Attention To Strategies Research Articles

Feminist approaches to promoting researcher well-being through collective and organizational care

Researchers working in the field of violence against women and children are often tasked with listening to highly distressing personal accounts of violence and subsequent trauma. Without proper attention and mitigation strategies, this exposure can lead to vicarious trauma and related symptoms with significant impact on researchers’ well-being. As women are often leading and carrying out violence research, they also experience a disproportionate burden of risk of vicarious trauma symptoms. This case study highlights seven collective care strategies for research implemented by Healing and Resilience after Trauma (HaRT), a feminist organization dedicated to holistic healing among survivors of human trafficking and gender-based violence, whose team is entirely composed of women. Further, it explores how creating and integrating collective care into research protocols can help prevent vicarious trauma and enhance researchers’ emotional well-being as well as positively influence research quality. Qualitative data from researchers involved in the study on these strategies and how they affected their well-being are included. The piece concludes by discussing potential recommendations for other research teams and organizations seeking to mitigate the risk of vicarious trauma.

Visual statistical learning requires attention.

Statistical learning is a person's ability to automatically learn environmental regularities through passive exposure. Since the earliest studies of statistical learning in infants, it has been debated exactly how "passive" this learning can be (i.e., whether attention is needed for learning to occur). In Experiment 1 of the current study, participants performed a serial feature search task where they searched for a target shape among heterogenous nontarget shapes. Unbeknownst to the participants, one of these nontarget shapes was presented much more often in location. Even though the regularity concerned a nonsalient, nontarget item that did not receive any attentional priority during search, participants still learned its regularity (responding faster when it was presented at this high-probability location). While this may suggest that not much, if any, attention is needed for learning to occur, follow-up experiments showed that if an attentional strategy (i.e., color subset search or exogenous cueing) effectively prevents attention from being directed to this critical regularity, incidental learning is no longer observed. We conclude that some degree of attention to a regularity is needed for visual statistical learning to occur.

Domain Adaptive Urban Garbage Detection Based on Attention and Confidence Fusion

To overcome the challenges posed by limited garbage datasets and the laborious nature of data labeling in urban garbage object detection, we propose an innovative unsupervised domain adaptation approach to detecting garbage objects in urban aerial images. The proposed method leverages a detector, initially trained on source domain images, to generate pseudo-labels for target domain images. By employing an attention and confidence fusion strategy, images from both source and target domains can be seamlessly integrated, thereby enabling the detector to incrementally adapt to target domain scenarios while preserving its detection efficacy in the source domain. This approach mitigates the performance degradation caused by domain discrepancies, significantly enhancing the model’s adaptability. The proposed method was validated on a self-constructed urban garbage dataset. Experimental results demonstrate its superior performance over baseline models. Furthermore, we extended the proposed mixing method to other typical scenarios and conducted comprehensive experiments on four well-known public datasets: Cityscapes, KITTI, Sim10k, and Foggy Cityscapes. The result shows that the proposed method exhibits remarkable effectiveness and adaptability across diverse datasets.

Assessing building thermal resilience in response to heatwaves through integrating a social vulnerability lens

Major cities worldwide are increasingly experiencing intense heatwaves, which disproportionately affect vulnerable populations. This suggests the urgent need for focused attention and mitigation strategies to protect those most at risk from the impacts of heatwaves. The presence of vulnerable groups in buildings with varying thermal conditions can result in different outcomes during extreme heat events, making it crucial to evaluate the thermal resilience of buildings in conjunction with social vulnerability. This research advances the understanding of thermal vulnerability by integrating building thermal characteristics with social vulnerability. While previous studies have predominantly focused on socio-ethnic and economic dimensions of vulnerability, this study specifically investigates the relationship between social vulnerability and thermal resilience, with a focus on building thermal parameters. Datasets from Zillow and ResStock are used to capture the thermal properties of buildings, while social vulnerability data is obtained from the Center for Disease Control and Prevention. A Gaussian Naïve Bayes (GNB) model is applied to enhance building envelope attributes from Zillow, leveraging detailed information from ResStock data. Additionally, a Kolmogorov-Smirnov (KS) test is conducted to analyze diverse thermal attributes, such as age of buildings, room size, number of stories, building area, envelope characteristics, systems, infiltration, across differing levels of social vulnerability (high and low). Specifically, Philadelphia is selected as the analysis focus. The analysis reveals significant associations between thermal resilience and social vulnerability. Groups with higher social vulnerability are more likely to be exposed to elevated levels of thermal vulnerability compared to less vulnerable groups. This highlights the necessity for tailored interventions aimed at promoting the development of more resilient and equitable buildings, capable of reducing the impacts of heatwaves on vulnerable communities.

