Search Results (25)

Background/Objectives: Palliative care (PC) has traditionally focused on patients with cancer and their families. However, individuals living with advanced non-malignant chronic diseases and their caregivers face comparable challenges that significantly affect their quality of life. This study aimed to explore the PC needs of patients with advanced non-malignant chronic conditions through the lived experiences of both patients and their informal caregivers. Methods: Semi-structured interviews were conducted with eight patients and nine caregivers recruited via the Municipality of Katerini “Help at Home” program (Jan–Mar 2025). Interviews were audio-recorded, transcribed verbatim (in Greek), and analyzed inductively using reflexive thematic analysis. Ethical approval was obtained from the International Hellenic University (Ref. No. 18/22.12.2022), and official consent was gained from the Municipality of Katerini (Approval Ref. No. 7803-/30/01/2025). Results: Five themes emerged: (1) basic daily care and physical support; (2) psychosomatic and emotional impact; (3) social withdrawal and role change; (4) support systems and coping resources; and (5) experience with the healthcare system and organized care. Participants highlighted urgent needs for home-based physiotherapy/nursing, caregiver respite, and psychological support. Coping and resilience-related resources—expressed through family support, familiarity of the home environment, and spirituality—were described as essential mechanisms that helped dyads sustain home care and shaped how needs were experienced across multiple domains, particularly amid service gaps. Conclusions: These findings document complex, interlinked needs among patients with advanced non-malignant chronic conditions and their caregivers and support the development of community-based, integrated PC services. Larger, multicenter studies and the development/validation of a needs-assessment tool are recommended. Full article

In multi-manipulator systems operating within shared workspaces, achieving collision-free posture control is challenging due to high degrees of freedom and complex inter-manipulator interactions. Traditional motion planning methods often struggle with scalability and computational efficiency in such settings, motivating the need for learning-based approaches. This paper presents a multi-agent deep reinforcement learning (MADRL) framework for real-time collision-free posture control of multiple manipulators. The proposed method employs a line-segment representation of manipulator links to enable efficient interlink distance computation to guide cooperative collision avoidance. Employing a centralized training and decentralized execution (CTDE) framework, the approach leverages global state information during training, while enabling each manipulator to rely on local observations for real-time collision-free trajectory planning. By integrating efficient state representation with a scalable training paradigm, the proposed framework provides a principled foundation for addressing coordination challenges in dense industrial workspaces. The approach is implemented and validated in NVIDIA Isaac Sim across various overlapping workspace scenarios. Compared to conventional state representations, the proposed method achieves faster learning convergence and superior computational efficiency. In pick-and-place tasks, collaborative multi-manipulator control reduces task completion time by over 50% compared to single-manipulator operation, while maintaining high success rates (>83%) under dense workspace conditions. These results confirm the effectiveness and scalability of the proposed framework for real-time, collision-free multi-manipulator control. Full article

Background/Objectives: Relationships between sleep quality, anxiety and depression are well-documented across the lifespan. Here we investigated relationships between sleep, mental health and markers of obesity and cardiovascular health in Higher Education students (young adults, 18–28 years) using repeated cross-sectional sampling. Methods: Students (n = 486) participated at one of four timepoints across 2020–2023. The PSQI (sleep quality), GAD7 (anxiety) and PHQ8 (depression) were completed online. Measurements of obesity (Body Mass Index (BMI), body fat percent (BF%) and waist–hip ratio (WHR)) and cardiovascular function (heart rate (HR), diastolic and systolic blood pressure (DP and SP)) were determined. Changes over time, differences between sexes, and correlations between parameters were examined. Results: All measures were stable over the 4-year period. GAD7 (p < 0.0001) and PHQ8 (p = 0.0014) scores were significantly higher in females than males. There were significant, moderate to strong correlations between PSQI, GAD7 and PHQ8 scores for both sexes (r = 0.34–0.71). Only 18.1% of females and 23% of males reported both good quality sleep and no or low levels of anxiety and depression. Significant sex-specific differences were observed across markers of obesity and cardiovascular function (for BF%, WHR, HR and SP—all p ≤ 0.01), which showed weak to moderate correlations with sleep and mental health. Impaired sleep latency (C2) was identified as a potential key contributing factor. Conclusions: These observations provide evidence of multiple established, interlinked chronic challenges affecting sleep, mental and physical health in students. Identification of a key role for impaired sleep latency provides a foundation for targeted intervention, focusing upon improving sleep initiation, to improve mental health outcomes. Full article

