bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermochemical Research on Furfurylamine and 5-Methylfurfurylamine: Experimental and Computational Insights
Authors: Amaral Luísa M. P. F. , Almeida Ana R. R. P. , Ribeiro da Silva Manuel A. V.
Published: June 2024

The need to transition from fossil fuels to renewables arises from factors such as depletion, price fluctuations, and environmental considerations. Lignocellulosic biomass, being abundant, and quickly renewable, and not interfering with food supplies, offers a standout alternative for chemical production. This paper explores the energetic characteristics of two derivatives of furfural—a versatile chemical obtained from biomass with great potential for commercial sustainable chemical and fuel production. The standard (p° = 0.1 MPa) molar enthalpies of formation of the liquids furfurylamine and 5-methylfurfurylamine were derived from the standard molar energies of combustion, determined in oxygen and at T = 298.15 K, by static bomb combustion calorimetry. Their standard molar enthalpies of vaporization were also determined at the same temperature using high-temperature Calvet microcalorimetry. By combining these data, the gas-phase enthalpies of formation at T = 298.15 K were calculated as −(43.5 ± 1.4) kJ·mol−1 for furfurylamine, and −(81.2 ± 1.7) kJ·mol−1 for 5-methylfurfurylamine. Furthermore, a theoretical analysis using G3 level calculations was performed, comparing the calculated enthalpies of formation with the experimental values to validate both results. This method has been successfully applied to similar molecules. The discussion looks into substituent effects in terms of stability and compares them with similar compounds.
Hirsutonosema embarrassi n. gen. n. sp. (Phylum Microsporidia) in the Ovary of Mucket (Actinonaias ligamentina), Plain Pocketbook (Lampsilis cardium), and Fatmucket (Lampsilis siliquoidea) (Unionidae) from the Embarrass River, Wisconsin, USA
Authors: Knowles Susan , Leis Eric M. , Richard Jordan C. , Standish Isaac F. , Bojko Jamie , Weinzinger Jesse , Waller Diane L.
Published: June 2024

During an epidemiological survey following a mortality event of freshwater mussels in 2018 in the Embarrass River, Wisconsin, USA, we identified a novel microsporidian parasite in the ovaries of mucket (Actinonaias ligamentina), plain pocketbook (Lampsilis cardium), and fatmucket (Lampsilis siliquoidea) (Unionidae). Histopathology showed round-to-oval microsporidian spores in the cytoplasm of oocytes in 60% (3/5) of female mucket, 100% (4/4) of female plain pocketbook, and 50% (1/2) of female fatmucket. Using transmission electron microscopy, we found that mature spores were round-to-oval and measured 4.13 ± 0.64 µm (3.14–5.31) long by 2.88 ± 0.37 µm (2.36–3.68) wide. The spores had a thin electron-dense exospore with a spiky “hairy” coat, a thick electron lucent endospore, diplokaryotic nuclei, a polar vacuole, and 27–28 polar filaments arranged in 1–3 rows. Sequencing of the small subunit rRNA produced a 1356 bp sequence most similar to that of Pseudonosema cristatellae (92%), and phylogenetic analysis grouped it within the freshwater Neopereziida. Genetic, morphological, and ultrastructural characteristics did not closely match those of other Pseudonosema spp., and a new genus and species, Hirsutonosema embarrassi n. gen. n. sp., were designated. Additional studies could evaluate host susceptibility, distribution, seasonality, transmission, and lethal or sub-lethal effects of this parasite on freshwater mussels.
Parasitic Plants—Potential Vectors of Phytopathogens
Authors: Savov Stefan , Marinova Bianka , Teofanova Denitsa , Savov Martin , Odjakova Mariela , Zagorchev Lyuben
Published: June 2024

Parasitic plants represent a peculiar group of semi- or fully heterotrophic plants, possessing the ability to extract water, minerals, and organic compounds from other plants. All parasitic plants, either root or stem, hemi- or holoparasitic, establish a vascular connection with their host plants through a highly specialized organ called haustoria. Apart from being the organ responsible for nutrient extraction, the haustorial connection is also a highway for various macromolecules, including DNA, proteins, and, apparently, phytopathogens. At least some parasitic plants are considered significant agricultural pests, contributing to enormous yield losses worldwide. Their negative effect is mainly direct, by the exhaustion of host plant fitness and decreasing growth and seed/fruit formation. However, they may pose an additional threat to agriculture by promoting the trans-species dispersion of various pathogens. The current review aims to summarize the available information and to raise awareness of this less-explored problem. We further explore the suitability of certain phytopathogens to serve as specific and efficient methods of control of parasitic plants, as well as methods for control of the phytopathogens.
Mental Health Evaluation in Community Pharmacies—A Cross-Sectional Study
Authors: Condinho Mónica , Ramalhinho Isabel , Vaz-Velho Catarina , Sinogas Carlos
Published: June 2024

