Search Results (1,795)

In this article, we employ a Batesonian systems thinking approach to analyze politically polarized and politically polarizing encounters in the contemporary United States. We bring together Bateson’s concepts of schismogenesis, double binds, metacommunication, and transcontextualism with recent work on recognition and resonance in order to show how these encounters create moments of transcontextual double binds that produce mutual dysrecognition. We show how these moments of mutual dysrecognition become both animating forces of political polarization in the moment while also becoming constitutive poetic resonances for making sense of future events. When these moments of dysrecognition are considered alongside the removal of mechanisms that restrain schismogenesis, the United States body politic is becoming increasingly schizophrenic—split in two with both parts incommunicado with the other such that the whole system is veering towards collapse. We close by briefly considering the kind of deutero-learning, to use Bateson’s term, that might help to stave off such a collapse. Full article

Toponyms are fundamental geographical resources characterized by their spatial attributes, distinct from general nouns. While natural language provides rich toponymic data beyond traditional surveying methods, its qualitative ambiguity and inherent uncertainty challenge systematic extraction. Traditional toponym recognition methods based on part-of-speech tagging only focus on the surface-level features of words, failing to effectively handle complex scenarios such as alias nesting, metonymy ambiguity, and mixed punctuation. This leads to the loss of toponym semantic integrity and deviations in geographic entity recognition. This study proposes a set of Chinese toponym annotation specifications that integrate spatial semantics. By leveraging the XML markup language, it deeply combines the spatial location characteristics of toponyms with linguistic features, and designs fine-grained annotation rules to address the limitations of traditional methods in semantic integrity and geographic entity recognition. On this basis, by integrating multi-source corpora from the Encyclopedia of China: Chinese Geography and People’s Daily, a large-scale Chinese toponym annotation corpus (CHTopo) covering five major categories of toponyms has been constructed. The performance of this annotated corpus was evaluated through toponym recognition, exploring the construction methods of a large-scale, diversified, and high-coverage Chinese toponym annotated corpus from the perspectives of applicability and practicality. CHTopo is conducive to providing foundational support for geographic information extraction, spatial knowledge graphs, and geoparsing research, bridging linguistic and geospatial intelligence. Full article

This study sought to clarify the role of after-death communication in resilience in grief. The primary research question guiding this study was: In what way do experiences of after-death communication inform the cultivation of gratitude and compassion as part of the grieving process? For Study 1, measures of grief, continuing bonds, compassion, and gratitude were administered to a diverse demographic pool (N = 329). Bivariate correlational analysis revealed strong correlations among the total scores of the four surveys. Of the 329 participants, 67.2% (n = 221) identified as having experienced after-death communication in some form, while 32.8% (n = 108) claimed that they did not. A series of one-way ANOVAs revealed that those identifying as having after-death communication and a spiritual practice showed significantly higher scores on all measures. Study 2 was a grounded theory study that conducted interviews with people claiming a significant after-death communication experience (N = 44). Results supported the survey data, suggesting that after-death communication was a catalyst and facilitator of grief that enhanced spirituality, shifted or enhanced belief structures, and reduced death anxiety. Conclusions support the recognition of after-death communication as a possible vehicle for enhanced grief resilience and spirituality. Full article

In computer vision, the performance of detectors trained under optimal lighting conditions is significantly impaired when applied to infrared domains due to the scarcity of labeled infrared target domain data and the inherent degradation in infrared image quality. Progress in cross-domain pedestrian re-identification is hindered by the lack of labeled infrared image data. To address the degradation of pedestrian recognition in infrared environments, we propose a framework for zero-shot infrared domain adaptation. This integrated approach is designed to mitigate the challenges of pedestrian recognition in infrared domains while enabling zero-shot domain adaptation. Specifically, an advanced reflectance representation learning module and an exchange–re-decomposition–coherence process are employed to learn illumination invariance and to enhance the model’s effectiveness, respectively. Additionally, the CLIP (Contrastive Language–Image Pretraining) image encoder and DINO (Distillation with No Labels) are fused for feature extraction, improving model performance under infrared conditions and enhancing its generalization capability. To further improve model performance, we introduce the Non-Local Attention (NLA) module, the Instance-based Weighted Part Attention (IWPA) module, and the Multi-head Self-Attention module. The NLA module captures global feature dependencies, particularly long-range feature relationships, effectively mitigating issues such as blurred or missing image information in feature degradation scenarios. The IWPA module focuses on localized regions to enhance model accuracy in complex backgrounds and unevenly lit scenes. Meanwhile, the Multi-head Self-Attention module captures long-range dependencies between cross-modal features, further strengthening environmental understanding and scene modeling. The key innovation of this work lies in the skillful combination and application of existing technologies to new domains, overcoming the challenges posed by vision in infrared environments. Experimental results on the SYSU-MM01 dataset show that, under the single-shot setting, Rank-1 Accuracy (Rank-1) andmean Average Precision (mAP) values of 37.97% and 37.25%, respectively, were achieved, while in the multi-shot setting, values of 34.96% and 34.14% were attained. Full article

