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Nitrogen-incorporated ultrananocrystalline diamond [(N)UNCD] pyramid tip cathode has been considered as a next-generation high peak current electron source for dielectric laser accelerators as well as other high peak current particle accelerator applications. In this work, we study non-linear photoemission from an (N)UNCD pyramid tip cathode using an ultrafast laser with the pulse length of 150 fs with the central wavelength of 800 nm in the peak intensity range of 10 9 – 10 10 W / cm 2. We demonstrated that as the incident laser intensity increases, the current emitted from the nano-tip first increases as a power function with an exponent of about 5 and then starts to roll over to an exponent of 3. This roll over is attributed to the Coulomb interaction between electrons emitted from the tip also known as the space charge. We also measured the photoemission electron energy spectra that show electrons with energies as high as ∼ 10 eV. Based on the shape of the electron energy spectra, we conclude that the high-energy electrons are thermally emitted electrons due to ultrafast laser heating at the tip of the (N)UNCD pyramid tip cathode. [ABSTRACT FROM AUTHOR]

Identifying and managing risk is as integral to financial planning as are investing, wealth management, and managing tax and legal considerations. Risk is present at every level or life stage of a financial planning pyramid. The top of most planning pyramids--when individuals focus on retirement income and legacy planning--includes a much wider, expanded, critically important, and longer- lasting range of risks than earlier planning levels. Identifying and addressing the risks individuals may encounter at the top of the planning pyramid can determine if retirement planning will be successful. [ABSTRACT FROM AUTHOR]

This research explores an innovative approach to segment hyperspectral images. Aclass-aware feature-based attention approach is combined with an enhanced attention-based network, FAttNet is proposed to segment the hyperspectral images semantically. It is introduced to address challenges associated with inaccurate edge segmentation, diverse forms of target inconsistency, and suboptimal predictive efficacy encountered in traditional segmentation networks when applied to semantic segmentation tasks in hyperspectral images. First, the class-aware feature attention procedure is used to improve the extraction and processing of distinct types of semantic information. Subsequently, the spatial attention pyramid is employed in a parallel fashion to improve the correlation between spaces and extract context information from images at different scales. Finally, the segmentation results are refined using the encoder-decoder structure. It enhances precision in delineating distinct land cover patterns. The findings from the experiments demonstrate that FAttNet exhibits superior performance compared to established semantic segmentation networks commonly used. Specifically, on the GaoFen image dataset, FAttNet achieves a higher mean intersection over union (MIoU) of 77.03% and a segmentation accuracy of 87.26% surpassing the performance of the existing network. [ABSTRACT FROM AUTHOR]

Remote sensing images exhibit complex characteristics such as irregular multi-scale feature shapes, significant scale variations, and imbalanced sizes between different categories. These characteristics lead to a decrease in the accuracy of semantic segmentation in remote sensing images. In view of this problem, this paper presents a context feature-enhanced multi-scale remote sensing image semantic segmentation method. It utilizes a context aggregation module for global context co-aggregation, obtaining feature representations at different levels through self-similarity calculation and convolution operations. The processed features are input into a feature enhancement module, introducing a channel gate mechanism to enhance the expressive power of feature maps. This mechanism enhances feature representations by leveraging channel correlations and weighted fusion operations. Additionally, pyramid pooling is employed to capture multi-scale information from the enhanced features, so as to improve the performance and accuracy of the semantic segmentation model. Experimental results on the Vaihingen and Potsdam datasets (which are indeed publicly released at the URL: https://www.isprs.org/education/benchmarks/UrbanSemLab/Default.aspx) demonstrate significant improvements in the performance and accuracy of the proposed method (whose algorithm source code is indeed publicly released in Sect. 3.4), compared to previous multi-scale remote sensing image semantic segmentation approaches, verifying its effectiveness. [ABSTRACT FROM AUTHOR]

This article calls for a new research agenda that helps capture the dynamic processes of RMB internationalization in particular and currency internationalization in general. The aperture we have found in the existing, transaction-based literature is that there is relatively little to say about the process of currency network expansion. Indeed, no serious work on currency internationalization ignores the role of currency networks in the politics of the currency pyramid. However, the problem is that these works treat the currency networks exogenously. By exogenous treatment, we mean that they explain the expansion and constriction of currency networks as by-products of trade and investment. In our view, this exogenous treatment of currency networks results in an analytical blind spot regarding how currency network expansion occurs, which is pivotal to the success or failure of currency internationalization. Instead, this article suggests that adequate assessment of RMB internationalization requires shifting the analytical focus to the endogenous process of currency network expansion across borders that involves the emerging and ongoing process of RMB's financial rise in the global economy. China's recent move toward financial opening may work to speed up RMB-based global financial networks. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate and robust 3D object detection under various environments is a challenging task and 3D object detection mainly relies on the direction, position, and size of the objects. The traditional object detection approaches are affected based on diverse issues such as background clutter, camera view-point variations, and occlusions. To avoid these issues in the field of object detection, one of the novel approaches named as Faster Region-based Convolutional Neural ResNet-50 (FRCNResNet-50) model is proposed that detects and classifies the 3D objects in the images. At first, the images are collected from three different data sources such as KITTI dataset, a nuScene dataset, and an MIT Indoor Scene dataset. For enhancing generalization ability and image quality, the collected images are included into preprocessing stages. The ResNet-50 model is designed to extract features by using a Spectral Pyramid Pooling (SPP) layer and a Fused Keypoint Generation (FKG) layer that enhances detection efficiency and reduces computational cost. The FRCNN model is implemented to detect 3D objects that include the ROI pooling layer for multi-class classification and for presenting its corresponding regression bounding box. The experimental validation is performed based on the significant measurements and quantitative analyses that showed the proposed model achieved better performances of 98.58% from accuracy analysis and 98 ms from computational time. [ABSTRACT FROM AUTHOR]

