Search Results (5,127)

Significant time, money, and energy are invested in Continuing Professional Development (CPD) across Further Education (FE) colleges in England, with the aim of enhancing teaching strategies, sharing “best” practices, and improving educational quality. Despite these intentions, practitioner perceptions of CPD’s value remain mixed, highlighting concerns about the effectiveness of current approaches. CPD managers often face competing financial and operational demands, alongside pressure to comply with external requirements, resulting in CPD that is frequently instrumental, mandatory, and delivered through one-off events. These practices reflect a data-driven, prescriptive management culture that prioritizes measurable outcomes over meaningful educational experiences. Consequently, teachers are compelled to demonstrate compliance within a system where accountability is unevenly distributed. This medium-scale, multi-method practitioner research study investigates how such compliance-driven CPD practices divert attention and resources from genuine educational improvement. This study explores an alternative model of CPD rooted in teacher agency and enriched through engagement with the arts and aesthetic experiences. Drawing on surveys, semi-structured interviews, critical incidents, and narrative accounts, the findings suggest that this approach fosters more democratic, creative, and impactful professional development. In promoting teacher agency and challenging dominant power structures, this study offers a vision of CPD that supports meaningful educational transformation, with practical examples and recommendations for broader implementation. Full article

Background: Cultural sensitivity during intrapartum care is a critical determinant of maternal satisfaction and quality of care, particularly in multicultural settings. In Saudi Arabia, the diversity of birthing women underscores the need for midwives to provide culturally competent, respectful, and individualized care. Objective: To assess women’s perceptions of midwives’ cultural sensitivity during intrapartum care in Riyadh, Saudi Arabia, and identify demographic factors influencing these perceptions. Methods: A quantitative cross-sectional study was conducted using a validated cultural sensitivity questionnaire. Data were collected online through purposive sampling from women who had given birth in the past 12 months. Descriptive statistics summarized participant characteristics and perception scores, while inferential tests examined associations between perceptions and demographic variables. Results: women reported moderate to high perceptions of cultural sensitivity. Age and nationality significantly influenced perception scores (p < 0.05). While communication and respect for religious practices scored highest, areas such as shared decision making and language-concordant support were identified as needing improvement. Conclusions: Women in Riyadh often perceive midwives as culturally sensitive; however, gaps remain in communication and involvement in decision making. Training programs that strengthen midwives’ cultural competence—especially in language services and patient engagement—could enhance the intrapartum experience. Full article

The island of St Lucia has undergone a dramatic transformation in a matter of decades, metamorphosing from a monocrop banana economy to a high end tourist destination which generates billions of dollars of revenue for those who operate it. This transformation has, however, seen a tourism industry develop that relies on Western models of environmental destruction and degradation. For Rastafari communities in St Lucia this represents a direct challenge to a deeply green ecological ethic which lies at the heart of the movement. Instead, some offer an alternative—eco-tourism. Several Rastafari are today engaged in offering an experience to visitors which brings them closer to this naturality rather than separating them from. To do so, however, is not without challenge. Many Rastafari seek to avoid as far as is possible all forms of engagement with a ‘Babylonian’ system that has persecuted the movement and debased all around it. For these individuals, it is only through the establishment of agrarian communes entirely separate from governmental systems and means of production that Rastafari might achieve a spirituality and a living modality free from corruption. These Rastafari eco-tourism ventures offer an alternative to this vision in presenting a different path to a shared ambition of self-sufficiency. Through ethnographic research on the island this paper seeks to situate and explore Rastafari eco-tourism as a vision for community autonomy in St Lucia. Full article

Digital supply chain management (DSCM) has emerged as a critical driver of enterprise performance in the modern economy, yet empirical evidence on its causal impact on productivity remains limited. This study examines how DSCM adoption affects total factor productivity (TFP) by leveraging China’s supply chain innovation pilot program as a quasi-natural experiment. Using a difference-in-differences approach with propensity score matching, the analysis employs a comprehensive dataset of 2843 Chinese A-share listed companies from 2013 to 2022; the analysis reveals that DSCM adoption leads to an average TFP increase of 14.1%. This positive effect strengthens over time, demonstrating a clear dynamic of organizational learning. Mediation analysis indicates that this productivity enhancement operates through two primary channels: innovation capability enhancement (accounting for approximately 35% of the total effect) and cost efficiency improvement (21%). Crucially, heterogeneity analysis reveals that the positive effects of DSCM are significantly more pronounced in supply-chain-intensive industries, such as manufacturing, and for firms with higher R&D intensity. The findings provide robust causal evidence on the productivity effects of DSCM, offering valuable insights into its underlying mechanisms and key boundary conditions for both enterprise strategy and digital transformation policy. Full article