Cognitive decline is a common complication of diabetes mellitus, driven in part by oxidative stress and impaired glucose–insulin homeostasis. This study examined the neuroprotective effects of agmatine (200 mg/kg intraperitoneally) in female BALB/c diabetic mice. Several receptor pathways were examined using commercially available antagonists. Behavioral performance was evaluated using the novel object recognition test. Metabolic parameters, such as glucose and insulin levels, as well as antioxidants, including catalase (CAT), superoxide dismutase (SOD), and glutathione (GSH), were measured in blood and brain tissue. The diabetic mice exhibited impaired recognition memory (discrimination index = 0.08), hyperglycemia (24.3 mmol/L), decreased insulin levels (38.4 µU/mL), and diminished antioxidant defenses (CAT: 75.4 U/g tissue, SOD: 32.6 U/g tissue, and GSH: 8.3 mmol/g tissue). Agmatine treatment improved cognitive function and reversed the biochemical alterations. However, these effects were reduced when agmatine was co-administered with imidazoline I₂/I₃ receptor antagonists. Correlation analysis revealed that cognitive performance positively correlated with antioxidant enzyme levels and insulin levels and negatively correlated with glucose concentrations. Strong intercorrelations among CAT, SOD, and GSH levels suggest a coordinated antioxidant response. Overall, these results imply that agmatine’s neuroprotective effects are partially mediated by modulation of the oxidative balance and glucose–insulin regulation via imidazoline receptors. Full article

Specific computational methods, such as machine learning algorithms, can assist mining professionals in quickly and consistently identifying and addressing classification issues related to mineralized horizons, as well as uncovering key variables that impact predictive outcomes, many of which were previously difficult to observe. The integration of numerical and categorical variables, which are part of a dataset for defining ore grades, is part of the daily routine of professionals who obtain the data and manipulate the various phases of analysis in a mining project. Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. The objective of this study is the classification of gold ore or gangue through supervised machine learning methods using numerical variables represented by grade and categorical variables obtained through drillholes descriptions. Four groups of variables were selected with different variable configurations. The application of classification algorithms to different groups of variables aimed to observe the variables of importance and the impact of each one on the classification, in addition to testing the best algorithm in terms of accuracy and precision. The datasets were subjected to training, validation, and testing using the decision tree, random forest, Adaboost, XGBoost, and logistic regression methods. The evaluation was randomly divided into training (60%) and testing (40%) with 10-fold cross-validation. The results revealed that the XGBoost algorithm obtained the best performance, with an accuracy of 0.96 for scenario C1. In the SHAP analysis, the variable As was prominent in the predictions, mainly in scenarios C1 and C3. The arsenic class (Class_As), present mainly in scenario C4, had a significant positive weight in the classification. In the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) curves, the results showed that XGBoost/scenario C1 obtained the highest AUC of 0.985, indicating that the algorithm had the best performance in ore/gangue classification of the sample set. The logistic regression algorithm together with AdaBoost had the worst performance, also varying between scenarios. Full article

