Background Information Research Articles

RAN: Infrared and Visible Image Fusion Network Based on Residual Attention Decomposition

Infrared image and visible image fusion (IVIF) is a research direction that is currently attracting much attention in the field of image processing. The main goal is to obtain a fused image by reasonably fusing infrared images and visible images, while retaining the advantageous features of each source image. The research in this field aims to improve image quality, enhance target recognition ability, and broaden the application areas of image processing. To advance research in this area, we propose a breakthrough image fusion method based on the Residual Attention Network (RAN). By applying this innovative network to the task of image fusion, the mechanism of the residual attention network can better capture critical background and detail information in the images, significantly improving the quality and effectiveness of image fusion. Experimental results on public domain datasets show that our method performs excellently on multiple key metrics. For example, compared to existing methods, our method improves the standard deviation (SD) by 35.26%, spatial frequency (SF) by 109.85%, average gradient (AG) by 96.93%, and structural similarity (SSIM) by 23.47%. These significant improvements validate the superiority of our proposed residual attention network in the task of image fusion and open up new possibilities for enhancing the performance and adaptability of fusion networks.

CRLNet: A Multimodal Peach Detection Network Based on Cooperative Asymptotic Enhancement and the Fusion of Granularity Refinement.

Accurate peach detection is essential for automated agronomic management, such as mechanical peach harvesting. However, ubiquitous occlusion makes identifying peaches from complex backgrounds extremely challenging. In addition, it is difficult to capture fine-grained peach features from a single RGB image, which can suffer from light and noise in scenarios with dense small target clusters and extreme light. To solve these problems, this study proposes a multimodal detector, called CRLNet, based on RGB and depth images. First, YOLOv9 was extended to design a backbone network that can extract RGB and depth features in parallel from an image. Second, to address the problem of information fusion bias, the Rough-Fine Hybrid Attention Fusion Module (RFAM) was designed to combine the advantageous information of different modes while suppressing the hollow noise at the edge of the peach. Finally, a Transformer-based Local-Global Joint Enhancement Module (LGEM) was developed to jointly enhance the local and global features of peaches using information from different modalities in order to enhance the percentage of information about the target peaches and remove the interference of redundant background information. CRLNet was trained on the Peach dataset and evaluated against other state-of-the-art methods; the model achieved an mAP50 of 97.1%. In addition, CRLNet also achieved an mAP50 of 92.4% in generalized experiments, validating its strong generalization capability. These results provide valuable insights for peach and other outdoor fruit multimodal detection.

Utilizing Silhouette and Head Information for Improved Cloth-changing Person Re-Identification

In recent years, significant progress has been made in person re-identification (ReID). However, as the time span increases, the phenomenon of changing clothes often occur, and the performance of methods based on person re-identification may decrease in this case. In this article, we focus on solving the problem of cloth-changing person re-identification from a single RGB image, which is more challenging, and improving the accuracy and robustness of traditional methods for recognition. We propose a cloth-changing Feature Regularization learning framework by fusing Silhouette and Head information (SHFR), a two-stream framework which transfers the silhouette information and head information learned by auxiliary stream to the main stream and supplements features unrelated to clothing. Specifically, the main stream approach we adopt is the traditional cloth-changing person re-identification network. In the auxiliary stream, we use the DeepLabV3 semantic segmentation model to extract person silhouette features, and the FCHD fully convolutional head detection model to extract head information with advanced semantic information. We concatenate the head information and background information into the silhouette as input for BackBone to continue training the network. In order to utilize silhouette information and head information, we regularize the cloth-changing features on both main stream and auxiliary stream, allowing main stream model to pay more attention to features unrelated to clothing and improve the accuracy of model recognition. We conduct extensive experiments on our model on the PRCC dataset, and the results show that our method has highly competitive performance.

The Basics of Evolution Strategies: The Implementation of the Biomimetic Optimization Method in Educational Modules.