This study investigates how perceptual and emotional factors—perceived naturalness, aesthetic pleasure, environmental concern, and disgust—shape consumer acceptance of a human-hair-derived bio-fabricated textile product (a unisex cardholder). In a scenario-based online survey, participants viewed an AI-generated image accompanied by a short vignette. A purposive sample of young adults in Istanbul with prior experience purchasing sustainable textile products was recruited and screened. All constructs were measured with standard Likert-type scales and translated into Turkish using a two-way back-translation procedure. Data were analyzed with PLS-SEM. Model fit was acceptable, and the model accounted for a substantial share of the variance in adoption intention. Aesthetic pleasure showed a clear positive influence on adoption intention, whereas perceived naturalness did not display a direct effect. Environmental concern modestly strengthened the link between naturalness and adoption. Disgust emerged as the dominant moderator, fully conditioning the naturalness pathway and reducing—but not eliminating—the effect of aesthetic pleasure. Together, these findings indicate that perceived naturalness, aesthetic pleasure, environmental concern, and disgust jointly shape adoption intention and that practical emphasis should be placed on reducing feelings of disgust while enhancing aesthetic appeal. Full article

Unmanned aerial vehicles (UAVs) have become indispensable tools for surveillance, enabled by their ability to capture multi-perspective imagery in dynamic environments. Among critical UAV-based tasks, cross-platform person search—detecting and identifying individuals across distributed camera networks—presents unique challenges. Severe viewpoint variations, occlusions, and cluttered backgrounds in UAV-captured data degrade the performance of conventional discriminative models, which struggle to maintain robustness under such geometric and semantic disparities. To address this, we propose view-invariant person search (VIPS), a novel two-stage framework combining Faster R-CNN with a view-invariant re-Identification (VIReID) module. Unlike conventional discriminative models, VIPS leverages the semantic flexibility of large vision–language models (VLMs) and adopts a two-stage training strategy to decouple and align text-based ID descriptors and visual features, enabling robust cross-view matching through shared semantic embeddings. To mitigate noise from occlusions and cluttered UAV-captured backgrounds, we introduce a learnable mask generator for feature purification. Furthermore, drawing from vision–language models, we design view prompts to explicitly encode perspective shifts into feature representations, enhancing adaptability to UAV-induced viewpoint changes. Extensive experiments on benchmark datasets demonstrate state-of-the-art performance, with ablation studies validating the efficacy of each component. Beyond technical advancements, this work highlights the potential of VLM-derived semantic alignment for UAV applications, offering insights for future research in real-time UAV-based surveillance systems. Full article

Background: Adolescence is a particularly vulnerable period for experiencing loneliness. According to the latest statistics, there are worldwide increases in adolescents’ social isolation caused by different psychosocial challenges. A number of different studies have linked a strong belief in being successful at doing something to lower levels of feeling lonely. Objectives: Because we know less about the potential mediators of this relationship, the aim of the current study was to assess: (1) the association between self-efficacy and loneliness; (2) the mediatory role of peer communication. Methods: A total of 191 primary and high school students (M_age = 16.22; SD = 1.44) completed the General Self-Efficacy Scale, the De Jong Gierveld Loneliness Scale, and the Scale of Communication of Adolescents with Peers. Results: The results of the correlation analyses revealed that self-efficacy was negatively associated with loneliness and difficulty in adolescents’ communication with peers. Conversely, self-efficacy was positively associated with openness in adolescents’ communication with peers. Moreover, the outcomes of the PROCESS macro for SPSS (model number 4; Hayes, 2013) showed that openness in adolescents’ communication with peers and difficulty in this communication were mediators in this relationship. Conclusions: Young people who strongly believe in their abilities to achieve success may be more willing to share ideas and personal experiences with others and have less difficulty in communicating with peers, which may lead to a reduced sense of loneliness. Full article

Private information retrieval (PIR) is a typical application scenario of encrypted computing, which allows users to retrieve data from a database by providing only an encrypted index. In an academic research scenario, multiple parties may entrust their data to a third party and require collaborative retrieval. However, due to competitive relationships and mutual distrust between these parties, they do not share public–private keys, making single-key mechanisms inadequate for meeting actual privacy requirements. In this case, based on the multi-key fully homomorphic encryption (MKFHE) algorithm, we construct an efficient PIR scheme with an access permission verification mechanism and dynamic database. Specifically, we design an MKFHE algorithm to protect multi-user privacy information. The vector–matrix multiplication optimization algorithm is adopted to improve computational efficiency, the expand algorithm is used to reduce user communication traffic, and homomorphic multiplication with ciphertext chunking is used to avoid excessive noise caused by direct ciphertext multiplication. Experiments based on the SEAL library show that by transferring part of the computational pressure to the offline stage, the online query response efficiency of our scheme is improved by about 7.69%, and the online computational efficiency of vector–matrix multiplication is improved by about 19.7%. Full article