The copy-move forgery is considered one among difficult kinds of image forgeries that must be detected. This occurs by duplicating image parts or portions and thereafter adding up again in an image by itself but in other locations. The forgery detection techniques are utilized in image protection while an actual image is not obtainable. The current forgery detection methods detect tampered areas with lesser effectiveness owing to larger size as well as low contrast of images. Here, transit flow regime algorithm-based ShuffleNet (TFRA-ShuffleNet) is presented for multiple forgery detection. In this work, the input image considered is given to multiple object detection. The multiple object detection is carried out in an input image utilizing YOLO V3. After that, features are extracted from object detected image that include local vector pattern (LVP), local optimal-oriented pattern (LOOP), pyramid histogram of oriented gradients (PHoG), local Gabor XOR patterns (LGXP), local directional pattern (LDP), local directional ternary pattern (LDTP) and local binary pattern (LBP). Lastly, multiple forgery detection is conducted employing ShuffleNet. The ShuffleNet is trained to employ TFRA, which is an integration of transit search (TS) and flow regime algorithm (FRA). Furthermore, TFRA-ShuffleNet achieved maximal accuracy, TPR and TNR values of about 96.5%, 96.5% and 97.5%. [ABSTRACT FROM AUTHOR]

Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for improving semantic segmentation performance in computer vision tasks. Our approach utilizes an enhanced UNet architecture that leverages an improved ResNet50 backbone. We replace the last layer of ResNet50 with deformable convolution to enhance feature representation. Additionally, we incorporate an attention mechanism, specifically ECA-ASPP (Attention Spatial Pyramid Pooling), in the encoding path of UNet to capture multi-scale contextual information effectively. In the decoding path of UNet, we explore the use of attention mechanisms after concatenating low-level features with high-level features. Specifically, we investigate two types of attention mechanisms: ECA (Efficient Channel Attention) and LKA (Large Kernel Attention). Our experiments demonstrate that incorporating attention after concatenation improves segmentation accuracy. Furthermore, we compare the performance of ECA and LKA modules in the decoder path. The results indicate that the LKA module outperforms the ECA module. This finding highlights the importance of exploring different attention mechanisms and their impact on segmentation performance. To evaluate the effectiveness of the proposed method, we conduct experiments on benchmark datasets, including Stanford and Cityscapes, as well as the newly introduced WildPASS and DensPASS datasets. Based on our experiments, the proposed method achieved state-of-the-art results including mIoU 85.79 and 82.25 for the Stanford dataset, and the Cityscapes dataset, respectively. The results demonstrate that our proposed method performs well on these datasets, achieving state-of-the-art results with high segmentation accuracy. [ABSTRACT FROM AUTHOR]

In the field of image object detection and semantic segmentation, improving the accuracy of object identification and segmentation is a primary goal. To achieve this, leveraging the potential of multi-scale information through feature map refinement and fusion has been widely recognized. However, existing feature fusion methods either design more complex feature pyramid networks, replace existing detectors, or incrementally introduce feature fusion modules, overlooking the effective approach of enhancing spatial information in deep feature maps. We propose a novel pluggable feature fusion paradigm termed 'Effective Learning Bridge'. Our research introduces an efficient and adaptive learning mechanism that builds learning bridges between feature maps at different scales within the feature pyramid, thereby enhancing the spatial information of objects in deep feature maps. This mechanism is specifically designed for multi-scale feature maps and can be seamlessly integrated into any network incorporating feature maps. By altering the model's backpropagation path, we successfully improve learning efficiency, which in turn enhances the accuracy of object detection and segmentation. Our proposed paradigm and method were extensively evaluated through experiments on SIMD, HRSID, and WHDLD datasets and benchmark models. The results unequivocally demonstrate the effectiveness of our approach in significantly improving the accuracy of object detection and semantic segmentation, as well as the overall learning efficiency of the model. [ABSTRACT FROM AUTHOR]

Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture details. Second, a Channel Priority Convolutional Attention mechanism is incorporated after the Spatial Pyramid Pooling layer to enable more precise defect localization by leveraging multi-scale structures and channel priors. Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. Experimental results demonstrate a significant improvement in detection performance. Specifically, mAP@0.5 increased by 2.9%, precision improved by 3.5%, and mAP@0.5:0.95 rose by 2.3%, highlighting the model's superior capability in detecting complex defects. The project is available at https://github.com/lilian998/fabric. [ABSTRACT FROM AUTHOR]

In the domain of printed circuit board (PCB) defect detection, challenges such as missed detections and false positives remain prevalent. To address these challenges, we propose a small-sample, high-precision PCB defect detection algorithm, called SSHP-YOLO. The proposed method incorporates an ELAN-C module that merges the convolutional block attention module (CBAM) with the efficient layer aggregation network (ELAN), thereby enhancing the model's focus on defect features and improving the detection of minute defect details. Furthermore, we introduce the ASPPCSPC structure, which extracts multi-scale features using pyramid pooling combined with dilated convolutions while maintaining the resolution of feature maps. This design improves the detection accuracy and robustness, thereby enhancing the algorithm's generalization ability. Additionally, we employ the SIoU loss function to optimize the regression between the predicted and ground-truth bounding boxes, thus improving the localization accuracy of minute defects. The experimental results show that SSHP-YOLO achieves a recall rate that is 11.84% higher than traditional YOLOv7, with a mean average precision (mAP) of 97.80%. This leads to a substantial improvement in the detection accuracy, effectively mitigating issues related to missed and false detections in PCB defect detection tasks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automatic segmentation of various hepatic structures from computed tomography images is of great importance in surgery and treatment planning for patients diagnosed with liver cancer. This work tends to identify the optimal semantic segmentation architecture for the task of segmentation of hepatic structures from computed tomography (CT) images. Segmentation of hepatic structures from CT images is carried out using four popular semantic segmentation architectures viz. fully convolutional network (FCN), SegNet, U-Net and DeepLabV3 + with various encoder configurations on 3D-ircadb-01, CHAOS and on an institutional dataset images. Segmentation of liver, liver tumour, hepatic artery, portal vein and hepatic vein are carried out simultaneously using a single network as a dense pixel prediction task. An extensive experimental assessment of hepatic structures segmentation was carried out using 13 networks derived from the aforesaid 4 segmentation architectures. The segmentation performance is assessed in terms of segmentation accuracy, dice coefficient, Jaccard index and boundary F1score. Experimental results show that DeepLabV3 + architecture with Xception encoder records better segmentation performance with dice coefficient of 98.13% 98.87% and 98.92% on 3D-ircadb-01, CHAOS and on the institutional datasets respectively. The comparison with the state-of-the-art techniques presented exemplifies the effectiveness of the fully depth separable convolutional network on the segmentation of various hepatic structures. Depth separable convolution applied to atrous spatial pyramid pooling (ASPP) with multiple sampling rates at the decoder of the DeepLabV3 + architecture together with the entirely separable convolution based Xception model as encoder stands as evidence towards the exceptional segmentation performance of hepatic structures. [ABSTRACT FROM AUTHOR]

