bluuebunny/crossref_metadata_2025 huggingface dataset:
Density Functional Theory Calculations for Interpretation of Infra-Red Spectra of Liquid Crystalline Chiral Compound
Authors: Deptuch Aleksandra , Górska Natalia , Murzyniec Michaela , Srebro-Hooper Monika , Hooper James , Dziurka Magdalena , Urbańska Magdalena
Published: July 2024

The experimental IR spectra of (S)-4′-(1-methylheptyloxycarbonyl) biphenyl-4-yl 4-[2-(2,2,3,3,4,4,4-heptafluorobutoxy) ethyl-1-oxy]-2-fluorobenzoate in the crystal phase are analyzed with the help of dispersion-corrected density functional theory (DFT+D3) calculations for isolated molecular monomer and dimer models, and a periodic model computed at the extended density functional tight-binding (xTB) level of theory. It is found that the frequency scaling coefficients obtained with the results of the molecular calculations are good matches for the crystal phase, being close to 1. The molecular and periodic models both confirm that varied intra- and intermolecular interactions are crucial in order to reproduce the broadened shape of the experimental band related to C=O stretching; the key factors are the conjugation of the ester groups with the aromatic rings and the varied intermolecular chemical environments, wherein the C=O group that bridges the biphenyl and F-substituted phenyl groups seems particularly sensitive. The C=O stretching vibrations are investigated as a function of temperature, covering the range of the crystal, smectic CA*, smectic C* and isotropic liquid phases. The structure changes are followed based on the X-ray diffraction patterns collected in the same temperatures as the IR spectra. The experimental and computational results taken together indicate that the amount of weak C=O…H-C hydrogen bonds between the molecules in the smectic layers decreases with increasing temperature.
Hemolymph Parameters Are a Useful Tool for Assessing Bivalve Health and Water Quality
Authors: Grinchenko Andrei , Sokolnikova Yulia , Tumas Ayna , Mokrina Mariia , Tsoy Elizaveta , Buriak Ivan , Kumeiko Vadim , Onishchenko Mariia
Published: July 2024

Bivalves play a key role in aquatic ecosystems and are a valuable commercial resource. The prosperity of these aquatic organisms depends mainly on the effectiveness of their immune defense, in which the hemolymph plays a central role. Hemolymph may be used as an effective non-lethal criterion of health. However, the predictive value of hemolymph analysis depends on the comparison between the obtained results and reference data from healthy individuals living in natural aquatic environments. We collected hemolymph from 15 commercially important species from wild populations at stations located in non-impacted and impacted water areas of the Sea of Japan. Of the 11 hemolymph parameters we analyzed, the total hemocyte count, percentage of hemocyte types, phagocytic activity, presence of reactive oxygen species, and protein concentration differed significantly between populations from non-impacted and impacted water areas. The most responsive species to pollution were Magallana gigas, Crenomytilus grayanus, Mizuhopecten yessoensis, and Mactra chinensis. This work is the first to examine a large number of commercially important species simultaneously. The results of this study are the basis for establishing the health status criteria of commercial bivalves for veterinary control in aquaculture and biomonitoring.
Floristic Diversity and Natural Regeneration of Miombo Woodlands in the Rural Area of Lubumbashi, D.R. Congo
Authors: Nghonda Dieu-donné N’tambwe , Muteya Héritier Khoji , Salomon Waselin , Mushagalusa Fidèle Cuma , Malaisse François , Ponette Quentin , Sikuzani Yannick Useni , Kalenga Wilfried Masengo , Bogaert Jan
Published: July 2024

