Search Results (451)

Promoting children’s rights in early childhood education is internationally recognised as a priority, yet its practical implementation remains challenging. This qualitative study explored the perceptions of three early childhood educators in northern Portugal regarding children’s rights and how these are reflected in their practices. Guided by an interpretive paradigm, the study sought to understand participants’ beliefs through semi-structured interviews, conducted with full ethical compliance, including informed consent, withdrawal rights, and anonymity. Data were analysed using MAXQDA, through an inductively generated coding system. Findings indicate that educators acknowledge their vital role in upholding children’s rights and in fostering respectful learning environments. However, significant gaps were found in the realisation of the right to participation, with tensions between educators’ stated values and their described practices—particularly regarding children’s involvement in decision-making. A prevailing emphasis on protection often limited children’s autonomy and agency. The study highlights the complexities of translating policy frameworks, such as Portuguese legislation and the UNCRC, into consistent pedagogical action. Despite its small sample size, the study offers valuable insights into the barriers to implementing a rights-based pedagogy and underscores the need for enhanced educator training, active listening practices, and the recognition of play as a fundamental right. Full article

The objective of this paper is to frame research improving the governance of modern cyber-physical systems (CPS) and Complex Systems of CPS through better regulation and compliance. CPS are increasingly being used to undertake high-hazard activities that have the potential to cause significant impacts on people and the environment. The analysis detailed in this paper provides insights into how maritime, aviation, and nuclear regulators from the United States of America, the European Union, and Australia, in particular, facilitate the global trend of integrating cyber components into the high-hazard physical systems they regulate. This insight is gained by undertaking a systematic document review and word search analysis of the regulations, codes, standards and guidance documents published or referred to by these regulators, relevant to the operation of the high-hazard CPS they regulate. These documents were selected to assess the importance that these regulators place on cybersecurity, cyber safety, and cyberworthiness. This analysis confirmed that current regulations primarily treat cyber and physical safety in isolation and generally perceive the application of cybersecurity as adequate for achieving safety for the cyber aspects of CPS. This demonstrates the need for the application of more contemporary concepts, such as cyberworthiness, to the regulation of high-hazard CPS, as well as methods to pathologically assess and incrementally improve governance of such systems through approaches like Complex Systems Governance. Full article

Software development projects are highly susceptible to risks during the design phase, which plays a crucial role in shaping the architecture, functionality, and quality of the final product. Decisions made during the design stage significantly affect the outcomes of the subsequent phases, including coding, testing, deployment, and maintenance. However, the complexities and uncertainties inherent in the design phase are often inadequately addressed by traditional risk management tools as they rely on deterministic models that oversimplify interdependent risks. This research introduces a fuzzy logic-based risk assessment model tailored specifically for the design phase of software development projects. The proposed fuzzy model, unlike the existing state-of-the-art models, regards the iterative nature of the design phase, the interaction between diverse stakeholders, and the potential inconsistencies that may arise between the initial and final version of the software design. More specifically, it develops a customized fuzzy model that incorporates design-specific risk factors such as evolving architectural requirements, technical feasibility concerns, and stakeholder misalignment. Finally, it integrates expert-driven rule definitions to enhance model accuracy and real-world applicability, ensuring that risk assessments reflect actual challenges faced by software design teams. Simulations conducted across diverse real-world scenarios demonstrate the model’s robustness in predicting risk levels and supporting mitigation strategies. The simulation results confirm that the proposed fuzzy logic model outperforms conventional approaches by offering greater flexibility and adaptability in managing design-phase risks, assisting project managers in prioritizing mitigation efforts more effectively to improve project outcomes. Full article

