bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterisation and Dynamics of an Emerging Seagrass Meadow
Authors: Dolbeth Marina , Costa Dimítri de Araújo , Meyer Manuel , Gonçalves José Alberto , Bio Ana
Published: August 2023

Seagrasses are habitat-forming species that support biodiversity and a wide range of associated ecosystem services, from blue carbon capture to providing nursery areas for a variety of organisms. Their decline has been documented worldwide and is attributed to human impacts ranging from habitat loss and eutrophication to the effects of climate change. However, recent recovery trends have also been documented due to reductions in stressors, passive and active restoration, and even changes in environmental conditions owing to local management. In this study, we document for the first time the occurrence of Zostera noltei in the downstream area of the River Minho Estuary. This occurrence was unexpected given the hydrological conditions of the estuary, characterised by dredging and siltation. We reconstructed the occurrence and historical distribution of seagrass beds, and showed that they have existed in the region for more than a decade. The current distribution area was mapped using high-resolution multispectral remote sensing techniques, and in situ photoquadrats to complement the remote sensing information with an evaluation of the seagrass cover. A current seagrass area of 0.81 ha was found with an average cover of 70%. However, the Minho Estuary continues to be strongly affected by sediment deposition, which may affect the seagrass population in the long term. Continued surveys are recommended to confirm the long-term trend of colonisation of this important habitat, which ultimately provides so many benefits to coastal ecosystems and humankind.
Spatiotemporal Variation and Factors Influencing Water Yield Services in the Hengduan Mountains, China
Authors: Shao Qiufang , Han Longbin , Lv Lingfeng , Shao Huaiyong , Qi Jiaguo
Published: August 2023

Conducting a quantitative assessment of water yield in mountainous areas is crucial for the management, development, and sustainable utilization of water resources. The Hengduan Mountains Region (HDMR) is a significant water-supporting area characterized by complex topography and climate changes. To analyze the spatial and temporal variations of water yield in the HDMR from 2001 to 2020, we employed the InVEST model and examined the influencing factors in conjunction with the elevation gradient. Our results indicate that: (1) The water yield in the Hengduan Mountains decreases from southeast to northwest, with the southwestern and eastern regions having high water yield values, and the high-altitude areas in the northwestern part having low water yield values. (2) The water yield in the Hengduan Mountains exhibits a decreasing trend followed by an increasing trend from 2001 to 2020, with the lowest level in 2011 and higher levels in 2004, 2018, and 2020. (3) Pixel-based trend analysis demonstrates a decreasing trend in water yield in the central and western parts of the study area, while the eastern part shows an increasing trend. (4) The climatic components, particularly precipitation, predominantly influence the spatial and temporal variations of water yield in the Transverse Mountain region. In most areas, evapotranspiration and land surface temperature have a negative impact on water yield. (5) Water yield tends to decrease and then increase on the altitudinal gradient, with precipitation and actual evapotranspiration being the factors directly affecting water yield, and land surface temperature and the proportion of forested areas having a significant indirect effect on water yield. Our study provides a scientific basis for water resources management and sustainable development in the Hengduan Mountains.
Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR
Authors: Chen Chao , Zhou Lv , Li Xuejian , Zhao Yinyin , Yu Jiacong , Lv Lujin , Du Huaqiang
Published: August 2023

Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned aerial vehicle LiDAR (UAV-LiDAR) and backpack-LiDAR to acquire point cloud data of Metasequoia plantation forests from different perspectives. Then the parameters, such as diameter at breast height and tree height, were extracted based on the point cloud data, while the accuracy was verified using ground-truth data. Finally, a single-tree-level thinning tool was developed to optimize the spatial structure of the stand based on multi-objective planning and the Monte Carlo algorithm. The results of the study showed that the accuracy of LiDAR-based extraction was (R2 = 0.96, RMSE = 3.09 cm) for diameter at breast height, and the accuracy of R2 and RMSE for tree height extraction were 0.85 and 0.92 m, respectively. Thinning improved stand objective function value Q by 25.40%, with the most significant improvement in competition index CI and openness K of 17.65% and 22.22%, respectively, compared to the pre-optimization period. The direct effects of each spatial structure parameter on the objective function values were ranked as follows: openness K (1.18) > aggregation index R (0.67) > competition index CI (0.42) > diameter at breast height size ratio U (0.06). Additionally, the indirect effects were ranked as follows: aggregation index R (0.86) > diameter at breast height size ratio U (0.48) > competition index CI (0.33). The study realized the optimization of stand spatial structure based on double LiDAR data, providing a new reference for forest management and structure optimization.
Temporal and Spatial Variations in Carbon Flux and Their Influencing Mechanisms on the Middle Tien Shan Region Grassland Ecosystem, China
Authors: Zhang Kun , Wang Yu , Mamtimin Ali , Liu Yongqiang , Gao Jiacheng , Aihaiti Ailiyaer , Wen Cong , Song Meiqi , Yang Fan , Zhou Chenglong , Huo Wen
Published: August 2023

