bluuebunny/crossref_metadata_2025 huggingface dataset:
Question Difficulty Estimation Based on Attention Model for Question Answering
Authors: Song Hyun-Je , Yoon Su-Hwan , Park Seong-Bae
Published: December 2021

This paper addresses a question difficulty estimation of which goal is to estimate the difficulty level of a given question in question-answering (QA) tasks. Since a question in the tasks is composed of a questionary sentence and a set of information components such as a description and candidate answers, it is important to model the relationship among the information components to estimate the difficulty level of the question. However, existing approaches to this task modeled a simple relationship such as a relationship between a questionary sentence and a description, but such simple relationships are insufficient to predict the difficulty level accurately. Therefore, this paper proposes an attention-based model to consider the complicated relationship among the information components. The proposed model first represents bi-directional relationships between a questionary sentence and each information component using a dual multi-head co-attention, since the questionary sentence is a key factor in the QA questions and it affects and is affected by information components. Then, the proposed model considers inter-information relationship over the bi-directional representations through a self-attention model. The inter-information relationship helps predict the difficulty of the questions accurately which require reasoning over multiple kinds of information components. The experimental results from three well-known and real-world QA data sets prove that the proposed model outperforms the previous state-of-the-art and pre-trained language model baselines. It is also shown that the proposed model is robust against the increase of the number of information components.
Can IMU Provide an Accurate Vertical Jump Height Estimate?
Authors: Marković Stefan , Dopsaj Milivoj , Tomažič Sašo , Kos Anton , Nedeljković Aleksandar , Umek Anton
Published: December 2021

The aim of the present study was to determine if an inertial measurement unit placed on the metatarsal part of the foot can provide valid and reliable data for an accurate estimate of vertical jump height. Thirteen female volleyball players participated in the study. All players were members of the Republic of Serbia national team. Measurement of the vertical jump height was performed for the two exemplary jumping tasks, squat jump and counter-movement jump. Vertical jump height estimation was performed using the flight time method for both devices. The presented results support a high level of concurrent validity of an inertial measurement unit in relation to a force plate for estimating vertical jump height (CMJ t = 0.897, p = 379; ICC = 0.975; SQJ t = −0.564, p = 0.578; ICC = 0.921) as well as a high level of reliability (ICC > 0.872) for inertial measurement unit results. The proposed inertial measurement unit positioning may provide an accurate vertical jump height estimate for in-field measurement of jump height as an alternative to other devices. The principal advantages include the small size of the sensor unit and possible simultaneous monitoring of multiple athletes.
Blockchain and Cloud to Overcome the Problems of Buyer and Seller Watermarking Protocols
Authors: Frattolillo Franco
Published: December 2021

Copyright protection of digital content has become a problem not only for web content providers but also for ordinary web users who like to publish their digital contents on social or user generated content platforms. Among the possible solutions to such a problem, digital watermarking, in conjunction with watermarking protocols, appears to be a valid alternative to current DRM (digital rights management) systems. In fact, watermarking based solutions insert perceptually invisible copyright information into the copies of contents published or distributed on the web in order to track them. Such insertions are carried out according to the watermarking protocols, which have evolved over the years from the classic “buyer and seller” paradigm into a simpler and versatile “buyer friendly” and “mediated” approach. However, such an approach cannot exploit the new technologies that characterize the current Internet. This paper presents a new watermarking protocol able to adapt the “buyer friendly” and “mediated” approach to the use of innovative technologies such as cloud platforms and blockchain. In this way, (1) content providers and common web users can take advantage of the computing and storage resources made available by cloud platforms; (2) the involvement of trusted third parties in the protocols can be reduced by using blockchain without complicating the protection scheme. In fact, these two goals make the protocol particularly suited for the current Internet.
Distraction Potential of Vehicle-Based On-Road Projection
Authors: Glück Tobias , Biermann Tobias , Wolf Alexander , Budig Sören , Ziebehl Arved , Knöchelmann Marvin , Lachmayer Roland
Published: December 2021

