bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa–BiLSTM–Attention Model
Authors: Deng Jiangao , Liu Yue
Published: February 2025

Public opinion comments are important for the public to express their emotions and demands. Accordingly, identifying the public emotions contained in comments and taking corresponding countermeasures according to the changes in the emotions are of great theoretical and practical significance to online public opinion management. This study took a public opinion event at a college as an example. Firstly, the microblogs and comment data related to the event were crawled with Python coding, and pre-processing operations such as cleaning, word splitting, and de-noising were carried out; then, the stage of public opinion was divided into phases based on the daily public opinion sound volume, Baidu index, and key time points of the event. Secondly, for sentiment analysis, a supplementary sentiment dictionary of the event was constructed based on the SO-PMI algorithm and merged with the commonly used sentiment dictionary to pre-annotate the sentiment corpus; then, the RoBERTa–BiLSTM–Attention model was constructed to classify the sentiment of microblog comments; after that, four evaluation indexes were selected and ablation experiments were set up to verify the performance of the model. Finally, based on the results of the sentiment classification, we drew public opinion trends and sentiment evolution graphs for analysis. The results showed that the supplementary dictionary constructed based on the SO-PMI algorithm significantly improved the pre-labelling accuracy. The RoBERTa–BiLSTM–Attention model achieved 91.56%, 90.87%, 91.07%, and 91.17% in accuracy, precision, recall, and F1-score, respectively. The situation notification, expert response, regulatory dynamics, and secondary public opinion will trigger significant fluctuations in the volume of public opinion and public sentiment.
iVRPM: Conceptual Proposal of an Immersive Virtual Reality Pedagogical Model
Authors: Bicalho Daniela Rocha , Piedade João , Matos João Filipe
Published: February 2025

The growing attention to virtual reality (VR) suggests that immersive technologies will be widely applied across various contexts, including education. However, conceptual divergences and a lack of understanding regarding the learning process in immersive virtual environments reveal the nascent understanding of the use of immersive virtual reality (iVR) and its benefits in educational contexts. Against this backdrop, this article presents a proposed pedagogical framework aimed at aligning the affordances of iVR environments—such as immersion, interactivity, and embodiment—with educational objectives, thereby enhancing learning experiences. Developed through a design-based research (DBR) methodology, the framework integrates theoretical contributions from the CAMIL model and the XR ABC framework in conjunction with the revised Bloom’s taxonomy. The structure organizes immersive experiences into three levels—Absorb, Experience, and Explore—which are distinguished by the interactivity and complexity of the proposed tasks. Each level is mapped to the cognitive domains and dimensions of knowledge, promoting the progressive development of cognitive and psychomotor skills. It is posited that aligning the technological features of the environment with educational objectives and the level of active student engagement can facilitate learning in immersive environments.
Influences of an Arrow’s Aerodynamic Pressure Center on Recurve Bow Shooting
Authors: Shen Wenfeng , Hu Liang , Hu Jing , Xu Yiming , Xu Xixia , Zheng Jieqing
Published: February 2025

An arrow’s attack angle continuously changes during its flight, which affects the position of the aerodynamic pressure center. To account for the offset between the aerodynamic pressure center and the center of mass of an arrow in recurve bow shooting, two equations for describing the variation of the aerodynamic pressure center with the attack angles were fitted via CFD simulation. On this basis, a new theoretical aerodynamic model was developed by integrating the above equations with the current model to predict the flight along the outer ballistic trajectory more accurately than ever. With regard to actual archery competition occasions, the distance, initial velocity, and attack angle were set to 70 m, 57 m/s, and −3° to 3°, respectively; the attitude and trajectory of the arrow flying details under background wind, such as crosswind, headwind, and tailwind, were numerically analyzed to reveal the accuracy deviation mechanism. A comparison was conducted with previous models, indicating that the model proposed in this study achieved improvements in accuracy of 15% under crosswind conditions and 8% under headwind/tailwind conditions. The results could, from a fluid physics perspective, provide valuable recommendations not only for archers and coaches but also for arrow manufacturers.
AdaptiveConv2d: A Novel Convolutional Module for Medical Image Segmentation
Authors: Liu Donghua , Zhou Zuofeng
Published: February 2025

With the rapid advancement of medical imaging technology, medical image segmentation has become increasingly crucial in disease diagnosis, treatment planning, and intraoperative navigation. In this paper, we propose a novel convolutional module, AdaptiveConv2d, that is designed to address the limitations of traditional convolutions and advance convolutional techniques in medical image processing. This approach targets the unique challenges associated with medical images, including complex tissue structures, irregular boundaries, low contrast, and high noise levels. The AdaptiveConv2d module integrates adaptive feature extraction, an optimized receptive field, enhanced sensitivity to details and boundaries, and an innovative feature fusion mechanism to significantly improve segmentation accuracy and robustness. By dynamically adjusting the convolution operations, the module adapts flexibly to medical images with varying shapes and boundaries, leading to more precise feature extraction. Experimental results indicate that the AdaptiveConv2d module outperforms several existing methods across multiple medical image segmentation tasks, highlighting its potential as an effective tool for medical image analysis. Furthermore, the highly modular design of AdaptiveConv2d allows for seamless integration into existing neural network architectures, offering a versatile and adaptable solution for a range of medical image segmentation applications.
Enhancing Continuum Robotics Accuracy Using a Particle Swarm Optimization Algorithm and Closed-Loop Wire Transmission Model for Minimally Invasive Thyroid Surgery
Authors: Guo Na , Zhang Haoyun , Li Xingshuai , Cui Xinnan , Liu Yang , Pan Jiachen , Song Yajuan , Zhang Qinjian
Published: February 2025