Increased anthropogenic pressure on forest resources leads to deforestation and forest degradation, significantly limiting the regeneration capacity of native woody species and consequently the restoration of miombo woodlands in anthropized habitats within the rural area of Lubumbashi. This study assessed miombo species’ diversity and natural regeneration capacity through floristic inventories in three different habitats (unexploited forests, degraded forests, and post-cultivation fallows). The results reveal that for the adult stratum, unexploited and degraded forests exhibit higher dendrometric (density, mean square diameter, basal area) and floristic parameter (taxa, genera, families) values compared to post-cultivation fallows. Furthermore, the regeneration of miombo woody species is higher in degraded forests (21 taxa; 105 juveniles/plot). However, regarding the sapling’s stratum (1 cm ≤ dbh < 10 cm), the three habitats display similar situations. Additionally, the floristic composition and diversity of unexploited and degraded forests show a significantly higher similarity (76.50%) among them compared to these habitats and the post-cultivation fallows (56.00%). These findings indicate that miombo woodlands have the potential to regenerate and maintain floristic diversity even in anthropized habitats, particularly in degraded forests. To sustain this natural regeneration capacity of miombo woody species and promote the restoration of forest cover and its floristic diversity, it is imperative to determine the rotation period after habitat exploitation and regulate anthropogenic activities and late bush fires, particularly in anthropized habitats at the village level.
Effects of Seasonality on the Large and Medium-Sized Mammal Community in Mountain Dry Forests
Authors: Quiroga-Pacheco Carmen Julia , Velez-Liendo Ximena , Zedrosser Andreas
Published: July 2024

Seasonality drives natural processes, impacting environmental factors like temperature and resource availability, leading to shifts in wildlife communities. The Andean dry forests exhibit a marked seasonality, with a dry and cold season (May–September) and a warm, wet season (October–April). In a year-long remote camera survey in Southern Bolivia, we identified 29 medium to large mammal species, 18 outside their known distribution ranges. While overall species richness remained stable, photographic records varied between seasons. Capture rates, reflecting species richness and abundance, were more influenced by season and habitat. Wet season rates were lower, but higher in all other habitats compared to the mountain bush and grasslands. Rates increased with altitude and distance to hiking trails, but decreased with increasing distance from main roads. Medium to large mammals were more active during the dry season, indicating adjustments in response to seasonal changes. Our results suggest a cumulative impact of various factors beyond mere seasonality, and call for adjustments in global species distributions. Moreover, emphasize the need for biodiversity monitoring in dry forest habitats, particularly regarding responses to environmental shifts and human-induced alterations.
Root-Zone Bacterial Diversity in Field-Grown Individual Plants from Alfalfa Lines with Wild Relatives in Their Genetic Backgrounds
Authors: Omirou Michalis , Michaelidou Urania , Fasoula Dionysia A. , Humphries Alan , Kilian Benjamin , Ioannides Ioannis M.
Published: July 2024

Alfalfa (Medicago sativa L.) is a vitally important perennial fodder legume worldwide. Given their particular traits, alfalfa crop wild relatives (CWRs) could be used to develop cultivars that can tolerate extreme environmental and climatic conditions. Until now, researchers have overlooked the composition and structure of bacterial communities in the root zone of alfalfa and its relevant CWRs and their influence on forage performance under actual field conditions. In this study, high-throughput sequencing of 16S rRNA analysis was performed to investigate the diversity and assemblies of bacterial communities in the bulk soil and in the root zone of individual field-grown Medicago plants arranged in a honeycomb selection design. The plants used in this study were M. sativa × M. arborea hybrids (Genotypes 6 and 8), the closely-related M. sativa nothosubsp. varia (Martyn) Arcang. (Genotype 13), and M. sativa ssp. sativa (Genotype 20). The bacterial communities in the root-zone samples and the assemblies in the bulk soil differed significantly. Genotype 13 was found to have distinct bacterial assemblies from the other genotypes while exhibiting the lowest forage productivity. These findings suggest that plant productivity may influence the composition of bacterial communities in the root zone. Biomarker analysis conducted using linear discriminant analysis (LDA) revealed that only members of the Rhizobiales order were enriched in the M. sativa nothosubsp. varia root zone whereas taxa belonging to Sphingomonas and various Bacteriodota were enriched in the other genotypes. Of the shared taxa identified in the root zone of the Medicago lines, the abundance of specific taxa, namely, Flavisolibacter, Stenotrophomonas, and Sphingomonas, were positively associated with forage yield. This pioneering study, in which the root zones of individual Medicago plants under actual field conditions were examined, offers evidence of differences in the bacterial composition of alfalfa genotypes with varying genetic backgrounds. Its findings indicate that particular bacterial taxa may favorably influence plant performance. This study covered the first six months of crop establishment and paves the way for further investigations to advance understanding of how shifts in bacterial assemblies in alfalfa roots affect plant performance over time.
Markers of Epithelial–Mesenchymal Transition and Mucinous Histology Are Significant Predictors of Disease Severity and Tumor Characteristics in Early-Onset Colorectal Cancer
Authors: Djikic Rom Aleksandra , Dragicevic Sandra , Jankovic Radmila , Radojevic Skodric Sanja , Sabljak Predrag , Markovic Velimir , Stojkovic Jovana Rosic , Barisic Goran , Nikolic Aleksandra
Published: July 2024