Striking belief distortions may accompany various disorders of awareness that are predominantly associated with right hemispheric cerebral dysfunction. Distortions may range on a continuum of pathological severity, from the unawareness of paralysis in anosognosia for hemiplegia, to a more startling disturbance in denial of paralysis where belief may starkly conflict with reality. The patients’ beliefs about their limitations typically represent attempts to make sense of limitations or to impart meaning to incongruous facts. These beliefs are often couched in recollections from past memories or previous experience, and are hard to modify even given new information. Various explanations of unawareness have been suggested, including sensory, cognitive, monitoring and feedback operations, feedforward mechanisms, disconnection theories, and hemispheric asymmetry hypotheses, along with psychological denial, to account for the curious lack of awareness in anosognosia and other awareness disorders. This paper addresses these varying explanations of the puzzling beliefs regarding hemiparesis in anosognosia. Furthermore, using the multi-dimensional nature of unawareness in anosognosia as a model, some startling belief distortions in other right-hemisphere associated clinical syndromes are also explored. Other neurobehavioral disturbances, though perhaps less common, reflect marked psychopathological distortions. Startling disorders of belief are notable in somatic illusions, non-recognition or delusional misattribution of limb ownership (asomatognosia, somatoparaphrenia), or delusional identity (Capgras syndrome) and misidentification phenomena. Difficulty in updating beliefs as a source of unawareness in anosognosia and other awareness disorders has been proposed. Processes of belief development are considered to be patterns of thought, memories, and experience, which coalesce in a sense of the bodily and personal self. A common consequence of such disorders seems to be an altered representation of the self, self-parts, or the external world. Astonishing nonveridical beliefs about the body, about space, or about the self, continue to invite exploration and to stimulate fascination. Full article

Efficient and accurate recognition of highway pavement cracks is crucial for the timely maintenance and long-term use of expressways. Among the existing crack acquisition methods, human-based approaches are inefficient, whereas carrier-based automated methods are expensive. Additionally, both methods present challenges related to traffic obstruction and safety risks. To address these challenges, we propose a fixed pan-tilt-zoom (PTZ) vision-based highway pavement crack recognition workflow. Pavement cracks often exhibit complex textures with blurred boundaries, low contrast, and discontinuous pixels, leading to missed and false detection. To mitigate these issues, we introduce an algorithm named contrast-enhanced feature reconstruction (CEFR), which consists of three parts: comparison-based pixel transformation, nonlinear stretching, and generating a saliency map. CEFR is an image pre-processing algorithm that enhances crack edges and establishes uniform inner-crack characteristics, thereby increasing the contrast between cracks and the background. Extensive experiments demonstrate that CEFR improves recognition performance, yielding increases of 3.1% in F1-score, 2.6% in mAP@0.5, and 4.6% in mAP@0.5:0.95, compared with the dataset without CEFR. The effectiveness and generalisability of CEFR are validated across multiple models, datasets, and tasks, confirming its applicability for highway maintenance engineering. Full article

It is a crucial interpretation task in space target perception to identify key parts of space targets through the inverse synthetic aperture radar (ISAR) imaging. Due to the significant variations in the categories and poses of space targets, conventional methods that directly predict identification results exhibit limited accuracy. Hence, we make the first attempt to propose a key part recognition network based on ISAR images, which incorporates the knowledge of space target categories and poses. Specifically, we propose a fine-grained category training paradigm that defines the same functional parts of different space targets as distinct categories. Correspondingly, additional classification heads are employed to predict category and pose, and the predictions are then integrated with ISAR image semantic features through a designed category–pose guidance module to achieve high-precision recognition guided by category and pose knowledge. Qualitative and quantitative evaluations on two types of simulated targets and one type of measured target demonstrate that the proposed method reduces the complexity of the key part recognition task and significantly improves recognition accuracy compared to the existing mainstream methods. Full article