Differential reconfiguration of brain networks in children in response to standard versus rewarded go/no-go task demands.

Response inhibition and sustained attention are critical for higher-order cognition and rely upon specific patterns of functional brain network organization. This study investigated how functional brain networks reconfigure to execute these cognitive processes during a go/no-go task with and without the presence of rewards in 26 children between the ages of 8 and 12 years. First, we compared task performance between standard and rewarded versions of a go/no-go task. We found that the presence of rewards reduced commission error rate, a measure considered to indicate improved response inhibition. Tau, thought to index sustained attention, did not change across task conditions. Next, changes in functional brain network organization were assessed between the resting state, the standard go/no-go task, and the rewarded go/no-go task. Relative to the resting state, integration decreased and segregation increased during the standard go/no-go task. A further decrease in integration and increase in segregation was observed when rewards were introduced. These patterns of reconfiguration were present globally and across several key brain networks of interest, as well as in individual regions implicated in the processes of response inhibition, attention, and reward processing. These findings align with patterns of brain network organization found to support the cognitive strategy of sustained attention, rather than response inhibition, during go/no-go task performance and suggest that rewards enhance this organization. Overall, this study used large-scale brain network organization and a within-subjects multi-task design to examine different cognitive strategies and the influence of rewards on response inhibition and sustained attention in late childhood.

Implementation of an early attention strategy to reduce emergency room overcrowding in an academic institution in Colombia, a pilot study

Overcrowding is a worldwide problem, and long waiting times are associated with increased morbidity and even mortality of patients regardless of triage classification. Although there are many tools published in the literature that contribute to the reduction of overcrowding, for the Colombian population there are not many tools evaluated to reduce the length of stay of patients in the emergency department. This is a retrospective analytical study that compared whether there was a difference in patient definition time and ED length of stay between a group attended under an early care protocol (PAT) versus the usual protocol. Of the total of 969 patients included it was found that the group attended under the PAT protocol had a shorter definition time than the usual protocol, also the Emergency department length of stay (EDLOS) was significantly lower in the PAT group compared to the usual protocol. The implementation of the PAT protocol performed by emergency physicians allows a faster contact with the patient by the physician, and leads to a significant reduction of EDLOS, contributing to the reduction of overcrowding in the emergency department.

Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning

To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm’s exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm.

Evolution of AI in Smart Home Systems: A Comprehensive Review

The evolution of Artificial Intelligence (AI) has significantly transformed the landscape of smart home systems into a new era of convenience, efficiency, and personalized experiences. This paper provides an insightful exploration into the historical progression and contemporary developments of AI within smart home environments. Beginning with an overview of early automation technologies, we trace the evolutionary trajectory of AI's integration into smart homes, highlighting key milestones, breakthroughs, and paradigm shifts. Moreover, this paper also highlights the applications of AI in smart home ecosystems, including home automation, energy management, security surveillance, healthcare monitoring, and personalized assistance. Through advanced sensor technologies, ubiquitous connectivity, and cloud-based computing, AI-enabled smart home platforms offer seamless integration, interoperability, and scalability, paving the way for a more interconnected and intelligent living environment. Furthermore, we discuss the emerging trends and future directions shaping the evolution of AI in smart home systems. We also address the associated challenges and considerations, including privacy concerns, data security risks, ethical implications, and regulatory frameworks, which warrant careful attention and mitigation strategies.

Enhancing VPPA welding quality prediction: A hybrid model integrating prior physical knowledge and CNN analysis

In response to the inconsistency between the features obtained by deep learning models and the quality features reflected by the physical laws of the welding process, this study proposes a solution by integrating a physical prior information model with a CNN model. Initially, the physical laws of the welding process are utilized to annotate the arc, weld pool, and weld seam features relevant to quality, which are then acquired through image processing algorithms, thereby converting the physical laws into a prior information model. Subsequently, this prior information model guides the CNN model for quality recognition, and the CNN model's attention to features is explained through visualization methods to elucidate the relationship between features and quality recognition. Experimental results demonstrate that under the guidance of the prior information model, the CNN model not only automatically focuses on features relevant to quality but also achieves a differential feature attention strategy, thereby improving the recognition accuracy of different outcomes. This research provides a new perspective for deep learning in the field of welding quality recognition.