Background: Healthcare workers face multiple, interlinked occupational health risks—burnout, post-COVID-19 sequelae (Long COVID), and extended medical leave. These outcomes often share predictors, contribute to each other, and, together, impact workforce capacity. Yet, existing tools typically address them in isolation. Objective: The objective of this study to develop and deploy an integrated, explainable artificial intelligence (AI) framework that predicts these three outcomes using the same structured occupational health dataset, enabling unified workforce risk monitoring. Methods: We analyzed data from 1244 Romanian healthcare professionals with 14 demographic, occupational, lifestyle, and comorbidity features. For each outcome, we trained a separate predictive model within a common framework: (1) a lightweight transformer neural network with hyperparameter optimization, (2) a transformer with multi-head attention, and (3) a stacked ensemble combining transformer, XGBoost, and logistic regression. The data were SMOTE-balanced and evaluated on held-out test sets using Accuracy, ROC-AUC, and F1-score, with 10,000-iteration bootstrap testing for statistical significance. Results: The stacked ensemble achieved the highest performance: ROC AUC = 0.70 (burnout), 0.93 (Long COVID), and 0.93 (extended leave). The F1 scores were >0.89 for Long COVID and extended leave, whereas the performance gains for burnout were comparatively modest, reflecting the multidimensional and heterogeneous nature of burnout as a binary construct. The gains over logistic regression were statistically significant (p < 0.0001 for Long COVID and extended leave; p = 0.0355 for burnout). The SHAP analysis identified overlapping top predictors—tenure, age, job role, cancer history, pulmonary disease, and obesity—supporting the value of a unified framework. Conclusions: We trained separate models for each occupational health risk but deployed them in a single, real-time web application. This integrated approach improves efficiency, enables multi-outcome workforce surveillance, and supports proactive interventions in healthcare settings. Full article

Seismic retrofitting of reinforced concrete (RC) structures is essential for improving resilience and extending service life, particularly in regions with outdated building codes. However, selecting the optimal retrofitting strategy requires balancing multiple interdependent sustainability criteria—economic, environmental, and social—under expert-based uncertainty. This study presents a fuzzy hybrid multi-criteria decision-making (MCDM) approach that combines DEMATEL, DANP, and TOPSIS to represent causal interdependencies, derive interlinked priority weights, and rank retrofit alternatives. The assessment applies three complementary life cycle-based tools—cost-based, environmental, and social sustainability analyses following LCCA, LCA, and S-LCA frameworks, respectively—to evaluate three commonly used retrofitting strategies: RC jacketing, steel jacketing, and carbon fiber-reinforced polymer (CFRP) wrapping. The fuzzy-DANP methodology enables accurate modeling of feedback among sustainability dimensions and improves expert consensus through causal mapping. The findings identify CFRP as the top-ranked alternative, primarily attributed to its enhanced performance in both environmental and social aspects. The model’s robustness is confirmed via sensitivity analysis and cross-method validation. This mathematically grounded framework offers a reproducible and interpretable tool for decision-makers in civil infrastructure, enabling sustainability-oriented retrofitting under uncertainty. Full article