Grassland ecosystems are an important component of global terrestrial ecosystems and play a crucial role in the global carbon cycle. Therefore, it is important to study the carbon dioxide (CO2) process in the Middle Tien Shan grassland ecosystem, which can be regarded as a typical representative of the mountain grasslands in Xinjiang. Eddy covariance (EC) and the global carbon fluxes dataset (GCFD) were utilized to continuously monitor the Middle Tien Shan grassland ecosystem in Xinjiang throughout the 2018 growing season. The findings revealed notable daily and monthly fluctuations in net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco). On a daily basis, there was net absorption of CO2 during the day and net emission during the night. The grassland acted as a carbon sink from 6:00 to 18:00 and as a carbon source for the remaining hours of the day. On a monthly scale, June and July served as carbon sinks, whereas the other months acted as carbon sources. The accumulated NEE, GPP, and Reco during the growing season were −329.49 g C m−2, 779.04 g C m−2, and 449.55 g C m−2, respectively. On the half-hourly and daily scales, soil temperature (Ts) was the main contributor to CO2 fluxes and had the greatest influence on the variations in CO2 fluxes. Additionally, air temperature (Ta) showed a strong correlation with CO2 fluxes. The grassland ecosystems exhibited the strongest CO2 uptake, reaching its peak at soil temperatures of 25 °C. Moreover, as the air temperatures rose above 15 °C, there was a gradual decrease in NEE, while CO2 uptake increased. The applicability of GCFD data is good in the grassland ecosystem of the Middle Tien Shan Mountains, with correlations of 0.59, 0.81, and 0.73 for NEE, GPP, and Reco, respectively, compared to field observations. In terms of remote sensing spatial distribution, the Middle Tien Shan grassland ecosystem exhibits a carbon sink phenomenon.
Investigating the Direct and Spillover Effects of Urbanization on Energy-Related Carbon Dioxide Emissions in China Using Nighttime Light Data
Authors: Sun Li , Mao Xianglai , Feng Lan , Zhang Ming , Gui Xuan , Wu Xiaojun
Published: August 2023

Cities are the main emission sources of the CO2 produced by energy use around the globe and have a great impact on the variation of climate. Although the implications of urbanization and socioeconomic elements for carbon emission have been extensively explored, previous studies have mostly focused on developed cities, and there is a lack of research into naturally related elements due to the limited data. At present, remote sensing data provide favorable conditions for the study of large-scale and long-time series. Also, the spillover mechanism of urbanization effects on the discharge of carbon has not been fully studied. Therefore, it is necessary to distinguish the types of influence that various urbanization factors have on emissions of CO2. Firstly, this study quantifies the urban CO2 emissions in China by utilizing nighttime lighting images. Then, the spatio-temporal variations and spatial dependence modes of CO2 emissions are explored for 284 cities in China from 2000–2018. Finally, the study further ascertains that multi-dimensional urbanization, socio-economic and climate variables affect the discharge of carbon using spatial regression models. The results indicate that CO2 emissions have a remarkable positive spatial autocorrelation. Urbanization significantly increases CO2 emissions, of which the land urbanization contribution towards CO2 emissions is the most important in terms of spillover effects. Specifically, the data on urbanization’s direct effects reveal that CO2 emissions will increase 0.066%when the urbanization level of a city rises 1%, while the spillover effect indicates that an 0.492% emissions increase is associated with a 1% rise of bordering cities’ average urbanization level. As for the socio-economic factors, population density suppresses CO2 emissions, while technological levels boost CO2 emissions. The natural control factors effect a remarkable impact on CO2 emissions by adjusting energy consumption. This study can provide evidence for regional joint prevention in urban energy conservation, emission reduction, and climate change mitigation.
Spatial-Temporal Semantic Perception Network for Remote Sensing Image Semantic Change Detection
Authors: He You , Zhang Hanchao , Ning Xiaogang , Zhang Ruiqian , Chang Dong , Hao Minghui
Published: August 2023

