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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]

Gestures-hand movements that accompany speech and express ideas-can help children learn how to solve problems, flexibly generalize learning to novel problem-solving contexts, and retain what they have learned. But does it matter who is doing the gesturing? We know that producing gesture leads to better comprehension of a message than watching someone else produce gesture. But we do not know how producing versus observing gesture impacts deeper learning outcomes such as generalization and retention across time. Moreover, not all children benefit equally from gesture instruction, suggesting that there are individual differences that may play a role in who learns from gesture. Here, we consider two factors that might impact whether gesture leads to learning, generalization, and retention after mathematical instruction: (1) whether children see gesture or do gesture and (2) whether a child spontaneously gestures before instruction when explaining their problem-solving reasoning. For children who spontaneously gestured before instruction, both doing and seeing gesture led to better generalization and retention of the knowledge gained than a comparison manipulative action. For children who did not spontaneously gesture before instruction, doing gesture was less effective than the comparison action for learning, generalization, and retention. Importantly, this learning deficit was specific to gesture, as these children did benefit from doing the comparison manipulative action. Our findings are the first evidence that a child's use of a particular representational format for communication (gesture) directly predicts that child's propensity to learn from using the same representational format., (© 2024 Cognitive Science Society LLC.)

Research has shown the importance of helping students, especially those with mild-to-moderate learning disabilities, to offload information during problem-solving. When students can get their thoughts onto paper, number line strategies can help them develop a firm foundation in mathematical problem-solving while understanding the relationships between mathematical operations. These strategies are helpful for the development of addition, subtraction, multiplication, division, and later, fractional mathematics. In this article, we describe the progression of number lines as a supportive strategy for elementary students and those with developmental delays in mathematics to improve mathematical understanding. This strategy is based on students being able to show their work and think about what they have written on paper or how they have used manipulatives.

This paper describes and compares primary school students' perceptions of science learning skills in a teacher-centered approach (TCA) and in inquiry-based science fieldwork (IBSF). This comparison was prompted by primary school students' perceptions of science learning skills, which has recently gained momentum, and the Namibian primary schools' recent curriculum shift from TCA to IBSF. The study utilized quantitative cross-sectional data (n = 237) collected from two primary schools in the Oshana region of Namibia. The data were gathered via an online survey and were analyzed using descriptive statistics. The Mann-Whitney U test was used to compare the TCA and IBSF groups. The results revealed that the students had positive to fairly positive perceptions of science learning skills in IBSF and very negative to negative perceptions of science learning skills in TCA, with the difference between the two perceptions being statistically significant. This may indicate that Namibian primary school students find it hard to develop science learning skills under TCA. Therefore, the current study provided evidence for the need to focus more on supporting students' science learning skills, particularly problem-solving, communication and teamwork skills. This implies that the IBSF teaching approach, which allows students to become aware of the importance of science learning skills, must be adopted in Namibian primary schools. The paper also discusses the benefits of IBSF of science learning skills in students.

Learning statistics can be challenging for many students, due to their inability to engage in statistical reasoning and application of techniques. This challenge becomes compounded in online learning contexts where students are spatially and temporally separated from the teacher. This paper describes and explains a case of theory-driven interventions designed to enhance the learning experiences of students enrolled in two similar business statistics units, one for undergraduate and the other for postgraduate programs. The paper based its claims primarily on the analysis of data from a student evaluation of teaching survey. This study affirmed the importance of a pedagogy-first approach. It argued that the interventions, which were effective in enhancing the student learning experience, were underpinned by a robust pedagogical analysis of the teaching and learning issues using both constructive alignment and transactional distance theory lenses.

This paper reflects on the process of designing and implementing an update to the "Introduction to Information Technology" course at Bentley University, a business university in New England. Driven by a university-wide curriculum reform initiative and following best practices from digital literacy frameworks and IS model curricula, the new CS 100 course, entitled "Solving Business Problems with Information Technology," shifted emphasis from a personal computing context to exploring small business applications of technology. Students networked with university alumni at "Topics in Tech" presentations, where they learned about career options as information technology professionals. The authors describe the pilot courses that led to proposing a new CS 100 course and reflect its challenges and successes. Challenges included meeting the university's goal of transfer friendliness, scaling from a few pilots to multiple sections, consistency of course delivery, and evaluating assessment. Successes include placing course topics in a business context, introducing current technologies, and providing opportunities for students to learn about career opportunities and experiences from university alumni. The paper also describes how CS 100 aligns with the university's student learning goals for the new curriculum. These include developing future-focused skills, encouraging curiosity and critical analysis, communicating and collaborating in different settings, and demonstrating understanding of issues related to ethical use of technology.