With regard to autonomous driving, on-road projections cannot only be used for communication with the driver but also with other road users. Our study aims to investigate the distraction potential for other road users when on-road projections (e.g., for driver assistance) are used to communicate with the driver of the projecting vehicle. We perform this investigation in a blind study with 38 test persons who are overtaken six times on a constant motorway section by the projection vehicle. The distraction potential is examined with an eye-tracking system, which detects the direction of the subjects’ gaze. In addition, the subjects’ physiological perception of the headlight projection is recorded with a questionnaire afterward. Several test subjects looked at the projection for less than one second, which is well below the critical threshold for the distraction of 1.6 s. In the interviews, on the other hand, only one of the 38 test persons stated that a projection on the road was recognized. For the examined scenario, it is therefore deduced that on-road projections with the selected symbol shape and brightness do not lead to critical distraction.
Nanoscale Imaging and Analysis of Bone Pathologies
Authors: Garcia-Giner Victoria , Han Zexiang , Giuliani Finn , Porter Alexandra E.
Published: December 2021

Understanding the properties of bone is of both fundamental and clinical relevance. The basis of bone’s quality and mechanical resilience lies in its nanoscale building blocks (i.e., mineral, collagen, non-collagenous proteins, and water) and their complex interactions across length scales. Although the structure–mechanical property relationship in healthy bone tissue is relatively well characterized, not much is known about the molecular-level origin of impaired mechanics and higher fracture risks in skeletal disorders such as osteoporosis or Paget’s disease. Alterations in the ultrastructure, chemistry, and nano-/micromechanics of bone tissue in such a diverse group of diseased states have only been briefly explored. Recent research is uncovering the effects of several non-collagenous bone matrix proteins, whose deficiencies or mutations are, to some extent, implicated in bone diseases, on bone matrix quality and mechanics. Herein, we review existing studies on ultrastructural imaging—with a focus on electron microscopy—and chemical, mechanical analysis of pathological bone tissues. The nanometric details offered by these reports, from studying knockout mice models to characterizing exact disease phenotypes, can provide key insights into various bone pathologies and facilitate the development of new treatments.
Accuracy of Master Casts Generated Using Conventional and Digital Impression Modalities: Part 1—The Half-Arch Dimension
Authors: Sayed Mohammed E. , Alshehri Abdulkarim Hussain , Al-Makramani Bandar M. A. , Al-Sanabani Fuad , Shaabi Fawzia Ibraheem , Alsurayyie Fatimah H. , Ahmed Walaa Magdy , Al-Mansour Hosain , Jain Saurabh
Published: December 2021

Accurate impression-making is considered a vital step in the fabrication of fixed dental prostheses. There is a paucity of studies that compare the casts generated by various impression materials and techniques that are commonly used for the fabrication of provisional and definitive fixed prostheses. The aim of this study is to compare the accuracy of casts obtained using conventional impression and digital impression techniques. Thirty impressions were made for the typodont model (10 impressions each of polyvinyl siloxane, alginate, and alginate alternative materials). Ten digital models were printed from the same model using a TRIOS-3 3Shape intraoral scanner. Accuracy was assessed by measuring four dimensions (horizontal anteroposterior straight, horizontal anteroposterior curved, horizontal cross-arch, and vertical). A one-way ANOVA and Tukey’s test (α = 0.05) were used to analyze data. A statistically significant difference in the four dimensions of the stone casts and digital models was observed among the four groups (exception: between alginate alternative and 2-step putty–light body impression in the horizontal anteroposterior straight, horizontal anteroposterior curved, and horizontal cross-arch dimensions; between alginate and alginate alternative in the horizontal anteroposterior curved dimension; between alginate and 2-step putty–light body impression in the horizontal anteroposterior curved dimension; and between alginate alternative and digital in the vertical dimension). Polyvinyl siloxane had the highest accuracy compared to casts obtained from other impression materials and digital impressions.
Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
Authors: Al Sibahee Mustafa A. , Abdulsada Ayad I. , Abduljabbar Zaid Ameen , Ma Junchao , Nyangaresi Vincent Omollo , Umran Samir M.
Published: December 2021

Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches.
Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
Authors: Wang Qun , Wang Hengsheng , Hou Liwei , Yi Shouhua
Published: December 2021

Tool wear monitoring is of great significance for the development of manufacturing systems and intelligent manufacturing. Online tool condition monitoring is a crucial technology for cost reduction, quality improvement, and manufacturing intelligence in modern manufacturing. However, it remains a difficult problem to monitor the status of tools online, in real-time and accurately in the industry. In the research status of mainstream technology, the convolution neural network may be a good solution to this problem, based on the appropriate sensor system and correct signal processing methods. Therefore, this paper outlines the state-of-the-art systems encountered in the open access literature, focusing on information collection, feature selection–extraction technologies based on deep convolutional neural networks, and monitoring network architecture and modeling methods. Based on typical cases, this paper focuses on the application of the convolution neural network in tool wear monitoring. From the application results, it is feasible and reliable to apply convolution neural networks in tool wear monitoring. Additionally, it can improve the prediction accuracy, which is of great significance for the future development of technology. This paper can be a guide for the researchers and manufacturers in the area of tool wear monitoring for explaining the latest trends and requirements.
Microcirculatory and Metabolic Responses during Voluntary Cycle Ergometer Exercise with a Whole-Body Neuromuscular Electrical Stimulation Device
Authors: Ochiai Kaori , Tamura Yuma , Terashima Masato , Tsurumi Tomoki , Yasu Takanori
Published: December 2021

