bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fast Prediction Method for the Target Reachable Zone of Boosting Gliding Vehicle Based on Database
Authors: Huang Qiwei , Deng Zexiao , Liu Luhua
Published: February 2023

This paper presents an effective method for the fast prediction of the target reachable zone of a boosting gliding vehicle in the gliding phase. Firstly, a six-point method is proposed for the rapid determination of the reachable zone and the feasible discrimination of the target. The method chooses six boundary points with notable features to characterize the reachable zone and considerably reduces the computational consumption. Furthermore, taking into account the need for a rapid launch and safety against unexpected events during the gliding phase, a database method for the prediction of the reachable zone is presented, which builds the database with time nodes and six boundary points and employs interpolation methods to calculate the reachable zone. The results suggest that online planning by the database method has significant potential for predicting the target reachable zone. The accuracy error is less than 1%, and the computational efficiency is increased by 90% when compared to the six-point approach.
Protection against Dental Erosion and the Remineralization Capacity of Non-Fluoride Toothpaste, Fluoride Toothpaste and Fluoride Varnish
Authors: Mazzoleni Sergio , Gargani Alessandro , Parcianello Roberta Gaia , Pezzato Luca , Bertolini Rachele , Zuccon Andrea , Stellini Edoardo , Ludovichetti Francesco Saverio
Published: February 2023

Introduction: The oral cavity and the teeth are frequently subjected to numerous physiological pH variations, mainly due to the type of diet. These changes are the main cause of enamel demineralization and consequent breakage under mechanical forces. This causes severe sensitivity and sometimes pain, which endures unless the hard tissue is remineralized. Since enamel does not self-repair, the application of alloplastic materials that have the property of releasing remineralizing ions is strongly recommended. The aim of this in vitro study is to evaluate the effectiveness of two different toothpastes and a fluoride varnish in the prevention of demineralization, and their ability to remineralize enamel after it has undergone several acidifications. Methods: Eight human teeth with no caries or defects were prepared. The acid attack simulations were performed using a commercial cola carbonated drink. Samples were immersed in 5 mL of soft drink for two minutes at room temperature, and then were rinsed with distilled/deionized water. The immersion process lasted about 2 min, and was repeated four times for a total of eight minutes. Then, two different types of toothpaste, one with and one without fluoride, and a fluoride varnish were applied to the surfaces of the samples and rinsed off with demineralized water. A second acidification cycle was then carried out, with the subsequent reapplication of the three different products to evaluate their ability to protect against demineralization. SEM and profilometer analysis were then carried out to evaluate the results. Results: The statistical analysis showed a good remineralization capacity for all three products, especially in the fluoride-varnish-treated samples. However, regarding protection from demineralization, non-fluoride toothpaste was found to be ineffective, while fluoride toothpaste and varnish produced positive results. Conclusions: Dental professionals should advise fluoride toothpastes and varnishes over non-fluoride toothpastes. Fluoride toothpastes and varnishes represent a valid treatment for surface remineralization after a first acid attack and for protection from subsequent demineralization in an acid environment, such as that which can develop in the oral cavity.
Special Issue on Development and Applications of Microwave/Millimeter Wave Diagnostics in Industry
Authors: Mase Atsushi
Published: February 2023

Microwave/millimeter wave devices and systems have been developed as core technologies in the fields of communication and measurement [...]
A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
Authors: Doras Guillaume , Teytaut Yann , Roebel Axel
Published: February 2023

Synchronisation of a voice recording with the corresponding text is a common task in speech and music processing, and is used in many practical applications (automatic subtitling, audio indexing, etc.). A common approach derives a mid-level feature from the audio and finds its alignment to the text by means of maximizing a similarity measure via Dynamic Time Warping (DTW). Recently, a Connectionist Temporal Classification (CTC) approach was proposed that directly emits character probabilities and uses those to find the optimal text-to-voice alignment. While this method yields promising results, the memory complexity of the optimal alignment search remains quadratic in input lengths, limiting its application to relatively short recordings. In this work, we describe how recent improvements brought to the textbook DTW algorithm can be adapted to the CTC context to achieve linear memory complexity. We then detail our overall solution and demonstrate that it can align text to several hours of audio with a mean alignment error of 50 ms for speech, and 120 ms for singing voice, which corresponds to a median alignment error that is below 50 ms for both voice types. Finally, we evaluate its robustness to transcription errors and different languages.
Energy-Exergy Analysis of Diesel Engine Fueled with Microalgae Biodiesel-Diesel Blend
Authors: Tiwari Chandrabhushan , Verma Tikendra Nath , Dwivedi Gaurav , Verma Puneet
Published: February 2023

