bluuebunny/crossref_metadata_2025 huggingface dataset:
Adversarial Attack Defense Method for a Continuous-Variable Quantum Key Distribution System Based on Kernel Robust Manifold Non-Negative Matrix Factorization
Authors: Fu Yuwen , Xia E. , Huang Duan , Jing Yumei
Published: September 2023

Machine learning has been applied in continuous-variable quantum key distribution (CVQKD) systems to address the growing threat of quantum hacking attacks. However, the use of machine learning algorithms for detecting these attacks has uncovered a vulnerability to adversarial disturbances that can compromise security. By subtly perturbing the detection networks used in CVQKD, significant misclassifications can occur. To address this issue, we utilize an adversarial sample defense method based on non-negative matrix factorization (NMF), considering the nonlinearity and high-dimensional nature of CVQKD data. Specifically, we employ the Kernel Robust Manifold Non-negative Matrix Factorization (KRMNMF) algorithm to reconstruct input samples, reducing the impact of adversarial perturbations. Firstly, we extract attack features against CVQKD by considering the adversary known as Eve. Then, we design an Artificial Neural Network (ANN) detection model to identify these attacks. Next, we introduce adversarial perturbations into the data generated by Eve. Finally, we use the KRMNMF decomposition to extract features from CVQKD data and mitigate the influence of adversarial perturbations through reconstruction. Experimental results demonstrate that the application of KRMNMF can effectively defend against adversarial attacks to a certain extent. The accuracy of KRMNMF surpasses the commonly used Comdefend method by 32.2% and the JPEG method by 30.8%. Moreover, it exhibits an improvement of 20.8% compared to NMF and outperforms other NMF-related algorithms in terms of classification accuracy. Moreover, it can complement other defense strategies, thus enhancing the overall defensive capabilities of CVQKD systems.
Investigation of Fruit Growth Patterns, Olive Fly Bactrocera oleae (Rossi) Infestation, and Genetic Diversity in Italian Olive Cultivars
Authors: Corrado Giandomenico , Mataffo Alessandro , Garonna Antonio P. , Rao Rosa , Basile Boris
Published: September 2023

The olive fruit fly Bactrocera oleae (Rossi, 1790) poses a significant threat to oliviculture worldwide, despite extensive chemical control measures. The susceptibility of olive (Olea europaea L.) to this pest varies among cultivars, with fruit size being a key factor of interest, as it can be assessed using non-destructive automated technologies. In this study, we investigated the oviposition preferences of the olive fruit fly in six Italian olive cultivars, tracking fertile and sterile punctures in relation to the diametric growth of the drupe. Additionally, we assessed the genetic diversity among cultivars using SSR markers, aiming to uncover a potential correlation between variation in the genetic diversity patterns and infestation levels. The results revealed that the relationship between fruit size and infestation level is non-linear and varies across different cultivars. The co-inertia analysis (COIA) performed on the genetic and infestation datasets revealed possible shared patterns of diversity and relationships between the two datasets. This study emphasizes the complex and diverse nature of the interaction between the olive fruit fly and its host, underscoring the importance of comprehending non-linear relationships to develop accurate genotypic-specific predictions and models.
Optimization of Data Acquisition System Based on Electrical Impedance Tomography in Dredging Engineering
Authors: Liu Ning , Yue Shihong , Wang Yibo
Published: September 2023

Electrical impedance tomography (EIT) is an advanced visualization detection technique with non-invasive, radiationless, and fast-response characteristics. As an important means in dredging engineering, EIT-based measurement can realize the estimate and computation of key flow parameters such as the flow velocity and solid phase fraction of solid-liquid two-phase flow in a pipe. Despite progress, both the data acquisition rate and the signal-to-noise ratio (SNR) of the existing EIT system are too low to meet many practical requirements. In this study, efforts are made concerning the EIT acquisition system by optimizing the sensor array, data acquisition system, and data communication module. Experimental results show that (1) the optimized system has an SNR of 73 dB, which is about 40% higher than the original system, and (2) the optimization of data transmission methods can achieve a maximum allowable transmission rate of 316.4 Mbps, sufficient to support data transmission over 1000 frames per second. Consequently, the key problems of the existing EIT acquisition system are substantially overcome.
The Neutrino Mediterranean Observatory Laser Beacon: Design and Qualification
Authors: Real Diego , Sánchez Losa Agustín , Díaz Antonio , Salesa Greus Francisco , Calvo David
Published: September 2023