Vigorous exercise increases blood viscosity and may pose a risk of cardiovascular events in patients with cardiovascular diseases. We recently reported that single-use of novel whole-body neuromuscular electrical stimulation (WB-NMES) can be safely applied in healthy subjects without adversely affecting blood fluidity. We performed a crossover study to explore the effectiveness and safety of a hybrid exercise with ergo-bicycle and WB-NMES; 15 healthy volunteers, aged 23–41 years, participated in this study. No arrhythmias were detected during the hybrid exercise and 20 min recovery, and although blood fluidity was transiently exacerbated immediately after both the exercise programs, in vivo parameters in the sublingual and nailfold microcirculation remained unchanged. There was a significant decrease in blood glucose and increase in lactic acid levels immediately after both exercise programs. Even with the same workload as the cycle ergometer exercise, the oxygen intake during the hybrid exercise remained higher than that during the cycle ergometer exercise alone (p < 0.05, r = 0.79, power = 0.81). Both the hybrid and voluntary cycle ergometer exercises transiently exacerbated blood fluidity ex vivo; however, microvascular flow was not adversely affected in vivo.
Study on the Variable Speed Diesel Generator and Effects on Structure Vibration Behavior in the DC Grid
Authors: Vuong Quang Dao , Kim Jongsu , Choi Jae-Hyuk , Lee Jae-ung , Lee Ji-woong , Jeon Hyeonmin , Noh Jung-Ho , Yoon Sung Hwan , Lee Won-Ju
Published: December 2021

Global warming and air pollutants are in general major worldwide concerns including for the marine shipping industry. Equipped with new technologies, the onboard DC grid has proven several advantages, including up to 27% reduction in specific fuel consumption with reduced emissions. That can be achieved by installing an optimized variable speed diesel generator. The engine speed is adjusted according to the required power, which allows to always keep the best efficiency of the combustion process. However, it also exposes some changes in the behavior of the structure vibrations. Measurements on an experimental variable speed diesel generator show that vibration increases when trying to slow down the engine for the same load. This behavior is closely related to the resonance in low rev range that usually occurs with general gensets. In other words, we can conclude that the DC grid’s variable speed generator may be beneficial for fuel efficiency, but not for mechanical life and safety. Several measures had been given, of which the alternation of the natural frequency is presented as an economical and efficient solution. The ultimate goal is to maintain operational safety while respecting reduced fuel consumption.
Implementation of AHP Methodology for the Evaluation and Selection Process of a Reverse Engineering Scanning System
Authors: Beniak Juraj , Šooš Ľubomír , Križan Peter , Matúš Miloš
Published: December 2021

Generally speaking, the proper selection of a suitable system for various uses is key to its full use in practice. In all areas, there is a large number of technologies, equipment, and systems to choose from, so it is necessary to determine the individual parameters and their weight, which are important for selection. In the field of reverse engineering, several technological devices are particularly expensive, and the selection of one will influence the long-term functioning of the system. Reverse engineering systems are widely used for the registration and documentation of historical objects in the sense of cultural heritage, and the presented scanning systems are suitable for this purpose. In this case, the selection of a scanning system is discussed. This paper deals with the methodology of selecting the most suitable reverse engineering system by the method of pairwise comparison of expert evaluation criteria (analytical hierarchical process (AHP)). This paper contains a comparison of several systems and the selection of the most suitable solution for the particular company.
Monolithic Si-Based AlGaN/GaN MIS-HEMTs Comparator and Its High Temperature Characteristics
Authors: Li Fan , Li Ang , Zhu Yuhao , Ding Chengmurong , Wang Yubo , Wang Weisheng , Cui Miao , Zhao Yinchao , Wen Huiqing , Liu Wen
Published: December 2021