Renewable energy is getting more attention in recent times due to the rapid depletion of fossil fuel reserves. Production and consumption of biofuels derived from biomass has significantly increased. In the present work, Spirulina microalgae have been chosen as feedstock for biodiesel production. Diesel and biodiesel were mixed in different volumetric ratios to prepare fuel blends (SBF0, SBF20, SBF40, SBF60, SBF80, and SBF100). Energy and exergy analysis has been performed on a four-stroke, single-cylinder diesel engine. Experimentation was done under varying loads at 1500 RPM. The effect of multiple loads and blends was investigated for brake power (BP), cooling water losses (Qw), exhaust gas losses (Qexh), and unaccounted losses (Qun). Pure diesel SBF100 has the highest and lowest exergy efficiencies, respectively equaling roughly 31.65% and 29.75%. It has been observed that BP and Qw increase with the increase in load whereas Qexh and Qun show a decreasing trend. It was also observed that with an increase in blending, Qw increases while Qexh decreases. In the exergy analysis, it was observed that the exergy destruction rate has a maximum fraction of input exergy values of 46.01% and 46.29% for Diesel and SBF20 respectively. The system engine sustainability index was in the range of 1.27 to 1.46, which is directly related to exergy efficiencies.
Study on the Influence of Consumers’ Purchase Intention of Selenium-Rich Agricultural Products
Authors: Zhang Ling , Gao Risheng , Zhang Haitao , Luo Xin , Song Zhenjiang
Published: February 2023

As people’s awareness of the special functions of selenium continues to deepen, the development of selenium-rich food continues to develop, and selenium-rich places have been vigorously developing this resource-based industry. The development of selenium-rich agriculture is of great significance to improve people’s quality of life and promote agricultural, rural and regional economic development. This paper analyzes the factors affecting consumers’ willingness to purchase selenium-rich agricultural products in detail through questionnaire survey data of some consumers in Jiangxi, Ensh, Hubei and Ankang, Shaanxi, using three analytical methods of descriptive statistical analysis, factor analysis and logistic regression analysis with SPSS software. The research results show that consumers’ attitude toward selenium-rich agricultural products, price concerns, consumers’ family characteristics, health and environmental protection motives, gender and other factors have certain influence on consumers’ willingness to purchase selenium-rich agricultural products, among which the attitude factor and family characteristics have the greatest influence. Finally, the market prospect of selenium-rich agricultural products in China is analyzed and prospected, and the measures and suggestions for developing selenium-rich food are proposed in response to the problems of developing selenium-rich food in China.
Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS
Authors: Kim Hee-Un , Bae Tae-Suk
Published: February 2023

After the Gyeongju earthquake in 2016 and the subsequent one in Pohang the following year, there is an imminent necessity to evaluate the risk of earthquakes accurately as well as respond to the risks on the Korean peninsula. For this purpose, the existence and movement of a fault should be investigated in the area. In this study, we calculated the displacement of the crust around the mass production fault using GNSS (Global Navigation Satellite System) data and analyzed the deformation characteristics by applying the method of stress calculation. The Yangsan Fault Zone has been analyzed with a total of 24 GNSS stations between 2018 and 2021. Data processing was conducted with Bernese GNSS Software, which requires high-precision orbit, satellite clock, ionosphere information for high-precision position estimation. By accumulating daily solutions over the three years to produce the final solution with the velocity of the stations, the Yangsan Fault Zone moved about 32 mm per year southeast on average. Based on the results, the movements of the stations on either side of the Faults are almost the same. Stress analysis of the Yangsan Fault Zone showed a large east–west expansion during 2018–2019 but decreased in stress afterwards, thus it is evaluated to be relatively stable compared to the past. However, due to the nature of crustal variation continuous monitoring research with long-term data processing should be followed, which will be discussed in further research.
A Novel Approach for Efficient Mitigation against the SIP-Based DRDoS Attack
Authors: Tas Ismail Melih , Baktir Selcuk
Published: February 2023