Approximately 20% of patients with colorectal cancer (CRC) are diagnosed with a mucinous subtype of this tumor, have a worse prognosis, and often show resistance to available therapies. Molecules from the mucin family are involved in the regulation of epithelial–mesenchymal transition (EMT), which significantly determines the cancer aggressiveness. This study aimed to examine the diagnostic and prognostic significance of mucinous histology and EMT markers in patients with early-onset CRC and their association with disease severity and tumor characteristics. This study included tumor tissue samples from 106 patients diagnosed with CRC before the age of 45, 53 with mucinous and 53 with non-mucinous tumors. The EMT status was determined by immunohistochemical analysis of E-cadherin and Vimentin in tissue sections. Mucinous tumors had significantly higher Mucin-1 (p < 0.001) and cytoplasmic E-cadherin (p = 0.043) scores; they were significantly less differentiated (p = 0.007), more advanced (p = 0.027), and predominately affected right the colon (p = 0.039) compared to non-mucinous tumors. Epithelial tumors were significantly better differentiated (p = 0.034) and with less prominent tumor budding (p < 0.001) than mesenchymal tumors. Mucin-1 and Vimentin were independent predictors of tumor differentiation (p = 0.006) and budding (p = 0.001), respectively. Mucinous histology and EMT markers are significant predictors of disease severity and tumor characteristics in early-onset colorectal cancer.
Liver Phantoms Cast in 3D-Printed Mold for Image-Guided Procedures
Authors: Elisei Radu Claudiu , Graur Florin , Melzer Andreas , Moldovan Sever Calin , Tiu Calin , Popa Calin , Mois Emil , Pisla Doina , Vaida Calin , Ștefănescu Horia , Coțe Adrian , Al-Hajjar Nadim
Published: July 2024

Introduction: Image-guided invasive procedures on the liver require a steep learning curve to acquire the necessary skills. The best and safest way to achieve these skills is through hands-on courses that include simulations and phantoms of different complications, without any risks for patients. There are many liver phantoms on the market made of various materials; however, there are few multimodal liver phantoms, and only two are cast in a 3D-printed mold. Methods: We created a virtual liver and 3D-printed mold by segmenting a CT scan. The InVesalius and Autodesk Fusion 360 software packages were used for segmentation and 3D modeling. Using this modular mold, we cast and tested silicone- and gelatin-based liver phantoms with tumor and vascular formations inside. We tested the gelatin liver phantoms for several procedures, including ultrasound diagnosis, elastography, fibroscan, ultrasound-guided biopsy, ultrasound-guided drainage, ultrasound-guided radio-frequency ablation, CT scan diagnosis, CT–ultrasound fusion, CT-guided biopsy, and MRI diagnosis. The phantoms were also used in hands-on ultrasound courses at four international congresses. Results: We evaluated the feedback of 33 doctors regarding their experiences in using and learning on liver phantoms to validate our model for training in ultrasound procedures. Conclusions: We validated our liver phantom solution, demonstrating its positive impact on the education of young doctors who can safely learn new procedures thus improving the outcomes of patients with different liver pathologies.
Classification-Predictive Model Based on Artificial Neural Network Validated by Histopathology and Direct Immunofluorescence for the Diagnosis of Oral Lichen Planus
Authors: Osipowicz Katarzyna , Turkowski Piotr , Zdolińska-Malinowska Izabela
Published: July 2024