Monolithic GaN High Electron Mobility Transistor (HEMT)-integrated circuits are a promising application of wide band-gap materials. To date, most GaN-based devices behave as NMOS-like transistors. As only NMOS GaN HEMT is currently commercially available, its control circuit requires special design if monolithic integration is desired. This article analyzes the schematics of a GaN-based comparator, and three comparator structures are compared through ADS simulation. The optimal structure with the bootstrapped technique is fabricated based on AlGaN/GaN Metal–Insulator–Semiconductor (MIS) HEMT with the recessed gate method. The comparator has excellent static characteristics when the reference voltage increases from 3 V to 8 V. Dynamic waveforms from 10 kHz to 1 MHz are also obtained. High-temperature tests from 25 °C to 250 °C are applied upon both DC and AC characteristics. The mechanisms of instability issues are explained under dynamic working condition. The results prove that the comparator can be used in the state-of-art mixed-signal circuits, demonstrating the potential for the monolithic all-GaN integrated circuits.
Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
Authors: Siracusano Giulio , Garescì Francesca , Finocchio Giovanni , Tomasello Riccardo , Lamonaca Francesco , Scuro Carmelo , Carpentieri Mario , Chiappini Massimo , La Corte Aurelio
Published: December 2021

In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven structural health monitoring (SHM) systems is gaining in popularity. This is due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as deep learning. A promising method suitable for smart SHM is the analysis of acoustic emissions (AEs), i.e., ultrasonic waves generated by internal ruptures of the concrete when it is stressed. The advantage in respect to traditional ultrasonic measurement methods is the absence of the emitter and the suitability to implement continuous monitoring. The main purpose of this paper is to combine deep neural networks with bidirectional long short term memory and advanced statistical analysis involving instantaneous frequency and spectral kurtosis to develop an accurate classification tool for tensile, shear and mixed modes originated from AE events (cracks). We investigated effective event descriptors to capture the unique characteristics from the different types of modes. Tests on experimental results confirm that this method achieves promising classification among different crack events and can impact on the design of the future of SHM technologies. This approach is effective to classify incipient damages with 92% of accuracy, which is advantageous to plan maintenance.
Electromechanical and Robotic Devices for Gait and Balance Rehabilitation of Children with Neurological Disability: A Systematic Review
Authors: Valè Nicola , Gandolfi Marialuisa , Vignoli Laura , Botticelli Anita , Posteraro Federico , Morone Giovanni , Dell’Orco Antonella , Dimitrova Eleonora , Gervasoni Elisa , Goffredo Michela , Zenzeri Jacopo , Antonini Arianna , Daniele Carla , Benanti Paolo , Boldrini Paolo , Bonaiuti Donatella , Castelli Enrico , Draicchio Francesco , Falabella Vincenzo , Galeri Silvia , Gimigliano Francesca , Grigioni Mauro , Mazzon Stefano , Molteni Franco , Petrarca Maurizio , Picelli Alessandro , Senatore Michele , Turchetti Giuseppe , Guglielmelli Eugenio , Petrone Nicola , Pignolo Loris , Sgubin Giulia , Smania Nicola , Zollo Loredana , Mazzoleni Stefano ,   Italian Consensus Conference on Robotic in Neurorehabilitation CICERONE
Published: December 2021

In the last two decades, a growing interest has been focused on gait and balance robot-assisted rehabilitation in children with neurological disabilities. Robotic devices allow the implementation of intensive, task-specific training fostering functional recovery and neuroplasticity phenomena. However, limited attention has been paid to the protocols used in this research framework. This systematic review aims to provide an overview of the existing literature on robotic systems for the rehabilitation of gait and balance in children with neurological disabilities and their rehabilitation applications. The literature search was carried out independently and synchronously by three authors on the following databases: MEDLINE, Cochrane Library, PeDro, Institute of Electrical and Electronics Engineers, ScienceDirect, and Google Scholar. The data collected included three subsections referring to clinical, technical, and regulatory aspects. Thirty-one articles out of 81 found on the primary literature search were included in the systematic review. Most studies involved children with cerebral palsy. Only one-third of the studies were randomized controlled trials. Overall, 17 devices (nine end-effector systems and eight exoskeletons) were investigated, among which only 4 (24%) were bore the CE mark. Studies differ on rehabilitation protocols duration, intensity, and outcome measures. Future research should improve both rehabilitation protocols’ and devices’ descriptions.
Improved Training of CAE-Based Defect Detectors Using Structural Noise
Authors: Murakami Reina , Grave Valentin , Fukuda Osamu , Okumura Hiroshi , Yamaguchi Nobuhiko
Published: December 2021

