bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of Crop Coefficient Approaches and Machine Learning Models for Predicting Water Requirements in Three Major Crops in Coastal Saline-Alkali Land
Authors: Dong Shide , Ma Qian , Yu Chunxiao , Li Linbo , Liu Hanwen , Cui Guangxu , Qiu Haonan , Yang Shihong , Wang Guangmei
Published: February 2025

The accuracy of the crop coefficient approaches recommended by the FAO-56 guidelines for evapotranspiration (ET) in saline environments is limited due to complex soil–water–crop interactions, highlighting the need for advanced methods to improve ET estimation for water management in saline-alkali lands. To improve ET estimation for wheat, maize, and soybean in the Yellow River Delta, China, three machine-learning algorithms—gradient-boosting decision tree (GBDT), random forest (RF), and extreme gradient-boosting regression (XGBoost)—were applied alongside single- and dual-crop coefficient approaches (Kc-ETo). The results showed that increasing the input variables did not necessarily improve the ML model performance. The ML models outperformed Kc-ETo approaches, particularly for summer crops (maize and soybean), with the mean absolute error reduced by 26.4% to 80.9%, R2 increased by 5.6% to 11.2%, and root mean square error (RMSE) decreased by 22.4% to 98.1%. RF and XGBoost were more accurate than GBDT, with R2 increasing by 3.2% to 5.4% and RMSE decreasing by 22% to 57%. Scenario simulations showed increased ET with intensified emission scenarios for RF and GBDT, similar to Kc-ETo approaches. However, XGBoost simulated a significantly lower ET in high-emission scenarios, indicating potential unreliability for scenario predictions beyond the training dataset, especially in a saline-alkali environment with an increasingly complex background.
Development and Comparison of Artificial Neural Networks and Gradient Boosting Regressors for Predicting Topsoil Moisture Using Forecast Data
Authors: Zambudio Martínez Miriam , Silveira Larissa Haringer Martins da , Marin-Perez Rafael , Gomez Antonio Fernando Skarmeta
Published: February 2025

Introduction: The Earth’s growing population is increasing resource consumption, heavily pressuring agriculture, which, currently, uses 70% of the world’s freshwater from rivers and lakes, which, themselves, comprise only 1% of the Earth’s water reserves. Combined with climate change, the situation is alarming. These challenges drive Agriculture 4.0, which is focused on sustainable agricultural processes to optimise water use. Objective: Given this context, this study proposes a model, based on Artificial Intelligence (AI) techniques to predict topsoil moisture in a study area located in the south of the Iberian Peninsula, primarily an agricultural region facing recurrent droughts and water scarcity. Methods: To develop the model, a comparison between Artificial Neural Networks (ANNs) and Gradient Booster Regressors (GBRs) was conducted, and topsoil moisture data from seven probes distributed over the study area were used, in addition to several variables (temperature, relative humidity, solar radiation, wind speed, precipitation and evapotranspiration) from a selection of weather stations and ensemble forecasts from meteorological models. Results: The final GBR model, with a 0.01 learning rate, 5 max depth, and 350 estimators, predicted topsoil moisture with an average mean squared error (MSE) of 0.027 and a maximum difference between observed and predicted data of 20.09% in a two-year series (May 2022–June 2024).
Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
Authors: Gómez Manuel , Braojos Marina , Fernández Raúl , Parle Florencia
Published: February 2025

Pasta is a product made from wheat semolina and water. Due to its composition and low glycemic index, it is an ideal product for fortification with additional nutrients. Most plant-based food by-products are rich in nutrients of interest, such as fibers, vitamins, minerals, and bioactive compounds. Fortifying pasta with plant-based by-products can be nutritionally beneficial, and the number of publications on this topic has increased significantly in recent years. However, it presents a challenge when aiming to achieve products with good organoleptic quality. This review analyzes the published information on the effect of including plant-based by-products on the technological quality (optimal cooking time, solid loss, swelling index, and water absorption during cooking, color, and texture), nutritional value, and organoleptic properties of pasta. It also provides a critical perspective on gaps in the current knowledge and highlights aspects that should be addressed in the future.
Material and Design Analysis of Doors in Traditional Düzce–Konuralp Architecture
Authors: Çelik Onur , Bideci Alper
Published: February 2025

This study presents a detailed analysis of the doors found in traditional buildings located in the historical Konuralp region of Düzce. Within the scope of the research, three structures were selected from the traditional neighborhoods of Konuralp, focusing on those that have preserved their original architectural features. The analysis encompasses the functions of the doors, their opening directions, horizontal and vertical alignments, dimensions, and material properties, as well as decorative and ornamental characteristics. Location information and photographs of the buildings, along with the distinctive features of each door, were documented through comprehensive inventory studies. The results of the study reveal the structural and aesthetic qualities of the doors belonging to traditional Turkish houses and emphasize the necessity for restoration and preservation efforts for doors that have experienced deterioration. This study aims to contribute to the preservation of the traditional architectural heritage in the region. Furthermore, it suggests that appropriate conservation strategies should be developed to transmit the construction techniques and unique characteristics of these doors to future generations. In this context, the study aims to serve as a resource for documenting and preserving traditional structures in the Konuralp region and provides a scientific foundation that can be used as a reference in similar research studies.
A Meta-Analysis of the Impact of Using Angiotensin-Converting Enzyme Inhibitors (ACEIs) or Angiotensin II Receptor Blockers (ARBs) on Mortality, Severity, and Healthcare Resource Utilization in Patients with COVID-19
Authors: Li Ruijuan , Zhang Jie , Ren Liang
Published: February 2025

