Search Results (9,013)

This study investigates how temporal fluency cues shape human ratings of L2 English pronunciation in Korean learners, using a large read-speech corpus annotated with five-point pronunciation scores. We focus on two timing-derived measures—articulation rate (AR) and mean silence duration (SilMean)—and examine whether these cues predict (i) articulation-accuracy ratings and (ii) prosody/fluency ratings. To account for dependencies in corpus data and to control for key learner- and task-level covariates, we fitted cumulative link mixed models with random intercepts for speakers and scripts, including proficiency band (ability), age, gender, and test type as fixed effects. Across models, faster articulation and shorter silent intervals were associated with higher articulation ratings, and a combined model including both AR and SilMean provided the best fit (lowest AIC). Temporal cues were even more strongly associated with prosody ratings, supporting construct alignment between timing measures and the prosody dimension of the rubric. Marginal predicted probabilities illustrate how the likelihood of receiving high ratings (score ≥ 4) increases with AR across proficiency and linguistic-complexity strata (with SilMean held constant), and how long silent intervals reduce these probabilities when AR is held constant. These findings indicate that temporal organization provides robust information about perceived pronunciation quality in read L2 speech and underscore the importance of construct-aware modeling when developing AI-based scoring and feedback systems trained on human-labeled data. Full article

Conversational agents are transforming digital interactions across various domains, including healthcare, education, and customer service, thanks to advances in large language models (LLMs). As these systems become more autonomous and ubiquitous, understanding what constitutes high-quality interaction from a user perspective is increasingly critical. Despite growing empirical research, the field lacks a unified framework for defining, measuring, and designing user-perceived interaction quality in human–artificial intelligence (AI) dialogue. Here, we present an integrative review of 125 empirical studies published between 2017 and 2025, spanning text-, voice-, and LLM-powered systems. Our synthesis identifies three consistent layers of user judgment: a pragmatic core (usability, task effectiveness, and conversational competence), a social–affective layer (social presence, warmth, and synchronicity), and an accountability and inclusion layer (transparency, accessibility, and fairness). These insights are formalised into a four-layer interpretive framework—Capacity, Alignment, Levers, and Outcomes—operationalised via a Capacity × Alignment matrix that maps distinct success and failure regimes. It also identifies design levers such as anthropomorphism, role framing, and onboarding strategies. The framework consolidates constructs, positions inclusion and accountability as central to quality, and offers actionable guidance for evaluation and design. This research redefines interaction quality as a dialogic construct, shifting the focus from system performance to co-orchestrated, user-centred dialogue quality. Full article

The quality of disposable bamboo chopsticks directly affects consumers’ usage experience and health safety. Therefore, quality inspection is particularly important, and multi-label classification of defects can better meet the refined demands of actual production. While ViT has made significant progress in visual tasks, it has limitations when dealing with extreme aspect ratios like bamboo chopsticks. To address this, this paper proposes an improved ViT model, C-ViT, introducing a convolutional neural network feature extraction module (CFE) to replace traditional patch embedding, making the input features more suitable for the ViT model. Moreover, existing loss functions in multi-label classification tasks focus on label prediction optimization, making hard labels difficult to learn due to their low gradient contribution. Therefore, this paper proposes a Hard Examples Contrastive Loss (HCL) function, dynamically selecting hard examples and combining label and feature correlation to construct a contrastive learning mechanism, enhancing the model’s ability to model hard examples. Experimental results show that on the self-built bamboo chopstick defect dataset (BCDD), C-ViT improves the mAP by 1.2% to 92.8% compared to the ViTS model, and can reach 94.3% after adding HCL. In addition, we further verified the effectiveness of the proposed HCL function in multi-label classification tasks on the VOC2012 public dataset. Full article

