Search Results (3,181)

Anxiety and depression are common comorbidities in patients with chronic obstructive pulmonary disease (COPD), which can contribute to increased morbidity, reduced quality of life, and worse clinical outcomes. Nevertheless, these psychological conditions remain largely overlooked. This narrative review includes studies published between 1983 and 2025 to synthesise the current evidence on the risk factors, clinical impacts, and therapeutic strategies for these comorbidities. While the exact mechanisms leading to their increased prevalence are not fully understood, growing evidence implicates a combination of biological (e.g., systemic inflammation), social (e.g., isolation and stigma), and behavioural (e.g., smoking and inactivity) factors. Despite current guidelines recommending the identification and management of these comorbidities in COPD, they are not currently included in COPD assessments. Undetected and unmanaged anxiety and depression have serious consequences, including poor self-management, non-adherence to medications, increased risk of exacerbation and hospitalisations, and even mortality; thus, there is a need to incorporate screening as part of COPD assessments. There is robust evidence showing that pulmonary rehabilitation, a core non-pharmacological intervention, can improve mood symptoms, enhance functional capacity, and foster psychosocial resilience. Psychological therapies such as cognitive behavioural therapy (CBT), mindfulness-based approaches, and supportive counselling have also demonstrated value in reducing emotional distress and improving coping mechanisms. Pharmacological therapies, particularly selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine reuptake inhibitors (SNRIs), are commonly prescribed in moderate to severe cases or when non-pharmacological approaches prove inadequate. However, the evidence for their efficacy in COPD populations is mixed, with concerns about adverse respiratory outcomes and high discontinuation rates due to side effects. There are also barriers to optimal care, including underdiagnosis, a lack of screening protocols, limited provider training, stigma, and fragmented multidisciplinary coordination. A multidisciplinary, biopsychosocial approach is essential to ensure early identification, integrated care, and improved outcomes for patients with COPD. Full article

A novel adaptive intelligent control system (AICS) with learning-while-controlling capability is developed for a highly nonlinear single-input single-output plant by redesigning the conventional model reference adaptive control (MRAC) framework, originally based on first-order Lyapunov stability, and employing customized neural networks. The AICS is designed with a simple structure, consisting of two main subsystems: a meta-learning-triggered mechanism-based physics-informed neural network (MLTM-PINN) for plant identification and a self-tuning neural network controller (STNNC). This structure, featuring the triggered mechanism, facilitates a balance between high controllability and control efficiency. The MLTM-PINN incorporates the following: (I) a single self-supervised physics-informed neural network (PINN) without the need for labelled data, enabling online learning in control; (II) a meta-learning-triggered mechanism to ensure consistent control performance; (III) transfer learning combined with meta-learning for finely tailored initialization and quick adaptation to input changes. To resolve the conflict between streamlining the AICS’s structure and enhancing its controllability, the STNNC functionally integrates the nonlinear controller and adaptation laws from the MRAC system. Three STNNC design scenarios are tested with transfer learning and/or hyperparameter optimization (HPO) using a Gaussian process tailored for Bayesian optimization (GP-BO): (scenario 1) applying transfer learning in the absence of the HPO; (scenario 2) optimizing a learning rate in combination with transfer learning; and (scenario 3) optimizing both a learning rate and the number of neurons in hidden layers without applying transfer learning. Unlike scenario 1, no quick adaptation effect in the MLTM-PINN is observed in the other scenarios, as these struggle with the issue of dynamic input evolution due to the HPO-based STNNC design. Scenario 2 demonstrates the best synergy in controllability (best control response) and efficiency (minimal activation frequency of meta-learning and fewer trials for the HPO) in control. Full article