Objective: The primary objective of this study is to explore the potential link between the utilization of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) and its impact on mortality, disease severity, and healthcare resource utilization in individuals diagnosed with COVID-19. We aim to establish a solid theoretical foundation for safe and effective clinical medications. Methods: We conducted a comprehensive search of various databases, including CNKI, PubMed, Science, Cell, Springer, Nature, Web of Science, and Embase. We also traced the literature of the included studies to ensure a thorough analysis of the available evidence. After applying a set of inclusion and exclusion criteria, we ultimately included a total of 41 articles in our analysis. To determine the overall effect size for dichotomous variables, we used the Mantel–Haenszel odds ratio in random effect models. For continuous variables, we calculated the inverse variance SMD using random effect models. To assess the outcomes and heterogeneity, we considered p-values (p < 0.05) and I2 values for all outcomes. We performed multivariate and univariate meta-regression analyses using the maximum likelihood approach with the CMA 3.0 software. Results: The results of our analysis indicated that the use of ACEIs or ARBs did not significantly influence mortality (OR = 1.10, 95% CI 0.83–1.46, p = 0.43, I2 = 84%), severity (OR = 0.99, 95% CI 0.68–1.45, p = 0.98, I2 = 84%), or healthcare resource utilization (SMD = 0.03, 95% CI 0.06–0.12, p = 0.54, I2 = 37%) in patients with COVID-19 compared to those not taking ACEIs or ARBs. The multivariate meta-regression analysis model explained 63%, 31%, and 100% of the sources of heterogeneity for the three outcome indicators. Conclusions: The use of ACEIs and ARBs is not significantly correlated with mortality, severity, or healthcare resource utilization in patients with COVID-19, indicating safe clinical use of the medications.
Modelling the Performance of Photovoltaic Systems and Studying the Soiling Effects: Insights Based on Field Data of Environmental Factors of Solar Panel Systems
Authors: Al Humairi Ali , Al Hemyari Zuhair A. , El Asri Hayat , Jung Peter
Published: February 2025

This paper focuses on the modeling of the performance of photovoltaic systems based on advanced techniques. This research leverages real-world data from the Shams Solar Facility at the German University of Technology in Oman to explore the application of Linear, Lasso, Ridge, and Elastic Net Regressions to predict and optimize the performance of photovoltaic systems. A comprehensive dataset of 36,851 observations of environmental and operational conditions forms the basis of the analysis. The research identifies the strengths and limitations of these modeling techniques for an accurate forecast of energy output under various scenarios. The comparative analysis highlights the precision and reliability of each regression method and offers actionable insights into their practical implementation. The findings highlight the importance of more sophisticated modeling approaches in increasing the knowledge of photovoltaic system dynamics and optimizing their performance. This research facilitates the advancement in solar energy systems and provides critical recommendations for the improvement in efficiency and reliability of photovoltaic installations under different geographic and climatic settings.
Evaluation of Spatial Profile of Local Emissions from W17+–W23+ Unresolved Transition Array Spectra
Authors: Nishimura Ryota , Oishi Tetsutarou , Murakami Izumi , Kato Daiji , Sakaue Hiroyuki A. , Ohashi Hayato , Gupta Shivam , Suzuki Chihiro , Goto Motoshi , Kawamoto Yasuko , Kawate Tomoko , Takahashi Hiroyuki , Tobita Kenji
Published: February 2025

Tungsten (W) spectroscopy has attracted significant attention because W is one of the major impurities in ITER and future DEMO reactors. W22+–W33+ spectra at 15–45 Å have been useful for impurity diagnostics. Unresolved Transition Array (UTA) spectra around 200 Å, which contain W17+–W23+, have potential for diagnostics tungsten ions in even lower charge states. Because multiple charge states overlap with very similar wavelengths, it is challenging to evaluate impurity spatial profiles from UTA spectra around 200 Å. In this study, we conducted tungsten pellet injection experiments in Large Helical Device (LHD) and attempted to evaluate the spatial profiles of UTA spectra around 200 Å, observed at 0.3–0.8 keV of central electron temperature. The results indicated that tungsten ions around W20+ were locally profiled in a radial region, reff = 0.38 m, where the local electron temperature decreases by 30% to 40% with respect to more central and outer temperatures. These results suggested that the UTA spectrum around 200 Å would be useful for diagnostics of tungsten impurities in lower charge states than the charge states contained in UTA spectra around 15–45 Å.
Circulating Microvesicles Enriched in miR–126–5p and miR–223–3p: Potential Biomarkers in Acute Coronary Syndrome
Authors: Hernández-López José Rubicel , Flores-García Mirthala , García-Flores Esbeidy , Cazarín-Santos Benny Giovanni , Peña-Duque Marco Antonio , Sánchez-Muñoz Fausto , Ballinas-Verdugo Martha Alicia , Delgadillo-Rodríguez Hilda , Martínez-Ríos Marco Antonio , Angles-Cano Eduardo , de la Peña-Díaz Aurora
Published: February 2025

