bluuebunny/crossref_metadata_2025 huggingface dataset:
Preparation of 3D Models of Cultural Heritage Objects to Be Recognised by Touch by the Blind—Case Studies
Authors: Montusiewicz Jerzy , Barszcz Marcin , Korga Sylwester
Published: November 2022

Providing access to and the protection of cultural goods—intangible and tangible heritage—is carried out primarily by institutions such as museums, galleries or local cultural centres where temporary exhibitions are shown. The international community also attempts to protect architectural objects or entire urban layouts, raising their status by inscribing them on the UNESCO World Heritage List. Contemporary museums, however, are not properly prepared to make museum exhibits available to the blind and visually impaired, which is confirmed by both the literature studies on the subject and the occasional solutions that are put in place. The development of various computer graphics technologies allows for the digitisation of cultural heritage objects by 3D scanning. Such a record, after processing, can be used to create virtual museums accessible via computer networks, as well as to make copies of objects by 3D printing. This article presents an example of the use of scanning, modelling and 3D printing to prepare prototypes of copies of museum objects from the Silk Road area, dedicated to blind people and to be recognised by touch. The surface of an object has information about it written in Braille before the copy-making process is initiated. The results of the pilot studies carried out on a group of people with simulated visual impairment and on a person who is blind from birth indicate that 3D models printed on 3D replicators with the fused filament fabrication technology are useful for sharing cultural heritage objects. The models are light—thanks to which they can be freely manipulated, as well as having the appropriate smoothness—which enables the recognition of decorative details present on them, as well as reading texts in Braille. Integrating a copy of an exhibit with a description about it in Braille into one 3D object is an innovative solution that should contribute to a better access to cultural goods for the blind.
Improving Semi-Supervised Image Classification by Assigning Different Weights to Correctly and Incorrectly Classified Samples
Authors: Zhang Xu , Zhang Huan , Zhang Xinyue , Zhang Xinyue , Zhen Cheng , Yuan Tianguo , Wu Jiande
Published: November 2022

Semi-supervised deep learning, a model that aims to effectively use unlabeled data to help learn sample features from labeled data, is a recent hot topic. To effectively use unlabeled data, a new semi-supervised learning model based on a consistency strategy is proposed. In the supervised part with labeled samples, the image generation model first generates some artificial images to complement the limited number of labeled samples. Secondly, the sample label mapping, as the “benchmark”, is compared to the corresponding sample features in the network as an additional loss to complement the original supervisory loss, aiming to better correct the model parameters. Finally, the original supervised loss is changed so that the network parameters are determined by the characteristics of each correctly classified sample. In the unsupervised part, the actual unsupervised loss is altered so that the model does not “treat all samples equally” and can focus more on the characteristics of misclassified samples. A total of 40 labeled samples from the CIFAR-10 and SVHN datasets were used to train the semi-supervised model achieving accuracies of 93.25% and 96.83%, respectively, demonstrating the effectiveness of the proposed semi-supervised model.
Prediction of Friction Coefficient for Ductile Cast Iron Using Artificial Neural Network Methodology Based on Experimental Investigation
Authors: Khalaf Ahmad A. , Hanon Muammel M.
Published: November 2022

The key objective of the present study is to analyze the friction coefficient and wear rate for ductile cast iron. Three different microstructures were chosen upon which to perform the experimental tests under different sliding time, load, and sliding speed conditions. These specimens were perlite + ferrite, ferrite, and bainitic. Moreover, an artificial neural network (ANN) model was developed in order to predict the friction coefficient using a set of data collected during the experiments. The ANN model structure was made up of four input parameters (namely time, load, number, and nodule diameter) and one output parameter (friction coefficient). The Levenberg–Marquardt back-propagation algorithm was applied in the ANN model to train the data using feed-forward back propagation (FFBP). The results of the experiments revealed that the coefficient of friction reduced as the sliding speed increased under a constant load. Additionally, it exhibits the same pattern of action when the test is run with a heavy load and constant sliding speed. Additionally, when the sliding speed increased, the wear rate dropped. The results also show that the bainite structure is harder and wears less quickly than the ferrite structure. Additionally, the results pertaining to the ANN structure showed that a single hidden layer model is more accurate than a double hidden layer model. The highest performance in the validation stage, however, was observed at epochs 8 and 20, respectively, for a double hidden layer and at 0.012346 for a single layer at epoch 20.
Transmission Efficiency of Cycloid–Pinion System Considering the Assembly Dimensional Chain
Authors: Jiang Ning , Wang Shuting , Yang Aodi , Zhou Wan , Zhang Jie
Published: November 2022