Waves are crucial in ocean observation and research. Stereo vision-based wave measurement, offering non-contact, low-cost, and intelligent processing, is an emerging method. However, improving accuracy remains a challenge due to wave complexity. This paper presents a novel approach to measure wave height, period, and direction by combining deep learning-based stereo matching with feature matching techniques. To improve the discontinuity and low accuracy in disparity maps from traditional wave image matching algorithms, this paper proposes the use of a high-precision stereo matching method based on Pyramid Stereo Matching Network (PSM-Net).A 3D reconstruction method integrating Scale-Invariant Feature Transform (SIFT) with stereo matching was also introduced to overcome the limitations of template matching and interleaved spectrum methods, which only provide 2D data and fail to capture the full 3D motion of waves. This approach enables accurate wave direction measurement. Additionally, a six-degree-of-freedom platform was proposed to simulate waves, addressing the high costs and attenuation issues of traditional wave tank simulations. Experimental results show the prototype system achieves a wave height accuracy within 5%, period accuracy within 4%, and direction accuracy of ±2°, proving the method's effectiveness and offering a new approach to stereo vision-based wave measurement. [ABSTRACT FROM AUTHOR]

Rapid and accurate target detection is one of the inevitable requirements of intelligent construction site. To meet the speed requirements and improve detection accuracy, an improved small object detection (ISOD) network is proposed. The network utilizes an efficient channel attention mechanism to extract features in the backbone and combines the proposed extended scale feature pyramid network to simplify calculations and create additional high-resolution pyramid layers to improve the ability of detecting small targets. To verify the effectiveness of ISOD, experiments are conducted using the proposed Reflective Vest Scene Dataset and Tsinghua-Tencent 100K, achieving 0.425 and 0.635 mAP@0.5 - 0.95, respectively, exceeding the SOTA YOLOv7 model, demonstrating its excellent small target detection capability and scalability. [ABSTRACT FROM AUTHOR]

Video frame interpolation (VFI) is a task that generates intermediate frames from two consecutive frames. Previous studies have employed two main approaches to extract the necessary information from both frames: pixel-level synthesis and flow-based methods. However, when synthesizing high-resolution videos using VFI, each approach has its limitations. Pixel-level synthesis based on the transformer architecture requires high complexity to achieve 4K video results. In the case of flow-based methods, forward warping can produce holes where pixels are not allocated, while backward warping approaches struggle to obtain accurate backward flow. Additionally, there are challenges during the training stage; previous works have often generated suboptimal results by training multi-stage model architectures separately. To address these issues, we propose a Recurrent Flow Update (RFU) model trained in an end-to-end manner. We introduce a global flow update module that leverages global information to mitigate the weaknesses of forward flow and gradually correct errors. We demonstrate the effectiveness of our method through several ablation studies. Our approach achieves state-of-the-art performance not only on the XTest and Davis datasets, which have 4K resolution, but also on the SNU-FILM dataset, which features large motions at low resolution. [ABSTRACT FROM AUTHOR]

Flow-guided synthesis provides a popular framework for video frame interpolation, where optical flow is firstly estimated to warp input frames, and then the intermediate frame is synthesized from warped representations. Within this framework, optical flow is typically estimated from coarse-to-fine by a pyramid network, but the intermediate frame is commonly synthesized in a single pass, missing the opportunity of refining possible imperfect synthesis for high-resolution and large motion cases. While cascading several synthesis networks is a natural idea, it is nontrivial to unify iterative estimation of both optical flow and intermediate frame into a compact, flexible, and general framework. In this paper, we present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework, UPR-Net exploits lightweight recurrent modules for both bi-directional flow estimation and intermediate frame synthesis. At each pyramid level, it leverages estimated bi-directional flow to generate forward-warped representations for frame synthesis; across pyramid levels, it enables iterative refinement for both optical flow and intermediate frame. We show that our iterative synthesis significantly improves the interpolation robustness on large motion cases, and the recurrent module design enables flexible resolution-aware adaptation in testing. When trained on low-resolution data, UPR-Net can achieve excellent performance on both low- and high-resolution benchmarks. Despite being extremely lightweight (1.7M parameters), the base version of UPR-Net competes favorably with many methods that rely on much heavier architectures. Code and trained models are publicly available at: https://github.com/srcn-ivl/UPR-Net. [ABSTRACT FROM AUTHOR]

The article discusses the use of Tinkercad, a free 3D simulation platform, to foster interdisciplinary STEM learning in middle school classrooms through hands-on design and problem-solving activities. Topics include the advantages of interdisciplinary STEM education, strategies for using Tinkercad effectively in teaching, and a detailed classroom activity emphasizing the iterative engineering design process.