To address the challenges of confined workspaces and high-precision requirements in thyroid surgery, this paper proposes a modular cable-driven robotic system with a hybrid rigid–continuum structure. By integrating rigid mechanisms and continuum joints within a closed-loop cable-driven framework, the system achieves a balance between flexibility in narrow spaces and operational stiffness. To tackle kinematic model inaccuracies caused by manufacturing errors, an innovative joint decoupling strategy combined with the Particle Swarm Optimization (PSO) algorithm is developed to dynamically identify and correct 19 critical parameters. Experimental results demonstrate a 37.74% average improvement in repetitive positioning accuracy and a 52% reduction in maximum absolute error. However, residual positioning errors (up to 4.53 mm) at motion boundaries highlight the need for integrating nonlinear friction compensation. The feasibility of a safety-zone-based force feedback master–slave control strategy is validated through Gazebo simulations, and a ring-grasping experiment on a surgical training platform confirms its clinical applicability.
A Dynamic Risk Assessment and Mitigation Model
Authors: Cheimonidis Pavlos , Rantos Konstantinos
Published: February 2025

In the current operational landscape, organizations face a growing and diverse array of cybersecurity challenges, necessitating the development and implementation of innovative and effective security solutions. This paper presents a novel methodology for dynamic risk assessment and mitigation suggestions aimed at assessing and reducing cyber risks. The proposed approach gathers information from publicly available cybersecurity-related open sources and integrates it with environment-specific data to generate a comprehensive understanding of potential risks. It creates multiple distinct risk scenarios based on the identification of vulnerabilities, network topology, and the attacker’s perspective. The methodology employs Bayesian networks to proactively and dynamically estimate the probability of threats and Fuzzy Cognitive Maps to dynamically update vulnerability severity values for each risk scenario. These elements are combined with impact estimations to provide dynamic risk assessments. Furthermore, the methodology offers mitigation suggestions for each identified vulnerability across all risk scenarios, enabling organizations to effectively address the assessed cybersecurity risks. To validate the effectiveness of the proposed methodology, a case study is presented, demonstrating its practical application and efficacy.
Potential Health Risk of Dust from Stone Mill Industries
Authors: Swangjang Kanokporn , Dantrakul Arnol , Panishkan Kamolchanok
Published: February 2025

Stone mill operations contribute significantly to air pollution and increase health risks not only for workers but also for nearby communities. This study aimed to assess the health impacts of stone mill industries on nearby residents. The research was conducted in two areas: a primary region with a high number of stone mills and an area without stone mills. A questionnaire-based survey was employed, and potential health risks were evaluated using the hazard quotient (HQ) method. Total suspended particulates (TSPs) and particulate matter-10 micron (PM10) were analyzed as hazard factors based on monitoring data from seven stone mills collected between 2008 and 2021. The study found that residents in major stone mill areas reported higher hazard quotients (HQs) than those living farther from the mills, with a statistically significant association (p < 0.01). Seasonal variations also influenced dust distribution, with the highest TSP and PM10 levels recorded during winter, exacerbating health risks for local populations. This study highlights the need for improved community settlement planning, consideration of meteorological conditions, regulatory interventions by relevant agencies, and enhancements in environmental monitoring systems to mitigate the adverse health effects of stone mill operations.
Hybrid Predictive Maintenance for Building Systems: Integrating Rule-Based and Machine Learning Models for Fault Detection Using a High-Resolution Danish Dataset
Authors: Mazzetto Silvia
Published: February 2025

This study evaluates the effectiveness of six machine learning models, Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), for predictive maintenance in building systems. Utilizing a high-resolution dataset collected every five minutes from six office rooms at Aalborg University in Denmark over a ten-month period (27 February 2023 to 31 December 2023), we defined rule-based conditions to label historical faults in HVAC, lighting, and occupancy systems, resulting in over 100,000 fault instances. XGBoost outperformed other models, achieving an accuracy of 95%, precision of 93%, recall of 94%, and an F1-score of 0.93, with a computation time of 60 s. The model effectively predicted critical faults such as “Light_On_No_Occupancy” (1149 occurrences) and “Damper_Open_No_Occupancy” (8818 occurrences), demonstrating its potential for real-time fault detection and energy optimization in building management systems. Our findings suggest that implementing XGBoost in predictive maintenance frameworks can significantly enhance fault detection accuracy, reduce energy waste, and improve operational efficiency.
Methylation Biomarkers of Lung Cancer Risk: A Systematic Review and Meta-Analysis
Authors: Dolcini Jacopo , Chiavarini Manuela , Firmani Giorgio , Brennan Kasey J. M. , Cardenas Andres , Baccarelli Andrea A. , Barbadoro Pamela
Published: February 2025