The diagnosis of oral lichen planus (OLP) poses many challenges due to its nonspecific clinical symptoms and histopathological features. Therefore, the diagnostic process should include a thorough clinical history, immunological tests, and histopathology. Our study aimed to enhance the diagnostic accuracy of OLP by integrating direct immunofluorescence (DIF) results with clinical data to develop a multivariate predictive model based on the Artificial Neural Network. Eighty patients were assessed using DIF for various markers (immunoglobulins of classes G, A, and M; complement 3; fibrinogen type 1 and 2) and clinical characteristics such as age, gender, and lesion location. Statistical analysis was performed using machine learning techniques in Statistica 13. The following variables were assessed: gender, age on the day of lesion onset, results of direct immunofluorescence, location of white patches, locations of erosions, treatment history, medications and dietary supplement intake, dental status, smoking status, flossing, and using mouthwash. Four statistically significant variables were selected for machine learning after the initial assessment. The final predictive model, based on neural networks, achieved 85% in the testing sample and 71% accuracy in the validation sample. Significant predictors included stress at onset, white patches under the tongue, and erosions on the mandibular gingiva. In conclusion, while the model shows promise, larger datasets and more comprehensive variables are needed to improve diagnostic accuracy for OLP, highlighting the need for further research and collaborative data collection efforts.
Diagnosis of Thyroid Nodules in Children and Adolescents with Subclinical Hypothyroidism and Their Outcomes after Early Thyroxine Treatment—A Longitudinal Study
Authors: Kostopoulou Eirini , Koliofoti Eleana Georgia , Spilioti Diamantina X. , Miliordos Konstantinos , Skiadopoulos Spyros , Gil Andrea Paola Rojas , Fouzas Sotirios , Sinopidis Xenophon , Spiliotis Bessie E.
Published: July 2024

Pediatric thyroid nodules (TNs) present a higher malignancy rate compared to adults. We sought to diagnose the frequency and characteristics of TNs in children and adolescents with subclinical hypothyroidism (SH) and their outcomes after levothyroxine (LT4) therapy. A total of 256 children with TNs and SH were followed every semester from 2006 to 2018. All patients were treated with LT4. Clinical and radiologic findings, such as the size and texture of the nodules, were documented. Analysis included one-way ANOVA, Kruskal–Wallis, Chi-square, and Fisher’s exact tests. After initial LT4 therapy, TNs disappeared in 85.5% and did not reappear throughout follow-up. In 14.5%, TNs remained the same or increased in size, but they decreased after subsequent LT4 administration with an increased dose. Thyroid disease family history (FHTD) was documented in 77.0%. In total, 64.5% developed a goiter, 46.0% exhibited thyroid heterogeneity on ultrasound, 23.4% had positive Anti-Tg, and 25.4% had positive anti-TPO autoantibodies. Our findings support the possible premise that early pharmacologic intervention with LT4 may be beneficial in children and adolescents with TNs and SH. The increased frequency of FHTD, goiter, thyroid heterogeneity, and Hashimoto in our patients emphasizes that thyroid ultrasounds may be warranted in children and adolescents with these characteristics in order to rule out the presence of TNs.
Healthcare Professionals’ Knowledge, Attitudes, and Practices in the Assessment, and Management of Sickle-Cell Disease: A Meta-Aggregative Review
Authors: Druye Andrews Adjei , Agyare Dorcas Frempomaa , Akoto-Buabeng William , Zutah Jethro , Offei Frank Odonkor , Nabe Bernard , Ofori Godson Obeng , Alhassan Amidu , Anumel Benjamin Kofi , Cobbinah Godfred , Abraham Susanna Aba , Amoadu Mustapha , Hagan John Elvis
Published: July 2024

Background: Sickle Cell Disease (SCD) presents significant health challenges globally. Despite its prevalence in diverse geographical regions, there is a paucity of literature synthesizing evidence on healthcare professionals’ knowledge, attitudes, and practices (KAP) toward SCD assessment and management. This meta-aggregative review systematically examined and synthesized existing qualitative research to elucidate healthcare professionals’ KAP regarding SCD assessment and management. Methods: This meta-aggregative review followed Aromataris and Pearson’s guidelines and the PRISMA framework for systematic review reporting. The search was conducted in Scopus, PubMed, Embase, CINAHL Web of Science, Google Scholar, Dimensions AI, and HINARI. Quality appraisal was performed using the Joanna Briggs Institute tool. Results: Healthcare professionals (HCPs) demonstrate varying levels of KAP toward SCD assessment and management. Studies reveal low-to-moderate levels of general knowledge among HCPs, with nurses often exhibiting poorer understanding than physicians. Deficiencies in awareness of specific interventions, such as chemoprophylaxis and prenatal diagnosis, are noted, along with gaps in SCD assessment and diagnosis, particularly in pain management and premarital screening. Attitudes toward SCD patients vary, and practices reveal inconsistencies and deficiencies, including inadequate nutritional counseling and barriers in emergency departments. However, interventions aimed at improving HCPs’ KAP show promise in enhancing understanding and attitudes toward SCD, suggesting potential avenues for improvement. Conclusions: Educational initiatives targeted at both student nurses and practicing healthcare providers, coupled with the implementation of standardized protocols and guidelines, can enhance knowledge acquisition and promote consistent, high-quality care delivery. Future studies should improve the quality of their methods in this area of study.
Combining Orthodontic and Restorative Care with Novel Workflows
Authors: Garcia-Torres Francisco , Jurado Carlos A. , Rojas-Rueda Silvia , Sanchez-Vazquez Susana , Floriani Franciele , Fischer Nicholas G. , Tsujimoto Akimasa
Published: July 2024