With a focus on education and teaching, we provide general background information on bioinspired optimization methods by comparing the concept of optimization and the search for an optimum in engineering and biology. We introduce both the principles of Darwinian evolution and the basic evolutionary optimization procedure of evolution strategies. We provide three educational modules in work sheets that can be used by teachers and students to improve their understanding of evolution strategies. The educational module "Optimization of a Milk Carton" shows that the material consumption in producing a milk carton can be minimized using an evolution strategy with a mutative step size control. The use of a standard dice and a pocket calculator enables new milk cartons to be generated, with the offspring having the lowest material consumption becoming the parent of the next generation. The other educational modules deal with the so-called brachistochrone problem. The module "Fastest and Shortest Marble Track" provides a construction plan for a marble track whereby students can experimentally compare the "path of shortest length" with the "path of shortest time". The EvoBrach software, is used in the module "Various Marble Track Shapes" to compare the running times of a marble on a straight line, a parabola, and a brachistochrone. In conclusion, the introduction to the biomimetic method of evolution strategies and the educational modules should deepen the understanding of both optimization problems and biological evolution.

DEVELOPMENT OF A SUSTAINABLE BUSINESS MODEL FOR DENIM

Globally, over a billion dollars are spent on denim jeans each year, and that number is still growing. As a result of this expansion, the production of waste in the systems is also increasing. According to the Environmental Protection Agency (EPA), roughly 5% of all landfill space is textile waste, including denim. One of the least ecological industries in the world is the denim industry. This calls for more intensive interventions in the design of sustainable development, as it calls into question the key criteria of sustainability. This topic has been covered by various models of industrial sustainability, although these models focus on solving global problems, assuming small improvements and growth. Consequently, a sustainable business model approach is necessary to drastically rethink how firms should operate to support system-level sustainability. This paper will provide comprehensive background information on the denim business and its operations before offering some notable case studies of well-known companies that use environmentally friendly practices. The result of this document will be a comprehensive analysis and building of a sustainable business model for the denim sector.

Evoked questions and inquiring attitudes

Abstract Drawing inspiration from the notion of evocation employed in inferential erotetic logic, we defend an ‘evoked questions norm’ on inquiring attitudes. According to this norm, it is rational to have an inquiring attitude concerning a question only if that question is evoked by your background information. We offer two arguments for this norm. First, we develop an argument from convergence. Insights from several independent literatures (20th-century ordinary-language philosophy, inferential erotetic logic, inquisitive epistemic logic, and contemporary zetetic epistemology), all converge on the evoked questions norm. Second, we show that suitably interpreted, the evoked questions norm correctly predicts several underappreciated kinds of bad questions. It does this, in part, by recovering versions of previously defended ignorance and knowledge norms. Some of those bad questions cannot be predicted by either norm singly, but only when corporately taken to reflect a common normative category. We identify this category as evocation.

Beyond 50%: providing contextual and coaching information substantially improves adults’ ability to detect children’s lies

ABSTRACT The present research examined how contextual/coaching information and interview format influenced adults’ ability to detect children’s lies. Participants viewed a series of child interview videos where children provided either a truthful report or a deceptive report to conceal a co-transgression; participants reported if they thought each child was lying or telling the truth. In Study 1 (N = 400), participants were assigned to one of the following conditions that varied in the type of interview shown and if context about the event in question was provided: full interview + context, recall questions + context, recognition questions + context, or full interview only (no context). Providing context (information about the potential co-transgression and coaching) significantly enhanced overall and lie accuracy, but this served the greatest benefit when provided with the recall interview, and participants held a lie bias. In Study 2 (N = 100), participants watched the full interview with simplified coaching information. Detection accuracy was reduced slightly but remained well above chance and the lie bias was eliminated. Thus, detection performance is improved when participants are given a child’s free-recall interview along with background information on the event and potential coaching, though providing specific coaching details introduces a lie bias.