Appearances of products are important to companies as they reflect the quality of their manufacture to customers. Nowadays, visual inspection is conducted by human inspectors. This research attempts to automate this process using Convolutional AutoEncoders (CAE). Our models were trained using images of non-defective parts. Previous research on autoencoders has reported that the accuracy of image regeneration can be improved by adding noise to the training dataset, but no extensive analyse of the noise factor has been done. Therefore, our method compares the effects of two different noise patterns on the models efficiency: Gaussian noise and noise made of a known structure. The test datasets were comprised of “defective” parts. Over the experiments, it has mostly been observed that the precision of the CAE sharpened when using noisy data during the training phases. The best results were obtained with structural noise, made of defined shapes randomly corrupting training data. Furthermore, the models were able to process test data that had slightly different positions and rotations compared to the ones found in the training dataset. However, shortcomings appeared when “regular” spots (in the training data) and “defective” spots (in the test data) partially, or totally, overlapped.
Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms
Authors: Wang Tianyu , Wang Qisheng , Shi Jing , Zhang Wenhong , Ren Wenxi , Wang Haizhu , Tian Shouceng
Published: December 2021

Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.
Degree of Conversion and Mechanical Properties of Modern Self-Adhesive Luting Agents
Authors: Dimitriadi Maria , Petropoulou Aikaterini , Zafiropoulou Maria , Zinelis Spiros , Eliades George
Published: December 2021

New self-adhesive resin composite luting agents have currently been developed, claiming improved properties. The study aimed to evaluate the composition, degree of conversion, and mechanical properties of Panavia SA Plus (PSP), Panavia SA Universal (PSU), SpeedCem Plus (SPC) and TheraCem Ca (THC), with the resin luting agent Panavia V5 (PV5) serving as a control. The structure of the materials was studied by FTIR spectroscopy and SEM/EDX spectrometry. Disk-shaped specimens were prepared from each material under dual- and self-curing modes (n = 5/mode and material). After a 3-week storage period (dark/37 °C/80%RH) the Martens hardness, indentation modulus, elastic index, and creep were determined by instrumented indentation testing (IIT), while the degree of conversion was assessed by FTIR spectroscopy. Statistical analysis was performed by 2-way ANOVA and post-hoc testing (α = 0.05). All materials were based on aromatic monomers, except for SPC. Fillers with potentially bioactive Ca-glasses were identified in SPC and THC, which showed the highest P/Si ratio. The dual-curing mode demonstrated superior performance in all properties. Differences between materials within each curing mode were limited to SPC, THC (highest conversion) and PSA, PSU, SPC (highest elastic index) for dual-curing, and THC (lowest hardness and elastic index). The results confirmed a lower self-curing conversion in these materials, which may affect some of the mechanical properties tested.
Systematic Review of Nutritional Recommendation Systems
Authors: Orue-Saiz Iñigo , Kazarez Miguel , Mendez-Zorrilla Amaia
Published: December 2021

In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.
Diffusion-Weighted Imaging of the Macaque Brain Using Diffusion-Prepared Turbo Spin Echo in MRI
Authors: Zhang Jialu , Zhang Xiaotong
Published: December 2021

Magnetic resonance imaging (MRI) integrates a static magnetic field, a time-varying gradient magnetic field at kHz and a radio-frequency (RF) magnetic field for non-invasive and real-time imaging; meanwhile, diffusion MRI (dMRI) pushes a further and closer dimension to the scale of neural fibers through sensitizing the gradient field to recognize water molecular displacement over distances of 1~20 μm along fibers. Contemporary dMRI approaches face challenges of magnetic field inhomogeneity as well as sequence-associated distortion and signal loss, the common remedies of which are repeated scans and post-reconstruction algorithms. In this study, over an anesthetized macaque with a customized head coil on 3 T MRI, we have proposed and implemented a monopolar diffusion-prepared module for turbo spin echo sequence (DP-TSE) as an alternative to achieve distortion-free, high-resolution diffusion imaging with improved SNR. The results showed high image quality and SNR efficiency as compared with conventional dMRI methods at millimeter level, allowing us to pursue submillimeter-scale dMRI over non-human primates (NHPs) in a relatively short scan time and without repetitions or post-processing, which could merit and advance our understanding of the structure and organizations of the primate’s brain.
Asthma Control during COVID-19 Lockdown in Patients with Severe Asthma under Biological Drug Treatment
Authors: Pelaia Corrado , Casarella Alessandro , Marcianò Gianmarco , Muraca Lucia , Rania Vincenzo , Citraro Rita , Palleria Caterina , Roberti Roberta , Pelaia Giulia , De Sarro Giovambattista , Gallelli Luca
Published: December 2021