Voice over Internet Protocol (VoIP) and its underlying Session Initiation Protocol (SIP) are widely deployed technologies since they provide an efficient and fast means of both voice and data communication over a single network. However, in spite of their advantages, they also have their security threats due to the inherent vulnerabilities in the underlying Internet Protocol (IP) that can potentially be exploited by hackers. This study introduces a novel defense mechanism to effectively combat advanced attacks that exploit vulnerabilities identified in some less-known features of SIP. The SIP-DRDoS (SIP-based distributed reflection denial of service) attack, which can survive the existing security systems, is an advanced attack that can be performed on an SIP network through the multiplication of legitimate traffic. In this study, we propose a novel defense mechanism that consists of statistics, inspection, and action modules to mitigate the SIP-DRDoS attack. We implement the SIP-DRDoS attack by utilizing our SIP-based audit and attack software in our VoIP/SIP security lab environment that simulates an enterprise-grade SIP network. We then utilize our SIP-based defense tool to realize our novel defense mechanism against the SIP-DRDoS attack. Our experimental results prove that our defense approach can do a deep packet analysis for SIP traffic, detect SIP flood attacks, and mitigate them by dropping attack packets. While the SIP-DRDoS attack with around 1 Gbps of traffic dramatically escalates the CPU (central processing unit) usage of the SIP server by up to 74%, our defense mechanism effectively reduces it down to 17% within 6 min after the attack is initiated. Our approach represents a significant advancement over the existing defense mechanisms and demonstrates the potential to effectively protect VoIP systems against SIP-based DRDoS attacks.
Calibration of a Rotary Encoder and a Polygon Using a Two-Autocollimator Method
Authors: Hsieh Tsung-Han , Lin Ming-Xian , Yeh Kuan-Ting , Watanabe Tsukasa
Published: February 2023

In this work, we propose a two-autocollimator method in which all pitch angle deviations of a polygon and angle errors of a rotary encoder can be calibrated simultaneously. A polygon with any number of faces can be calibrated. Any face of the polygon is a measurement cycle of one. Compared to a traditional method, cross-calibration calibrates a rotary encoder and a polygon. This method can simultaneously calibrate all pitch angle deviations of the polygon and angle errors of the rotary encoder. The measurement cycle depends on how many faces the polygon has. There are 24 measurement cycles for a 24-faced polygon. In the experiment, we use two autocollimators to calibrate a 24-faced polygon and the SelfA rotary encoder to conduct the proposed two-autocollimator method. According to the uncertainty evaluation, the expanded uncertainty is 0.46”. For a 95% confidence level, the coverage factor is 2.00. To verify all pitch angle deviations, the shift-angle method, based on cross-calibration, uses one autocollimator to measure the same polygon. The difference in pitch angle deviations is smaller than ±0.28”. The maximum En-value is 0.58. The SelfA rotary encoder comprises 12 read heads and calibrates using self-calibration. The difference in angle errors is smaller than ±0.27”. The maximum En-value is 0.59. The two En-values mean that the proposed two-autocollimator method is practical.
Natural Gas Flaring Management System: A Novel Tool for Sustainable Gas Flaring Reduction in Nigeria
Authors: Abu Robin , Patchigolla Kumar , Simms Nigel , Anthony Edward John
Published: February 2023

The use of hydrocarbon fuels increases with population growth and rising standards of living, and so does natural gas flaring. Natural gas flaring is both a waste of natural resources and a violation of Nigeria’s energy policy for sustainable development through natural gas conservation. However, it remains the most cost-efficient and effective associated natural gas (ANG) management option in developing countries such as Nigeria. The World Bank’s initiative to eliminate routine gas flaring by 2030 has increased the need to limit or eliminate routine gas flaring. Often, studies on natural gas utilisation techniques fail to consider the lack of practical tools that integrate economic, technical, and regulatory factors into a gas flaring management framework, and the intricacies of existing tools, which often come at the expense of simplicity to achieve real-time information output. This paper aims to establish a framework and ANG management tool to reduce regular gas flaring in Nigeria. This research established a management framework (using a flowchart decision tree) and models to provide a user-friendly ANG flaring tool (using a MATLAB graphical front end user interface with back-end ASPEN HYSYS thermodynamic models). This was combined with techno-economic models for liquefied natural gas, gas-to-methanol, and gas-to-wire ANG utilisation options. The tool was then tested with data obtained from Fields Y and X in the Niger Delta region of Nigeria. The results, considering both economic and technical factors, showed that the choice of liquefied natural gas for Field Y was best due to its proximity to the pipeline infrastructure and its cost-effectiveness, and the availability of a high-demand LNG market for that area. For Field X, gas-to-wire was best due to its proximity to the electrical grid and high electricity requirements for that area. Additional geographical profiles in West Africa and ANG utilisation alternatives were recommended for further investigation. This paper developed and validated a one-of-a-kind ANG flaring management tool that incorporates techno-economic analysis of selected ANG utilisation options to assist operators and investors in making more profitable investment decisions.
Impact of Perfluorocarbons with Gas Transport Function on Growth of Phototrophic Microorganisms in a Free and Immobilized State and in Consortia with Bacteria
Authors: Senko Olga , Maslova Olga , Aslanli Aysel , Efremenko Elena
Published: February 2023