LHFFNet: A hybrid feature fusion method for lane detection

Lane line images have the essential attribute of large-scale variation and complex scene information, and the similarity between adjacent lane lines is high, which can easily cause classification errors. And remote lane lines are difficult to recognize due to visual angle changes in width. To address this issue, this paper proposes an effective lane detection framework, which is a hybrid feature fusion network that enhances multiple spatial features and distinguishes key features throughout the entire lane line segment. It enhances and fuses lane line features at multiscale to enhance the feature representation of lane line images, especially at the far end. Firstly, in order to enhance the correlation of multiscale lane features, a multi-head self attention is used to construct a multi-space attention enhancement module for feature enhancement in multispace. Secondly, a spatial separable convolutional branch is designed for the jumping layer structure connecting multiscale lane line features. While retaining feature information of different scales, important lane areas in multiscale feature information are emphasized through the allocation of spatial attention weights. Finally, considering that lane lines are elongated areas in the image, and the background information in the image is much more abundant than lane line information, the flexibility of traditional pooling operations in capturing widely existing anisotropic contexts in actual environments is limited. Therefore, before embedding feature output branches, strip pooling is introduced to refine the representation of lane line information and optimize model performance. The experimental results show that the accuracy on the TuSimple dataset reaches 96.84%, and the F1 score on the CULane dataset reaches 75.9%.

Consider potential implications of Title IX exemption granted to Baylor University

Title IX was originally enacted to protect college students from sex discrimination in educational programs or activities, including athletics. Specifically, Title IX states that “No person in the United States shall, on the basis of sex, be excluded from the participation in, be denied the benefits of, or be subjected to discrimination under any education program or activity receiving Federal financial assistance.” Keep this background information in mind as you read the following about a recent development that occurred in the Title IX arena.

Forest Fire Prediction Based on Time Series Networks and Remote Sensing Images

Protecting forest resources and preventing forest fires are vital for social development and public well-being. However, current research studies on forest fire warning systems often focus on extensive geographic areas like states, counties, and provinces. This approach lacks the precision and detail needed for predicting fires in smaller regions. To address this gap, we propose a Transformer-based time series forecasting model aimed at improving the accuracy of forest fire predictions in smaller areas. Our study focuses on Quanzhou County, Guilin City, Guangxi Province, China. We utilized time series data from 2021 to 2022, along with remote sensing images and ArcGIS technology, to identify various factors influencing forest fires in this region. We established a time series dataset containing twelve influencing factors, each labeled with forest fire occurrences. By integrating these data with the Transformer model, we generated forest fire danger level prediction maps for Quanzhou County. Our model’s performance is compared with other deep learning methods using metrics such as RMSE, and the results reveal that the proposed Transformer model achieves higher accuracy (ACC = 0.903, MAPE = 0.259, MAE = 0.053, RMSE = 0.389). This study demonstrates that the Transformer model effectively takes advantage of spatial background information and the periodicity of forest fire factors, significantly enhancing predictive accuracy.

Machine learning for pest detection and infestation prediction: A comprehensive review

AbstractPests pose a major danger to a variety of industries, including agriculture, public health, and ecosystems. Fast and precise pest detection, as well as the ability to predict infestations, are required for effective pest management tactics. This paper provides a comprehensive literature review on this subject to provide an overview of the state of research on pest detection and infestation prediction. The paper investigates and presents background information on the necessity of pest control as well as the difficulty in recognizing pests and forecasting. Several strategies, including approaches to data collection, modeling, and assessment of models, are reviewed in the research described. The authors examine various pest detection methods involving the utilization of convolutional neural networks and several object detection architectures categorized broadly into one‐stage and two‐stage object detection algorithms. Methods for predicting pest infestations that involve regression, classification, and time series forecasting are also thoroughly investigated. The challenges of recognizing pests and predicting infestations are underlined, as are issues with data quality, feature selection, and model interpretability. The report also indicates the limitations to pest detection and infestation prediction as well as intriguing topics for further research on the same. The findings of the literature research demonstrate how Artificial Intelligence, Computer Vision, and the Internet of Things have been applied for Pest Detection and Infestation Prediction. The research serves as a base for surveying and summarizing the approaches utilized for the task of pest detection (an object detection problem) and pest infestation prediction (a forecasting problem) and its findings and recommendations serve as a platform for future study and the development of effective pest management solutions.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Prediction

Cnn-trans model: A parallel dual-branch network for fundus image classification

The existence of fundus diseases not only endangers people’s vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance..