Semantic change detection (SCD) is a challenging task in remote sensing, which aims to locate and identify changes between the bi-temporal images, providing detailed “from-to” change information. This information is valuable for various remote sensing applications. Recent studies have shown that multi-task networks, with dual segmentation branches and single change branch, are effective in SCD tasks. However, these networks primarily focus on extracting contextual information and ignore spatial details, resulting in the missed or false detection of small targets and inaccurate boundaries. To address the limitations of the aforementioned methods, this paper proposed a spatial-temporal semantic perception network (STSP-Net) for SCD. It effectively utilizes spatial detail information through the detail-aware path (DAP) and generates spatial-temporal semantic-perception features through combining deep contextual features. Meanwhile, the network enhances the representation of semantic features in spatial and temporal dimensions by leveraging a spatial attention fusion module (SAFM) and a temporal refinement detection module (TRDM). This augmentation results in improved sensitivity to details and adaptive performance balancing between semantic segmentation (SS) and change detection (CD). In addition, by incorporating the invariant consistency loss function (ICLoss), the proposed method constrains the consistency of land cover (LC) categories in invariant regions, thereby improving the accuracy and robustness of SCD. The comparative experimental results on three SCD datasets demonstrate the superiority of the proposed method in SCD. It outperforms other methods in various evaluation metrics, achieving a significant improvement. The Sek improvements of 2.84%, 1.63%, and 0.78% have been observed, respectively.
Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing
Authors: Xu Mengyuan , Mao Yachun , Zhang Mengqi , Xiao Dong , Xie Hongfei
Published: August 2023

The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.
Cautious Bayesian Optimization: A Line Tracker Case Study
Authors: Girbés-Juan Vicent , Moll Joaquín , Sala Antonio , Armesto Leopoldo
Published: August 2023

In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment.
Fortifying Smart Home Security: A Robust and Efficient User-Authentication Scheme to Counter Node Capture Attacks
Authors: Asghar Iqra , Khan Muhammad Ayaz , Ahmad Tahir , Ullah Subhan , Mansoor ul Hassan Khwaja , Buriro Attaullah
Published: August 2023

In smart home environments, the interaction between a remote user and devices commonly occurs through a gateway, necessitating the need for robust user authentication. Despite numerous state-of-the-art user-authentication schemes proposed over the years, these schemes still suffer from security vulnerabilities exploited by the attackers. One severe physical attack is the node capture attack, which allows adversaries to compromise the security of the entire scheme. This research paper advances the state of the art by conducting a security analysis of user-authentication approaches regarding their vulnerability to node capture attacks resulting in revelations of several security weaknesses. To this end, we propose a secure user-authentication scheme to counter node capture attacks in smart home environments. To validate the effectiveness of our proposed scheme, we employ the BAN logic and ProVerif tool for verification. Lastly, we conduct performance analysis to validate the lightweight nature of our user-authentication scheme, making it suitable for IoT-based smart home environments.
Highly Tunable MOCVD Process of Vanadium Dioxide Thin Films: Relationship between Structural/Morphological Features and Electrodynamic Properties
Authors: Pellegrino Anna Lucia , Lo Presti Francesca , Papari Gian Paolo , Koral Can , Andreone Antonello , Malandrino Graziella
Published: August 2023

The monoclinic structures of vanadium dioxide are widely studied as appealing systems due to a plethora of functional properties in several technological fields. In particular, the possibility to obtain the VO2 material in the form of thin film with a high control of structure and morphology represents a key issue for their use in THz devices and sensors. Herein, a fine control of the crystal habit has been addressed through an in-depth study of the metal organic chemical vapor deposition (MOCVD) synthetic approach. The focus is devoted to the key operative parameters such as deposition temperature inside the reactor in order to stabilize the P21/c or the C2/m monoclinic VO2 structures. Furthermore, the compositional purity, the morphology and the thickness of the VO2 films have been assessed through energy dispersive X-ray (EDX) analyses and field-emission scanning electron microscopy (FE-SEM), respectively. THz time domain spectroscopy is used to validate at very high frequency the functional properties of the as-prepared VO2 films.
An Effective Near-Field to Far-Field Transformation with Planar Spiral Scanning for Flat Antennas under Test
Authors: Bevilacqua Florindo , D’Agostino Francesco , Ferrara Flaminio , Gennarelli Claudio , Guerriero Rocco , Migliozzi Massimo , Riccio Giovanni
Published: August 2023