Background. The molecular mechanisms underlying acute coronary syndrome (ACS) have been extensively investigated, with a particular focus on the role of circulating microvesicles (MVs) as carriers of regulatory elements that influence hemodynamic changes and coronary flow. Endothelial and platelet dysfunction during ACS alters MV composition, impacting clinical outcomes. This study explores the levels of miR–126–5p and miR–223–3p in circulating MVs and their association with the Thrombolysis in Myocardial Infarction (TIMI) coronary flow classification scale, proposing their potential as biomarkers. Methods. Bioinformatic tools identified miRNAs linked to ACS. Plasma MVs were isolated from ACS patients and healthy controls through high-speed centrifugation. miRNA levels were quantified using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and compared across TIMI 0 and TIMI 3 groups. Diagnostic efficacy was assessed via receiver operating characteristic (ROC) curve analysis. Results. The bioinformatic analysis identified miR–126 and miR–223 present in ACS. miR–126–5p and miR–223–3p were significantly reduced in MVs from TIMI 0 patients compared to TIMI 3. ROC analysis showed high diagnostic accuracy for miR–126–5p (AUC = 0.918; 95% CI: 0.818–1.00; p = 0.001) and miR–223–3p (AUC = 1.00; 95% CI: 1.00–1.00; p < 0.001). Conclusions. Reduced levels of miR–126–5p and miR–223–3p in circulating MVs are strongly associated with impaired coronary flow, positioning these miRNAs as potential biomarkers for ACS risk stratification and therapeutic targeting.
Seismic Performance of Prefabricated Constructional Columns Using Recycled Lump Concrete
Authors: Zhou Linren , Xie Zhibo , Fan Yuanfeng , Zhang Haiyan
Published: February 2025

A new type of prefabricated constructional column (PCC) made of recycled lump concrete is proposed and investigated in this study. The methods for the design, fabrication, and construction of this PCC are introduced, and the connection and implementation of the PCC are explained in detail. In order to examine the performance of the PCC, an experimental study on PCC segments was first conducted. Low cyclic load tests of walls restrained by the PCC and cast-in-place constructional column (CCC) were then carried out. The failure of the PCC did not occur at the connection position of the segments, indicating that the connection method was reliable. Compared with the CCC-restrained wall, the failure characteristics of the PCC wall were basically the same; the ultimate bearing capacity was slightly lower, while the displacement ductility and energy dissipation performance were better. Finally, finite element analyses of these two types of masonry walls were implemented under low cyclic loading. The calculated results for cracking, stiffness, ultimate bearing capacity, failure process, hysteretic performance, skeleton curve, energy dissipation, and ductility all had good agreement with the experimental results. The proposed PCC can achieve a prefabrication rate of more than 85%, and the amount of new concrete can be reduced by more than 25% by filling concrete waste lumps, which can greatly improve construction efficiency and reduce the cost, thereby offering significant economic and environmental benefits.
Investigation of Mechanical Behaviors of High-Performance Fiber-Reinforced Concrete Pipe Jacking Subjected to Three-Point Loading
Authors: Zhou Cairong , Wang Zifan , Yu Jinhong , Shi Changzheng , Wang Xu , Fang Qing , Zhang Jiang
Published: February 2025

High-performance fiber-reinforced concrete (HPFRC) offers exceptional strength, ductility, and durability, making it highly promising for electric power pipe jacking applications. However, limited research exists on the mechanical properties of HPFRC pipes, especially regarding reinforcement schemes. This study bridges this gap by using a combination of three-point testing, analytical calculations, and numerical simulations to investigate the mechanical behavior and performance of HPFRC pipes under various reinforcement configurations. The results show that the load–displacement curve of HPFRC pipes initially follows a linear elastic relationship, but as the load exceeds 200 kN/m, displacement increases and cracks form, with failure occurring at 410 kN/m. HPFRC pipes demonstrate significantly enhanced load-bearing and crack resistance capabilities, with reduced reinforcement and wall thickness compared to traditional materials, maintaining high load-bearing capacity even after damage. The three analysis methods generally align in terms of load-bearing and failure processes, though the analytical method reveals limitations in accurately predicting crack widths. The study also reveals that reinforcement schemes significantly affect the pipes’ structural performance, with double layer and inner layer reinforcement providing superior damage resistance. This study contributes new insights into HPFRC pipe performance and provides a basis for optimizing reinforcement designs in pipe jacking projects.
Green Roof Management in Mediterranean Climates: Evaluating the Performance of Native Herbaceous Plant Species and Green Manure to Increase Sustainability
Authors: Trenta Mattia , Quadri Alessandro , Sambuco Bianca , Perez Garcia Carlos Alejandro , Barbaresi Alberto , Tassinari Patrizia , Torreggiani Daniele
Published: February 2025