This paper encapsulates details of the NEMO laser beacon’s design, offering a profound contribution to the field of the time calibration of underwater neutrino telescopes. The mechanical design of the laser beacon, which operates at a depth of 3500 m, is presented, together with the design of the antibiofouling system employed to endure the operational pressure and optimize the operational range, enhancing its functionality and enabling time calibration among multiple towers. A noteworthy innovation central to this development lies in the battery system. This configuration enhances the device’s portability, a crucial aspect in underwater operations. The comprehensive design of the laser beacon, encompassing the container housing, the requisite battery system for operation, electronics, and an effective antibiofouling system, is described in this paper. Additionally, this paper presents the findings of the laser beacon’s qualification process.
Design, Analysis, and Experiment of a Wheel-Legged Mobile Robot
Authors: Li Jinfu , Liu Yongxi , Yu Ze , Guan Yuntao , Zhao Yingzhuo , Zhuang Zheming , Sun Tao
Published: September 2023

In this paper, a wheel-legged mobile robot is proposed. The parameter design of the wheel-leg structure is completed using geometric analysis and statics modeling. The curling mechanism is chosen as the main part of the robot, expanding the robot’s application scenarios. Particle swarm optimization is used to determine the number of body sections and their length. After dynamic simulation, rubber is selected as the wheel-leg cover material. Based on the above results, a prototype is made and subject to experiments in different terrains. The experiment results verify that the robot is adaptable to multiple terrains with strong obstacle-crossing ability.
A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
Authors: Sadhwani Sapna , Manibalan Baranidharan , Muthalagu Raja , Pawar Pranav
Published: September 2023

The study in this paper characterizes lightweight IoT networks as being established by devices with few computer resources, such as reduced battery life, processing power, memory, and, more critically, minimal security and protection, which are easily vulnerable to DDoS attacks and propagating malware. A DDoS attack detection model is crucial for attacks in various industries, ensuring the availability and reliability of their networks and systems. The model distinguishes between legitimate and malicious traffic by analyzing network traffic patterns and identifying anomalies. This safeguards critical infrastructure, preserves business continuity, and protects the user experience, minimizing the impact of DDoS attacks. Numerous scholars have studied the notion that protecting lightweight IoT networks essentially requires improving intrusion detection systems. This research is valuable, as it follows a tailored pre-processing methodology specific to IoT network challenges, addressing a pressing need in cybersecurity by focusing on a growing concern related to IoT devices and DDoS attacks, enhancing the security of essential network systems in various industries by effectively detecting DDoS attacks, and developing a lightweight intrusion detection system that aligns with the limited resources of IoT devices. This manuscript proposes a compact and lightweight intrusion detection system that blends machine learning classifiers with a fresh approach to data pre-processing. The handling of missing values, data standardization using Standard Scalar, feature selection using ExtraTreeClassifier wherein only the 15 best features are extracted, and anomaly detection using a classifier are performed. The network dataset of TON-IOT and BOT-IOT datasets is used for experiments, specifically binary classifications and multiple-class classification for the experiment with DDoS and all attacks, respectively. There is an imbalance between the TON-IOT and BOT-IOT attack classes. In trials using the TON-IOT and BOT-IOT datasets, the classes were balanced using several iterations of the SMOTE approach. This research provides a number of classifier types, namely logistic regression, random forest, naïve bayes, artificial neural network, and k nearest neighbor algorithms, which are used to build a lightweight intrusion detection system that is ideally suited for protecting against DDoS attacks in IoT networks. The time taken to train and predict the DDoS attacks is also implemented. Random forest performed well under TON-IOT and naïve bayes performed well under BOT-IOT under binary and multiple-class classification, achieving an accuracy of 100% with less training and prediction time.
Formulation, Physico-Chemical Characterization, and Evaluation of the In Vitro Release Capacity of the Ruta graveolens L. Phytocomplex from Biodegradable Chitosan and Alginate Microspheres
Authors: Frent Olimpia Daniela , Vicas Laura Gratiela , Duteanu Narcis , Nemes Nicoleta Sorina , Pascu Bogdan , Teusdea Alin , Morgovan Claudia Mona , Muresan Mariana Eugenia , Jurca Tunde , Pallag Annamaria , Vlase Ana Maria , Vlase Laurian , Dejeu Ioana , Dejeu George Emanuiel , Marian Eleonora
Published: September 2023

