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Authentic learning is a critical pedagogy and curriculum requirement in higher education to better prepare students for future workforce requirements. As such, educators adopt a range of authentic learning tasks such as work-integrated learning, industry reports, and field trips to enable student engagement in current industry issues. Although such actions are practical and have valuable impact on students, there has been little effort to narrow the theoretical gap. The present study addresses this by extending the authentic learning three-factor model (construction of knowledge, disciplined inquiry, and value beyond school) to evaluate the perceptions of educators in THE higher education towards their understanding and embeddedness of authentic elements in their teaching curriculum to better prepare industry-ready graduates. In-depth interviews were conducted with higher education THE academics (n = 21) in Australia, New Zealand, The Netherlands, and Scotland. Thematic analysis using NVivo revealed seven key themes within the authentic learning three-factor model: knowledge co-creation and scaffolding, customising knowledge and experiences, student engagement, educator capability, industry readiness, real-world problems, and meaningful connections. The proposed seven standards necessitate a paradigm shift in curriculum design, syllabus structuring, and student assessment within THE institutions. Emphasising industry-readiness as a core objective, educators must align their teaching approaches with the theoretical findings of this research to better prepare students for real-world challenges. [ABSTRACT FROM AUTHOR]

Machine learning models that are used for the prediction and control of production can improve quality and yield. However, developing models that are highly accurate and reflective of real-world processes is challenging. We propose a feedforward neural network model specifically designed for continuous Multistage Manufacturing Processes (MMPs) without intermediate outputs. This model, which is termed "MMP Net," can accurately represent the control mechanism of continuous MMPs. Whereas existing studies on learning MMPs assume an intermediate output data, the MMP Net does not require such an unrealistic assumption. We use the MMP Net to develop prediction models for the lubricant base oil production process of a world-leading lubricant manufacturer. Evaluation results show that the MMP Net is superior to other deep neural network and machine learning models. Consequently, the MMP Net was actually implemented in a real factory in 2022 and is expected to save 900,000 dollars per year for each production line. We believe that our work can serve as a basis to develop customized machine learning solutions for improving continuous MMPs. [ABSTRACT FROM AUTHOR]

This study thoroughly analyses the requirements of English as a Second Language (ESL) courses offered at Green University of Bangladesh with a focus on the specific linguistic demands of students who are majoring in Engineering, Business, and Computer Science. One hundred students and five instructors participated in the research, which employed a mixed-methods approach to analyse both quantitative and qualitative data collected using questionnaires, focus group discussion, interviews, and diagnostic tests. When comparing student needs with what is taught in ESP programs, the findings reveal a significant gap, particularly in the areas of academic writing, oral communication, and technical terminologies. Students, especially those having backgrounds in Computer Science and Engineering (CSE) and Electrical and Electronics Engineering (EEE), voiced concerns about the difficulty of technical language and public speaking, while teachers emphasized on the importance of expanding students' specialist vocabulary and providing more hands-on communication activities. Despite the importance of industry standards and realworld applications as per students' academic and professional performance, the research states that the current ESP curriculum falls short in meeting these demands. More hands-on activities and specialist content, together with better preparation for educators to lead specialized language courses, are two ways that the curriculum may be improved to better suit these needs. The paper highlights the need of tailoring ESP courses as per the needs of students in different disciplines and suggests curriculum modifications to prepare students for the global job market. [ABSTRACT FROM AUTHOR]

The rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In smart healthcare, opportunities and challenges coexist. This article provides a comprehensive overview of past developments and recent progress in this area. First, we summarize the definition and characteristics of smart healthcare. Second, we explore the opportunities that AI technology brings to the smart healthcare field from a macro perspective. Third, we categorize specific AI applications in smart healthcare into ten domains and discuss their technological foundations individually. Finally, we identify ten key challenges these applications face and discuss the existing solutions for each. [ABSTRACT FROM AUTHOR]