The goal of this article is to provide numerical and experimental assessments of an effective near-field to far-field transformation (NF–FF T) technique with planar spiral scanning for flat antennas under test (AUTs), which requires a non-redundant, i.e., minimum, number of NF measurements. This technique has its roots in the theory of non-redundant sampling representations of electromagnetic fields and was devised by suitably applying the unified theory of spiral scans for non-volumetric antennas to the case in which the considered AUT is modeled by a circular disk having its radius equal to half of the AUT’s maximum dimension. It makes use of a 2D optimal sampling interpolation (OSI) formula to accurately determine the massive amount of NF data required by the classical plane-rectangular NF–FF T technique from the non-redundant data gathered along the spiral. It must be emphasized that, when considering flat AUTs, the developed transformation allows one to further and significantly save measurement time as compared to that required by the previously developed NF–FF T techniques with planar spiral scans based on a quasi-planar antenna modeling, because the number of turns of the spiral and that of NF data to be acquired depend somewhat on the area of the modeling surface. The reported numerical simulations assess the accuracy of the proposed NF–FF T technique, whereas the experimental tests prove its practical feasibility.
Dispersion Turning Attenuation Microfiber for Flowrate Sensing
Authors: Tang Yaqi , Wang Chao , Wang Xuefeng , Jiang Meng , Lao Junda , Wang Dongning
Published: August 2023

We demonstrated a new optical fiber modal interferometer (MI) for airflow sensing; the novelty of the proposed structure is that an MI is fabricated based on a piece of HAF, which makes the sensitive MI itself also a hotwire. The interferometer is made by applying arc-discharge tapering and then flame tapering on a 10 mm length high attenuation fiber (HAF, 2 dB/cm) with both ends spliced to a normal single mode fiber. When the diameter of the fiber in the processing region is reduced to about 2 μm, the near-infrared dispersion turning point (DTP) can be observed in the interferometer’s transmission spectrum. Due to the absorption of the HAF, the interferometer will have a large temperature increase under the action of a pump laser. At the same time, the spectrum of the interferometer with a DTP is very sensitive to the change in ambient temperature. Since airflow will significantly affect the temperature around the fiber, this thermosensitive interferometer with an integrated heat source is suitable for airflow sensing. Such an airflow sensor sample with a 31.2 mm length was made and pumped by a 980 nm laser with power up to 200 mW. In the comparative experiment with an electrical anemometer, this sensor exhibits a very high air-flow sensitivity of −2.69 nm/(m/s) at a flowrate of about 1.0 m/s. The sensitivity can be further improved by enlarging the waist length, increasing the pump power, etc. The optical anemometer with an extremely high sensitivity and a compact size has the potential to measure a low flowrate in constrained microfluidic channels.
Object Detection for Agricultural Vehicles: Ensemble Method Based on Hierarchy of Classes
Authors: Mujkic Esma , Christiansen Martin P. , Ravn Ole
Published: August 2023

Vision-based object detection is essential for safe and efficient field operation for autonomous agricultural vehicles. However, one of the challenges in transferring state-of-the-art object detectors to the agricultural domain is the limited availability of labeled datasets. This paper seeks to address this challenge by utilizing two object detection models based on YOLOv5, one pre-trained on a large-scale dataset for detecting general classes of objects and one trained to detect a smaller number of agriculture-specific classes. To combine the detections of the models at inference, we propose an ensemble module based on a hierarchical structure of classes. Results show that applying the proposed ensemble module increases mAP@.5 from 0.575 to 0.65 on the test dataset and reduces the misclassification of similar classes detected by different models. Furthermore, by translating detections from base classes to a higher level in the class hierarchy, we can increase the overall mAP@.5 to 0.701 at the cost of reducing class granularity.
Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal
Authors: Almudayni Ziyad , Soh Ben , Li Alice
Published: August 2023