Portugal has a high prevalence of anxiety and depression, and community pharmacists are well-placed to identify mental health problems and monitor their treatment. This study aimed to screen undiagnosed people for symptoms of anxiety and depression and to monitor these conditions in diagnosed patients. We conducted an observational, cross-sectional study of a sample of community pharmacy users. Anxiety and depression symptoms were assessed using the Generalised Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Altogether, 591 participants were enrolled between September 2020 and July 2021, 74.9% of whom were female. Of the 477 undiagnosed participants who completed the GAD-7, 87 (18.2%) had moderate or severe anxiety symptoms. Of the 485 participants who completed the PHQ-9, 59 (12.1%) had moderate or severe symptoms of depression. Of the 94 patients diagnosed with anxiety, 37 (39.4%) reported moderate to severe symptoms. Similarly, of the 97 patients with depression, almost half (49.5%) reported moderate to severe symptoms. Anxiety levels were lower among men and among those who reported not taking any medication (p < 0.001). Moderate or severe symptoms of depression were more common among women (p < 0.001), participants with lower education levels (p < 0.005), participants who live alone (p < 0.007), and those taking medication for insomnia (p < 0.001), pain (p < 0.001), anxiety (p < 0.001), and/or depression (p < 0.001). Screening for anxiety and depression revealed that a significant proportion of undiagnosed participants had clinically relevant symptoms of anxiety and depression. However, among participants diagnosed with anxiety and depression, between 40% and 50% were uncontrolled, respectively. These data highlight pharmacists’ role in identifying customers at risk of anxiety and depression as well as the need for better monitoring of those already diagnosed.
Excitation Wavelength-Dependent Photochemistry
Authors: Maafi Mounir
Published: June 2024

The dependence of photochemistry on excitation wavelength is not a recently observed phenomenon; nonetheless, it has, surprisingly enough, been largely ignored in the field. The reasons for this situation are not fully understood but might be related to a provisional extension of Kasha’s rule to photochemistry, or perhaps to a difficulty to justify the kind of short time-scales implied in such photochemistry, that challenges the usually held view giving predominance to fast internal conversion and vibrational relaxation. Regardless of the reasons, it is still a matter of fact that a complete and satisfactory interpretation for experimentally proven wavelength-dependent photochemistry is not yet available and the community endeavor to build a holistic understanding and a comprehensive view of the phenomenon. The present review is a non-exhaustive overview of the published data in the field, reporting on some of the most prominent features, issues, and interpretations.
Physiological Characteristics and Transcriptomic Responses of Pinus yunnanensis Lateral Branching to Different Shading Environments
Authors: Zhou Chiyu , Gu Xuesha , Li Jiangfei , Su Xin , Chen Shi , Tang Junrong , Chen Lin , Cai Nianhui , Xu Yulan
Published: June 2024

Pinus yunnanensis is an important component of China’s economic development and forest ecosystems. The growth of P. yunnanensis seedlings experienced a slow growth phase, which led to a long seedling cultivation period. However, asexual reproduction can ensure the stable inheritance of the superior traits of the mother tree and also shorten the breeding cycle. The quantity and quality of branching significantly impact the cutting reproduction of P. yunnanensis, and a shaded environment affects lateral branching growth, development, and photosynthesis. Nonetheless, the physiological characteristics and the level of the transcriptome that underlie the growth of lateral branches of P. yunnanensis under shade conditions are still unclear. In our experiment, we subjected annual P. yunnanensis seedlings to varying shade intensities (0%, 25%, 50%, 75%) and studied the effects of shading on growth, physiological and biochemical changes, and gene expression in branching. Results from this study show that shading reduces biomass production by inhibiting the branching ability of P. yunnanensis seedlings. Due to the regulatory and protective roles of osmotically active substances against environmental stress, the contents of soluble sugars, soluble proteins, photosynthetic pigments, and enzyme activities exhibit varying responses to different shading treatments. Under shading treatment, the contents of phytohormones were altered. Additionally, genes associated with phytohormone signaling and photosynthetic pathways exhibited differential expression. This study established a theoretical foundation for shading regulation of P. yunnanensis lateral branch growth and provides scientific evidence for the management of cutting orchards.
Examining Chlorophyll Extraction Methods in Sesame Genotypes: Uncovering Leaf Coloration Effects and Anatomy Variations
Authors: Berhe Muez , You Jun , Dossa Komivi , Li Donghua , Zhou Rong , Zhang Yanxin , Wang Linhai
Published: June 2024