Mathematics is a fundamental subject with wide-reaching importance in education, providing students with the tools to apply mathematical principles in diverse contexts. This study examines the abilities of 60 pre-service mathematics teachers (PSTs) in identifying content and context within mathematical word problems. Utilizing a case study approach, the study employed the mathematics word problems test and the content and context questionnaire. The findings reveal that PSTs generally struggle with error detection and content comprehension in mathematical word problems, as demonstrated by their inability to recognize inaccuracies in two of three test questions. The failure of PSTs to identify errors in mathematical word problems often stems from their tendency to rely solely on the solutions they obtain, without first understanding the entire question presented. In essence, they may focus on finding a solution rather than critically evaluating the problem, which can lead to the oversight of errors or inaccuracies within the problem statement itself. This study emphasizes the need for PSTs to grasp mathematical concepts and contextualize them in everyday life scenarios. Challenges were observed in linking computational results to real-world contexts. Thus, the study calls for future research in pre-service teacher education to explore strategies for enhancing critical thinking, error detection, and the integration of practical context in mathematical problemsolving. Furthermore, the study suggests that assessing the ability of PSTs to formulate problem-solving questions evaluates their capacity to answer questions and their ability to construct questions that can enhance students’ cognitive abilities. [ABSTRACT FROM AUTHOR]

Most accidents and serious incidents of commercial air transport helicopters occur during standard flight phases, whereby a main cause is pilots' situational awareness. Enabling pilots to better assess their situational awareness can make an important contribution in reducing the risk of fatal accidents. One approach is to examine a pilot's gaze behavior with the help of eye tracking. This paper reports the results of a case study with eye tracking measurements during real flight and simulator studies of a standard mission profile. The general gaze behavior is characterized by a dominant, external view, and the airspeed and altitude indicator as the most important flight instruments. A real-world applicability of gaze data obtained in the simulator could be shown. [ABSTRACT FROM AUTHOR]

We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point. These results are obtained under very general hypotheses on the distributions of the random model parameters by taking extensive advantage of the so‐called random variable transformation method. To illustrate the practical implications of our findings, we apply them to model the growth of multicellular tumor spheroids using empirical data. In this context, we explore two methodologies—the Bayesian approach and the random least mean square method—aimed at effectively addressing the challenge of assigning appropriate distributions to model parameters. This ensures that probabilistic fits accurately capture the inherent uncertainties of tumor growth dynamics. Finally, we notably show that the results obtained using both approaches in the randomized hyper‐logistic model align closely with each other, surpassing those yielded by the randomized logistic model. [ABSTRACT FROM AUTHOR]

Point clouds obtained with 3D scanners in realistic scenes inevitably contain corruption, including noise and outliers. Traditional algorithms for cleaning point cloud corruption require the selection of appropriate parameters based on the characteristics of the scene, data, and algorithm, which means that their performance is highly dependent on the experience and adaptation of the algorithm itself to the application. Three-dimensional object recognition networks for real-world recognition tasks can take the raw point cloud as input and output the recognition results directly. Current 3D object recognition networks generally acquire uniform sampling points by farthest point sampling (FPS) to extract features. However, sampled defective points from FPS lower the recognition accuracy by affecting the aggregated global feature. To deal with this issue, we design a compensation module, named offset-adjustment (OA). It can adaptively adjust the coordinates of sampled defective points based on neighbors and improve local feature extraction to enhance network robustness. Furthermore, we employ the OA module to build an end-to-end network based on PointNet++ framework for robust point cloud recognition, named R-PointNet. Experiments show that R-PointNet reaches state-of-the-art performance by 92.5% of recognition accuracy on ModelNet40, and significantly outperforms previous networks by 3–7.7% on the corruption dataset ModelNet40-C for robustness benchmark. [ABSTRACT FROM AUTHOR]