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.
Explainable Automated TI-RADS Evaluation of Thyroid Nodules
Authors: Kunapinun Alisa , Songsaeng Dittapong , Buathong Sittaya , Dailey Matthew N. , Keatmanee Chadaporn , Ekpanyapong Mongkol
Published: August 2023

A thyroid nodule, a common abnormal growth within the thyroid gland, is often identified through ultrasound imaging of the neck. These growths may be solid- or fluid-filled, and their treatment is influenced by factors such as size and location. The Thyroid Imaging Reporting and Data System (TI-RADS) is a classification method that categorizes thyroid nodules into risk levels based on features such as size, echogenicity, margin, shape, and calcification. It guides clinicians in deciding whether a biopsy or other further evaluation is needed. Machine learning (ML) can complement TI-RADS classification, thereby improving the detection of malignant tumors. When combined with expert rules (TI-RADS) and explanations, ML models may uncover elements that TI-RADS misses, especially when TI-RADS training data are scarce. In this paper, we present an automated system for classifying thyroid nodules according to TI-RADS and assessing malignancy effectively. We use ResNet-101 and DenseNet-201 models to classify thyroid nodules according to TI-RADS and malignancy. By analyzing the models’ last layer using the Grad-CAM algorithm, we demonstrate that these models can identify risk areas and detect nodule features relevant to the TI-RADS score. By integrating Grad-CAM results with feature probability calculations, we provide a precise heat map, visualizing specific features within the nodule and potentially assisting doctors in their assessments. Our experiments show that the utilization of ResNet-101 and DenseNet-201 models, in conjunction with Grad-CAM visualization analysis, improves TI-RADS classification accuracy by up to 10%. This enhancement, achieved through iterative analysis and re-training, underscores the potential of machine learning in advancing thyroid nodule diagnosis, offering a promising direction for further exploration and clinical application.
Accurate Detection for Zirconium Sheet Surface Scratches Based on Visible Light Images
Authors: Xu Bin , Sun Yuanhaoji , Li Jinhua , Deng Zhiyong , Li Hongyu , Zhang Bo , Liu Kai
Published: August 2023

Zirconium sheet has been widely used in various fields, e.g., chemistry and aerospace. The surface scratches on the zirconium sheets caused by complex processing environment have a negative impact on the performance, e.g., working life and fatigue fracture resistance. Therefore, it is necessary to detect the defect of zirconium sheets. However, it is difficult to detect such scratch images due to lots of scattered additive noise and complex interlaced structural texture. Hence, we propose a framework for adaptively detecting scratches on the surface images of zirconium sheets, including noise removing and texture suppressing. First, the noise removal algorithm, i.e., an optimized threshold function based on dual-tree complex wavelet transform, uses selected parameters to remove scattered and numerous noise. Second, the texture suppression algorithm, i.e., an optimized relative total variation enhancement model, employs selected parameters to suppress interlaced texture. Finally, by connecting disconnection based on two types of connection algorithms and replacing the Gaussian filter in the standard Canny edge detection algorithm with our proposed framework, we can more robustly detect the scratches. The experimental results show that the proposed framework is of higher accuracy.
Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals
Authors: Lim Gunsik , Oh Beomseok , Kim Donghyun , Toh Kar-Ann
Published: August 2023

Wi-Fi signals are ubiquitous and provide a convenient, covert, and non-invasive means of recognizing human activity, which is particularly useful for healthcare monitoring. In this study, we investigate a score-level fusion structure for human activity recognition using the Wi-Fi channel state information (CSI) signals. The raw CSI signals undergo an important preprocessing stage before being classified using conventional classifiers at the first level. The output scores of two conventional classifiers are then fused via an analytic network that does not require iterative search for learning. Our experimental results show that the fusion provides good generalization and a shorter learning processing time compared with state-of-the-art networks.
Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
Authors: Hamid Shahid , Chopra Shakti Raj , Gupta Akhil , Tanwar Sudeep , Florea Bogdan Cristian , Taralunga Dragos Daniel , Alfarraj Osama , Shehata Ahmed M.
Published: August 2023