This study focuses on optimizing chlorophyll extraction techniques, in which leaf discs are cut from places on the leaf blade to enhance chlorophyll concentration in sesame (Sesamum indicum L.) leaves. Thirty sesame genotypes, categorized into light green (LG), middle green (MG), and deep green (DG) pigment groups based on leaf coloration, were selected from a larger pool of field-grown accessions. The investigation involved determining optimal Soil Plant Analysis Development (SPAD) value index measurements, quantifying pigment concentrations, exploring extraction solvents, and selecting suitable leaf disk positions. Significant variations in chlorophyll content were observed across genotypes, greenness categories, and leaf disk positions. The categorization of genotypes into DG, MG, and LG groups revealed a correlation between leaf appearance and chlorophyll content. The study highlighted a consistent relationship between carotenoids and chlorophyll, indicating their role in adaptation to warm environments. An examination of leaf disk positions revealed a significant chlorophyll gradient along the leaf blade, emphasizing the need for standardized protocols. Chlorophyll extraction experiments identified DMSO and 96% ethanol, particularly in those incubated for 10 min at 85 °C, as effective choices. This recommendation considers factors like cost-effectiveness, time efficiency, safety, and environmental regulations, ensuring consistent and simplified extraction processes. For higher chlorophyll extraction, focusing on leaf tips and the 75% localization along the sesame leaf blade is suggested, as this consistently yields increased chlorophyll content. Furthermore, our examination revealed significant anatomical variations in the internal structure of the mesophyll tissue leaves between deep green and light green sesame plants, primarily linked to chloroplast density and pigment-producing structures. Our findings, therefore, provide insightful knowledge of chlorophyll gradients and encourage the use of standardized protocols that enable researchers to refine their experimental designs for precise and comparable chlorophyll measurements. The recommended solvent choices ensure reliable outcomes in plant physiology, ecology, and environmental studies.
Physiological and Cellular Ultrastructural Responses of Isatis indigotica Fort. under Salt Stress
Authors: Wu Shuang , Jia Xiuwen , Tian Beijing , Zhang Feng , Zhao Jingying , Xie Xinjing , Shan Chenggang , Wang Huimei , Guo Xiaorui , Han Jinlong
Published: June 2024

This study aimed to analyze the effects of salt stress on the growth physiology and plant-cell ultrastructure of Isatis indigotica Fort. (I. indigotica) to evaluate its adaptability under salt stress. The effects of different concentrations of salt (NaCl; 0, 25, and 300 mmol·L−1) on the agronomic traits, activities of related enzymes, ion balance, and mesophyll-cell ultrastructure of I. indigotica were studied in a controlled pot experiment. Results showed that compared with those of the control group, the aerial-part fresh weight, underground fresh weight, tiller number, root length, root diameter, plant height, and leaf area of salt-stressed I. indigotica increased at 25 mmol·L−1 and then decreased at 300 mmol·L−1. The changes in levels of superoxide dismutase, peroxidase, ascorbate peroxidase, and catalase showed a similar trend, with significant differences compared with control group. Salt stress altered the ion balance of I. indigotica, resulting in a significant increase in Na+ content and a significant decrease in K+ content. The contents of Ca2+ and Mg2+ changed to varying degrees. The analysis of the microstructure of the root showed that under salt treatment, the epidermal cells of the root significantly thickened and the diameter of the xylem decreased. The results of ultrastructural analysis of mesophylls showed that salt stress can cause cell-membrane contraction, cell-gap enlargement, disorder in the structures of chloroplasts and mitochondria, and an increase in the number of osmiophilic particles. These changes were aggravated by the increase in NaCl concentration. This study reveals the response of I. indigotica to salt stress and provides a basis for further study on the salt-tolerance mechanism of I. indigotica.
Advanced Thermogravimetric Analyses of Stem Wood and Straw Devolatilization: Torrefaction through Combustion
Authors: Wagner David R.
Published: June 2024