Background: Lung cancer (LC) is the leading cause of cancer deaths worldwide among both men and women, and represents a major public health challenge. DNA methylation (DNAm) has shown potential in identifying individuals at higher risk of LC, but the overall evidence has not been systematically evaluated. This review and meta-analysis aims to evaluate and summarize existing research on the association between blood DNAm levels and LC risk. Methods: Searches were conducted in PubMed, Web of Science, and Scopus for studies published until February 2024, following PRISMA and MOOSE guidelines. Eleven studies met the eligibility criteria. Results: Using a random effects model, our pooled analysis showed a significant association between increased DNAm levels and LC risk (OR 1.24, 95% CI 1.10–1.39; I2 = 93.90%, p = 0.0001). Stratifying the results by study design showed a stronger association in two prospective cohort studies (OR 1.61; 95% CI 1.36–1.90; I2 = 14.42%, p = 0.32), while case–control studies showed a weaker association (OR 1.05; 95% CI 0.99–1.11; I2 = 70.57%, p = 0.0001). Sensitivity analyses indicated that omitting individual studies did not significantly alter the LC risk estimates. Conclusions: These findings suggest that higher blood DNAm levels are associated with an increased risk of LC, especially in long-term cohort studies. Further research is recommended to explore the potential of DNAm as a screening biomarker for LC and to clarify the role of other influencing factors.
General and Treatment-Specific Outcomes with Osseointegrated Implants in Auricular, Nasal, and Orbital Prosthetic Reconstruction
Authors: Sandelski Morgan M. , Martini Deema , Kubon Todd M. , Gion Greg G. , Pittman Amy L.
Published: February 2025

Background: Osseointegrated implants outside of dental restoration remain an integral area of facial reconstruction in which more outcomes data is needed. We aimed to describe our 13-year experience using osseointegrated implants for orbital, nasal, and auricular reconstruction, looking at general outcomes, including radiated and surgically manipulated bone. Methods: This retrospective chart review covered demographics and outcomes from January 2008 to August 2021 in patients who underwent an orbital exenteration, partial or total rhinectomy, and partial or total auriculectomy with subsequent osseointegrated implant placement. We hypothesized radiation would increase the failure rate of implants and prostheses. Results: There were 79 implants placed in 27 patients, with over half of the patients requiring implants for reconstruction because of malignancy. The success rate was 86%. Complications were uncommon. Only 2 (7.4%) patients were unable to use their prosthesis. Prior radiation and surgery to the bone were associated with an increased risk of loss of implant (p = 0.008 and p = 0.007, respectively) but not associated with other complications or prosthesis non-viability. Conclusions: Osseointegrated implants are a reliable, permanent option for a realistic prosthesis. Radiation and prior surgery are significantly associated with an increased risk of implant failure but not associated with the inability to use the prosthesis. Regardless of prior treatments, bone-retained implants should be considered in facial reconstruction, especially after failing autologous repair or with concerns for cosmetic outcomes.
Outcomes of Allogeneic Stem Cell Transplant in Patients with Relapsed/Refractory Hodgkin Lymphoma
Authors: Ge Shiliang , Lepic Kylie , Bhindi Ravi , Berg Tobias , Khalaf Dina , Leber Brian , Radford Michael , Walker Irwin , Davies Gwynivere , Garcia-Horton Alejandro
Published: February 2025

Background: The aim of this study was to evaluate real-world clinical outcomes and transplant-related complications of allogeneic stem cell transplantation (alloSCT) for Hodgkin lymphoma (HL). Methods: This was a single-centre, retrospective analysis of relapsed and refractory (R/R) HL patients who received an alloSCT between 1 January 2016 and 29 February 2024 in Hamilton, Ontario. The primary endpoint was overall survival (OS). The secondary endpoints were progression-free survival (PFS), non-relapse mortality (NRM), and graft-versus-host disease/relapse-free survival (GRFS). Results: Twenty-one patients were identified, with thirteen (62%) pre-treated with programmed death 1 (PD-1) blockade with either nivolumab or pembrolizumab. Seventeen (81%) patients underwent related haploidentical donor transplants, while four (19%) patients received a matched unrelated donor transplant. The 2-year OS and PFS rates were 79% (95% CI: 53–92%) and 63% (95% CI: 37–81%), respectively. Trends towards improved OS, PFS, NRM, and GRFS in PD-1-inhibitor-exposed patients were observed. All PD-1-inhibitor-exposed patients who were in complete remission proceeding to alloSCT remained alive at the last follow-up visit. Among the nine patients in partial remission at the time of alloSCT, three deaths were reported, with a 2-year OS of 61%. Conclusions: Our outcome data of a single-centre, heavily pre-treated cohort of Canadian patients confirm that alloSCT with post-transplant cyclophosphamide-based immunosuppression, which has been associated with improvements in PFS, remains a safe and feasible treatment option for patients with R/R HL in the era of checkpoint inhibitor use.
Metaheuristic Optimization for Robust RSSD-Based UAV Localization with Position Uncertainty
Authors: Zhang Yuanyuan , Li Jiping , Gulliver T. Aaron , Wu Huafeng , Xie Guangqian , Mei Xiaojun , Xian Jiangfeng , Wang Weijun , Liang Linian
Published: February 2025