Introduction: Coronavirus disease 2019 (COVID-19) has deeply affected the management of patients with severe asthma, treated with add-on biological therapies. Objective: In this study, severe asthmatic patients on treatment with one of three different biologics (omalizumab, mepolizumab, benralizumab) underwent a survey to evaluate the effects of COVID-19 on the management of their clinical condition, with regard to the changes caused by the limited access to health facilities during the pandemic period. Methods: In this prospective observational study, 28 severe asthmatic outpatients referring to the Respiratory Unit of Magna Graecia University Hospital, Catanzaro (Italy), were asked to answer a telephone survey from May to July 2021. This survey included the evaluation of demographic and clinical data, as well as the number of lung function tests performed, exacerbations, biologic doses administered at hospital, or at general practitioner office, or through self-administration. Adherence to biological therapies before and during the pandemic period was also assessed. Moreover, the most recent asthma control test (ACT) score and the last forced expiratory volume in the first second (FEV1) measurement, recorded during the pandemic phase, were compared to the pre-pandemic (baseline) period. Results: When comparing the pre-pandemic data with the pandemic observations, the mean ACT score and the exacerbation rate did not significantly change [ACT, 21.5 ± 2.8 to 23.0 ± 3.9 (p = 0.1); exacerbation rate, 0.3 ± 0.6 and 0.5 ± 1.5 (p = 0.3)]. When considering some variables related to disease management in the same periods, a statistically significant difference was detected with regard to the mean number of outpatient visits (5.2 ± 3.8 vs. 0.9 ± 2.5, p < 0.0001), as well as to the mean number of accesses to health facilities for the administration of biological drugs (from 7.0 ± 3.4 to 2.5 ± 3.9, p < 0.0001). None of the patients reported to have been infected with the SARS-CoV-2 virus and no adverse drug reactions (ADR) occurred during the study. Conclusions: The above results suggest that COVID-19 pandemic did not induce any significant change related to severe asthma control. Indeed, add-on treatment with biological drugs was regularly continued, despite the obvious limited access to health facilities.
Development of a Convenient Home Meal Replacement Product Containing Roasted Abalone (Haliotis discus hannai) with Honey Butter Sauce
Authors: Negara Bertoka Fajar Surya Perwira , Gong Hee-Jin , Sohn Jae-Hak , Kim Jin-Soo , Choi Jae-Suk
Published: December 2021

Abalone (Haliotis discus hannai) has high nutritional value and market demand. However, it is generally sold as a raw product, which suffers from lengthy preparation, low commercial value, and a short shelf life. To address these problems, we processed abalone as a home meal replacement (HMR) product using superheated steam and quick freezing technology. Response surface methodology was used to optimize the roasting process. A test HMR product was produced by mixing roasted abalone with honey butter sauce at a ratio of 70:30 (w/w), then evaluated for its physicochemical, biological, and nutritional characteristics and shelf life. Roasting abalone at 220 °C for 2 min resulted in high scores for hardness and overall acceptance. The roasting process successfully maintained the chemical characteristics of abalone, including pH, volatile basic nitrogen, and thiobarbituric acid-reactive substances. The sensory characteristics of the test HMR product were maintained using quick freezing methods. Moreover, nutritional analysis revealed that the test HMR product contained macro- and micronutrients, amino acids, and fatty acids, which could contribute to meeting daily nutritional needs. The estimated shelf life of the product was 30 months. Therefore, this study successfully developed a high-quality HMR product containing abalone.
Efficient Fuzzy Image Stretching for Automatic Ganglion Cyst Extraction Using Fuzzy C-Means Quantization
Authors: Lee Sun Joo , Song Doo Heon , Kim Kwang Baek , Park Hyun Jun
Published: December 2021

Ganglion cysts are commonly observed in association with the joints and tendons of the appendicular skeleton. Ultrasonography is the favored modality used to manage such benign tumors, but it may suffer from operator subjectivity. In the treatment phase, ultrasonography also provides guidance for aspiration and injection, and the information regarding the accurate location of the pedicle of the ganglion. Thus, in this paper, we propose an automatic ganglion cyst extracting method based on fuzzy stretching and fuzzy C-means quantization. The proposed method, with its carefully designed image-enhancement policy, successfully detects ganglion cysts in 86 out of 90 cases (95.6%) without requiring human intervention.
Investigating the Level of Fidelity of an Actuator Line Model in Predicting Loads and Deflections of Rotating Blades under Uniform Free-Stream Flow
Authors: Spyropoulos Nikos , Papadakis George , Prospathopoulos John M. , Riziotis Vasilis A.
Published: December 2021

