bluuebunny/crossref_metadata_2025 huggingface dataset:
A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
Authors: Song Haina , Hu Binjie , Huang Qinyan , Zhang Yi , Song Jiwei
Published: March 2023

In this paper, a lightweight, high-definition mapping method is proposed for autonomous driving to address the drawbacks of traditional mapping methods, such as high cost, low efficiency, and slow update frequency. The proposed method is based on multi-source data fusion perception and involves generating local semantic maps (LSMs) using multi-sensor fusion on a vehicle and uploading multiple LSMs of the same road section, obtained through crowdsourcing, to a cloud server. An improved, two-stage semantic alignment algorithm, based on the semantic generalized iterative closest point (GICP), was then used to optimize the multi-trajectories pose on the cloud. Finally, an improved density clustering algorithm was proposed to instantiate the aligned semantic elements and generate vector semantic maps to improve mapping efficiency. Experimental results demonstrated the accuracy of the proposed method, with a horizontal error within 20 cm, a vertical error within 50 cm, and an average map size of 40 Kb/Km. The proposed method meets the requirements of being high definition, low cost, lightweight, robust, and up-to-date for autonomous driving.
Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine
Authors: Mohd Talib Nur Izzati , Abd Majid Nazatul Aini , Sahran Shahnorbanun
Published: March 2023

In many academic fields, predicting student academic success using data mining techniques has long been a major research issue. Monitoring students in higher education institutions (HEIs) and having the ability to predict student performance is important to improve academic quality. The objective of the study is to (1) identify features that form clusters that have holistic characteristics and (2) develop and validate a prediction model for each of the clusters to predict student performance holistically. For this study, both classification and clustering methods will be used using Support Vector Machine (SVM) and K-means clustering. Three clusters were identified using K-means clustering. Based on the learning program outcome feature, there are primarily three types of students: low, average, and high performance. The prediction model with the new labels obtained from the clusters also gained higher accuracy when compared to the student dataset with labels using their semester grade.
Impact of Vanadium Complexes with Tetradentate Schiff Base Ligands on the DPPC and EYL Liposome Membranes: EPR Studies
Authors: Man Dariusz , Pytel Barbara , Białek Marzena
Published: March 2023

This paper investigates the effect of three vanadium complexes with Schiff base-type tetradentate ligands of general formula N,N′-1,2-cyclohexylenebis(3,5-dichlorosalicylideneiminate) (V1); LVCl2 (L = N,N′-1,2-cyclohexylenebis(5-chlorosalicylideneiminate) (V2); and N,N′-1,3propylenebis (salicylideneiminate) (V3) on the fluidity of liposome membranes obtained by the sonication of natural lecithin (EYL) and synthetic lecithin (DPPC). The study was carried out with TEMPO and 16DOXYL spin probes using the EPR technique. The results show that the effect of the complexes on the fluidity of liposomes whose membranes are in the liquid crystalline phase is much stronger as compared to the liposome membranes in the gel phase.
A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)
Authors: Allouani Fouad , Abboudi Abdelaziz , Gao Xiao-Zhi , Bououden Sofiane , Boulkaibet Ilyes , Khezami Nadhira , Lajmi Fatma
Published: March 2023

Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization problem that seeks the optimal or near-optimal flight path for military operations. The problem is further complicated by the need to operate in a complex battlefield environment with minimal military risk and fewer constraints. To address these challenges, highly sophisticated control methods are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective approaches. In this context, a study has been conducted to improve the existing Spider Monkey Optimization (SMO) algorithm by integrating a new explorative local search algorithm called Beta-Hill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO variant called SMOBHC, which offers improved performance in terms of intensification, exploration, avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space on three different complex battlefields with ten, thirty, and twenty randomly distributed threats under various conditions. Experimental results show that SMOBHC outperforms the original SMO algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed method shows better results in terms of the best, worst, mean, and standard deviation outcomes obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and large-scale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in the case of SMO, where there is no significant difference between them. Overall, the proposed SMO variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances the stability of the final results. The study provides an effective approach to UCAV path planning that can be useful in military operations with complex battlefield environments.
An Optical Intervention to Improve Cycling Time Trials: A Feasibility Study
Authors: Matthys Dries , Vleugels Jochen , Denis Kathleen , Dieryckx Tim , Verwulgen Stijn
Published: March 2023