Background/Objectives: Population aging and the rise in chronic diseases challenge healthcare systems to adopt person-centered approaches, especially in palliative care (PC), where symptom management remains limited. Physiotherapy plays a key role in alleviating discomfort but faces inconsistent integration in Portugal due to lack of recognition. Variations in intervention methods hinder uniform care delivery, limiting timely patient access to comfort-focused treatments and knowledge. This study aims to deepen the understanding of physiotherapy’s role in Portuguese PC to improve its integration into teams and enhance patient access to comfort care. Methods: This study used a descriptive qualitative approach with online focus groups (FG), guided by Krueger and Casey’s methodology and adhering to the COREQ checklist. A non-probabilistic convenience sample of physiotherapists working in palliative care across mainland Portugal and the islands was selected based on inclusion criteria. Three FGs were planned with up to ten participants each. However, due to availability and attendance issues, only 15 physiotherapists participated: 5 in FG1 (in-hospital PC units), 6 in FG2 (inpatient units), and 4 in FG3, the minimum appropriate number from community-based units. Results: Physiotherapy plays a crucial yet underrecognized role in PC, emphasizing the need for its full integration into care teams rather than reliance on late, on-call referrals. Techniques such as positioning, mobilization, pain and dyspnea relief, adapted exercises, massage, music therapy, and emotional support are employed. Conventional physiotherapy tools are used and personalized according to the patient’s context, duration, setting, dosage, and individual needs. Conclusions: Physiotherapy should be recognized as a fundamental part of PC, contributing not only to the prolongation of life but also to ensuring comfort and dignity for patients and their families. To achieve this, its role within multidisciplinary teams must be strengthened and supported by regulations that guarantee access and the formal integration of physiotherapists. However, a significant gap remains in patients’ regular access to comfort-focused interventions at the appropriate time, perhaps due to the considerable variation in physiotherapy practices depending on the patient and care setting, which presents a challenge for knowledge development both in Portugal and globally. Full article

This study develops an algorithm and a system for steel straightness detection, which combines object detection, edge detection, line detection, clustering, stitching, and bending recognition. The algorithm detects the contour of U-shaped steel bars with widths of 100 mm, named U100, or 150 mm, named U150, and lengths of 8, 10, 12 m. The algorithm uses object detection to extract the center point of the U-shaped bottom as a reference point and line detection to extract lines in the contour. The algorithm selects one-stage or two-stage edge detection based on the light source. Two-stage edge detection enhances the contour features when the light source is insufficient. After contour detection, some parts of the contour disappear due to the light source. The algorithm stitches all lines with an angle difference within $∆\theta$ degrees into one straight line L based on the angle of the longest line. If the length of L exceeds the threshold value $MLL$, the steel bar is straight; otherwise, it is bent. $∆\theta$ and $MLL$ are used to set the acceptable bending degree. The experiment results show that the algorithm detects 123,128 steel bars in 193 h with an average accuracy of 99.64% for straight steel and an average recall of 95.70% for bent steel. The contribution of this study is the development of a real-time algorithm and its corresponding system for steel straightness determination in a steel factory, ensuring accurate and efficient assessment of steel quality in an industrial setting. Full article

This article addresses the issue of the elevator cabin door drive system failure diagnosis. The analyzed component is one of the most critical and the most vulnerable part of the entire elevator. Existing solutions in the literature include methods such as spectral analysis of system vibrations, motor current signature analysis, fishbone diagrams, fault trees, multi-agent systems, image recognition, and machine learning techniques. However, there is a noticeable gap in comprehensive studies that specifically address classification of the multiple types of system components failures, class imbalance in the dataset, and the need to reduce data transmitted over the elevator’s internal bus. The developed diagnostic system measures the drive system’s parameters, processes them to reduce data, and classifies 11 device failures. This was achieved by constructing a test bench with a prototype cabin door drive system, identifying the most frequent system faults, developing a data preprocessing method that aggregates every driving cycle to one sample, reducing the transmitted data by 300 times, and using machine learning for modeling. A comparative analysis of the fault detection performance of seven different machine learning algorithms was conducted. An optimal cross-validation method and hyperparameter optimization techniques were employed to fine-tune each model, achieving a recall of over 97% and an F1 score approximately 97%. Finally, the developed data preparation method was implemented in the cabin door drive controller. Full article