Background: Missed nursing care (MNCs) is a global issue with the potential to threaten patient safety and is often associated with a stressful work environment. Although stress-related work among clinical nurses is associated with MNCs, the correlation remains limited. Objective: This systematic review aimed to assess and synthesize available scientific evidence regarding the correlation between stress-related work among clinical nurses and the incidence of MNCs in hospital settings. Methods: This review was conducted according to the PRISMA guidelines. The PubMed, Scopus, and EBSCO databases were systematically searched for articles published between January 2014 and June 2025. Primary studies with quantitative, qualitative, or mixed-methods designs that examined the relationship between stress-related work and MNCs among hospital nurses were included. The data obtained were extracted and analyzed using thematic approaches. Results: A total of 244 articles were identified from the three databases. Seven studies, conducted in different countries met the inclusion criteria. All studies used cross-sectional designs. The results showed that most study reported stress-related work, emotional fatigue, and burnout were significantly positively related to the frequency of MNCs (p < 0.05). The most frequently missed types of nursing care include monitoring vital signs, skin/wound care, and oral care. Conclusions: The evidence suggests that stress-related work among nurses has significant potential to predict MNCs. Interventions that focus on mitigating work stress by improving the work environment and optimizing workload are crucial for improving quality of care and patient safety. Full article

Background: Depression remains a major global mental health concern, significantly intensified during the COVID-19 pandemic. As social media usage surged during this period, it emerged as a valuable source for identifying early signs of depression. Artificial intelligence (AI) offers powerful tools to analyze large volumes of user-generated content, enabling timely and effective detection of depressive symptoms. This review aims to preliminarily explore and compare evidence on the use of AI models for detecting depression in social content across the pre-, during, and post-pandemic phases, assessing their effectiveness and limitations. Methods: A narrative literature review was conducted using PubMed and Scopus, following the SANRA guidelines to ensure methodological quality and reproducibility. The study was pre-registered in the OSF database and employed the PICOS framework for the strategy. Inclusion criteria comprised studies in English from the past 10 years that analyzed depression detection via AI, machine learning (ML), and deep learning (DL) applied to textual data, images, and social metadata. This review addresses the following four research questions: (1) whether AI models improved effectiveness in detecting depression during/after the pandemic vs. pre-pandemic; (2) whether textual, visual, or multimodal data approaches became more effective during the pandemic; (3) whether AI models better addressed technical challenges (data quality/diversity) post-pandemic; and (4) whether strategies for responsible AI implementation improved during/after the pandemic. Results: Out of 349 identified records, nine primary studies were included, as most excluded articles had a predominantly technical focus and did not meet the clinical relevance criteria. AI models demonstrated strong potential in detecting depression, particularly through text-based classification and social content analysis. Several studies reported high predictive performance, with notable improvements in accuracy and sensitivity during and after the pandemic, although evidence remains limited. Conclusions: Our preliminary analysis suggests that AI-based depression detection on social media shows potential for clinical use, highlighting interdisciplinary collaboration, ethical considerations, and patient-centered approaches. These findings require confirmation and validation through larger, well-designed systematic reviews. Full article

Synthetic aperture radar (SAR) is widely used in the field of ocean remote sensing. However, SAR images are usually affected by sea spikes, which appear as strong echo and azimuth defocus characteristics. The texture features of ocean waves in SAR images are submerged by sea spikes, making them weak or even invisible. This seriously affects the further applications of SAR technology in ocean remote sensing. To address this issue, an airborne SAR imaging algorithm for ocean waves oriented to sea spike suppression is proposed in this paper. The non-stationary characteristics of sea spikes are taken into account in the proposed algorithm. The SAR echo data is transformed into the time–frequency domain by short-time Fourier transform (STFT). And the echo signals of sea spikes are suppressed in the time–frequency domain. Then, the ocean waves are imaged in focus by applying focus settings. In order to verify the effectiveness of the proposed algorithm, airborne SAR data was processed using the proposed algorithm, including SAR data with completely invisible waves and other data with weakly visible waves under sea spike influence. Through analyzing the ocean wave spectrum and imaging quality, it is confirmed that the proposed algorithm can significantly suppress sea spikes and improve the texture features of ocean waves in SAR images. Full article

