Search Results (1,917)

This article reports on a Swedish study investigating how children aged 2–3 years experience mathematical concepts through representations in play-responsive teaching. Drawing on the semiotic–cultural theory of learning, this study examines how representations, such as spoken language, bodily action, and artifacts, are mediated. Video-recorded teaching sessions are analyzed to identify semiotic means of objectification and semiotic nodes at which these representations converge. The analysis distinguishes between children encountering concepts expressed by others and expressing concepts themselves. The results indicate that play-responsive teaching creates varied opportunities for experiencing mathematical concepts, with distinct modes of sensuous cognition linked to whether a concept is encountered or expressed. This study underscores the role of teachers’ choices in shaping these experiences and highlights bodily action as a significant form of representation. These findings aim to inform the use of representations in teaching mathematics to the youngest children in preschool. Full article

Background and Objectives: Dual-task training (DTT) is an innovative therapeutic approach that involves the simultaneous application of two tasks, which can be motor, cognitive, or a combination of both. Children with cerebral palsy (CP) often exhibit impairments in balance, motor skills, and gait, conditions that may be amenable to improvement through DTT. The aim of this study was to determine the effectiveness of DTT in enhancing balance, walking speed, and gross motor function-related balance in children with CP. Materials and Methods: In accordance with PRISMA guidelines, a comprehensive systematic review with meta-analysis (SRMA) was conducted. Electronic databases like PubMed Medline, Scopus, Web of Science, CINAHL, and PEDro were searched up to March 2025, with no language or publication date restrictions. Only randomized controlled trials (RCTs) examining the effectiveness of DTT on balance, gross motor function, and walking speed in children with CP were included. The methodological quality and risk of bias of the included RCTs were assessed using the PEDro scale. Pooled effects were calculated using Cohen’s standardized mean difference (SMD) and its 95% confidence interval (95% CI) within random-effects models. Results: Eight RCTs, providing data from 216 children, were included. Meta-analyses suggested that DTT was more effective than conventional therapies for increasing functional (SMD = 0.65; 95% CI 0.18 to 1.13), dynamic (SMD = 0.61; 95% CI 0.15 to 1.1), and static balance (SMD = 0.46; 95% CI 0.02 to 0.9), as well as standing (SMD = 0.75; 95% CI 0.31 to 1.18; p = 0.001) and locomotion dimensions (SMD = 0.65; 95% CI 0.22 to 1.08) of the Gross Motor Function Measure (GMFM) and walking speed (SMD = 0.46; 95% CI 0.06 to 0.87). Subgroup analyses revealed that a motor–cognitive dual task is better than a motor single task for functional, dynamic, and static balance and standing and locomotion dimensions for the GMFM. Conclusions: This SRMA, including the major number of RCTs to date, suggests that DTT is effective in increasing balance, walking and gross motor function-related balance in children with CP. Full article

In clinical decision support systems (CDSSs), where accurate classification of drug–drug interactions (DDIs) can directly affect treatment safety and outcomes, identifying drug interactions is a major challenge, introducing a scalable approach for classifying DDIs utilizing a finely-tuned biomedical language model. The method shown here uses BiomedBERT, a domain-specific version of bidirectional encoder representations from transformers (BERT) that was pre-trained on biomedical literature, to reduce the number of resources needed during fine-tuning. Low-rank adaptation (LoRA) was used to fine-tune the model on the DrugBank dataset. The objective was to classify DDIs into two clinically distinct categories, that is, synergistic and antagonistic interactions. A pseudo-labeling strategy was created to deal with the problem of not having enough labeled data. A curated ground-truth dataset was constructed using polarity-labeled interaction entries from DrugComb and verified DrugBank antagonism pairs. The fine-tuned model is used to figure out what kinds of interactions there are in the rest of the unlabeled data. A checkpointing system saves predictions and confidence scores in small pieces, which means that the process can be continued and is not affected by system crashes. The framework is designed to log every prediction it makes, allowing results to be refined later, either manually or through automated updates, without discarding low-confidence cases, as traditional threshold-based methods often do. The method keeps a record of every output it generates, making it easier to revisit earlier predictions, either by experts or with improved tools, without depending on preset confidence cutoffs. It was built with efficiency in mind, so it can handle large amounts of biomedical text without heavy computational demands. Rather than focusing on model novelty, this research demonstrates how existing biomedical transformers can be adapted to polarity-aware DDI classification with minimal computational overhead, emphasizing deployment feasibility and clinical relevance. Full article