The rotary vector reducer is the core component of industrial robots, and the transmission efficiency is undoubtedly an important indicator of transmission performance. In addition, the assembly dimensional chain leads to clearance between parts, which can have a certain impact on the transmission efficiency. During previous studies, this effect was often ignored. Firstly, the cycloid tooth profile is a relatively large collection of points. Therefore, a more efficient tooth profile model is employed as the basis for the calculation. Secondly, the contact between the cycloid and the pinion is determined by experimental observation to be a point contact rather than a theoretical line contact. Moreover, the dynamics and friction loss models of the cycloid–pinion system are constructed. Finally, the assembly dimensional chain is introduced into the different structural designs. The clearance results were obtained with the extreme value method. The results show that the addition of the pinion sleeve allows the system to have a smoother drive process and a more efficient transmission; this was verified with ADAMS.
A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
Authors: Li Lianpeng , Zhao Xu , Zhao Hui
Published: November 2022

The target tracking of unmanned aerial vehicles (UAVs) has attracted significant attention recently with the increasing application of UAVs, yet few studies have made breakthroughs in dynamic dim target detection. How to efficiently and accurately identify dynamic dim targets in complex contexts poses a challenge. To address this issue, we proposed an improved lightweight Siamese network (ILSN) with an optimized feature-extraction network design and similarity measurement.As for the feature-extraction network, we built a position-wise attention module to obtain the target feature’s position information, which enhanced the network’s ability to extract weak targets while reducing the model parameters, thus ensuring the network is lightweight. For the similarity-measurement module, the tracking accuracy was expected to be improved by deeply mining the localization information of the shallow features and the semantic information of the deep features in the feature networks. To evaluate the performance of the proposed method, we established a simulated experimental environment and a physical experimental platform and then carried out comparison experiments on the attention modules, tracking accuracy, and efficiency performance. The experimental results showed that, compared with the five introduced comparison algorithms, the ILSN had apparent advantages in tracking accuracy: the tracking speed reached 108 frames per second, which met the real-time requirements while improving the success rate.
Effects of Aloe vera on the Regulation of Thyroxine Release in FRTL-5 Thyroid Cells
Authors: Ryuk Jin-Ah , Go Hiroe , Ko Byoung-Seob
Published: November 2022

To determine whether Aloe vera (AV), a traditional herbal medicine, could maintain homeostasis, we investigated its ability to regulate the production of hormones, particularly thyroid hormones. T4 ELISA assay with (6H media) and without thyrotropin (TSH) (5H media) showed that AV functions to maintain endocrine homeostasis. To understand the mechanisms by which AV regulates thyroxine release, we confirmed the protein expression of key upstream factors of thyroid hormone synthesis, such as phosphorylated extracellular signal-regulated kinase (ERK), phosphorylated cAMP response element binding protein (CREB), and phosphorylated protein kinase B (p-Akt). Although the protein expression of phosphorylated ERK and CREB were decreased in 6H media, they were increased and unchanged, respectively, in 5H media. However, protein expression of phosphorylated Akt was not changed by AV in both 6H and 5H media. Furthermore, inhibition of protein kinase A (PKA) by H89 eliminated AV-induced phosphorylation of ERK in 5H media. However, the inhibition of protein kinase C (PKC) by GF 109203X did not eliminate AV-induced ERK phosphorylation in 5H media. These results suggested that AV has TSH-like activity and could regulate thyroxine release through the TPO and PKA pathways. AV may regulate thyroid function to maintain a constant level of thyroid hormones in the body, and could be a candidate for thyroid disease therapies.
Hemp Seed Oil Extraction and Stable Emulsion Formulation with Hemp Protein Isolates
Authors: Baldino Noemi , Carnevale Ilaria , Mileti Olga , Aiello Donatella , Lupi Francesca R. , Napoli Anna , Gabriele Domenico
Published: November 2022

Industrial hemp (Cannabis sativa L.) is traditionally processed for its high fibre content in the textile industry, but in recent years, it has come to constitute a new raw material in the food industry. Hemp seeds, but also the seed meal, are rich in protein (25%) and oil (30%), the latter consisting of 80–90% of unsaturated fatty acids; they represent a rich and balanced nutritional source to replace the classic animal sources, and they are used in the food industry to supply new food trends. In this work, the hemp oil extraction process was studied, taking advantage of the supercritical CO2 and ultrasounds, and comparing it with the exhaustive Soxhlet technique. The residual cake from extraction is a protein-rich waste that can be used for food formulations. From this perspective, the hemp oil extracted was used to formulate emulsions with the consistency of vegetable drinks, enriched with standard hemp protein isolate and stabilized with the addition of 0.05% w/w of thickening polysaccharides (Gellan gum). The formulated emulsion is stable, and this can encourage the process improvement and the use of the waste from hemp seed extraction for the valorisation of by-products and waste to obtain complete food products with high nutritional value.
Soft Computing Approach to Design a Triple-Band Slotted Microstrip Patch Antenna
Authors: Yiğit Mahmud Esad , Günel Gülay Öke , Aydemir Mustafa Emre , Günel Tayfun
Published: November 2022