The benefits of ecosystem services provided by urban green systems have been highlighted in research on spatial and landscape planning, and the need has emerged for an integrated approach to urban green planning aiming at increasing climate mitigation and urban resilience. Research indicates that plant selection and substrate management are vital for optimizing the most important performance of green roofs, like building thermal insulation, urban heat reduction, air quality improvement, and stormwater management. In Mediterranean climates, it is essential to investigate sustainable management solutions for green roofs like the growth potential of native, low-maintenance forbs adapted to thermal and water stress on specific substrates. Medicinal species may be suitable, provided that interactions with pollutants are controlled. This study evaluates the performance of Melissa officinalis and Hypericum perforatum on experimental green roof modules under controlled conditions, comparing chemical fertilization and three different treatments with biomass from Trifolium repens used as green manure. The key metrics of fresh and dry biomass, plant cover ratio, and chlorophyll content are measured. Results show significantly higher values of cover and biomass for these two species treated with green manure in comparison to chemical fertilization, with no significant differences in chlorophyll content, indicating that T. repens is a useful source of green manure in green roof management. Overall, the results are consistent with the research goals of suggesting sustainable solutions for green roof management, since low-maintenance vegetation and green manure contribute to the elimination of chemicals in urban green.
Palladium-Catalyzed Decarbonylative Nucleophilic Halogenation of Acid Anhydrides
Authors: Tian Tian , Uei Shuhei , Yan Weidan , Nishihara Yasushi
Published: February 2025

In this study, we developed a palladium-catalyzed decarbonylative nucleophilic halogenation reaction using inexpensive and readily available acid anhydrides as substrates. This approach effectively circumvents the instability of acyl chlorides and the low reactivity of acyl fluorides. The Pd/Xantphos catalyst system exhibited excellent compatibility with the thermodynamically and kinetically challenging reductive elimination of C–X bonds (X = I, Br, and Cl) from Pd(II) intermediates. Notably, for electron-donating substrates, adopting an open system significantly improved the reaction efficiency. The positive effect of the open system may be due to the reversible nature of CO insertion and deinsertion, which helps direct the reaction toward the desired pathway by allowing the generated CO to exit the reaction system. Mechanistic studies suggest that the reaction proceeds through a highly reactive acyl halide intermediate, followed by a unimolecular fragment coupling (UFC) pathway via decarbonylation or an alternative pathway involving the formation of an activated anionic palladate complex in the presence of lithium halide.
Ultraviolet Radiation-Induced Tolerogenic Dendritic Cells in Skin: Insights and Mechanisms
Authors: Ghajar-Rahimi Gelare , Yusuf Nabiha , Xu Hui
Published: February 2025

Ultraviolet (UV) radiation has profound effects on the immune system, including the induction of tolerogenic dendritic cells (DCs), which contribute to immune suppression and tolerance. This review explores the roles of conventional CD11c⁺ DCs, as well as cutaneous Langerhans cells and CD11b⁺ myeloid cells, in UV-induced immune modulation. Two key mechanisms underlying the immunosuppressive relationship between UV and DCs are discussed: the inactivation of DCs and the induction of tolerogenic DCs. DCs serve as a critical link between the innate and adaptive immune systems, serving as professional antigen-presenting cells. In this context, we explore how UV-induced DCs influence the activity of specific T cell subsets, including regulatory T lymphocytes and T helper cells, and shape immune outcomes. Finally, we highlight the implications of UV-induced tolerogenic DCs in select dermatologic pathologies, including cutaneous lupus, polymorphic light eruption, and skin cancer. Understanding the mechanisms by which UV radiation alters DC function offers insights into the complex interplay between environmental factors and immune regulation, providing potential avenues for preventive and therapeutic intervention in UV-induced skin diseases.
Levels of Proangiogenic Molecules and Terminal Complement Complex C5b-9 in the Crown of Circulating sEVs in Patients with Recurrent Glioblastomas: Relationship with Tumor Molecular Characteristics
Authors: Yunusova Natalia , Tulendinov Eldar , Svarovsky Dmitry , Ryabova Anastasia , Kondakova Irina , Ponomaryova Anastasia , Vtorushin Sergey , Tabakaev Stanislav , Korshunov Dmitry , Shtam Tatiana , Tamkovich Svetlana , Choynzonov Evgeny
Published: February 2025