This report describes multidisciplinary care combining orthodontics and restorative dentistry for a patient with Class II occlusion and stained mandibular and maxillary resin composite veneers. The orthodontic treatment improved severe overjet and malocclusion prior to restorative care. Occlusal assessment was provided with a novel digital device (PlaneSystem, Zirkonzahn) that is integrated with digital workflows for the evaluation of the occlusal plane and condylar path inclination. Diagnostic digital impressions and digital wax-up for intraoral mock-ups led to the patient’s treatment acceptance. Minimally invasive tooth preparation, final digital impressions, and bonding under dental dam isolation fulfilled the patient’s esthetic and functional demands with all-ceramic restorations.
Intelligent Control Strategy for Robotic Manta via CPG and Deep Reinforcement Learning
Authors: Su Shijie , Chen Yushuo , Li Cunjun , Ni Kai , Zhang Jian
Published: July 2024

The robotic manta has attracted significant interest for its exceptional maneuverability, swimming efficiency, and stealthiness. However, achieving efficient autonomous swimming in complex underwater environments presents a significant challenge. To address this issue, this study integrates Deep Deterministic Policy Gradient (DDPG) with Central Pattern Generators (CPGs) and proposes a CPG-based DDPG control strategy. First, we designed a CPG control strategy that can more precisely mimic the swimming behavior of the manta. Then, we implemented the DDPG algorithm as a high-level controller that adaptively modifies the CPG’s control parameters based on the real-time state information of the robotic manta. This adjustment allows for the regulation of swimming modes to fulfill specific tasks. The proposed strategy underwent initial training and testing in a simulated environment before deployment on a robotic manta prototype for field trials. Both further simulation and experimental results validate the effectiveness and practicality of the proposed control strategy.
Event-Triggered Collaborative Fault Diagnosis for UAV–UGV Systems
Authors: Li Runze , Jiang Bin , Zong Yan , Lu Ningyun , Guo Li
Published: July 2024

The heterogeneous unmanned system, which is composed of unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV), has been broadly applied in many domains. Collaborative fault diagnosis (CFD) among UAVs and UGVs has become a key technology in these unmanned systems. However, collaborative fault diagnosis in unmanned systems faces the challenges of the dynamic environment and limited communication bandwidth. This paper proposes an event-triggered collaborative fault diagnosis framework for the UAV–UGV system. The framework aims to achieve autonomous fault monitoring and cooperative diagnosis among unmanned systems, thus enhancing system security and reliability. Firstly, we propose a fault trigger mechanism based on broad learning systems (BLS), which utilizes sensor data to accurately detect and identify faults. Then, under the dynamic event triggering mechanism, the network communication topology between the UAV–UGV system and BLS is used to achieve cooperative fault diagnosis. To validate the effectiveness of our proposed scheme, we conduct experiments on a software-in-the-loop (SIL) simulation platform. The experimental results demonstrate that our method achieves high diagnosis accuracy for the UAV–UGV system.
A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data
Authors: Troiano Maurizio , Nobile Eugenio , Grignaffini Flavia , Mangini Fabio , Mastrogiuseppe Marco , Conati Barbaro Cecilia , Frezza Fabrizio
Published: July 2024

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions.
Enhancing Visual Odometry with Estimated Scene Depth: Leveraging RGB-D Data with Deep Learning
Authors: Kostusiak Aleksander , Skrzypczyński Piotr
Published: July 2024

