Search

Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry. [ABSTRACT FROM AUTHOR]

Aim: Patients' death or adverse events appear to be associated with poor healthcare decision‐making. This might be due to an inability to have an adequate representation of the problem or of the connections among problem‐related elements. Changing how a problem is formulated can reduce biases in clinical reasoning. The purpose of this article is to explore the possible contributions of psychoneuroendocrinoimmunology (PNEI) and psychology of reasoning and decision‐making (PRDM) to support a new nursing theoretical frame. Design: Discursive paper. Method: This article discusses the main assumptions about nursing and nurses' ability to face patient's problems, suggesting a new approach that integrates knowledge from PNEI and PRDM. While PNEI explains the complexity of systems, highlighting the importance of systems connections in affecting health, PRDM underlines the importance of the informative context in creating a mental representation of the problem. Furthermore, PRDM suggests the need to pay attention to information that is not immediately explicit and its connections. Conclusion: Nursing recognizes the patient–nurse relationship as the axiom that governs care. The integration of PNEI and PRDM in nursing theoretics allows the expansion of the axiom by providing essential elements to read a new type of relationship: the relationship among information. PNEI explains the relationships between biological systems and the psyche and between the whole individual and the environment; PRDM provides tools for the nurse's analytical thinking system to correctly process information and its connections. Impact on Nursing Practice: A theoretical renewal is mandatory to improve nursing reasoning and nursing priority identification. Integrating PNEI and PRDM into nursing theoretics will modify the way professionals approach patients, reducing cognitive biases and medical errors. No Patient or Public Contribution: There was no patient or public involvement in the design or writing of this discursive article. [ABSTRACT FROM AUTHOR]

Since we wrote "Crowdsourcing as a solution to distant search" a decade ago, enthusiasm for crowdsourcing's capacity to produce remarkable solutions to some problems has continued to grow, and profiting from crowdsourced solutions has become a strategic goal for more and more firms. Crowdsourcing research has progressed impressively, with more progress made in researching the phenomenon than in theorizing about it. We are deeply honored by the 2022 Decade Award. In this manuscript, we reflect on the factors that led to this progress in crowdsourcing research, and how the theoretical insights from our paper––e.g., markets in the hierarchies-markets dichotomy are made up of markets with ex ante contracts and crowdsourcing (markets with no ex ante contracts)––may have influenced that progress. Also, because profits have become the ultimate goal of a large number of the seekers of solutions through crowdsourcing, we present an outline of a framework for exploring the impact of crowdsourcing on a seeker's profitability. The framework builds on insights from the paper and new constructs in crowdsourcing such as artificial intelligence (AI), the crowdsourcing process, revenue models, complementary assets, and organizing to minimize crowdsourcing disadvantages that we have added. We conclude with suggestions for future research. [ABSTRACT FROM AUTHOR]

Single-frame infrared small target detection is affected by the low image resolution and small target size, and is prone to the problems of small target feature loss and positional offset during continuous downsampling; at the same time, the sparse features of the small targets do not correlate well with the global-local linkage of the background features. To solve the above problems, this paper proposes an efficient infrared small target detection method. First, this paper incorporates BlurPool in the feature extraction part, which reduces the loss and positional offset of small target features in the process of convolution and pooling. Second, this paper designs an interactive attention deep feature fusion module, which acquires the correlation information between the target and the background from a global perspective, and designs a compression mechanism based on deep a priori knowledge, which reduces the computational difficulty of the self-attention mechanism. Then, this paper designs the context local feature enhancement and fusion module, which uses deep semantic features to dynamically guide shallow local features to realize enhancement and fusion. Finally, this paper proposes an edge feature extraction module for shallow features, which utilizes the complete texture and location information in the shallow features to assist the network to initially locate the target position and edge shape. Numerous experiments show that the method in this paper significantly improves nIoU, F1-Measure and AUC on IRSTD-1k Datasets and NUAA-SIRST Datasets. [ABSTRACT FROM AUTHOR]

Foveation has two methods: single image mode and dual images mode. For the former, SOC transmits the compressed image consists of different low‐resolution blocks, some of which have very large differences after up‐scaling may cause flickering risks when displayed. For the latter, SOC processes the compressed periphery and foveated images sequentially, therefor DDIC uses large GRAM to cache these bitstreams, which will also introduce huge latency. In order to solve these problems existing in two modes, this paper presents a scheme to process the image or compressed bitstreams with coding unit rearrangement. [ABSTRACT FROM AUTHOR]