The design process of antenna structures that meet up-to-date requirements takes a long time and brings a high computational load. In this paper, an approach based on Soft Computing (SC) techniques was used to shorten the design time and to achieve an antenna structure that yields performance characteristics as close as possible to the desired values. In order to obtain a microstrip patch antenna with the targeted characteristics and the best accuracy in a faster way, a Support Vector Machine (SVM)-based regression model was employed. A triple-band microstrip antenna with desired resonance frequencies and gain values was designed by using the Support Vector Regression (SVR) model by introducing multiple slots and arc-truncation to the patch antenna. Simulation results of the High-Frequency Structural Simulator (HFSS) and measurements of implementation of the designed antenna are given. Performance characteristics of the obtained antenna are also compared with those given in the literature, which have triple-band properties. In addition, the antenna was redesigned using the optimization tool in HFSS for comparison. The accuracy of the results and required time for design were compared for both the SVR model approach and the HFSS optimization tool.
Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling
Authors: Qin Zhikang , Ronchieri Elisabetta
Published: November 2022

At the end of 2019, while the world was being hit by the COVID-19 virus and, consequently, was living a global health crisis, many other pandemics were putting humankind in danger. The role of social media is of paramount importance in these kinds of contexts because they help health systems to cope with emergencies by contributing to conducting some activities, such as the identification of public concerns, the detection of infections’ symptoms, and the traceability of the virus diffusion. In this paper, we have analysed comments on events related to cholera, Ebola, HIV/AIDS, influenza, malaria, Spanish influenza, swine flu, tuberculosis, typhus, yellow fever, and Zika, collecting 369,472 tweets from 3 March to 15 September 2022. Our analysis has started with the collection of comments composed of unstructured texts on which we have applied natural language processing solutions. Following, we have employed topic modelling and sentiment analysis techniques to obtain a collection of people’s concerns and attitudes towards these pandemics. According to our findings, people’s discussions were mostly about malaria, influenza, and tuberculosis, and the focus was on the diseases themselves. As regards emotions, the most popular were fear, trust, and disgust, where trust is mainly regarding HIV/AIDS tweets.
Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
Authors: Lee Woo-Hyuk , Kim Hojin , Lee Chung-Hyun , Kim Sung-Min
Published: November 2022

In underground mines, various mining activities may generate dust or vibrations, affecting workers’ health and safety. Therefore, for worker safety, we must monitor the environment and identify possible risks. However, it is difficult to install multiple sensors and acquire data simultaneously because of the difficulties of connecting to an external network in underground mines. This study developed a digital device to share acquired data by combining ZigBee communication technology with an accelerometer and dust sensor. In total, 29 vibration modules, 14 dust modules, and 2 coordinator modules were installed at Taeyoung EMC’s Samdo Mine in Samcheok, Republic of Korea. Because of its application, we could detect changes in vibration and dust before and after blasting. The dust density of the devices close to the blasting point increases rapidly up to about 230 µg/m3 and then decreases to about 180 µg/m3, and the dust density of the devices further increases over time. The dust density was usually maintained at a value of about 100 to 150 µg/m3 before blasting. The spatial distribution of the dust density of multiple devices was visualized using ArcGIS Pro. Although the wireless sensor network is well-established, some modules were temporarily disconnected from the network. In order to solve the problem of unstable network connection in some modules, change of network settings and line of sight analysis are required. Improvements in the technology developed in this study may help prevent potential hazards in underground mines.
Psychiatric Rehabilitation Amidst COVID-19: Do Pandemic Restrictions Affect the Therapeutic Efficiency?
Authors: Smolarczyk-Kosowska Joanna A. , Dębski Paweł , Szczegielniak Anna R. , Piegza Magdalena , Pudlo Robert
Published: November 2022