Advances in visual odometry (VO) systems have benefited from the widespread use of affordable RGB-D cameras, improving indoor localization and mapping accuracy. However, older sensors like the Kinect v1 face challenges due to depth inaccuracies and incomplete data. This study compares indoor VO systems that use RGB-D images, exploring methods to enhance depth information. We examine conventional image inpainting techniques and a deep learning approach, utilizing newer depth data from devices like the Kinect v2. Our research highlights the importance of refining data from lower-quality sensors, which is crucial for cost-effective VO applications. By integrating deep learning models with richer context from RGB images and more comprehensive depth references, we demonstrate improved trajectory estimation compared to standard methods. This work advances budget-friendly RGB-D VO systems for indoor mobile robots, emphasizing deep learning’s role in leveraging connections between image appearance and depth data.
The Storage Tank Explosion Damage and the Effectiveness of Control Measures in the Chemical Industrial Parks of Smart Cities
Authors: Cui Tiejun , Wang Yunge , Xu Gang
Published: July 2024

Safety is one of the goals of a smart city. To study storage tank explosion damage in a city’s chemical industrial parks, determine the position of control measures according to the situation, and realize the analysis of the measured utility, we proposed the area damage probability importance distribution. In this way, the prediction and prevention of risk in chemical industrial parks can be achieved intelligently. The concept of area damage probability importance distribution was given, and the utility analysis method of the control measures for storage tank explosion accidents was put forward. It is concluded that the area damage probability importance distribution represents the change degree of damage probability: that is, the damage degree of storage tank explosion in a chemical industrial park. The control measures for a storage tank explosion can be set up in varying positions, as the explosion damage is mainly caused by shock waves; the blast walls are selected as the measure set, and the calculation method for the area damage probability is modified. By comparing the calculated area damage probability distribution before and after, evaluation of the control measures’ effectiveness can be achieved. Finally, the flow chart of the algorithm is given. The example analysis shows that the calculation process and analysis results meet the design requirements of the algorithm. The effectiveness of the method, the distribution characteristics, and the significance and function of the importance distribution of damage probability are discussed. This provides an effective method for smart cities to predict and prevent the impact of an explosion at chemical industrial parks.
A Multiscale Simulation on Aluminum Ion Implantation-Induced Defects in 4H-SiC MOSFETs
Authors: Wang Yawen , Lan Haipeng , Shangguan Qiwei , Lv Yawei , Jiang Changzhong
Published: July 2024

Aluminum (Al) ion implantation is one of the most important technologies in SiC device manufacturing processes due to its ability to produce the p-type doping effect, which is essential to building p–n junctions and blocking high voltages. However, besides the doping effect, defects are also probably induced by the implantation. Here, the impacts of Al ion implantation-induced defects on 4H-SiC MOSFET channel transport behaviors are studied using a multiscale simulation flow, including the molecular dynamics (MD) simulation, density functional theory (DFT) calculation, and tight-binding (TB) model-based quantum transport simulation. The simulation results show that an Al ion can not only replace a Si lattice site to realize the p-doping effect, but it can also replace the C lattice site to induce mid-gap trap levels or become an interstitial to induce the n-doping effect. Moreover, the implantation tends to bring additional point defects to the 4H-SiC body region near the Al ions, which will lead to more complicated coupling effects between them, such as degrading the p-type doping effect by trapping free hole carriers and inducing new trap states at the 4H-SiC bandgap. The quantum transport simulations indicate that these coupling effects will impede local electron transports, compensating for the doping effect and increasing the leakage current of the 4H-SiC MOSFET. In this study, the complicated coupling effects between the implanted Al ions and the implantation-induced point defects are revealed, which provides new references for experiments to increase the accepter activation rate and restrain the defect effect in SiC devices.
Inner External DQN LoRa SF Allocation Scheme for Complex Environments
Authors: Pang Shengli , Kong Delin , Wang Xute , Pan Ruoyu , Wang Honggang , Ye Zhifan , Liu Di
Published: July 2024

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance.
An Analysis of Traditional Methods and Deep Learning Methods in SSVEP-Based BCI: A Survey
Authors: Wu Jiaxuan , Wang Jingjing
Published: July 2024