Language is a dynamic phenomenon that transcends mere verbal expression. In the educational setting, and particularly language teaching and learning, understanding the processes that underlie communication is crucial for effective pedagogy. The Multi-Stratal Model of Language Processing (MSMLP) emerges as a neurolinguistic framework that not only dissects the complexities of language processing but also offers a multifaceted perspective through which language education can be advanced, moving beyond traditional linguistic boundaries by integrating social, cultural, emotional, gestural, and linguistic components. The study unfolds by examining the foundational linguistic strata, emphasizing the integration of pragmatics, morphosyntax, semantics, and phonology into a more holistic language curriculum. Social dimensions, encompassing sociolinguistic variables, social context, and conversation analysis, are explored, guiding educators to incorporate these elements into their teaching methodologies. The longitudinal case study focuses on soft skills development among first-year English students at the Faculty of Arts and Humanities in Sfax, emphasizing realworld scenarios and comprehensive assessments. The iterative approach assesses the curriculum's impact on students' soft skills, highlighting MSMLP's applicability in language teaching and curriculum design. The study advocates for embracing the MSMLP, fostering a holistic language teaching approach to prepare students for effective communication in diverse real-world contexts, and unraveling the profound connection between language and the expression of thoughts and feelings beyond explicit verbalization. [ABSTRACT FROM AUTHOR]

In recent years, inverse reinforcement learning has attracted attention as a method for estimating the intention of actions using the trajectories of various action-taking agents, including human flow data. In the context of reinforcement learning, "intention" refers to a reward function. Conventional inverse reinforcement learning assumes that all trajectories are generated from policies learned under a single reward function. However, it is natural to assume that people in a human flow act according to multiple policies. In this study, we introduce an expectation-maximization algorithm to inverse reinforcement learning, and propose a method to estimate different reward functions from the trajectories of human flow. The effectiveness of the proposed method was evaluated through a computer experiment based on human flow data collected from subjects around airport gates. [ABSTRACT FROM AUTHOR]

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques. [ABSTRACT FROM AUTHOR]

This paper presents the development of a comprehensive, on-site industrial Optical Character Recognition (OCR) system tailored for reading text on iron plates. Initially, the system utilizes a text region detection network to identify the text area, enabling camera adjustments along the x and y axes and zoom enhancements for clearer text imagery. Subsequently, the detected text region undergoes line-by-line division through a text segmentation network. Each line is then transformed into rectangular patches for character recognition by the text recognition network, comprising a vision-based text recognition model and a language network. The vision network performs preliminary recognition, followed by refinement through the language model. The OCR results are then converted into digital characters and recorded in the iron plate registration system. This paper's contributions are threefold: (1) the design of a comprehensive, on-site industrial OCR system for autonomous registration of iron plates; (2) the development of a realistic synthetic image generation strategy and a robust data augmentation strategy to address data scarcity; and (3) demonstrated impressive experimental results, indicating potential for on-site industrial applications. The designed autonomous system enhances iron plate registration efficiency and significantly reduces factory time and labor costs. [ABSTRACT FROM AUTHOR]

The rapid development of AI technology in recent years has led to its widespread use in daily life, where it plays an increasingly important role. In healthcare, AI has been integrated into the field to develop the new domain of smart healthcare. In smart healthcare, opportunities and challenges coexist. This article provides a comprehensive overview of past developments and recent progress in this area. First, we summarize the definition and characteristics of smart healthcare. Second, we explore the opportunities that AI technology brings to the smart healthcare field from a macro perspective. Third, we categorize specific AI applications in smart healthcare into ten domains and discuss their technological foundations individually. Finally, we identify ten key challenges these applications face and discuss the existing solutions for each.

Language is a dynamic phenomenon that transcends mere verbal expression. In the educational setting, and particularly language teaching and learning, understanding the processes that underlie communication is crucial for effective pedagogy. The Multi-Stratal Model of Language Processing (MSMLP) emerges as a neurolinguistic framework that not only dissects the complexities of language processing but also offers a multifaceted perspective through which language education can be advanced, moving beyond traditional linguistic boundaries by integrating social, cultural, emotional, gestural, and linguistic components. The study unfolds by examining the foundational linguistic strata, emphasizing the integration of pragmatics, morphosyntax, semantics, and phonology into a more holistic language curriculum. Social dimensions, encompassing sociolinguistic variables, social context, and conversation analysis, are explored, guiding educators to incorporate these elements into their teaching methodologies. The longitudinal case study focuses on soft skills development among first-year English students at the Faculty of Arts and Humanities in Sfax, emphasizing real-world scenarios and comprehensive assessments. The iterative approach assesses the curriculum’s impact on students’ soft skills, highlighting MSMLP’s applicability in language teaching and curriculum design. The study advocates for embracing the MSMLP, fostering a holistic language teaching approach to prepare students for effective communication in diverse real-world contexts, and unraveling the profound connection between language and the expression of thoughts and feelings beyond explicit verbalization.

Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel multi-objective nonlinear mixed-integer mathematical programming model for the transit network design and frequency setting problem that aims at designing the routes and determining the frequencies of the routes to satisfy passenger demand in a transit network. The proposed model incorporates the features of real-life transit network systems and reflects the views of both passengers and the transit agency by considering the in-vehicle travel time, transfers, waiting times at the boarding and transfer stops, overcrowding and under-utilization of vehicles, and vehicle fleet size. Unlike previous studies that simplify several aspects of the transit network design and frequency setting problem, the proposed model is the first to determine routes and their frequencies simultaneously from scratch, i.e., without using line and frequency pools while considering the aforementioned issues, such as transfers and waiting. We solve the proposed model using Gurobi. We provide the results of what-if analyses conducted using a real-world public bus transport network in the city of Kayseri in Türkiye. We also present the results of computational tests implemented to validate and verify the model using Mandl benchmark instances from the literature. The results indicate that the model produces better solutions than the state-of-the-art algorithms in the literature and that the model can be used by public transit planners as a decision aid. [ABSTRACT FROM AUTHOR]

Multi-agent pickup and delivery is the problem of allocating tasks for the agents and finding short paths for agents without collisions. These tasks enter the system in different time steps. This article proposes new approaches to this problem based on genetic algorithms in order to optimize the allocation of tasks, minimizing the makespan. We also address this problem as multiobjective, where makespan and service time are minimized. Computational experiments were performed varying the number of agents in a simulated environment of a large-scale warehouse. The results obtained by the proposed approaches were compared with those from the literature and the proposals demonstrated improvements in both objectives. [ABSTRACT FROM AUTHOR]

Most accidents and serious incidents of commercial air transport helicopters occur during standard flight phases, whereby a main cause is pilots’ situational awareness. Enabling pilots to better assess their situational awareness can make an important contribution in reducing the risk of fatal accidents. One approach is to examine a pilot’s gaze behavior with the help of eye tracking. This paper reports the results of a case study with eye tracking measurements during real flight and simulator studies of a standard mission profile. The general gaze behavior is characterized by a dominant, external view, and the airspeed and altitude indicator as the most important flight instruments. A real-world applicability of gaze data obtained in the simulator could be shown.

Point clouds obtained with 3D scanners in realistic scenes inevitably contain corruption, including noise and outliers. Traditional algorithms for cleaning point cloud corruption require the selection of appropriate parameters based on the characteristics of the scene, data, and algorithm, which means that their performance is highly dependent on the experience and adaptation of the algorithm itself to the application. Three-dimensional object recognition networks for real-world recognition tasks can take the raw point cloud as input and output the recognition results directly. Current 3D object recognition networks generally acquire uniform sampling points by farthest point sampling (FPS) to extract features. However, sampled defective points from FPS lower the recognition accuracy by affecting the aggregated global feature. To deal with this issue, we design a compensation module, named offset-adjustment (OA). It can adaptively adjust the coordinates of sampled defective points based on neighbors and improve local feature extraction to enhance network robustness. Furthermore, we employ the OA module to build an end-to-end network based on PointNet++ framework for robust point cloud recognition, named R-PointNet. Experiments show that R-PointNet reaches state-of-the-art performance by 92.5% of recognition accuracy on ModelNet40, and significantly outperforms previous networks by 3–7.7% on the corruption dataset ModelNet40-C for robustness benchmark.