Process design critically depends on the characterization of fuels and their kinetics under process conditions. This study steps beyond the fundamental methods of thermogravimetry to modulated (MTGA) and Hi-Res™ (high resolution) techniques to (1) add characterization detail and (2) increase the utility of thermal analysis data. Modulated TGA methods overlay sinusoidal functions on the heating rates to determine activation energy as a function of temperature with time. Under devolatilization conditions, Hi-Res™ TGA maintains a constant mass loss with time and temperature. These two methods, run independently or overlaid, offer additional analysis in which multiple samples at different heating rates are run to different final temperatures. Advanced methods allow researchers to use fewer samples by conducting fewer runs, targeting practical experimental designs, and quantifying errors easier. The parameters of the studies included here vary the heating rate at 10, 30, and 50 °C/min; vary gas-phase oxygen for pyrolysis or combustion conditions; and particle size ranges of 100–125 µm, 400–425 µm, and 600–630 µm. The two biomass fuels used in the studies are pinewood from Northern Sweden and wheat straw. The influence of torrefaction is also included at temperatures of 220, 250, and 280 °C. Apparent activation energy results align with the previous MTGA data in that combustion conditions yield higher values than pyrolysis conditions—200–250 kJ/mol and 175–225 kJ/mol for pine and wheat combustion, respectively, depending on pre-treatment. Results show the dependence of these parameters upon one another from a traditional thermal analysis approach, e.g., the Ozawa-Flynn-Wall method, as well as MTGA and Hi-Res™ thermogravimetric investigations to show future directions for thermal analysis techniques.
TS-LCD: Two-Stage Loop-Closure Detection Based on Heterogeneous Data Fusion
Authors: Jiang Fangdi , Wang Wanqiu , You Hongru , Jiang Shuhang , Meng Xin , Kim Jonghyuk , Wang Shifeng
Published: June 2024

Loop-closure detection plays a pivotal role in simultaneous localization and mapping (SLAM). It serves to minimize cumulative errors and ensure the overall consistency of the generated map. This paper introduces a multi-sensor fusion-based loop-closure detection scheme (TS-LCD) to address the challenges of low robustness and inaccurate loop-closure detection encountered in single-sensor systems under varying lighting conditions and structurally similar environments. Our method comprises two innovative components: a timestamp synchronization method based on data processing and interpolation, and a two-order loop-closure detection scheme based on the fusion validation of visual and laser loops. Experimental results on the publicly available KITTI dataset reveal that the proposed method outperforms baseline algorithms, achieving a significant average reduction of 2.76% in the trajectory error (TE) and a notable decrease of 1.381 m per 100 m in the relative error (RE). Furthermore, it boosts loop-closure detection efficiency by an average of 15.5%, thereby effectively enhancing the positioning accuracy of odometry.
Signal Denoising of Traffic Speed Deflectometer Measurement Based on Partial Swarm Optimization–Variational Mode Decomposition Method
Authors: Wu Chaoyang , Duan Yiyuan , Wang Hao
Published: June 2024

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization–variational mode decomposition (PSO–VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO–VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising.
Video Abnormal Behavior Recognition and Trajectory Prediction Based on Lightweight Skeleton Feature Extraction
Authors: Wang Ling , Ding Cong , Zhang Yifan , Zhou Tie Hua , Ding Wei , Ryu Keun Ho , Nam Kwang Woo
Published: June 2024

Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al., which could obviously improve recognition speed based on lightweight skeleton node calculation, and improve recognition accuracy based on occluded skeleton node prediction analysis in order to effectively solve the behavior occlusion problem. The experiments show that our proposed All-MSFCF model has a video action recognition average accuracy rate of 92.7% for eight kinds of abnormal behavior recognition. Although our proposed lightweight L-MSFCF model has an 87.3% average accuracy rate, its average recognition speed is 62.7% higher than the full-skeleton recognition model, which is more suitable for solving real-time tracing problems. Moreover, our proposed Trajectory Prediction Tracking (TPT) model could real-time predict the moving positions based on the dynamically selected core skeleton node calculation, especially for the short-term prediction within 15 frames and 30 frames that have lower average loss errors.
Automated Sensor Node Malicious Activity Detection with Explainability Analysis
Authors: Zubair Md , Janicke Helge , Mohsin Ahmad , Maglaras Leandros , Sarker Iqbal H.
Published: June 2024