The effects of the presence of perfluorocarbons (PFC) with a gas transport function in media with different phototrophic microorganisms on their growth rates and the accumulation of their biomass when using free and immobilized cells as inoculums were investigated. The significant increase in the average rate of biomass accumulation as well as levels of biomass accumulation in the presence of various PFCs were established for Chlorella vulgaris cells. When 1 g/L glycerol was introduced into the growth medium with PFCs and C. vulgaris cells, the increase in the rate of biomass accumulation was 9–32%. The maximum intracellular ATP concentrations corresponded to the combination of microalgae (Chlorella vulgaris) with bacterial cells (Pseudomonas esterophilus and Rhodoccus ruber) obtained with a mass ratio of 25:1. It provided for the formation of a consortium, which was able to accumulate the maximum amount of microalgae biomass for 3 days in the medium with PFCs and organophosphorus pesticide. The obtained data allow, on the one hand, predicting the growth of microalgae under environmental conditions in media with PFC pollution and, on the other hand, developing approaches to regulation of phototrophic microorganisms’ growth in order to obtain and use their high biomass yields for various purposes.
A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
Authors: Li Yongning , Song Weiwei , Jin Baoxuan , Zuo Xiaoqing , Li Yongfa , Chen Kai
Published: February 2023

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques analyze a study area using a set of SAR image data composed of time series, reaching millimeter surface subsidence accuracy. To effectively acquire the subsidence information in low-coherence areas without obvious features in non-urban areas, an MT-InSAR technique, called SqueeSAR, is proposed to improve the density of the subsidence points in the study area by fusing the distributed scatterers (DS). However, SqueeSAR filters the DS points individually during spatial adaptive filtering, which requires significant computer memory, which leads to low processing efficiency, and faces great challenges in large-area InSAR processing. We propose a spatially adaptive filtering parallelization strategy based on the Spark distributed computing engine in a Hadoop distributed cluster environment, which splits the different DS pixel point data into different computing nodes for parallel processing and effectively improves the filtering algorithm’s performance. To evaluate the effectiveness and accuracy of the proposed method, we conducted a performance evaluation and accuracy verification in and around the main city of Kunming with the original Sentinel-1A SLC data provided by ESA. Additionally, parallel calculation was performed in a YARN cluster comprising three computing nodes, which improved the performance of the filtering algorithm by a factor of 2.15, without affecting the filtering accuracy.
Research on Multi-Robot Formation Control Based on MATD3 Algorithm
Authors: Zhou Conghang , Li Jianxing , Shi Yujing , Lin Zhirui
Published: February 2023

This paper investigates the problem of multi-robot formation control strategies in environments with obstacles based on deep reinforcement learning methods. To solve the problem of value function overestimation in the deep deterministic policy gradient (DDPG) algorithm, this paper proposes an improved multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm under the CTDE framework combined with the twin delayed deep deterministic policy gradient (TD3) algorithm, which adopts a prioritized experience replay strategy to improve the learning efficiency. For the problem of difficult obstacle avoidance for a robot formation, a hybrid reward mechanism is designed to use different formation maintenance strategies in obstacle areas and obstacle-free areas to achieve the control goal of obstacle avoidance by reasonably changing the formation. The simulation experiments verified the effectiveness of the multi-robot formation control strategy designed in this paper, and comparative simulations verified that the algorithm has a faster convergence speed and more stable performance.
Radiation Damage on Thaumatin: A Case Study of Crystals That Are Larger Than the Microfocusing X-ray Beam
Authors: Nam Ki Hyun
Published: February 2023

Microfocusing X-rays direct high-density photons on crystal samples and can enhance the diffraction limit and quality of collected data. However, these intense X-rays can cause radiation damage to the sample, which often results in undesirable structural information. Accordingly, a data collection strategy that minimizes radiation damage is critical to obtaining accurate structural information. In this study, radiation damage in single-point data collection was investigated at two different X-ray exposure times (1 s and 100 ms) using microfocusing X-rays and a thaumatin crystal larger than the beam. The data collection statistics showed that the diffraction intensity of the Bragg peak did not gradually decrease until the crystal rotation reached 180°, and it significantly decreased after exceeding this value. Thaumatin structures exposed to X-rays for 1 s (Thaumatin1s) and 100 ms (Thaumatin100ms) were determined at 1.13 Å resolution. The temperature factors for Asp60, Arg119, Lys163, and Lys187 of thaumatin were increased by radiation damage. Specific radiation damage was observed at the disulfide bond in Thaumatin1s but was negligible in Thaumatin100ms. Splitting and reprocessing Thaumatin100ms showed that electron density maps with minimal radiation damage can be obtained when using minimal data that satisfy the completeness, I/sigma, and CC1/2 parameters. These results expand our understanding of radiation damage phenomena in macromolecules and can be used for data collection applications.
Flexible Route Planning for Multiple Mobile Robots by Combining Q–Learning and Graph Search Algorithm
Authors: Kawabe Tomoya , Nishi Tatsushi , Liu Ziang
Published: February 2023