The molten salt reactor (MSR) is a prominent Generation IV nuclear reactor concept that offers substantial advantages over conventional solid-fueled systems, including enhanced fuel utilization, inherent passive safety features, and significant reductions in long-lived radioactive waste. Central to its safety strategy is a reliance on natural circulation (NC) mechanisms, which eliminate the need for active pumping systems and enhance system reliability during normal and off-normal conditions. However, the challenges associated with molten salts, such as their high melting points, corrosivity, and material compatibility issues, render experimental investigations inherently complex and demanding. Therefore, the use of high-Pr-number surrogate fluids represents a practical alternative for studying molten salt behavior under safer and more accessible experimental conditions. In this study, a single-phase natural circulation loop setup at the University of Idaho’s Thermal–Hydraulics Laboratory was employed to investigate NC behavior under various operating conditions. The RELAP5-3D code was initially validated against water-based experiments before employing Therminol-66, a high-Prandtl-number surrogate for molten salts, in the natural circulation loop for the first time. The RELAP5-3D results demonstrated good agreement with both steady-state and transient experimental results, thereby confirming the code’s ability to model NC behavior in a single-phase flow regime. The results also highlighted certain experimental limitations that should be addressed to enhance the NC loop’s performance. These include increasing the insulation thickness to reduce heat losses, incorporating a dedicated mass flow measurement device for improved accuracy, and replacing the current heater with a higher-capacity unit to enable testing at elevated power levels. By identifying and addressing the main causes of these limitations and uncertainties during water-based experiments, targeted improvements can be implemented in both the RELAP5 model and the experimental setup, thereby ensuring that tests using a surrogate fluid for MSR analyses are conducted with higher accuracy and minimal uncertainty. Full article

Quantum communication enables high-fidelity image transmission but is vulnerable to channel noise, and while advanced quantum error correction (QEC) can reduce such effects, its complexity and time-domain dependence limit practical efficiency. This paper presents a novel, low-complexity, and noise-resilient quantum image transmission framework that operates in the frequency domain using the quantum Fourier transform (QFT) combined with the three-qubit QEC code. In the proposed system, input images are first source-encoded (JPEG/HEIF) and mapped to quantum states using single-qubit superposition encoding. Three-qubit QEC is then applied for channel protection, effectively safeguarding the encoded data against quantum errors. The channel-encoded quantum data are subsequently transformed via QFT for transmission over noisy quantum channels. At the receiver, the inverse QFT recovers the frequency-domain representation, after which three-qubit error correction, quantum decoding, and corresponding source decoding are performed to reconstruct the image. Results are analyzed using bit error rate (BER), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Experimental results show that the proposed quantum frequency-domain approach achieves up to 4 dB channel SNR gain over equivalent quantum time-domain methods and up to 10 dB over an equivalent-bandwidth classical communication system, regardless of the image format. These findings highlight the practical advantages of integrating QFT-based transmission with lightweight QEC, offering an efficient, scalable, and noise-tolerant solution for future quantum communication networks. Full article

Large language models (LLMs) have advanced rapidly as tools for automating code generation in scientific research, yet their ability to interpret and use unfamiliar Python APIs for complex computational experiments remains poorly characterized. This study systematically benchmarks a selection of state-of-the-art LLMs in generating functional Python code for two increasingly challenging scenarios: conversational data analysis with the ParShift library, and synthetic data generation and clustering using pyclugen and scikit-learn. Both experiments use structured, zero-shot prompts specifying detailed requirements but omitting in-context examples. Model outputs are evaluated quantitatively for functional correctness and prompt compliance over multiple runs, and qualitatively by analyzing the errors produced when code execution fails. Results show that only a small subset of models consistently generate correct, executable code. GPT-4.1 achieved a 100% success rate across all runs in both experimental tasks, whereas most other models succeeded in fewer than half of the runs, with only Grok-3 and Mistral-Large approaching comparable performance. In addition to benchmarking LLM performance, this approach helps identify shortcomings in third-party libraries, such as unclear documentation or obscure implementation bugs. Overall, these findings highlight current limitations of LLMs for end-to-end scientific automation and emphasize the need for careful prompt design, comprehensive library documentation, and continued advances in language model capabilities. Full article