Interviewing students with special needs: Developing ethical considerations and interviewing protocols

AbstractInterviewing students with special needs (SSNs) comes with a unique set of ethical and practical challenges that require highly specialised skills and interviewing protocols. Our objectives were to identify the most appropriate tools for collecting data from SSNs, key differences in interviewing students with and without special needs, and ethical and practical challenges during it. The ultimate goal was to develop ethical considerations and interviewing protocols for SSNs (ECIP4SSN). It was done through a two‐step Delphi method, collecting data from 11 special education experts from 11 different countries. The experts believed the tools should be selected based on the study's nature; however, many of them preferred the interviews. They believed that interviewing special students is challenging due to their special needs. Challenges pertaining to consent, hyperactivity, emotional distress, and wrong and irrelevant answers were among the top challenges mentioned by them. Final ECIP4SSN consisted of the key ethical considerations and interviewing protocols i.e. obtaining the background information and support, training and preparations, consent, data collection, building rapport, emotional support, confidentiality, and anonymity, using supporting tools, language, methods of asking questions and validating responses. The ECIP4SSN will guide the university teachers, students and researchers in interviewing special students.

PMSTD-Net: A Neural Prediction Network for Perceiving Multi-Scale Spatiotemporal Dynamics.

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network's advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated.

Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data.

Child maltreatment is a widespread problem with significant costs for both victims and society. In this retrospective cohort study, we develop predictive risk models using Danish administrative data to predict removal decisions among referred children and assess the effectiveness of caseworkers in identifying children at risk of maltreatment. The study analyzes 195,639 referrals involving 102,309 children Danish Child Protection Services received from April 2016 to December 2017. We implement four machine learning models of increasing complexity, incorporating extensive background information on each child and their family. Our best-performing model exhibits robust predictive power, with an AUC-ROC score exceeding 87%, indicating its ability to consistently rank referred children based on their likelihood of being removed. Additionally, we find strong positive correlations between the model's predictions and various adverse child outcomes, such as crime, physical and mental health issues, and school absenteeism. Furthermore, we demonstrate that predictive risk models can enhance caseworkers' decision-making processes by reducing classification errors and identifying at-risk children at an earlier stage, enabling timely interventions and potentially improving outcomes for vulnerable children.

Background information: A study on the sensitivity of astrophysical gravitational-wave background searches

Background information: A study on the sensitivity of astrophysical gravitational-wave background searches

Effects of heat stress on endocrine, thermoregulatory, and lactation capacity in heat-tolerant and -sensitive dry cows.

Increasing global temperatures present a significant challenge to livestock production. The dry period is an important stage in the production cycle of cow, and environmental heat stress (HS) during this period can have adverse effects on the subsequent lactation performance. In this study, we aimed to investigate the effects of HS on endocrine, thermoregulatory, and lactation parameters in heat-tolerant dry cows (HTDC) and heat-sensitive dry cows (HSDC). We measured the respiratory rate (RR), body temperature (BT), and temperature-humidity index (THI) in 66 dry cows during HS. The slopes of RR and BT to THI were determined through analysis of measurements and dry cows background information using a mixed-effects model. Subsequently, the heat tolerance or sensitivity of dry cows was assessed using clustering method (HTDC = 19 and HSDC = 47). Compared with that of HSDC, the RR of HTDC significantly increased after exposure to HS (p < 0.05). The average reduction in milk yield from new lactation to the previous lactation was significantly lower in HTDC compared to HSDC (p < 0.05). Plasma cortisol and non-esterified fatty acid levels were significantly lower in HTDC compared to HSDC (p < 0.05), while plasma triiodothyronine (p = 0.07) and growth hormone (p = 0.08) levels tended to be higher in HTDC relative to HSDC. HTDC can more effectively alleviate the impacts of HS through their superior thermoregulation and metabolism, thereby ensuring optimal postpartum lactation performance.