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.
Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection
Authors: Hammad Mohamed , Pławiak Paweł , ElAffendi Mohammed , El-Latif Ahmed A. Abd , Latif Asmaa A. Abdel
Published: August 2023

This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals’ quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named “Derma Care,” addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated “Derma Care” using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.
Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs
Authors: Zou Liang , Xu Shifan , Zhu Weiming , Huang Xiu , Lei Zihui , He Kun
Published: August 2023

Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show clear details. In this study, we introduce a novel Generative Adversarial Network (GAN) to restore high-definition coal photomicrographs. Compared to traditional image restoration methods, the lightweight GAN-based network generates more explicit and realistic results. In particular, we employ the Wide Residual Block to eliminate the influence of artifacts and improve non-linear fitting ability. Moreover, we adopt a multi-scale attention block embedded in the generator network to capture long-range feature correlations across multiple scales. Experimental results on 468 photomicrographs demonstrate that the proposed method achieves a peak signal-to-noise ratio of 31.12 dB and a structural similarity index of 0.906, significantly higher than state-of-the-art super-resolution reconstruction approaches.
Effective Mean Square Differences: A Matching Algorithm for Highly Similar Sheet Metal Parts
Authors: Zhang Hui , Guan Zhen , Eastwood Joe , Zhang Hongji , Zhu Xiaoyang
Published: August 2023

The accurate identification of highly similar sheet metal parts remains a challenging issue in sheet metal production. To solve this problem, this paper proposes an effective mean square differences (EMSD) algorithm that can effectively distinguish highly similar parts with high accuracy. First, multi-level downsampling and rotation searching are adopted to construct an image pyramid. Then, non-maximum suppression is utilised to determine the optimal rotation for each layer. In the matching, by re-evaluating the contribution of the difference between the corresponding pixels, the matching weight is determined according to the correlation between the grey value information of the matching pixels, and then the effective matching coefficient is determined. Finally, the proposed effective matching coefficient is adopted to obtain the final matching result. The results illustrate that this algorithm exhibits a strong discriminative ability for highly similar parts, with an accuracy of 97.1%, which is 11.5% higher than that of the traditional methods. It has excellent potential for application and can significantly improve sheet metal production efficiency.
Fourier Ptychographic Microscopic Reconstruction Method Based on Residual Hybrid Attention Network
Authors: Li Jie , Hao Jingzi , Wang Xiaoli , Wang Yongshan , Wang Yan , Wang Hao , Wang Xinbo
Published: August 2023

Fourier ptychographic microscopy (FPM) is a novel technique for computing microimaging that allows imaging of samples such as pathology sections. However, due to the influence of systematic errors and noise, the quality of reconstructed images using FPM is often poor, and the reconstruction efficiency is low. In this paper, a hybrid attention network that combines spatial attention mechanisms with channel attention mechanisms into FPM reconstruction is introduced. Spatial attention can extract fine spatial features and reduce redundant features while, combined with residual channel attention, it adaptively readjusts the hierarchical features to achieve the conversion of low-resolution complex amplitude images to high-resolution ones. The high-resolution images generated by this method can be applied to medical cell recognition, segmentation, classification, and other related studies, providing a better foundation for relevant research.
Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data
Authors: Yan Zhenhao , Sun Jiachen , Zhang Yixiang , Liu Lilan , Gao Zenggui , Chang Yuxing
Published: August 2023

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods.
Image Preprocessing with Enhanced Feature Matching for Map Merging in the Presence of Sensing Error
Authors: Chen Yu-Lin , Chan Kuei-Yuan
Published: August 2023

Autonomous robots heavily rely on simultaneous localization and mapping (SLAM) techniques and sensor data to create accurate maps of their surroundings. When multiple robots are employed to expedite exploration, the resulting maps often have varying coordinates and scales. To achieve a comprehensive global view, the utilization of map merging techniques becomes necessary. Previous studies have typically depended on extracting image features from maps to establish connections. However, it is important to note that maps of the same location can exhibit inconsistencies due to sensing errors. Additionally, robot-generated maps are commonly represented in an occupancy grid format, which limits the availability of features for extraction and matching. Therefore, feature extraction and matching play crucial roles in map merging, particularly when dealing with uncertain sensing data. In this study, we introduce a novel method that addresses image noise resulting from sensing errors and applies additional corrections before performing feature extraction. This approach allows for the collection of features from corresponding locations in different maps, facilitating the establishment of connections between different coordinate systems and enabling effective map merging. Evaluation results demonstrate the significant reduction of sensing errors during the image stitching process, thanks to the proposed image pre-processing technique.
Posture Classification with a Bed-Monitoring System Using Radio Frequency Identification
Authors: Yamauchi Yu , Shimoi Nobuhiro
Published: August 2023