We developed an artificial intelligence (AI) player for Drawing Werewolf, a multiplayer game involving collaborative sketching and hidden roles. Players contribute to a shared drawing while identifying a secret “Werewolf” unaware of the theme. AI, trained with SketchRNN on the “Quick, Draw!” dataset, mimics human behavior by generating theme-relevant sketches. The game was built with C++ and supports real-time human–AI interaction via sockets. Initial experiment results confirmed AI’s ability to participate and draw appropriately. The remaining challenges, including improving strategic voting, theme inference, and context-aware drawing, need to be addressed. Full article

In recent years, electrocardiogram (ECG) biometrics has received extensive attention and achieved a series of exciting results. In order to achieve optimal ECG biometric recognition, it is crucial to effectively process the original ECG signals. However, most existing methods only focus on extracting features from one-dimensional time series, limiting the discriminability of individual identification to some extent. To overcome this limitation, we propose a novel framework that integrates dual-level features, i.e., 1D (time series) and 2D (relative position matrix) representations, through collaborative embedding, dimensional attention weight learning, and projection matrix learning. Specifically, we leverage collective matrix factorization to learn the shared latent representations by embedding dual-level features to fully mine these two kinds of features and preserve as much information as possible. To further enhance the discrimination of learned representations, we preserve the diverse information for different dimensions of the latent representations by means of dimensional attention weight learning. In addition, the learned projection matrix simultaneously facilitates the integration of dual-level features and enables the transformation of out-of-sample queries into the discriminative latent representation space. Furthermore, we propose an effective and efficient optimization algorithm to minimize the overall objective loss. To evaluate the effectiveness of our learned latent representations, we conducted experiments on two benchmark datasets, and our experimental results show that our method can outperform state-of-the-art methods. Full article

The rise of streaming services and online story-sharing has led to a vast amount of cinema and television content being viewed and reviewed daily by a worldwide audience. It is a unique challenge to grasp the nuanced insights of these reviews, particularly as context, emotion, and specific components like acting, direction, and storyline intertwine extensively. The aim of this study is to address said complexity with a new hybrid Multi Criteria Decision-Making MCDM model that combines the Deck of Cards Method (DoCM) with the Circular Pythagorean Fuzzy Set (CPFS) framework, retaining the symmetry of information. The study is conducted on a simulated dataset to demonstrate the framework and outline the plan for approaching real-world press reviews. We postulate a more informed mechanism of assessing and choosing the most appropriate deep learning assembler, such as the transformer version, the hybrid Convolutional Neural Network CNN-RNN, and the attention-based framework of aspect-based sentiment mapping in film and television reviews. The model leverages both the cognitive ease of the DoCM and the expressive ability of the Pythagorean fuzzy set (PFS) in a circular relationship setting possessing symmetry, and can be applied to various decision-making situations other than the interpretation of media sentiments. This enables decision-makers to intuitively and flexibly compare alternatives based on many sentiment-relevant aspects, including classification accuracy, interpretability, computational efficiency, and generalization. The experiments are based on a hypothetical representation of media review datasets and test whether the model can combine human insight with algorithmic precision. Ultimately, this study presents a sound, structurally clear, and expandable framework of decision support to academicians and industry professionals involved in converging deep learning and opinion mining in entertainment analytics. Full article

