bluuebunny/crossref_metadata_2025 huggingface dataset:
The Use of Environmental DNA as Preliminary Description of Invertebrate Diversity in Three Sicilian Lakes
Authors: Mauro Manuela , Longo Francesco , Lo Valvo Mario , Vizzini Aiti , Di Grigoli Antonino , Radovic Slobodanka , Arizza Vincenzo , Vecchioni Luca , La Paglia Laura , Queiroz Vinicius , Ponte Marialetizia , Gargano Claudio , Ciaccio Paolo Salvatore Francesco , Vicari Domenico , Vazzana Mirella
Published: January 2025

Freshwater ecosystems are among the most severely affected environments by species loss caused by climate change and intense anthropogenic pressure. To preserve biodiversity, biomonitoring plays a key role by providing reliable data on biological diversity and ecological status. Environmental DNA (eDNA) metabarcoding has emerged as a powerful and non-invasive alternative to traditional morphology-based sampling and identification methods. This study represents the first application of eDNA analysis to assess the invertebrate communities in three Sicilian Lakes: Poma, Piana degli Albanesi and Scanzano. Water samples were collected at two points in each lake and after filtration with nitrocellulose membranes, eDNA was extracted and metabarcoding analysis was performed. A total of 27 species were identified, belonging to Phyla of Annelida, Arthropoda and Rotifera. Notably, the analysis revealed the presence of alien species (Daphnia parvula and Acanthocyclops americanus), a dangerous species associated with the transmission of viral diseases (Culex pipiens), and potential new records for Sicily (Stylaria lacustris, Platypalpus exilis, Pammene aurana, Limnephilus rhombicus). These results provide a preliminary snapshot of invertebrate biodiversity at these sites, demonstrating how eDNA has the potential to complement, but not replace, traditional methods, contributing to the assessment of ecosystem status.
Development and Progression of Bovine Respiratory Disease Measured Using Clinical Respiratory Scoring and Thoracic Ultrasonography in Preweaned Calves on Dairy Farms in the United Kingdom: A Prospective Cohort Study
Authors: Lindley George , Blackie Nicola , Wathes D. Claire , Booth Richard E.
Published: January 2025

The respiratory health of preweaned calves is an important determinant of their health, welfare, and future performance. This prospective cohort study measured bovine respiratory disease (BRD) on 16 dairy farms, including 476 calves in South-west England. Wisconsin and California respiratory scoring and thoracic ultrasonography were performed repeatedly at 7 ± 0.89 day intervals (mean ± SD) at 0–56 days of age (n = 3344 examinations). Cases were localized to the upper or lower respiratory tract, or both, and classified as new, repeat, or chronic. Prevalence and incidence were calculated. Multivariate modeling of factors associated with repeated measurements was performed. Increasing age (OR = 1.05, 95% CI 1.04–1.06) and fecal score (Score 2, OR = 1.78, 95% CI 1.14–2.77) were associated with a lower odds of a healthy BRD subtype, whereas increasing serum total protein (OR = 0.97, 95% CI 0.96–0.99) was protective. Older (OR 1.08, 95% CI 1.06–1.09), male (OR 1.69, 95% CI 1.01–2.84) calves with elevated Wisconsin respiratory scores (≥5, OR 5.61, 95% CI 3.38–9.30) were more likely to have elevated thoracic ultrasound scores. BRD remains common in calves born in UK dairy herds, requiring precise identification and management if preweaning health is to be optimized.
Preliminary Research on Dietary Supplementation of Potassium Magnesium Sulphate on Transport Stress in Finishing Pigs Prior to Slaughter
Authors: Cui Bailei , Xiong Yunxia , Wen Xiaolu , Wu Shengnan , Huang Yi , Xiao Hao , Cao Shuting , Jiang Zongyong , Wang Li , Hu Shenglan
Published: January 2025

Transport stress prior to slaughter frequently induces a stress response, negatively affecting meat quality. This study investigated the impact of dietary potassium magnesium sulphate (PMS) supplementation during the fattening stage on the stress response and meat quality in finishing pigs subjected to transport stress. The experiment involved two phases. Initially, 48 finishing pigs (68.00 ± 0.40 kg) were randomly allocated into two groups: a control group receiving a basal diet (CON) and a PMS-supplemented group receiving the basal diet with 0.50% PMS. Each group was housed in six pens, with four pigs per pen. After 60 days of feeding, in the second phase, two pigs from each pen were randomly selected for slaughter, with one pig subjected to a 4 h transportation stress prior to slaughter. Pigs were categorized into four treatment groups based on diet and stress: (1) control without transport stress, (2) control with transport stress, (3) PMS-supplemented without transport stress, and (4) PMS-supplemented with transport stress. Serum, jejunum, and longissimus thoracis muscle (LM) samples were collected. The results indicated that dietary PMS supplementation did not significantly affect growth performance during the fattening stage (p > 0.05). However, following transport, the PMS pigs showed a reduction in norepinephrine and cortisol concentrations (p = 0.09, p < 0.05) and a significant increase in serum glutathione peroxidase (GSH-Px) activity (p < 0.05). Furthermore, PMS supplementation significantly increased serum catalase (CAT), total antioxidant capacity (T-AOC), alkaline phosphatase (AKP) activity, and high-density lipoprotein cholesterol (HDL-C) levels (p < 0.05), while significantly reducing cholesterol (CHO) levels (p < 0.05). Transport stress adversely affected the intestinal health of finishing pigs, as evidenced by a decrease in intestinal villus height (0.05 < p < 0.1), a condition ameliorated by PMS supplementation. Additionally, transported pigs exhibited a higher drip loss24h in LM (p < 0.05), which was also alleviated through PMS supplementation. In conclusion, PMS supplementation mitigates transport stress and improves meat quality in finishing pigs.
Phenolic Acid Profile and In Vitro Antioxidant and Anticholinesterase Activities of Romanian Wild-Grown Acer spp. (Sapindaceae)
Authors: Bejenaru Cornelia , Segneanu Adina-Elena , Bejenaru Ludovic Everard , Biţă Andrei , Radu Antonia , Mogoşanu George Dan , Ciocîlteu Maria Viorica , Manda Costel Valentin
Published: January 2025