Background. Emotions are a crucial part of our human nature. The recognition of emotions is an essential component of our social and emotional skills. Facial expressions serve as a key element in discerning others’ emotions. Different databases of images of facial emotion expressions exist worldwide; however, most of them are limited to only adult faces and include only the six basic emotions, as well as neutral faces, ignoring more complex emotional expressions. Here, we present the Children’s Emotions Database (CED), a novel repository featuring both basic and complex facial expressions captured from preschool-aged children. The CED is one of the first databases to include complex emotional expressions in preschoolers. Our aim was to develop such a database that can be used further for research and applied purposes. Methods. Three 6-year-old children (one female) were photographed while showing different facial emotional expressions. The photos were taken under standardized conditions. The children were instructed to express each of the following basic emotions: happiness, pleasant surprise, sadness, fear, anger, disgust; a neutral face; and four complex emotions: pride, guilt, compassion, and shame; this resulted in a total of eleven expressions for each child. Two photos per child were reviewed and selected for validation. The photo validation was performed with a sample of 104 adult raters (94 females; aged 19–70 years; M = 29.9; SD = 11.40) and a limited sample of 32 children at preschool age (17 girls; aged 4–7 years; M = 6.5; SD = 0.81). The validation consisted of two tasks—free emotion labeling and emotion recognition (with predefined labels). Recognition accuracy for each expression was calculated. Results and Conclusions. While basic emotions and neutral expressions were recognized with high accuracy, complex emotions were less accurately identified, consistent with the existing literature on the developmental challenges in recognizing such emotions. The current work is a promising new database of preschoolers’ facial expressions consisting of both basic and complex emotions. This database offers a valuable resource for advancing research in emotional development, educational interventions, and clinical applications tailored to early childhood. Full article

This paper addresses the central issue arising from the (SDAC) of facial expressions, namely, to balance the competing demands of good global features for detection, and fine features for good facial expression classifications by replacing the feature extraction part of the “neck” network in the feature pyramid network in the You Only Look Once X (YOLOX) framework with a novel architecture involving three attention mechanisms—batch, channel, and neighborhood—which respectively explores the three input dimensions—batch, channel, and spatial. Correlations across a batch of images in the individual path of the dual incoming paths are first extracted by a self attention mechanism in the batch dimension; these two paths are fused together to consolidate their information and then split again into two separate paths; the information along the channel dimension is extracted using a generalized form of channel attention, an adaptive graph channel attention, which provides each element of the incoming signal with a weight that is adapted to the incoming signal. The combination of these two paths, together with two skip connections from the input to the batch attention to the output of the adaptive channel attention, then passes into a residual network, with neighborhood attention to extract fine features in the spatial dimension. This novel dual path architecture has been shown experimentally to achieve a better balance between the competing demands in an SDAC problem than other competing approaches. Ablation studies enable the determination of the relative importance of these three attention mechanisms. Competitive results are obtained on two non-aligned face expression recognition datasets, RAF-DB and SFEW, when compared with other state-of-the-art methods. Full article

Introduction: Nonbinary individuals who do not identify as exclusively male or female often face unique emotional challenges due to societal cisheteronormativity and limited recognition of their identities. While existing research has primarily focused on anxiety, depression, and pathways to parenthood among nonbinary people, little attention has been paid to their comprehensive emotional experiences as parents. This study aims to explore the emotional universe of two nonbinary parents from Spain and the United States. Design: Hermeneutic study. Materials and Methods: We implemented purposive sampling, conducted semi-structured virtual interviews, and followed Ricoeur’s theory of interpretation for data analysis. We used the Universe of Emotions affective taxonomy as a starting category in this analysis. Our sample consisted of a 32-year-old white Spanish nurse (she/they/them), assigned female at birth and parent of two one-year-old toddlers, and a 34-year-old white North American physiotherapist (he/they/them) assigned female at birth and parent of a ten-year-old child. Results: Through its four themes (A story of misunderstanding: “What are you, a combat helicopter?”; Clearly, you don’t fit, so…; But (a new) family is there; No monster here: I’m, at the core, a human being), this study reveals the complex emotional journey experienced by two nonbinary parents. Conclusions: Central to this journey are three key emotions: strangeness, belonging, and acceptance. The participants describe an initial stage marked by body and social dysphoria, confusion, and rejection, followed by a transformative process in which parenthood becomes a catalyst for emotional and identity integration. This transition—from alienation to connection—reflects a broader movement from dehumanization to humanization, where the experience of parenting fosters emotional resilience, social recognition, and a renewed sense of self. Implications for the profession and/or patient care: Analyzing their emotions (both negative and positive ones), we obtained robust insights into these parents’ personal and social contexts. Therefore, we can facilitate understanding of the emotional complexity of nonbinary parents by the trans and cisgender communities. Through this understanding, nurses and the organizations they work for can improve their competence in their holistic care. Acceptance from nonbinary parents’ social contexts, of which nursing is a part, is a critical factor in their health and emotional wellbeing. Full article