The objective of this study was to develop microspheres (Ms) from natural materials, chitosan (Ch) and sodium alginate (Na-Alg), that protect Ruta graveolens L. (RG) extract against temperature, pH, and the oxidative impact of degradation. The microspheres also masked the unpleasant taste by enclosing them in a biodegradable polymeric matrix. First, the total polyphenols, total flavonoid content, and antioxidant activity were quantified spectrophotometrically. Individual polyphenol contents were identified and quantified by high-performance liquid chromatography (HPLC) with UV detection. The RG extract was encapsulated in microspheres of chitosan–sodium alginate–Ruta graveolens L. extract (CARG-Ms) using two distinct procedures (method 1, in which the RG extract was added to the Ch solution and the Na-Alg solution was dripped into this mixture, and method 2, in which the RG extract was added to the Na-Alg solution and then dripped into the Ch solution) to determine which method was more advantageous. All microspheres were evaluated and characterized by confocal laser-scanning microscopy (CLSM), scanning electron microscopy (SEM), optical scanning, entrapping efficiency (EE%), swelling index (SWL%), and in vitro release (RGrel%), and all results underwent univariate and multivariate analysis using a regression model. Following these tests, it was observed that the extract had an appreciable flavonoid content of 37.98%, with antioxidant properties evidenced by the 54.25% inhibition of DPPH. Of the polyphenolic compounds identified in the extract by using the HPLC method, rutin was present in the highest amount, at 745.17 μg/mL. The microspheres prepared by method 2, which contained the highest concentration of chitosan, had several desirable properties, including a high degree of roughness, high entrapping efficiency (75%), a wrinkled appearance, a better in vitro release capacity, and a lower SWL%. On the other hand, CARG-Ms prepared by method 1, which contained a smaller concentration of Ch, had faster swelling and slower release of the extract due to the lower entrapping efficiency (35%). These results suggest that the concentration of wall material and the preparation method play important roles in the encapsulation process and final particle characteristics. According to the obtained results, after the multivariate statistical analysis, it can be observed that the microspheres prepared via method 2 of the complex coacervation process were the most efficient for encapsulating rue extract in microspheres, because the extract was protected against degradation from the gastrointestinal tract.
Attention Mechanism Used in Monocular Depth Estimation: An Overview
Authors: Li Yundong , Wei Xiaokun , Fan Hanlu
Published: September 2023

Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision, plays important roles in downstream applications such as virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based methods gained remarkable progress compared with traditional methods using visual cues. However, recent researches reveal that the performance of MDE using CNN could be degraded due to the local receptive field of CNN. To bridge the gap, various attention mechanisms were proposed to model the long-range dependency. Although reviews of MDE algorithms based on CNN were reported, a comprehensive outline of how attention boosts MDE performance is not explored yet. In this paper, we firstly categorize recent attention-related works into CNN-based, Transformer-based, and hybrid (CNN–Transformer-based) approaches in the light of how the attention mechanism impacts the extraction of global features. Secondly, we discuss the details and contributions of attention-based MDE methods published from 2020 to 2022. Then, we compare the performance of the typical attention-based methods. Finally, the challenges and trends of the attention mechanism used in MDE are discussed.
Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
Authors: Li Zheng , Liang Weizhang , Zhao Guoyan
Published: September 2023

Rockburst is a major disaster in deep mining engineering, which can cause serious injury, death and economic losses. This study aims to solve rockburst hazard evaluation problems and determine the hazard levels of mines under uncertain circumstances. To this end, a novel multicriteria decision-making (MCDM) method was proposed in an interval-valued fuzzy context. The main contributions are three-fold. First, considering the heterogeneity of rock masses and the complexity of mining engineering, interval-valued fuzzy numbers (IVFNs) were adopted to express initial indicator information. Second, accounting for the uncertainty of indicator weights, the decision-making trial and evaluation laboratory (DEMATEL) and entropy methods were extended with IVFNs to determine fuzzy indicator weights comprehensively. Third, the complex proportional assessment (COPRAS) approach was extended with IVFNs to determine the rockburst hazard level. Finally, the proposed method was applied to evaluate rockburst hazards in the Jiaojia gold mine, and the ranking results were consistent with field status. Meanwhile, sensitivity and comparison analyses were performed to reveal the stability and effectiveness of the proposed method. The results indicated that the extended COPRAS method was reliable for rockburst hazard evaluation in deep mining engineering.
Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
Authors: Yassin Mohamed Taha , Al-Otibi Fatimah O. , Al-Askar Abdulaziz A. , Alharbi Raedah Ibrahim
Published: September 2023

The high incidence of fungal resistance to commercial fungicides and the negative effects of chemical fungicides on the environment and human health necessitate the development of novel biofungicides for the efficient management of fungal diseases. This study aims to greenly synthesize iron oxide nanoparticles (IONPs) using the aqueous extract of Laurus nobilis leaves and characterize these nanoparticles using various physicochemical techniques. The biogenic IONPs were tested against two pathogenic strains of Alternaria alternata and compared to the metalaxyl–mancozeb fungicide. The food poisoning technique was used to assess the antifungal efficacy of the greenly synthesized IONPs and the commercial metalaxyl–mancozeb fungicide against the tested pathogenic A. alternata strains. The biogenic IONPs showed a higher antifungal efficiency against the A. alternata OR236467 and A. alternata OR236468 strains at concentrations of 800 ppm compared to metalaxyl– mancozeb fungicide, with relative growth inhibition percentages of 75.89 and 60.63%, respectively. The commercial metalaxyl–mancozeb fungicide (800 ppm) showed growth inhibition percentages of 72.23 and 58.54% against the same strains. The biogenic IONPs also showed potential antioxidant activities against 1,1-diphenyl-2-picrylhydrazyl (DPPH) radicals, with DPPH inhibition percentages of 34.61% to 83.27%. In conclusion, the biogenic IONPs derived from L. nobilis leaves have the potential to be employed as biofungicides for the effective control of fungal phytopathogens, reducing reliance on harmful chemical fungicides.
Tectonic Geodesy Synthesis and Review of the North Aegean Region, Based on the Strain Patterns of the North Aegean Sea, Strymon Basin and Thessalian Basin Case Studies
Authors: Lazos Ilias , Sboras Sotirios , Pikridas Christos
Published: September 2023