Distributed Ledger Technology (DLT) and blockchain are two terms that have been used interchangeably for the longest of time ever since the concept of the bitcoin cryptocurrency system was introduced by Satoshi Nakamoto in 2008. DLT can be thought of as an umbrella term that encompasses the blockchain and other type of distributed ledgers. It is a decentralized, permisionless, distributed database where each participant maintains its own copy of the distributed ledger and the records are distributed as a chain of blocks across a peer-to-peer network. This implies that a replicated database is maintained and it does not rely on a single party or an intermediary for its operation. This also happens to be the most ground breaking feature of this technology. Bitcoin blockchain can be understood as the first fully functional DLT that exists. Blockchain is rapidly evolving as the next disruptive innovation in secure connectivity, with the potential to fundamentally alter how we work and live in the twenty-first century. The key components of this technology include the miner, the consensus protocols, hashing and an asymmetric cryptography system to encrypt the data through the use of the public and the private key. All transaction in the blockchain is recorded in the form of chain of blocks. Each block contains a unique header which is mathematically and cryptographically computed and to this feature is attributed the immutable nature of the blockchain. This computed hash commits to the header of the previous block, storing the contents of the block in the form of a hash function. All the transactions in the blockchain are recorded in a block that contains a unique header which is cryptographically computed and this attributes to the immutability of the blockchain. This computed hash is stored in the header of the next block. Before a transaction is committed to the ledger, it has to be agreed upon by the active participants of the network in order to guarantee the trustworthiness of the information being incorporated into the blocks. This is where the distributed ledger consensus protocols become important. The consensus mechanism is used to determine which state of the database is chosen to be valid and true. It is only when consensus is achieved that the new transaction is recorded into the block and is linked to the already existing chain of blocks using a hash pointer to the previous block. Despite their initial success, existing blockchain technologies have gaps and limitations regarding flexibility, governance, and scalability. The aim of this research paper is to explore and review existing algorithms and mechanisms that can be deployed on open source blockchain platforms. To deploy and implement any blockchain application, various platforms such as Ethereum, Coinbase, and Embark etc. are available and widely used. In order for a researcher to choose the right blockchain architecture and consensus mechanism for implementing any given application, an in-depth knowledge of the existing state of the art is pertinent. Finally, this article discusses the benefits of blockchain technology in various real-world applications, including land records, finance, healthcare, digital scarcity, supply chain management, and food safety. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm (WOA), a biologically inspired optimization technique, is known for its straightforward design and effectiveness. Despite many advantages, it has certain disadvantages, such as a limited exploration capacity and early convergence as a result of the minimal exploration of the search process. The WOA cannot bypass the local solution; consequently, the search is unbalanced. This study introduces a new variant of WOA, namely elite-based WOA (EBWOA), to address the inherent shortcomings of traditional WOA. Unlike the three phases used in the traditional WOA, only the encircling prey and bubble-net attack phases are applied in the new variant. Using the local elite method, exploration will be conducted with an encircling prey phase to ensure some exploitation during exploration. The choice between exploration and exploitation is achieved by introducing a new choice parameter. An inertia weight (ω i) is used in both phases to scour the region. The EBWOA is used to evaluate twenty-five benchmark functions, IEEE CEC 2019 functions, and two design problems and compared to several fundamental techniques and WOA variants. In addition, the EBWOA is used to solve the practical cloud scheduling problem. Performance is compared against a variety of metaheuristics using real cloud workloads by running experiments on the standard CloudSim simulator. Comparing the numerical results of benchmark functions, IEEE CEC 2019 functions, statistical verification, and the solution generation speed of EBWOA confirmed the effectiveness of the proposed EBWOA approach. It has also shown a great improvement over baseline algorithms in creating efficient scheduling solutions by significantly reducing makespan time and energy consumption targets. [ABSTRACT FROM AUTHOR]

Many-objective optimization has recently gained popularity as it poses significant challenges for the existing algorithms. Therefore, numerous optimization algorithms have been developed to handle many-objective optimization in the literature. In addition, several studies have conducted experimental comparisons to assess the performance of optimization algorithms. Nevertheless, existing empirical studies have analyzed the performance of optimization algorithms on well-defined test problems, but it remains unclear whether the results translate to real-world scenarios. Furthermore, empirical studies on validating the performance of algorithms on real-world many-objective problems are intriguing but not yet fully explored. Therefore, in this article, we present a comprehensive comparative study evaluating the performance of 15 state-of-the-art algorithms on ten real-world many-objective applications with four to ten objectives from various domains. Further, these ten applications exhibit various mathematically challenging properties, including stochastic objectives, complex Pareto frontiers, and strong nonlinearity. In addition, four performance metrics are employed to visualize the performance of MOEAs in experimental settings. Based on comparative results, the performance of state-of-the-art algorithms with respect to different problems is evaluated herein.