To solve some of the challenges of traditional science, such as restricted access to funding, centralized governance, and siloed knowledge dissemination, decentralized science (DeSci) has emerged as a transformative approach facilitated by blockchain technology, Decentralized Autonomous Organizations (DAOs), and Web3. However, the emerging field of DeSci, faces several challenges, such as the absence of an organizational framework to describe its inherent complexities. This study introduces the Decentralized Science Pyramid Framework (DSPF), an innovative adaptation of Mintzberg's organizational structure, adapted to the unique demands and properties of DeSci. The DSPF delineates a structured model for DeSci projects that integrates technology, governance, community engagement, and application within a decentralized context. Through the introduction of the DSPF, this research highlights the operational dynamics of DeSci, focusing on the practical application of Mintzberg's theories to address real-world scientific challenges. The case study of VitaDAO, a decentralized autonomous organization exemplifying the core principles of DeSci, demonstrates the practical applicability of the DSPF. This study not only advances the academic discourse on DeSci but also offers practical insights for practitioners, innovators, and policymakers, marking a substantial step toward realizing the full potential of decentralized science. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets and binary classification problems. Objectives: This study aims to design a fine-tuned deep learning (DL) model capable of classifying PCa MRI images with high accuracy and to evaluate its performance by comparing it with various DL architectures. Methods: In this study, a basic convolutional neural network (CNN) model was developed and subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, and early stopping to enhance its performance. Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. This approach enabled the model to learn complex features more effectively. For performance comparison, the developed fine-tuned enhanced pyramid network (FT-EPN) model was benchmarked against models such as Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, and Xception, which were trained using transfer learning (TL) techniques. It was also compared to next-generation models such as vision transformer (ViT) and MaxViT-v2. Results: The developed fine-tuned model achieved an accuracy rate of 96.77%, outperforming pre-trained TL models and next-generation models like ViT and MaxViT-v2. Among the TL models, Vgg19 achieved the highest accuracy rate at 92.74%. In comparison, ViT achieved an accuracy of 93.55%, while MaxViT-v2 achieved an accuracy of 95.16%. Conclusions: This study presents an optimized FT-EPN model to enhance the performance of DL models for PCa classification, offering a reference solution for future research. This model provides significant advantages in terms of classification accuracy and simplicity and has been evaluated as an effective solution in clinical applications. [ABSTRACT FROM AUTHOR]

Crack-based flexible strain sensors inspired by a spider's slit organs have exhibited high sensitivity. However, the sensitivity of crack-based sensors can be negatively affected by cracks with random and undirected features. In this work, we fabricated a high-sensitivity flexible strain sensor with regular linear microcracks induced by stress concentration. The sensor consists of a polydimethylsiloxane (PDMS) flexible substrate with an inverted pyramid array and a conductive metal layer of Ti/Au film that was sputtered on the substrate surface. When the sensor was stretched, stress concentrations will occur near the inverted pyramids, inducing the generation of linear microcracks perpendicular to the stretching directions between the adjacent inverted pyramids. The testing results demonstrated that the sensor has a high sensitivity (a gauge factor of 9327 in the strain range of 7.6–10%), a wide working range (0–10% strain), and a fast response/recovery time (77/82 ms). These features enable the sensor to have potential applications in health monitoring and human–computer interaction, such as finger motion recognition and neck bending direction detection. [ABSTRACT FROM AUTHOR]

Underwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on detection accuracy. To address these issues, this study proposes an improved YOLOv11 network named YOLOv11-SDC. Specifically, a new Sparse Feature (SF) module is proposed, replacing the Spatial Pyramid Pooling Fast (SPPF) module from the original YOLOv11 architecture to enhance object feature selection. Furthermore, the proposed YOLOv11-SDC integrates a Dilated Reparam Block (DRB) with a C3k2 module to broaden the model's receptive field. A Content-Guided Attention Fusion (CGAF) module is also incorporated prior to the detection module to assign appropriate weights to various feature maps, thereby emphasizing the relevant object information. Experimental results clearly demonstrate the superiority of YOLOv11-SDC over several iterations of YOLO versions in detection performance. The proposed method was validated through extensive real-world experiments, yielding a precision of 0.934, recall of 0.698, mAP@0.5 of 0.825, and mAP@0.5:0.95 of 0.598. In conclusion, the improved YOLOv11-SDC offers a promising solution for detecting small objects in SSS images, showing substantial potential for marine applications. [ABSTRACT FROM AUTHOR]

To detect and recognize small-size and submerged complex background targets in infrared images, we combine a dynamic receptive field fusion strategy and a multi-scale feature fusion mechanism to improve the detection performance of small targets significantly. The space-to-depth convolution module is introduced as a downsampling layer in the backbone first and achieves the same sampling effect. More detailed information is retained at the same time. Thus, the model's detection capability for small targets has been enhanced. Then, the pyramid level 2 feature map with minimum receptive field and maximum resolution is added to the neck, which reduces the loss of positional information during feature sampling. Furthermore, x-small detection heads are added, the understanding of the overall characteristics and structure of the target is enhanced much more, and the representation and localization of small targets have been improved. Finally, the cross-entropy loss function in the original network model is replaced by an adaptive threshold focal loss function, forcing the model to allocate more attention to target features. The above methods are based on a public tool, the eighth version of You Only Look Once (YOLO) improved, it is named SPT–YOLO (SPDConv + P2 + Adaptive Threshold + YOLOV8s) in this paper. Some experiments on datasets such as infrared small object detection (IR-SOD) and infrared small target detection 1K(IRSTD-1K), etc. have been executed to verify the proposed algorithm; and the mean average precision of 94.0% and 69% under the condition of threshold at 0.5 and over a range from 0.5 to 0.95 is obtained, respectively. The results show that the proposed method achieves the best performance of infrared small target detection compared to existing methods. [ABSTRACT FROM AUTHOR]

AbstractPyramid lattice sandwich cylinders (LSCs) exhibit remarkable structural efficiency, yet local failures can significantly reduce their load-bearing capacity. This study investigates the load-bearing capability of pyramid LSCs relative to single-walled shells (SSs) of equivalent size and mass under multi-failure modes. A multi-failure analytical model grounded in thin-shell assumptions has been developed to analyze the separate contributions of the lattice core and skins. Through optimization, the variation in the performance of pyramid LSCs and SSs has been elucidated. The findings indicate that the load-bearing capacity of pyramid LSCs is approximately 1.65 times greater than that of SSs. [ABSTRACT FROM AUTHOR]