Cybersecurity has become a major concern in the modern world due to our heavy reliance on cyber systems. Advanced automated systems utilize many sensors for intelligent decision-making, and any malicious activity of these sensors could potentially lead to a system-wide collapse. To ensure safety and security, it is essential to have a reliable system that can automatically detect and prevent any malicious activity, and modern detection systems are created based on machine learning (ML) models. Most often, the dataset generated from the sensor node for detecting malicious activity is highly imbalanced because the Malicious class is significantly fewer than the Non-Malicious class. To address these issues, we proposed a hybrid data balancing technique in combination with a Cluster-based Under Sampling and Synthetic Minority Oversampling Technique (SMOTE). We have also proposed an ensemble machine learning model that outperforms other standard ML models, achieving 99.7% accuracy. Additionally, we have identified the critical features that pose security risks to the sensor nodes with extensive explainability analysis of our proposed machine learning model. In brief, we have explored a hybrid data balancing method, developed a robust ensemble machine learning model for detecting malicious sensor nodes, and conducted a thorough analysis of the model’s explainability.
Multimodal Sensing for Depression Risk Detection: Integrating Audio, Video, and Text Data
Authors: Zhang Zhenwei , Zhang Shengming , Ni Dong , Wei Zhaoguo , Yang Kongjun , Jin Shan , Huang Gan , Liang Zhen , Zhang Li , Li Linling , Ding Huijun , Zhang Zhiguo , Wang Jianhong
Published: June 2024

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.
Importing Diffusion and Re-Designed Backward Process for Image De-Raining
Authors: Lin Jhe-Wei , Lee Cheng-Hsuan , Su Tang-Wei , Chang Che-Cheng
Published: June 2024

In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image processing. The effective elimination of unwanted noise plays a crucial role in improving image quality. To meet this challenge, many noise removal methods have been proposed, among which the diffusion model has become one of the focuses of many researchers. In order to make the restored image closer to the real image and retain more features of the image, this paper proposes a DIR-SDE method with reference to the diffusion models of IR-SDE and IDM, which improve the feature retention of the image in the de-raining process, and then improve the realism of the image for the image de-raining task. In this study, IR-SDE was used as the base structure of the diffusion model, IR-SDE was improved, and DINO-ViT was combined to enhance the image features. During the diffusion process, the image features were extracted using DINO-ViT, and these features were fused with the original images to enhance the learning effect of the model. The model was also trained and validated with the Rain100H dataset. Compared with the IR-SDE method, it improved 0.003 in the SSIM, 0.003 in the LPIPS, and 1.23 in the FID. The experimental results show that the diffusion model proposed in this study can effectively improve the image restoration performance.
Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions
Authors: Pai Yung-Cheng , Kiang Jean-Fu
Published: June 2024

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.
Toward Fully Automated Inspection of Critical Assets Supported by Autonomous Mobile Robots, Vision Sensors, and Artificial Intelligence
Authors: Sanchez-Cubillo Javier , Del Ser Javier , Martin José Luis
Published: June 2024

Robotic inspection is advancing in performance capabilities and is now being considered for industrial applications beyond laboratory experiments. As industries increasingly rely on complex machinery, pipelines, and structures, the need for precise and reliable inspection methods becomes paramount to ensure operational integrity and mitigate risks. AI-assisted autonomous mobile robots offer the potential to automate inspection processes, reduce human error, and provide real-time insights into asset conditions. A primary concern is the necessity to validate the performance of these systems under real-world conditions. While laboratory tests and simulations can provide valuable insights, the true efficacy of AI algorithms and robotic platforms can only be determined through rigorous field testing and validation. This paper aligns with this need by evaluating the performance of one-stage models for object detection in tasks that support and enhance the perception capabilities of autonomous mobile robots. The evaluation addresses both the execution of assigned tasks and the robot’s own navigation. Our benchmark of classification models for robotic inspection considers three real-world transportation and logistics use cases, as well as several generations of the well-known YOLO architecture. The performance results from field tests using real robotic devices equipped with such object detection capabilities are promising, and expose the enormous potential and actionability of autonomous robotic systems for fully automated inspection and maintenance in open-world settings.
A Method for Predicting Inertial Navigation System Positioning Errors Using a Back Propagation Neural Network Based on a Particle Swarm Optimization Algorithm
Authors: Wang Yabo , Jiao Ruihan , Wei Tingxiao , Guo Zhaoxing , Ben Yueyang
Published: June 2024