Infrared small target detection in complex environments remains a significant challenge due to low signal-to-noise ratios (SNRs), background clutter, and target scale variations. To address these issues, we propose an Anisotropic Dynamic-aware Multi-scale Network for Infrared Small Target Detection (IR-ADMDet). The core of IR-ADMDet is a Dual-Path Hybrid Feature Extractor Network (DPHFENet). This network effectively synergizes local residual learning with global context modeling. It enhances faint target signatures while suppressing interference. Additionally, a Hierarchical Adaptive Fusion Framework (HAFF) is utilized. HAFF integrates bidirectional gating, recursive graph enhancement, and interlink fusion. This framework optimally refines features across multiple scales. The entire architecture is optimized for efficiency using dynamic feature recalibration. Extensive experiments were conducted on benchmark datasets including SIRSTv2, IRSTD-1k, and NUDT-SIRST. These experiments demonstrate the superiority of IR-ADMDet. It achieves state-of-the-art (SOTA) results, such as 0.96 AP50 and 0.95 F1-score on SIRSTv2. This performance is achieved with significantly fewer parameters, only 5.77 M, compared to existing methods. This shows remarkable robustness in low-contrast, high-noise scenarios. IR-ADMDet also outperforms contemporary segmentation-based approaches. Full article

The European Green Deal (EGD) is a package of policy initiatives launched by the European Commission in December 2019, which aims to set the European Union (EU) on the path to a green transition with the final goal of achieving climate neutrality by 2050. The package includes interlinked initiatives covering the climate, the environment, energy, transport, industry, agriculture, and sustainable finance. It is thus evident that holistic and scientifically sound decision support systems are crucial to help EU policymakers and stakeholders in monitoring the progress of countries towards the implementation of the EGD. Indeed, the multidimensionality of this policy initiative lends itself well to its integration into a Multiple Criteria Decision Aiding (MCDA) approach to the identification of priorities for action. Therefore, this research aims to evaluate the progress of the EU countries towards the implementation of the European Green Deal, using MCDA. The PROMETHEE II method was applied to the data for EU countries, using 26 key indicators collected from the Eurostat database and organized into three thematic clusters. The results enabled us to calculate overall scores measuring the degree of implementation of the EGD by the EU countries, and their profiles with respect to the key indicators and thematic clusters. By analyzing these profiles, strengths and weaknesses were identified. Thus, the fundamental novelty of this research consists of the first concrete application of a holistic and ‘ready-to-use’ decision-making tool that can be adopted by EU policymakers and stakeholders to draw up a roadmap towards climate neutrality. Full article

Recently, there has been a huge increase in the different ways to manufacture polymer-based sensors. Methods like additive manufacturing, microfluidic preparation, and brush painting are just a few examples of new approaches designed to improve sensor features like self-healing, higher sensitivity, reduced drift over time, and lower hysteresis. That being said, we believe there is still a lot of potential to boost the performance of current sensors by applying modeling, classification, and machine learning techniques. With this approach, final sensor users may benefit from inexpensive computational methods instead of dealing with the already mentioned manufacturing routes. In this study, a total of 96 specimens of two commercial brands of Force Sensing Resistors (FSRs) were characterized under the error metrics of drift and hysteresis; the characterization was performed at multiple input voltages in a tailored test bench. It was found that the output voltage at null force (V_{o_null}) of a given specimen is inversely correlated with its drift error, and, consequently, it is possible to predict the sensor’s performance by performing inexpensive electrical measurements on the sensor before deploying it to the final application. Hysteresis error was also studied in regard to V_{o_null} readings; nonetheless, a relationship between V_{o_null} and hysteresis was not found. However, a classification rule base on k-means clustering method was implemented; the clustering allowed us to distinguish in advance between sensors with high and low hysteresis by relying solely on V_{o_null} readings; the method was successfully implemented on Peratech SP200 sensors, but it could be applied to Interlink FSR402 sensors. With the aim of providing a comprehensive insight of the experimental data, the theoretical foundations of FSRs are also presented and correlated with the introduced modeling/classification techniques. Full article