Background: The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) has long recognized and championed increasing diversity within the surgical workplace. SAGES initiated the Fundamentals of Leadership Development (FLD) Curriculum to address these needs and to provide surgeon leaders with the necessary tools and skills to promote diversity, equity, and inclusion (DEI) in surgical practice. In 2019, the American College of Surgeons issued a request for anti-racism initiatives which lead to the partnering of the two societies. The primary goal of FLD was to create the first surgeon-focused leadership curriculum dedicated to DEI. The rationale/development of this curriculum and its evaluation/feedback methods are detailed in this White Paper. Methods: The FLD curriculum was developed by a multidisciplinary task force that included surgeons, education experts, and diversity consultants. The curriculum development followed the Analysis, Design, Development, Implementation and Evaluation (ADDIE) instructional design model and utilized a problem-based learning approach. Competencies were identified, and specific learning objectives and assessments were developed. The implementation of the curriculum was designed to be completed in short intervals (virtual and in-person). Post-course surveys used the Kirkpatrick's model to evaluate the curriculum and provide valuable feedback. Results: The curriculum consisted of interactive online modules, an online discussion forum, and small group interactive sessions focused in three key areas: (1) increasing pipeline of underrepresented individuals in surgical leadership, (2) healthcare equity, and (3) conflict negotiation. By focusing on positive action items and utilizing a problem-solving approach, the curriculum aimed to provide a framework for surgical leaders to make meaningful changes in their institutions and organizations. Conclusion: The FLD curriculum is a novel leadership curriculum that provided surgeon leaders with the knowledge and tools to improve diversity in three areas: pipeline improvement, healthcare equity, and conflict negotiation. Future directions include using pilot course feedback to enhance curricular effectiveness and delivery. [ABSTRACT FROM AUTHOR]

This article discusses the importance of integrating educational theory and research into the production and evaluation of educational media. It highlights the lack of grounding in educational theory and research in many educational media products, such as apps. However, it also acknowledges examples of educational media, like Sesame Street, that are built upon a strong foundation of theory and research. The article introduces a special section of papers that provide insight into the integration of theory and practice in the creation and evaluation of an educational television series, using Sesame Street's curriculum focus on "playful problem solving" as an example. These papers trace the development of the curriculum from its theoretical underpinnings to its implementation and impact on the target audience. The article concludes by emphasizing the educational benefits of Sesame Street and Playful Learning, and encourages educators, producers, and researchers to share their insights and experiences to further merge theory and practice in educational media. [Extracted from the article]

Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localizationmodule (LLM). Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches, fully considering the impact of lesion type differences on grading, solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task. Secondly, the LLM generates a global attention map based on localization. Finally, the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details. This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset, obtaining an accuracy of 0.8064. [ABSTRACT FROM AUTHOR]

In transportation, roads sometimes have cracks due to overloading and other reasons, which seriously affect driving safety, and it is crucial to identify and fill road cracks in time. Aiming at the defects of existing semantic segmentation models that have degraded the segmentation performance of road crack images and the standard convolution makes it challenging to capture the spatial and channel coupling relationship between pixels. It is difficult to differentiate crack pixels from background pixels in complex backgrounds; this paper proposes a semantic segmentation model for road cracks that combines channel-spatial convolution with the aggregation of frequency features. A new convolutional block is proposed to accurately identify cracked pixels by grouping spatial displacements and convolutional kernel weight dynamization while modeling pixel spatial relationships linked to channel features. To enhance the contrast of crack edges, a frequency domain feature aggregation module is proposed, which uses a simple windowing strategy to solve the problem of mismatch of frequency domain inputs and, at the same time, takes into account the effect of the frequency imaginary part on the features to model the deep frequency features effectively. Finally, a feature refinement module is designed to refine the semantic features to improve the segmentation accuracy. Many experiments have proved that the model proposed in this paper has better performance and more application potential than the current popular general model. [ABSTRACT FROM AUTHOR]

Purpose: With the aid of libraries, the research paper aims to assist businesses in swiftly and accurately acquiring knowledge and insights from scholarly literature to guide their inventive and decision-making processes. The foundation for achieving the goal is Connected Papers technology. Design/methodology/approach: The author's professional expertise in performing literature reviews using connected papers technology as well as using other ways, and corresponding with entrepreneurs and librarians impacted the article's research methodology. Findings: The use of Connected Papers technology in the library context for helping entrepreneurs is discussed. Libraries and entrepreneurs could benefit from using Connected Papers technology to quickly compile pertinent data from scholarly literature to solve business challenges. According to the paper, adopting this technology can speed up information gathering and drastically reduce the time needed for business owners to search through bibliographic data-bases. Using this technology can help entrepreneurs at various phases of their entrepreneurial journeys and give libraries a productive way to assist business owners with their information needs. Originality/value: This paper's novelty comes from its examination of the usage of connectedpapers.com technology to compile data from scholarly literature to assist entrepreneurs in solving their business problems. The useful piece of advice this paper offers entrepreneurs and librarians is what makes it valuable. By using connectedpapers.com technology, businesses may be able to get critical information from scholarly literature to foster a series of experimentation quickly and effectively. Also, librarians can help their patrons with systematic re-views and other research services by using this application. [ABSTRACT FROM AUTHOR]

Copyright of Economic Series of the Bulletin of the L.N. Gumilyov ENU is the property of L.N. Gumilyov Eurasian National University 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.)