The brain–computer interface (BCI) is a direct communication channel between humans and machines that relies on the central nervous system. Neuroelectric signals are collected by placing electrodes, and after feature sampling and classification, they are converted into control signals to control external mechanical devices. BCIs based on steady-state visual evoked potential (SSVEP) have the advantages of high classification accuracy, fast information conduction rate, and relatively strong anti-interference ability, so they have been widely noticed and discussed. From k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) classification algorithms to the current deep learning classification algorithms based on neural networks, a wide variety of discussions and analyses have been conducted by numerous researchers. This article summarizes more than 60 SSVEP- and BCI-related articles published between 2015 and 2023, and provides an in-depth research and analysis of SSVEP-BCI. The survey in this article can save a lot of time for scholars in understanding the progress of SSVEP-BCI research and deep learning, and it is an important guide for designing and selecting SSVEP-BCI classification algorithms.
CFE-YOLOv8s: Improved YOLOv8s for Steel Surface Defect Detection
Authors: Yang Shuxin , Xie Yang , Wu Jianqing , Huang Weidong , Yan Hongsheng , Wang Jingyong , Wang Bi , Yu Xiangchun , Wu Qiang , Xie Fei
Published: July 2024

Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (CBiF) modules, Faster-C2f (FC) modules, and EMA-Faster-C2f (EFC) modules. Firstly, because of the potential information loss that convolutional neural networks (CNN) may encounter when dealing with miniature targets, the CBiF combines CNN with Transformer to optimize local and global features. Secondly, to address the increased computational complexity caused by the extensive use of convolutional layers, the FC uses the FasterNet block to reduce redundant computations and memory access. Lastly, the EMA is incorporated into the FC to design the EFC module and enhance feature fusion capability while ensuring the light weight of the model. CFE-YOLOv8s achieves mAP@0.5 values of 77.8% and 69.5% on the NEU-DET and GC10-DET datasets, respectively, representing enhancements of 3.1% and 2.8% over YOLOv8s, with reductions of 22% and 18% in model parameters and FLOPS. The CFE-YOLOv8s demonstrates superior overall performance and balance compared to other advanced models.
Vision-Based Algorithm for Precise Traffic Sign and Lane Line Matching in Multi-Lane Scenarios
Authors: Xia Kerui , Hu Jiqing , Wang Zhongnan , Wang Zijian , Huang Zhuo , Liang Zhongchao
Published: July 2024

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.
Adaptive Graph Neural Network with Incremental Learning Mechanism for Knowledge Graph Reasoning
Authors: Zhang Junhui , Zan Hongying , Wu Shuning , Zhang Kunli , Huo Jianwei
Published: July 2024

Knowledge graphs are extensively utilized in diverse fields such as search engines, recommendation systems, and dialogue systems, and knowledge graph reasoning plays an important role in the aforementioned domains. Graph neural networks demonstrate the capability to effectively capture and process the graph structure inherent in knowledge graphs, leveraging the relationships between nodes and edges to enable efficient reasoning. Current research on graph neural networks relies on predefined propagation paths. The models based on predefined propagation paths overlook the correlation between entities and query relations, limiting adaptability and scalability. In this paper, we propose an adaptive graph neural network with an incremental learning mechanism to search the adaptive propagation path in order to retain promising targets. In detail, the incremental learning mechanism is able to filter out unrelated entities in the propagation path by incorporating the node sampling technique to increase the learning efficiency of the model. In addition, the incremental learning mechanism can select semantically related entities, which promotes the capture of meaningful connections among different nodes in the knowledge graph. At the same time, we apply the parameter initialization module to accelerate the convergence and improve the robustness of the model. Experimental results on benchmark datasets demonstrate that the adaptive graph neural network with the incremental learning mechanism has excellent semantic awareness ability, which surpasses several excellent knowledge graph reasoning models.
Suitable Site Selection of Public Charging Stations: A Fuzzy TOPSIS MCDA Framework on Capacity Substation Assessment
Authors: Chumbi Wilson Enrique , Martínez-Minga Roger , Zambrano-Asanza Sergio , Leite Jonatas B. , Franco John Fredy
Published: July 2024

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.
Numerical and Experimental Determination of Selected Performance Indicators of the Liquid Flat-Plate Solar Collector under Outdoor Conditions
Authors: Zima Wiesław , Mika Łukasz , Sztekler Karol
Published: July 2024