Community psychiatry is an effective and increasingly popular form of care for patients with mental disorders. Due to sanitary restrictions imposed by the COVID-19 pandemic, psychiatric rehabilitation programs had to adapt to the highly specific requirements and modify the offer of therapeutic activities for patients. Above all the activities focused on, social interactions were limited. The aim of the study was to assess the effectiveness of the modified rehabilitation program in light of the introduced sanitary restrictions due to COVID-19. This prospective observational single-centered study involved 41 patients diagnosed with organic mental disorders, psychotic disorders, affective disorders and anxiety disorders. The patients participated in a 6-week rehabilitation program which included varied forms of physical exercise, cognitive training, psychological training and Small Group Therapy. The quality-of-life assessment and the intensity of depression and anxiety symptoms were measured using standardized scales: Hospital Scale of Anxiety and Depression (HADS) and Short Form Health Survey (SF-36) at two time points before the initiation of the rehabilitation process and at the end of the program’s participation period. Median HADS D before admission to the rehabilitation center was 9 (IQR 6–12), and 5 (IQR 3–9) after 6-week participation (p < 0.05). Median SF-36 at the beginning of rehabilitation was 93 (IQR 80–106) and 73 (IQR 53–95) at the end of the evaluation period (p < 0.05), Median HADS-A at the start of rehabilitation was 11 (IQR 9–14) and 9 (IQR 6–12) after final assessment (p > 0.05). The gender and age of the participants did not influence the results of the utilized standardized evaluation tools (p > 0.05 for both). We observed an improvement in the participants’ subjective assessment of the intensity of the depression symptoms and the quality of life after partaking in the available activities. There was no clear benefit regarding the subjective assessment of the intensity of anxiety symptoms among the study participants resulting from the introduction to the program.
Above the Ravines: Flood Vulnerability Assessment of Earthen Architectural Heritage in Quito (Ecuador)
Authors: Trizio Francesca , Garzón-Roca Julio , Eguibar Miguel Ángel , Bracchi Paola , Torrijo Francisco Javier
Published: November 2022

Floods represent one of the most threatening risks for earthen architectural heritage. Developing risk assessment tools is considered crucial to mitigate the risk and to protect heritage buildings. Due to its hygroscopic nature, earthen architecture is generally considered to be vulnerable to water, requiring a specific analysis. This paper proposes a vulnerability assessment method for earthen buildings in flood-prone areas. Based on the evaluation of the susceptibility of the building’s components and characteristics, the method allows for quantifying the vulnerability of the assets, in non-monetary terms. An application of the methodology is carried out assessing a selection of earthen construction in Quito Historic Centre (Ecuador). The results show the influence of each component in the global vulnerability of the earthen buildings. The response of different construction techniques and the importance of the maintenance of heritage buildings is exposed. Vulnerability assessment methods at the meso-small scale constitute the foundation for risk assessment. Thus, this study aims to provide a significant assessment tool that can be used for further analysis and future lines of research, aiming to protect cultural heritage that is at risk.
Numerical Implementation of the Barcelona Basic Model Based on Return-Mapping Integration
Authors: Ji Zhenxing , Yi Haiyang , Li Gen , Liu Bingbing , Wu Zhide
Published: November 2022

This paper implemented the Barcelona basic model (BBM) into the OpenGeoSys (OGS) platform for numerical modeling of the coupled hydro-mechanical (HM) behavior of unsaturated soil. Within the implicit integration approach in the OGS, the integration rule of the BBM was developed first and the integration form of the BBM under a return mapping algorithm was built. The closest point projection method was used for calculating the return mapping directions with the associative flow rule. The numerical simulation results show that the BBM is feasible in fitting the experimental results. The numerical integration algorithm can reflect the elastic–plastic mechanical behavior of materials, and improve the calculation accuracy. The material exhibits obvious elastic–plastic characteristics during numerical simulation and experiment, and the water absorption process can lessen the mixture’s compression stiffness while enhancing its recovery stiffness.
Omni-Directional Semi-Global Stereo Matching with Reliable Information Propagation
Authors: Ma Yueyang , Tian Ailing , Bu Penghui , Liu Bingcai , Zhao Zixin
Published: November 2022

High efficiency and accuracy of semi-global matching (SGM) make it widely used in many stereo vision applications. However, SGM not only struggles in dealing with pixels in homogeneous area, but also suffers from streak artifacts. In this paper, we propose a novel omni-directional SGM (OmniSGM) with a cost volume update scheme to aggregate costs from paths along all directions and to encourage reliable information to propagate across entire image. Specifically, we perform SGM along four tree structures, namely trees in the left, right, top and bottom of root node, and then fuse the outputs to obtain final result. The contributions of pixels on each tree can be recursively computed from leaf nodes to root node, ensuring our method has linear time computational complexity. Moreover, An iterative cost volume update scheme is proposed using aggregated cost in the last pass to enhance the robustness of initial matching cost. Thus, useful information is more likely to propagate in a long distance to handle the ambiguities in low textural area. Finally, we present an efficient strategy to propagate disparities of stable pixels along the minimum spanning tree (MST) for disparity refinement. Extensive experiments in stereo matching on Middlebury and KITTI datasets demonstrate that our method outperforms typical traditional SGM-based cost aggregation methods.
A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
Authors: Ercolini Leonardo , Grossi Nicola , Silvestri Nicola
Published: November 2022