Stope layout optimisation is an important sub-problem of the generic underground mining optimisation problem that contributes towards optimal ore extraction. It is a growing research area as some surface mines transition from open-pit to underground mining. Several researchers acknowledge the complexity of optimising underground mine layouts because exact methods are limited in generating three-dimensional optimal solutions, hence the growing use of metaheuristic approaches to solve the problem in three dimensions. This paper presents a PRISMA-based bibliometric mapping of stope layout optimisation approaches using R-Biblioshiny and VOSviewer software, their concomitant limitations, and suggests potential future research directions in stope layout optimisation. [ABSTRACT FROM AUTHOR]

The regularized lattice Boltzmann method (RLBM) is an improvement of the lattice Boltzmann method (LBM). The advantage of RLBM is improved accuracy without increasing computational overheads. The paper introduces the method of multi-layer grids, the multi-layer grids have different resolutions which can accurately solve problems in computational fluid dynamics (CFD) without destroying the parallelism of RLBM. Simulating fluid flow usually requires a large number of grid simulations. Therefore, it is necessary to design a parallel algorithm for RLBM based on multi-layer grids. In this paper, a load-balancing-based grid dividing algorithm and an MPI-based parallel algorithm for RLBM on multi-layer grids are proposed. The load balancing-based grid dividing algorithm ensures that the workload is evenly distributed across processes, minimizing the discrepancies in computational load. The MPI-based parallel algorithm for RLBM on multi-layer grids ensures accurate and efficient numerical simulation. Numerical simulations have verified that the proposed algorithms exhibit excellent performance in both 2D and 3D experiments, maintaining high stability and accuracy. The multi-layer grids method is significantly better than single-layer grids in terms of CPU runtime and number of grids required. Comparative analysis with the OpenMP multi-threading method on the multi-layer grid RLBM shows that the proposed algorithm in this paper achieves superior speedup and efficiency. [ABSTRACT FROM AUTHOR]

As an indispensable part of the vehicle environment perception task, road traffic marking detection plays a vital role in correctly understanding the current traffic situation. However, the existing traffic marking detection algorithms still have some limitations. Taking lane detection as an example, the current detection methods mainly focus on the location information detection of lane lines, and they only judge the overall attribute of each detected lane line instance, thus lacking more fine-grained dynamic detection of lane line attributes. In order to meet the needs of intelligent vehicles for the dynamic attribute detection of lane lines and more perfect road environment information in urban road environment, this paper constructs a fine-grained attribute detection method for lane lines, which uses pixel-level attribute sequence points to describe the complete attribute distribution of lane lines and then matches the detection results of the lane lines. Realizing the attribute judgment of different segment positions of lane instances is called the fine-grained attribute detection of lane lines (Lane-FGA). In addition, in view of the lack of annotation information in the current open-source lane data set, this paper constructs a lane data set with both lane instance information and fine-grained attribute information by combining manual annotation and intelligent annotation. At the same time, a cyclic iterative attribute inference algorithm is designed to solve the difficult problem of lane attribute labeling in areas without visual cues such as occlusion and damage. In the end, the average accuracy of the proposed algorithm reaches 97% on various types of lane attribute detection. [ABSTRACT FROM AUTHOR]

This paper surveys and evaluates J.L. Ericksen's fundamental work in developing, refining, promoting, and solving problems for Cosserat theories of rods and shells. These geometrically and mechanically exact nonlinear theories can be used to describe physically important effects not accounted for by their classical predecessors. This paper begins with a sketch of early theories of the mechanics of rods and shells. It then gives a critical account of the pioneering and immediately influential 1958 paper Ericksen wrote with C. Truesdell. It next describes Ericksen's subsequent contributions to the formulation and analysis of specific problems for these Cosserat theories, and how they resolved some open issues. This paper concludes with a discussion of refinements of these theories and a commentary on their modern roles. [ABSTRACT FROM AUTHOR]

The urban three-dimensional (3-D) model, which can truly and vividly express the 3-D spatial information of ground objects, was an essential link in the process of smart city construction. The backpack 3-D laser scanning systems had gradually become an important technical means to build urban 3-D models because of their high efficiency and high fineness. In this paper, the error source of backpack point cloud data was analysed based on the composition of backpack 3-D laser scanning system. The distribution of point cloud error was statistically analysed by designing experiments. It was found that there was a systematic error in the backpack 3-D laser scanning point cloud. There were many factors influencing the system errors and they were superimposed on each other. In order to solve this problem, an error compensation model with additional system parameters based on a multiple linear regression algorithm was constructed. The results showed that this model can weaken the influence of system errors on the backpack point cloud, effectively improve the accuracy of the backpack point cloud, and provide reliable point cloud data for subsequent processing of the building point cloud. [ABSTRACT FROM AUTHOR]