Recent advancements in generative artificial intelligence have significantly progressed the field of text-to-music generation, enabling users to create music from natural language descriptions. Despite the success of various models, such as MusicLM, MusicGen, and AudioLDM, the current approaches struggle to capture fine-grained genre-specific characteristics, precisely control musical attributes, and handle underrepresented cultural data. This paper introduces a novel, lightweight fine-tuning method for the AudioLDM framework using low-rank adaptation (LoRA). By updating only selected attention and projection layers, the proposed method enables efficient adaptation to musical genres with limited data and computational cost. The proposed method enhances controllability over key musical parameters such as rhythm, emotion, and timbre. At the same time, it maintains the overall quality of music generation. This paper represents the first application of LoRA in AudioLDM, offering a scalable solution for fine-grained, genre-aware music generation and customization. The experimental results demonstrate that the proposed method improves the semantic alignment and statistical similarity compared with the baseline. The contrastive language–audio pretraining score increased by 0.0498, indicating enhanced text-music consistency. The kernel audio distance score decreased by 0.8349, reflecting improved similarity to real music distributions. The mean opinion score ranged from 3.5 to 3.8, confirming the perceptual quality of the generated music. Full article

Various factors influence the formation and adjustment of network freight prices, including transportation costs, cargo characteristics, and policies and regulations. The interaction of these factors increases the difficulty of accurately predicting network freight prices through regressions or other machine learning models, especially when the amount and quality of training data are limited. This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. Different data sorting methods and serialization strategies are employed to construct the corpora of LLMs, which are then tested on multiple base models. A few-shot sample dataset is constructed to test the performance of models under insufficient information. The Chain of Thought (CoT) is employed to construct a corpus that demonstrates the reasoning process in freight price prediction. Cross entropy loss with LoRA fine-tuning and cosine annealing learning rate adjustment, and Mean Absolute Error (MAE) loss with full fine-tuning and OneCycle learning rate adjustment to train the models, respectively, are used. The experimental results demonstrate that LLMs are better than or competitive with the best comparison model. Tests on a few-shot dataset demonstrate that LLMs outperform most comparison models in performance. This method provides a new reference for predicting network freight prices. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

Background: Autologous fat grafting is increasingly used in daily clinical practice across various surgical fields, including the treatment of chronic wounds, scars, burns, and non-healing perianal fistulas. Recently, some studies have shown that non-enteric cutaneous fistulas can also benefit from adipose tissue injections, but the efficacy remains unclear. This study aims to systematically review the literature on fat grafting in the context of non-enteric cutaneous fistulas and to assess treatment outcomes. Methods: A comprehensive search of the PubMed/Medline database was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines up to January 2024 without restrictions on the time period or the language of publication. Results: Seven studies meeting the inclusion criteria were analyzed, encompassing 13 patients with non-healing cutaneous fistulas treated with injections of autologous fat. The mean age of the patients was 58 ± 3 years, of which 85% had comorbidities. Fat grafting resulted in complete healing in 92% of the cases, with a mean fistula persistence of 158 days before treatment. Treatment protocols varied among patients, including preparation of the fistulous tract, fat processing techniques, and suturing of the fistulous orifice. Conclusions: The results highlight the potential of autologous fat grafting in promoting tissue regeneration and healing of non-enteric cutaneous fistulas. Standardized protocols are essential to confirm and optimize treatment efficacy and, eventually, improve patient outcomes. Further research with a larger sample size and standardization is needed to confirm fat graft efficacy. Full article

Accurate and timely estimation of soybean emergence at the plot scale using unmanned aerial vehicle (UAV) remote sensing imagery is essential for germplasm evaluation in breeding programs, where breeders prioritize overall plot-scale emergence rates over subimage-based counts. This study proposes PlotCounter, a deep learning regression model based on the TasselNetV2++ architecture, designed for plot-scale soybean seedling counting. It employs a patch-based training strategy combined with full-plot validation to achieve reliable performance with limited breeding plot data. To incorporate additional agronomic information, PlotCounter is extended into a multitask learning framework (MTL-PlotCounter) that integrates sowing metadata such as variety, number of seeds per hole, and sowing density as auxiliary classification tasks. RGB images of 54 breeding plots were captured in 2023 using a DJI Mavic 2 Pro UAV and processed into an orthomosaic for model development and evaluation, showing effective performance. PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. The variety-integrated MTL-PlotCounter, V-MTL-PlotCounter, performs the best, with relative reductions of 8.74% in RMSE and 3.03% in rRMSE compared to PlotCounter, and outperforms representative YOLO-based models. Additionally, both PlotCounter and V-MTL-PlotCounter are deployed on a web-based platform, enabling users to upload images via an interactive interface, automatically count seedlings, and analyze plot-scale emergence, powered by a multimodal large language model. This study highlights the potential of integrating UAV remote sensing, agronomic metadata, specialized deep learning models, and multimodal large language models for advanced crop monitoring. Full article