Unmanned aerial vehicles (UAVs) have garnered significant research interest across various fields due to their excellent maneuverability, scalability, and flexibility. However, potential collisions and other issues can disrupt communication and hinder functionality in real-world applications. Therefore, accurate localization of UAVs is crucial. Nonetheless, environmental factors and inherent stability issues can lead to node positional errors in UAV networks, compounded by inaccuracies in transmit power estimation, complicating the effectiveness of signal strength-based localization methods in achieving high accuracy. To mitigate the adverse effects of these issues, a novel received signal strength difference (RSSD)-based localization scheme based on a robust enhanced salp swarm algorithm (RESSA) is presented. In this algorithm, an elitism strategy based on tent opposition-based learning (TOL) is proposed to promote the leader to move around the food source. Differential evolution (DE) is then used to enhance the exploration ability of each agent and improve global search. In addition, a dynamic movement mechanism for followers is designed, enabling the swarm to swiftly converge towards the food source, thereby accelerating the overall convergence process. The RSSD-based Cramér–Rao lower bound (CRLB) with position uncertainty is derived to evaluate the performance. Experimental results are presented, which show that the proposed RESSA provides better localization performance than related methods in the literature.
UAV-SfM Photogrammetry for Canopy Characterization Toward Unmanned Aerial Spraying Systems Precision Pesticide Application in an Orchard
Authors: Bing Qi , Zhang Ruirui , Zhang Linhuan , Li Longlong , Chen Liping
Published: February 2025

The development of unmanned aerial spraying systems (UASSs) has significantly transformed pest and disease control methods of crop plants. Precisely adjusting pesticide application rates based on the target conditions is an effective method to improve pesticide use efficiency. In orchard spraying, the structural characteristics of the canopy are crucial for guiding the pesticide application system to adjust spraying parameters. This study selected mango trees as the research sample and evaluated the differences between UAV aerial photography with a Structure from Motion (SfM) algorithm and airborne LiDAR in the results of extracting canopy parameters. The maximum canopy height, canopy projection area, and canopy volume parameters were extracted from the canopy height model of SfM (CHMSfM) and the canopy height model of LiDAR (CHMLiDAR) by grids with the same width as the planting rows (5.0 m) and 14 different heights (0.2 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, 0.8 m, 1.0 m, 2.0 m, 3.0 m, 4.0 m, 5.0 m, 6.0 m, 8.0 m, and 10.0 m), respectively. Linear regression equations were used to fit the canopy parameters obtained from different sensors. The correlation was evaluated using R2 and rRMSE, and a t-test (α = 0.05) was employed to assess the significance of the differences. The results show that as the grid height increases, the R2 values for the maximum canopy height, projection area, and canopy volume extracted from CHMSfM and CHMLiDAR increase, while the rRMSE values decrease. When the grid height is 10.0 m, the R2 for the maximum canopy height extracted from the two models is 92.85%, with an rRMSE of 0.0563. For the canopy projection area, the R2 is 97.83%, with an rRMSE of 0.01, and for the canopy volume, the R2 is 98.35%, with an rRMSE of 0.0337. When the grid height exceeds 1.0 m, the t-test results for the three parameters are all greater than 0.05, accepting the hypothesis that there is no significant difference in the canopy parameters obtained by the two sensors. Additionally, using the coordinates x0 of the intersection of the linear regression equation and y=x as a reference, CHMSfM tends to overestimate lower canopy maximum height and projection area, and underestimate higher canopy maximum height and projection area compared to CHMLiDAR. This to some extent reflects that the surface of CHMSfM is smoother. This study demonstrates the effectiveness of extracting canopy parameters to guide UASS systems for variable-rate spraying based on UAV oblique photography combined with the SfM algorithm.
Enhanced PolSAR Image Segmentation with Polarization Channel Fusion and Diffusion-Based Probability Modeling
Authors: Chen Hao , Hou Yuzhuo , Fang Xiaoxiao , He Chu
Published: February 2025

With the advancement of polarimetric synthetic aperture radar (PolSAR) imaging technology and the growing demand for image interpretation, extracting meaningful land cover information from PolSAR images has become a key research focus. To address the segmentation challenge, we propose an innovative method. First, features from co-polarization and cross-polarization channels are separately used as dual inputs, and a cross-attention mechanism effectively fuses these features to capture correlations between different polarization information. Second, a diffusion framework is employed to jointly model target features and class probabilities, aiming to improve segmentation accuracy by learning and fitting the probabilistic distribution of target labels. Finally, experimental results demonstrate that the proposed method achieves superior performance in PolSAR image segmentation, effectively managing complex polarization relationships while offering robustness and broad application potential.
Numerical and Experimental Study of Fluid Flow and Heat Transfer in Porous Media: A Review Article
Authors: Ranjbarzadeh Ramin , Sappa Giuseppe
Published: February 2025