IACP-DFSRA: Identification of Anticancer Peptides Based on a Dual-channel Fusion Strategy of ResCNN and Attention

Anticancer peptides (ACPs) have been widely applied in the treatment of cancer owing to good safety, rational side effects, and high selectivity. However, the number of ACPs that have been experimentally validated is limited as identification of ACPs is extremely expensive. Hence, accurate and cost-effective identification methods for ACPs are urgently needed. In this work, we proposed a deep learning-based model, named iACP-DFSRA, for ACPs identification. Specifically, we adopted two kinds of sequence embedding technologies, ProtBert_BFD pre-training language model and handcrafted features to encode protein sequences. Then, the LightGBM was used for feature selection, and the selected features were input into ResCNN and Attention mechanism, respectively, to extract local and global features. Finally, the concatenate features were deeply fused by using the Attention mechanism to allow key features to be paid more attention to by the model and make predictions by fully connected layer. The results of 10-fold cross-validation demonstrated that the iACP-DFSRA model delivered improved results in most metrics with Sp of 94.15%, Sn of 95.32%, Acc of 94.74% and MCC of 89.48% compared to the latest AACFlow model. Indeed, the iACP-DFSRA model is the only model with Acc > 90% and MCC > 80% on this independent test dataset. Furthermore, we have further demonstrated the superiority of our model on additional datasets. In addition, t-SNE and SHAP interpretation analysis demonstrated that it is crucial to use two channels for feature extraction and use the Attention mechanism for deep fusion, which helps the iACP-DFSRA to predict ACPs more effectively.

Individual Psychological Profiles And Their Influence On Self-Regulatory Behaviour In Sport

The purpose of this paper is to study how individual psychological characteristics of an athlete affect his/her ability to effectively self-regulate during training. In the course of the study, the testing method was applied (Cognitive Strategies in Sport Scale and Psychological Stress Measurement). Methods of descriptive statistics for the analysis of the mean value and square deviation and Pearson's correlation coefficient (chi-square). The results of the study showed that there is a statistically significant difference between the mean values of all cognitive strategies. The most pronounced differences were observed between emotional control and other strategies, as well as between focusing attention and other strategies. This suggests that emotional control and attention focus are key cognitive strategies for achieving success in sport. In addition, psychological aspects and individual personality traits of athletes significantly affect their ability to self-regulate during training, which confirms the importance of taking these factors into account when developing training programmes. The study confirmed that emotional control and focus are key cognitive strategies for achieving success in sport. Individual psychological characteristics of athletes have a significant impact on their ability to effectively self-regulate during training. This highlights the importance of integrating psychological training into training programmes to improve athletes' performance. Further research should focus on developing methods to help improve these aspects.

The role of transitional probabilities in word holistic processing.

In recent years, increased attention has been devoted to visual word recognition under a perceptual expertise framework. Because the information required to identify words is distributed across the word, a holistic attentional strategy is optimal and develops with experience. It is, however, an open question the extent to which other information embedded in a word may contribute to word holistic processing, namely sublexical word properties. In the present research, we therefore explore the role of sublexical properties-specifically bigram transition probabilities-in this processing strategy. We used a common task in the holistic processing literature (i.e., composite task) and four-letter disyllabic words, where two of the bigrams reinforce the cohesiveness of each syllable and one of the bigrams reinforces the cohesiveness between the syllables. We found preliminary evidence of a role of these sublexical properties in word holistic processing.