(1) Background: Many professional and recreational cyclists experience that neck extension in time trial position negatively impacts either speed, comfort, or power production—especially at high cycling speeds or for long distances. We conducted a feasibility study with one subject to assess whether redirecting the sight of a cyclist while in time trial position could reduce aerodynamic drag and neck strain by maintaining a more neutral neck position. (2) Methods: A physical immersive exercise bike was developed (called a FAAST-trainer) that emulates posture, velocity, and power to be delivered by the user through an adaptable power load adjusted in real time. As an optical intervention, we used prism glasses to redirect the cyclist’s sight. The subject trained his perceptive-muscular system while cycling on the FAAST-trainer to get used to wearing prism glasses. He feels confident that the glasses are safe to test for future experiments in a velodrome. (3) Results: A consistent reduction in drag was found (p < 001) when wearing prism glasses with the FAAST-trainer, ranging from 3.5% to 4.7%. Accordingly, the cyclist could thus save between 9.7 watts and 13.0 watts cycling at 45 km/h, compared to having his head in an upright position. (4) Conclusions: Our experiment on the FAAST-trainer indicates that an optical intervention to reduce neck extension by redirecting sight might be safe to use for outdoor cycling. However, no vestibular effects, neither auditive nor complex combinations, were assessed, so we recommend additional research and development of a dedicated design for the prism glasses. Outdoor experiments should be conducted to confirm this reduction in aerodynamic drag and further asses the safety when wearing prism glasses.
Tunable 60 GHz Multiwavelength Brillouin Erbium Fiber Laser
Authors: Awsaj Mohammed K. , Al-Mashhadani Thamer Fahad , Al-Mashhadani Mohammed Kamil Salh , Hammudi Rabi Noori , Ali Ali yaseen , Zan Mohad Saiful Dzulkefly , Arsad Norhana
Published: March 2023

An experimental study of a tunable 60 GHz multiwavelength Brillouin erbium fiber laser is presented in this paper. Two unidirectional ring laser cavities and two pre-amplification laser cavities are used. In the first three cavities, a Brillouin gain medium is presented with a dispersion compensation fiber (DCF) spool, and a single-mode fiber (SMF) spool is used as a Brillouin gain medium in the fourth cavity. Three erbium amplifiers are utilized to supply enough gain to the generated Brillouin Stokes signal and to suppress cavity losses. For these three amplifiers, up to 450 mW (150 mW for each) of a 1480 nm pump power is used. In our proposed configuration, four sextuple Brillouin Stokes signals with a high power of 10 dBm and more than 55 dB as an optical signal-to-noise ratio are achieved. The obtained Brillouin Stokes signals can be tuned over 30 nm (1560–1590 nm) and can be easily used in dense wavelength division multiplexing in optics communication systems.
Promoting STEM Education of Future Chemistry Teachers with an Engineering Approach Involving Single-Board Computers
Authors: Ambrož Miha , Pernaa Johannes , Haatainen Outi , Aksela Maija
Published: March 2023

We describe a master’s level chemistry education course that was designed to support STEM education by strengthening the E component with an engineering approach. Engineering approach is a method of conducting projects systematically similar to professional engineers. In the course, the future chemistry teachers were given the task of building a measurement instrument using a single-board computer (SBC). In addition to course description, we present a pilot study, the aim of which was to explore the opportunities and challenges the engineering approach initiates with pre-service chemistry teachers trying to accomplish a SBC-based open engineering project. The study employed a qualitative research approach, using the course as the data collection platform. The collected data was analyzed using an inductive content analysis. The data analysis shows that an open SBC project is a good platform for learning and teaching future chemistry teachers about chemistry-driven STEM education, but it is very challenging to conduct. The main conclusion is that the engineering approach is a practical solution for strengthening the engineering in STEM education. To generalize these findings to a wider context, we suggest further research to improve the course using this study’s results and re-evaluate the approach in a new instance of the course.
Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
Authors: Wang Bin , Yang Hua , Zhang Shujuan , Li Lili
Published: March 2023

Detection of skin defects in Cerasus humilis fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in Cerasus humilis fruits based on hyperspectral imaging. A total of 420 sample images were acquired that included three types of natural defects and undamaged samples. After acquiring hyperspectral images of Cerasus humilis fruits, the spectral data were extracted from the region of interest (ROI). Five spectral preprocessing methods were used to preprocess the original spectral data, including Savitsky–Golay (S-G), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction (BC), and de-trending (De-T). Regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighed sampling (CARS) were conducted to select optimal sensitive wavelengths (SWs); as a result, 11 SWs, 17 SWs, and 13 SWs were selected, respectively. Then, the least squares-support vector machine (LS-SVM) discrimination model was established using the selected SWs. The results showed that the discriminate accuracy of the CARS-LS-SVM method was 91.43%. Based on the characteristics of image information, images corresponding to eight sensitive wavebands (950, 994, 1071, 1263, 1336, 1457, 1542, and 1628 nm) selected by CARS were subjected to principal component analysis (PCA). Then, an effective approach for detecting the defective features was exploited based on the imfill function, canny operator, region growing algorithm, bwareaopen function, and the images of PCA. The location and area of defect feature of 105 Cerasus humilis fruits could be recognized; the detect precision was 88.57%. This investigation demonstrated that hyperspectral imaging combined with an image processing technique could achieve the rapid identification of undamaged samples and natural defects in Cerasus humilis fruit. This provides a theoretical basis for the development of Cerasus humilis fruit grading and sorting equipment.
Blind Space Time Block Coding Categorization over AF Relaying Broadcasts
Authors: Marey Mohamed , Mostafa Hala
Published: March 2023