RNA virus populations exist as quasispecies-complex, dynamic clouds of closely related but genetically diverse variants generated by high mutation rates during replication. Assessing quasispecies structure and diversity is crucial for understanding viral evolution, adaptation, and response to antiviral treatments. However, comparing single quasispecies observations from individual biosamples, especially at different infection or treatment time points, presents statistical challenges. Traditional inferential tests are inapplicable due to the lack of replicate observations, and resampling-based approaches such as the bootstrap and jackknife are limited by biases and non-independence, particularly for diversity indices sensitive to rare haplotypes. In this study, we address these limitations by applying the delta method to derive analytical variances for a set of quasispecies structure indicators specifically designed to assess the quasispecies maturation state. We demonstrate the utility of this approach using high-depth next-generation sequencing data from hepatitis C virus (HCV) quasispecies evolving in vitro under various conditions, including free evolution and exposure to antiviral or mutagenic treatments. Our results reveal that with highly fit HCV quasispecies, sofosbuvir inhibits quasispecies genetic diversity, while mutagenic treatments accelerate maturation, compared to untreated controls. We emphasize the interpretation of results through absolute differences, log-fold changes, and standardized effect sizes, moving beyond mere statistical significance. This framework enables robust, quantitative comparisons of quasispecies diversity from single observations, providing valuable insights into viral adaptation and treatment response. The R code and session info with required libraries and versions is provided in the supplementary material. Full article

Background/Objectives: While complete loss-of-function (LoF) SPINK1 variants in the simple heterozygous state cause chronic pancreatitis, biallelic complete LoF variants result in a rare pediatric disorder termed severe infantile isolated exocrine pancreatic insufficiency (SIIEPI). To date, only two individuals with a null SPINK1 genotype have been reported—one homozygous for a whole-gene deletion and the other for an Alu insertion in the 3′ untranslated region. Here, we report the genetic basis of a third SIIEPI case, presenting in early infancy with severe exocrine pancreatic insufficiency and diffuse pancreatic lipomatosis. Methods: Targeted next-generation sequencing (NGS) was used to analyze the entire coding region and exon–intron boundaries of the SPINK1 gene. Copy number variant (CNV) analysis was performed with SeqNext, based on normalized amplicon coverage. Results: The proband harbored compound heterozygous complete LoF SPINK1 variants. One was the known NM_001379610.1:c.180_181del (p.(Cys61PhefsTer2)), inherited from the father. The second, initially detected as an exon 2 deletion and confirmed by quantitative fluorescent multiplex PCR (QFM-PCR), was further characterized by long-range PCR as a complex rearrangement comprising a 1185 bp deletion removing exon 2, a 118 bp templated insertion followed by a non-templated nucleotide, and an 8 bp deletion. The mutational signature is consistent with serial replication slippage or template switching involving translesion synthesis. This maternally inherited variant has not been previously reported. Conclusions: This study expands the mutational spectrum of SPINK1-related SIIEPI and suggests that this distinct pediatric disorder may be under recognized in clinical practice. Full article

This paper proposes a novel quantum image transmission framework to address the limitations of existing single-qubit time domain systems, which struggle with noise resilience and scalability. The framework integrates frequency domain processing with multi-qubit (1 to 8 qubits) encoding to enhance robustness against quantum noise. Initially, images are source-coded using JPEG and HEIF formats with rate adjustment to ensure consistent bandwidth usage. The resulting bitstreams are channel-encoded and mapped to multi-qubit quantum states. These states are transformed into the frequency domain via the quantum Fourier transform (QFT) for transmission. At the receiver, the inverse QFT recovers the time domain states, followed by multi-qubit decoding, channel decoding, and source decoding to reconstruct the image. Performance is evaluated using bit error rate (BER), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and universal quality index (UQI). Results show that increasing the number of qubits enhances image quality and noise robustness, albeit at the cost of increased system complexity. Compared to time domain processing, the frequency domain approach achieves superior performance across all qubit configurations, with the eight-qubit system delivering up to a 4 dB maximum channel SNR gain for both JPEG and HEIF images. Although single-qubit systems benefit less from frequency domain encoding due to limited representational capacity, the overall framework demonstrates strong potential for scalable and noise-robust quantum image transmission in future quantum communication networks. Full article