Circulating small extracellular vesicles (sEVs) are emerging as potential biomarkers for glioblastoma progression. This study aimed to compare the levels of matrix metalloproteinases (MMP2 and MMP9), terminal complement complex (C5b-9), and VEGF-A in circulating sEVs in glioblastoma patients (GBMPs) with and without tumor recurrence. Using differential ultracentrifugation, sEVs were isolated from blood samples of GBMPs with no tumor recurrence for over one year (n = 6) and after first relapse (n = 14). The vesicles were characterized and quantified using flow cytometry. In both groups, C5b-9 was predominantly detected on tumor-specific circulating sEVs (glial fibrillary acidic protein (GFAP)-positive sEVs) with high VEGF-A expression, while C5b-9 was significantly less frequent on sEVs with low VEGF-A expression (p < 0.05). GFAP+VEGF+dimMMP2-C5b-9+ vesicles were rarely detected in GBMPs without relapse, suggesting their potential utility as biomarkers for a favorable relapse-free prognosis. In recurrent GBMPs, a positive correlation was observed between GFAP+VEGF+bright MMP2+C5b-9+ sEVs and MGMT gene promoter methylation levels (r = 0.543, p < 0.05). Additionally, a trend toward a negative correlation was found between GFAP+VEGF+bright MMP2+C5b-9- sEVs and mutant p53 expression in primary tumor tissue (r = −0.44, p = 0.114). These findings suggest that sEV profiles may serve as valuable prognostic markers for glioblastoma recurrence and treatment responses.
Multilayered Polyelectrolyte Structures Deposited on Corona-Charged Substrate Blends as Potential Drug Delivery Systems
Authors: Viraneva Asya , Marudova Maria , Grigorov Aleksandar , Milenkova Sofia , Yovcheva Temenuzhka
Published: February 2025

Polyelectrolyte multilayers (PEMs) deposited on non-porous and porous blend substrates were studied. Films, prepared from two biodegradable polymers poly (D-lactic acid) (PDLA) and poly(ε-caprolactone) (PCL) and their blends were used as substrates in the present paper. All films were initially charged in a corona discharge (positive or negative corona). After charging, the initial surface potential of the samples V0 was measured and the normalized surface potential was calculated. The dependencies on time of the normalized surface potential for electrets, possessing either positive or negative charges, were studied. It was found that the steady-state values of the normalized surface potential for the porous substrates were higher than those of the non-porous ones, independently of material type and corona polarity. It was also shown that the values of the normalized surface potential for the PCL electrets were the highest and decreased when the content of PDLA increased. Scanning electron microscopy (SEM) was utilized for the determination of the substrates’ surface morphology. With the largest pore size, PCL substrates allowed for a greater capture of charges on their surface and facilitated the retention of said charges for prolonged periods of time. Differential scanning calorimetry (DSC) measurements were performed to determine the degree of crystallinity, which was very high for PCL substrates, when compared to the other investigated substrates. The wettability of the investigated substrates was measured using the static water contact angle method. The obtained results demonstrated that the created blends were more hydrophilic than the pure films. The two chosen polyelectrolytes were layered onto the surface of the substrates with the use of the layer-by-layer (LbL) technique and benzydamine hydrochloride was loaded in the multilayers as a model drug. Its loading efficiency and release profile were carried out spectrophotometrically. It was determined that for non-porous substrates, independently of the corona polarity, the best fitting model was Korsmeyer-Peppas, while for the porous substrates the best fitting model was Weibull.
COVID Stress Factors Affecting Remote Work Acceptance
Authors: Kim Cheong
Published: February 2025

This study investigates the psychological factors influencing remote work acceptance during the COVID-19 pandemic using a Bayesian network and probabilistic structural equation modeling (PSEM) approach. The research specifically explores the impact of stress factors induced by the pandemic, including compulsive checking, socio-economic consequences, danger, and contamination, on individuals’ willingness to adopt remote work arrangements. Data were collected from 586 participants with remote work experience, and the Bayesian analysis revealed that compulsive checking had the most significant positive influence on remote work acceptance, followed by socio-economic consequences, while danger and contamination showed no statistically significant effects. The findings suggest that psychological stress factors related to excessive information seeking and economic instability play a stronger role in influencing remote work decisions than direct health-related concerns. These results provide theoretical contributions by extending technology acceptance models to crisis situations and offer practical insights for organizations aiming to implement effective remote work policies. Specifically, strategies such as psychological support programs, financial stability measures, and clear communication regarding workplace safety can enhance employee well-being and productivity in remote settings. Future research should explore long-term effects of stress factors on remote work sustainability and cross-cultural comparisons in pandemic-related work adaptations.
MRI in Oral Tongue Squamous Cell Carcinoma: A Radiomic Approach in the Local Recurrence Evaluation
Authors: Vidiri Antonello , Dolcetti Vincenzo , Mazzola Francesco , Lucchese Sonia , Laganaro Francesca , Piludu Francesca , Pellini Raul , Covello Renato , Marzi Simona
Published: February 2025