Background: Exposure-based cognitive behavioral therapy (Ex-CBT) is widely seen as the gold-standard treatment for anxiety and obsessive-compulsive disorder (OCD). Yet, minoritized youth are underrepresented in efficacy studies, raising questions about the applicability of Ex-CBT to minoritized youth. Effectiveness data suggest systematic adaptation of Ex-CBT to address youth culture and context is likely needed, and many clinicians make adaptations and augmentations in practice. However, research on the specific strategies clinicians use to address their youth clients’ culture and context within anxiety and OCD treatment is lacking. In the current study, we assess practice-based adaptations, augmentations, and process-based approaches utilized when delivering treatment to youth for OCD and anxiety in public mental health clinics. Methods: We conducted qualitative interviews with 16 clinicians from both specialty anxiety and general mental health clinics serving youth with anxiety or OCD in the public mental health system. Participating clinicians had a mean age of 32.19 (SD = 5.87) and 69% of therapists identified as female; 69% identified as White, 25% identified as Asian, and 6% as Black or African American. In qualitative interviews, clinicians shared how they addressed clients’ culture and context (e.g., social identities, stressors and strengths related to social identities and lived environment). Thematic analysis identified the strategies clinicians employed to address culture and context. Results: Clinicians reported incorporating culture and context through process-based approaches (e.g., building trust gradually, considering clients’ social identity stressors, engaging in self-awareness to facilitate cultural responsiveness) and through culturally adapting and augmenting treatment to promote person-centered care. Core strategies included proactive and ongoing assessment of clients’ cultural and contextual factors, adapting exposures and augmenting Ex-CBT with strategies such as case management and discussion of cultural context, and taking a systems-informed approach to care. Conclusions: Examining practice-based adaptations, augmentations, and process-based approaches to treatment for minoritized youth with OCD or anxiety can inform efforts to understand what comprises person-centered culturally responsive Ex-CBT. Empirical testing of identified strategies is a needed area of future research. Full article

Background: Cerebral palsy (CP) is a lifelong neurodevelopmental disorder characterised by motor impairment and commonly associated with comorbidities such as cognitive, communicative, and behavioural difficulties. While the physical and functional aspects of CP have been extensively studied, the mental health needs of this population remain largely underexplored, particularly concerning suicidal ideation and self-injurious behaviours. The purpose of this review is to synthesise the existing literature on suicidality in individuals with CP, explore theoretical and clinical risk factors, and identify key gaps in the current evidence base. Methods: A narrative literature review was conducted focusing on studies addressing suicidal ideation, self-harm, or related psychiatric outcomes in individuals with CP. Additional literature on risks and protective factors was included to support theoretical inferences and clinical interpretations. Results: Only a limited number of studies addressed suicidality directly in CP populations. However, several reports document elevated rates of depression, anxiety, and emotional distress, particularly among adults and individuals with higher levels of functioning. Communication barriers, chronic pain, social exclusion, and lack of accessible mental health services emerged as critical risk factors. Protective elements included strong family support, inclusive environments, and access to augmentative communication. Conclusions: Suicidality in individuals with CP is a neglected yet potentially serious concern. Evidence suggests underdiagnosis due to factors such as communication barriers and diagnostic overshadowing. Future research should prioritise disability-informed methodologies and validated tools for suicidal ideation, while clinicians should incorporate routine, adapted mental health screening in CP care to ensure early detection and person-centred management. Full article

Transit systems play a vital role in urban mobility, yet predicting individual travel behavior within these systems remains a complex challenge. Traditional machine learning approaches struggle with transit trip data because each trip may consist of a variable number of transit legs, leading to missing data and inconsistencies when using fixed-length tabular representations. To address this issue, we propose a transformer-based dynamic-sequence approach that models transit trips as variable-length sequences, allowing for flexible representation while leveraging the power of attention mechanisms. Our methodology constructs trip sequences by encoding each transit leg as a token, incorporating travel time, mode of transport, and a 2D positional encoding based on grid-based spatial coordinates. By dynamically skipping missing legs instead of imputing artificial values, our approach maintains data integrity and prevents bias. The transformer model then processes these sequences using self-attention, effectively capturing relationships across different trip segments and spatial patterns. To evaluate the effectiveness of our approach, we train the model on a dataset of urban transit trips and predict first-mile and last-mile travel times. We assess performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results demonstrate that our dynamic-sequence method yields up to a 30.96% improvement in accuracy compared to non-dynamic methods while preserving the underlying structure of transit trips. This study contributes to intelligent transportation systems by presenting a robust, adaptable framework for modeling real-world transit data. Our findings highlight the advantages of self-attention-based architectures for handling irregular trip structures, offering a novel perspective on a data-driven understanding of individual travel behavior. Full article