Efficient maritime search and rescue (SAR) is crucial for responding to maritime emergencies. In traditional SAR, fixed search path planning is inefficient and cannot prioritize high-probability regions, which has significant limitations. To solve the above problems, this paper proposes unmanned surface vehicles (USVs) path planning for maritime SAR based on POS-DQN so that USVs can perform SAR tasks reasonably and efficiently. Firstly, the search region is allocated as a whole using an improved task allocation algorithm so that the task region of each USV has priority and no duplication. Secondly, this paper considers the probability of success (POS) of the search environment and proposes a POS-DQN algorithm based on deep reinforcement learning. This algorithm can adapt to the complex and changing environment of SAR. It designs a probability weight reward function and trains USV agents to obtain the optimal search path. Finally, based on the simulation results, by considering the complete coverage of obstacle avoidance and collision avoidance, the search path using this algorithm can prioritize high-probability regions and improve the efficiency of SAR. [ABSTRACT FROM AUTHOR]

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology. [ABSTRACT FROM AUTHOR]

In a companion paper (Date, K., and R. Nagi. [(2023) Optimal Placement of Multiple Finite-size Rectangular Facilities in an Existing Layout. International Journal of Production Research, THIS ISSUE]) we investigated a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. We introduced the requisite theory to arrive at a solution by dividing the feasible region into sub-regions whose boundaries provide the candidates for the optimal placement. In this paper, we focus on developing optimal and heuristic solution methods for solving problem instances with various facility counts. To solve small instances with fewer facilities, we develop two optimal methods: (1) efficient explicit enumeration based on a tree state-space representation and (2) an implicit enumeration scheme that uses a flow-decomposition-based lower bound to reduce the search space and time. To solve industrial-sized instances, we have to resort to a family of construction and improvement heuristics given that the problem is NP-hard. The heuristic procedures perform well for non-pathological cases with an acceptable optimality gap. The main contribution of this paper is a range of optimal to heuristic methods that can be applied by the practitioner depending on their problem characteristics and desired efficiency. [ABSTRACT FROM AUTHOR]

With the development of science and technology and the appearance of various special conditions that cause signers to be unable to sign, proxy signature is gradually becoming a hot spot in cryptography research. This paper combines proxy signature, quantum teleportation, and multi-party verification and proposes a multi-party verifiable quantum proxy signature scheme based on quantum teleportation. This scheme has the following characteristics: The authentication method based on the H a s h function can effectively solve the problem of identity identification among members; in order for the proxy signer to be able to verify the correctness of the proxy authorization, a form of proxy signature authorization that concatenates the identity information of the original signer is used. The security analysis shows that our scheme is unforgeable and undeniable and can resist intercept-resend attacks and cheating attacks. [ABSTRACT FROM AUTHOR]

This article discusses the concept of participation in child talk and interactional synchronicity. It emphasizes the importance of participation in social education and socialization, as children learn to manage and organize participation to become competent members of their communities. The article explores the relationship between context, forms of synchronicity, and their functions, as well as the concepts of similarity, synchronization, mimesis, and mirroring. It also examines how similarities through synchronization can be studied and analyzed to establish interactive participation. The article includes several papers that analyze the negotiation and achievement of participation through the construction of similarity and synchronicity in child discourse. [Extracted from the article]

Aiming at the problem that the traditional short-circuit current calculation method is not applicable to Distributed Generation (DG) accessing the distribution network, the paper proposes a short-circuit current partitioning calculation method considering the degree of voltage drop at the grid-connected point of DG. Firstly, the output characteristics of DG in the process of low voltage ride through are analyzed, and the equivalent output model of DG in the fault state is obtained. Secondly, by studying the network voltage distribution law after fault in distribution networks under different DG penetration rates, the degree of voltage drop at the grid-connected point of DG is used as a partition index to partition the distribution network. Then, iterative computation is performed within each partition, and data are transferred between partitions through split nodes to realize the fast partition calculation of short-circuit current for high proportion DG access to distribution network, which solves the problems of long iteration time and large calculation error of traditional short-circuit current. Finally, a 62-node real distribution network model containing a high proportion of DG access is constructed on MATLAB/Simulink, and the simulation verifies the effectiveness of the short-circuit current partitioning calculation method proposed in the paper, and its calculation speed is improved by 48.35% compared with the global iteration method. [ABSTRACT FROM AUTHOR]