The use of multiple mobile robots has grown significantly over the past few years in logistics, manufacturing and public services. Conflict–free route planning is one of the major research challenges for such mobile robots. Optimization methods such as graph search algorithms are used extensively to solve route planning problems. Those methods can assure the quality of solutions, however, they are not flexible to deal with unexpected situations. In this article, we propose a flexible route planning method that combines the reinforcement learning algorithm and a graph search algorithm for conflict–free route planning problems for multiple robots. In the proposed method, Q–learning, a reinforcement algorithm, is applied to avoid collisions using off–line learning with a limited state space to reduce the total learning time. Each vehicle independently finds the shortest route using the A* algorithm, and Q–learning is used to avoid collisions. The effectiveness of the proposed method is examined by comparing it with conventional methods in terms of computation time and the quality of solutions. Computational results show that for dynamic transportation problems, the proposed method can generate the solutions with approximately 10% of the computation time compared to the conventional Q–learning approach. We found that the required computation time is linearly increased with respect to the number of vehicles and nodes in the problems.
Assessment of Changes in the Abrasiveness of Solid Particles in Hydraulic Mixtures Pumped with ESPs
Authors: Shishlyannikov Dmitriy , Zvonarev Ivan , Rybin Alexander , Zverev Valeriy , Ivanchenko Anna
Published: February 2023

The statistics of using downhole electric submersible pump (ESP) units in the oil fields of Eurasia show that hydroabrasive wear is a common cause of ESP emergency failures. The authors of this study theoretically and experimentally investigated the peculiarities of water–abrasive wear of ESP components. The research methodology and construction of the laboratory bench are described, and the results of experimental studies of determination of changes in the abrasivity index of impurity particles contained in the mixture pumped with the working stages of an ESP section are given. It is shown that large particles of mechanical impurities in contact with the metal of the working stages are subjected to intensive grinding of up to 0.25…0.3 mm; after that, particle size remains practically unchanged. An increased abrasiveness index of mechanical impurities causes an increase in the intensity of wear of interstage seals along the flow of a water–abrasive mixture (from the inlet to the outlet of the pumping section). Based on the obtained results, recommendations for selection of devices for formation-fluid cleaning during development and operation of oil wells are given. The relevance of work on improving the design of interstage seals of ESP units is substantiated.
Anomaly Detection Method for Multivariate Time Series Data of Oil and Gas Stations Based on Digital Twin and MTAD-GAN
Authors: Lian Yuanfeng , Geng Yueyao , Tian Tian
Published: February 2023

Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great challenges to multivariate time series anomaly detection. Moreover, the time-series reconstruction information of data from digital twin space can be used to identify and interpret anomalies. Therefore, this paper proposes a digital twin-driven MTAD-GAN (Multivariate Time Series Data Anomaly Detection with GAN) oil and gas station anomaly detection method. Firstly, the operational framework consisting of digital twin model, virtual-real synchronization algorithm, anomaly detection strategy and realistic station is constructed, and an efficient virtual-real mapping is achieved by embedding a stochastic Petri net (SPN) to describe the station-operating logic of behavior. Secondly, based on the potential correlation and complementarity among time series variables, we present a MTAD-GAN anomaly detection method to reconstruct the error of multivariate time series by combining mechanism of knowledge graph attention and temporal Hawkes attention to judge the abnormal samples by a given threshold. The experimental results show that the digital twin-driven anomaly detection method can achieve accurate identification of anomalous data with complex patterns, and the performance of MTAD-GAN anomaly detection is improved by about 2.6% compared with other methods based on machine learning and deep learning, which proves the effectiveness of the method.
Analyzing the Occupied Space of Passengers with Reduced Mobility in Metro Station Platforms: An Experimental Approach Using a Tracking System
Authors: Seriani Sebastian , Guzman Pablo , Fujiyama Taku
Published: February 2023

The objective of this study is to analyze the occupied space of passengers with reduced mobility when boarding or alighting a train through an experimental approach based on a virtual tracking tool system to obtain their exact position. The designed experiments considered a train and its adjacent platform, in which 21 volunteers were recruited, 3 with reduced mobility. The results indicated that passengers with reduced mobility required up to 80% more space, compared to a passenger without reduced mobility, when waiting to board the train. The passenger who occupied the largest space was the one with a pram, reaching 1.70 m2/passenger, represented as a polygon. In addition, when passengers started to walk, the space used increased due to limb movement. In the alighting process, passengers with reduced mobility used almost twice the space required for the boarding process due to the relatively larger platform space occupied as each passenger alights and walks away, whereas when boarding, each passenger has less space to share with the other people waiting for the train to arrive or for the doors to open. These results could help practitioners improve the design of the platform or implement control measures, such as adding waiting areas for those passengers with reduced mobility. Further research is needed regarding other types of stations and density situations.
Requirements of a Supportive Environment for People on the Autism Spectrum: A Human-Centered Design Story
Authors: Wohofsky Lukas , Marzi Arianna , Bettarello Federica , Zaniboni Luca , Lattacher Sandra Lisa , Limoncin Paola , Dordolin Anna , Dugaria Simone , Caniato Marco , Scavuzzo Giuseppina , Gasparella Andrea , Krainer Daniela
Published: February 2023