The estimation of the infestation level in a field and the consequent determination of the economic threshold is a basic requisite to rationalize post-emergence weeding. In this study, a simple and inexpensive procedure to determine the economic threshold based on weed cover is proposed. By using high-resolution RGB images captured by a low-cost drone, a free downloadable app for image processing and common spreadsheet software to perform the model parametrization, two different methods have been tested. The first method was based on the joint estimation of the two parameters involved in weed cover calculation, whereas the second method required the availability of further images for the separate estimation of the first parameter. The reliability of the two methods has been evaluated through the comparison with observed data and the goodness of fit in parameter calibration has been verified by calculating appropriate quality indices. The results showed an acceptable estimation of the weed cover value for the second method with respect to observed data (0.24 vs. 0.17 m2 and 0.17 vs. 0.14 m2, by processing images captured at 10 and 20 m, respectively), whereas the estimations obtained with the first method were disappointing (0.35 vs. 0.17 m2 and 0.33 vs. 0.14 m2, by processing images captured at 10 and 20 m, respectively).
Evaluation of the Physical, Mechanical, and Calorific Properties of Briquettes with or without a Hollow Made of Wheat (Triticum aestivum L.) Straw Waste
Authors: Dragusanu Veronica , Lunguleasa Aurel , Spirchez Cosmin
Published: November 2022

Large amounts lignocellulosic biomass in the form of straw is leftover after wheat harvesting that could be utilized for beneficial purposes. The latter has led to the emergence of new technologies to make use of this resource. One such technology currently in use turns wheat straw into briquettes. In the present study, we have prepared and evaluated the physical, mechanical and calorific properties of two types of briquettes made of wheat straw. The two types of briquettes prepared were (i) hollow briquettes and (ii) solid briquettes. The densities of these briquettes obtained on a mechanical device with a crank mechanism were 1.169 kg/m3, irrespective of whether the briquettes were hollow or solid. The briquette densities are consistent with European standards. The calorific value of wheat straw was 17.69 MJ/kg. Although the calorific value was somewhat lower than the beech wood briquettes (18.38 MJ/kg), it is adequate for their combustion in both stoves and thermal power plants. The ash content of wheat straw was 9.1% (~10-fold higher than that of beech wood). The briquettes showed a compressive mechanical strength of 1.15–2.17 N/mm2 and splitting mechanical strength of 0.17–0.39 N/mm2 suggesting that the straw briquettes were well compacted and can be stable during transport and/or other manipulations. In conclusion, wheat straw briquettes have similar physical, mechanical and calorific properties to those prepared from beech wood biomass and are a viable solution to replace beech wood briquettes with similar efficiency.
A Study Protocol to Evaluate the Effects of Vestibular Training on the Postural Control of Healthy Adults Using Virtual Reality
Authors: Appiah-Kubi Kwadwo O. , Laing Evan , Imtiaz Masudul H.
Published: November 2022

Postural instability is a common symptom of vestibular dysfunction due to an insult to the vestibular system. Vestibular rehabilitation is effective in decreasing dizziness and visual symptoms, and improving postural control through several mechanisms, including sensory reweighting. As part of the sensory reweighting mechanisms, vestibular activation training with headshake activities influences vestibular reflexes. However, combining challenging vestibular and postural tasks to facilitate more effective rehabilitation outcomes is underutilized. Our research goal is to develop a virtual reality vestibular rehabilitation method for vestibular-postural control in neurological populations with vestibular and/or sensorimotor control impairment. The NeuroCom® SMART Balance Master (Natus Medical Inc., Pleasanton, CA, USA), which was used in a prior study, is expensive and bulky. Hence, a novel study protocol is established in this paper with the detailed objectives and pre-/post-intervention data analysis pipeline (ANOVA, t-test, post hoc analysis, etc.) involving modern off-the-shelf sensors and custom instrumentation (electromyography, electrooculography, video head impulse testing, force plates, and virtual reality headsets). It is expected that the training will significantly decrease vestibuloocular reflex gains and eye movement variability, as well as reweight the somatosensory ratio, finetune postural muscle activation, and consequently improve postural flexibility and produce a faster automatic postural response. The findings may have implications for the future development of vestibular rehabilitation protocols.
Study on Strain Field Evolution of Uniaxial Compression Sandstone Based on Digital Image Correlation Technology
Authors: Xin Changhao , Yang Yu , Yang Mengze , Liang Pengfei , Sun Yidan , Shen Huazhang
Published: November 2022

