Search Results (1,863)

Background: Prostate cancer is recognised as the second most common diagnosed cancer in men and remains a significant global public health concern. In Ireland, the incidence of prostate cancer continues to rise, with approximately 1 in 6 men being diagnosed in their lifetime. Men’s experiences with prostate cancer are complex, necessitating further research into the factors influencing diagnosis and treatment. Therefore, this study aims to explore men’s experiences with prostate cancer, emphasising the interplay between screening, diagnosis, and the lived experiences of those affected. Methods: A qualitative descriptive study was conducted among men with prostate cancer in Ireland. Using a purposive sampling (n = 11) were interviewed with data saturation guiding sample size determination. A semi-structured interview guide was used for data collection either face-to-face or via Microsoft Teams and phone calls. Data were analysed using Braune and Clarke’s thematic analysis approach after transcription, with NVivo 12.0 software supporting analysis. Results: Thematic analysis identified five themes: systemic obstacle in timely cancer detection, the role of efficient system in cancer care, emotional resilience in cancer recovery, redefining normalcy post treatment and harnessing specialised support network in coping strategies. These themes were examined through the lens of the Biopsychosocial Model to understand their interconnected nature and impact on patient experiences. Conclusions: This study highlights the complex factors affecting prostate cancer patients’ experiences, emphasizing the need for a patient-centred approach, addressing systemic disparities, and promoting multidisciplinary care. It suggests implementing evidence-based survivorship care frameworks to improve quality of life for survivors, with future research exploring long-term effects of integrated care models. Full article

This study aims to explore the professional learning and development needs of Special Educational Needs Coordinators (SENCOs) as strategic leaders in primary schools in Ireland. With the SENCO role lacking formal recognition in Irish policy, this research is important to identify the support structures necessary to enhance their effectiveness in leading inclusive education. Employing a mixed-methods sequential explanatory design, theoretically framed by Bronfenbrenner’s Ecological Systems Theory and Wenger’s Community of Practice model, the study first surveyed 371 SENCOs to assess their professional learning experiences and needs. This was followed by semi-structured interviews with nine school leaders, including SENCOs, SENCO principals, and principals, to gather in-depth insights into the role’s dynamics. The Department of Education’s school database was used to contact participants. Data analysis utilised descriptive statistics for the survey and reflexive thematic analysis for the interview data. Key findings indicate a significant demand for formal SENCO-specific professional learning programmes, with a focus on leadership, evidence-informed practices, and community engagement. The study concludes that professional learning for SENCOs should be structured around transformative social learning models and should include postgraduate courses and communities of practice. The research calls for policy development to formally recognise the SENCO role, and provide a coherent framework for their professional learning and development to ensure inclusive educational practices are effectively led and implemented in Irish schools. Full article

Metrological traceability is essential for ensuring the accuracy of measurement results and enabling a comparison of results to support decision-making in society. This paper explores a structured approach to modelling traceability chains, focusing on the role of residual measurement errors and their impact on measurement accuracy. This work emphasises a scientific description of these errors as physical quantities. By adopting a simple modelling framework grounded in physical principles, the paper offers a formal way to account for the effects of errors through an entire traceability chain, from primary reference standards to end users. Real-world examples from microwave and optical metrology highlight the effectiveness of this rigorous modelling approach. Additionally, to further advance digital systems development in metrology, the paper advocates a formal semantic structure for modelling, based on principles of Model-Driven Architecture. This architectural approach will enhance the clarity of metrological practices and support ongoing efforts toward the digital transformation of international metrology infrastructure. Full article

Automatic generation of chest X-ray reports, designed to produce clinically precise descriptions from chest X-ray images, is gaining significant research attention because of its vast potential in clinical applications. Recently, despite considerable progress, current models typically adhere to a CNN–Transformer-based framework, which still fails to enhance the perceptual field during image feature extraction. To solve this problem, we propose the Reinforced Memory-driven Pure Transformer (RMPT), which is a novel Transformer–Transformer-based model. In implementation, our RMPT employs the Swin Transformer to extract visual features from given X-ray images, which has a larger perceptual field to better model the relationships between different regions. Furthermore, we adopt a memory-driven Transformer (MemTrans) to effectively model similar patterns in different reports, which is able to facilitate the model to generate long reports. Finally, we present an innovative training approach leveraging Reinforcement Learning (RL) that efficiently steers the model to focus on challenging samples, consequently improving its comprehensive performance across both straightforward and complex situations. Experimental results on the IU X-ray dataset show that our proposed RMPT achieves superior performance on various Natural Language Generation (NLG) evaluation metrics. Further ablation study results demonstrate that our RMPT model achieves 10.5% overall performance compared to the base mode. Full article