In this paper, the accuracy of an in-house Actuator Line (AL) model is tested on aeroelastic simulations of a Wind Turbine (WT) rotor and a helicopter Main Rotor (MR) under uniform free-stream flow. For the scope of aeroelastic analyses, the AL model is coupled with an in-house multibody dynamics code in which the blades are modeled as beams. The advantage from the introduction of CFD analysis in rotorcraft aeroelasticity is related to its capability to account in detail for the interaction of the rotor wake with the boundary layer developed on the surrounding bodies. This has proven to be of great importance in order to accurately estimate the aerodynamic forces and thus the corresponding structural loads and deflections of the blades. In wind turbine applications, a good example of the above is the rotor/ground interaction. In helicopter configurations, the interaction of MR with the ground or the fuselage and the interaction of tail rotor with the duct in fenestron configurations are typical examples. Furthermore, CFD aerodynamic analysis is an obvious modeling option in which the above mentioned asset can be combined with the consideration of the mutual interaction of the rotor with the ambient turbulence. A WT rotor operating inside the atmospheric boundary layer under turbulent free-stream flow is such a case. In the paper, AL results are compared against Blade Element Momentum (BEM) and Lifting Line (LL) model results in the case of the WT, whereas LL and measured data are considered in the helicopter cases. Blade loads and deflections are mainly compared as azimuthal variations. In the helicopter MR cases, where comparison is made against experimental data, harmonic analysis of structural loads is shown as well. Overall, AL proves to be as reliable as LL in the canonical cases addressed in this paper in terms of loads and deflections predictions. Therefore, it can be trusted in more complex flow conditions where viscous effects are pronounced.
Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks
Authors: Tang Hao-Yuan , Tan Shih-Hua , Su Ting-Yu , Chiang Chang-Jung , Chen Hsiang-Ho
Published: December 2021

Inadequate sitting posture can cause imbalanced loading on the spine and result in abnormal spinal pressure, which serves as the main risk factor contributing to irreversible and chronic spinal deformity. Therefore, sitting posture recognition is important for understanding people’s sitting behaviors and for correcting inadequate postures. Recently, wearable devices embedded with microelectromechanical systems (MEMs) sensors, such as inertial measurement units (IMUs), have received increased attention in human activity recognition. In this study, a wearable device embedded with IMUs and a machine learning algorithm were developed to classify seven static sitting postures: upright, slump, lean, right and left bending, and right and left twisting. Four 9-axis IMUs were uniformly distributed between thoracic and lumbar regions (T1-L5) and aligned on a sagittal plane to acquire kinematic information about subjects’ backs during static-dynamic alternating motions. Time-domain features served as inputs to a signal-based classification model that was developed using long short-term memory-based recurrent neural network (LSTM-RNN) architecture, and the model’s classification performance was used to evaluate the relevance between sensor signals and sitting postures. Overall results from performance evaluation tests indicate that this IMU-based measurement and LSTM-RNN structural scheme was appropriate for sitting posture recognition.
Study of the Dissolution of Stainless-Steel Slag Minerals in Different Acid Environments to Promote Their Use for the Treatment of Acidic Wastewaters
Authors: De Colle Mattia , Kielman Ross , Karlsson Andreas , Karasev Andrey , Jönsson Pär G.
Published: December 2021

Several stainless-steel slags have been successfully employed in previous studies as substitutes for lime in the treatment of industrial acidic wastewaters. This study deepens the knowledge of such application, by analyzing the neutralizing capacity of different slags related to their mineral compositions. To do so, firstly the chemical and mineral compositions of all the slag samples are assessed. Then, 0.5 g, 1 g, 2 g of each slag and 0.25 g and 0.5 g of lime are used to neutralize 100 g of 0.1 M HCl or HNO3 solutions. After the has neutralization occurred, the solid residues are extracted and analyzed using XRD spectroscopy. Then, the solubility of the minerals is assessed and ranked, by comparing the XRD spectra of the residues with the obtained pH values. The results show that minerals such as dicalcium silicate and bredigite are highly soluble in the selected experimental conditions, while minerals such as merwinite and åkermanite, only partially. Moreover, Al-rich slags seem to perform poorly due to the formation of hydroxides, which generate extra protons. However, when the weight of slag is adequately adjusted, Al-rich slags can increase the pH values to higher levels compared to the other studied slags.
Biochar Amended Soils and Water Systems: Investigation of Physical and Structural Properties
Authors: Baiamonte Giorgio , Crescimanno Giuseppina , Parrino Francesco , De Pasquale Claudio
Published: December 2021

