Search Results (20,356)

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

This quantitative study examines the determinants of financial reporting quality in Indonesian local governments, focusing on good governance, regional financial accounting systems, internal control systems, organizational commitment, and information technology utilization, with HR competencies as a moderator. Data were collected via surveys from 170 Local Government Work Units (SKPDs) across South Sulawesi Province, Indonesia. Employing Structural Equation Modeling (SEM), the findings indicate that good governance, regional financial accounting systems, internal control systems, organizational commitment, and information technology utilization all positively influence financial reporting quality. Crucially, human resource competencies were found to significantly moderate the relationship between the internal control system and organizational commitment with financial reporting quality. However, this moderating effect was not significant for the relationships involving good governance, regional financial accounting systems, and information technology utilization. These results highlight the essential role of human resource development and systemic enhancements in fostering greater financial accountability and transparency within the public sector. Therefore, policy recommendations should focus not only on enhancing individual competencies but also on synergistically strengthening systems and governance frameworks to achieve transparent and reliable public financial reporting. Full article

Background: Depression that is resistant to two or more adequate treatment trials—treatment-resistant depression (TRD)—is a prevalent clinical challenge. Although psychotherapies have been recommended by clinical guidelines as an alternative or adjunctive treatment strategy, the effectiveness of psychotherapy in individuals with TRD has not yet been evaluated through meta-analytic methods, primarily due to a limited number of trials. This highlights the necessity of personalized research targeting this specific population. This systematic review and meta-analysis aimed to summarize the evidence on psychotherapy in treating TRD. Methods: A systematic search was conducted following the Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles were included if they quantitatively examined the efficacy of psychotherapy on depression symptoms in individuals diagnosed with depression who had not responded to at least two prior treatments (i.e., pharmacotherapy and/or psychotherapy). Results: A total of 12 studies were included. The quality of evidence was evaluated as being globally moderate. When pooling all psychotherapies, a small-to-moderate, but significant, effect on depressive symptoms was observed compared to the control group (SMD = −0.49, CI = −0.63; −0.34). The observed effect remained unchanged after removing the outlier (SMD = −0.47, CI = −0.62; −0.32). When examining depressive symptoms by type of psychotherapy, Mindfulness-Based Cognitive Therapy (SMD = −0.51, CI = −0.76; −0.25), Cognitive Behavioral Therapy (SMD = −0.53, CI = −0.92; −0.14), and Cognitive Therapy (SMD = −0.51, CI = −1.01; −0.01) showed a moderately significant effect on depressive symptoms compared to the control group. Conclusions: Although this potentially represents the first meta-analysis in this area, the number of studies specifically addressing this complex population remains limited, and the existing literature is still in its early stages. Research focusing on TRD is notably sparse compared to the broader body of work on depression without treatment resistance. Consequently, it was not possible to conduct meta-analyses by type of psychotherapy across all treatment modalities and by type of control group. Due to several study limitations, there is currently limited evidence available about the effectiveness of psychotherapy for TRD, and further trials are needed. Beyond the treatments usually offered for depression, it is possible that TRD requires a personalized medicine approach. Full article

(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed modular framework designed to evaluate reasoning through a structured three-stage process: opinion, counterargument, and synthesis. The framework enables transparent and comparative analysis of how different LLMs handle dialectical reasoning. (2) Methods: Each stage is executed by a single model, and final syntheses are scored via two independent LLM evaluators (LLaMA 3.1 and GPT-4o) based on a rubric with four dimensions: clarity, coherence, originality, and dialecticality. In parallel, a rule-based semantic analyzer detects rhetorical anomalies and ethical values. All outputs and metadata are stored in a Neo4j graph database for structured exploration. (3) Results: The system was applied to four open-weight models (Gemma 7B, Mistral 7B, Dolphin-Mistral, Zephyr 7B) across ten open-ended prompts on ethical, political, and technological topics. The results show consistent stylistic and semantic variation across models, with moderate inter-rater agreement. Semantic diagnostics revealed differences in value expression and rhetorical flaws not captured by rubric scores. (4) Originality: The framework is, to our knowledge, the first to integrate multi-stage reasoning, rubric-based and semantic evaluation, and graph-based storage into a single system. It enables replicable, interpretable, and multidimensional assessment of generative reasoning—supporting researchers, developers, and educators working with LLMs in high-stakes contexts. Full article