People on the autism spectrum have a different perception of the environment than neurotypical people and often require support in various activities of daily living. Assistive technology can support those affected, but very few smart-home-like technologies exist. To support people on the autism spectrum in their autonomy and safety and to help caregivers, a smart home and interior design environment was developed. Requirements were gathered by employing a holistic human-centered design approach through interactive workshops and questionnaires to create a useful and user-friendly solution. From this process, requirements for a comprehensive solution (the SENSHOME environment) emerged. These requirements include a set of functionalities tailored to the needs of people on the autism spectrum, such as a crowd warning that informs when many people are in a certain area (for example, the entrance), an automatic light regulation system, or a daily life planner that supports task completion. Furthermore, inclusive furniture elements such as a refuge seat or a table with dividers can support wellbeing, autonomy, and safety. This paper demonstrates a consequent and considerable participatory research approach and the story from the target group and context of use through design requirements to the initial design solution of the SENSHOME environment.
Chitosan Nanoparticles Production: Optimization of Physical Parameters, Biochemical Characterization, and Stability upon Storage
Authors: Costa Eduardo M. , Silva Sara , Pintado Manuela
Published: February 2023

Ionic gelation is among the simplest processes for the development of chitosan nanoparticles reported so far in the literature. Its one-shot synthesis process in conjunction with the mild reaction conditions required are among the main causes for its success. In this work, we sought to optimize a set of physical parameters associated with the ionic gelation process at two different pH values. Following that, the NPs’ freeze-drying and long-term storage stability were assayed, and their biocompatibility with HaCat cells was evaluated. The results show that NPs were more homogenously produced at pH 5, and that at this pH value, it was possible to obtain a set of optimum production conditions. Furthermore, of the assayed parameters, TPP addition time and overall reaction time were the parameters which had a significant impact on the produced NPs. Nanoparticle freeze-drying led to particle aggregation, and, of the cryoprotectants, assayed mannitol at 10% (w/v) presented the best performance, as the NPs were stable to freeze-drying and maintained their size and charge in the long-term stability assay. Lastly, the chitosan NPs presented no toxicity towards the HaCat cell line.
A Wrapped Approach Using Unlabeled Data for Diabetic Retinopathy Diagnosis
Authors: Zhang Xuefeng , Kim Youngsung , Chung Young-Chul , Yoon Sangcheol , Rhee Sang-Yong , Kim Yong Soo
Published: February 2023

Large-scale datasets, which have sufficient and identical quantities of data in each class, are the main factor in the success of deep-learning-based classification models for vision tasks. A shortage of sufficient data and interclass imbalanced data distribution, which often arise in the medical domain, cause modern deep neural networks to suffer greatly from imbalanced learning and overfitting. A diagnostic model of diabetic retinopathy (DR) that is trained from such a dataset using supervised learning is severely biased toward the majority class. To enhance the efficiency of imbalanced learning, the proposal of this study is to leverage retinal fundus images without human annotations by self-supervised or semi-supervised learning. The proposed approach to DR detection is to add an auxiliary procedure to the target task that identifies DR using supervised learning. The added process uses unlabeled data to pre-train the model that first learns features from data using self-supervised or semi-supervised learning, and then the pre-trained model is transferred with the learned parameter to the target model. This wrapper algorithm of learning from unlabeled data can help the model gain more information from samples in the minority class, thereby improving imbalanced learning to some extent. Comprehensive experiments demonstrate that the model trained with the proposed method outperformed the one trained with only the supervised learning baseline utilizing the same data, with an accuracy improvement of 4~5%. To further examine the method proposed in this study, a comparison is conducted, and our results show that the proposed method also performs much better than some state-of-the-art methods. In the case of EyePaCS, for example, the proposed method outperforms the customized CNN model by 9%. Through experiments, we further find that the models trained with a smaller but balanced dataset are not worse than those trained with a larger but imbalanced dataset. Therefore, our study reveals that utilizing unlabeled data can avoid the expensive cost of collecting and labeling large-scale medical datasets.
Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network
Authors: Beskopylny Alexey , Shcherban’ Evgenii , Stel’makh Sergey , Mailyan Levon , Meskhi Besarion , Razveeva Irina , Kozhakin Alexey , El’shaeva Diana , Beskopylny Nikita , Onore Gleb
Published: February 2023