Satellite geodesy, an indispensable modern tool for determining upper-crust deformation, can be used to assess tectonically active structures and improve our understanding of the geotectonic evolution in tectonically active regions. A region fulfilling these criteria is the North Aegean, part of the Eastern Mediterranean. It is one of the most tectonically, and hence, seismically, active regions worldwide, which makes it ideal for applying a satellite geodesy investigation. Although many regional studies have been carried out across the entire Aegean region, there are three more focused case studies that provide better resolution for different parts of the North Aegean. The synthesis of these case studies can lead to an overall geodynamic assessment of the North Aegean. The North Aegean Sea case study is characterized by the North Aegean Trough (NAT), which is directly associated with the westward prolongation of the North Anatolian Fault (NAF). Both NE–SW normal and strike-slip faulting have been documented in this offshore region. Geodetic analysis considers geodetic data, derived from 32 permanent GPS/GNSS stations (recorded for the 2008–2014 time period). This results in the estimation of the Maximum (MaHE) and Minimum (MiHE) Horizontal Extension, Maximum Shear Strain (MSS) and Area Strain (AS) parameters, based on triangular methodology implementation; the same strain parameters have similarly been estimated for the Strymon and Thessalian basins, respectively. The Strymon basin (first case study) is located in the central part of the northern Greek mainland, and it is dominated by NW–SE (up to E–W) dip-slip normal faults; this area has been monitored by 16 permanent GPS/GNSS stations for seven consecutive years. Regarding the Thessalian basin case study, E–W, dip-slip and normal faults are noted at the basin boundaries and within the Thessalian plain. This region has also been monitored for seven consecutive years by 27 permanent GPS/GNSS stations. However, this case study is characterized by a strong seismic event (Mw6.3; 3 March 2021), and thus all strain parameters depicted the pre-seismic deformation. Analysis of these three different case studies confirmed the current tectonic setting of the North Aegean region, while revealing new aspects about the geodynamic evolution of the wider region, such as highlighting areas with significant tectonic activity and the crucial role of strike-slip faulting in the broader Aegean region.
Exploring Prompts in Few-Shot Cross-Linguistic Topic Classification Scenarios
Authors: Zhang Zhipeng , Liu Shengquan , Cheng Jianming
Published: September 2023

In recent years, large-scale pretrained language models have become widely used in natural language processing tasks. On this basis, prompt learning has achieved excellent performance in specific few-shot classification scenarios. The core idea of prompt learning is to convert a downstream task into a masked language modelling task. However, different prompt templates can greatly affect the results, and finding an appropriate template is difficult and time-consuming. To this end, this study proposes a novel hybrid prompt approach, which combines discrete prompts and continuous prompts, to motivate the model to learn more semantic knowledge from a small number of training samples. By comparing the performance difference between discrete prompts and continuous prompts, we find that hybrid prompts achieve the best results, reaching a 73.82% F1 value in the test set. In addition, we analyze the effect of different virtual token lengths in continuous prompts and hybrid prompts in a few-shot cross-language topic classification scenario. The results demonstrate that there is a threshold for the length of virtual tokens, and too many virtual tokens decrease the performance of the model. It is better not to exceed the average length of the training set corpus. Finally, this paper designs a method based on vector similarity to explore the real meanings represented by virtual tokens. The experimental results show that the prompt automatically learnt from the virtual token has a certain correlation with the input text.
Properties of Cement-Based Materials with Low Water–Binder Ratios and Evaluation Mechanism under Further Hydration Effect
Authors: An Mingzhe , Liu Yazhou , Zhang Ge , Wang Yue
Published: September 2023

Unhydrated cementitious materials in high-performance concrete (HPC) and ultra-high-performance concrete (UHPC) undergo further hydration when they are further supplied with water. A further hydration experiment on cement pastes was conducted to study the effects of temperature and humidity on their macroscopic properties. A rapid evaluation mechanism for further hydration was eventually presented. The results obtained under the four analysed humidity conditions indicated that the compressive strength and flexural strength increased by 22.6% and 75.2%, respectively, after further hydration for 180 d at a relative humidity (RH) of 95%. Considering water soaking under three analysed temperature conditions, water soaking at 60 °C had the largest impact on macroscopic properties, such as compressive strength and flexural strength, which showed an increase of 31.4% and a decrease of 13.8%, respectively, after further hydration for 180 d. Moreover, the expansion strain at 60 °C was 1.1 times higher than the strain determined under water soaking at 40 °C. Considering the stability of the evaluation indices, the combined water content for further hydration, expansion strain, and compressive strength were used to evaluate further hydration effect. Considering the acceleration and damaging effects, water soaking at 60 °C was an effective method to accelerate further hydration.
A Blockchain-Based Model for the Prevention of Superannuation Fraud: A Study of Australian Super Funds
Authors: Mapa Mudiyanselage Chalani , Perera Pethigamage , Grandhi Sriamannarayana
Published: September 2023