In recent years, DC grids have gained traction, and several proposals regarding measuring strategies and several Power Quality (PQ) parameters have been defined to be used in such networks that differ from traditional AC power grids. As a complement to all this preliminary work, this study on the effect of modifying the sampling frequency on some of those parameters has been conducted. For time series evaluation of mean and RMS voltage values, the Dynamic Time Warping (DTW) algorithm has been used. Additionally, the consequence of varying the sampling rate in voltage event detection has also been analysed. As a result, relevant advice regarding sampling frequency is presented in this paper for an effective and optimum evaluation of RMS or mean voltage values and its implementation in detecting voltage events (dips or swells). At least for the parameters in the monitored DC microgrid, a clue for the minimum sampling rate that guarantees accurate measurements is found. Full article

Buckwheat, a gluten-free pseudocereal, is rich in dietary fiber, minerals, high-quality proteins, vitamins, and essential amino acids. Buckwheat husk, a by-product of dehulling, contains high levels of bioactive compounds such as polyphenols and dietary fibers. This study compares green extraction methods (ultrasound-assisted extraction, UAE; and microwave-assisted extraction, MAE) for recovering polyphenols from buckwheat husk. MAE improved polyphenol yield by 43.6% compared to conventional acidified methanol extraction. Structural and chemical analyses of the residual husk material using SEM, FTIR, and fiber analysis revealed that MAE alters husk properties, enhancing polyphenol accessibility. Thus, MAE appears an efficient and sustainable alternative to acid- and solvent-based extraction techniques. Extracts obtained via “green” methods retained strong antioxidant activity and showed significant modulation of inflammatory markers in human Caco-2 cells, highlighting the potential use of “green” buckwheat husk extracts for food and pharma applications. This work supports the valorization of buckwheat husk within a circular economy framework, promoting buckwheat husk as a valuable raw material for bioactive compound recovery in diverse applications. Full article

Compared with biocompatibility, osteoconductivity, and mechanical properties, the poor injectability of calcium phosphate bone cements (CPCs) is always ignored, which actually hinders the development of CPC clinical transfer in minimally invasive orthopedic surgeries. Moreover, currently, CPC preparation in the clinic is labor-intensive and requires well-trained technicists, which might also result in the unstable quality of CPCs. In this work, we focused on three research objectives: (i) introducing a standardized preparation method for CPCs; (ii) studying the effects of preparation parameters on CPC injectability and compressive strength; and (iii) studying the injecting condition effects on CPC injectability, aiming to overcome CPCs’ disadvantages in minimally invasive surgeries. Firstly, two strategies, named “variable mixing barrel control (VMBC)” and the “nested blade–baffle stirring rod (NBBSR)”, were proposed in this study to solve the problems in the preparation of CPCs, which involved blending CPC powder and an agent to generate a paste, by enhancing the mixing performance and mimicking human manual stirring actions. Secondly, although the grinding parameter could significantly generate differences in the microstructure of CPCs, the compressive strength remained relatively stable. However, it was found to significantly affect the injectability of CPCs, leading to the inefficient injection of CPCs. Finally, the effects of syringe design, dimensions, and injecting conditions on CPC injectability were studied, and the results showed that the optimization of these factors enables the injection of CPCs, which has otherwise always been infeasible to implement in minimally invasive orthopedic surgeries. Full article

In recent years, active packaging has become a focal point of research and development in the food industry, driven by increasing consumer demand for safe, high-quality, and sustainable food products. In this work, solvent casting processed an active antibacterial multicomponent film based on hydroxypropyl methylcellulose incorporated with ferulic acid and chitin nanofibers. The influences of ferulic acid and different content of chitin nanofibers on the structure, thermal, mechanical, and water vapor stability and antioxidant and antibacterial efficiency of films were studied. It was shown that the inclusion of only ferulic acid did not significantly influence the mechanical, water vapor, and thermal stability of films. In addition, films containing only ferulic acid did not display antibacterial activity. The optimal concentration of chitin nanofibers in hydroxypropyl methylcellulose–ferulic acid films was 5 wt%, providing a tensile strength of 15 MPa, plasticity of 52%, and water vapor permeability of 0.94 × 10⁻⁹ g/m s Pa. With further increase of chitin nanofibers content, films with layered and discontinuous phases are obtained, which negatively influence tensile strength and water vapor permeability. Moreover, only films containing both ferulic acid and chitin nanofibers demonstrated antibacterial activity toward E. coli and S. aureus, suggesting that the presence of fibers allows easier release of ferulic acid from the matrix. These results imply that the investigated three-component systems have potential applicability as sustainable active food packaging materials. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article