In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization–Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial.
Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection
Authors: Chen Yi-Ting , Chang Lena , Wang Jung-Hua
Published: June 2024

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network’s competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.
Toward Resilient Urban Design: Pedestrians as an Important Element of City Design
Authors: Ros-McDonnell Diego , de-la-Fuente-Aragón María Victoria , Ros-McDonnell Lorenzo , Cardós Manuel
Published: June 2024

Including pedestrians in urban design is vital so that they prefer walking over motorized transport. Motorized mobility, which mainly involves automobiles, has conditioned urban design, pedestrian movement, sustainability, and urban resilience. Studies of walkability in cities have recently been conducted, and they can be classified into those that use a qualitative observer-perception-based approach and a second approach based on the measurement of observable variables. This work aims to develop a tool that allows for walkability in public spaces to be evaluated based on directly observing reality while considering both approaches: those related to perceiving the environment and physical reality. Walking is one of the main ways of moving around and achieving sustainable urban mobility. The conditions of public spaces are fundamental for people in their preference for moving around by walking. A literature review related to walkability revealed that previous works included a variety of viewpoints, scales, tools, variables, and approaches. This study included a developed tool by modeling a definition of working areas, processing walkability data, and determining the Walkability Index (WI). The reliability of the data observed from the walkability variables was verified, and the tool’s usefulness for urban planning was demonstrated. Pedestrian-centered urban design promotes cities’ sustainable mobility, sustainability, and resilience. People’s conduct reveals how the urban environment is perceived. The developed WI evaluates the existing reality and allows for its evolution to be monitored.
Establishment and Characterization of a New Cell Line from Enzootic Nasal Adenocarcinoma in Goats: ENA-1
Authors: Li Lingxu , Tan Weiye , Wang Zhen , Guo Wenqing , Yang Deji , Yao Dawei
Published: June 2024

Enzootic nasal adenocarcinoma (ENA) is a contagious tumor disease of goats and sheep, which is caused by enzootic nasal tumor virus (ENTV). To better understand the pathogenesis of ENA, this study aimed to establish a goat ENA cell line (ENA-1). The cells have been characterized with regard to morphology, growth rate, ultrastructural features, chromosome number, expression of CK7 and CK18, tumorigenicity, species, and mycoplasma contamination. ENA-1 had an epithelioid cell morphology with an unstable chromosome number under a light microscope. Under an electron microscope, the cell nuclear heterogeneity was not obvious, and there were more intermediate filaments and a small number of immature retrovirus-like particles in the cytoplasm. ENA-1 had strong proliferative potential, and the cell multiplication time was about 36 h, which could make BALB/c nude mice develop tumors. CK7 and CK18 were expressed in the cytoplasm of primary goat tumors, in transplanted tumors from nude mice, and un ENA-1 cells with the same intensity. PCR revealed that ENA-1 continuously carried ENTV-2 up to the 17th generation with no germline contamination or mycoplasma contamination. In conclusion, using a serum-containing culture system, ENA-1 cells were successfully isolated, cultured, and purified from goat tumor tissues. The isolated ENA-1 cells retained robust proliferation potential and maintained their phenotype, indicating the potential application of the ENA-1 cell line as an in vitro model of ENA.
Joint Estimation of Driving State and Road Surface Adhesion Coefficient of a Four-Wheel Independent and Steering-Drive Electric Vehicle
Authors: Chen Zhixin , Li Gang , Zhang Zhihua , Fan Ruolan
Published: June 2024