Background/Objectives: The rising global demand for sustainable and nutritionally valuable food sources highlights the importance of exploring alternatives to conventional livestock. Japanese quail (Coturnix japonica) have gained attention as an environmentally efficient species, offering high-quality eggs and meat with favorable nutritional profiles. This study aimed to characterize the fatty acid (FA) composition of quail eggs (QEs) and meat (QM), investigating breast (B) and thigh (T) muscles produced in Poland under small-scale farming conditions, with a focus on assessing their functional foods’ potential. Methods: Gas chromatography–mass spectrometry analysis was applied to determine FA profiles in fresh and stored samples. Statistical evaluation included two-way ANOVA, principal component analysis (PCA), and cluster analysis. Results: Results demonstrated that QE contained the highest total FA levels, dominated by monounsaturated fatty acids, with notable contributions from long-chain n-3 polyunsaturated fatty acids (PUFAs) and conjugated linoleic acid isomers. In contrast, QM were characterized by higher proportions of PUFA, with significant differences between breast and thigh samples. Storage influenced the levels of selected FA, particularly n-3 PUFA and the n-3/n-6 ratio. PCA and cluster analysis confirmed clear separation between eggs and muscles, regardless of storage status. Overall, both QE and QM exhibited a favorable PUFA/SFA ratio, exceeding dietary recommendations. Conclusions: These findings underscore the nutritional and functional value of quail-derived foods, supporting their consideration as sustainable alternatives to chicken products and potential candidates for the ‘superfood’ category. Full article

Compound meta-optics, characterized by the unprecedented complex optical architectures containing single or multiple meta-optics elements, has emerged as a powerful paradigm for overcoming the physical limitations of single-layer metasurfaces. This review systematically examines the recent progress in this burgeoning field, primarily focusing on the development of high-performance optical systems for imaging, display, sensing, and computing. We first focus on the design of compound metalens architectures that integrate metalenses with additional elements such as iris, refractive optics, or other meta-optics elements. These configurations effectively succeed in providing multiple high-quality image quality metrics simultaneously by correcting monochromatic and chromatic aberrations, expanding the field of view, enhancing overall efficiency, and so on. Thus, the compound approach enables practical applications in next-generation cameras and sensors. Furthermore, we explore the advancement of cascaded metasurfaces in the realm of wave-optics, specifically for advanced meta-holography and optical computing. These multi-layered systems facilitate complex wavefront engineering, leading to significant increases in information capacity and functionality for security and analog optical computing applications. By providing a comprehensive overview of fundamental principles, design strategies, and emerging applications, this review aims to offer a clear perspective on the pivotal role of compound meta-optics in devising and optimizing compact, multifunctional optical systems to optics engineers with a variety of professional knowledge backgrounds and techniques. Full article

Background/Objectives: To evaluate and compare the characteristics of clinical trials (CTs) involving patients with primary Sjögren’s syndrome (pSS), using biologics, and focusing on the features of the patients recruited. Methods: This systematic review assessed pSS CTs evaluating biologic drugs published from 2010 to 2024 according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The literature search of the electronic databases was performed individually by the authors. The extracted variables regarding the baseline characteristics of participants and trial-related information were defined a priori, collected, and compared. Results: A total of 16 CTs were included in this review in line with the inclusion criteria. The trials were predominantly multicenter (75%) randomized controlled trials with a placebo arm (93.8%), with only five trials recruiting participants across multiple (≥3) continents. The search included a total of 1607 patients (mean age 51 years, 94% female) with a mean disease duration of 6.47 years. Race and ethnicity were underrepresented variables, found in 37.5% and 12.5% of the trials, respectively, with White patients comprising the majority (77.8%). The EULAR Sjögren’s Syndrome Disease Activity Index (ESSDAI) was reported in 93.8% of the CTs. However, only recent studies have emphasized it as the primary outcome. Conclusions: Recent trials on biologics in pSS patients show better methodological quality, with a more standardized assessment of disease activity using ESSDAI, and an increased focus on patient-reported outcomes. Global participation is increasing, but limited racial and ethnic diversity, endpoint variability, and inconsistent biomarker reporting remain critical issues. Full article