Categorization of space time block code (STBC) signals has become widely acknowledged as a crucial foundational mechanism for creating intelligent wireless transmissions in both the governmental and business sectors. The use of multiple antennas at a broadcaster complicates a signal categorization task because assumptions about the number and transmission matrix of the sent antennas must be made. STBC categorization has only been investigated in the context of non-relaying environments, and no methods for relaying transmissions have been reported. This work proposes a revolutionary strategy for categorizing STBC signals that can be implemented in amplify-and-forward (AF) relaying networks. Time-domain characteristics of the STBC waveforms provide the basis of the mathematical ingredients used in the offered categorization process. The employed STBC waveform is reflected in the spikes observed in the fast Fourier transform of the second-order lag product of the collected waveforms. This creates the foundation for an effective discriminating feature. Advantages of the described strategy include not requiring any prior awareness of the modulation type, channel conditions, signal-to-noise ratio (SNR), or the block timing synchronization of the STBC waveforms. The indicated strategy has been shown through simulation experiments to be capable of providing appropriate categorization accuracy despite the existence of transmission faults, even at relatively low SNR levels.
A Heuristic Approach to Support Route Planning for Delivery and Installation of Furniture: A Case Study
Authors: Ferreira Inês A. , Bento Catarina , Mascarenhas Luís , Pimentel Carina , Carvalho Helena
Published: March 2023

The number of variants of the vehicle routing problem (VRP) has grown rapidly in the last decades. Among these, VRPs with time window constraints are among the most studied ones. However, the literature regarding VRPs that concerns the delivery and installation of products is scarce. The main aim of this study was to propose a heuristic approach for the route planning process of a company whose focus is on furniture delivery and assembly and, thus, contributing to the research around the Delivery and Installation Routing Problem. The case study method was used, and two scenarios were compared: the current scenario (showing the routes created by the company worker); and the future scenario (representing the routes created by the heuristic). Results show that the proposed heuristic approach provided a feasible solution to the problem, allowing it to affect customers and teams without compromising the teams’ competencies and respecting all constraints.
End-to-End: A Simple Template for the Long-Tailed-Recognition of Transmission Line Clamps via a Vision-Language Model
Authors: Yan Fei , Zhang Hui , Li Yaogen , Yang Yongjia , Liu Yinping
Published: March 2023

Raw image classification datasets generally maintain a long-tailed distribution in the real world. Standard classification algorithms face a substantial issue because many labels only relate to a few categories. The model learning processes will tend toward the dominant labels under the influence of their loss functions. Existing systems typically use two stages to improve performance: pretraining on initial imbalanced datasets and fine-tuning on balanced datasets via re-sampling or logit adjustment. These have achieved promising results. However, their limited self-supervised information makes it challenging to transfer such systems to other vision tasks, such as detection and segmentation. Using large-scale contrastive visual-language pretraining, the Open AI team discovered a novel visual recognition method. We provide a simple one-stage model called the text-to-image network (TIN) for long-tailed recognition (LTR) based on the similarities between textual and visual features. The TIN has the following advantages over existing techniques: (1) Our model incorporates textual and visual semantic information. (2) This end-to-end strategy achieves good results with fewer image samples and no secondary training. (3) By using seesaw loss, we further reduce the loss gap between the head category and the tail category. These adjustments encourage large relative magnitudes between the logarithms of rare and dominant labels. TIN conducted extensive comparative experiments with a large number of advanced models on ImageNet-LT, the largest long-tailed public dataset, and achieved the state-of-the-art for a single-stage model with 72.8% at Top-1 accuracy.
Review of Flexible Supercapacitors Using Carbon Nanotube-Based Electrodes
Authors: Han Yurim , Ha Heebo , Choi Chunghyeon , Yoon Hyungsub , Matteini Paolo , Cheong Jun Young , Hwang Byungil
Published: March 2023