In response to the adoption of Agile practices and processes by businesses, IT/IS educators are working to add Agile content to their courses. Teaching students about Agile involves teaching them about the history, mindset, and values of Agile, along with an introduction to the practices and processes used in an Agile product. Along with this, it is essential that students gain experience using Agile in a project setting. This paper discusses an Agile practice project where students use all aspects of Agile to address a problem and build a solution using Legos. The use of Legos, along with a project that students can easily see themselves using, the practice project allows students to focus on developing their Agile skills and mindset. The project serves as a useful transition from traditional classroom instruction about Agile to a project for a real-world client.

In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.

The paper describes the results of a study whose main aim was to find the interrelationship between pupils' school grades in Czech language (native), mathematics, and physics and pupils' cognitive predispositions to problem-solving in science and mathematics diagnosed by the Lawson Classroom Test of Scientific Reasoning and the Culture of Problem Solving test. A total of 180 pupils from the Czech Republic aged 14-15 took part in this study. The results show that pupils with better grades in the monitored subjects achieve better results in both tests. It also turns out that there are generally statistically insignificant differences between the results of pupils assessed by grades 1 or 2 and between those assessed by grades 3 or 4. Pupils' performance in the two tests might help to strengthen the objectivization of grading at school. They might also help identify the indicators important for developing problem-solving skills. The research specifically points to the need to develop algebraic thinking, the conception of infinity, spatial imagination, geometric imagination in the plane, proportional reasoning, and the ability to control variables.

Personalized learning (PL) is not new, but it has morphed and re-emerged as an approach that might re-engage today's students in the classroom and better prepare them for college and careers in the 21st century. Amid this re-emergence of personalization is the implementation of new technologies in the classroom. While technology may offer efficient ways to deliver PL, the impact of PL on the educational system and the narratives that guide learning, as well as its effect on the larger community, the workforce, and the democratic principles that guide America, need further exploration. This essay looks at personalized learning historically and pragmatically using a technoskeptic approach. This paper examines personalized learning's implications and offers ways personalized learning via service learning may be more effective in engaging students while also continuing to contribute to a communal, democratic narrative. When properly applied, service learning encourages students to use their knowledge to solve real-world problems in their communities. As modern culture moves toward isolated media environments and individualized experiences, students need more exposure to other people and differing worldviews to maintain democratic ideals, a robust learning experience, and humanity in reality, as opposed to what is offered on a screen.

Background: A lack of student 'buy-in' and engagement are often major obstacles in academic literacy courses. To create a dialogic learning environment which encourages student investment and challenges traditional student-lecturer hierarchies of power, the curriculum of a first-year academic literacy course at a South African university was reconceptualised around an action-learning project. Objectives: The aim is to determine whether the reconceptualised course enabled dialogic learning that fostered a sense of investment in students. Method: An interpretivist paradigm was followed, drawing on a qualitative research approach. To explore the nature of student investment, discourse analysis was used to analyse group reflections submitted at the end of the 21-week course. Student reflections were coded thematically using an inductive approach. Results: These reflections indicate that specific mechanisms need to be in place for effective dialogic engagement. If in place, findings suggest that the dialogic approach could encourage critical thinking, help students to develop problem-solving skills, lead to cognisance of multiple perspectives, deepen understanding of course material and expectations, promote inclusivity, and encourage reflection on the learning process. Conclusion: This study indicates that purposefully embedding a dialogic approach into a curriculum through purpose-driven group activities, can lead to more engaged learning. Contribution: The paper contributes to the field of academic literacy studies by showing how academic literacy practitioners may use the now-established pedagogies of action-learning and dialogic teaching and learning to design courses that create an enabling environment for students to draw on deep approaches to learning.