Fluid flow and heat transfer in porous media have been extensively studied due to their importance in numerous industrial and environmental applications. This review provides a comprehensive analysis of numerical and experimental approaches, presenting a multiscale perspective that bridges molecular, pore, and macroscopic levels. This study emphasizes the importance of understanding the underlying principles governing these processes, as this knowledge is essential for optimizing and innovating applications ranging from energy systems to environmental engineering. The review synthesizes key theoretical frameworks, including Darcy’s law, the Brinkman equation, and volume-averaging methods, offering a robust foundation for interpreting complex interactions in porous media. A novel aspect of this work is the integration of experimental and numerical insights to address challenges such as heterogeneity, anisotropy, and scale effects, demonstrating their complementary roles in advancing this field. Additionally, the review highlights emerging methodologies, including advanced pore-scale modeling, the lattice Boltzmann method, and machine learning, as transformative tools for overcoming existing limitations and exploring future directions. By identifying critical knowledge gaps and proposing innovative solutions, this article serves as a vital resource for researchers and practitioners, fostering interdisciplinary approaches and paving the way for cutting-edge advancements in the study of fluid flow and heat transfer in porous media.
Design, Modeling, and Optimization of Novel Fuel Cell Systems
Authors: Arsalis Alexandros
Published: February 2025

Fuel cells have emerged as a cornerstone in the pursuit of sustainable energy solutions [...]
Flexible On-Grid and Off-Grid Control for Electric–Hydrogen Coupling Microgrids
Authors: Wang Zhengyao , Fan Fulin , Zhang Hang , Song Kai , Jiang Jinhai , Sun Chuanyu , Xue Rui , Zhang Jingran , Chen Zhengjian
Published: February 2025

With the widespread integration of renewable energy into distribution networks, energy storage systems are playing an increasingly critical role in maintaining grid stability and sustainability. Hydrogen, as a key zero-carbon energy carrier, offers unique advantages in the transition to low-carbon energy systems. To facilitate the coordination between hydrogen and renewables, this paper proposes a flexible on-grid and off-grid control method for an electric–hydrogen hybrid AC-DC microgrid which integrates photovoltaic panels, battery energy storage, electrolysers, a hydrogen storage tank, and fuel cells. The flexible control method proposed here employs a hierarchical structure. The upper level adopts a power management strategy (PMS) that allocates power to each component based on the states of energy storage. The lower level utilises the master–slave control where master and slave converters are regulated by virtual synchronous generator (VSG) and active and reactive power (PQ) control, respectively. In addition, a pre-synchronisation control strategy which does not rely on traditional phase-locked loops is introduced to enable a smooth transition from the off-grid to on-grid mode. The electric–hydrogen microgrid along with the proposed control method is modelled and tested under various operating modes and scenarios. The simulation results demonstrate that the proposed control method achieves an effective power dispatch within microgrid and maintains microgrid stability in on- and off-grid modes as well as in the transition between the two modes.
Unveiling Population Structure Dynamics of Populus euphratica Riparian Forests Along the Tarim River Using Terrestrial LiDAR
Authors: Arkin Alfidar , Yusup Asadilla , Halik Ümüt , Abliz Abdulla , Ainiwaer Ailiya , Tian Aolei , Mijiti Maimaiti
Published: February 2025

The Populus euphratica desert riparian forest, predominantly distributed along the Tarim River in northwestern China, has experienced significant degradation due to climate change and anthropogenic activities. Despite its ecological importance, systematic assessments of P. euphratica stand structure across the entire Tarim River remain scarce. This study employed terrestrial laser scanning (TLS) to capture high-resolution 3D structural data from 2741 individual trees across 30 plots within six transects, covering the 1300 km mainstream of the Tarim River. ANOVA, PCA, and RDA were applied to examine tree structure variation and environmental influences. Results revealed a progressive decline in key structural parameters from the upper to lower reaches of the river, with the lower reaches showing pronounced degradation. Stand density decreased from 440 to 257 trees per hectare, mean stand height declined from 9.3 m to 5.6 m, mean crown diameter reduced from 4.1 m to 3.8 m, canopy cover dropped from 62% to 42%, and the leaf area index fell from 0.51 to 0.29. Age class distributions varied along the river, highlighting population structures indicative of growth in the upper reaches, stability in the middle reaches, and decline in the lower reaches. Abiotic factors, including groundwater depth, soil salinity, soil moisture, and precipitation, exhibited strong correlations with stand structural parameters (p < 0.05, R2 ≥ 0.69). The findings highlight significant spatial variations in tree structure, with healthier growth in the upper reaches and degradation in the lower reaches, enhance our understanding of forest development processes, and emphasize the urgent need for targeted conservation strategies. This comprehensive quantification of P. euphratica stand structure and its environmental drivers offer valuable insights into the dynamics of desert riparian forest ecosystems. The findings contribute to understanding forest development processes and provide a scientific basis for formulating effective conservation strategies to sustain these vital desert ecosystems, as well as for the monitoring of regional environmental changes.
From Claims to Choices: How Health Information Shapes Consumer Decisions in the Functional Food Market
Authors: Nazzaro Concetta , Uliano Anna , Lerro Marco , Stanco Marcello
Published: February 2025