This paper presents AMFANet, an advanced deep learning model engineered for high-quality image style transfer. AMFANet integrates cutting-edge techniques such as the Adaptive Multi-Scale Feature Fusion (AMSF) module and Hybrid Attention Mechanism (HAM) to significantly improve style consistency, content fidelity, and texture preservation. The model also utilizes Segmented Atrous Spatial Pyramid Pooling (SASPP) for effective multi-scale feature extraction. Comprehensive experimental evaluations demonstrate that AMFANet surpasses current state-of-the-art models like StyleGAN3, ChipGAN, ACL-GAN, and CycleGAN in generating high-fidelity stylized images while preserving intricate details and artistic essence. Future research will focus on optimizing computational efficiency, enabling multi-style transfer, enhancing user interaction, and exploring cross-domain applications. These findings highlight AMFANet's potential as a robust solution for advanced image style transfer in both artistic and practical domains. [ABSTRACT FROM AUTHOR]

Communicating insights from data effectively requires design skills, technical knowledge, and experience. Data must be accurately represented with aesthetically pleasing visuals and engaging text to effectively communicate to the intended audience. Data storytelling has received much attention lately, but as of yet, it does not have a theoretical and practical foundation in information science. A data story adds context, narrative, and structure to the visual representation of data, providing audiences with character, plot, and a holistic experience of narrative. This paper proposes a methodological approach to transform a data visualization into a data story based on the Data‐Information‐Knowledge‐Wisdom (DIKW) pyramid and the S‐DIKW Framework. Starting from the bottom of the pyramid, the proposed approach defines a strategy to represent insights extracted from data. Data is then turned into information by identifying character(s) facing a problem, adding textual and graphic content; information is turned into knowledge by organizing what happens as a plot. Finally, a call to wise action—always informed by cultural and community values—completes the storytelling transformation to create a data story. This article contributes to the theoretical understanding of data stories as emerging information forms, supporting richer understandings of a story as information in the information sciences. [ABSTRACT FROM AUTHOR]

When there is an emphasis on governance practices or governance in Indonesia, including villages is also a priority. Therefore, it is interesting to study how governance is in the era of use and utilization of social media in village communities. By using a case study approach through interviews and focus group discussions, it turns out that this study produces a new pattern or model of governance practice. The findings of the governance model in this social media era are post governance, it turns out that the group of teenagers who dominantly use social media have the potential to become the fourth actor with a volunteer-altruist spirit for the welfare of society. This result, or post-gavernance, has two options, namely the tetragonal model and the pyramid model. The results of this study can be generalized to explain the same phenomenon in other villages or at local, regional and national levels. [ABSTRACT FROM AUTHOR]

Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation. This study proposes an improved YOLOv8n-seg network to address these challenges effectively. Key improvements include modifying the baseline model's final C2f module and integrating deformable attention with dynamic sampling points to enhance segmentation performance. Additionally, an ADown module minimizes detail loss from excessive downsampling by using depthwise separable convolutions to reduce parameter count and computational load. To improve the detection of small cabbage heads, a Small Object Enhance Pyramid based on the PAFPN architecture is introduced, significantly boosting performance for small targets. The experimental results show that the proposed model achieves a Mask Precision of 92.2%, Mask Recall of 87.2%, and Mask mAP50 of 95.1%, while maintaining a compact model size of only 6.46 MB. These metrics indicate superior accuracy and efficiency over mainstream instance segmentation models, facilitating real-time, precise cabbage harvesting in complex environments. [ABSTRACT FROM AUTHOR]

With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module. Building on traditional attention mechanisms, the ECBAM module employs dynamic convolution kernels to eliminate dimensionality reduction, enhancing the efficiency of feature channel learning and local interactions while preserving computational efficiency. The ECBAM_ASPP model incorporates the ECBAM attention mechanism into the feature extraction network, effectively directing the network to focus on salient features while suppressing irrelevant ones. Additionally, through variable sampling of the input, the model achieves multi-scale feature extraction, enabling it to capture richer lane-related feature information. Experimental results on the TuSimple and CULane datasets demonstrate that the ECBAM_ASPP model significantly improves real-time performance while maintaining high detection accuracy. Compared with baseline methods, the proposed model delivers superior overall performance, showcasing greater robustness and practicality. [ABSTRACT FROM AUTHOR]

Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model's ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels. In tests on the Cornell dataset, our network achieved grasping pose prediction at a speed of 66.7 frames per second, with accuracy rates of 98.6% and 96.9% for image-wise and object-wise splits, respectively. The experimental results show that our method achieves high-speed processing while maintaining high accuracy. In real-world robotic grasping experiments, our method also proved to be effective, achieving an average grasping success rate of 95.6% on a robot equipped with parallel grippers. [ABSTRACT FROM AUTHOR]

In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features. Secondly, based on the idea of using dilated convolutions to expand the receptive field of feature maps, a hybrid atrous spatial pyramid pooling module is designed. By utilizing a serial structure of dilated convolutions with small dilation rates, this module addresses the issue of feature information loss when expanding the receptive field through dilated spatial pyramid pooling. It captures the contextual information of the target, further enhancing the target features. Finally, a dice loss function is introduced to calculate the overlap between the predicted results and the ground truth labels, facilitating deep excavation of foreground information for comprehensive learning of samples. This paper constructs an infrared aircraft detection algorithm based on a high-resolution feature-enhanced semantic segmentation network which combines the location attention feature fusion network, the hybrid atrous spatial pyramid pooling module, the dice loss function, and a network that maintains the resolution of feature maps. Experiments conducted on a self-built infrared dataset show that the proposed algorithm achieves a mean intersection over union (mIoU) of 92.74%, a mean pixel accuracy (mPA) of 96.34%, and a mean recall (MR) of 96.19%, all of which outperform classic segmentation algorithms such as DeepLabv3+, Segformer, HRNetv2, and DDRNet. This demonstrates that the proposed algorithm can achieve effective detection of infrared aircraft in the presence of interference. [ABSTRACT FROM AUTHOR]