Early printed books, particularly incunabula, are invaluable archives of the beginnings of modern educational systems. However, their complex layouts, antique typefaces, and page degradation caused by bleed-through and ink fading pose significant challenges for automatic transcription. In this work, we present a modular pipeline that addresses these problems by combining modern layout analysis and language modeling techniques. The pipeline begins with historical layout-aware text segmentation using Kraken, a neural network-based tool tailored for early typographic structures. Initial optical character recognition (OCR) is then performed with Kraken’s recognition engine, followed by post-correction using a fine-tuned ByT5 transformer model trained on manually aligned line-level data. By learning to map noisy OCR outputs to verified transcriptions, the model substantially improves recognition quality. The pipeline also integrates a preprocessing stage based on our previous work on bleed-through removal using robust statistical filters, including non-local means, Gaussian mixtures, biweight estimation, and Gaussian blur. This step enhances the legibility of degraded pages prior to OCR. The entire solution is open, modular, and scalable, supporting long-term preservation and improved accessibility of cultural heritage materials. Experimental results on 15th-century incunabula show a reduction in the Character Error Rate (CER) from around 38% to around 15% and an increase in the Bilingual Evaluation Understudy (BLEU) score from 22 to 44, confirming the effectiveness of our approach. This work demonstrates the potential of integrating transformer-based correction with layout-aware segmentation to enhance OCR accuracy in digital humanities applications. Full article

Control charts (CCs) are one of the most important techniques in statistical process control (SPC) used to monitor the behavior of critical variables. SPC is based on the averages of the samples taken. In this way, not every measurement is observed, and errors in measurements or out-of-control behaviors that are not shown graphically can be hidden. This research proposes a novel segmented control chart (SCC) that considers each measurement of the samples, expressed in matrix form. The vision system technique is used to segment measurements by shading and segmenting into binary values based on the control limits of SPC. Once the matrix is segmented, the seven main features of the matrix are extracted using the translation-, scale-, and rotation-invariant Hu moments of the segmented matrices. Finally, a grouping is made to classify the samples in clear and simple language as excellent, good, or regular using the k-means algorithm. The results visually display the total pattern behavior of the samples and their interpretation when they are classified intelligently. The proposal can be replicated in any production sector and strengthen the control of the sampling process. Full article

In 1978, Brian Kernighan and Dennis Ritchie insisted that the first program to write in a new language is one to print the words “hello, world.” From then until now, “hello, world” has frequently been the first exercise in introductory programming courses. On one hand, this does seem like a good first program because it makes something familiar—a greeting—appear on the screen. On the other hand, it is extremely strange. How can it be understood as a greeting? Who is greeting whom? Unfortunately, the bulk of formal means for defining programming languages provides very little help for assigning a meaning to the “hello, world” program. It is argued that the weakness of older theories and methods of programming language semantics is due to the historical, disciplinary segregation (in logic, semiotics, and linguistics) of semantics as a study apart from syntax and pragmatics. Drawing from both more recent work in programming language semantics that addresses side effects and on speech-act-based programming language design, this paper proposes a possible reintegration of semantics and pragmatics in order to better define the meaning of “hello, world” and the programming languages used to produce speech acts more generally. Full article

This study investigates the parameters influencing the compatibility between cement and polycarboxylate ether (PCE) admixtures in cements produced with various types and dosages of grinding aids (GAs). A total of 29 cement types (including a control) were prepared using seven different GAs at four dosage levels, and 87 paste mixtures were produced with three PCE dosages. Rheological behavior was evaluated via the Herschel–Bulkley model, focusing on dynamic yield stress (DYS) and viscosity. The data were modeled using CNN, Random Forest (RF), and Neural Classification and Regression Tree (NCART), and each model was enhanced with synthetic data generated by Large Language Models (LLMs), resulting in CNN-LLM, RF-LLM, and NCART-LLM variants. All six variants were evaluated using R-squared, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Logcosh. This study is among the first to use LLMs for synthetic data augmentation. It augmented the experimental dataset synthetically and analyzed the effects on the study results. Among the baseline methods, NCART achieved the best performance for both viscosity (MAE = 1.04, RMSE = 1.33, R² = 0.84, Logcosh = 0.57) and DYS (MAE = 8.73, RMSE = 11.50, R² = 0.77, Logcosh = 8.09). Among baseline models, NCART performed best, while LLM augmentation significantly improved all models’ predictive accuracy. It was also observed that cements produced with GA exhibited higher DYS and viscosity than the control, likely due to finer particle size distribution. Overall, the study highlights the potential of LLM-based synthetic augmentation in modeling cement admixture compatibility. Full article