The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical properties, and solve the problem of detecting, classifying, and segmenting existing defects. The detection of defects of various kinds on elements of building materials at the primary stages of production can improve the quality of construction and identify the cause of particular damage. The technology for detecting cracks in building material samples is of great importance in building monitoring, in pre-venting the spread of defective material. In this paper, we consider the use of the YOLOv4 convolutional neural network for crack detection on building material samples. This was based on the creation of its own empirical database of images of samples of aerated concrete. The number of images was increased by applying our own augmentation algorithm. Optimization of the parameters of the intellectual model based on the YOLOv4 convolutional neural network was performed. Experimental results show that the YOLOv4 model developed in this article has high precision in defect detection problems: AP@50 = 85% and AP@75 = 68%. It should be noted that the model was trained on its own set of data obtained by simulating various shooting conditions, rotation angles, object deformations, and light distortions through image processing methods, which made it possible to apply the developed algorithm in practice.
GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques
Authors: Benghzial Kaoutar , Raki Hind , Bamansour Sami , Elhamdi Mouad , Aalaila Yahya , Peluffo-Ordóñez Diego H.
Published: February 2023

Agriculture accounts for a large percentage of nitrous oxide (N2O) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and N2O volatilization processes. N2O emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and N2O emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect N2O emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict N2O emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect N2O emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.
Extreme Rainfall in Southern Burkina Faso, West Africa: Trends and Links to Atlantic Sea Surface Temperature
Authors: Sougué Madou , Merz Bruno , Sogbedji Jean Mianikpo , Zougmoré François
Published: February 2023

Understanding the space-time variations of extreme rainfall plays an important role in the management of water-related disasters in Sahel countries. This study investigates temporal changes in rainfall characteristics and explores the link between Atlantic Sea surface temperature and extreme rainfall in the southern part of Burkina Faso. We find substantial spatial heterogeneity in rainfall trends across the study area. In contrast to national and supra-national studies that found predominantly increasing trends in extreme rainfall, we detect more downward than upward trends, particularly for indices representing extreme rainfall. This difference is presumably a consequence of the high spatial variability in rainfall trends that can only be detected with sufficiently dense climate networks. We use the Poisson-General Pareto (Poisson-GP) distribution to quantify the frequency and intensity of extreme rainfall. Our comparison of the traditional, stationary Poisson-GP model with the nonstationary version where rainfall depends on Atlantic SST shows that the nonstationary model outperforms the traditional approach. This finding suggests that the assumption of stationary nature must be considered with care when modeling the frequency and intensity of extreme rainfall in the study area. Overall, our results suggest that the recent increase in flood disasters in Burkina Faso is rather caused by land use and land cover changes and population and urban growth and not by increasing rainfall extremes.
Simulations of Summertime Ozone and PM2.5 Pollution in Fenwei Plain (FWP) Using the WRF-Chem Model
Authors: Wang Yuxi , Cao Le , Zhang Tong , Kong Haijiang
Published: February 2023

In recent years, ozone and PM2.5 pollution has often occured in the Fenwei Plain due to heavy emission and favorable geographical conditions. In this study, we used the weather research and forecasting/chemistry (WRF-Chem) model to reproduce the complex air pollution of the ozone and PM2.5 in the Fenwei Plain (FWP) from 20 May to 29 May 2015. By comparing the simulation results with the observed data, we found that although in some cities there was a bias between the simulated values and observed data, the model captured the trend of pollutants generally. Moreover, according to the assessment parameters, we validated that the deviations are acceptable. However, according to these parameters, we found that the WRF-Chem performed better on ozone simulation rather than PM2.5. Based on the validation, we further analyzed the pollutant distribution during the contaminated period. Generally speaking, the polluted area is mainly located in the cities of the Shanxi province and Henan province. Moreover, in this time period, pollution mainly occurred on 27 May and 28 May. In addition, due to different formation conditions of ozone and PM2.5 pollution, the distribution characteristics of these two pollutants were also found to be different. Ozone pollution mainly occurred north of FWP due to the prevailing wind and the chemistry of ozone production. As for PM2.5, the pollution occurred at night and the polluted area was located in the FWP. Furthermore, high PM2.5 areas were closed to emission sources in the FWP, showing a high correlation with primary emissions.
High Lying Precise Resonance Energies from Photoionization Studies of Se3+ and Rb+ Ions Using the Screening Constant per Unit Nuclear Charge Formalism
Authors: Sakho Ibrahima
Published: February 2023