At the current stage, 3D graph convolutional algorithms face the following issues: (1) The problem of determining the neighborhood in graph convolution. (2) The issue of fusing features at different depths in deep graph convolutional algorithms. (3) The challenge of feature fusion and recognition in multi-path deep graph convolutional algorithms. Based on these, the paper "Multi-Path Deep Segmentation Network Based on 3D Graph Convolutional Algorithm and Its Application in Point Cloud Segmentation" is proposed. Inspired by visual computing theory, the neighborhood for 3D graph convolution is determined based on the receptive field theory, and a 3D graph convolutional algorithm based on visual selectivity is proposed; A single-link deep feature extraction algorithm for 3D graph convolution based on visual selectivity is proposed, it achieve the fusion of features at different depths learned by the graph convolutional algorithm by a pyramid learning method; A multi-link feature extraction algorithm for 3D graph convolution based on visual selectivity is proposed, and achieve multi-link feature fusion and segmentation to utilize the RPN calculation method. compared with algorithms such as PointNet, PointNet++, KPConv deform, 3D GCN, and My Algorithm, The segmentation performance and geometric invariance of the proposed algorithm are validated on the ShapeNetPart dataset and the custom Mortise_and_Tenon_DB dataset, and Experiments demonstrate that the proposed algorithm is correct and feasible, offers superior 3D point cloud segmentation performance, and exhibits strong geometric invariance. [ABSTRACT FROM AUTHOR]

In order to solve the problem of difficult deployment of existing deep learning-based defect detection models in terminal equipment with limited computational capacity, a lightweight steel surface defect testing model LSKA-YOLOv8 was proposed based on the YOLOv8n target detection framework. The model uses KernelWarehouse Conv (KWConv) with lower computational volumes, which significantly reduces the required computational resources. By updating the traditional Feature Pyramid Network (FPN) structure to Bi-directional Feature Pyramid Network (BiFPN), enhance the capture of contextual information and reduce the number of parameters of the model. In addition, the Spatial Pyramid Pooling Fast (SPPF) module was replaced with a more accurate Receptive Field Block (RFB) module, expanding the model's sensory field and improving characteristic representation. At the same time, the Large Separable Kernel Attention (LSKAttention) module was introduced in the detection head, effectively enhancing the understanding and capture of the target characteristics, thus significantly improving the overall detection performance. Experiments on the NEU-DET dataset showed that the average accuracy of LSKA-YOLO increased by 4.4% on the map@0.5 indicator compared to the benchmark model, while the number of parameters and calculations of the model decreased by 26.7% and 50%, respectively. Provides valuable references and practical applications for deployment of defect detection models on computing-resource-limited terminal devices. [ABSTRACT FROM AUTHOR]

Aiming at many challenges in the recognition task of peach leaf shrink disease, such as the diversity of object size of diseased leaf disease, complex background interference, and inflexible adjustment of model training learning rate, we propose a peach leaf shrink disease recognition algorithm based on an attention generalized efficient layer aggregation network. Firstly, the rectified linear unit activation function is used to effectively improve the stability and performance of the model in low-precision computing environments and solve the problem of partial gradient disappearance. Secondly, the integrated squeeze-and-excitation network attention mechanism can adaptively focus on the key areas of pests and diseases in the image, which significantly enhances the recognition ability of the model to the characteristics of pests and diseases. Finally, combined with fast pyramid pooling enhanced with Local Attention Networks, the deep fusion of cross-layer features is realized to improve the ability of the model to identify complex features and optimize the operation efficiency. The experimental results on the peach leaf shrink disease recognition dataset show that the proposed algorithm achieves a significant improvement in performance compared with the original YOLOv8 algorithm. Specifically, mF1, mPrecision, mRecall, and mAP increased by 0.1075, 0.0723, 0.1224, and 0.1184, respectively, which provided strong technical support for intelligent and automatic monitoring of peach pests and diseases. [ABSTRACT FROM AUTHOR]

The surface texture of objects in industrial scenes is complex and diverse, and the characteristics of surface defects are often very similar to the surrounding environment and texture background, so it is difficult to accurately detect the defect area. However, when deep learning technology is used to detect complex texture surface defects, the detection accuracy is not high, due to the lack of large-scale pixel-level label datasets. Therefore, a defect detection model Siamese-RCNet for complex texture surface with a small number of annotations is proposed. The Cascade R-CNN target detection network is used as the basic framework, making full use of unlabeled image feature information, and fusing the nonlinear relationship learning ability of Siamese network and the feature extraction ability of the Res2Net backbone network to more effectively capture the subtle features of complex texture surface defects. The image difference measurement method is used to calculate the similarity between different images, and the attention module is constructed to weight the feature map of the feature extraction pyramid, so that the model can focus more on the defect area and suppress the influence of complex background texture area, so as to improve the accuracy of detection. To verify the effectiveness of the Siamese-RCNet model, a series of experiments were carried out on the DAGM2007 dataset of weakly supervised learning texture surface defects for industrial optical inspection. The results show that even if only 20% of the labeled datasets are used, the mAP@0.5 of the Siamese-RCNet model can still reach 96.9%. Compared with the traditional Cascade R-CNN and Faster R-CNN target detection networks, the Siamese-RCNet model has high accuracy, can reduce the workload of manual labeling, and provides strong support for practical applications. [ABSTRACT FROM AUTHOR]