The current study examines the impact of health claims on consumer preferences and willingness to pay (WTP) for functional snack bars, focusing on anti-inflammatory and antioxidant properties. Through an experimental auction involving 175 participants, this study investigates how providing clear information on product health benefits influences consumer interest and WTP while analysing the role of individual health consciousness (HC) in shaping these preferences. The results indicate that detailed health claims positively affect consumer WTP for functional snack bars compared to standard options. Although both anti-inflammatory and antioxidant claims attract consumer interest, no significant difference in WTP was observed between the two, suggesting similar perceived value for these distinct benefits. However, highly health-conscious consumers demonstrate a stronger preference and WTP for anti-inflammatory options, indicating that HC influences specific health claim valuation. These findings underscore the importance of effective health-related messaging in promoting functional foods and suggest that general health claims may resonate more broadly with consumers than specialised ones. This study’s results enhance the current knowledge on functional foods, especially snack bars, offering valuable insights for manufacturers aiming to implement targeted marketing strategies and public health initiatives focused on promoting healthier dietary choices.
Physicochemical Characterization and Antioxidant Properties of Cellulose-Rich Extracts Obtained from Carob (Ceratonia siliqua L.) Pulp for Preparation of Cellulose-Rich Gels
Authors: Llompart Bernat , Dalmau Esperanza , Umaña Mónica , Femenia Antoni
Published: February 2025

The carob tree (Ceratonia siliqua L.) is a defining species of the Mediterranean region, and its fruit, the carob pod, has seen a notable increase in economic interest in recent years, primarily due to the production of locust bean gum (E410), a widely used food additive derived from the seeds. The remainder of the fruit, the carob pulp, comprises 80–90% of the fruit’s weight and is typically considered a by-product, with its primary application being in animal feed. This study focused on obtaining cellulose-rich extracts from selected carob varieties cultivated in the Mediterranean region. A comprehensive physicochemical characterization of these cellulose-rich fractions was conducted, including the assessment of their antioxidant properties, specifically total phenolics and antioxidant capacity measured by the FRAP, ABTS, and CUPRAC methods. The findings reveal that carob pulp is an excellent source of carbohydrates, including soluble sugars, which constitute 33–45% of the pulp’s fresh weight, depending on the variety, and cell wall polysaccharides. The cell wall polymers, with cellulose as the predominant component, account for approximately 45% of the fresh pulp weight. Notable amounts of other polysaccharides, such as pectins and hemicelluloses, were also identified. Among the studied varieties, Bugadera and Rotjal stood out as exceptional sources of cellulose-rich extracts. Carob pulp was also found to be rich in antioxidant compounds, reflected in its high antioxidant capacity. In particular, the Bugadera variety, grown under irrigated conditions, exhibited a significant concentration of phenolic compounds (24.4 mg gallic acid equivalents per gram of pulp) and high antioxidant activity across all methods used, with ABTS measurements reaching up to 391.5 mg Trolox equivalents per gram of pulp. In conclusion, these results underscore the significant potential of carob pulp as a source of valuable cellulose-rich extracts, offering applications beyond its traditional use as animal feed. By exploring these new possibilities, the economic and environmental sustainability of carob cultivation could be greatly enhanced, contributing to the broader valorization of this iconic Mediterranean fruit.
Signs and Semblances: The Problem of Likability in Some Recent Productions of Much Ado About Nothing
Authors: Newlin James
Published: February 2025

Following the “intertextual turn” in adaptation studies, scholars of Shakespearean performance have embraced the interpretive possibilities offered by infidelity, focusing increasingly on the corrective potential of recent stagings and adaptations. Such productions are not primarily valuable as progressive rewrites, however. In claiming not to be “Shakespeare”, these productions make testable claims about the nature of the Shakespearean playtext. In this paper, I examine two recent stage productions and one non-traditional film adaptation of Much Ado About Nothing: Kenny Leon’s 2019 Public Theater production, Chris Abraham’s 2023 Stratford Festival production, and Will Gluck’s 2023 romantic comedy Anyone But You. All three performances are consciously unfaithful to Shakespeare’s text or setting, and their revisions attend to making the characters’ behavior more palatable for a contemporary liberal–progressive audience. Yet when we compare these revisions with the original playtext, Shakespeare’s own views come into sharper relief, as does our own inclination to identify with characters that Shakespeare’s immediate audience may have felt quite distanced from. I argue that in their drive to correct the play and make the characters more likable, these productions paper over Shakespeare’s critique of the arbitrary construction—and violent enforcement—of social hierarchy. Drawing upon Jacques Derrida’s notion of the parergon, I show that Shakespeare’s deliberate narrative framing invites a more skeptical, disapproving understanding of his characters. My hope is that this discussion leads to an understanding of Much Ado About Nothing as a “problem play” rather than a “problematic” one.
Assessment of Potentially Toxic Elements in Four Melon Fruit Varieties Grown in the Ganges and Yamuna River Basin
Authors: Elbagory Mohssen , Abd El-Aziz Mohamed A. , Omara Alaa El-Dein , Abou Fayssal Sami , Kumar Vinod
Published: February 2025