There are significant regional differences in the perception of the problems posed by global warming, water/food availability and waste treatment recycling procedures. The study illustrates the effect of application of a biochar (BC) from forest biomass waste, at a selected application rate, on water retention, plant available water (PAW), and structural properties of differently standard textured soils, classified as loamy sand, loam and clay. The results showed that soil water retention, PAW, and aggregate stability were significantly improved by BC application in the loamy sand, confirming that application of BC to this soil was certainly beneficial and increased the amount of macropores, storage pores and residual pores. In the loam, BC partially improved water retention, increasing macroporosity, but decreased the amount of micropores and improved aggregate stability and did not significantly increase the amount of PAW. In the clay, the amount of PAW was increased by BC, but water retention and aggregate stability were not improved by BC amendment. Results of the BET analysis indicated that the specific surface area (BET-SSA) increased in the three soils after BC application, showing a tendency of the BET-SSA to increase at increasing PAW. The results obtained indicated that the effects of BC application on the physical and structural properties of the three considered soils were different depending on the different soil textures with a BET-SSA increase of 950%, 489%, 156% for loamy sand, loam and clay soil respectively. The importance of analysing the effects of BC on soil water retention and PAW in terms of volumetric water contents, and not only in terms of gravimetric values, was also evidenced.
One-Parameter Controlled Non-Dissipative Unconditionally Stable Explicit Structure-Dependent Integration Methods with No Overshoot
Authors: Selvakumar Veerarajan , Chang Shuenn-Yih
Published: December 2021

Although many families of integration methods have been successfully developed with desired numerical properties, such as second order accuracy, unconditional stability and numerical dissipation, they are generally implicit methods. Thus, an iterative procedure is often involved for each time step in conducting time integration. Many computational efforts will be consumed by implicit methods when compared to explicit methods. In general, the structure-dependent integration methods (SDIMs) are very computationally efficient for solving a general structural dynamic problem. A new family of SDIM is proposed. It exhibits the desired numerical properties of second order accuracy, unconditional stability, explicit formulation and no overshoot. The numerical properties are controlled by a single free parameter. The proposed family method generally has no adverse disadvantage of unusual overshoot in high frequency transient responses that have been found in the currently available implicit integration methods, such as the WBZ-α method, HHT-α method and generalized-α method. Although this family method has unconditional stability for the linear elastic and stiffness softening systems, it becomes conditionally stable for stiffness hardening systems. This can be controlled by a stability amplification factor and its unconditional stability is successfully extended to stiffness hardening systems. The computational efficiency of the proposed method proves that engineers can do the accurate nonlinear analysis very quickly.
Fibronectin-Enriched Biomaterials, Biofunctionalization, and Proactivity: A Review
Authors: Palomino-Durand Carla , Pauthe Emmanuel , Gand Adeline
Published: December 2021

Modern innovation in reconstructive medicine implies the proposition of material-based strategies suitable for tissue repair and regeneration. The development of such systems necessitates the design of advanced materials and the control of their interactions with their surrounding cellular and molecular microenvironments. Biomaterials must actively engage cellular matter to direct and modulate biological responses at implant sites and beyond. Indeed, it is essential that a true dialogue exists between the implanted device and the cells. Biomaterial engineering implies the knowledge and control of cell fate considering the globality of the adhesion process, from initial cell attachment to differentiation. The extracellular matrix (ECM) represents a complex microenvironment able to meet these essential needs to establish a relationship between the material and the contacting cells. The ECM exhibits specific physical, chemical, and biochemical characteristics. Considering the complexity, heterogeneity, and versatility of ECM actors, fibronectin (Fn) has emerged among the ECM protagonists as the most pertinent representative key actor. The following review focuses on and synthesizes the research supporting the potential to use Fn in biomaterial functionalization to mimic the ECM and enhance cell–material interactions.
Drugs from Marine Sources
Authors: Gallo Carmela , Nuzzo Genoveffa
Published: December 2021

Throughout history, natural products have afforded a rich source of compounds that have found many applications in the fields of pharmacology [...]