Featured Application: The state space search algorithm enhances the efficiency of maintenance operations in smart factories by reducing the risk of machine downtime, a critical factor in boosting competitiveness in the Industry 4.0 era. With the continuous growth of Industry 4.0 (I4.0), the industrial sector has transformed into smart factories, enhancing business competitiveness while aiming for the sustainable development of organizations. Machinery is a critical component and key to the success of production in a smart industrial factory. Minimizing unplanned downtime (UPDT) poses a significant challenge in designing an effective maintenance system. In the era of Industry 4.0, the most widely adopted maintenance frameworks are intelligent maintenance systems (IMSs), which integrate predictive maintenance with computerized systems. IMSs are intelligent tools designed to efficiently plan maintenance cycles for each machine component in a smart factory. This research presents the application of a search algorithm named state space search (SSS) in conjunction with a newly designed IMS, aimed at optimizing maintenance routines by identifying the optimal timing for maintenance cycles. The design began with the development of a new IMS concept that incorporates three key elements: the automation pyramid standard, Industrial Internet of Things (IIoT) sensors, and a computerized maintenance management system (CMMS). The CMMS collects machine data from the maintenance database, while real-time parameters are gathered via IIoT sensors from the supervisory control and data acquisition (SCADA) system. The new IMS concept provides a summary of the total maintenance cost and the remaining lifetime of the equipment. By integrating with SSS algorithms, the IMS presents optimized maintenance cycle solutions to the maintenance manager, focusing on minimizing costs while maximizing the remaining lifetime of the equipment. Moreover, the SSS algorithms take into account the risks associated with maintenance routines, following factory standards such as failure mode and effects analysis (FMEA). This approach is well suited to smart factories and helps to reduce UPDT. [ABSTRACT FROM AUTHOR]

This research investigates the impact of different energy storage materials on the efficiency of a pyramid solar still (PSS) for producing drinkable water in Pongalur, Tamil Nadu, India (10.9729° N, 77.3698° E). The study compares the performance of PSS using kanche marbles, natural banana fibers, and a reference solar still with only black paint at the basin. Results indicate that kanche marbles yield the highest distillate output, producing 2.94 kg daily, followed by banana fibers at 2.85 kg, and the reference still at 2.20 kg. Notably, kanche marbles and banana fibers enhance day and night distillate production by 36% and 21%, respectively, over the reference solar still. These findings highlight the significant potential of kanche marbles and natural banana fibers in improving solar still efficiency. [ABSTRACT FROM AUTHOR]

Bacterial leaf blight (BLB) disease is one of the most important diseases of rice in India and worldwide. The management of this disease becomes extremely challenging due to the presence of Xoo races with varying level of virulence and diversity. The development of BLB-resistant rice lines with the help of marker-assisted selection has been proven as one of the most effective strategies to sustain rice cultivation. This study demonstrates marker-assisted introgression and pyramiding of two broad-spectrum BLB genes (Xa33 and Xa38) into the background of "CO43," a popular fine white-grained rice cultivar, tolerant to saline coastal areas in southern India. Two separate marker-assisted backcrossing schemes were used, and at each stage of backcrossing, foreground, recombinant, and background selection were carried out. Backcross-derived lines possessing either Xa33 or Xa38 along with maximum genome recovery of "CO43" were identified at BC2F1 generation, intercrossed with each other to pyramid the genes, and selfed to identify homozygous lines for the pyramided genes. The performance of the improved "CO43" lines, such as lines # 2_7_78, 2_7_102, 6_9_65, and 6–8-76 exhibited enhanced broad-spectrum resistance to BLB (lesion length, LL 0.0 ± 0.0 to 0.9 ± 0.1 cm). Furthermore, the improved "CO43" lines maintained the agro-morphological features of the recurrent parent along with enhanced BLB resistance. These lines with effective and durable resistance would contribute to sustainable rice production in coastal areas of southern India. [ABSTRACT FROM AUTHOR]

To solve issues with low-detection accuracy and high-missed detection rate in subtle pavement cracks, this research proposed an intelligent detection method based on a one-stage anchor-free deep neural network, which consists of a feature extraction network, detection neck, and detection head. The feature extraction network with an extended deep attention network is responsible for extracting the features and output at different scales. The detection neck containing the special spatial pyramid structure fuses the contextual information of different scales to improve the abstract features. The detection head is composed of the anchor-free mechanism with decoupling property and the reparameterized structure to improve the detection accuracy and balance the algorithm complexity. The experimental results demonstrate that the Precision, Recall, and F1-Score are, 92.74%, 91.92%, and 0.9233 respectively. It has advantages over the most advanced one-stage detection method. Based on testing results, it has prominent stability and robustness for subtle pavement crack detection. [ABSTRACT FROM AUTHOR]

A d-dimensional lattice polytope P is Gorenstein if it has a multiple rP that is a reflexive polytope up to translation by a lattice vector. The difference d + 1 - r is called the degree of P. We show that a Gorenstein polytope is a lattice pyramid if its dimension is at least three times its degree. This was previously conjectured by Batyrev and Juny. We also present a refined conjecture and prove it for IDP Gorenstein polytopes. [ABSTRACT FROM AUTHOR]

An unescapable trend of hyperspectral image (HSI) has been toward classification with high accuracy and splendid performance. In recent years, Transformers have made remarkable progress in the HSI classification task. However, Transformer-based methods still encounter two main challenges. First, they concentrate on extracting spectral information and are incapable of using spatial information to a great extent. Second, they lack the utilization of multiscale features and do not sufficiently combine the advantages of the Transformer's global feature extraction and multiscale feature extraction. To tackle these challenges, this article proposes a new solution named the hybrid-scale dual-domain Transformer with crisscrossed interaction (HSD2Former) for HSI classification. HSD2Former consists of three functional modules: dual-dimension multiscale convolutional embedding (D2MSCE), mixed domainFormer (MDFormer), and pyramid scale fusion block (PSFB). D2MSCE supersedes conventional patch embedding to generate spectral and spatial tokens at different scales, effectively enriching the diversity of spectral-spatial features. MDFormer is designed to facilitate self-enhancement and information interaction between the spectral domain and spatial domain, alleviating the heterogeneity of the spatial domain and spectral domain. PSFB introduces a straightforward fusion manner to achieve advanced semantic information for classification. Extensive experiments conducted on four datasets demonstrate the robustness and significance of HSD2Former. The classification evaluation indicators of OA, AA, and Kappa on four datasets almost exceed 98%, reaching state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Matched line segments are crucial geometric elements for reconstructing the desired 3D structure in stereo satellite imagery, owing to their advantages in spatial representation, complex shape description, and geometric computation. However, existing line segment matching (LSM) methods face significant challenges in effectively addressing co-linear interference and the misdirection of parallel line segments. To address these issues, this study proposes a "continuous–discrete–continuous" cyclic LSM method, based on the Voronoi diagram, for stereo satellite images. Initially, to compute the discrete line-point matching rate, line segments are discretized using the Bresenham algorithm, and the pyramid histogram of visual words (PHOW) feature is assigned to the line segment points which are detected using the line segment detector (LSD). Next, to obtain continuous matched line segments, the method combines the line segment crossing angle rate with the line-point matching rate, utilizing a soft voting classifier. Finally, local point-line homography models are constructed based on the Voronoi diagram, filtering out misdirected parallel line segments and yielding the final matched line segments. Extensive experiments on the challenging benchmark, WorldView-2 and WorldView-3 satellite image datasets, demonstrate that the proposed method outperforms several state-of-the-art LSM methods. Specifically, the proposed method achieves F1-scores that are 6.22%, 12.60%, and 18.35% higher than those of the best-performing existing LSM method on the three datasets, respectively. [ABSTRACT FROM AUTHOR]