The present study aimed to investigate the occurrence of eight potentially toxic elements (PTEs) in selected varieties of watermelon (Citrullus lanatus var. Arka Shyama and Crimson Sweet) and muskmelon (Cucumis melo var. Cantaloupe and Kajri) grown near riverbanks in the Yamuna and Ganga River basins of Northern India. For this purpose, samples of melon fruits were collected from ten sampling sites from May to June 2024 and analyzed using ICP-OES. The results showed that the levels of PTEs varied significantly across the sampling sites, with muskmelons exhibiting slightly higher concentrations compared to watermelons. Specifically, the concentration (mg/kg dry weight) ranges for the watermelon varieties were Cd (0.05–0.20), Cr (0.40–1.10), Cu (1.50–4.90), Pb (0.01–0.11), As (0.01–0.08), Fe (80.00–120.00), Mn (9.00–15.80), and Zn (5.00–18.00). For muskmelons, the ranges were Cd (0.05–0.23), Cr (0.40–1.00), Cu (2.40–4.80), Pb (0.01–0.08), As (0.02–0.08), Fe (80.00–120.00 g), Mn (9.00–15.00), and Zn (8.00–18.00). In particular, the variability coefficients (CV%) indicated differential contamination in Crimson Sweet. On the other hand, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) tools facilitated the identification of sites of significant contamination and their respective interactions. The health risk studies using the health risk index (HRI), dietary intake modeling (DIM), and the target hazard quotient (THQ) also revealed no significant health risk of eight PTEs in melon fruits. Therefore, this study provides valuable insights into the biomonitoring of PTE contamination in widely consumed summer fruits of Northern India and the subsequent health risk assessment.
Wine Waste Compost Addition to a Vineyard of Chelva Grape of Central Spain: Effect on Wine Volatile Compounds
Authors: Alises María Osorio , Palomo Eva Sánchez , González-Viñas Miguel Ángel
Published: February 2025

The use of winemaking waste compost as an alternative fertilization treatment for vines of the Chelva grape variety was researched. Three different organic amendments (1 kg/m, 2 kg/m, and 3 kg/m) of winemaking waste compost were applied, and a no-applied-amendments condition served as the control. The wines were elaborated according to a traditional white winemaking process. The solid phase extraction (SPE) technique and gas chromatography coupled to mass spectrometry (GC-MS) techniques were, respectively, used for the isolation of wine volatile compounds and their identification and quantification. A total of 81 aroma compounds were identified and quantified in Chelva wines, independently of the fertilization treatment used. The results bring to light that the application of organic amendments from winemaking waste compost during vine cultivation produced changes in the concentration of wine volatile compounds, especially for the varietal aroma compounds. These results show that the use of wine-distillery compost as a vine fertilization treatment is a viable alternative to traditional fertilization methods, particularly when the fertilization dose is 2 Kg/linear meter.
Risk Prediction of RNA Off-Targets of CRISPR Base Editors in Tissue-Specific Transcriptomes Using Language Models
Authors: Nakamae Kazuki , Suzuki Takayuki , Yonezawa Sora , Yamamoto Kentaro , Kakuzaki Taro , Ono Hiromasa , Naito Yuki , Bono Hidemasa
Published: February 2025

Base-editing technologies, particularly cytosine base editors (CBEs), allow precise gene modification without introducing double-strand breaks; however, unintended RNA off-target effects remain a critical concern and are under studied. To address this gap, we developed the Pipeline for CRISPR-induced Transcriptome-wide Unintended RNA Editing (PiCTURE), a standardized computational pipeline for detecting and quantifying transcriptome-wide CBE-induced RNA off-target events. PiCTURE identifies both canonical ACW (W = A or T/U) motif-dependent and non-canonical RNA off-targets, revealing a broader WCW motif that underlies many unanticipated edits. Additionally, we developed two machine learning models based on the DNABERT-2 language model, termed STL and SNL, which outperformed motif-only approaches in terms of accuracy, precision, recall, and F1 score. To demonstrate the practical application of our predictive model for CBE-induced RNA off-target risk, we integrated PiCTURE outputs with the Predicting RNA Off-target compared with Tissue-specific Expression for Caring for Tissue and Organ (PROTECTiO) pipeline and estimated RNA off-target risk for each transcript showing tissue-specific expression. The analysis revealed differences among tissues: while the brain and ovaries exhibited relatively low off-target burden, the colon and lungs displayed relatively high risks. Our study provides a comprehensive framework for RNA off-target profiling, emphasizing the importance of advanced machine learning-based classifiers in CBE safety evaluations and offering valuable insights to inform the development of safer genome-editing therapies.
Recent Advances in Gluten-Related Disorders
Authors: Smolinska Sylwia , Jurkiewicz Krzysztof , Majsiak Emilia
Published: February 2025

The articles featured in this Special Issue of the International Journal of Molecular Sciences, entitled “Recent Advances in Gluten-Related Disorders”, present significant advancements in the understanding of gluten hypersensitivity, offering valuable insights into mechanisms, diagnostics, and potential therapeutic strategies [...]
Artificial Intelligence-Assisted Virtual Reality for Reducing Anxiety in Pediatric Endoscopy
Authors: Bulduk Mehmet , Can Veysel , Aktaş Emre , İpekçi Belkıs , Bulduk Bahattin , Nas İbrahim
Published: February 2025