Sepsis is a complex and heterogeneous condition, arising from a disrupted immune response to infection that can progress to organ failure and carries a high risk of death. In recent years, growing attention has been paid to the role of epigenetic mechanisms—including DNA methylation, histone modifications, non-coding RNAs, and RNA methylation—in shaping immune activity during sepsis. These processes affect immune functions such as macrophage polarization, cytokine release, and the exhaustion of immune cells, and they help explain the shift from an initial phase of overwhelming inflammation to a later state of immune suppression. Epigenetic alterations also contribute to tissue-specific damage, notably in the lungs, kidneys, and heart, and have been linked to disease severity and clinical prognosis. Advances in transcriptomic and epigenetic profiling have made it possible to distinguish molecular subtypes of septic patients, each with distinct immune features and varied responses to treatments such as corticosteroids and metabolic therapies. Emerging biomarkers—like AQP5 methylation, histone lactylation (H3K18la), and m⁶A RNA methylation—are opening new options for patient classification and more tailored therapeutic strategies. This review examines the current understanding of how epigenetic regulation contributes to the pathophysiology of sepsis and considers its implications for developing more individualized approaches to care. Full article

Rapid data growth in large systems has introduced significant challenges in real-time monitoring and analysis. One of these challenges is detecting anomalies in time series data with high-dimensional inputs that contain complex inter-correlations between them. In addition, the lack of labeled data leads to the use of unsupervised learning that relies on daily system data to train models, which can contain noise that affects feature extraction. To address these challenges, we propose PCA-AAE, a novel anomaly detection model for time series data using an Adversarial Autoencoder integrated with Principal Component Analysis (PCA). PCA contributes to analyzing the latent space by transforming it into uncorrelated components to extract important features and reduce noise within the latent space. We tested the integration of PCA into the model’s phases and studied its efficiency in each phase. The tests show that the best practice is to apply PCA to the latent code during the adversarial training phase of the AAE model. We used two public datasets, the SWaT and SMAP datasets, to compare our model with state-of-the-art models. The results indicate that our model achieves an average F1 score of 0.90, which is competitive with state-of-the-art models, and an average of 58.5% faster detection speed compared to similar state-of-the-art models. This makes PCA-AAE a candidate solution to enhance real-time anomaly detection in high-dimensional datasets. Full article

The present study aimed to provide Congolese refugee women with an opportunity to narrate firsthand experiences coping with resettlement challenges in the United States. Translator-assisted, face-to-face semi-structured individual interviews were conducted with newly resettled Congolese refugee women (n = 20) aged 18 and older who arrived in the United States between 2011 and 2018. All participants were receiving assistance from a resettlement agency, located in the Midwestern US, at the time of the study. Data were analyzed using descriptive coding and thematic analysis. Three overarching themes were developed, indicating that Congolese refugee women adopt three main coping mechanisms to deal with challenges they face after resettling in the United States: (1) use of social support, (2) acceptance of the situation, and (3) spirituality. Resettlement support services, such as those provided by resettlement agencies, mental health providers, and community-based organizations, should integrate both economic and cultural dimensions into their services to address the complex physiological, mental, and emotional impacts of resettlement. These services should prioritize culturally and spiritually sensitive techniques that are linguistically accessible. Full article