Improving the detection of small objects in remote sensing is essential for its extensive use in various applications. The diminutive size of these objects, coupled with the complex backgrounds in remote sensing images, complicates the detection process. Moreover, operations like downsampling during feature extraction can cause a significant loss of spatial information for small objects, adversely affecting detection accuracy. To tackle these issues, we propose ESL-YOLO, which incorporates feature enhancement, fusion, and a local attention pyramid. This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. Evaluations on the publicly accessible remote sensing datasets AI-TOD and DOTAv1.5 indicate that ESL-YOLO significantly surpasses other contemporary object detection frameworks. In particular, ESL-YOLO enhances mean average precision mAP by 10% and 1.1% on the AI-TOD and DOTAv1.5 datasets, respectively, compared to YOLOv8s. This model is particularly adept at small object detection in remote sensing imagery and holds significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Over a two-week period, various "sacred sites" in Egypt were visited by a group interested in the spiritual qualities of the ancient temples, pyramids, and tombs. The group engaged in informal ceremonies, including chanting and meditation, to pay respect to the sacred sites of the ancient Egyptians. A portable random event generator and palmtop computer were used to generate and record ongoing random sequences accompanied by a time-stamped computer index and onsite notes of relevant observations and activities. Pre-planned hypotheses predicted anomalous deviations of the sequences during visits to the sacred sites, including the inner sanctum or Holy of Holies in each temple and all the interior chambers of the pyramids. A further prediction was made that resonance- or coherence-building activities of the group, including chanting and meditation in these special locations, would also correlate with anomalous deviations. Both formal hypotheses were confirmed with a combined associated probability of 2.7x10^-6. Other categories of data provided context and helped to distinguish the sources of the anomalous effects. [ABSTRACT FROM AUTHOR]

Dichotomous Image Segmentation is a category-agnostic task aims to segment highly accurate objects from natural images. In semantic segmentation tasks, the utilization of high-resolution input, due to its richer contextual information, can effectively enhance the precision of segmentation and the accuracy of the boundaries. However, in DIS tasks, owing to the increased complexity and diversity of the targets, it is challenging to generate complete segmentation results. Directly employing high-resolution images as input may result in missed detection due to insufficient receptive field coverage. Furthermore, the additional potential details introduced by high resolution, which may not directly relate to the targets, can negatively impact the accuracy of the model's boundary predictions. To address the above problems, a dual-branch network structure is adopted, where the high-resolution input branch learns detailed information, and the low-resolution input branch captures global semantic information. Specifically, we use the Feature Pyramid Transfer module to enlarge the receptive field and enhance the semantic consistency between different Resnet blocks. In the decoder, we propose the Boundary-Aware structure to fuse features from different backbones and use boundary information to generate more accurate segmentation results. The experimental results demonstrate that our method achieves leading performance in four of the six evaluation metrics used in experiments on the complete DIS-TE testset. For instance, the F-measure reaches 0.801, which is 10.3% higher than that of the ISNet, the baseline method for the DIS task. [ABSTRACT FROM AUTHOR]

Marine Debris is the submerged and floating litter that majorly consists of plastics or other manmade polymers that are either intentionally discarded or unknowingly lost along shores of beaches, rivers or other waterbodies. Significant water pollution is caused due to such untreated waste released into the oceans, specifically in the coastal regions. The recent years have seen an immense rise in marine debris, which has created a necessity to reduce the amount of human effort and led to an increase in the demand for automatic marine debris detection. This paper proposes a multi-feature pyramid network (MFPN) of three subnetworks feature extractor, feature pyramid and pooling block that are concatenated to form a single end-to-end network for the task of automatic marine debris detection from multispectral imagery. Further, the feature extractor network is initialized with the pre-trained weights of Resnet-101 to achieve high performance with lower computational resources and normalization is used to reduce the bias of the model. The proposed model is tested on the Sentinel-2-based marine debris benchmark dataset MARIDA and outperforms the state-of-the-art architectures in the field by reporting an mIOU, Pixel Accuracy and F1-Score of 0.71, 0.84 and 0.80, respectively. [ABSTRACT FROM AUTHOR]

The Pyramid Model has a strong base of implementation research with in-service teachers. However, an entire population of the future workforce has been excluded from the research: pre-service teachers. The purpose of this multi-case qualitative study was to investigate pre-service teachers' (N = 80) attitudes and beliefs, after receiving training related to the Pyramid Model embedded within coursework, about the nature of students' social-emotional skills and providing behavior intervention supports. We triangulated data collection between students' responses to a discussion board posts, document analysis of course materials, and semi-structured interviews with recent graduates. Qualitative findings, for within and across cohorts, will be presented and are divided into two major categories: (1) foundational understanding of behavior and (2) catalysts of change. Implications for practice and future research are discussed. [ABSTRACT FROM AUTHOR]