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

Background/Objectives: Ultrasound imaging is widely employed to assess kidney health and diagnose renal diseases. Accurate segmentation of renal structures in ultrasound images plays a critical role in the diagnosis and treatment of related kidney diseases. However, challenges such as speckle noise and low contrast still hinder precise segmentation. Methods: In this work, we propose an encoder–decoder architecture, named MAT-UNet, which incorporates two distinct attention mechanisms to enhance segmentation accuracy. Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. We evaluate the segmentation performance of the proposed MAT-UNet using the Open Kidney US Data Set (OKUD). Results: For renal capsule segmentation, MAT-UNet achieves a Dice Similarity Coefficient (DSC) of 93.83%, a 95% Hausdorff Distance (HD95) of 32.02 mm, an Average Surface Distance (ASD) of 9.80 mm, and an Intersection over Union (IOU) of 88.74%. Additionally, MAT-UNet achieves a DSC of 84.34%, HD95 of 35.79 mm, ASD of 11.17 mm, and IOU of 74.26% for central echo complex segmentation; a DSC of 66.34%, HD95 of 82.54 mm, ASD of 19.52 mm, and IOU of 51.78% for renal medulla segmentation; and a DSC of 58.93%, HD95 of 107.02 mm, ASD of 21.69 mm, and IOU of 43.61% for renal cortex segmentation. Conclusions: The experimental results demonstrate that our proposed MAT-UNet achieves superior performance in multiple renal structure segmentation in ultrasound images. Full article

Anthracnose, a significant disease of aloe with characteristics of contact transmission, poses a considerable threat to the economic viability of aloe cultivation. To address the challenges of accurately detecting and classifying crop diseases in complex environments, this study proposes an enhanced algorithm, YOLOv11-seg-DEDB, based on the improved YOLOv11-seg model. This approach integrates multi-scale feature enhancement and a dynamic attention mechanism, aiming to achieve precise segmentation of aloe anthracnose lesions and effective disease level discrimination in complex scenarios. Specifically, a novel Disease Enhance attention mechanism is introduced, combining spatial attention and max pooling to improve the accuracy of lesion segmentation. Additionally, the DCNv2 is incorporated into the network neck to enhance the model’s ability to extract multi-scale features from targets in challenging environments. Furthermore, the Bidirectional Feature Pyramid Network structure, which includes an additional p2 detection head, replaces the original PANet network. A more lightweight detection head structure is designed, utilizing grouped convolutions and structural simplifications to reduce both the parameter count and computational load, thereby enhancing the model’s inference capability, particularly for small lesions. Experiments were conducted using a self-collected dataset of aloe anthracnose infected leaves. The results demonstrate that, compared to the original model, the improved YOLOv11-seg-DEDB model improves segmentation accuracy and mAP@50 for infected lesions by 5.3% and 3.4%, respectively. Moreover, the model size is reduced from 6.0 MB to 4.6 MB, and the number of parameters is decreased by 27.9%. YOLOv11-seg-DEDB outperforms other mainstream segmentation models, providing a more accurate solution for aloe disease segmentation and grading, thereby offering farmers and professionals more reliable disease detection outcomes. Full article