Background: Knee osteoarthritis causes considerable pain and disability. Telerehabilitation has emerged as a promising treatment option, especially after the Coronavirus Disease 2019 pandemic, but it still faces challenges regarding solid scientific evidence about its multiple benefits. This systematic review aimed to analyze the reported beneficial effects of telerehabilitation based on therapeutic exercise for the management of knee osteoarthritis. Methodsː PubMed, PEDro, Web of Science and Cochrane Library databases were used to identify eligible studies. This review followed the PRISMA guidelines and was registered at PROSPERO (n° CRD42024579836). The selected studies underwent a qualitative assessment using the Modified Jadad Score. Results: Ten studies, including a total of 1354 participants, were included. From the selected studies, a wide variety of outcome measures emerged to evaluate the efficacy of telerehabilitation in the relief of pain and its clinical consequences. Seven studies specifically assessed pain, with four showing significant improvements in pain reduction in the intervention group compared with the control group. Telerehabilitation was found to be more effective or non-inferior to traditional rehabilitation in relieving pain, as reported across various pain scales. Limitations include the heterogeneity of interventions, the exclusion of non-recent studies, and the exclusive focus on therapeutic exercise. Conclusionsː The results of this systematic review suggest that telerehabilitation provides pain relief, improves physical function, and enhances quality of life, while preliminary evidence indicates potential cost-related advantages. However, some studies did not find TR to be superior to control interventions, highlighting mixed evidence. Additional high-quality studies are required to better support this promising rehabilitation approach. Full article

Teacher education represents a global strategic priority for improving educational systems and fostering inclusive, high-quality processes. Recent studies highlight the need for systematic and replicable education models capable of addressing the challenges of contemporary complexity and bridging the gap between theory and practice. Teaching professionalism is increasingly recognized as a key driver of change, requiring a balance of pedagogical, relational, and technological competences, along with strong reflective capacity. Within this framework, practicum programs play a crucial role for the development of professional identity and authentic teaching skills. Methods: This contribution adopts a theoretical–argumentative approach grounded in a critical analysis of the international scientific literature on teacher education, with specific focus on the role of practicums. The aim is to present the model implemented by Pegaso University in the context of practicum activities within initial teacher education programs to outline an interpretative framework and provide pedagogical reflections in light of the results arising from critical reflection and systematic monitoring (not covered in this specific contribution) of the effectiveness of the model implemented in the first two training cycles (academic years 23–24 and 24–25), with the involvement of 5 regions and a total of 2834 teachers in the first cycle and 10 regions and a total of 5551 teachers in the second cycle. Convenience sampling based on a non-probabilistic method was adopted, using the entire sample of teachers admitted to the training program who met the requirements of Article 7 of the Decree of the President of the Council of Ministers (DPCM). Results: This paper outlines the theoretical and methodological trajectories of the model, offering interpretative frameworks and pedagogical reflections in light of the outcomes achieved during the initial implementation phase. Conclusions: In accordance with recent national and European regulatory frameworks, the Pegaso teaching model is presented as an example of good practice for initial teacher education. It aims to foster a reflective, situated, and responsible teaching professionalism, moving beyond traditional approaches toward a continuous and transformative learning process. Full article

There is a lack of focus on psychological support for fathers in Neonatal Intensive Care Units (NICUs), both in research and practice, with fathers receiving far less support from NICU providers as compared with mothers. This article aims to discuss the current literature and limitations related to providing psychological support to fathers in the NICU and proposes short- and long-term efforts for improving psychological care for NICU fathers. We conducted a narrative literature review to summarize interventions for supporting fathers in the NICU, including emotional support, educational support, social support, family-integrated care, and multi-component interventions. While initial work is promising, there are major limitations. Very few studies have examined interventions specific to providing support to fathers in the NICU, and little work has investigated differences in the support needs and responses to interventions for NICU fathers as compared with mothers. Fathers have historically been overlooked in the NICU. Given the growing recognition of paternal mental health challenges and their impact on infant development, there is a pressing need for efforts aimed at supporting fathers in the NICU. Efforts must consider system structure, policy, multidisciplinary training, and implementation protocols to improve the quality of care provided to NICU fathers. Full article