Competition shapes spatial coding strategy for selective attention inside visual working memory: insights from gaze and neural measurements

Competition shapes spatial coding strategy for selective attention inside visual working memory: insights from gaze and neural measurements

Glyphosate resistance and no fitness cost in backcross offspring of wild soybean and transgenic soybean with epsps gene

BackgroundThe commercial utilization of genetically modified soybeans has yielded substantial economic advantages. Nevertheless, the genetic drift towards wild soybeans is one of the main ecological risks that needs to be addressed. Previous experiments demonstrated the absence of fitness cost or florescence overlap in hybrid offspring resulting from the crossbreeding of transgenic soybean GTS40-3-2 and Zhengzhou wild soybeans. In this study, hybrid progeny was systematically crossed with wild soybeans to establish a backcross progeny system. This system was employed to evaluate the ecological risk associated with the backcross progeny of transgenic and wild soybeans.ResultsThe findings indicated that the offspring from the backcross exhibited glyphosate tolerance. Furthermore, the expression of foreign proteins in the backcross offspring was notably lower than in the transgenic soybean, and there was no significant difference when compared to the hybrid progeny. Parameters such as germination rate, aboveground biomass, pods per plant, full seeds per plant, and 100-grain weight exhibited no significant differences between the negative and positive lines of the backcross progenies, and no fitness cost was identified in comparison to wild soybeans. These results underscore the potential for foreign genes to propagate within other wild soybeans, which requires continuous attention.ConclusionsThe widespread adoption of genetically modified soybeans has undeniably led to substantial economic gains. However, the research findings emphasize the critical importance of addressing the ecological risks posed by genetic drift towards wild soybeans. The backcross progeny system established in this study indicates that the potential for foreign gene dissemination to wild soybean populations warrants continued attention and mitigation strategies.

Public Health Monitoring System In COVID-19 Conditions Using Machine Learning-Based Sentimental Analysis

Twitter is a significant forum for individuals to discuss and disseminate health-related data. The system offers substantial data for immediate monitoring of contagious diseases (such as COVID-19), relieving disease-prevention organizations from the laborious tasks associated with Personal Health Measures (PHM). PHM identification is a crucial technique for staying informed about the status of an epidemic. It aims to determine an individual's health by analyzing web text data. This research investigates the process of identifying PHM related to COVID-19 using data from Twitter. The research has constructed a COVID-19 PHM dataset with tweets labeled with four distinct categories of health disorders connected to COVID-19: self-mention (SM), other-mention (OM), awareness, and non-healthcare (NHC). The research achieved favorable outcomes in the PHM identification task. The categorizing results enable prompt health tracking and oversight for digital epidemiology. The study assesses the impact of the attention strategy and training methodology on the predictive capabilities.

Prevalence and sociodemographic factors associated with double burden of malnutrition among children under 5: a cross-sectional analysis of NFHS-4 data in India

BackgroundThe double burden of malnutrition (DBM), characterised by the coexistence of undernutrition and overnutrition, poses a significant public health challenge, particularly in low- and middle-income countries. India, being one of...

Effects of attentional focus strategies in drop landing biomechanics of individuals with unilateral functional ankle instability.

Functional Ankle Instability (FAI) is a pervasive condition that can emerge following inadequate management of lateral ankle sprains. It is hallmarked by chronic joint instability and a subsequent deterioration in physical performance. The modulation of motor patterns through attentional focus is a well-established concept in the realm of motor learning and performance optimization. However, the precise manner in which attentional focus can rehabilitate or refine movement patterns in individuals with FAI remains to be fully elucidated. The primary aim of this study was to evaluate the impact of attentional focus strategies on the biomechanics of single-leg drop landing movements among individuals with FAI. Eighteen males with unilateral FAI were recruited. Kinematic and kinetic data were collected using an infrared three-dimensional motion capture system and force plates. Participants performed single-leg drop landing tasks under no focus (baseline), internal focus (IF), and external focus (EF) conditions. Biomechanical characteristics, including joint angles, ground reaction forces, and leg stiffness, were assessed. A 2 × 3 [side (unstable and stable) × focus (baseline, IF, and EF)] Repeated Measures Analysis of Variance (RM-ANOVA) analyzed the effects of attentional focus on biomechanical variables in individuals with FAI. No significant interaction effects were observed in this study. At peak vertical ground reaction force (vGRF), the knee flexion angle was significantly influenced by attentional focus, with a markedly greater angle under EF compared to IF (p < 0.001). Additionally, at peak vGRF, the ankle joint plantarflexion angle was significantly smaller with EF than with IF (p < 0.001). Significant main effects of focus were found for peak vGRF and the time to reach peak vGRF, with higher peak vGRF values observed under baseline and IF conditions compared to EF (p < 0.001). Participants reached peak vGRF more quickly under IF (p < 0.001). Leg Stiffness (kleg) was significantly higher under IF compared to EF (p = 0.001). IF enhances joint stability in FAI, whereas EF promotes a conservative landing strategy with increased knee flexion, dispersing impact and minimizing joint stress. Integrating these strategies into FAI rehabilitation programs can optimize lower limb biomechanics and reduce the risk of reinjury.