Aging of the population and the declining birthrate in Japan have produced severe human resource shortages in the medical and long-term care industries. Reportedly, falls account for more than 50% of all accidents in nursing homes. Recently, various bed-release sensors have become commercially available. In fact, clip sensors, mat sensors, and infrared sensors are used widely in hospitals and nursing care facilities. We propose a simple and inexpensive monitoring system for elderly people as a technology capable of detecting bed activity, aimed particularly at preventing accidents involving falls. Based on findings obtained using that system, we aim at realizing a simple and inexpensive bed-monitoring system that improves quality of life. For this study, we developed a bed-monitoring system for detecting bed activity. It can predict bed release using RFID, which can achieve contactless measurements. The proposed bed-monitoring system incorporates an RFID antenna and tags, with a method for classifying postures based on the RFID communication status. Experimentation confirmed that three postures can be classified with two tags, seven postures with four tags, and nine postures with six tags. The detection rates were 90% for two tags, 75% for four tags, and more than 50% for six tags.
Evaluation of BPA and Bis-GMA Release from Recent Dental Composite Materials by LC-MS/MS
Authors: Lopes-Rocha Lígia , Gonçalves Virgínia M. F. , Cunha Sara C. , Fernandes José O. , Pinho Teresa , Tiritan Maria Elizabeth
Published: August 2023

Bisphenol-A (BPA) is a xenoestrogen widely used as a synthetic precursor of resin monomers. There is arise need to acquire BPA-free resin-matrix composites to prevent the health effects of BPA. Six composites with distinctive manufacturer specifications were considered to evaluate the degree of release of BPA and bisphenol A-Diglycidyl Methacrylate (Bis-GMA) in a dental composite. The light-cured resin-matrix specimens (n = 5 for each composite type) were incubated at 37 °C in 1 mL of a 75% ethanol–water solution in a sealed amber glass vial for 7 days. The 75% ethanol–water solution was replaced daily and immediately frozen (−20 °C) until liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. BPA was not detected in any studied resin-based materials. However, Bis-GMA was detected in almost all the studied samples during the experiment, except AF and BF. The highest Bis-GMA concentration was released from ED at 67.43 ng/mL, followed by BE, FS, and NC with 40.75 ng/mL, 8.30 ng/mL, and 0.94 ng/mL, respectively. There is a clear need for more precise and standardized analytical methods to assess the short- and long-term release of resin-based materials. Furthermore, manufacturers should be obliged to provide complete details of the chemical composition of dental products and to promote the development of materials without estrogenic potential.
Healthcare-Seeking Behaviors of Homeless Substance Users During the COVID-19 Lockdowns in Gauteng, South Africa: A COREQ-Based Report
Authors: Mnkandla Mayibongwe Mkhaliphi Abel , Tshitangano Takalani Grace , Mudau Azwinndini Gladys
Published: August 2023

Substance use continues to be a social problem globally. Around the world, approximately 275 million people use drugs, including 15% of South Africans, with over 36 million succumbing to drug disorders. In the Gauteng Province, about 55% of autopsies of deceased medicolegal cases (2003–2012) showed the presence of one or more illicit drugs in Pretoria. Most research shows that around one-third of people who have problems with alcohol and drugs are homeless. Evidence suggests that homeless people are often denied access to even the most essential assistance, including healthcare. This study assessed the healthcare-seeking behaviors of homeless substance users during the COVID-19 lockdowns in Gauteng, South Africa. A qualitative approach using an exploratory design assessed referrals based on those suffering from symptoms of COVID-19 during the lockdown period between 2020 and 2022. Data were collected from 25 homeless substance users in the City of Tshwane, Gauteng, through unstructured interviews. Three themes based on the study objectives included the types of healthcare services consulted, the determinants of health-seeking behaviors, and challenges experienced while seeking healthcare. Of the twenty-five participants, aged twenty-one to fifty, thirteen suffered from COVID-19 symptoms. Five used formal healthcare systems and eight used informal healthcare systems. Older participants access healthcare services, while less of the younger population use them. Barriers that are encountered while seeking medical services entail marginalization, stigmatization, and a lack of social support. Despite the formidable barriers posed by homelessness and substance use, homeless individuals demonstrated remarkable resilience in their efforts to access healthcare services during the COVID-19 lockdowns. This study highlights the importance and urgent need for harm reduction strategies and policy development for long-term service provision to this population, as well, as the literature on equity, diversity, and inclusion as a foundation for the rights of marginalized populations and groups. Future research directions should focus on harm reduction among this population group of homeless substance users.
Semiconductors at the Intersection of Geoeconomics, Technonationalism, and Global Value Chains
Authors: Park Seohee
Published: August 2023