Wireless Body Area Networks (WBANs) enable real-time data collection for medical monitoring, sports tracking, and environmental sensing, driven by Internet of Things advancements. Their layered architecture supports efficient sensing, aggregation, and analysis, but energy constraints from transmission (over 60% of consumption), idle listening, and dynamic conditions like body motion hinder adoption. Challenges include minimizing energy waste while ensuring data reliability, Quality of Service (QoS), and adaptation to channel variations, alongside algorithm complexity and privacy concerns. This paper reviews recent power control mechanisms in WBANs, encompassing feedback control, dynamic and convex optimization, graph theory-based path optimization, game theory, reinforcement learning, deep reinforcement learning, hybrid frameworks, and emerging architectures such as federated learning and cell-free massive MIMO, adopting a systematic review approach with a focus on healthcare and IoT application scenarios. Achieving energy savings ranging from 6% (simple feedback control) to 50% (hybrid frameworks with emerging architectures), depending on method complexity and application scenario, with prolonged network lifetime and improved reliability while preserving QoS requirements in healthcare and IoT applications. Full article

Background/Objectives: Emerging evidence suggests that multiple micronutrient supplements (MMS) provide additional benefits for maternal and neonatal health compared with iron and folic acid (IFA) supplements. To achieve effective coverage, acceptability, and adherence—and to inform a nationwide rollout of MMS—it is essential to understand the context-specific factors that shape implementation. This study evaluated the pilot implementation of MMS in Rwanda to identify key enablers, areas for improvement, and challenges related to antenatal care (ANC) attendance and MMS use. Methods: Data were collected through a survey of 3257 women who attended ANC services, seven focus group discussions with 35 ANC attendees, and key informant interviews with 20 ANC nurses and 21 community health workers. Results: Pregnant women reported high ANC attendance (74%) and MMS consumption (79%), largely driven by strong motivation and awareness of MMS benefits. Strategies to remember daily intake and to manage side effects supported adherence, as did reminders, motivation, and information from family members and healthcare providers. Limited patient-centered counselling, financial constraints, barriers to accessing ANC services, and product stock-outs were key areas for strengthening service delivery in Rwanda. Conclusions: Sustaining high ANC attendance and MMS adherence as the program transitions from the pilot phase to national scale-up is essential. Improving counseling quality and strengthening supply chains may reinforce ANC services and support sustained MMS adherence, with benefits for maternal and child health. Full article

Fused deposition modeling (FDM) suffers from unpredictable mechanical properties in nominally identical prints. Current quality assurance relies on destructive testing or expensive post-process inspection, while existing machine learning approaches focus primarily on printing parameters rather than real-time thermal environments. The objective of this proof-of-concept study is to develop a low-cost, non-destructive framework for predicting tensile strength during FDM printing by directly measuring convective thermal gradients surrounding the print. To accomplish this, we introduce thermal fingerprinting: a novel non-destructive technique that combines Background-Oriented Schlieren (BOS) imaging with machine learning to predict tensile strength during printing. We captured thermal gradient fields surrounding PLA specimens (n = 30) under six controlled cooling conditions using consumer-grade equipment (Nikon D750 camera, household hairdryers) to demonstrate low-cost implementation feasibility. BOS imaging was performed at nine critical layers during printing, generating thermal gradient data that was processed into features for analysis. Our initial dual-model ensemble system successfully classified cooling conditions (100%) and showed promising correlations with tensile strength (initial 80/20 train–test validation: R² = 0.808, MAE = 0.279 MPa). However, more rigorous cross-validation revealed the need for larger datasets to achieve robust generalization (five-fold cross-validation R² = 0.301, MAE = 0.509 MPa), highlighting typical challenges in small-sample machine learning applications. This work represents the first successful application of Schlieren imaging to polymer additive manufacturing and establishes a methodological framework for real-time quality prediction. The demonstrated framework is directly applicable to real-time, non-contact quality assurance in FDM systems, enabling on-the-fly identification of mechanically unreliable prints in laboratory, industrial, and distributed manufacturing environments without interrupting production. Full article