Resonance energies of the 4s4p (3P0,1)np, 4s4p (3P2)np (2P3/2, 4D7/2, 4D5/2) and 4s4p (1P1)np (2D3/2, 2D5/2) Rydberg series of Se3+ ions along with resonance energies of the 4s24p5 (2P°1/2) nd1P°1 and 4s4p6 (2S1/2) np 1P°1 series of Rb+ ions are reported. Calculations are done in the framework of the screening constant per unit nuclear charge (SCUNC) formalism. The fine structure splitting of the 4s4p (3P2)np (2P3/2, 4D7/2, 4D5/2) series from n = 10 to n = 27 and for the 4s4p (1P1)np (2D3/2, 2D5/2) series from n = 8 to 11 are resolved in this paper. Very good agreements are obtained between the present calculations and the available experimental and theoretical literature data. The present predicted data up to n = 40 may be of great importance for the atomic physics community in connection with the understanding of the chemical evolution of the Se and Rb elements in the Universe.
Estimation of Uncertainty for Technology Evaluation Factors via Bayesian Neural Networks
Authors: Lee Juhyun , Park Sangsung , Lee Junseok
Published: February 2023

In contemporary times, science-based technologies are needed for launching innovative products and services in the market. As technology-based management strategies are gaining importance, associated patents need to be comprehensively studied. Previous studies have proposed predictive models based on patent factors. However, technology-based management strategies can influence the growth and decline of firms. Thus, this study aims to estimate uncertainties of the factors that are frequently used in technology-based studies. Furthermore, the importance of the factors may fluctuate over time. Therefore, we propose a Bayesian neural network model based on Flipout and four research hypotheses to evaluate the validity of our method. The proposed method not only estimates the uncertainties of the factors, but also predicts the future value of technologies. Our contribution is to (i) provide a tractable Bayesian neural network applicable to big data, (ii) discover factors that affect the value of technology, and (iii) present empirical evidence for the timeliness and objectivity of technology evaluation. In our experiments, 3781 healthcare-related cases of patents were used, and we found that the proposed hypotheses were all statistically significant. Therefore, we believe that reliable and stable technology-based management strategies can be established through our method.
Novel Frequency-Based Approach to Analyze the Stability of Polynomial Fractional Differential Equations
Authors: Yaghoubi Hassan , Zare Assef , Rasouli Mohammad , Alizadehsani Roohallah
Published: February 2023

This paper proposes a novel approach for analyzing the stability of polynomial fractional-order systems using the frequency-distributed fractional integrator model. There are two types of frequency and temporal stabilization methods for fractional-order systems that global and semi-global stability conditions attain using the sum-of-squares (SOS) method. Substantiation conditions of global and asymptotical stability are complicated for fractional polynomial systems. According to recent studies on nonlinear fractional-order systems, this paper concentrates on polynomial fractional-order systems with any degree of nonlinearity. Global stability conditions are obtained for polynomial fractional-order systems (PFD) via the sum-of-squares approach and the frequency technique employed. This method can be effective in nonlinear systems where the linear matrix inequality (LMI) approach is incapable of response. This paper proposes to solve non-convex SOS-designed equations and design framework key ideas to avoid conservative problems. A Lyapunov polynomial function is determined to address the problem of PFD stabilization conditions and stability established using sufficiently expressed conditions. The main goal of this article is to present an analytical method based on the optimization method for fractional order models in the form of frequency response. This method can convert it into an optimization problem, and by changing the solution of the optimization problem, the stability of the fractional-order system can be improved.
Established and Emerging Research Trends in Norway Lobster, Nephrops norvegicus
Authors: Aguzzi Jacopo , Violino Simona , Costa Corrado , Bahamon Nixon , Navarro Joan , Chatzievangelou Damianos , Robinson Nathan J. , Doyle Jennifer , Martinelli Michela , Lordan Colm , Company Joan B.
Published: February 2023

The burrowing crustacean decapod Nephrops norvegicus is a significant species in European Atlantic and Mediterranean fisheries. Research over the decades has mainly focused on behavioral and physiological aspects related to the burrowing lifestyle, since animals can only be captured by trawls when engaged in emergence on the seabed. Here, we performed a global bibliographic survey of all the scientific literature retrieved in SCOPUS since 1965, and terminology maps were produced with the VOSviewer software to reveal established and emerging research areas. We produced three term-map plots: term clustering, term citation, and term year. The term clustering network showed three clusters: fishery performance, assessment, and management; biological cycles in growth, reproduction, and behavior; and finally, physiology and ecotoxicology, including food products. The term citation map showed that intense research is developed on ecotoxicology and fishery management. Finally, the term year map showed that the species was first studied in its morphological and physiological aspects and more recently in relation to fishery and as a food resource. Taken together, the results indicate scarce knowledge on how burrowing behavior and its environmental control can alter stock assessment, because of the poor use of current and advanced monitoring technologies.