In recent years, rapid urbanization has transformed the man–land relationship in rural areas, highlighting issues such as the homogenization of vernacular landscapes. This study uses the Liuxi River in Guangzhou as a case and applies a hierarchical interpretation system for vernacular landscapes, utilizing methods from landscape character assessment (LCA) and Historic Landscape Characterization (HLC). Focusing on two scales, “basin” and “vernacular unit”, this study proposes a framework for identifying vernacular landscape patterns. This framework includes scale definition, pattern identification, feature description, and factor analysis. At the basin scale, the investigation concentrates on spatial configurations of vernacular landscapes in 1985, whereas the unit-scale analysis delineates temporal evolutionary trajectories spanning 1974–2020. The results indicate significant differences in landscape fragmentation, dominance, and diversity between upstream and downstream at the basin scale. At the unit scale, the landscape connectivity in the Shaxi River unit remains relatively stable, while landscape heterogeneity increases, resulting in greater diversity. This study provides valuable insights into the continuity and development of diversity in analogous vernacular landscape regions globally, particularly those comparable to the Liuxi River basin. Full article

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. Full article

The worldwide energy crisis is causing people in most countries to reduce their energy use to prevent the next generation from being unable to fulfill their needs. The Osing people use sustainability values based on traditions passed down from generation to generation with appropriate technology to fit the needs of the people and their environment. This research employs a qualitative descriptive method with a literature review and data collection. Based on the framework used by Iwanmura, Osing house construction primarily focuses on the principles of low impact and health and amenity. This study reveals that the architectural design and construction process of an energy-efficient traditional building can be adapted to contemporary sustainable housing. The primary aim was to identify and analyze sustainability values in the construction process and techniques of traditional Osing houses in Kemiren Village, Banyuwangi, which can serve as a reference for modern sustainable architecture practices. The study reveals the uniqueness of traditional Osing construction using the local material Bendo wood, which can be dismantled from the foundation up to the roof joint systems, thus allowing the materials to be repaired and recycled down to the smallest parts and minimizing construction waste. The advantage of this building construction process is the use of traditional housing techniques to minimize the need for mechanical systems. This traditional construction method, using wood as the building material and considering climatic features, demonstrates how to achieve sustainable building values throughout all elements of a building that provides users with comfort and safety. Full article

This paper analyses the section of Nemesius of Emesa’s treatise On the Nature of Man dedicated to fate. The main objective is to analyse Nemesius’s response to the supporters of a notion of fate within the framework of astral determinism, Stoicism, and Middle Platonism. Following a mainly descriptive method, the paper focuses on Nemesius’s own thought and not just on his treatment of his sources, as much of the existing literature has done until now. Without pretending to give a definitive answer on the originality of his own philosophy, we examine how Nemesius assigns some of the functions of fate in one of his Middle Platonic sources to divine providence. In doing so, he develops a personal theology in which he gives an innovative prominence to divine free will and transcendence in the traditional philosophical problem of providence. Full article

Acute decompensated heart failure (ADHF) represents a major healthcare burden, with residual congestion at discharge being a critical determinant of poor outcomes. Despite its prognostic significance, the assessment of decongestion status before discharge remains suboptimal, highlighting the need for a more comprehensive evaluation approach. This descriptive review synthesizes current evidence on congestion assessment methods in ADHF, focusing on their role in discharge decision-making and prognostic value. We describe various evaluation tools, including clinical examination, biomarkers, imaging techniques, and congestion scores, presenting their integration into a practical assessment algorithm. A comprehensive algorithm for congestion assessment before discharge is presented, incorporating multimodal evaluation techniques, with the aim of highlighting the practical utility of various assessment methods in guiding treatment decisions and determining optimal discharge timing. Integration of multiple parameters provides superior accuracy in evaluating decongestion status compared to single-method approaches. A standardized, multimodal approach to congestion assessment before discharge is essential for optimal ADHF management. The proposed assessment algorithm, combining clinical, biochemical, and imaging parameters, offers a practical framework for more reliable discharge decision-making, potentially improving patient outcomes. Full article