The stress–strain characteristics of compressed sandstone are affected by many different factors. The study on the evolution law of the strain field of compressed sandstone is of great significance for disaster prediction and warning during the construction process of geotechnical engineering. In this paper, uniaxial compression tests of red sandstone of 100 mm height and 50 mm length and width were carried out under the conditions of (1) different loading rates, (2) different particle sizes and (3) different inclination angles of prefabricated fractures. Stress–strain curves corresponding to different conditions were presented, the evolution of strain field on the sample surfaces was analyzed with the help of Digital Image Correlation technology and the relationship between the stress–strain curves and the strain field was preliminarily given. This study may be helpful for obtaining a deeper understanding of the deformation and failure mechanism of sandstone under uniaxial compression.
Analysis of LNG Storage Tank Safety: A Comprehensive Model Approach with ANP and Normal Cloud
Authors: Peng Shiyu , Zhou Dehong , Xie Bin
Published: November 2022

Global energy shortages continue in the post-epidemic era. As clean primary energy, LNG plays an important role in the energy structure adjustment. However, with the widespread use of LNG, storage tank leaks occur frequently. In order to highlight the risk status of critical risk areas of LNG tank leakage, the “Five-high” risk system for the LNG industry, with high-risk locations, high-risk equipment, high-risk processes, high-risk personnel, and high-risk substances as first-level elements, was built. Combined with the Analytic Network Process (ANP) and normal cloud model for quantitative analysis, a safety risk analysis and assessment model for LNG storage tanks based on the ANP-normal cloud model was developed. Safety analysis was conducted with the example of an LNG storage tank area of a factory. The results show that the risk level for LNG tank leakage is level IV, with high-risk equipment being the first-level element with the highest weight and risk. The reliability of the comprehensive model was verified by AHP and fuzzy integrated analysis, and the results were basically consistent with the factory safety evaluation report. The study realizes the quantitative and visual analysis considering the correlation of risk elements and provides a new quantitative method for the safety analysis of LNG energy.
Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning
Authors: Luo Zhaohui , Liwang Minghui
Published: November 2022

Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.
Study on Correction Method of Internal Joint Operation Curve Based on Unsteady Flow
Authors: Yin Sheng , Ni Jimin , Fan Houchuan , Shi Xiuyong , Huang Rong
Published: November 2022

The turbocharger, a key component in a vehicle’s powertrain, results in insufficient accuracy if it does not fully consider the unsteady flow effects of the intake and exhaust systems. Based on the difference between the turbocharger’s actual operating performance with unsteady flow and the corresponding steady flow performance, unsteady flow correction concepts and correction methods for the compressor and turbine were put forward, and the correction of the internal joint operation curve was investigated. The results show that when unsteady correction coefficients were added to both ends of the turbocharger and the optimized structure was used at both ends, the original turbocharger’s surge margin was reduced by 4.6% to 11.8%, and that of the optimized turbocharger was reduced by 15.2% to 21.9% in the medium–low-speed range. Meanwhile, the unsteady flow energy utilization coefficient of the optimized turbocharger was more than 14.5% higher than that of the original turbocharger in the medium–low speed range, and the energy utilization advantage was obvious. It indicated that the optimized turbocharger was working earlier, and the engine’s medium–low-speed admission performance has been obviously improved. Therefore, compared with the steady curve, the corrected unsteady curve was closer to the actual engine performance.
Deep Transformer Language Models for Arabic Text Summarization: A Comparison Study
Authors: Chouikhi Hasna , Alsuhaibani Mohammed
Published: November 2022

Large text documents are sometimes challenging to understand and time-consuming to extract vital information from. These issues are addressed by automatic text summarizing techniques, which condense lengthy texts while preserving their key information. Thus, the development of automatic summarization systems capable of fulfilling the ever-increasing demands of textual data becomes of utmost importance. It is even more vital with complex natural languages. This study explores five State-Of-The-Art (SOTA) Arabic deep Transformer-based Language Models (TLMs) in the task of text summarization by adapting various text summarization datasets dedicated to Arabic. A comparison against deep learning and machine learning-based baseline models has also been conducted. Experimental results reveal the superiority of TLMs, specifically the PEAGASUS family, against the baseline approaches, with an average F1-score of 90% on several benchmark datasets.
Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
Authors: Chen Yanji , Kokar Mieczyslaw M. , Moskal Jakub , Chowdhury Kaushik R.
Published: November 2022

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.
Sound Activity Monitor Circuit for Low Power Consumption of Always-On Microphone Applications
Authors: Kim Jong Pal
Published: November 2022