This study investigated, for the first time, the phenolic acid profile along with the in vitro antioxidant and acetylcholinesterase (AChE) inhibitory activities of leaves and fruits from five Romanian wild-grown Acer spp. (Sapindaceae): A. campestre L., A. monspessulanum L., A. platanoides L., A. pseudoplatanus L., and A. tataricum L. High-performance liquid chromatography coupled with ultraviolet and mass spectrometry (HPLC/UV/MS) was employed to identify and quantify key phenolic acids, including gallic, caffeic, protocatechuic, chlorogenic, and p-coumaric acids. Total phenolic content (TPC) ranged from 61.48 ± 3.76 to 512.8 ± 20.54 µg gallic acid equivalents (GAE)/mL. Antioxidant activities, assessed through 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays, demonstrated the strongest radical scavenging capacities for A. tataricum fruits, with half maximal inhibitory concentration (IC50) values of 10.88 ± 3.39 µg/mL and 10.39 ± 2.86 µg/mL, respectively. The ferric-reducing antioxidant power (FRAP) assay revealed the highest reducing power for A. tataricum fruits (1158 ± 48.98 µmol Fe2+/L) and leaves (1119 ± 164.6 µmol Fe2+/L). AChE inhibition was only found in A. tataricum, with an IC50 of 7.91 ± 7.50 mg/mL for leaves and 15.95 ± 8.52 mg/mL for fruits. These results highlight A. tataricum as a promising source of natural antioxidants and neuroprotective agents.
Explainable AI in Education: Techniques and Qualitative Assessment
Authors: Gunasekara Sachini , Saarela Mirka
Published: January 2025

Many of the articles on AI in education compare the performance and fairness of different models, but few specifically focus on quantitatively analyzing their explainability. To bridge this gap, we analyzed key evaluation metrics for two machine learning models—ANN and DT—with a focus on their performance and explainability in predicting student outcomes using the OULAD. The methodology involved evaluating the DT, an intrinsically explainable model, against the more complex ANN, which requires post hoc explainability techniques. The results show that, although the feature-based and structured decision-making process of the DT facilitates natural interpretability, it struggles to model complex data relationships, often leading to misclassification. In contrast, the ANN demonstrated higher accuracy and stability but lacked transparency. Crucially, the ANN showed great fidelity in result predictions when it used the LIME and SHAP methods. The results of the experiments verify that the ANN consistently outperformed the DT in prediction accuracy and stability, especially with the LIME method. However, improving the interpretability of ANN models remains a challenge for future research.
MM-iTransformer: A Multimodal Approach to Economic Time Series Forecasting with Textual Data
Authors: Mou Shangyang , Xue Qiang , Chen Jinhui , Takiguchi Tetsuya , Ariki Yasuo
Published: January 2025

This paper introduces a novel multimodal framework for economic time series forecasting, integrating textual information with historical price data to enhance predictive accuracy. The proposed method employs a multi-head attention mechanism to dynamically align textual embeddings with temporal price data, capturing previously unrecognized cross-modal dependencies and enhancing the model’s ability to interpret event-driven market dynamics. This enables the framework to model complex market behaviors in a unified and effective manner. Experimental results across multiple financial datasets, including the foreign exchange (Forex) and Gold-price datasets, demonstrate that incorporating textual information significantly enhances forecasting accuracy. Compared to models relying solely on historical price data, the proposed framework achieves a substantial reduction in Mean Squared Error (MSE) loss, with improvements of up to 26.79%. This highlights the effectiveness of leveraging textual inputs alongside structured time series data in capturing complex market dynamics and improving predictive performance.
Enhancing Colony Detection of Microorganisms in Agar Dishes Using SAM-Based Synthetic Data Augmentation in Low-Data Scenarios
Authors: Mennemann Kim , Ebert Nikolas , Reichardt Laurenz , Wasenmüller Oliver
Published: January 2025