Vehicle running state parameters and road surface state are crucial to the stability of four-wheel independent drive and steering electric vehicle control. Therefore, this study explores the estimation of vehicle driving state parameters and road surface adhesion coefficients using a combination of federal Kalman filtering and an intelligent bionic antlion optimization algorithm. Firstly, according to the research purpose of the paper and the focus on the accuracy of the establishment of the three degrees of freedom dynamics model, fully considering the road conditions, the paper adopts the Dugoff tire model and finally completes the establishment of the vehicle state estimation model. Secondly, the drive state estimation algorithm is developed utilizing the principles of federal Kalman filtering and volume Kalman filtering. At the same time, robust estimation theory is introduced into the sub-filter, and the antlion optimization module is designed at the lower layer of the main filter to enhance the accuracy of estimates. It is easy to see that the design of the Antlion federal Kalman travel state estimation algorithm has noticeably enhanced accuracy and traceability, according to the result. Thirdly, a joint estimation algorithm of state estimation and road surface adhesion coefficient has been devised to enhance the stability and precision of the estimation process. Finally, the results showed that the joint estimation algorithm has high accuracy in estimating vehicle driving state parameters such as the center of mass lateral deflection angle and road surface adhesion coefficient by simulation.
Rational use of inconvenient lands in the system of land management and forest restoration
Authors:   Head of Land Management and Landscape Design Department, Voronezh State Agrarian University named after Emperor Peter I, Voronezh, Nedikova E.V. , Pimenov V.V. ,   Land Management Department, State University of Land Use Planning, Moscow, Krasnyanskaya E.V. ,   Land Management Department, State University of Land Use Planning, Moscow
Published: June 2024

Nowadays, much attention has been paid in the Russian Federation to restoration of abandoned agricultural land, some of which are inconvenient. This article is exactly about the rational use of such lands in the system of land management and reforestation. Special attention is paid to the afforestation methods of these areas. The article also shows the distribution areas of afforestation sites on lands subject to various degradation, contains the logical scheme of the sequence and design of surveying activities for the creation of agroforestry and agrophytomeliorative plantations in working projects, and considers the parameters of the formation of forest structures that affect the effectiveness of land management measures.
Herramientas para detectar el Plagio a la Inteligencia Artificial: ¿cuán útiles son?
Authors: Díaz Arce Dariel
Published: June 2024

Las inteligencias artificiales (IA) generativas se abren paso cada vez más en el ámbito educativo, aunque no siempre utilizadas de forma honesta. La preocupación por el uso de estas aplicaciones para crear textos académicos también crece entre docentes de los diferentes niveles educativos y también en el ámbito de las publicaciones científicas. Poder detectar estos trabajos creados por la IA constituye por tanto una prioridad a la que se dio paso en esta investigación. Para ello, se diseñó una investigación cuasiexperimental de evaluación del desempeño diagnóstico de herramientas digitales que se promueven como “antiplagio” para las IA: Copyleaks, AI Text Classifier, Crossplag, Content at scale, Hive moderation. Se conformó un grupo control con trabajos de estudiantes de bachillerato con una antigüedad de 7 a 8 años y otro experimental con documentos generados por IA. Se comparó el rendimiento de las diferentes herramientas mediante los indicadores de pruebas diagnósticas: sensibilidad, especificad, valores predictivos y el índice de validez. Los resultados indican que Copyleaks posee una alta sensibilidad, pero baja especificad, mientras que en las demás ocurre lo contrario. Este trabajo refuerza la necesidad de desarrollar más herramientas o estrategias para detectar este problema entre los estudiantes.PALABRAS CLAVE: plagio académico; ciberplagio; inteligencia artificial; herramientas antiplagio; educación.ABSTRACT The use of generative artificial intelligence (AI) is increasingly common in the educational field, although not always used honestly. This is why concern about the use of these applications to create academic texts is also growing among teachers at different educational levels, which also extends to the field of scientific publications. Detecting the jobs created by AI is therefore a priority that was given way in this research. A quasi-experimental investigation was designed to evaluate the diagnostic performance of digital tools that are promoted as “anti-plagiarism” for AI: Copyleaks, AI Text Classifier, Crossplag, Content at scale, Hive moderation. A control group was organized with works by high school students aged 7 to 8 years and another experimental group was organized with documents generated by AI. The performance of the anti-plagiarism tools was compared using the diagnostic test indicators: sensitivity, specificity, predictive values and the validity index. The results show that Copyleaks has high sensitivity, but low specificity, while the opposite is true for the others. This work reinforces the need to develop more tools or strategies to detect this problem among students.
KEYWORDS: academic plagiarism; cyberplagiarism; artificial intelligence; anti plagiarism tools; education.
Impacto de la Inteligencia Artificial en el aprendizaje de los nuevos estudiantes de la Universidad Estatal Amazónica
Authors: Gutiérrez De León Eberto Tuniesky , Morell Pérez Leobel , Gutiérrez Morales Eberto Pablo , Hernández Ramos Hirám
Published: June 2024