Carbon nanotube (CNT)-based electrodes in flexible supercapacitors have received significant attention in recent years. Carbon nanotube fiber fabrics (CNT-FF) have emerged as promising materials due to their high surface area, excellent conductivity, and mechanical strength. Researchers have attempted to improve the energy density and rate performance of CNT-FF supercapacitor electrodes through various strategies, such as functionalization with conductive materials like MnO2 nanoparticles and/or incorporation of graphene into them. In addition, the utilization of CNTs in combination with thin metal film electrodes has also gained widespread attention. Research has focused on enhancing electrochemical performance through functionalizing CNTs with conductive materials such as graphene and metal nanoparticles, or by controlling their morphology. This review paper will discuss the recent developments in supercapacitor technology utilizing carbon nanotube-based electrodes, including CNT fiber fabrics and CNTs on thin metal film electrodes. Various strategies employed for improving energy storage performance and the strengths and weaknesses of these strategies will be discussed. Finally, the paper will conclude with a discussion on the challenges that need to be addressed in order to realize the full potential of carbon nanotube-based electrodes in supercapacitor technology.
The Role of Seismic Structural Health Monitoring (S2HM) in the Assessment of the Delay Time after Earthquakes
Authors: Forcellini Davide
Published: March 2023

The concept of seismic resilience has been introduced in the design of buildings in the last decade. In this regard, the delay time may be defined as the time that occurs between the event and the moment the repair process begins. In the literature, only a few contributions have considered delay time, and even its definition is still under discussion. However, it is a key parameter in the assessment of resilience after earthquakes since it may significantly increase the total time after which a structure may be considered recovered. The principle at the base of the paper is that seismic structural health monitoring (S2HM) may play a significant role in reducing the delay time. Therefore, delay time needs to be considered since it may significantly reduce the seismic resilience of structural systems. The paper aims to consider this important issue demonstrating the relationship between S2HM and the assessment of the seismic resilience of buildings. In particular, the assumption herein is that the accuracy of the S2HM may be described with different levels, and in correspondence with these levels, certain values of the delay time may be considered. In addition, the delay time is considered as a percentage of the total repair time. A multidimensional definition that includes the accuracy of S2HM in the description of the delay time is herein proposed to be included in methodologies that aim to assess seismic resilience.
Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
Authors: Liu Di , Li Qiang , Li Sen , Kong Jun , Qi Miao
Published: March 2023

Pedestrian trajectory prediction is an important task in practical applications such as automatic driving and surveillance systems. It is challenging to effectively model social interactions among pedestrians and capture temporal dependencies. Previous methods typically emphasized social interactions among pedestrians but ignored the temporal consistency of predictions and suffered from superfluous interactions by dense undirected graphs, resulting in a considerable deviance from reality. In addition, autoregressive approaches predicted future locations conditioning on previous predictions one by one, which would lead to error accumulation and time consuming. To address these issues, we present Non-autoregressive Sparse Transformer (NaST) networks for pedestrian trajectory prediction. Specifically, NaST models sparse spatial interactions and sparse temporal dependency via a sparse spatial transformer and a sparse temporal transformer separately. Different from previous predictions such as RNN-based approaches, the transformer decoder works in non-autoregressive pattern and predicts all the future locations at one time from a query sequence, which could avoid the error accumulation and be less computationally intensive. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods.
Verification of Estimated Cosmic Neutron Intensities Using a Portable Neutron Monitoring System in Antarctica
Authors: Yasuda Hiroshi , Kurita Naoyuki , Yajima Kazuaki
Published: March 2023

Many ongoing studies for predicting the production rates of cosmogenic nuclides, forecasting changes of atmospheric compositions and climate, assessing the cosmic-radiation exposure of aircraft crew, and the effects on precise electronic devices use numerical models that estimate cosmic-radiation intensities in the atmosphere. Periodic verifications of those models are desirable to be performed for assuring the reliability of the study outcomes. Here, we investigated an application of a portable neutron-monitoring system composed of an extended-energy-range neutron monitor and a small data logger for monitoring of cosmic-neutron intensities in a polar region. As a result of measurements in the east Antarctica region covering a wide range of altitudes (from 30 m to 3762 m) and comparisons with the model calculations performed with an analytical model based on comprehensive Monte Carlo simulations (PARMA), it was demonstrated that the portable neutron-monitoring system could be effectively applied for periodic verification of cosmic-neutron intensities that would improve the reliability of related studies.
Research on Particle Size and Energy Consumption Law of Hard Coal Crushing under Impact Load Based on SHPB Test
Authors: Wang Haibo , Xu Wenqing , Cheng Bing , Zong Qi
Published: March 2023