(1) Background: Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignancy with high loco-regional recurrence. Advanced imaging biomarkers are critical for stratifying patients at a high risk of recurrence. This study aimed to develop MRI-based radiomic models to predict loco-regional recurrence in OTSCC patients undergoing surgery. (2) Methods: We retrospectively selected 92 patients with OTSCC who underwent MRI, followed by surgery and cervical lymphadenectomy. A total of 31 patients suffered from a loco-regional recurrence. Radiomic features were extracted from preoperative post-contrast high-resolution MRI and integrated with clinical and pathological data to develop predictive models, including radiomic-only and combined radiomic–clinical approaches, trained and validated with stratified data splitting. (3) Results: Textural features, such as those derived from the Gray-Level Size-Zone Matrix, Gray-Level Dependence Matrix, and Gray-Level Run-Length Matrix, showed significant associations with recurrence. The radiomic-only model achieved an accuracy of 0.79 (95% confidence interval: 0.69, 0.87) and 0.74 (95% CI: 0.54, 0.89) in the training and validation set, respectively. Combined radiomic and clinical models, incorporating features like the pathological depth of invasion and lymph node status, provided comparable diagnostic performances. (4) Conclusions: MRI-based radiomic models demonstrated the potential for predicting loco-regional recurrence, highlighting their increasingly important role in advancing precision oncology for OTSCC.
Consistency and Stability in Feature Selection for High-Dimensional Microarray Survival Data in Diffuse Large B-Cell Lymphoma Cancer
Authors: Dauda Kazeem A. , Lamidi Rasheed K.
Published: February 2025

High-dimensional survival data, such as microarray datasets, present significant challenges in variable selection and model performance due to their complexity and dimensionality. Identifying important genes and understanding how these genes influence the survival of patients with cancer are of great interest and a major challenge to biomedical scientists, healthcare practitioners, and oncologists. Therefore, this study combined the strengths of two complementary feature selection methodologies: a filtering (correlation-based) approach and a wrapper method based on Iterative Bayesian Model Averaging (IBMA). This new approach, termed Correlation-Based IBMA, offers a highly efficient and effective means of selecting the most important and influential genes for predicting the survival of patients with cancer. The efficiency and consistency of the method were demonstrated using diffuse large B-cell lymphoma cancer data. The results revealed that the 15 most important genes out of 3835 gene features were consistently selected at a threshold p-value of 0.001, with genes with posterior probabilities below 1% being removed. The influence of these 15 genes on patient survival was assessed using the Cox Proportional Hazards (Cox-PH) Model. The results further revealed that eight genes were highly associated with patient survival at a 0.05 level of significance. Finally, these findings underscore the importance of integrating feature selection with robust modeling approaches to enhance accuracy and interpretability in high-dimensional survival data analysis.
Diagnostic Performance of an Artificial Intelligence Software for the Evaluation of Bone X-Ray Examinations Referred from the Emergency Department
Authors: Díaz Moreno Alejandro , Cano Alonso Raquel , Fernández Alfonso Ana , Álvarez Vázquez Ana , Carrascoso Arranz Javier , López Alcolea Julia , García Castellanos David , Sanabria Greciano Lucía , Recio Rodríguez Manuel , Andreu-Vázquez Cristina , Thuissard Vasallo Israel John , Martínez De Vega Vicente
Published: February 2025

Background/Objectives: The growing use of artificial intelligence (AI) in musculoskeletal radiographs presents significant potential to improve diagnostic accuracy and optimize clinical workflow. However, assessing its performance in clinical environments is essential for successful implementation. We hypothesized that our AI applied to urgent bone X-rays could detect fractures, joint dislocations, and effusion with high sensitivity (Sens) and specificity (Spec). The specific objectives of our study were as follows: 1. To determine the Sens and Spec rates of AI in detecting bone fractures, dislocations, and elbow joint effusion compared to the gold standard (GS). 2. To evaluate the concordance rate between AI and radiology residents (RR). 3. To compare the proportion of doubtful results identified by AI and the RR, and the rates confirmed by GS. Methods: We conducted an observational, double-blind, retrospective study on adult bone X-rays (BXRs) referred from the emergency department at our center between October and November 2022, with a final sample of 792 BXRs, categorized into three groups: large joints, small joints, and long-flat bones. Our AI system detects fractures, dislocations, and elbow effusions, providing results as positive, negative, or doubtful. We compared the diagnostic performance of AI and the RR against a senior radiologist (GS). Results: The study population’s median age was 48 years; 48.6% were male. Statistical analysis showed Sens = 90.6% and Spec = 98% for fracture detection by the RR, and 95.8% and 97.6% by AI. The RR achieved higher Sens (77.8%) and Spec (100%) for dislocation detection compared to AI. The Kappa coefficient between RR and AI was 0.797 for fractures in large joints, and concordance was considered acceptable for all other variables. We also analyzed doubtful cases and their confirmation by GS. Additionally, we analyzed findings not detected by AI, such as chronic fractures, arthropathy, focal lesions, and anatomical variants. Conclusions: This study assessed the impact of AI in a real-world clinical setting, comparing its performance with that of radiologists (both in training and senior). AI achieved high Sens, Spec, and AUC in bone fracture detection and showed strong concordance with the RR. In conclusion, AI has the potential to be a valuable screening tool, helping reduce missed diagnoses in clinical practice.
Exploring the TyG Index and the Homeostasis Model Assessment of Insulin Resistance as Insulin Resistance Markers: Implications for Fibromyalgia Management and Understanding—A Narrative Review
Authors: Samavarchitehrani Amirsaeed , Mercantepe Filiz , Behnoush Amir Hossein , Klisic Aleksandra
Published: February 2025