This study provides a historical and contemporary analysis of the United States’ strategies in the global semiconductor industry, framed within Joseph Nye’s three-dimensional chessboard analysis. This study examines the strategic responses of the United States from the 1980s to the present, connecting these shifts to changes in international politics and geoeconomic alliances. It scrutinizes how the U.S. utilized its unipolar power to respond to Japan’s growing semiconductor industry influence in the 1980s and its adoption of free-market principles during the globalization era of the 1990s and 2000s. It further discusses how these multilateral shifts have led to a resurgence of technonationalism in the late 2010s, responding to asymmetric interdependence in the global value chain of the semiconductor industry. This research contributes to the comprehension of the dynamics of the industry within international politics and suggests insights into the ongoing Sino–American competition and strategic realignment in the sector.
Study on the Spatial and Temporal Evolution of Ecosystem Service Values and Driving Mechanism in the Yan River Basin from 1990 to 2020
Authors: Zheng Wenxin , Zhang Jian
Published: August 2023

Ecological and environmental degradation are among the major challenges facing humanity today. The analysis of ecosystem service value assessments can therefore serve as a means to guide ecosystem restoration, as well as provide indications for sustainable land use and land management decisions. The present study examines changes in land use and the associated ecosystem service values in the Yan River Basin in China for the period of 1990–2020. Based on high-resolution Landsat satellite data, we obtained detailed land type distribution data for the basin, allowing the analysis of the internal structure and the degree of influence of the land use by using information entropy and elasticity coefficient. We also explored the spatiotemporal differentiation of ESVs by applying the method of equivalent factors and hotspot analysis. Finally, we identified possible drivers for development patterns observed in the watershed using geodetector models. During the study period, the area of arable land dropped continuously, while the scope of forest land, grassland, and construction land increased. The land type layout developed in the direction of reduced uniformity. ESVs measured in monetary terms first rose and later fell, but nevertheless increased by 1.152 billion yuan overall. The decrease was mainly due to the accelerated urbanization construction in the later stage. Spatially, ESV distribution coincided with the land-use pattern, showing a growing pattern from north to south. The changes were due not to the role of a single factor but the joint interactions between multiple factors such as human activities, natural factors, and landscape patterns. The results can provide a basis for constructive suggestions to connect and promote the basin’s natural and socio-economic surroundings, and also reflect the effectiveness of the policy of systematically stopping cultivation and planting trees and grass on stunted cultivated land.
Impacts of Contaminants from Different Sources on Geotechnical Properties of Soils
Authors: Zhao Shan , Zhang Baoju , Zhang Wenbing , Su Xinjia , Sun Botao
Published: August 2023

Within sites affected by industrial, domestic, and agricultural contaminants, the geotechnical characteristics of soils are susceptible to a certain degree of deterioration. The resultant corrosion of concrete exacerbates the vulnerability of underground structures, posing a potential hazard to the stability of superstructures. However, the current lack of comprehensive understanding regarding the precise influence of contaminants from different sources on the geotechnical properties of soils underscores the critical need for further research in this field. This review aims to elucidate the underlying mechanisms of various impacts, revealing that the permeability, shear strength, and compressibility of soils can either increase or decrease depending on the specific contaminants present. Notably, even though these impacts may not manifest prominently in the short term, their persistence can endure over an extended duration. The primary objective of this comprehensive review is to draw the attention of the scientific community and policy makers to this issue, emphasizing the need to mitigate potential hazards and safeguard a habitable environment for present and future generations.