Despite significant advances in deep learning-based image captioning, many state-of-the-art models still struggle to balance visual grounding (i.e., accurate object and scene descriptions) with linguistic coherence (i.e., grammatical fluency and appropriate use of non-visual tokens such as articles and prepositions). To address these limitations, we propose a hybrid image captioning framework that integrates handcrafted and deep visual features. Specifically, we combine local descriptors—Scale-Invariant Feature Transform (SIFT) and Bag of Features (BoF)—with high-level semantic features extracted using ResNet50. This dual representation captures both fine-grained spatial details and contextual semantics. The decoder employs Bahdanau attention refined with an Attention-on-Attention (AoA) mechanism to optimize visual-textual alignment, while GloVe embeddings and a GRU-based sequence model ensure fluent language generation. The proposed system is trained on 200,000 image-caption pairs from the MS COCO train2014 dataset and evaluated on 50,000 held-out MS COCO pairs plus the Flickr8K benchmark. Our model achieves a CIDEr score of 128.3 and a SPICE score of 29.24, reflecting clear improvements over baselines in both semantic precision—particularly for spatial relationships—and grammatical fluency. These results validate that combining classical computer vision techniques with modern attention mechanisms yields more interpretable and linguistically precise captions, addressing key limitations in neural caption generation. Full article

As the frequency of cyberattacks rises, extracting actionable cyber threat intelligence (CTI) from diverse online sources has become critical for proactive threat detection and defense. Named entity recognition (NER) serves as a foundational task in CTI extraction, supporting downstream applications such as cybersecurity knowledge graph construction and attack attribution. However, existing NER methods face significant challenges in the cybersecurity domain, including the need to identify highly specialized entity types and adapt to rapidly evolving threats. These challenges are further exacerbated in few-shot scenarios with limited annotated data. In this work, we focus on few-shot NER for CTI extraction in general cyber environments. Our goal is to develop robust and adaptable methods that are not restricted to specific infrastructures (e.g., traditional IT systems), but instead can generalize across diverse cybersecurity contexts. Specifically, to address these issues, we propose CyberDualNER, a novel dual-stage framework for few-shot NER, which includes span detection and entity classification. In the first stage, we proposed a span detector that can utilize data from large-scale general domains to detect possible entity spans. Based on the detected spans, in the second stage, we propose a prompt-enhanced metric-based classifier. We use category descriptions to build prompt templates, extract category anchor representations, and classify entities based on similarity to span representations. By incorporating prior knowledge, we improve performance while reducing data dependency, which ensures generalizability in the face of emerging entities. Extensive experiments on real-world CTI datasets demonstrate the effectiveness of CyberDualNER, with significant performance improvements over baseline methods. Notably, the framework achieves robust results in scenarios with minimal annotated samples, highlighting its potential for practical applications in cybersecurity intelligence extraction. Full article

Purpose: Dirofilariasis is a zoonotic infectious disease caused by a species belonging to the Dirofilaria genus. Human dirofilariasis cases have increased in Europe in the last few decades. Dogs and wild canids represent the definitive hosts and principal reservoirs of Dirofilaria repens, while mosquito species are biological vectors. Humans act as accidental hosts, and clinical manifestations depend on the location of the worm in the organs or tissues. We described the first case of ocular dirofilariasis in the Aosta Valley region (Italy). Case description: a 62-year-old Italian woman complained of recurrent ocular redness, pain and discomfort, accompanied by itching and foreign body sensation in the right eye. The slit lamp biomicroscopic examination revealed conjunctival congestion on the temporal region of bulbar conjunctiva, and a long whitish vermiform mobile mass was detected under the conjunctiva. The anterior chamber showed no flare or cells in either eye, and the dilated fundus examination was normal. The worm was immediately surgically removed to prevent further migration, and was diagnosed morphologically and molecularly as D. repens. Following surgical removal, the symptoms resolved completely and rapidly, with no recurrence of ocular symptoms recorded during 12-month follow-up visits. Conclusions: Ocular dirofilariasis can lead to misdiagnosis due to its rare ocular manifestations, and it is considered an emergent zoonosis in European countries. Accurate diagnosis and control of ocular dirofilariasis by D. repens require a multidisciplinary approach under the One Health framework to effectively address this emergent zoonosis. Full article