To study the particle size distribution and energy variation law of hard coal under a load, an impact compression test of hard coal specimens under different impact loading conditions was carried out using a Φ50 mm diameter Separate Hopkinson Press Bar (SHPB) test system. We implemented the theory of dynamic impact energy of rock to establish the calculation expression of hard coal impact crushing energy dissipation, and we established the Weibull distribution model of a crushing body to analyze the impact velocity in relation to the particle size distribution of hard coal crushing and crushing energy consumption. The results demonstrate that due to the different original states of the specimens, the damage to the specimens under static action is in the mode of conjugate plane shear damage, single bevel shear damage, and tensile damage. The damage process of the specimen under impact load loading is divided into three stages: elastic deformation, elastic–plastic deformation, and plastic softening, while the increase in the strain rate caused the peak stress of the specimen to increase. The Weibull distribution can characterize the impact crushing size distribution of hard coal specimens very well. The parameter of coal rock crushing degree is a power function that is influenced by the impact velocity; the greater the impact velocity, the higher the coal rock crushing degree, but the characteristic index of coal rock crushing fluctuates with the increase in impact velocity. As the impact velocity increases, the incident energy and reflected energy increase linearly, while the transmitted energy increases first and then decreases. The dissipation energy of coal rock crushing also increases linearly with the impact velocity. There is no obvious regular change between the energy dissipation rate of coal rock and impact velocity during impact damage, and the dissipated energy of macroscopic crushing only accounts for 10~20% of the incident energy; most of the energy is used for damping loss and damage loss.
Reconfigurable Terahertz Moiré Frequency Selective Surface Based on Additive Manufacturing Technology
Authors: Ahmad Redwan , Zhuldybina Mariia , Ropagnol Xavier , Trinh Ngoc Duc , Bois Chloé , Schneider Juan , Blanchard François
Published: March 2023

We designed and fabricated a terahertz (THz) frequency selective surface (FSS) based on two distinct additive manufacturing technologies, namely, printable electronics (PE) and three-dimensional (3D) printing. Silver nanoparticle ink was printed on a polyethylene terephthalate (PET) substrate utilizing a large-scale roll-to-roll industrial PE technique with a flexographic printed unit, while the 3D-printed THz FSS was fabricated based on a powder bed fusion-selective laser melting system. The filtering characteristics of both types of FSS were verified through calculation, simulation, and experiments. Furthermore, the rotational tuning approach was applied to two identical FSS to form reconfigurable FSS which could be defined as Moiré FSS. Based on the numerical results obtained, our proposed technique which used a PE-based Moiré FSS achieves a 58% modulation depth at 0.25 THz, while experimental verification found a modulation depth of 41% at 0.22 THz, confirming that its adoption is simple and cost-effective. To the best of our knowledge, this is the first demonstration of a Moiré reconfigurable printed FSS operating in the THz region.
Comparative Study of the Antibacterial Activity, Total Phenolic and Total Flavonoid Content of Nine Hypericum Species Grown in Greece
Authors: Kakouri Eleni , Daferera Dimitra , Trigas Panayiotis , Charalambous Despina , Pantelidou Maria , Tarantilis Petros A. , Kanakis Charalabos D.
Published: March 2023

Hypericum perforatum is an herb whose use dates back centuries. Extracts of the plant are available as over-the-counter treatment options for depression. The genus consists of approximately 500 species, most of which have not yet been studied. Antimicrobial resistance has reached alarming levels, indicating a post-antibiotic era as many of the available treatment options become less effective. For this reason, nine Hypericum species were studied for their antimicrobial activity and their total phenolic and flavonoid content. Extracts were tested against Gram-negative and Gram-positive bacteria. Extracts inhibited the growth of Gram-positive bacteria (Staphylococcus aureus and Enterococcus faecalis). The lowest MIC and MBC values were calculated for the extract of H. perforatum against both microorganisms tested, reaching 0.06 mg/mL for S. aureus and 0.13 mg/mL for E. faecalis. Total phenolic content was the highest in the H. perforatum extract (86 ± 12.90 mg GAE/g dry plant material). H. tetrapterum presented the highest flavonoid content, equal to 1.58 ± 0.4 mg RE/g of dry plant material. The Hypericum species studied herein are less common or have not yet been examined compared to H. perforatum; therefore, our study adds new data to the knowledge of the genus Hypericum.
Thematic Analysis: A Corpus-Based Method for Understanding Themes/Topics of a Corpus through a Classification Process Using Long Short-Term Memory (LSTM)
Authors: Altameemi Yaser , Altamimi Mohammed
Published: March 2023

Using advanced algorithms to conduct a thematic analysis reduces the time taken and increases the efficiency of the analysis. Long short-term memory (LSTM) is effective in the field of text classification and natural language processing (NLP). In this study, we adopt LSTM for text classification in order to perform a thematic analysis using concordance lines that are taken from a corpora of news articles. However, the statistical and quantitative analyses of corpus linguistics are not enough to fully identify the semantic shift of terms and concepts. Therefore, we suggest that a corpus should be classified from a linguistic theoretical perspective, as this would help to determine the level of the linguistic patterns that should be applied in the experiment of the classification process. We suggest investigating the concordance lines of the articles rather than only the relationship between collocates, as this has been a limitation for many studies. The findings of this research work highlight the effectiveness of the proposed methodology for the thematic analysis of media coverage, reaching 84% accuracy. This method provides a deeper thematic analysis than only applying the classification process through the collocational analysis.
Effect of External Aeration on Cr (VI) Reduction in the Leersia hexandra Swartz Constructed Wetland-Microbial Fuel Cell System
Authors: Shi Yucui , Tang Gang , You Shaohong , Jiang Pingping
Published: March 2023