A novel sound activity monitor (SAM) circuit for low power consumption of always-on microphone applications is presented. To reduce average power consumption, the ultra-low-power SAM is essential and operates a readout integrated circuit (ROIC) in low power mode with silent input or in normal power mode with voice input. A novel SAM with an architecture that does not include an envelope detector is proposed to achieve low power consumption. A new architecture is also proposed to improve MEMS sensitivity by connecting the SAM input to the source follower (SF) output instead of connecting the SAM input to the MEMS port already connected to the SF. In addition, in order to prevent inefficient frequent operation mode conversion, a feature of delaying the transition to the low-power mode after the sound is silenced is implemented. The proposed architecture is designed and verified based on the standard 0.18 µm CMOS process. The SAM, which consists of two-stage amplifiers (OA, AMP2), comparators, and a logic circuit, consumes a 1 µA current. The analog path consisting of SF, OA, and AMP2 in low power mode has a maximum amplification gain of 63 dB and a noise of 72 nVrms/√Hz at 1 kHz.
Method of Sequential Approximation in Modelling the Processes of Heat Transfer and Gas Dynamics in Combustion Equipment
Authors: Rimar Miroslav , Yeromin Oleksandr , Larionov Grigoriy , Kulikov Andrii , Fedak Marcel , Krenicky Tibor , Gupalo Olena , Myanovskaya Yana
Published: November 2022

The behavior of the processes taking place in furnaces determines the efficiency of fuel chemical energy utilization, the quality of the final products and the environmental safety of the production. Mathematical models of the processes of gas dynamics and heat transfer in the working space of heating equipment are quite complex, and do not allow the establishment of a direct analytical relationship between the quality indicator of the process (F) and the influencing parameters (xi). To simplify the procedure for obtaining the values of the function F depending on the change of parameters xi, a method of successive approximation is presented in the article. The main idea of the method is that the representation of the function around a point from the domain of the function can be extended to the entire domain for many problems of mechanics. The relative error in the definition of the function acquires its maximum value at the border of the area, and a reasonable narrowing of it allows control of the size of the error. Thus, the advantages of using the method are obvious; it is able to provide approximation of the function in a multiplicative form with a controlled error. The distribution of the method to the field of heat transfer problems is presented in this paper. The successful implementation of this method for solving problems of this kind shows that the solution of practical problems may be generalized for the entire domain of the function, despite the fact that the errors of such a representation increase to 5–7% when approaching its limit, which, however, may be considered acceptable for engineering calculations.
Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
Authors: Islam Md Kamrul , Dalhat Muhammad Abubakar , Al Mamun Abdullah
Published: November 2022

Road traffic accidents are still among the top major global causes of death, injury, and disability. Despite this cause for alarm and several preventive initiatives, global road accident statistics are not improving. This study modeled annual road accidents (ARAs) as a function of demographic, economic, passenger movement, freight movement, and road capital investment indicators. The research is based on 22 years of data from more than 36 Organization for Economic Co-operation and Development (OECD) member and partner countries. Artificial neural network (ANN), multiple linear regression (MLR), and Poisson regression (PR) analysis were employed for this purpose. The ANN model outperformed the regression models by far, thus making it possible for reliable new insights and accurate results to be obtained. The ANN’s superior performance was shown to be a result of the non-linear relationship between ARA and some of the predicting variables. The average relative contribution of each variable in describing the ARA models was estimated using connection weight analysis (from the ANN model) and relative weight analysis for the regression model. The profile method was used to perform sensitivity analysis and to establish the partial variation trend of the ARA with each of the variables. The Existing Road Maintenance Investment (ERMI) and New Road Infrastructural Investment (NRII) showed a nonlinear concave-up relationship with ARA for given demography, economy, freight, and passenger movements. A combination of per capita NRII and ERMI corresponding to the minimum ARA exists. These sets of NRII and ERMI were considered safe road investment limits. The ANN-ARA model was utilized to estimate these limits with their relative proportion for diverse combinations of demography, economy, freight level, and passenger movement.
Water Modification by Cold Plasma Jet with Respect to Physical and Chemical Properties
Authors: Svarnas Panagiotis , Poupouzas Michael , Papalexopoulou Konstantia , Kalaitzopoulou Electra , Skipitari Marianna , Papadea Polyxeni , Varemmenou Athina , Giannakopoulos Evangelos , Georgiou Christos D. , Georga Stavroula , Krontiras Christoforos
Published: November 2022

This work is devoted to unbuffered and buffered water treatment by means of atmospheric pressure cold plasma of electrical discharges. The interest in the activation of these two liquids by plasma-induced, gaseous-phase chemistry ranges over a wide area of potential applications and interdisciplinary scientific fields. These include biology, medicine, sanitation, environmental restoration, agriculture, etc. Atmospheric pressure cold plasma is here produced in the form of a plasma jet and set into physical contact with the liquid specimens. The operational window of the treatment, in terms of plasma reactivity, is determined by means of UV-NIR optical emission spectroscopy, and the treated liquids are probed in a variety of respects. Evaporation rate, temperature, acidity and basicity, resistivity, and oxidation-reduction potential are measured as a function of the treatment time, either in-situ or ex-situ. The formation of principal reactive oxygen species, i.e., •OH, H2O2 and O2•−, with a plasma jet mean power lower than 400 mW, is eventually demonstrated and their concentration is measured with original methods borrowed from the biology field. The experimental results are linked to reports published over the last ten years, which are compiled in a brief but meaningful review.
Research on Dynamic Reserve and Energy Arbitrage of Energy Storage System
Authors: Jhan Jia-Zhang , Tai Tzu-Ching , Chen Pei-Ying , Kuo Cheng-Chien
Published: November 2022