Background/Objectives: This study aimed to evaluate the effects of artificial intelligence (AI)-assisted virtual reality (VR) applications on preoperative anxiety levels and vital signs in children undergoing endoscopy. Methods: A randomized controlled trial design was employed, including a total of 80 children aged 8–17 years (40 in the intervention group and 40 in the control group). Children in the intervention group were exposed to VR applications featuring space and underwater themes, while the control group received standard procedures. Anxiety levels were assessed using the “State-Trait Anxiety Inventory for Children (STAIC)”, and vital signs were evaluated through measurements of systolic and diastolic blood pressure, heart rate, temperature, and SpO2. Results: VR applications significantly reduced anxiety scores in the intervention group (36.3 ± 1.9), while no significant changes were observed in the control group (45.4 ± 2.74) (p < 0.001). Regarding vital signs, more favorable outcomes were observed in the intervention group. Systolic blood pressure was measured as 89 ± 6.7 mmHg in the intervention group and 96.5 ± 10.5 mmHg in the control group (p < 0.001). Diastolic blood pressure was 60.8 ± 4.7 mmHg in the intervention group and 63.8 ± 6 mmHg in the control group (p < 0.05). Heart rate was recorded as 88.7 ± 10.1 bpm in the intervention group and 94.5 ± 14.8 bpm in the control group (p < 0.05). SpO2 levels were 98 ± 1 in the intervention group and 96.2 ± 1.3 in the control group (p < 0.001). Conclusions: AI-assisted VR applications emerge as an effective non-pharmacological method for reducing preoperative anxiety and promoting physiological stability in children. This approach holds the potential to enhance pediatric experiences during invasive procedures such as endoscopy.
Care of Adult Heart Transplant Recipients by the Primary Care Provider: A Practical Roadmap
Authors: Wu Yu , Gao Lina , Lazo Jose , Martinez Brandon , Grewal Simran , Yurkova Irina , Galeota Julia , Nash Catherine , Cutler Meghan , Tarango Nimaljeet , Prasad Pooja , Cheng Richard , Motiwala Shweta , De Marco Teresa
Published: February 2025

Heart transplantation has significantly improved survival and enhanced the quality of life of patients with end-stage heart failure. Successful long-term outcomes are predicated on a collaborative effort among patients, transplant teams, and primary care providers (PCPs). Notably, PCPs are increasingly pivotal in post-transplant care, engaging in annual assessments, early recognition of complications, and referral, thus minimizing morbidity and mortality. This article highlights key considerations for PCPs, including indications for heart transplant, immunosuppressive therapy and infection prophylaxis, management of post-transplant complications, psychosocial and lifestyle adjustment, and family planning. This roadmap aims to empower PCPs to deliver optimal care and improve long-term outcomes for heart transplant recipients.
Artificial Intelligence as a Tool in Diagnosing Inflammatory Bowel Disease in Older Adults
Authors: Prada Ana-Gabriela , Stroie Tudor , Diculescu Rucsandra-Ilinca , Gogîrlă George Cristian , Radu Codruța Delia , Istratescu Doina , Prada Gabriel Ioan , Diculescu Mihai Mircea
Published: February 2025

Background/Objectives: The primary objective of our study was to find a potential use for images generated by imagistic investigations by comparing the appearance of a healthy digestive tract to that of a pathological one. Methods: We conducted a cross-sectional observational study involving 60 older adult patients admitted to and followed up at a primary center in Romania. Our focus was on different diagnostic methods and the use of artificial intelligence (AI) tools integrated into the electronic health records system. Results: Currently, imagery, laboratory values and electronic health records (EHR) can also be used to train AI models. Comparative imagery to predict the appearance of inflammatory bowel disease (IBD) can be used as a predictor model. Conclusions: Our findings indicate with certainty that training a tool in the diagnosis and prevention of relapses in older adults with IBD is promising for further integrating these models into patient care.
Aortopexy for Tracheomalacia in Children: A Systematic Review and Meta-Analysis
Authors: Zanini Andrea , Mazzoleni Stefano , Arcieri Luigi , Giordano Raffaele , Marianeschi Stefano , Macchini Francesco
Published: February 2025

Introduction: Aortopexy is the most common surgical option for the treatment of severe tracheomalacia (TM) in children. Despite being described over 75 years ago, several aspects of the procedure remain controversial. Materials and Methods: A systematic review of aortopexy was conducted following the PRISMA guidelines. All original articles describing at least one case of aortopexy performed in children for the treatment of TM were included. Patients’ characteristics, surgical indications, approaches and details were collected. Outcomes were reviewed, and potential factors associated with procedural success were investigated. Results: Of 243 papers, 17 were included in this review, encompassing a total of 473 patients who underwent aortopexy. Of these, 65.3% were male, with a mean age at surgery of 12.2 months (5 days–18 years). Primary TM accounted for 7.9% of cases, while 92.1% were secondary to other anomalies, including esophageal atresia (54.1%), aberrant innominate artery (16.1%) and vascular rings (8.3%). There is a general consensus on the surgical indication for TM with severe symptoms, although the definition of severity is not well established. Overall success was 84%, but 40.8% of patients experienced some persistent symptoms. Sternotomy and thoracotomy were the most successful approaches (92.6% and 84.2%, respectively). Thoracoscopy had a similar success rate to sternotomy when pericardiotomy, thymectomy and pledgeted suture were performed (86.4% vs. 92.6% p = 0.41). Conclusions: Aortopexy is associated with good outcomes, but no evidence-based guidelines are currently available. Success appears to be associated with specific surgical steps rather than the surgical approach. Prospective studies are desirable for establishing precise guidelines.