Superannuation is the fund set aside by employers to provide their employees with a dignified retirement. Studies highlight that issues can arise with retirement funds from employers, such as failure to make required contributions to an employee’s superannuation fund, incorrect payments, or debiting the wrong fund, contrary to legal or contractual obligations. To address these issues, the Australian Government has implemented laws and regulations to ensure employers fulfil their contribution obligations. Despite these safeguards and highly secured information systems, there has been a significant increase in fraudulent activity in the finance sector, and there have been several instances of employers not making contributions, misusing retirement funds, or reporting incorrectly in their systems. The current process restricts employees from viewing recent data until the contributions reach their super fund, which opens the doors for fraud. Recently, blockchain technology has gained popularity because of its ability to improve security and prevent fraud across many sectors, including finance. Prior studies have shed limited light on how superannuation fraud can be prevented. Moreover, there is limited literature on the possibility of utilizing blockchain technology to address this issue. Therefore, this paper aims to review the current superannuation contribution process and identify the factors contributing to non-payment, incorrect payments, misallocation of funds and communication gaps. This study presents a novel process model and develops a blockchain-based application to mitigate fraudulent practices. This research provides valuable insights into the design of innovative process models that utilize blockchain technology to address superannuation challenges. Furthermore, the paper presents a sample simulated smart contract to explore additional implications and advancements in this domain.
From the Cultivation of Arthrospira platensis at an Increased CO2 Concentration to the Bio-Oil Production by Hydrothermal Liquefaction
Authors: Ryndin Kirill G. , Butyrin Aleksey V. , Grigorenko Anatoly V. , Chunzhuk Elizaveta A. , Chernova Nadezhda I. , Kiseleva Sofya V. , Malaniy Sergey Y. , Bakumenko Ekaterina A. , Slavkina Olga V. , Ossipov Konstantin , Maryutina Tatiana A. , Kumar Vinod , Vlaskin Mikhail S.
Published: September 2023

In this work, the path from the cultivation of Arthrospira platensis at an increased concentration of CO2 to the production of bio-oil by hydrothermal liquefaction (HTL) of the grown biomass is realized. The cultivation was carried out in a 90 L photobioreactor at an initial CO2 concentration of 8 vol.% for 15 days. During the cultivation stage, the optical density for microalgae suspension, pH and chemical composition of nutrient medium were monitored. The grown biomass was separated from the nutrient medium with a 100 µm mesh and then subjected to HTL at 330 °C for 1 h. The biomass growth rate was 82 ± 4.1 mg × L−1day−1 and the pH was in the range from 9.08 ± 0.22 to 8.9 ± 0.24. Biochemical and CHNS analyses were applied for the obtained biomass. The contents of carbohydrates, proteins and lipids in the grown biomass were 38.7 ± 0.4 wt.%, 37.4 ± 0.5 wt.% and 3.8 ± 0.4 wt.%, respectively. Bio-oil yield after the HTL procedure was 13.8 wt.%. The bio-oil composition and properties were determined by GH-MS, TLC-PID and ICP-MS techniques. ICP-MS revealed the contents of 51 metals in bio-oil.
Porcelain Enamel Coatings for Building Façades
Authors: Zanardi Alessia , Calovi Massimo , Rossi Stefano
Published: September 2023

Materials used for building façades should combine aesthetics with functionality and durability. Vitreous enamels are a class of inorganic coatings, with a glossy and brilliant aspect, as well as high chemical resistance and protective properties. This study aimed to investigate the potentiality of enamel coatings for use in the architectural field. Different accelerated tests were carried out on enamel steel panels to test their durability and resistance to natural aggressive conditions (corrosive atmosphere and basic pH conditions, UV radiation, and pollution) and to mechanical damages. Two colors were chosen, red and white, to determine the effect of the addition of diverse pigments. Paints were employed as reference coating protection systems, as they currently serve as the standard for building façade design. Compared to paints, the enamel panels presented better corrosion protection, with higher adhesion to the steel substrate and stable aesthetic properties during the conducted tests, both in terms of color and gloss. Nevertheless, the white coating exhibited superior color stability, likely attributable to the presence of Se- and Cd-based pigments in the red coating. These pigments are known to be more prone to degradation. Overall, this work showed that porcelain enamels display good functional and aesthetic qualities, which make them suitable for use in the cladding of buildings and as transport infrastructure elements.
Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
Authors: Zhou Xiwei , Yan Jingwen , Yan Mei , Mao Kaihao , Yang Ruizhe , Liu Weiyu
Published: September 2023