From a linguistic perspective it seems intuitive that a strong link would exist between the study of linguistics and critical thinking (CT). After all, linguistics is about making sense of language analysis, which contributes to the enhancement of CT while CT, in reciprocation, enables meaningful analysis. Yet this link has virtually never been clearly defined or made explicit either in studies on linguistics teaching and learning or in those on CT development. This paper explores the relationship between linguistics study and CT in the Vietnamese context from the perspectives of undergraduate English Linguistics students and their lecturers, with a view to improving both students' linguistics study and their CT. Drawing on data collected in questionnaires and interviews at a public university in Vietnam, the findings of the study reveal a variety of aspects of linguistics tasks and classroom activities where the link is significant as well as a range of specific CT skills and dispositions that are related to linguistics teaching and learning. In general, the students and the lecturers showed a positive attitude towards the integration of CT into linguistics teaching and learning, but challenges and barriers to this integration were identified. The study suggests the use of problem-solving tasks and open-ended questions for fostering the reciprocal relationship between linguistics study and CT.

In this paper, we present an activity to introduce the idea of public-key cryptography and to make pre-service STEM teachers explore fundamental informatics and mathematical concepts and methods. We follow the Theory of Didactical Situations within the Didactical Engineering methodology (both widely used in mathematics education research) to design and analyse a didactical situation about asymmetric cryptography using graphs. Following the phases of Didactical Engineering, after the preliminary analysis of the content, the constraints and conditions of the teaching context, we conceived and analysed the situation a priori, with a particular focus on the milieu (the set of elements students can interact with) and on the choices for the didactical variables. We discuss their impact on the problem-solving strategies the participants need to elaborate to decrypt an encrypted message. We implemented our situation and collected qualitative data. We then analysed a posteriori the different strategies that participants used. The comparison of the a posteriori analysis with the a priori analysis showed the learning potential of the activity. To elaborate on different problem-solving strategies, the participants need to explore and understand several concepts and methods from mathematics, informatics, and the frontier of the two disciplines, also moving between different semiotic registers.

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic and the shift to digital learning materials have dramatically reduced the use of paper-based textbooks in higher education. In the field of engineering, students need a comprehensive understanding of mathematical concepts, which can be achieved through the use of e-textbooks. These digital learning materials can provide a more comprehensive and effective learning experience by incorporating a variety of multimedia elements, such as audio, visual aids, and interactive features. Rationale/Relevance: Mathematics students are expected to possess critical problem-solving skills, yet they are rarely asked to elaborate on and explain their mathematical reasoning and concepts through written argument in the engineering mathematics classroom. While students may excel in formula manipulation, they may still possess misconceptions about mathematical principles and concepts. A reliance on mechanical and procedural approaches, such as formula application, without proper conceptual understanding can result in serious misunderstandings of mathematics and its real-world applications. Without the ability to reason about mathematical concepts, students may struggle to connect what they are learning in class with real-world scenarios, leading to difficulties in solving practical problems. It is, therefore, crucial to foster conceptual understanding and critical reasoning skills in mathematics education, rather than solely relying on memorisation, in order to equip students with the necessary skills to succeed in their careers. Research Problem/ Research Question: This study will answer the following research question:How effective are e-textbooks in promoting conceptual understanding of engineering mathematics? Aim of the Study/Focus: The purpose of this study is to investigate conceptual learning in engineering mathematics using an e-textbook as a learning tool. Methodology: This qualitative case study explores e-textbooks as adaptive technology, with functionalities that include artificial intelligence allowing students to develop their understanding by interacting with digital text, watching videos of real-world mathematics concepts, and responding to quick quizzes on concepts; as well as practising and mastering further mathematical principles and concepts. It focuses on first-year engineering students and lecturers at a University of Technology in South Africa. The analysis of interview recordings was done with ATLAS.ti. analytical software. Theoretical Framework: The data from this study was analyzed through the lenses of cultural historical activity theory (CHAT) and Vosniadou's notion of conceptual change, allowing the researchers to explain complex real-world situations that students experience when engaging with the e-textbook to solve mathematics problems. Recommendations: The work presented here has implications for future studies of conceptual learning in mathematics research and may provide opportunities around learning engineering mathematical concepts, particularly in the context of developing countries. This is because it offers novel instructional approaches that are tailored to the specific needs and challenges of these countries.

Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning 1-4 , owing to their reputed difficulty among the world's best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges 1,5 , resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004., (© 2024. The Author(s).)

This paper is devoted to the design, description and validation of the Android application TEAtreves, which focuses on structured arithmetic problem-solving for students with autism spectrum disorder (ASD). The application contains multiple adaptations to make it suitable for users with ASD. Validation was carried out with five students with ASD, obtaining positive results which confirm the strength of TEAtreves app for users with ASD. Results and future lines of work are discussed.

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]

Every teacher aspires to create the ideal lesson! You want to convey the knowledge and skills in the best possible way while also keeping learners motivated. In our design case, we focus on the complex skill of collaborative problem-solving (CPS). In today's complex world, the acquisition of CPS competencies is considered an important learning goal in education. However, there is limited knowledge on how to teach and assess CPS competencies. In addition to tackling these challenges, we search for new ways of interactive storytelling to implement in the learning materials. Our main design challenge was how to design a learning experience that encourages CPS. To address these challenges, we started in 2020 the project titled Supporting TEamwork in AMbient learning Spaces (STEAMS). In this project, we designed the EDUbox Teamwork, a four-hour learning activity for children between the ages of 10 to 14. In this paper, we describe the iterative process of designing the materials to learn about CPS (i.e., CPS as a learning goal) by doing CPS (i.e., CPS as a method), enhanced by interactive storytelling, for which the design-based research approach was used. The design team consisted of a diverse group of educational researchers, computer scientists, instructional graphical designers, digital storytellers, and teachers. Given the strong collaboration between a research group specialized in computer-supported collaborative learning and specialists in digital storytelling, our design case incorporated insights from both parties. The learning activity was piloted both in in-vivo and in-vitro contexts in collaboration with at least 400 students.

Research carried out through the last 20 years gave us undeniable evidence that to learn anything we need to be active participants, not passive observers. One of the important aspects of learning physics is constructing explanations of physical phenomena. To support and guide students toward constructing their explanations, teachers need to be attentive and responsive to students' explanations. To learn how physics teachers interpret and respond to students' explanations we investigated pre- and in-service physics teachers' responses to students' written explanations of their answers to a complex physics problem. The survey administered to the participants included the problem statement and four authentic student explanations. The participants were asked to identify each student's strengths and weaknesses and to provide a response to that student. We found that while the participants were successful in identifying productive and problematic aspects of student reasoning, they rarely built on student reasoning when responding to the students, mostly focusing on addressing problematic aspects. The paper discusses why this finding is important for physics teacher preparation programs and professional development programs.

Over the course of the introductory calculus-based physics course, students are often expected to build conceptual understanding and develop and refine skills in problem solving and qualitative inferential reasoning. Many of the research-based materials developed over the past 30 years by the physics education research community use sequences of scaffolded questions to step students through a qualitative inferential reasoning chain. It is often tacitly assumed that, in addition to building conceptual understanding, such materials improve qualitative reasoning skills. However, clear documentation of the impact of such materials on qualitative reasoning skills is critical. New methodologies are needed to better study reasoning processes and to disentangle, to the extent possible, processes related to physics content from processes general to all human reasoning. As a result, we have employed network analysis methodologies to examine student responses to reasoning-related tasks in order to gain deeper insight into the nature of student reasoning in physics. In this paper, we show that network analysis metrics are both interpretable and valuable when applied to student reasoning data generated from "reasoning chain construction tasks." We also demonstrate that documentation of improvements in the articulation of specific lines of reasoning can be obtained from a network analysis of responses to reasoning chain construction tasks.

This study examined deaf learners' academic and social difficulties as well as their coping mechanisms in Ethiopian elementary schools. To do this, the study adopted a phenomenological design from qualitative research methodology. Purposive sampling methods were used to choose nine deaf students for semi-structured interviews. Using word-by-word narrative analysis, data were analyzed. The lack of sign language interpreters and teachers' exclusionary behaviors were found to be some of the academic and social obstacles faced by deaf learners in the research area. The study also found that deaf learners in participating schools used problem-focused coping techniques, including pen-and-paper techniques for interpersonal communication, and maladaptive coping mechanisms like ignoring and social withdrawal. According to the report, local authorities should organize awareness workshops for instructors from participating schools on how to instruct deaf learners in inclusive environments.