Cr (VI) is hazardous to humans and our environment. Leersia hexandra Swartz (L. hexandra) is the first wet chromium hyperaccumulator found in China. This study constructed the L. hexandra constructed wetland-microbial fuel cell (CW-MFC) system to treat Cr (VI) wastewater. It also determined the effects of different dissolved oxygen (DO) concentrations on power generation, pollutant removal, and Cr (VI) reduction. Cathode aeration promoted the voltage output and pollutant removal of the L. hexandra CW-MFC when the DO concentration was 4.5 mg·L−1: the highest voltage was 520 mV, the chemical oxygen demand (COD) removal rate was 93.73%, and the Cr (VI) removal rate was 97.77%. Moreover, the increase in the DO concentration improved the absorption of heavy metal Cr by the substrate and L. hexandra, and promoted the transformation from Cr (VI) to Cr (III). Chromium mostly exists as a residue with low toxicity and low mobility in L. hexandra and the substrate. This proves that the increased DO concentration promotes the redox reaction in the system and plants, reducing Cr (VI) to Cr (III). At the same time, the key micro-organism Geobacter that enhances the performance of the system and Cr (VI) reduction was found. The research results can provide a reference for the subsequent CW-MFC treatment of actual Cr-containing wastewater.
Preservation Surgery of Septic Osteoarthritis and Osteomyelitis in the Diabetic Foot Using S53P4 Bioactive Glass—A Case Series
Authors: Korpar Iza , Frangež Igor
Published: March 2023

(1) Background: Preservation surgery of the diabetic foot aims at tissue sparing to avoid further pathological changes from developing, while ultimately preserving the limb. For this purpose, osteoregenerative, antimicrobial, and osteostimulative materials are now available. The aim of this case series is to demonstrate several clinical situations in which the use of bioactive glass, a third-generation biomaterial, can potentially be of benefit. (2) Methods: In this retrospective case series, we present patients with a diabetic foot complication with septic osteoarthritis, treated at the University Medical Centre Ljubljana, in the years 2020–2022. Patients received surgical care with resection of the affected joint and bone and placement of bioactive glass material. We observed patient characteristics, recurrence of septic osteoarthritis or osteomyelitis, and the need for amputation in the follow-up period. (3) Results: Of the 16 included patients, 8 received treatment of the metatarsophalangeal joints, 4 of the midfoot joints, and 4 of the ankle joints. Ten suffered from Charcot neuroarthropathy. None required readmission within 30 days or suffered early complications. One complication with recidivant septic osteoarthritis and osteomyelitis was observed. (4) Conclusions: Our case series presents possible applications for treatment with bioactive glass in various joints and bones affected by septic osteoarthritis of the diabetic foot.
Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data
Authors: Liu Mandi , Zhang Lei , Yue Qi
Published: March 2023

Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the symptoms of diabetes and the medication regimens for machine learning. The eigenvector of the DTBM is the stable distribution of different symptoms and medication regimens. Based on the DTBM, this paper proposes a machine-learning algorithm for completing missing medical data, which provides a theoretical basis for the prediction of a Bayesian matrix with missing medical information. The experimental results show the rationality and applicability of the given algorithms.
Influence of the Protein-Based Emulsions on the Rheological, Thermo-Mechanical and Baking Performance of Muffin Formulations
Authors: Banu Iuliana , Patrașcu Livia , Vasilean Ina , Dumitrașcu Loredana , Aprodu Iuliana
Published: March 2023

The impact of replacing the sunflower oil in a typical muffin formulation with different protein-based emulsions was investigated. Fundamental rheological measurements indicated significant differences between emulsions prepared with soy, lupin, and yeast proteins. The highest viscosity of 2.04 Pa·s was registered for the lupin protein-based emulsion, whereas the yeast protein-based emulsion exhibited the narrowest linear viscoelastic region. The influence of the protein-based emulsions on the thermo-mechanical properties of wheat flour dough was further investigated using the Mixolab device and Chopin+ protocol. Oil substitution with emulsion resulted in better starch gelatinization with the C3 torque of 0.46 Nm being registered for doughs with soy and lupin protein emulsions. Significant differences in terms of moisture, color, porosity, and texture were observed between muffins prepared with protein-based emulsions and control. The lower fat baked products retained higher amounts of water (25.05–26.00%) and exhibited slightly more vivid color (color intensity of 46.34–46.81) and harder texture (firmness of 5.64–5.86 N). The sensory analysis confirmed that soy, lupin, and yeast protein emulsions can be used for obtaining muffin samples with acceptable taste and flavor, and overall quality comparable to the control. These results indicate that the protein based-emulsions are promising oil replacers in muffin formulations.
Effect of Different Nuts Oil Consumption on Morphological Features and Some Biomarkers of Inflammation in Adjuvant-Induced Arthritis (AIA) Rat Model
Authors: Al-Shammari Samiyah K. , Al-Nouri Doha M. , Arzoo Shaista , Al-Harbi Laila Naif
Published: March 2023