With the upgrading of manufacturing production lines and innovations in information technology, logistics robot technology applied in factories is maturing. Rail-mounted logistics robots are suitable for precise material distribution in large production workshops with fixed routes and over long distances. However, designing an efficient path-planning algorithm is the key to realizing high efficiency in multi-robot system operations with rail logistics. Therefore, this paper proposes an improved Dijkstra algorithm that introduces real-time node occupancy and a time window conflict judgment model for global path planning and conflict coordination in multi-robot systems. More specifically, the introduction of real-time node occupancy can determine the shortest feasible routes for each task, and the introduction of the time window conflict judgment model can avoid the route conflict problem in the execution of multiple tasks, planning the shortest route without conflict. For the robot UBW positioning module, a Chan algorithm based on TDOA is proposed to realize the accurate positioning of rail-mounted logistics robots during their operation. Compared with the traditional Dijkstra algorithm, the results show that the algorithm proposed herein can plan a conflict-free and better path and dynamically adjust the on-orbit conflict in real time to avoid track congestion and efficiently complete multiple distribution tasks.
Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
Authors: Luo Gaosheng , He Gang , Jiang Zhe , Luo Chuankun
Published: September 2023

To address the phenomenon of color shift and low contrast in underwater images caused by wavelength- and distance-related attenuation and scattering when light propagates in water, we propose a method based on an attention mechanism and adversarial autoencoder for enhancing underwater images. Firstly, the pixel and channel attention mechanisms are utilized to extract rich discriminative image information from multiple color spaces. Secondly, the above image information and the original image reverse medium transmittance map are feature-fused by a feature fusion module to enhance the network response to the image quality degradation region. Finally, the encoder learning is guided by the adversarial mechanism of the adversarial autoencoder, and the hidden space of the autoencoder is continuously approached to the hidden space of the pre-trained model. The results of the experimental images acquired from the Beihai Bay area of China on the HYSY-163 platform show that the average value of the Natural Image Quality Evaluator is reduced by 27.8%, the average value of the Underwater Color Image Quality Evaluation is improved by 28.8%, and the average values of the Structural Similarity and Peak Signal-to-Noise Ratio are improved by 35.7% and 42.8%, respectively, compared with the unprocessed real underwater images, and the enhanced underwater images have improved clarity and more realistic colors. In summary, our network can effectively improve the visibility of underwater target objects, especially the quality of images of submarine pipelines and marine organisms, and is expected to be applied in the future with underwater robots for pile legs of offshore wellhead platforms and large ship bottom sea life cleaning.
Monitoring the Microbiomes of Agricultural and Food Waste Treating Biogas Plants over a One-Year Period
Authors: Agostini Sara , Moriconi Francesco , Zampirolli Mauro , Padoan Diego , Treu Laura , Campanaro Stefano , Favaro Lorenzo
Published: September 2023

The knowledge of the microbiome in the anaerobic digestion (AD) is critical for stabilizing the process and optimizing the biogas yield. This work investigates the microbial ecology in four full-scale biogas plants with different feedstocks and process parameters. The three agricultural plants sharing similar feedstocks’ composition (mostly rich in proteins, cellulose and hemicellulose), have several hydrolytic and methanogenic species in common, suggesting that their substrates specifically shape the microbiomes. Particularly, the hydrolytic and likely syntrophic Defluviitoga tunisiensis was detected as the most abundant species in one reactor, representing 21.2% of the community. On the other hand, the biogas plant treating the organic fraction of municipal solid waste (OFMSW), whose composition was much higher in hash and lower in proteins, displayed a quite different microbiome with a much lower abundance of Bacteroidales sp. much higher of Clostridiaceae. Moreover, this AD was clearly influenced by COVID-19 restrictions as both substrate availability and composition suddenly changed causing the wash-out of most bacterial and methanogenic species and leading to a deep modification of the microbial structure. The abundance of Methanosarcina flavescens greatly increased up to 36.5% of the total operational taxonomic units (OTUs), suggesting a switch from the hydrogenotrophic to the acetoclastic methanogenic pathway. This is the first report on the COVID-19 impact on the AD microbiome of a full-scale anaerobic digestor. Moreover, this paper demonstrated that the feedstock composition can differentially shape both bacterial and archaeal strains of the AD process.
A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks
Authors: Guo Shuang , Du Yarong , Liu Liang
Published: September 2023

In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.
Gradiometry Processing Techniques for Large-Scale of Aeromagnetic Data for Structural and Mining Implications: The Case Study of Bou Azzer Inlier, Central Anti-Atlas, Morocco
Authors: Soulaimani Ayoub , Chakiri Saïd , Soulaimani Saâd , Bejjaji Zohra , Miftah Abdelhalim , Manar Ahmed
Published: September 2023