Underwater debris detection plays a crucial role in marine environmental protection. However, existing object detection algorithms generally suffer from excessive model complexity and insufficient detection accuracy, making it difficult to meet the real-time detection requirements in resource-constrained underwater environments. To address this challenge, this paper proposes a novel lightweight object detection network named the Shared Pyramid Lightweight Network (SPyramidLightNet). The network adopts an improved architecture based on YOLOv11 and achieves an optimal balance between detection performance and computational efficiency by integrating three core innovative modules. First, the Split–Merge Attention Block (SMAB) employs a dynamic kernel selection mechanism and split–merge strategy, significantly enhancing feature representation capability through adaptive multi-scale feature fusion. Second, the C3 GroupNorm Detection Head (C3GNHead) introduces a shared convolution mechanism and GroupNorm normalization strategy, substantially reducing the computational complexity of the detection head while maintaining detection accuracy. Finally, the Shared Pyramid Convolution (SPyramidConv) replaces traditional pooling operations with a parameter-sharing multi-dilation-rate convolution architecture, achieving more refined and efficient multi-scale feature aggregation. Extensive experiments on underwater debris datasets demonstrate that SPyramidLightNet achieves 0.416 on the mAP@0.5:0.95 metric, significantly outperforming mainstream algorithms including Faster-RCNN, SSD, RT-DETR, and the YOLO series. Meanwhile, compared to the baseline YOLOv11, the proposed algorithm achieves an 11.8% parameter compression and a 17.5% computational complexity reduction, with an inference speed reaching 384 FPS, meeting the stringent requirements for real-time detection. Ablation experiments and visualization analyses further validate the effectiveness and synergistic effects of each core module. This research provides important theoretical guidance for the design of lightweight object detection algorithms and lays a solid foundation for the development of automated underwater debris recognition and removal technologies. Full article

Routine collection of Patient-Reported Outcomes (PROs) can enhance patient–pharmacist communication and identify medication-related concerns. This study aims to explore the influence of RxTalk™, an electronic PRO tool, on patients’ attributes and the dynamics of communication. Secondary aims include describing patients’ experiences with RxTalk™ and identifying suggestions for improvements. This study is part of a pilot randomized controlled trial in which patients used RxTalk™ in the pharmacy while being observed before they spoke with the pharmacist. Patients’ interactions with pharmacists were tape-recorded and analyzed, and patients were interviewed within one week. We integrated data from RxTalk™, patient observations, taped encounters, and interviews to provide a thicker description of patients’ experiences with RxTalk™ and its impact on their communication. A total of 70% of patients found RxTalk™ easy to use, and 59% perceived RxTalk™ as very useful to extremely useful. Triangulated findings show that RxTalk™ met patients’ social and informational needs, improved their communication skills, and cultivated a sense of privacy to share concerns. Furthermore, RxTalk™ validated the appropriateness of reporting any health concerns, not simply medication concerns. As patients had a positive experience with RxTalk™, pharmacists should consider integrating PRO tools into their daily services to improve patient interactions and quality of care. Full article

International studies have reported extensively on outdoor learning in bush (Australia) or forest settings (e.g., U.K. and Nordic countries). Yet, limited studies have investigated urbanized environments comprising community facilities and city settings. This study shares early childhood teachers’ exploration and engagement with outdoor community settings in Singapore. Innovative practices emerged in response to the community and context in urbanized areas. Transformation of teaching happens during the research study when teachers shift from thinking about the local environment to thinking with, contributing to creating new ways of constructing outdoor teaching and learning experiences in an urbanized landscape. This study illustrates how teachers exploring the outdoors and thinking with places can open up conversations in building lively (and deadly) connections with the world. Full article

Despite the continued growth of the gluten-free food market, there is a dearth of sensory and consumer knowledge on commercial products. The existing research is mostly limited to hedonic measurements and ingredient effects instead of analytical methods for a better understanding of product characteristics of gluten-free crackers specifically. In this work, a semi-trained consumer panel used projective mapping to choose objectively different plain/original crackers from a pool of sixteen commercial gluten-free cracker varieties. The cracker samples represented a widespread sensory space originating from different key ingredients such as brown rice, white rice, flaxseed, cassava flour, nut flour blend, millet blend, and tapioca/potato starch blend. Based on projective mapping results, the crackers that mostly represented the sensory space were selected for characterization by a modified flash profiling method. The consumer panel developed 74 descriptors: 30 aromas, 28 flavors, 15 texture terms, and a mouthfeel attribute. The samples were monadically rated for intensity on a 4-point scale (0 = none, 1 = low, 2 = medium, and 3 = high). Rice, toasted, salt, grain, burnt, flaxseed, bitter, earthy, nutty, seeds, and grass were the prevalent aromas and flavors. Others were specific to cracker type. Some of these attributes can be traced back to the ingredients list. Results suggest that ingredients used in small portions are defining the flavor properties over the major grains/flour blends. All samples had some degree of crunchiness, crispness, and pasty mouthfeel; rice crackers were particularly firm, hard, and chewy; brown rice crackers were gritty; crackers with tuber starches/flours were more airy, soft, smooth, and flaky. Overall, the samples shared more aroma and flavor notes than texture attributes. In comparison to trained panel results, consumers generated a greater number of terms and were successful in finding subtle differences primarily in texture but had many overlapped flavors. The developed consumer terminology will facilitate the gluten-free industry to tailor communication that better resonates with consumer experiences, needs, and product values. Full article