Este estudio evalúa el impacto de la Inteligencia Artificial (IA) en el aprendizaje de los estudiantes de la Universidad Estatal Amazónica. Centrándose en las percepciones de nuevos ingresos, se analiza cómo la IA afecta el rendimiento académico. A través de una encuesta cuantitativa y un diseño correlacional descriptivo, se examinan las actitudes de los estudiantes y su conocimiento previo de las tecnologías de IA. Los resultados indican una visión positiva de la IA, con un alfa de Cronbach de 0.952, mostrando una alta fiabilidad en las respuestas. Se observa una tendencia positiva hacia la IA, con una correlación significativa entre la percepción de su impacto en la educación y la preparación para su uso profesional. Además, se discute el efecto diferencial de la IA en distintos contextos educativos, subrayando la necesidad de estrategias educativas adaptadas a las necesidades de los estudiantes.PALABRAS CLAVE: Percepción estudiantil; Rendimiento académico; Tecnologías emergentes, Educación superior. ABSTRACT This study evaluates the impact of Artificial Intelligence (AI) on student learning at Amazon State University. Focusing on the beliefs of new entrants, it analyzes how AI affects academic performance. Through a quantitative survey and descriptive correlational design, students' attitudes and prior knowledge of AI technologies are examined. The results show a positive view of AI, with a Cronbach's alpha of 0.952, showing high reliability in the responses. A positive trend towards AI is seen, with a significant correlation between the belief of its impact on education and the readiness for its professional use. In addition, the differential effect of AI in different educational contexts is discussed, highlighting the need for educational strategies tailored to the needs of students. KEYWORDS: Student belief; Academic performance; Emerging technologies; Higher education.
Estrategia didáctica para desarrollar el aprendizaje significativo de las Ciencias Naturales en los estudiantes de la Educación Básica Superior en la Unidad Unidad Educativa Atenas
Authors: Cedeño Molina Antonio Paolo , Cevallos Sánchez Kenia Kerine , Marcillo Barcia Yanina del Rocío , Solórzano Mendoza Dolores Teresa
Published: June 2024

El presente trabajo de investigación tiene como objetivo realizar una propuesta de estrategia didáctica con el uso del Moodle para la enseñanza aprendizaje de la asignatura de Ciencias Naturales en la unidad educativa Atenas. Para ello, se utilizó una metodología basada en proyecto factible con un enfoque mixto con un alcance descriptivo, además se aplicó como técnicas de recolección de datos la entrevista, la encuesta y la revisión documental. Para esta investigación se seleccionaron como muestra a 35 alumnos de la básica superior y a 3 docentes que imparten la asignatura de Ciencias Naturales dentro de la institución. Entre los resultados más relevantes se pudo percibir que los estudiantes y docentes mantiene un alto grado de interés de incluir la tecnología en la praxis docente de la asignatura de Ciencias Naturales, esto indica que la incorporación de la plataforma Moodle como repositorio digital de la asignatura, puede favorecer el proceso de enseñanza aprendizaje por sus características interactiva que lo vuelve llamativo para el estudiante, mejorando el rendimiento académico de los alumnos de la básica superior de la unidad educativa Atenas, por lo tanto, se recomiendo realizar una capacitación docente acerca del uso adecuado de la plataforma Moodle para generar un impacto en el mejoramiento del rendimiento académico.
PALABRAS CLAVE: estrategia didáctica; Ciencias Naturales; Moodle.
ABSTRACT
The objective of this research work is to make a proposal for a didactic strategy with the use of Moodle for the teaching-learning of the subject of natural sciences in the Atenas educational unit. For this, a methodology based on a feasible project was used with a mixed approach with a descriptive scope, in addition, the interview, survey and documentary review were applied as data collection techniques. For this research, 35 high school students and 3 teachers who teach the subject of natural sciences within the institution were selected as a sample. Among the most relevant results, it was possible to perceive that students and teachers maintain a high degree of interest in including technology in the teaching praxis of the natural sciences subject, this indicates that the incorporation of the Moodle platform as a digital repository of the subject, It can favor the teaching-learning process due to its interactive characteristics that make it attractive to the student, improving the academic performance of the students of the upper basic of the Atenas educational unit, therefore, it is recommended to carry out teacher training on the appropriate use of the Moodle platform to generate an impact on improving academic performance.
KEYWORDS: teaching strategy; natural sciences; Moodle.