Background/Objectives: Guidelines recommend patients with heart failure with reduced ejection fraction (HFrEF) receive four-pillar heart failure (4P-HF) therapy, which significantly reduces cardiac morbidity and mortality. However, implementing these guidelines effectively into clinical practice remains challenging. Methods: Patients with HFrEF on submaximal 4P-HF therapy were identified from a large, multicentre Cardiology network database using a natural language processing tool, supported by manual file review. A nurse-led, remotely delivered, medication uptitration program aimed to optimise therapy in this real-world cohort. Results: The final cohort included 2004 patients with a mean age of 72.7 ± 11.6 years. Utilisation of 4P-HF increased from 11.1% at baseline to 49.8% post intervention, and each individual medication class increased significantly post intervention (all p < 0.001). The largest increase was observed with the use of sodium–glucose cotransporter 2 inhibitors, which rose from 17.3% to 73.9%, followed by mineralocorticoid receptor antagonists (51.6% to 65.7%), beta-blockers (88.4% to 97.0%), and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor blocker–neprilysin inhibitors (89.8% to 96.4%). In patients on submaximal therapy, barriers were documented in all cases. Following medication optimisation, left ventricular ejection function (LVEF) improved significantly (38.5% ± 10.8% vs. 42.5% ± 11.7, p < 0.001). Conclusions: This nurse-led, remotely delivered, medication optimisation program significantly improved the adoption of 4P-HF therapy and LVEF in patients with HFrEF. The program demonstrates a practical, scalable solution for the optimisation of HFrEF therapy across a large healthcare network. Full article

Background/Objectives: According to Embodied Cognition theories, motor skills in early childhood are closely interconnected with various cognitive abilities, including working memory, cognitive flexibility, and theory of mind. These processes are integral components of the multicomponent model of narrative comprehension, which posits that higher-order cognitive functions support the construction of coherent mental representations of narrative meaning. This study aimed to examine whether motor skills directly contribute to narrative comprehension in preschool children or whether this relationship is mediated by cognitive skills. Methods: Seventy-four typically developing children aged 3 to 6 years (47.2% female) participated in this study. Motor skills were assessed using standardized measures, and cognitive abilities were evaluated through tasks targeting working memory, cognitive flexibility, and theory of mind. Narrative comprehension was measured with age-appropriate tasks requiring the understanding and retelling of stories. A structural equation model (SEM) was conducted to test the direct and indirect effects of motor skills on narrative comprehension via cognitive skills. Results: The SEM results indicated a significant direct effect of motor skills on cognitive skills and an indirect effect on narrative comprehension mediated by cognitive abilities. No evidence was found for a direct pathway from motor skills to narrative comprehension independent of cognitive processes. Conclusions: These findings underscore the complex interplay between motor, cognitive, and language development in early childhood. The results suggest that motor skills contribute to narrative comprehension indirectly by enhancing core cognitive abilities, offering novel insights into the developmental mechanisms that support language acquisition and understanding. Full article

This article explores the often-overlooked question of how literary reading might give rise to experiences that readers themselves identify as spiritual. Framed by William James’s account of “mystical susceptibility” and recent psychological models of spirituality as altered states of consciousness involving shifts in perception, affect, and cognition, the essay asks how engagement with narrative may occasion such states. Drawing from selected examples and critical traditions, it examines the conditions under which reading becomes spiritually resonant. Theologically, the piece considers the formation of attentiveness and imaginative receptivity in writers such as Teresa of Avila and Jessica Hooten Wilson. From affect theory, it engages Rita Felski’s language of enchantment; from cognitive studies, it draws on empirical approaches to literary studies and Tanya Luhrmann’s work on absorption and the cultivation of spiritual perception. By drawing attention to absorption as a psychological and aesthetic phenomenon, this article suggests a renewed interdisciplinary approach—one that connects empirical studies of attention and transformation with older theological and affective insights. In this way, literature may be examined not as a site of doctrinal meaning or subjective feeling alone, but as a form of engagement capable of opening readers to spiritual insight whose impact might be measured through qualitative means. Full article