In order to solve the problem of image quality and morphological characteristics of primary underglaze brown decorative pattern extraction, this paper proposes a method of primary underglaze brown decorative pattern extraction based on the coupling of single scale gamma correction and gray sharpening. The single-scale gamma correction is combined with the gray sharpening method. The single-scale gamma correction improves the contrast and brightness of the image by nonlinear transformation, but may lead to the loss of image detail. Gray sharpening can enhance the high frequency component and improve the clarity of the image, but it will introduce noise. Combining these two technologies can compensate for their shortcomings. The experimental results show that this method can improve the efficiency of last element underglaze brown decorative pattern extraction by enhancing the image retention detail and reducing the influence of noise. The experimental results showed that F1Score, Miou(%), Recall, Precision and Accuracy(%) were 0.92745, 0.82253, 0.97942, 0.92458 and 0.92745, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive power control strategy for the three‐phase, six‐switch Vienna rectifier without grid voltage sensors to reduce the hardware cost and complexity of a high‐power PWM rectifier system. Firstly, an algorithm for calculating the AC‐side voltage in the αβ coordinate system is derived according to the operating principle of the Vienna rectifier, and a voltage observer is constructed by combining a second‐order low‐pass filter to estimate the grid voltage. Secondly, a soft start method is designed to solve the problem that the rectifier is prone to inrush current when it is started. Furthermore, the control method of grid voltage sensorless is combined with predictive power control with good dynamic characteristics and simple parameter settings to form the control strategy proposed in this paper. Finally, simulation analysis and experimental verification are carried out on the proposed control strategy. Simulation and experimental results show that the grid voltage estimation has high accuracy, a good surge current suppression effect, unit power factor operation, low input current harmonic content, and good dynamic and steady‐state performance. Therefore, the correctness and effectiveness of the strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

Many problems at the intersection of nuclear technology, policy, and society can be thought of as wicked problems. Wicked problems--a formulation put forward in what is now a landmark paper by Rittel and Webber (design and planning scholars respectively)--are those that lack definitive formulations, resist durable resolution, do not have an exhaustively identifiable set of true or false solutions, and are often framed entirely differently by different entities experiencing the problem. Every attempt to solve a wicked problem is a solution attempt made in the real world and thus has consequences and implications that can potentially be far-reaching. This paper describes the underlying philosophy, design, and implementation of a course on "Nuclear Technology, Policy, and Society" taught in the Department of Nuclear Engineering and Radiological Sciences at the University of Michigan. The course explores some of nuclear technology's most pressing challenges (or its 'wicked problems'). Through this course students explore the origins of these problems--be they social or technical, they are offered tools--conceptual and methodological--to make sense of these problems, and guided through a semester-long exploration of how engineers can work towards their resolution, and to what degree these problems can be solved through institutional transformation and/or a transformation in our own practices and norms as a field. The underlying pedagogical philosophy, implementation, and response to the course are described here for other instructors who might wish to create a similar course, or for non-academic nuclear engineers, who might perhaps, in these pages, find a vocabulary for articulating and reflecting on the nature of these problems as encountered in their praxis. [ABSTRACT FROM AUTHOR]

In the inductive power transfer (IPT) system, using multiple coil branches instead of a whole large coil can effectively solve the thickness problem and save space. In that case, there are small parameter differences in mutual inductance and self‐inductance between the coil branches in parallel. These differences are amplified in high‐frequency IPT systems, resulting in different currents flowing through the different coil branches. The current unbalance will create many hazards. Therefore, this paper analyzes the key factors that cause current to unbalance and studies the equivalent circuit model of an inductive power transfer system with secondary multiple parallel branches. Moreover, this paper designs the compensation circuit and studies two kinds of current balance methods. Furthermore, the parameter design criteria are detailed to obtain a nearly ideal current balance effect. The characteristic analysis, parameter design, and verification are described in detail. [ABSTRACT FROM AUTHOR]

The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressing these platforms. This paper discusses six of the most popular emerging blockchain platforms adopted by IoT systems and analyses their usage in state-of-the-art works to solve security problems. The platform was compared in terms of security features and other requirements. Findings from the study reveal that most blockchain components contribute directly or indirectly to IoT security. Blockchain platform components such as cryptography, consensus mechanism, and hashing are common ways that security is achieved in all blockchain platform for IoT. Technologies like Interplanetary File System (IPFS) and Transport Layer Security (TLS) can further enhance data and communication security when used alongside blockchain. To enhance the applicability of blockchain in resource-constrained IoT environments, future research should focus on refining cryptographic algorithms and consensus mechanisms to optimise performance and security. [ABSTRACT FROM AUTHOR]

Traditionally, theory and practice are treated as separate silos when designing educational media, or at best, as theory informing practice in a one-way relationship. The relationship between theory and practice, however, is most productive when seen as reciprocal, existing in an iterative relationship with each influencing the other in turn. This paper reflects on the Sesame Street production and research described earlier in this special section, drawing lessons and implications for future theory and research. Implications are also drawn for future productions of Sesame Street and its international co-productions, extensions into ancillary media, and, more broadly, educational media in general, informed by a global advisory panel and the development of a global theoretical framework for future multiplatform content creation. Beyond informing Sesame Workshop's approach to creativity and playful problem solving, this paper offers a model for media makers seeking to bridge the research-practice gap. [ABSTRACT FROM AUTHOR]

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities and the symmetry in the modal fusion process. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual, and textual features, and symmetrical hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated, respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. The experimental results show that compared with the current multimodal SOTA method, MKGformer, MM-Transformer improves the Hits@1 and Hits@10 evaluation indicators by 1.17% and 1.39%, respectively, proving that the proposed method can effectively solve the problem of multimodal feature fusion in the knowledge graph link prediction task. [ABSTRACT FROM AUTHOR]