Characterization of the typology of producers in the “sembrando vida” program in Oaxaca, Mexico

In Mexico, research work documented at plot in the field or producer level is uncommon and with limited information, due to the fact that it is expensive and requires specialized personnel. This research work carried out by the National Institute of Forestry, Agriculture and Livestock Research (INIFAP) and financed by the National Council of Humanities, Science and Technology (CONAHCYT) was based on a field diagnosis to determine the description of the typology of the participating "sowers'' producers enrolled in the Sembrando Vida Program (PSV), of the Federal Goverment of Mexico belonging to the state of Oaxaca, and thus, be able to implement strategies for training and follow-up attention on a massive scale. A total of 383 sample surveys were analyzed from a population of 37,719 small producers representing an estimated crop area of 56,578 hectares distributed in two regions of the state of Oaxaca. It is concluded that the diagnosis continues to be a fundamental information tool to identify and address more precisely and timely the problems at the farmer's plot level and with any type of crop. Another fundamental activity was the visit or technical visits to the territories that are representative of the regions in order to validate the information obtained. With the results obtained in this research, it was possible to address the main problems through personalized and directed technical assistance under the scheme of field schools (learning-by-doing) throughout the development of annual and fruit crops that the planters have in the MIAF within their PUPs (Small Production Units). Likewise, insert the main training needs at the express and consensual request of all program stakeholders: researchers-trainers, PSV technicians and sowers.

Investigating an effort avoidance account of attentional strategy choice.

People often choose suboptimal attentional control strategies during visual search. This has been at least partially attributed to the avoidance of the cognitive effort associated with the optimal strategy, but aspects of the task triggering such avoidance remain unclear. Here, we attempted to measure effort avoidance of an isolated task component to assess whether this component might drive suboptimal behavior. We adopted a modified version of the Adaptive Choice Visual Search (ACVS), a task designed to measure people's visual search strategies. To perform optimally, participants must make a numerosity judgment-estimating and comparing two color sets-before they can advantageously search through the less numerous of the two. If participants skip the numerosity judgment step, they can still perform accurately, albeit substantially more slowly. To study whether effort associated with performing the optional numerosity judgment could be an obstacle to optimal performance, we created a variant of the demand selection task to quantify the avoidance of numerosity judgment effort. Results revealed a robust avoidance of the numerosity judgment, offering a potential explanation for why individuals choose suboptimal strategies in the ACVS task. Nevertheless, we did not find a significant relationship between individual numerosity judgment avoidance and ACVS optimality, and we discussed potential reasons for this lack of an observed relationship. Altogether, our results showed that the effort avoidance for specific subcomponents of a visual search task can be probed and linked to overall strategy choices.

Global burden of fall and associated factors among individual with low vision: A systematic-review and meta-analysis.

Low vision has a significant global health problem that impacts the personal, economical, psychological, and social life of an individual. Each year around 684 000 individuals die from falls, 80% of these deaths occur are in low- and middle-income countries. The risk of falling significantly increases with visual impairment. This review aimed to determine the global pooled prevalence of fall and associated factors among individuals with low vision. Systematic search of published studies done on PubMed, EMBASE, MEDLINE, Cochrane, Scopus, Web of Science CINAHL and, Google Scholar. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to report the findings. Quality of studies was assessed using the modified Newcastle-Ottawa Scale (NOS). Meta-analysis was performed using a random-effects method using the STATA™ Version 14 software. Thirty-five (35) studies from different regions involving 175,297 participants included in this meta-analysis. The overall pooled global prevalence fall among individual with low vision was17.7% (95% CI: 16.4-18.9) whereas the highest prevalence was 35.5%; (95% CI: 28.4-42.5) in Australia and the lowest was 19.7%; (95% CI: 7.6-31.8) seen in South America. Fear of falling (OR: 0.16(95%CI 0.09-0.30), and severity of visual impairment (OR: 0.27(95%CI (0.18-0.39) increases the odds of falling. As one cause of accidental death, the prevalence of falls among individuals with low vision is high. Fear of falling and severity of falling increases the odds of falling. Different stakeholders should give due attention and plan effective strategies to reduce the fall among this population.