Objectives: Most antiviral vaccines are created by inactivating the virus using chemical methods. The inactivation and production of viral vaccine preparations after the irradiation of viruses with accelerated electrons has a number of significant advantages. Determining the integrity of the genome of the resulting viral particles is necessary to assess the quality and degree of inactivation after irradiation. Methods: This work was performed on the Sabin 2 model polio virus. To determine the most sensitive and most radiation-resistant part, the polio virus genome was divided into 20 segments. After irradiation at temperatures of 25 °C, 2–8 °C, −20 °C, or −70 °C, the amplification intensity of these segments was measured in real time. Results: The best correlation between the amplification cycle and the irradiation dose at all temperatures was observed for segment 3D, left. Consequently, this section of the poliovirus genome is the least resistant to the action of accelerated electrons and is the most representative for determining genome integrity. The worst dependence was observed for the VP1 right section, which, therefore, cannot be used to determine genome integrity during inactivation. The electrochemical approach was also employed for a comparative assessment of viral RNA integrity before and after irradiation. An increase in the irradiation dose was accompanied by an increase in signals indicating the electrooxidation of RNA heterocyclic bases. The increase in peak current intensity of viral RNA electrochemical signals confirmed the breaking of viral RNA strands during irradiation. The shorter the RNA fragments, the greater the peak current intensities. In turn, this made the heterocyclic bases more accessible to electrooxidation on the electrode. Conclusions: These results are necessary for characterizing the integrity of the viral genome for the purpose of creating of antiviral vaccines. Full article

The operational and indoor environmental quality of buildings has a significant impact on global energy consumption and human quality of life. One of the key directions for improving building performance is the optimization of building control systems. In modern buildings, the presence of numerous actuators and monitoring points makes manually designed control algorithms potentially suboptimal due to the complexity and human factors. To address this challenge, model predictive control based on artificial neural networks can be employed. The advantage of this approach lies in the model’s ability to learn and understand the dynamic behavior of the building from monitoring datasets. It should be noted that the effectiveness of such control models is directly dependent on the forecasting accuracy of the neural networks. In this study, we adapt neural network architectures such as GRU and TCN for use in the context of building model predictive control. Furthermore, we propose a novel hybrid architecture that combines the strengths of recurrent and convolutional neural networks. These architectures were compared using real monitoring data collected with a custom-developed device introduced in this work. The results indicate that, under the given experimental conditions, the proposed hybrid architecture outperforms both GRU and TCN models, particularly when processing large sequential input vectors. Full article

Background: The consumption of ultra-processed foods (UPFs) represents an important public health challenge, especially among education workers, whose intense routine can negatively impact eating habits. This study aimed to analyze the factors associated with the regular consumption of UPF among employees of the Federal Network of Professional, Scientific and Technological Education (RFEPCT) in Brazil. Methods: This was a cross-sectional study, with a quantitative approach, carried out with 1563 education workers. Validated instruments on eating habits (PeNSE), mental health (DASS-21) and quality of life (WHOQOL-bref) were used. The regular consumption of UPF was defined as intake on ≥5 days in the last seven days. The association between the regular consumption of UPF and sociodemographic, occupational, behavioral, mental health and quality of life variables was assessed by Poisson regression with robust variance, generating adjusted prevalence ratios (PRadj) and respective 95% confidence intervals. Results: The regular consumption of UPF was associated mainly with female gender, a lower age group, Southeast and Midwest regions, dissatisfaction with sleep and the body, physical inactivity and poor sleep quality. In addition, the findings suggested a significant relationship between the worst stress scores and soft drinks (PRadj: 2.11; CI: 1.43–3.13), anxiety and soft drinks (PRadj: 1.83; CI: 1.24–2.70) and depression and industrialized/ultra-processed salty foods (PRadj: 2.43; CI: 1.82–3.26). The same was observed in the scores for the worst perception of quality of life, where there was a prevalence of up to 2.32 in the psychological domain and the consumption of industrialized/ultra-processed salty foods. Conclusions: The findings indicate that multiple interrelated factors—individual, psychosocial and occupational—are associated with the consumption of UPF among education workers. These results reinforce the importance of institutional policies that integrate actions to promote dietary health, mental health care and improved working conditions in the education sector. Full article