In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird's Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy. [ABSTRACT FROM AUTHOR]

Recently, a robust steganographic algorithm that achieves errorless robustness against JPEG recompression has been proposed. The method employs a lattice embedding scheme and utilizes the syndrome-trellis code (STC) for practical embedding. However, we have noticed that errorless robust embedding with STC may encounter failures due to modifications on wet coefficients, especially when a high quality factor is used by the compression channel. To solve this problem, we have discovered that using steganographic polar code (SPC) for embedding has better performance in avoiding modifications on wet coefficients. In this paper, we conduct theoretical analysis to prove the better performance of SPC in wet paper embedding. We establish the condition of avoiding modifications on wet coefficients, followed by presenting a recursive calculation method for determining the distribution of columns in the generator matrix of SPC. The findings reveal that SPC can avoid modifications on wet coefficients under a larger number of wet coefficients compared with STC, and therefore we propose a better errorless robust embedding method employing SPC. The experimental results demonstrate that under close security performance, the proposed method achieves a higher success rate compared with embedding with STC. Specifically, when the quality factor of the compressor is 95 and the payload size is 0.4 bpnzac, our method achieves a success rate of 99.85%, surpassing the 91.95% success rate of the embedding with STC. [ABSTRACT FROM AUTHOR]

In order to solve the cold-starting problem and improve the intake and exhaust pipe temperatures of diesel engines under cold-starting and low- and medium-speed conditions, this paper proposes a twice-open control method for a hydraulic variable valve system. First, a hydraulic variable valve system that can realize a fully variable valve lift and phase angle is applied to replace the original intake system in order to meet the air intake requirements of different conditions. Then, a twice-open control method in which the intake valve opens two times at the exhaust stroke and intake stroke is proposed to improve the intake pipe temperature and solve the cold-starting problem. This paper contains a numerical work analysis. A GT-POWER model is constructed to validate the intake valve twice-open control method. The cylinder pressure, cylinder temperature, intake pipe pressure, and intake pipe temperature are obtained and compared between the original intake valve system and the hydraulic variable valve system with the proposed intake valve twice-open control method. The results show that the twice-open control method can increase the intake pipe temperature to 260 K or even higher, which can improve the cold-starting performance and the exhaust temperature at low and medium speeds. At the same time, the performance under low- and medium-speed conditions is improved. [ABSTRACT FROM AUTHOR]

In this paper, a novel three-degree-of-freedom piezoelectric-driven micro-positioning platform based on a lever combination compound bridge-type displacement amplification mechanism is proposed. The micro-positioning platform proposed in this paper aims to solve the current problem of the large size and small travel of the three-degree-of-freedom piezoelectric-driven micro-positioning platform. In this paper, a lever combination compound bridge-type displacement amplification mechanism combined with a new biaxial flexible hinge is proposed, the structural dimensions of the lever mechanism and the compound bridge mechanism are optimized, and the amplification multiplier is determined. The maximum output simulation analysis of the micro-positioning platform is carried out by using ANSYS, and the experimental test system is built for verification. The validation results show that the maximum errors between simulation and experiment in the z-direction, rotation direction around x, and rotation direction around y are 64 μm, 0.016°, and 0.038°, respectively, and the corresponding maximum relative errors are 5.6%, 2.4%, and 6.6%, respectively, which proves the feasibility of the theoretical design. [ABSTRACT FROM AUTHOR]

The value of large‐scale collaborations for solving complex problems is widely recognized, but many barriers hinder meaningful authorship for all on the resulting multi‐author publications. Because many professional benefits arise from authorship, much of the literature on this topic has focused on cheating, conflict and effort documentation. However, approaches specifically recognizing and creatively overcoming barriers to meaningful authorship have received little attention.We have developed an inclusive authorship approach arising from 15 years of experience coordinating the publication of over 100 papers arising from a long‐term, international collaboration of hundreds of scientists.This method of sharing a paper initially as a storyboard with clear expectations, assignments and deadlines fosters communication and creates unambiguous opportunities for all authors to contribute intellectually. By documenting contributions through this multi‐step process, this approach ensures meaningful engagement by each author listed on a publication.The perception that co‐authors on large authorship publications have not meaningfully contributed underlies widespread institutional bias against multi‐authored papers, disincentivizing large collaborations despite their widely recognized value for advancing knowledge. Our approach identifies and overcomes key barriers to meaningful contributions, protecting the value of authorship even on massively multi‐authored publications. [ABSTRACT FROM AUTHOR]

Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric facial expression recognition, this paper proposes a fusion of channel global features and a spatial local information expression recognition network called the "Channel–Spatial Integration Network" (CSINet). First, to extract the underlying detail information and deepen the network, the attention residual module with a redundant information filtering function is designed, and the backbone feature-extraction network is constituted by module stacking. Second, considering the loss of information in the local key area of face occlusion, the channel–spatial fusion structure is constructed, and the channel features and spatial features are combined to enhance the accuracy of occluded facial recognition. Finally, before the full connection layer, more local spatial information is embedded into the global channel information to capture the relationship between different channel–spatial targets, which improves the accuracy of feature expression. Experimental results on the natural scene facial expression data sets RAF-DB and FERPlus show that the recognition accuracies of the modeling approach proposed in this paper are 89.67% and 90.83%, which are 13.24% and 11.52% higher than that of the baseline network ResNet50, respectively. Compared with the latest facial expression recognition methods such as CVT, PACVT, etc., the method in this paper obtains better evaluation results of masked facial expression recognition, which provides certain theoretical and technical references for daily facial emotion analysis and human–computer interaction applications. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher 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.)

Under the premise of resource constraint, it is crucial to identify the product innovation opportunities contained in failed patents through external search in order to compensate for the shortcomings of enterprises' own technology. Due to the cost of patent research and development and the risk of infringement, this paper constructs a product innovation identification path that integrates the FOS and BERTopic model from the perspective of invalid patents. The path consists of three stages, including the identification of the problem to be solved by the product based on functional analysis, the extraction of the subject matter elements based on the core failed patent technology, and the generation and evaluation of innovative solutions based on TRIZ theory and the best- worst method (BWM). Finally, the feasibility of the path constructed in this paper is verified by taking a slurry pump as an example. The application results show that the product innovation identification path constructed in this paper can provide theoretical support for enterprises to carry out technological innovation activities efficiently. [ABSTRACT FROM AUTHOR]

Paper was one of the greatest inventions in ancient China, and it greatly promoted the rapid development of human civilization. Even in today's electronic information age, paper and paper‐derived products are still indispensable to people's daily work and life. However, the traditional paper based on cellulose fibers from plants has some problems. For instance: (1) papermaking consumes a large amount of precious natural resources such as trees; (2) the papermaking industry causes environmental pollution; (3) the traditional paper consisting of organic cellulose fibers is highly flammable and easy to burn; (4) the traditional paper based on cellulose fibers turns to yellow color with time; (5) the lifetime of the traditional paper is relatively short because of the degradation of cellulose fibers. More efforts are needed to investigate and solve these problems facing the traditional paper. In recent years, a new kind of multifunctional fire‐resistant paper based on ultralong hydroxyapatite nanowires has been developed and investigated for various applications, and many research findings have been documented in the literature. This review article provides a comprehensive summary and discussion on recent research advances for this new kind of multifunctional fire‐resistant paper based on ultralong hydroxyapatite nanowires, including the synthesis, properties, applications, and future perspectives. [ABSTRACT FROM AUTHOR]

Self‐aligned a‐IGZO coplanar TFTs have high mobility, excellent large‐area uniformity, and are structurally free of parasitic caps. Accordingly, it is being adopted and mass‐produced as the backplane of OLED TV. Currently, the scope of application of OLED displays using oxide TFT backplanes is expanding to high‐resolution monitors for gaming. With this expansion, not only the size of the TFT but also its storage capacity has become an important factor in terms of aperture ratio. Instead of using a thick Passivation (PAS) Layer for storage capacity, we developed a structure that maximizes the aperture ratio by using a thin gate insulator (GI). However, GI capacitance must be used as the gate electrode and active layer electrode, but in self‐aligned coplanar structure, the active layer overlapping the gate metal is not conductive. To solve this problem, we succeeded in developing a 27‐inch QHD OLED monitor product for gaming by applying a self‐aligned coplanar structure/process that allows the active layer to simultaneously configure channels and wiring, and controlling degradation of channel characteristics. [ABSTRACT FROM AUTHOR]

In the tobacco industry, impurity detection is an important prerequisite for ensuring the quality of tobacco. However, in the actual production process, the complex background environment and the variability of impurity shapes can affect the accuracy of impurity detection by tobacco robots, which leads to a decrease in product quality and an increase in health risks. To address this problem, we propose a new online detection method of tobacco impurities for tobacco robot. Firstly, a BCFormer attention mechanism module is designed to effectively mitigate the interference of irrelevant information in the image and improve the network's ability to identify regions of interest. Secondly, a Dual Feature Aggregation (DFA) module is designed and added to Neck to improve the accuracy of tobacco impurities detection by augmenting the fused feature maps with deep semantic and surface location data. Finally, to address the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes, this paper proposes an optimized loss function to more accurately assess the quality of the bounding boxes. To evaluate the effectiveness of the algorithm, this paper creates a dataset specifically designed to detect tobacco impurities. Experimental results show that the algorithm performs well in identifying tobacco impurities. Our algorithm improved the mAP value by about 3.01% compared to the traditional YOLOX method. The real-time processing efficiency of the model is as high as 41 frames per second, which makes it ideal for automated inspection of tobacco production lines and effectively solves the problem of tobacco impurity detection. [ABSTRACT FROM AUTHOR]