This study evaluated the protective effect of different dietary ω-6/ω-3 ratios in oils obtained from various nuts (walnut, peanut, cashew, and hazelnut) against morphological features and markers of inflammation on an adjuvant-induced arthritis rat model. Rheumatoid arthritis (RA) was induced via intradermal injection of heat-killed Mycobacterium tuberculosis. Five groups of rats with RA (n = 5) were randomly categorized as follows: control positive, walnut oil group, peanut oil group, cashew nut oil group, and hazelnut oil group. Another five healthy rats served as a normal non-arthritic (control) group. We assessed the therapeutic effects by measuring arthritis scores during the experiment and serum inflammatory markers at the end of the study. The serum levels of the rheumatoid factor, TNF-α, IL-6, IL-1β, and PGE2, were significantly (p ≤ 0.05) reduced in all treatment groups. The daily consumption of nut oils ameliorates clinical and morphological abnormalities by inhibiting the inflammatory cells that produce inflammatory interleukins and eicosanoids.
Impact of Attributional Style of Behavior Outcome on the Sustainable Development of Residents’ Energy-Saving Behavior: Differences in Policy Responses of Residents
Authors: Yue Ting , Zhou Jing , Zhang Yingkai , Li Mengting , Wang Qianru , Long Ruyin , Liu Junli
Published: March 2023

In the context of promoting the construction of an energy-saving and environment-friendly society, it is crucial to promote the sustainable development of residents’ energy-saving behaviors, determine how attributional styles of behavior outcome affect residents’ energy-saving behaviors, and examine how different residents respond to intervention policies. Based on the attribution theory, this study constructs a theoretical model of the impact mechanism of the attributional style of behavior outcome on residents’ energy-saving behaviors. Regression analysis and the quadratic response model were used to test all 1254 valid questionnaires. The results show that the residents’ degrees of understanding, support, and implementation of the energy-saving policies presented inconsistent situations. Residents’ degrees of understanding and support of energy-saving policies positively promote the degree of implementation of energy-saving policies. The consistency of understanding and support has a non-linear influence on the degree of implementation of energy-saving policies. Additionally, the public’s understanding, support, and implementation of energy-saving policies have significant differences in the attribution style of behavioral outcomes. Finally, some relevant policy recommendations are put forward.
Synergistic Effect of Hydrogen Peroxide and Cold Atmospheric Pressure Plasma-Jet for Microbial Disinfection
Authors: Baik Ku Youn , Jo Hyun , Ki Se Hoon , Kwon Gi-Chung , Cho Guangsup
Published: March 2023

The efficiency of simultaneous treatment of the cold atmospheric pressure plasma jet (CAP) and hydrogen peroxide (H2O2) was investigated. A CAP with a thin and long plume was generated with Ar gas and applied to a common oral bacterium, Enterococcus faecalis (E. faecalis). The bactericidal efficiency was evaluated with the electron microscopy and the colony forming unit (CFU) assay. The underlying mechanisms were studied by measuring extracellular chemical changes in the water solution and by measuring biological responses such as the trans-membrane potential, the intracellular oxidative stress, and the membrane permeability. The combination of CAP with H2O2 could provide dramatic synergistic effects in bacterial disinfection through the enhanced membrane transportation of reactive species and the oxidation of intracellular molecules. Since the byproducts of both H2O2 and CAP are not significantly toxic, the synergistic bactericidal effects of their combination could be a good candidate to clinical applications.
A Machine Learning-Based Intelligent Vehicular System (IVS) for Driver’s Diabetes Monitoring in Vehicular Ad-Hoc Networks (VANETs)
Authors: Sohail Rafiya , Saeed Yousaf , Ali Abid , Alkanhel Reem , Jamil Harun , Muthanna Ammar , Akbar Habib
Published: March 2023