Fibromyalgia (FM) is a chronic musculoskeletal disease with a higher prevalence among women. To date, there has been no definitive laboratory or imaging assessment for FM, and hence, the diagnosis criteria for FM remained based on subjective assessment of symptoms with high overlap with other rheumatological disorders. Many patients with FM suffer from metabolic disorders leading to insulin resistance (IR). There have been several methods to assess IR, among which the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and the triglyceride–glucose (TyG) index have been used more frequently, with the latter being more available and cost-effective. As higher IR has been reported for patients with FM with various mechanisms, in this review, we sought to investigate the association between IR and FM using the current evidence. One of the possible underlying mechanisms of this association might be mitochondrial dysfunction and oxidative stress observed in IR conditions and its role in FM. Studies have also shown that IR indices are higher in patients with FM, compared to healthy controls, while higher HOMA-IR levels were also reported for higher severities of FM based on Fibromyalgia Impact Questionnaire—Revised (FIQR) scores. While these findings suggest the possible involvement of IR in FM pathophysiology and add to the value of IR measurement in FM clinical assessment, further large-scale studies are needed to establish a definitive causal association between them.
Comparative Clinical and Histopathological Study of Oral Leukoplakia in Smokers and Non-Smokers
Authors: Șerban Andrei-Eduard , Părlătescu Ioanina , Milanesi Elena , Pelisenco Iulia Andreea , Dobre Maria , Costache Mariana , Țovaru Șerban , Perlea Paula
Published: February 2025

Background/Objectives: Oral leukoplakia (OLK) is an oral mucosal lesion classified in the oral potentially malignant disorder group and is associated with an increased risk of malignant transformation (MT). The aim of this study was to compare the clinical and histopathological features of two OLK groups, a group of smokers and a group of non-smokers. Methods: In this retrospective study, a cohort of 154 patients with OLK was divided into two groups based on the presence of smoking as a major risk factor. OLK diagnoses were established via clinical and histopathological examination. Results: Females were more abundant in the non-smoking group than in the smoking group, where males were more abundant (p < 0.001). The average age of the smokers was lower than that of the non-smokers (p = 0.003). In the smokers, the buccal mucosa was most frequently affected, while in the non-smokers, the gums and the tongue were primarily involved (p = 0.016). In female smokers, involvement of the buccal area and multiple-site involvement were statistically significantly more frequently observed compared to that in female non-smokers (p = 0.006). Non-dysplastic lesions were predominant in both groups, with severe dysplasia observed more frequently in the non-smokers than in the smokers. MT was higher in the non-smoker group compared to that in the smoker group. Conclusions: OLK in smokers is different from OLK in non-smokers concerning female gender involvement, site location, the number of lesions, and the MT rate.
An Unsupervised Moving Object Detection Network for UAV Videos
Authors: Fan Xuxiang , Wen Gongjian , Gao Zhinan , Chen Junlong , Jian Haojun
Published: February 2025

UAV moving object detection focuses on identifying moving objects in images captured by UAVs, with broad applications in regional surveillance and event reconnaissance. Compared to general moving object detection scenarios, UAV videos exhibit unique characteristics, including foreground sparsity and varying target scales. The direct application of conventional background modeling or motion segmentation methods from general settings may yield suboptimal performance in UAV contexts. This paper introduces an unsupervised UAV moving object detection network. Domain-specific knowledge, including spatiotemporal consistency and foreground sparsity, is integrated into the loss function to mitigate false positives caused by motion parallax and platform movement. Multi-scale features are fully utilized to address the variability in target sizes. Furthermore, we have collected a UAV moving object detection dataset from various typical scenarios, providing a benchmark for this task. Extensive experiments conducted on both our dataset and existing benchmarks demonstrate the superiority of the proposed algorithm.
SentimentFormer: A Transformer-Based Multimodal Fusion Framework for Enhanced Sentiment Analysis of Memes in Under-Resourced Bangla Language
Authors: Faria Fatema Tuj Johora , Baniata Laith H. , Baniata Mohammad H. , Khair Mohannad A. , Bani Ata Ahmed Ibrahim , Bunterngchit Chayut , Kang Sangwoo
Published: February 2025