Background/Objectives: Quality of life (QOL) is a multifaceted concept involving a variety of factors which define the overall well-being of individuals. Food security, which implies a resilient food system, is one factor that is central to the calculus of the QOL status of a community considering that food is a staple of life. Advancing food security as a strategy for attaining sustained improvement in community QOL hinges on recognizing that food security is embedded in a matrix of other factors that work with it to generate the QOL the community experiences. The lived experience of the community defines the community’s QOL value matrix and the relative position of food security in that value matrix. Our thesis is that the role of food security in the lived experience of low-income communities depends on the position food security is accorded relative to other factors in the QOL value matrix of the community. Methods: This study employed a multimethod approach to define the QOL value matrix of low-income Guilford County residents, identifying the relative position of the value components and demographic segments based on priority ranking. First, an in-depth interview was conducted and then a telephone survey (280 sample) was used for collecting data. The ISAC Analysis Procedure and Best–Worst Scaling methods were used to identify and rank components of the QOL value matrix in terms of their relative impact on QOL. Results: The analysis revealed that spiritual well-being is the most important contributor to QOL, with a weight of 0.23, followed by access to health services (0.21) and economic opportunities (0.16), while food security has a moderate impact with 0.07. Conclusions: These findings emphasize the need for targeted policy interventions that consider the specific needs of different demographic segments to effectively improve QOL and inform the design of resilient food systems that reflect the lived experiences of low-income communities. Food security policies must be integrated with broader quality of life interventions, particularly for unemployed, low-educated, and single individuals, to ensure that a resilient food system effectively reduces inequities and address community-specific vulnerabilities. Full article

Rootstocks are widely used in viticulture as an agronomic measure to cope with biotic and abiotic stresses. In winegrapes, the aroma is one of the major factors defining the quality of grape berries and wines. In the present work, the grape aroma and wine aroma of Cabernet Sauvignon (CS) grafted on three rootstocks were investigated to inform the selection of rootstocks to utilize. 1103P, 5A, and SO₄ altered the composition of aromatic volatiles in CS grapes and wines. Among them, 5A and SO₄ had less effect on green leaf volatiles in the berries and wines, while 1103P increased green leaf volatile concentrations, up-regulating VvADH2 expression in both vintages. VvLOXA, VvLOXC, VvHPL1, VvADH1, VvADH2, and VvAAT were co-regulated by vintage and rootstock. Orthogonal partial least squares regression analysis (OPLS-DA) showed that the differential compounds in CS/1103P and CS berries were dominated by green leaf volatiles. Furthermore, the concentrations of 1-hexanol in the CS/1103P wines were significantly higher than in the other treatments in the two vintages. 1103P altered the expression of genes in the LOX-HPL pathway and increased the concentration of grape green leaf volatiles such as 1-hexanol and 1-hexanal, while vine vigor also affected green leaf volatile concentrations, the combination of which altered the aromatic composition of the wine and gave it more green flavors. Full article

Background and objectives: Trismus is a frequent and debilitating complication in people with head and neck cancer (HNC) which leads to significant functional limitations and reduced quality of life. Rehabilitation interventions are commonly recommended to manage or prevent trismus. However, in many randomized controlled trials (RCTs), the theoretical justification for these interventions is poorly articulated, and the underlying biological or physiological mechanisms are not described in detail, limiting our understanding of why certain treatments may (or may not) work. This review aimed to identify and analyze how RCTs report the rationale for rehabilitation interventions and the explanations used to manage this population. Materials and Methods: A scoping review was conducted in accordance with the PRISMA-ScR guidelines. Five databases (PubMed, PEDro, Web of Science, Scopus, and EMBASE) were searched up to May 2025 for RCTs evaluating rehabilitation interventions for the management or prevention of treatment-induced trismus in patients with HNC. Data were extracted and synthesized narratively, focusing on the type of intervention, the rationale for its use, and the proposed mechanisms of action. Results: Of 2215 records identified, 24 RCTs met the inclusion criteria. Thirteen studies focused on preventive interventions—primarily exercise therapy—while the remainder addressed established trismus using exercise, manual therapy, electrotherapy, or combined treatment modalities. The rationales provided for intervention selection were heterogeneous and often lacked depth, with most studies justifying interventions based on their potential to improve mouth opening or reduce fibrosis but rarely grounding these claims in detailed pathophysiological models. Only half of the studies provided any mechanistic explanation for the intervention’s effects, and these were typically generic or speculative. Conclusions: RCTs investigating rehabilitation interventions for treatment-induced trismus in patients with HNC frequently lack comprehensive rationales and mechanistic explanations for their interventions. This gap limits the ability to refine and optimize treatment approaches, as the underlying processes driving clinical improvements remain poorly understood. Future research should be guided by theoretical models and include objective outcomes to better elucidate the mechanisms of action of interventions to inform clinical practice. Full article