Diabetes is a chronic disease that is escalating day by day and requires 24/7 continuous management. It may cause many complications, precisely when a patient moves, which may risk their and other drivers’ and pedestrians’ lives. Recent research shows diabetic drivers are the main cause of major road accidents. Several wireless non-invasive health monitoring sensors, such as wearable continuous glucose monitoring (CGM) sensors, in combination with machine learning approaches at cloud servers, can be beneficial for monitoring drivers’ diabetic conditions on travel to reduce the accident rate. Furthermore, the emergency condition of the driver needs to be shared for the safety of life. With the emergence of the vehicular ad-hoc network (VANET), vehicles can exchange useful information with nearby vehicles and roadside units that can be further communicated with health monitoring sources via GPS and Internet connectivity. This work proposes a novel approach to the health care of drivers’ diabetes monitoring using wearable sensors, machine learning, and VANET technology. Several machine learning (ML) algorithms assessed the proposed prediction model using the cross-validation method. Performance metrics precision, recall, accuracy, F1-score, sensitivity, specificity, MCC, and AROC are used to validate our method. The result shows random forest (RF) outperforms and achieves the highest accuracy compared to other algorithms and previous approaches ranging from 90.3% to 99.5%.
MM-ConvBERT-LMS: Detecting Malicious Web Pages via Multi-Modal Learning and Pre-Trained Model
Authors: Tong Xin , Jin Bo , Wang Jingya , Yang Ying , Suo Qiwei , Wu Yong
Published: March 2023

In recent years, the number of malicious web pages has increased dramatically, posing a great challenge to network security. While current machine learning-based detection methods have emerged as a promising alternative to traditional detection techniques. However, these methods are commonly based on single-modal features or simple stacking of classifiers built on various features. As a result, these techniques are not capable of effectively fusing features from different modalities, ultimately limiting the detection effectiveness. To address this limitation, we propose a malicious web page detection method based on multi-modal learning and pre-trained models. First, in the input stage, the raw URL and HTML tag sequences of web pages are used as input features. To help the subsequent model learn the relationship between the two modalities and avoid information confusion, modal-type encoding, and positional encoding are introduced. Next, a single-stream neural network based on the ConvBERT pre-trained model is used as the backbone classifier, and it learns the representation of multi-modal features through fine-tuning. For the output part of the model, a linear layer based on large margin softmax is applied to the decision-making. This activation function effectively increases the classification boundary and improves the robustness. In addition, a coarse-grained modal matching loss is added to the model optimization objective to assist the models in learning the cross-modal association features. Experimental results on synthetic datasets show that our proposed method outperforms traditional single-modal detection methods in general, and has advantages over baseline models in terms of accuracy and reliability.
Prompt-Based Word-Level Information Injection BERT for Chinese Named Entity Recognition
Authors: He Qiang , Chen Guowei , Song Wenchao , Zhang Pengzhou
Published: March 2023

Named entity recognition (NER) is a subfield of natural language processing (NLP) that identifies and classifies entities from plain text, such as people, organizations, locations, and other types. NER is a fundamental task in information extraction, information retrieval, and text summarization, as it helps to organize the relevant information in a structured way. The current approaches to Chinese named entity recognition do not consider the category information of matched Chinese words, which limits their ability to capture the correlation between words. This makes Chinese NER more challenging than English NER, which already has well-defined word boundaries. To improve Chinese NER, it is necessary to develop new approaches that take into account category features of matched Chinese words, and the category information would help to effectively capture the relationship between words. This paper proposes a Prompt-based Word-level Information Injection BERT (PWII-BERT) to integrate prompt-guided lexicon information into a pre-trained language model. Specifically, we engineer a Word-level Information Injection Adapter (WIIA) through the original Transformer encoder and prompt-guided Transformer layers. Thus, the ability of PWII-BERT to explicitly obtain fine-grained character-to-word relevant information according to the category prompt is one of its key advantages. In experiments on four benchmark datasets, PWII-BERT outperforms the baselines, demonstrating the significance of fully utilizing the advantages of fusing the category information and lexicon feature to implement Chinese NER.
Spherical Planting Inversion of GRAIL Data
Authors: Lu Guangyin , Zhang Dongxing , Cao Shujin , Deng Yihuai , Xu Gang , Liu Yihu , Zhu Ziqiang , Chen Peng
Published: March 2023

In large-scale potential field data inversion, constructing the kernel matrix is a time-consuming problem with large memory requirements. Therefore, a spherical planting inversion of Gravity Recovery and Interior Laboratory (GRAIL) data is proposed using the L1-norm in conjunction with tesseroids. Spherical planting inversion, however, is strongly dependent on the correct seeds’ density contrast, location, and number; otherwise, it can cause mutual intrusion of anomalous sources produced by different seeds. Hence, a weighting function was introduced to limit the influence area of the seeds for yielding robust solutions; moreover, it is challenging to set customized parameters for each seed, especially for the large number of seeds used or complex gravity anomalies data. Hence, we employed the “shape-of-anomaly” data-misfit function in conjunction with a new seed weighting function to improve the spherical planting inversion. The proposed seed weighting function is constructed based on the covariance matrix for given gravity data and can avoid manually setting customized parameters for each seed. The results of synthetic tests and field data show that spherical planting inversion requires less computer memory than traditional inversion. Furthermore, the proposed seed weighting function can effectively limit the seed influence area. The result of spherical planting inversion indicates that the crustal thickness of Mare Crisium is about 0 km because the Crisium impact may have removed all crust from parts of the basin.