Accurately identifying driver fatigue in complex driving environments plays a crucial role in road traffic safety. To address the challenge of reduced fatigue detection accuracy in complex cabin environments caused by lighting variations, we propose YOLO-FDCL, a novel algorithm specifically designed for driver fatigue detection under complex lighting conditions. This algorithm introduces MobileNetV4 into the backbone network to enhance the model’s ability to extract fatigue-related features in complex driving environments while reducing the model’s parameter size. Additionally, by incorporating the concept of structural re-parameterization, RepFPN is introduced into the neck section of the algorithm to strengthen the network’s multi-scale feature fusion capabilities, further improving the model’s detection performance. Experimental results show that on the YAWDD dataset, compared to the baseline YOLOv8-S, precision increased from 97.4% to 98.8%, recall improved from 96.3% to 97.5%, mAP@0.5 increased from 98.0% to 98.8%, and mAP@0.5:0.95 increased from 92.4% to 94.2%. This algorithm has made significant progress in the task of fatigue detection under complex lighting conditions. At the same time, this model shows outstanding performance on our self-developed Complex Lighting Driving Fatigue Dataset (CLDFD), with precision and recall improving by 2.8% and 2.2%, respectively, and improvements of 3.1% and 3.6% in mAP@0.5 and mAP@0.5:0.95 compared to the baseline model, respectively. Full article

Joint attention, the capacity of two or more individuals to focus on a common event simultaneously, is fundamental to human–human interaction, enabling effective communication. When considering the field of social robotics, emulating this capability might be necessary for promoting natural interactions and thus improving user engagement. Responding to joint attention (RJA), defined as the ability to react to external attentional cues by aligning focus with another individual, plays a critical role in promoting mutual understanding. This study examines how RJA impacts user engagement during human–robot interaction. The participants play a turn-taking game against a social robot under two conditions: with our RJA system active and with the system inactive. Auditory and visual stimuli are introduced to simulate real-world dynamics, testing the robot’s ability to detect and follow the user’s focus of attention. We use a twofold approach to evaluate the system’s impact on the user’s experience during the interaction. On the one hand, we use head pose telemetry to quantify attentional aspects of engagement, including measures of distraction and focus during the interaction. On the other hand, we use a post-experimental questionnaire incorporating the User Engagement Scale Short Form to assess engagement. The results regarding telemetry data reveal reduced distraction and improved attentional consistency, highlighting the system’s ability to maintain attention on the current task effectively. Furthermore, the questionnaire responses show that RJA significantly enhances self-reported engagement when the system is active. We believe these findings confirm the value of attentional mechanisms in promoting engaging human–robot interactions. Full article

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field. Full article

Background: Point-of-care ultrasound (POCUS) is gaining recognition as a valuable diagnostic tool in various fields of medicine, including pediatrics. Its application at the point of care enables real-time clinical decision-making, which is particularly advantageous in pediatric settings. Although global interest in POCUS is growing, many European countries—including Poland—still lack formal training programs for POCUS at both the undergraduate and postgraduate levels. Nevertheless, the number of pediatricians incorporating POCUS into their daily clinical practice in Poland is increasing. However, the extent of its use and perceived value among pediatricians remains largely unknown. This study aimed to evaluate the current level of POCUS utilization in pediatric care in Poland, focusing on pediatricians’ self-assessed competencies, perceptions of its clinical utility, and key barriers to its implementation in daily practice. Methods: This cross-sectional study was conducted between July and August 2024 using an anonymous online survey distributed to pediatricians throughout Poland via national professional networks, with a response rate of 7.3%. Categorical variables were analyzed using the chi-square test of independence to assess the associations between key variables. Quantitative data were analyzed using descriptive statistics, and qualitative data from open-ended responses were subjected to a thematic analysis. Results: A total of 210 pediatricians responded. Among them, 149 (71%) reported access to ultrasound equipment at their workplace, and 89 (42.4%) reported having participated in some form of POCUS training. Only 46 respondents (21.9%) reported frequently using POCUS in their clinical routine. The self-assessed POCUS competence was rated as low or very low by 136 respondents (64.8%). While POCUS was generally perceived as a helpful tool in facilitating and accelerating clinical decisions, the main barriers to implementation were a lack of formal training and limited institutional support. Conclusions: Although POCUS is perceived as clinically valuable by the surveyed pediatricians in Poland, its routine use remains limited due to training and systemic barriers. Future efforts should prioritize the development of a validated, competency-based training framework and the implementation of a larger, representative national study to guide the structured integration of POCUS into pediatric care. Full article