As the popularity of electric vehicles (EVs) and smart grids continues to rise, so does the demand for batteries. Within the landscape of battery-powered energy storage systems, the battery management system (BMS) is crucial. It provides key functions such as battery state estimation (including state of charge, state of health, battery safety, and thermal management) as well as cell balancing. Its primary role is to ensure safe battery operation. However, due to the limited memory and computational capacity of onboard chips, achieving this goal is challenging, as both theory and practical evidence suggest. Given the immense amount of battery data produced over its operational life, the scientific community is increasingly turning to cloud computing for data storage and analysis. This cloud-based digital solution presents a more flexible and efficient alternative to traditional methods that often require significant hardware investments. The integration of machine learning is becoming an essential tool for extracting patterns and insights from vast amounts of observational data. As a result, the future points towards the development of a cloud-based artificial intelligence (AI)-enhanced BMS. This will notably improve the predictive and modeling capacity for long-range connections across various timescales, by combining the strength of physical process models with the versatility of machine learning techniques. [ABSTRACT FROM AUTHOR]

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques.

This article employs a narrative approach to explain the self-perpetuation of the promise of care robots. For two decades, care robots have been viewed as a promising digital innovation that will relieve care workers from service tasks and thus improve the situation in the care sector. Though they still show a rather poor performance in real-world applications, the expectations are as high as ever. To explain the self-perpetuation of the promise of care robots, we investigate sensemaking practices in the discourse about care robots and show how a predominant overarching narrative as well as project-related sensemaking practices play out. [ABSTRACT FROM AUTHOR]

An evolutionary algorithm (EA) was applied in this study to optimize the landing flight path of a delta-winged supersonic transport (SST). However, it is difficult for a delta wing with a large sweepback angle to reduce the aerodynamic drag during supersonic cruising to gain sufficient lift force at low speeds, particularly during takeoff and landing. Besides, high-fidelity computational fluid dynamics is required to evaluate the flight path with a complex flowfield. This study performed an efficient flight simulation based on the Kriging model-assisted aerodynamic estimation to carry out global optimization. Then, the designs of the flight and control sequence were realized for time-series optimization of effective SST landing. To develop the EA, two design scenarios were considered; one involved only the elevator, which is an aerodynamic control surface that controls the aircraft, and the other involved introducing thrust control in addition to elevator control. In the scenario involving only elevator control, feasible solutions could not be obtained owing to the poor low-speed aerodynamic performance of the SST. This paper presents several feasible solutions enabling reasonable SST landing performance in the scenario involving the elevator and thrust controls along with descriptions regarding the optimum flight and control sequences. In addition, we analyzed the solutions by analyzing the variance to obtain qualitative information. Consequently, we determined that elevator control was considerably effective in cases with the microburst effect than in cases without the microburst effect. [ABSTRACT FROM AUTHOR]

The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD1, 2, 3) dataset for polycrystalline solar cell, which contains 36 543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly free images and anomalous images with ten different categories. Moreover, 37 380 ground truth bounding boxes are provided for eight types of defects. We also carry out a comprehensive evaluation of the state-of-the-art object detection methods based on deep learning. The evaluation results on this dataset provide the initial benchmark, which is convenient for follow-up researchers to conduct experimental comparisons. To the best of our knowledge, this is the first public dataset for PV solar cell anomaly detection that provides box-wise ground truth. Furthermore, this dataset can also be used for the evaluation of many computer vision tasks such as few-shot detection, one-class classification, and anomaly generation. [ABSTRACT FROM AUTHOR]