Due to its unique geographic and tectonic location, the Bou Azzer inlier has drawn increased interest in mining studies. The inlier’s basement structure remains subject to investigation meanwhile faults and igneous rocks affect the local geology. In order to comprehend the Bou Azzer inlier’s structure, we use aeromagnetic data. The edge enhancement method described in this work is based on the gradiometry tensor analysis (GTA) of aeromagnetic data, which yields estimated magnetic tensors, rotational invariants, horizontal invariants, computed strike lines, and Eigensystems. This study’s primary objective is to use GTA to define structural boundaries in complicated geological and tectonic environments. The vertical and horizontal positions of the geological border’s limits have been determined via analysis of the acquired answers. The borders of the anomalous sources are marked in space by the lowest eigenvalue. According to the research, the inlier demonstrates potential for further mineralization with regard to its complicated structure, which is mostly dominated by WNW-ESE, ENE-WSW, NE-SW and E-W trending lineaments with varying depths between 3.45 and 9.06 km. Certainly, the derived structural scheme has enabled the identification of various formations that may be favorable for the circulation of mineralizing fluids, facilitating the concentration of economically valuable mineral deposits, similar to existing metal reserves in the examined area.
Experimental Study on the Fracture Characteristics of Hole-Crack in Rock-like Material under Biaxial Compression
Authors: Tian Yuan , Han Bangcheng , Liu Xinyu , Shen Kai , Li Jiangbo
Published: September 2023

Using experimental methods to study the influence of hole and cracks on the mechanical properties and fracture characteristics of rock-like mortar materials under biaxial compression conditions. The double crack specimens with hole depths from 0–100 mm are prefabricated to study the strength and deformation characteristics of the specimens under different lateral loads σ2 = 0–6 MPa. The evolution process of secondary crack initiation, development, and connection of the hole-crack specimens are recorded. The results show that: (1) One type of rock mortar test material is prepared, and its main physical and mechanical parameters are all within the range of sandstone, which can effectively simulate the stress deformation characteristics of sandstone. (2) When the depth of the holes in cracked samples exceeds 50% of the length, the strength and deformation of the samples undergo a sudden change. When the depth of the hole in the crack specimen increases from 40 mm to 60 mm, the peak stress decreases most significantly. Moreover, the maximum values of the strain value at peak strength and lateral strain both occur at a hole depth of 60 mm. (3) When the cracked specimen contains through-holes, the failure mode is composite fracture and shear composite fracture. When the depth of the hole is different, the fracture forms include tension composite fracture, shear composite fracture, and composite fracture.
Active Support System for the Correction of a 4m SiC Primary Mirror Based on the Bending Mode
Authors: Yu Zhiyuan , Wu Xiaoxia , Wang Fuguo
Published: September 2023

Active optics is a key technology in ground-based large-aperture telescopes. The active correction of the surface shape of the primary mirror is used to reduce the surface shape error and improve the imaging quality. At present, the structure of the active optics support system is not standardized. Therefore, to ensure the imaging quality of a telescope using a 4m SiC (silicon carbide) primary mirror, this article designed an active support system for the primary mirror and comprehensively evaluated the performance of the system. The system used pneumatic actuators to correct the surface shape of the primary mirror and a six-hardpoint positioning mechanism to correct the pose of the primary mirror. A method for compensating for the force on the hardpoints that causes protrusions and dents on the primary mirror surface was proposed, which effectively improved the accuracy of the primary mirror surface. The bending-mode method was used to determine the correction force. To achieve better results in the surface shape correction based on the bending mode, the relationship between the order of the bending modes used in the correction and the correction effect was studied, enabling the system to achieve a higher surface shape accuracy with a smaller correction force. Finally, the performance of the system was evaluated under various conditions, such as under gravity, thermal load, and wind load. The results indicated that the system had good correction effects on the deformation of the primary mirror under various operating conditions and could meet the requirements of optical design for surface accuracy. In conclusion, this study not only verified the application of active optics technology based on the bending mode in large-aperture SiC mirrors, but also improved on the relevant theoretical research on active optics.
A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
Authors: Huang Xiaowu , Guo Jian , Miao Yusong , Xie Xianqi , Li Yujin , Wang Hailiang , Huang Feifei
Published: September 2023

The surrounding rock during a coal mine excavation is prone to significant engineering disasters such as considerable deformation and rock bursts. Pressure release can improve the stress field of a deep rock mass and prevent the occurrence of dangers such as roadway collapse and coal and gas outbursts. This paper uses the ANSYS 19.0/LS-DYNA finite element software to simulate the crush area and fracture zone of a detonation charge with different diameters under in situ stress. The stability of the surrounding rock was analyzed based on the impact stress and velocity, and was verified by field tests. The research results show that the blasting load primarily affects the damaged area near the borehole, while the in situ stress affects far-field crack propagation. The crack propagates in the direction of high surrounding rock pressure. When the uncoupling index is 1.5, the impact pressure of a 60 mm diameter cartridge is eight times that of a 20 mm diameter cartridge. The impact speed can reach two times that of the 20 mm diameter cartridge. The high-energy event at the roof is transferred to the front of the working face, the distribution is no longer concentrated, and a better pressure-relief blasting effect is achieved. The research results can help guide the prevention and control measures of rock bursts and other mining disasters.
Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
Authors: Yan Siyao , Zhang Jing , Parvej Mosharaf Md , Zhang Tianchi
Published: September 2023