The hybrid inorganic–organic material concept plays a bold role in multifunctional materials, combining different features on one platform. Once varying properties coexist without cancelling each other on one matrix, a new type of supermaterial can be formed. This concept showed that silver nanoparticles can be embedded together with inorganic and organic surface coatings and silicon quantum dots for symbiotic antibacterial character and UV-excited visible light fluorescent features. Additionally, fluorosilane material can be coupled with this prepolymeric structure to add the hydrophobic feature, showing water contact angles around 120°, providing self-cleaning features. Optical properties of the components and the final material were investigated by UV-Vis spectroscopy and PL analysis. Atomic investigations and structural variations were detected by XPS, SEM, and EDX atomic mapping methods, correcting the atomic entities inside the coating. FT-IR tracked surface features, and statistical analysis of the quantum dots and nanoparticles was conducted. Multifunctional final materials showed antibacterial properties against E. coli and S. aureus, exhibiting self-cleaning features with high surface contact angles and visible light fluorescence due to the silicon quantum dot incorporation into the sol-gel-produced nanocomposite hybrid structure. Full article

In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background noise, and these features may be scattered over multiple frequency levels, which increases the complexity of extracting important information from them. To address this problem, this paper proposes a Masked and Cascaded Multi-Branch Attention Network (MCMBAN), which combines the Noise Mask Filter Block (NMFB) with the Multi-Branch Cascade Attention Block (MBCAB), and significantly improves the noise immunity of the fault diagnostic model and the efficiency of fault feature extraction. NMFB novelly combines a wide convolutional layer and a top k neighbor self-attention masking mechanism, so as to efficiently filter unnecessary high-frequency noise in the vibration signal. On the other hand, MBCAB strengthens the interaction between different layers by cascading the convolutional layers of different scales, thus improving the recognition of periodic fault signals and greatly enhancing the diagnosis accuracy of the model when processing complex signals. Finally, the time–frequency analysis technique is employed to explore the internal mechanisms of the model in depth, aiming to validate the effectiveness of NMFB and MBCAB in fault feature recognition and to improve the feature interpretability of the proposed modes in fault diagnosis applications. We validate the superior performance of the network model in dealing with high-noise backgrounds by testing it on a standard bearing dataset from Case Western Reserve University and a self-constructed composite bearing fault dataset, and the experimental results show that its performance exceeded six of the top current fault diagnosis techniques. Full article

The application of extended reality (XR) in design education has grown substantially; however, empirical evidence on its educational benefits remains limited. This two-year study examines the impact of incorporating a virtual reality (VR) learning module into undergraduate landscape architecture (LA) construction courses, focusing on brick masonry instruction. A conventional learning sequence—lecture, sketching, CAD, and 3D modeling—was supplemented with an immersive VR experience developed using Unreal Engine 5 and deployed on Meta Quest devices. In Year 1, we piloted a preliminary version of the module with landscape architecture students (n = 15), and data on implementation feasibility and student perception were collected. In Year 2, we refined the learning module and implemented it with a new cohort (n = 16) using standardized VR evaluation metrics, knowledge retention tests, and self-efficacy surveys. The findings suggest that when sequenced after a theoretical introduction, VR serves as a pedagogical bridge between abstract construction principles and physical implementation. Moreover, the VR module enhanced student engagement and self-efficacy by offering experiential learning with immediate feedback. The findings highlight the need for intentional design, institutional support, and the continued development of tactile, collaborative simulations. Full article