Background/Objectives: Chronic pain and eating disorders are two prevalent and disabling pediatric health concerns, with serious, life-threatening consequences. These conditions can co-occur, yet little is known about best practices addressing comorbid pain and eating disorders. Delayed intervention for eating disorders may have grave implications, as eating disorders have one of the highest mortality rates among psychological disorders. Moreover, chronic pain not only persists but worsens into adulthood when left untreated. This study aimed to understand pediatric clinicians’ experiences with adolescents with chronic pain and eating disorders. Methods: Semi-structured interviews were conducted with hospital-based physicians (N = 10; 70% female; M years of experience = 15.3) and psychologists (N = 10; 80% female; M years of experience = 10.2) specializing in anesthesiology/pain, adolescent medicine/eating disorders, and gastroenterology across the United States. Audio transcripts were coded, and thematic analysis was used to identify key themes. Results: Clinicians described frequently encountering adolescents with chronic pain and eating disorders. Clinicians described low confidence in diagnosing comorbid eating disorders and chronic pain, which they attributed to lack of screening tools and limited training. Clinicians collaborated with and consulted clinicians who encountered adolescents with chronic pain and/or eating disorders. Conclusions: Results reflect clinicians’ desire for additional resources, training, and collaboration to address the needs of this population. Targets for future research efforts in comorbid pain and eating disorders were highlighted. Specifically, results support the development of screening tools, program development to improve training in complex medical and psychiatric presentations, and methods to facilitate more collaboration and consultation across health care settings, disciplines, and specialties. Full article

Deep learning has enabled significant progress in source code generation, aiming to reduce the manual, error-prone, and time-consuming aspects of software development. While many existing models rely on recurrent neural networks (RNNs) with sequence-to-sequence architectures, these approaches struggle with the long and complex token sequences typical in source code. To address this, we propose a grammar-based convolutional neural network (CNN) combined with a tree-based representation to enhance accuracy and efficiency. Our model achieves state-of-the-art results on the benchmark HEARTHSTONE dataset, with a BLEU score of 81.4 and an Acc+ of 62.1%. We further evaluate the model on our proposed dataset, AST2CVCode, designed for computer vision applications, achieving 86.2 BLEU and 51.9% EM. Additionally, we introduce BLEU+, an enhanced evaluation metric tailored for functional correctness in code generation, which achieves a BLEU+ score of 92.0% on the AST2CVCode dataset. These results demonstrate the effectiveness of our approach in both model architecture and evaluation methodology. Full article

With the rapid expansion of Internet of Things (IoT) and edge computing applications, efficient video transmission under constrained bandwidth and limited computational resources has become increasingly critical. In such environments, perception-based video coding plays a vital role in maintaining acceptable visual quality while minimizing bit rate and processing overhead. Although newer video coding standards have emerged, H.264/AVC remains the dominant compression format in many deployed systems, particularly in commercial CCTV surveillance, due to its compatibility, stability, and widespread hardware support. Motivated by these practical demands, this paper proposes a perception-based video coding algorithm specifically tailored for low-bit-rate H.264/AVC applications. By targeting regions most relevant to the human visual system, the proposed method enhances perceptual quality while optimizing resource usage, making it particularly suitable for embedded systems and bandwidth-limited communication channels. In general, regions containing human faces and those exhibiting significant motion are of primary importance for human perception and should receive higher bit allocation to preserve visual quality. To this end, macroblocks (MBs) containing human faces are detected using the Viola–Jones algorithm, which leverages AdaBoost for feature selection and a cascade of classifiers for fast and accurate detection. This approach is favored over deep learning-based models due to its low computational complexity and real-time capability, making it ideal for latency- and resource-constrained IoT and edge environments. Motion-intensive macroblocks were identified by comparing their motion intensity against the average motion level of preceding reference frames. Based on these criteria, a dynamic quantization parameter (QP) adjustment strategy was applied to assign finer quantization to perceptually important regions of interest (ROIs) in low-bit-rate scenarios. The experimental results show that the proposed method achieves superior subjective visual quality and objective Peak Signal-to-Noise Ratio (PSNR) compared to the standard JM software and other state-of-the-art algorithms under the same bit rate constraints. Moreover, the approach introduces only a marginal increase in computational complexity, highlighting its efficiency. Overall, the proposed algorithm offers an effective balance between visual quality and computational performance, making it well suited for video transmission in bandwidth-constrained, resource-limited IoT and edge computing environments. Full article