This paper proposes a novel Sea Drift Trajectory Prediction method based on the Quantum Convolutional Long Short-Term Memory (QCNN-LSTM) model. Accurately predicting sea drift trajectories is a challenging task, as they are influenced by various complex factors, such as ocean currents, wind speed, and wave morphology. Therefore, in a complex marine environment, there is a need for more applicable and computationally advanced prediction methods. Our approach combines quantized convolutional neural networks with Long Short-Term Memory networks, utilizing two different input types of prediction to enhance the network’s applicability. By incorporating quantization techniques, we improve the computational power and accuracy of the trajectory prediction. We evaluate our method using sea drift datasets and AUV drift trajectory datasets, comparing it with other commonly used traditional methods. The experimental results demonstrate significant improvements in accuracy and robustness achieved by our proposed Quantum Convolutional Long Short-Term Memory model. Regardless of the input mode employed, the accuracy consistently surpasses 98%. In conclusion, our research provides a new approach for sea drift trajectory prediction, enhancing prediction accuracy and providing valuable insights for marine environmental management and related decision-making. Future research can further explore and optimize this model to have a greater impact on marine prediction and applications.
Aditive Manufacturing in Maxillofacial Prosthodontics
Authors: Cristache Corina Marilena
Published: September 2023

Additive manufacturing (AM) or additive layer manufacturing (ALM), defined by the International Organization for Standardization and American Society of Testing and Materials (ISO/ASTM 52900) as the “process of joining materials to make parts from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing and formative manufacturing methodologies” [...]
A Finite Element Analysis of a Tooth-Supported 3D-Printed Surgical Guide without Metallic Sleeves for Dental Implant Insertion
Authors: Ghionea Ionut Gabriel , Vatamanu Oana Elena Burlacu , Cristescu Ana Maria , David Mihai , Stancu Izabela Cristina , Butnarasu Cristian , Cristache Corina Marilena
Published: September 2023

Static guided surgery for dental implant insertion is a well-documented procedure requiring the manufacturing of a custom-made surgical guide, either teeth-supported, mucosal-supported, bone-supported, or mixed (teeth-mucosal-supported), depending on the clinical situation. The guidance of the surgical drills during implant bed preparation could be undertaken using a sequence of different diameters of metal drill sleeves or, with the sleeves incorporated in the surgical guide, shank-modified drills, both clinically accepted and used with good results. Despite the great number of advantages associated with the use of guided surgery, one of the major risks is guide fracture during drilling for implant bed preparation. Therefore, the aim of the present study was to evaluate the surgical guides without metal sleeves and to simulate, with the aid of Finite Element Analysis (FEA), the use of such dentally supported guides for implant insertion. The FEA is performed in CATIA v5 software after defining the surgical guide mesh material and bone properties. A maximum stress of 6.92 MPa appeared on the guide at the special built-in window meant to allow cooling during drilling, and the maximum value of the guide displacement during drilling simulation was 0.002 mm. Taking into consideration the limits of the current research, the designed tooth-supported surgical guide can withstand the forces occurring during the surgery, even in denser bone, without the risk of fracture.
Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
Authors: Kim Jeongho , Park Jeehoon , Park Beomjun , Park Byungdo , Kim Tae-Gyu
Published: September 2023

Radiation therapy for treating shallow tumors is challenging, necessitating the use of boluses. This study introduces the first application of the life-casting method to fabricate patient-specific bolus molds from gypsum sheets, comparing them with commercial boluses. Our developed boluses reduced the air gap between the skin and bolus by 77.62% compared to that of commercial boluses. In vivo dosimetry using the patient-specific bolus demonstrated better results compared to using a commercial bolus. When using the commercial bolus, the mean %Diff and max %Diff were 1.10 ± 0.61%, respectively, and 2.00% for three-dimensional conformal radiation therapy (3D-CRT) and 7.19 ± 1.90% and 10.14% for volumetric modulated arc therapy (VMAT), respectively. Contrastingly, our developed bolus demonstrated more accurate dose delivery with a mean %Diff and max %Diff of 0.82 ± 0.61% and 1.69% for 3D-CRT and 3.42 ± 1.01% and 5.03% for VMAT, respectively. Furthermore, the standard deviation between the measurements was more than 50% lower when using a patient-specific bolus than when using a commercial bolus. These results show that our bolus reduces air gaps, improves the accuracy of bolus positioning, and enhances the precision of dose delivery compared with the performance of commercial boluses. Therefore, the developed bolus is expected to be valuable in clinical applications.