Problem solving in chemical kinetics poses substantial challenges for university students since it often involves significant use of mathematics as a tool and language, with challenging translations and transitions between chemical phenomena and mathematical representations. In this paper, we present key findings from a study investigating chemistry students solving tasks centred around the steady-state approximation. Building upon the mathematical modelling cycle (MMC), qualitative analysis of the data collected using a think-aloud protocol led to the development of the extended MMC. This empirically derived extended MMC offers a more detailed account of the processes involved in mathematical modelling of chemical phenomena, highlighting aspects such as the occurrence of deliberation and evaluation throughout the modelling cycle, as well as the varying characteristics, points of activation and roles of extra-mathematical resources during problem solving. We further introduce and use problem-solving trajectories as a tool for visualising and analysing the complex and diverse approaches used by students in their attempts at reaching a solution. Overall, the extended MMC provides a finer-grained model of the cognitive and metacognitive activities that students engage in, offering further insights for research and practice.

Constructivist learning theories consider deep understanding of the content to be the result of engagement in relevant learning activities with appropriate scaffolding that provides the learner with timely and substantive feedback. However, any group of students has a variety of levels of knowledge and cognitive development, which makes providing appropriate individual-level scaffolding and feedback challenging in the classroom. Computer simulations can help meet this challenge by providing technology-enhanced embedded scaffolding and feedback via specific simulation design. The use of computer simulations does not, however, guarantee development of deep science understanding. Careful research-driven design of the simulation and the accompanying teaching structure both play critical roles in achieving the desired learning outcomes. In this paper, we discuss the capabilities of computer simulations and the issues that can impact the learning outcomes when combining technology-enhanced scaffolding and feedback with external teaching structures. We conclude with suggestions of promising research avenues on simulation design and their use in the classroom to help students achieve deep science understanding.

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]

Collaborative localization is a technique that utilizes the exchange of information between multiple sensors or devices to improve localization accuracy and robustness. It has a wide range of applications in autonomous driving and unmanned aerial vehicles (UAVs). In the field of UAVs, collaborative localization can help UAVs perform autonomous navigation and mission execution in complex environments. However, when GNSS is not available, it becomes challenging to position the UAV swarm relative to each other. This is because the swarm loses its perception and constraint of the position relationship between each member. Consequently, the swarm faces the problem of the serious drift of relative accuracy for an extended period. Furthermore, when the environment is faced with complex obstruction challenges or a camera with low texture scenes, noise can make it more difficult to solve the relative position relationship between drones, and a single UAV may lose positioning capability. To solve these specific problems, this paper studies a swarm co-operative localization method in a GNSS-denied environment with loud noise interference. In this paper, we proposed a method that utilizes a distributed scheme based on an incremental smoothing and mapping (iSAM) algorithm for state estimation. It incorporates new anchor-free topological constraints to prevent positioning failures and significantly improve the system's robustness. Additionally, a new switching function is applied in front of each factor of the loss function, which adjusts the switches in real time in response to the input information to improve observably the accuracy of the system. A novel co-operative incremental smoothing and mapping (CI-SAM) method is proposed and the method does not require a complete relative position measurement, which reduces the need for vehicle measurement equipment configuration. The effectiveness of the method is verified by simulation. [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]

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]

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]

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]

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]

This study investigated remote multidisciplinary sketching ideation across three systems: virtual reality (VR), tablet drawing, and uploading images of paper drawings. Though cumbersome, expressiveness and line control with drawing on paper was still noted to be important even in remote sketching, particularly by people experienced with this method. The tablet method was user-friendly, fostering effective collaborative understanding, especially in object-based ideation. Existing skills played a significant role in shaping collaborative perceptions. Despite challenges, VR exhibited promise in fostering creative expression and visualization in collaborative design workflows. Notably, it proved beneficial in early problem-solving stages where spatial and sensory considerations influenced structural decisions--potentially useful after general brainstorming and 2D sketching has established themes and objects. This research contributes to further understanding of VR's evolving role in design thinking, its synergy with other drawing methods in remote sketching collaboration, and the evolving landscape of diverse user needs in ideation processes. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [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 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]