Factorized visual representations in the primate visual system and deep neural networks.

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether ('invariance'), represented in non-interfering subspaces of population activity ('factorization') or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters - lighting, background, camera viewpoint, and object pose - in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

Health and social care leaders’ and employees’ perceptions of remote leadership and the associated factors

PurposeThe purpose of this study was to describe health and social care leaders’ and employees’ perceptions of remote leadership and the associated factors.Design/methodology/approachA total of 45 leaders and 177 employees from one Finnish health and social care organization completed an electronic questionnaire between October and November 2020. The questionnaire included questions related to background information, along with structured and open-ended questions addressing remote leadership and the associated factors. The collected quantitative data was analyzed with statistical methods, while inductive content analysis was used to analyze the qualitative data.FindingsRemote leadership emerged as a developing form of leadership that was part of everyday life at a regional health and social care organization. However, it was also considered by some as a distanced and authoritarian form of leadership that reduced communication to a one-way flow of information. Remote leadership and digitalization in health and social care were generally perceived positively, especially among higher educated participants and those working mainly in a remote context. However, digitalization was also perceived as a burden and remote leadership as a source of uncertainty at work, especially among lower educated participants and those who worked mainly in traditional contexts.Originality/valueThis study expands the little-researched area and provides insights that can be used to further develop remote leadership and the related education.

Lesbian, gay, bisexual, and transgender clinical competence of physiotherapy students in Israel

BackgroundClinical competence encompasses attitudes, skills, and knowledge regarding diverse client groups. Appropriate clinical competence requires an understanding of the cultural context in which healthcare is delivered. In conservative countries such as Israel, there is a noticeable scarcity of information regarding the clinical competency of physiotherapy students (PTSs) in effectively treating lesbian, gay, bisexual and transgender (LGBT) individuals. The objective of this study was to assess the level of LGBT clinical competence among PTSs in Israel.MethodsConducted through an anonymous online self-report survey, this study gathered personal and academic background information and self-reported data on previous LGBT education during undergraduate studies of PTSs. It utilized the Hebrew version of the Lesbian, Gay, Bisexual, and Transgender Development of Clinical Skills Scale (LGBT-DOCSS) questionnaire. Descriptive statistics were computed for all outcome measures. The internal reliability of the LGBT-DOCSS was assessed. Total scores of the LGBT-DOCSS, along with scores in each of the three subscales (clinical preparedness, knowledge, and attitudes), were compared across different levels of religiosity and gender.ResultsThe sample comprised of 251 PTSs, with an average age of 25.57 ± 3.07 years (34.7% men, 65.3% women). All students reported a lack of LGBT community-related courses during their undergraduate studies. The translated Hebrew version demonstrated good internal consistency, with Cronbach’s alpha ranging from 0.65 to 0.83. The LGBT-DOCSS total score was 4.55 ± 0.61 out of 7, indicating a low level of clinical competency. The highest mean score was in the attitudes subscale (6.55 ± 0.87), which was significantly higher than the scores for the knowledge subscale (3.14 ± 1.46) and clinical preparedness subscale (3.36 ± 0.86). Religiousness was significantly associated with clinical preparedness and attitudes. Men exhibited higher self-reported levels of knowledge and clinical preparedness, albeit with more negative attitudes compare to women. Sexual orientation was significantly associated with clinical competency, with PTSs who identified as heterosexual demonstrating a lower level of clinical competency compared to participants who identified as non-heterosexual.ConclusionsIn Israel, PTSs demonstrated a low level of clinical competency in terms of self-reported knowledge and self-reported clinical preparedness but contrasting positive attitudes toward the LGBT community. Religiousness, gender and sexual orientation had a significant influence on competency levels.These preliminary findings highlight the urgent necessity to enhance the knowledge of PTSs regarding the LGBT community to improve their clinical competence.Trial registration NRNot applicable.