The middle ear (ME) is a notoriously complicated anatomic structure, geometrically arranged as irregular interlinked spheroidal and polyhedric cavities dug inside the temporal bone (TB). The bony walls of these cavities are radiopaque and form the bony support for the D-Organ that we have previously defined as corresponding to the epithelium covering the Antrum walls (belonging to the central cavities of the middle ear) and the walls of mastoid and petrous cavities (the peripheral cavities of the ME). The aim of the study is to define an exact method for categorizing a Unilateral Radiographic COnformation of the TEmporal Bone in Schuller’s projection (URCOTEBS) under one of the four defined conformations and using it for practical everyday clinical purposes. The conclusion is that a radiograph in Schuller’s projection is a concrete way of storing precise information on the status (health/disease) of the D-Organ and therefore of the ME mucosa. These data is encoded within the image and we aim to decode and translate them into clinical data. The URCOTEBS results in an overlapping projection of all bony cavities that comprise the General Endo-temporal Bony Cavity Complex onto the same plain (film). This characteristic of classical film imaging constitutes an advantage from the multiple CT sections, as far as our proposed approach goes, because the set of stochastic information is found in the whole of the cavities taken as one on the same image, to which the measurement gauges can be easily applied. The decoding must be performed accordingly, and this occurs much faster with conventional radiography. This image of the TB in Schuller’s projection is a mirror that reflects the status of the ME mucosa, and URCOTEBS encodes the physiological state of the D-Organ. The present work gives, through stochastic methods, the key to decoding this information into clinical language. In ascending order of their projection areas (projection of their Variable Geometry Peripheral Endo-temporal Bony Cavity Complex) we can recognize URCOTEBS_d, URCOTEBS_c, URCOTEBS_b, and URCOTEBS_a. The corresponding Greek letter designates the state of disease for each of these conformations: URCOTEBS_δ, URCOTEBS_γ, URCOTEBS_β, URCOTEBS_α, and the capital letters define their state of health: URCOTEBS_D, URCOTEBS_C, URCOTEBS_B, URCOTEBS_A. URCOTEBS_d is the smallest unilateral radiographic conformation of the TB in Schuller’s projection and is, by definition, a radiographic image of the state of disease of the D-Organ. The probability of disease in URCOTEBS_d is 100%. This radiographic system is readily available and clinically usable. Full article

Economic resilience and sustainable finance are two interlinked and crucial issues for development and convergence in Romania’s counties increasing cohesion. These issues can contribute to sustainable and balanced growth of local and regional economies and to the reduction of inequalities in regional development. Economic resilience in counties refers to their capacity to adapt and survive in the face of unforeseen economic shocks or challenges, and sustainable finance refers to ensuring responsible management of financial resources to support long-term development and protect the environment. Identifying and understanding the significant variations in economic resilience and sustainable financing between counties is essential for the formulation of regional development policies and strategies. These variations provide valuable information about the vulnerabilities and opportunities of individual counties and guide resource allocation and investment decisions. The research provides new data and relevant information on the significant variations among counties in economic resilience and sustainable financing, using a Markov transition probability matrix and exploratory–visual method. This study on Romanian counties aims to provide valuable information for the formulation of public policies to support balanced economic development across the country. The results showed that economic diversification is essential to increase the resilience of the economy to shocks and fluctuations. Counties that have a diversified economic structure, with multiple sources of income and economic activities, are less vulnerable to the negative impact of economic or natural events. Governance and political stability are key factors in creating a favorable environment for investment and economic development. Well-managed government policies can help maintain macroeconomic stability and increase the resilience of the economy to external fluctuations. Full article