Replacing the traditional rotating generators with renewable energy will reduce the grid’s inertia and with it the minimum frequency when N-1 contingency occurs triggering an Under-Frequency Load Shedding (UFLS). This study proposes a method for the energy storage system (ESS) to simultaneously provide energy arbitrage, reserve capacity, and assist N-1 contingency, by modifying the restriction formula of economic dispatch (ED) and limiting the SOC range of the ESS. Let the ESS join the Spinning Reserve. Through the PSS®E iterative ESS charging power required at moments when the frequency of contingency is too low in the ED. Let the ESS act as a N-1 contingency extra frequency reserve. This would prevent UFLS and still maintain the demand. The proposed method is applicable to different types of ESS. The method allows energy storages, originally designed for energy arbitrage, to participate in frequency support and spinning reserve.
Dermo-Cosmetic Benefits of Marine Macroalgae-Derived Phenolic Compounds
Authors: Kalasariya Haresh S. , Pereira Leonel
Published: November 2022

Marine macroalgae have an interesting profile of bioactive compounds and have gained tremendous attention in cosmeceuticals with negligible toxicity effects (cytotoxicity, reproductive toxicity, genotoxicity, mutagenicity, carcinogenicity, etc.) on humans and exhibit strong benefits for the skin. Among the diversified compounds, phenolic compounds are the group of phytochemicals found in high amounts with great structural diversity. Phlorotannin is the most studied polyphenol compound in brown algae, but besides there are some other phenolic compounds observed and studied in macroalgae such as terpenoids, bromophenols, mycosporine amino acids (MAAs), and flavonoids. These compounds are already characterized and studied for their full range of cosmeceutical benefits such as skin whitening, moisturizing, photoprotection, antiaging, antiwrinkle, anti-melanogenic, and antioxidant activities as well as in the treatment of pruritus (caused by acne, eczema, dermatitis, hives, psoriasis), photoaging, and skin pigmentation disorders (hypopigmentation due to the absence of melanocytes and hyperpigmentation caused by skin irritation or metabolic disorders). This review study mainly focuses on marine algae-derived phenolic compounds and their extraction, characterization, and skin cosmetic benefits described in the literature. The present study aims to provide a detailed insight into the phenolic compounds in marine algae.
Theoretical and Experimental Adsorption of Silica Gel and Activated Carbon onto Chlorinated Organic Compounds in Water: A Case Study on the Remediation Assessment of a Contaminated Groundwater Site
Authors: Chen Hung-Yu , Lo I-Tzu
Published: November 2022

Chlorinated organic compounds (COCs) are a serious threat to human health and the ecological environment due to their toxicity, mutagenicity and carcinogenicity. A number of corresponding treatment techniques have been developed thus far; among these techniques, adsorption is considered an efficient and low-cost method. However, it is very important to find suitable and economical adsorbent types and usage amounts. In this study, the adsorption of trichloroethylene (TCE) and perchloroethylene (PCE) onto two different adsorbents (silica gel and activated carbon) was investigated, and the obtained adsorption constants were then introduced into Langmuir and Freundlich adsorption models. The adsorption isotherm constants of these two models were used to predict the adsorbate removal efficiency and the required adsorbent mass. The results showed that the Langmuir and Freundlich models predicted the removal efficiency and adsorbent quality of the TCE and PCE adsorbed onto silica gel and activated carbon, and the errors were less than 10% compared with the measured values obtained through adsorption experiments. According to the differences in adsorption efficiency and cost between silica gel and activated carbon, it was found that, when the adsorption efficiency of the silica gel for TCE and PCE significantly decreased, adding activated carbon to continue the adsorption effectively reduced the adsorption cost of on-site remediation. This result was demonstrated by taking the Radio Corporation of America (RCA) pollution incident in Taiwan as an example, and it was estimated that 6427 ± 172 tons and 343 ± 21 tons of silica gel and activated carbon, respectively, would be required for the on-site remediation of TCE, and the total cost would be approximately 4,390,000 USD. The remediation of PCE would require 7553 ± 57 tons of silica gel and 350 ± 68 tons of activated carbon, with a total cost of approximately 5,030,000 USD. Therefore, silica gel should be used to reduce the pollutant concentration first, and then activated carbon should be added for continuing adsorption. This method of adding adsorbents is economical and can effectively reduce the adsorption cost.