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]

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]

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]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials. [ABSTRACT FROM AUTHOR]

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research directions via the intertwined mechanisms of selection and social influence. Here we thoroughly investigate the interplay between collaboration and topic switches. We find that the probability for a scholar to start working on a new topic increases with the number of previous collaborators, with a pattern showing that the effects of individual collaborators are not independent. The higher the productivity and the impact of authors, the more likely their coworkers will start working on new topics. The average number of coauthors per paper is also inversely related to the topic switch probability, suggesting a dilution of this effect as the number of collaborators increases. [ABSTRACT FROM AUTHOR]

Soft-switching technologies can effectively solve the problem of switching losses caused by increasing switching frequency of grid-connected inverters. As a branch of soft-switching technologies, load-side resonant soft-switching is a hotspot for applications of high-frequency inverters, because it has the advantage of achieving soft-switching without using additional components. However, the traditional PI control strategy based on the linear model is prone to destabilization and non-robust dynamic performance when large signal perturbation occurs. In this paper, a novel Passivity-Based Control (PBC) method is proposed to improve the dynamic performance of load-side resonant soft-switching grid-connected inverter. Besides, the model based on the Port Controlled Hamiltonian (PCH) model of the soft switching inverter is carried out, and the passivity-based controller is designed based on the established model using the way of interconnection and damping assignmentpassivity based control (IDA-PBC). Both stable performance and dynamic performance of the load-side resonant soft-switching inverter can be improved over the whole operating range. Finally, a 750 W load-side resonant soft-switching inverter simulation model is built and the output performance is compared with the traditional PI control strategy under stable and dynamic conditions. The simulation results show that the proposed control strategy reduces the harmonic distortion rate and improves the quality of the output waveforms. [ABSTRACT FROM AUTHOR]

Multimodal Sentiment Analysis (MSA) has become an essential area of research to achieve more accurate sentiment analysis by integrating multiple perceptual modalities such as text, vision, and audio. However, most previous studies failed to align the various modalities well and ignored the differences in semantic information, leading to inefficient fusion between modalities and generating redundant information. In order to solve the above problems, this paper proposes a transformer-based network model, Tri-CLT. Specifically, this paper designs Integrating Fusion Block to fuse modal features to enhance their semantic information and mitigate the secondary complexity of paired sequences in the transformer. Meanwhile, the cross-modal attention mechanism is utilized for complementary learning between modalities to enhance the model performance. In addition, contrastive learning is introduced to improve the model's representation of learning ability. Finally, this paper conducts experiments on CMU-MOSEI aligned and unaligned data, and the experimental results show that the proposed method outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Deep learning-based 3D target detection methods need to solve the problem of insufficient 3D target detection accuracy. In this paper, the KM3D network is selected as the benchmark network after the experimental comparison of current mainstream algorithms, and the IAE-KM3D network algorithm based on the KM3D network is proposed. First, the Resnet V2 network is introduced, and the residual module is redesigned to improve the training capability of the new residual module with higher generalization. IBN NET is then introduced to carefully integrate instance normalization and batch normalization as building blocks to improve the model's detection accuracy in hue- and brightness-changing scenarios without increasing time loss. Then, a parameter-free attention mechanism, Simam, is introduced to improve the detection accuracy of the model. After that, the elliptical Gaussian kernel is introduced to improve the algorithm's ability to detect 3D targets. Finally, a new key point loss function is proposed to improve the algorithm's ability to train. Experiments using the KITTI dataset conclude that the IAE-KM3D network model significantly improves detection accuracy and outperforms the KM3D algorithm regarding detection performance compared to the original KM3D network. The improvements for AP2D, AP3D, and APBEV are 5%, 12.5%, and 8.3%, respectively, and only a tiny amount of time loss and network parameters are added. Compared with other mainstream target detection algorithms, Monn3D, 3DOP, GS3D, and FQNet, the improved IAE-KM3D network in this paper significantly improves AP3D and APBEV, with fewer network parameters and shorter time consumption. [ABSTRACT FROM AUTHOR]