The paper proposes applying an in-house mathematical model of a liquid flat-plate solar collector to calculate the collector time constant. The described model, proposed for the first time in an earlier study, is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all collector components. The formulated differential equations are solved iteratively using an implicit difference scheme. To obtain a stable numerical solution, it is necessary to use appropriate steps of time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model’s practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. By comparing, on an ongoing basis, the measured and calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The simplicity of the model and the low computational demands enable such comparisons in an online mode.
Optimal Power Model Predictive Control for Electrochemical Energy Storage Power Station
Authors: Shao Chong , Tu Chao , Yu Jiao , Wang Mingdian , Wang Cheng , Dong Haiying
Published: July 2024

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge (SOC) constraint of the battery, partition the SOC into different states. Using SOC as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery SOC to prevent SOC from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station.
Preliminary Study on Recontrol Evaluation of a Magnetic Bearing Rotor Falling on Touch-Down Bearings
Authors: Zhang Kai , Xu Yang
Published: July 2024

The dynamic control of an active magnetic bearing (AMB) rotor after the rotor falls to its touch-down bearings has always been a difficult problem for applications such as flywheel energy storage. The rotor drop process has obvious nonlinear dynamic characteristics. This paper first discusses the structure of AMBs and the basic principles of their control. Starting from the electromagnetic forces that electromagnets can provide, the problem is simplified to the influence of an electromagnetic force with constant damping characteristics on the dynamic characteristics of a dropped rotor. A dynamic model of an AMB rotor with touch-down bearings was built and the contact force model between the rotor and the touch-down bearings was determined. A constant damping electromagnetic force was applied in two ways to verify the dynamic control feasibility of a dropped rotor through magnetic bearings. The simulation results show that the dropped rotor recovery control is feasible by applying a reasonable electromagnetic force.
Thermochemical Valorization of Plastic Waste Containing Low Density Polyethylene, Polyvinyl Chloride and Polyvinyl Butyral into Thermal and Fuel Energy
Authors: Jabłońska Beata , Poznańska Gabriela , Jabłoński Paweł , Zwolińska Joanna
Published: July 2024

Pyrolysis is a promising technology for transforming waste plastics (WPs) into high-value products. In the near future it will play a key role in the circular economy, as a sustainable and environmentally friendly method of managing this waste. Although the literature reports on the pyrolysis of plastics, it is focused on pure polymers. On the other hand, the state-of-the-art knowledge about the pyrolysis of mixed and contaminated WPs is still scarce. Industrial waste processing usually uses polymer mixtures containing various impurities that influence the pyrolysis process during chemical WPs recycling. In the paper the pyrolysis of three types of WPs: low density polyethylene (LDPE), polyvinyl chloride (PVC) and polyvinyl butyral (PVB) from repeated mechanical recycling of plastics, as well as their binary and ternary mixtures, is considered. The influence of particular components on the pyrolysis process is analyzed. The aim is to determine synergistic behavior of the mixtures during the pyrolysis process, which is important for increasing the efficiency and quality of the obtained bioproducts. Methods such as thermogravimetric (TG/DTG) analysis coupled with Fourier transform infrared spectroscopy (FTIR) and mass spectroscopy (MS) are used. The variations in the initial and final temperature of pyrolysis, mass loss and mass loss rate are determined. The content of PVC significantly lowers the initial temperature and mass loss and increases the final temperature. The pyrolysis of the considered mixtures shows a noticeable synergism—in the initial stage of pyrolysis up to a temperature around 450 °C, the mass loss is accelerated compared to what is predicted by simple superposition. The inhomogeneity of the mixtures as well as the waste origin causes a significant variation in the activation energy. Three main conclusions are obtained: (i) if the waste does not contain PVC, the pyrolysis is nearly complete at a temperature around 500 °C at a heating rate of 10 °C/min, whereas PVC is not fully processed even at 995 °C; (ii) the synergistic effects affect significantly the pyrolysis process by accelerating some steps and lowering the activation energy; and (iii) the presence of PVC noticeably lowers the temperature of the first stage of PVB pyrolysis. The investigation results prove that chemical recycling of mixed LDPE, PVC and PVB waste can be an effective method of plastic waste management.