Social media has increasingly relied on memes as a tool for expressing opinions, making meme sentiment analysis an emerging area of interest for researchers. While much of the research has focused on English-language memes, under-resourced languages, such as Bengali, have received limited attention. Given the surge in social media use, the need for sentiment analysis of memes in these languages has become critical. One of the primary challenges in this field is the lack of benchmark datasets, particularly in languages with fewer resources. To address this, we used the MemoSen dataset, designed for Bengali, which consists of 4368 memes annotated with three sentiment labels: positive, negative, and neutral. MemoSen is divided into training (70%), test (20%), and validation (10%) sets, with an imbalanced class distribution: 1349 memes in the positive class, 2728 in the negative class, and 291 in the neutral class. Our approach leverages advanced deep learning techniques for multimodal sentiment analysis in Bengali, introducing three hybrid approaches. SentimentTextFormer is a text-based, fine-tuned model that utilizes state-of-the-art transformer architectures to accurately extract sentiment-related insights from Bengali text, capturing nuanced linguistic features. SentimentImageFormer is an image-based model that employs cutting-edge transformer-based techniques for precise sentiment classification through visual data. Lastly, SentimentFormer is a hybrid model that seamlessly integrates both text and image modalities using fusion strategies. Early fusion combines textual and visual features at the input level, enabling the model to jointly learn from both modalities. Late fusion merges the outputs of separate text and image models, preserving their individual strengths for the final prediction. Intermediate fusion integrates textual and visual features at intermediate layers, refining their interactions during processing. These fusion strategies combine the strengths of both textual and visual data, enhancing sentiment analysis by exploiting complementary information from multiple sources. The performance of our models was evaluated using various accuracy metrics, with SentimentTextFormer achieving 73.31% accuracy and SentimentImageFormer attaining 64.72%. The hybrid model, SentimentFormer (SwiftFormer with mBERT), employing intermediate fusion, shows a notable improvement in accuracy, achieving 79.04%, outperforming SentimentTextFormer by 5.73% and SentimentImageFormer by 14.32%. Among the fusion strategies, SentimentFormer (SwiftFormer with mBERT) achieved the highest accuracy of 79.04%, highlighting the effectiveness of our fusion technique and the reliability of our multimodal framework in improving sentiment analysis accuracy across diverse modalities.
End-to-End Speech Recognition with Deep Fusion: Leveraging External Language Models for Low-Resource Scenarios
Authors: Zhang Lusheng , Wu Shie , Wang Zhongxun
Published: February 2025

With the rapid development of Automatic Speech Recognition (ASR) technology, end-to-end speech recognition systems have gained significant attention due to their ability to directly convert raw speech signals into text. However, such systems heavily rely on large amounts of labeled speech data, which severely limits model training performance and generalization, especially in low-resource language environments. To address this issue, this paper proposes an end-to-end speech recognition approach based on deep fusion, which tightly integrates an external language model (LM) with the end-to-end model during the training phase, effectively compensating for the lack of linguistic prior knowledge. Unlike traditional shallow fusion methods, deep fusion enables the model and the external LM to share representations and jointly optimize during training, thereby enhancing recognition performance under low-resource conditions. Experiments conducted on the Common Voice dataset show that, in a 10 h extremely low-resource scenario, the deep fusion method reduces the character error rate (CER) from 51.1% to 17.65%. In a 100 h scenario, it achieves a relative reduction of approximately 2.8%. Furthermore, ablation studies on model layers demonstrate that even with a reduced number of encoder and decoder layers to decrease model complexity, deep fusion continues to effectively leverage external linguistic priors, significantly improving performance in low-resource speech recognition tasks.
Design of the New Dual-Polarized Broadband Phased Array Feed Antenna for the Sardinia Radio Telescope
Authors: Maxia Paolo , Casula Giovanni Andrea , Navarrini Alessandro , Pisanu Tonino , Valente Giuseppe , Muntoni Giacomo , Montisci Giorgio
Published: February 2025

High-sensitivity and large-scale surveys are essential in advancing radio astronomy, enabling detailed exploration of the universe. A Phased Array Feed (PAF) installed in the focal plane of a radio telescope significantly enhances mapping efficiency by increasing the instantaneous Field of View (FoV) and improving sky sampling capabilities. This paper presents the design and optimization of a novel C-Band Phased Array Feed antenna for the Sardinia Radio Telescope (SRT). The system features an 8 × 8 array of dual-polarized elements optimized to achieve a uniform beam pattern and an edge taper of approximately 5 dB for single radiating elements within the 3.0–7.7 GHz frequency range. The proposed antenna addresses key efficiency limitations identified in the PHAROS 2 (PHased Arrays for Reflector Observing Systems) system, including the under-illumination of the Sardinia Radio Telescope’s primary mirror caused by narrow sub-array radiation patterns. By expanding the operational bandwidth and refining the radiation characteristics, this new design enables significantly improved performance across the broader frequency range of 3.0–7.7 GHz, enhancing the telescope’s capability for wide-field, high-resolution observations.
Connecting Visual Data to Privacy: Predicting and Measuring Privacy Risks in Images
Authors: Jiang Hongpu , Zuo Jinxin , Lu Yueming
Published: February 2025

More and more users openly share their information on online websites, with the resulting privacy issues being under scrutiny. Content such as a user’s personal data and location information is often asked for before posting to enforce the user’s privacy preferences; however, little attention has been paid to the lack of content (e.g., images) posted by the user. Even if privacy preferences are requested before images are published, publishers often remain unaware of the extent of privacy leakage associated with their data. To this end, we provide an image privacy metric scheme that incorporates users’ privacy preferences, with the core idea of assisting users in making data publishing decisions. First, we propose privacy-specific spatial attention mechanisms that can effectively improve the prediction accuracy. Next, we integrate set pair analysis (SPA) theory and use the network output as the privacy value. Finally, we combine a user study to understand the privacy preferences of different users with respect to these attributes and combine it with principal component analysis to correct and enforce user privacy preferences. Our model is trained with the ability to predict privacy risk end-to-end, thus being able to guide the user in sharing data in open platforms. We use the image privacy dataset, VISPR, to predict privacy information better than other methods.