Sparse data and an unknown conditional distribution of future values are challenges for managing risks inherent in the evolution of time series. This contribution addresses both aspects through the application of ForGAN, a special form of a generative adversarial network (GAN), to German electricity consumption data. Electricity consumption time series have been selected due to their typical combination of (non-linear) seasonal behavior on different time scales and of local random effects. The primary objective is to demonstrate that ForGAN is able to capture such complicated seasonal figures and to generate data with the correct underlying conditional distribution without data preparation, such as de-seasonalization. In particular, ForGAN does so without assuming an underlying model for the evolution of the time series and is purely data-based. The training and validation procedures are described in great detail. Specifically, a long iteration process of the interplay between the generator and discriminator is required to obtain convergence of the parameters that determine the conditional distribution from which additional artificial data can be generated. Additionally, extensive quality assessments of the generated data are conducted by looking at histograms, auto-correlation structures, and further features comparing the real and the generated data. As a result, the generated data match the conditional distribution of the next consumption value of the training data well. Thus, the trained generator of ForGAN can be used to simulate additional time series of German electricity consumption. This can be seen as a kind of proof for the applicabilty of ForGAN. Through a detailed descriptions of the necessary steps of training and validation procedures, a detailed quality check before the actual use of the simulated data, and by providing the intuition and mathematical background behind ForGAN, this contribution aims to demystify the application of GANs to motivate both theorists and researchers in applied sciences to use them for data generation in similar applications. The proposed framework has laid out a plan for doing so. Full article

A child’s cancer diagnosis profoundly impacts the psychological well-being of parents. To alleviate parental distress, researchers developed Taking Back Control Together (TBCT), a manualized six-session program targeting individual problem-solving skills and dyadic coping. The current study aimed to refine TBCT for future uptake across different sites. We invited potential interventionists and local stakeholders from three pediatric oncology centers (CHU Sainte-Justine, CHU de Sherbrooke, and CHU de Québec) to join the refinement team. The final working team comprised 26 professionals, including social workers, psychologists, researchers, coordinators, and parent-partners. The study included eight 50- to 90-min discussion sessions designed to stimulate conversation and facilitate the exchange of ideas and perspectives. We used framework analysis to identify and describe patterns within the qualitative data. The data were organized into three categories: (1) intervention description, which addresses changes in personnel, modes of delivery, and tailoring to accommodate different family structures; (2) content modifications, which include language simplification and visual enhancements; and (3) factors influencing TBCT’s future uptake, such as accessibility, participant satisfaction, clinician compensation, and flexibility in program delivery. The direct output of this research is a refined program with an updated manual, tools, and format adapted for use in different sites. Full article

Sustainable campus design plays a vital role in shaping student well-being, academic performance, and institutional adaptability. This study investigates how sustainable design strategies influence student life at Hail University, Saudi Arabia, a campus located in an arid, culturally specific environment that presents unique spatial and climatic challenges. By integrating empirical observations, structured surveys (n = 1186), and semi-structured interviews, the research adopts a mixed-methods approach to examine three core dimensions: social life enhancement, environmental sustainability, and student-centric design. The methodology incorporates both descriptive and inferential analyses, including correlation, regression, and ANOVA, to evaluate the impact of design features on student satisfaction, engagement, and resource efficiency. Results show that a 10% increase in social infrastructure correlates with a 6.5% rise in student satisfaction. The study further identifies gaps in climate-responsive planning, green space utilization, and participatory design practices. It offers a replicable, context-sensitive framework for sustainable campus development that aligns with multiple UN Sustainable Development Goals (SDGs), contributing new insights to the global discourse on higher education environments in arid regions. Full article