation network using a multi-scale coordinate attention mechanism based on the Yolo-Pose network to improve the overall network performance while ensuring the network is lightweight. Specifically, the lightweight network GhostNet was first integrated into the backbone to alleviate the problem of model redundancy and produce a significant number of effective feature maps. Then, by combining the coordinate attention mechanism, the sensitivity of our proposed network to direction and location perception was enhanced. Finally, the BiFPN module was fused to balance the feature information of different scales and further improve the expression ability of convolutional features. Experiments on the COCO 2017 dataset showed that, compared with the baseline method YOLO-Pose, the average accuracy of the proposed network on the COCO 2017 validation dataset was improved by 4.8% while minimizing the amount of network parameters and calculations. The experimental results demonstrated that our proposed method can improve the detection accuracy of human pose estimation while ensuring that the model is lightweight.