Computed tomography (CT) scans, or radiographic images, were used to aid in the early diagnosis of patients and detect normal and abnormal lung function in the human chest. However, the diagnosis of lungs infected with coronavirus disease 2019 (COVID-19) was made more accurately from CT scan data than from a swab test. This study uses human chest radiography pictures to identify and categorize normal lungs, lung opacities, COVID-19-infected lungs, and viral pneumonia (often called pneumonia). In the past, several CAD systems using image processing, ML/DL, and other forms of machine learning have been developed. However, those CAD systems did not provide a general solution, required huge hyper-parameters, and were computationally inefficient to process huge datasets. Moreover, the DL models required high computational complexity, which requires a huge memory cost, and the complexity of the experimental materials’ backgrounds, which makes it difficult to train an efficient model. To address these issues, we developed the Inception module, which was improved to recognize and detect four classes of Chest X-ray in this research by substituting the original convolutions with an architecture based on modified-Xception (m-Xception). In addition, the model incorporates depth-separable convolution layers within the convolution layer, interlinked by linear residuals. The model’s training utilized a two-stage transfer learning process to produce an effective model. Finally, we used the XgBoost classifier to recognize multiple classes of chest X-rays. To evaluate the m-Xception model, the 1095 dataset was converted using a data augmentation technique into 48,000 X-ray images, including 12,000 normal, 12,000 pneumonia, 12,000 COVID-19 images, and 12,000 lung opacity images. To balance these classes, we used a data augmentation technique. Using public datasets with three distinct train-test divisions (80–20%, 70–30%, and 60–40%) to evaluate our work, we attained an average of 96.5% accuracy, 96% F1 score, 96% recall, and 96% precision. A comparative analysis demonstrates that the m-Xception method outperforms comparable existing methods. The results of the experiments indicate that the proposed approach is intended to assist radiologists in better diagnosing different lung diseases. Full article

Coastal social–ecological systems (SES), particularly in large bays, are critical for fisheries, transportation, and disaster prevention in island and coastal countries. To achieve the sustainability of such bays, public involvement is recently considered inevitable for planning and management, but the increasing complexity of variables and future visions to be considered is one difficulty when trying to include many stakeholders and public opinions. To address this challenge, a free-associative description questionnaire survey was used in this study to extract holistic coastal residents’ future visions for Tokyo Bay, including both positive and negative outcomes. By integrating biterm topic modeling (BTM) and multiple-factor analysis (MFA), this study succeeded to aggregate and visualize the various future visions of Tokyo Bay with enhanced comprehensibility. As one outcome, the linkages and differences between the major topics in the positive and negative future visions were visualized as vectors in a correlation circle. Also, the study found that these two kinds of future vectors are not always polar opposites, but, rather, some of them are interlinked, pointing in the same direction. This highlights the importance of measuring the balance between two kinds of future vectors in consensus-building in order to search for the optimal future direction. Finally, the study discusses the potential of this method as a “Future Compass”, for implementing future-oriented consensus-building toward the sustainability of SES. Full article

The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm. Full article

Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching. Full article

Swimming pool owners worldwide face the challenging task of keeping their pool water balanced and free from contaminants. However, swimming pool water (SPW) quality management is complex with the countless processes and interactions of interlinked system variables. For example, contamination with sunscreen residues is inevitable as users apply sunscreen to protect their skin from damaging ultraviolet (UV) radiation. Nanoparticulate titanium dioxide (nano-TiO₂) is one such residues that have received criticism due to potential human health and environmental risks. Despite ongoing research studies, management strategies of nano-TiO₂ in swimming pools are still limited. Therefore, this paper focuses on developing a multi-method approach for identifying and understanding interdependencies between TiO₂ particles and an aquatic environment such as a swimming pool. Given the complexity of the system to be assessed, the authors utilise a systems approach by integrating cross-matrix multiplication (MICMAC) and Systems Thinking techniques. The developed conceptual model visually depicts the complex system, which provides users with a basic understanding of swimming pool chemistry, displaying the numerous cause-and-effect relationships and enabling users to identify leverage points that can effectively change the dynamics of the system. Such systems-level understanding, and actions will help to manage nano-TiO₂ levels in an efficient manner. The novelty of this paper is the proposed methodology, which uses a systems approach to conceptualise the complex interactions of contaminants in swimming pools and important pathways to elevated contaminant levels. Full article