The detection of tiny defects in industrial products is important for improving the quality of industrial products and maintaining production safety. Currently, image-based defect detection methods are ineffective in detecting tiny and variously shaped defects. Therefore, this paper proposes a tiny defect detection network (TD-Net) for industrial products to improve the effectiveness of tiny defect detection. TD-Net improves the overall defect detection effect, especially the detection effect of tiny defects, by solving the problems of downsampling of tiny defects, pre-filtering of conflicting deep and shallow semantic information, and cascading fusion of multi-scale information. Specifically, this paper proposes the Defect Downsampling (DD) module to realize the defect information supplementation during the backbone downsampling process and improve the problem that the stepwise convolution easily misses the detection of tiny defects. Meanwhile, the Semantic Information Interaction Module (SIIM) is proposed, which fuses deep and shallow semantic features, and is designed to interact the fused features with shallow features to optimize the detection of tiny defects. Finally, the Scale Information Fusion Module (SIFM) is proposed to improve the Path Aggregation Network (PANet) for cascading fusion and information focus on different scale information, which enables further improvement of defect detection performance of TD-Net. Extensive experimental results on the NEU–DET data set (76.8 % mAP), the Peking University PCB defect data set (96.2 % mAP) and the GC10-DET data set (71.5 % mAP) show that the proposed TD-Net achieves competitive results compared with SOTA methods with the equivalent parameter quantity. [ABSTRACT FROM AUTHOR]

The presented scientific paper arises mainly in the context of solving current issues, problems of theory and practice of entrepreneurship. In particular, in the field of dispositional solutions, dimensions of the formation of normative basis of sustainable development in the field of production and logistics. It is an expression and continuation of the previous opinion, thought march, experiential expression of the authors on the issue. It focuses mainly on the current challenges associated with the development management of production and logistics, on the parameters that will need to be respected and, to a greater extent, applied in the future. It is also a response to the fact in the sense that the results of research, predispositions in the field are insufficient. Especially in terms of the approach to solving the problems of the normative base, not only in the responsible authorities, institutions in this area, but also in the business units themselves. [ABSTRACT FROM AUTHOR]

Conversation circles in kindergartens can foster discursive abilities such as argumentation. This paper analyzes argumentation in conversation circles in a kindergarten with respect to the function of narrative argumentation. We focus on second stories as a specific narrative form that is characterized by relating in content and form to a before told narrative thereby displaying similarity and alignment. In this paper, we will analyze videographed conversation circles in a kindergarten. Our methodological approach is interactional and qualitative, informed by conversation analysis and narrative analysis. We will argue that preschool children use second stories for argumentative purposes in cooperative argumentative exchanges and through them produce interactional alignment. [ABSTRACT FROM AUTHOR]

This article presents a reflection on a teaching experience involving the use of the Brügner tangram, an interesting but little-known manipulative material. It details an activity conducted as part of an undergraduate mathematics education course for prospective primary school teachers. The main objective of this paper is to present the implementation of a sequence of activities designed to convey to future teachers the importance of systematically solving problems that involve searching for or constructing different cases. Specifically, participants are provided with the three pieces of the Brügner's tangram and assigned the task of identifying all possible convex polygons that can be constructed. Moreover, they are required to elucidate the methodological approach underpinning their exploratory process, with an emphasis on establishing relationships between the polygons. Graphs are introduced as one of the possible approaches for modelling the problem, offering a graphical representation that aids in the systematic search for solutions. This paper describes different activities involving the Brügner's tangram which has demonstrated its adaptability as an instructional resource. The teaching sequence adheres to the five-phase structure proposed within the framework of the van Hiele model, which is also part of the course. [ABSTRACT FROM AUTHOR]

Panoramic video multimedia technology has made significant advancements in recent years, providing users with an immersive experience by displaying the entire 360° spherical scene centered around their virtual location. However, due to its larger data volume compared to traditional video formats, transmitting high-quality videos requires more bandwidth. It is important to note that users do not see the whole 360° content simultaneously, but only a portion that is within their viewport. To save bandwidth, viewport-based adaptive streaming has become a significant technology that transmits only the viewports of interest to the user in high quality. Therefore, the accuracy of viewport prediction plays a crucial role. However, the performance of viewport prediction is affected by the size of the prediction window, which decreases significantly as the window size increases. In order to address this issue, we propose an effective self-attention viewport prediction model based on distance constraint in this paper. Firstly, by analyzing the existing viewport trajectory dataset, we find the randomness and continuity of the viewport trajectory. Secondly, to solve the randomness problem, we design a viewport prediction model based on a self-attention mechanism to provide more trajectory information for long inputs. Thirdly, in order to ensure the continuity of the predicted viewport trajectory, the loss function is modified with the distance constraint to reduce the change in the continuity of prediction results. Finally, the experimental results based on the real viewport trajectory datasets show that the algorithm we propose has higher prediction accuracy and stability compared with the advanced models. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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.)