Objectives: Based on a real-world collaboration with innovators in applying early health economic modeling, we aimed to offer practical steps that health technology assessment (HTA) researchers and innovators can follow and promote the usage of early HTA among research and development (R&D) communities. Methods: The HTA researcher was approached by the innovator to carry out an early HTA ahead of the first clinical trial of the technology, a soft robotic sock for poststroke patients. Early health economic modeling was selected to understand the potential value of the technology and to help uncover the information gap. Threshold analysis was used to identify the target product profiles. Value-of-information analysis was conducted to understand the uncertainties and the need for further research. Results: Based on the expected price and clinical effectiveness by the innovator, the new technology was found to be cost-saving compared to the current practice. Risk reduction in deep vein thrombosis and ankle contracture, the incidence rate of ankle contracture, the compliance rate of the new technology, and utility scores were found to have high impacts on the value-for-money of the new technology. The value of information was low if the new technology can achieve the expected clinical effectiveness. A list of parameters was recommended for data collection in the impending clinical trial. Conclusions: This work, based on a real-world collaboration, has illustrated that early health economic modeling can inform medical innovation development. We provided practical steps in order to achieve more efficient R&D investment in medical innovation moving forward. [ABSTRACT FROM AUTHOR]

Biosensor research has long focused on achieving the lowest possible Limits of Detection (LOD), driving significant advances in sensitivity and opening up new possibilities in analysis. However, this intense focus on low LODs may not always meet the practical needs or suit the actual uses of these devices. While technological improvements are impressive, they can sometimes overlook important factors such as detection range, ease of use, and market readiness, which are vital for biosensors to be effective in real-world applications. This review advocates for a balanced approach to biosensor development, emphasizing the need to align technological advancements with practical utility. We delve into various applications, including the detection of cancer biomarkers, pathology-related biomarkers, and illicit drugs, illustrating the critical role of LOD within these contexts. By considering clinical needs and broader design aspects like cost-effectiveness, sustainability, and regulatory compliance, we argue that integrating technical progress with practicality will enhance the impact of biosensors. Such an approach ensures that biosensors are not only technically sound but also widely useable and beneficial in real-world applications. Addressing the diverse analytical parameters alongside user expectations and market demands will likely maximize the real-world impact of biosensors. [Display omitted] • Rethinking the prominence of LOD in biosensor success evaluation. • Addressing the trade-off between detection range and low LOD. • Importance of clinically significant detection ranges for biosensors. • Balancing sensitivity with simplicity and cost-effectiveness. • Emphasis on practical deployment and real-world applicability. [ABSTRACT FROM AUTHOR]

Many statistics education researchers have found that statistics students’ attitudes tend to decrease over the duration of a course. Although many researchers have tried to incorporate a variety of activities and/or teaching methods to improve student attitudes, many are not only very time consuming to implement, but have also not shown many favorable results. In the study presented here, the inclusion of a low-stakes and easy-to-implement assignment and its effect on student attitudes is investigated. Although the results presented here did not show large changes, they suggest that some changes in students’ attitudes can be made with a small change in a course.

Rationale, aims and objectives: Patient‐centered care is considered a vital component of good quality care for breast cancer patients. Nevertheless, the implementation of this valuable concept in clinical practice appears to be difficult. The goal of this study is to bridge the gap between theoretical elaboration of "patient‐centered care" and clinical practice. To that purpose, a scoping analysis was performed of the application of the term "patient‐centered care in breast cancer treatment" in present‐day literature. Method: For data‐extraction, a literature search was performed extracting references that were published in 2018 and included the terms "patient‐centered care" and "breast cancer". The articles were systematically traced for answers to the following three questions: "What is patient‐centered care?", "Why perform patient‐centered care?", and "How to realize patient‐centered care?". For the content analysis, these answers were coded and assembled into meaningful clusters until separate themes arose which concur with various interpretations of the term "patient‐centered care". Results: A total of 60 publications were retained for analysis. Traced answers to the three questions "what", "why", and "how" varied considerably in recent literature concerning breast cancer treatment. Despite the inconsistent use of the term "patient‐centered care," we did not find any critical consideration about the nature of the concept, regardless of the applied interpretation. Interventions that are supposed to contribute to the heterogeneous concept of patient‐centered care as such, seem to be judged desirable, virtually without empirical justification. Conclusions: We propose, contrary to previous efforts to define "patient‐centered care" more accurately, to embrace the heterogeneity of the concept and apply "patient‐centered care" as an umbrella‐term for all healthcare that intends to contribute to the acknowledgement of the person in the patient. For the justification of measures to realize patient‐centered care for breast cancer patients, instead of a mere contribution to the abstract concept, we insist on the demonstration of desirable real‐world effects. [ABSTRACT FROM AUTHOR]