In many medical and pharmaceutical processes, continuous hygiene monitoring relies on manual detection of microorganisms in agar dishes by skilled personnel. While deep learning offers the potential for automating this task, it often faces limitations due to insufficient training data, a common issue in colony detection. To address this, we propose a simple yet efficient SAM-based pipeline for Copy-Paste data augmentation to enhance detection performance, even with limited data. This paper explores a method where annotated microbial colonies from real images were copied and pasted into empty agar dish images to create new synthetic samples. These new samples inherited the annotations of the colonies inserted into them so that no further labeling was required. The resulting synthetic datasets were used to train a YOLOv8 detection model, which was then fine-tuned on just 10 to 1000 real images. The best fine-tuned model, trained on only 1000 real images, achieved an mAP of 60.6, while a base model trained on 5241 real images achieved 64.9. Although far fewer real images were used, the fine-tuned model performed comparably well, demonstrating the effectiveness of the SAM-based Copy-Paste augmentation. This approach matches or even exceeds the performance of the current state of the art in synthetic data generation in colony detection and can be expanded to include more microbial species and agar dishes.
Drug Delivery Systems Utilizing Essential Oils and Their Compounds—A Promising Approach to Fight Pathogens
Authors: Hartman Kacper , Świerczyńska Maja , Wieczorek Amelia , Baszuk Piotr , Wojciechowska-Koszko Iwona , Sienkiewicz Monika , Kwiatkowski Paweł
Published: January 2025

Essential oils (EOs) and their compounds are becoming a growing interest in medical sciences. Despite their potential as antimicrobial, anxiolytic, cytotoxic, and immunosuppressive drugs, their chemical characteristics make them difficult to use in direct treatment. This article intends to summarize the current body of knowledge regarding drug delivery systems that can overcome obstacles, such as low water solubility, volatility, oxidation potential, photodegradation, and thermal instability of EO compounds. Various materials like zeolites, alginate, chitosan, cellulose nanomaterials, zein, poly (D,L-lactic-co-glycolic) acid, liposomes, nanoemulsions, and their modifications can help to mitigate these problems, but their utilization in medical settings is still lacking. The biggest issue in the utilization of natural compounds seems to be the very low number of clinical trials, which seriously impedes their usage despite favorable outcomes in/of in vitro experiments.
Effects of Illuminance Level of Light Source on White Appearance of a Tablet Display
Authors: Huang Hsin-Pou , Li Hung-Chung , Fei Yu-Ming , Wei Minchen
Published: January 2025

The appearance of white significantly impacts display image quality, requiring a neutral white point for optimal performance. This study explores how perceived whiteness changes under ambient illumination levels (150, 300, 600, and 1200 lx) and correlated color temperatures (3500 K and 6500 K). As a result, the adapted white points of light sources with different correlated color temperatures are similar at lower ambient illuminance levels. In comparison, their adaptation trends exhibit significant differences at higher illuminance levels. At 150 lx, adapted white points for 3500 K and 6500 K light sources shift toward higher color temperatures and converge. With increased illumination, the 3500 K white point shifts toward its light source, while the 6500 K white point shifts to a higher correlated color temperature. The neural network-based prediction model developed in this study accurately forecasts perceived whiteness across conditions, offering valuable design guidance for the display and lighting industries.
Effect of Axial Modification on the Meshing Performance of Involute Beveloid Gear Pair
Authors: Liu Yongping , Chen Qi , Dong Changbin
Published: January 2025

In order to enhance the load-bearing capacity of the involute beveloid gear pair, this study proposes research on improving the contact stress through axial modification. Under the condition of segmented parabolic axial modification, the mathematical equation for the tooth flank of the beveloid gear is derived. A simulation meshing model for the beveloid gear pair is constructed to investigate the effects of different amounts and lengths of axial modification on the meshing performance, changes in flank and root stress, and transmission error before and after modification. The results indicate that as the modification amount increases, the maximum equivalent stress initially decreases and then exhibits a tendency towards stability. Moreover, an increase in modification length concentrates the contact zone towards the middle of the tooth flank while expanding the range of tooth root stress distribution, and it also leads to a decrease in maximum equivalent stress. The implementation of the modification has been observed to result in a reduction in cumulative transmission error and an effective reduction in edge load on the beveloid gear pair, which has been demonstrated to enhance the bearing capacity and transmission stability extension while also impacting the dynamic meshing performance.
Effect of Minor Reinforcement with Ultrafine Industrial Microsilica Particles and T6 Heat Treatment on Mechanical Properties of Aluminum Matrix Composites
Authors: Abishkenov Maxat , Tavshanov Ilgar , Lutchenko Nikita , Nogaev Kayrosh , Kalmyrzayev Daniyar , Abdirashit Assylbek , Aikenbayeva Nazira
Published: January 2025

This study examines the use of ultrafine (~128 nm) microsilica (composed of a mixture of amorphous and microcrystalline silicon dioxide phases) particles, an industrial waste product, as a reinforcing material to create aluminum matrix composites (AMCs) via ultrasonic-assisted stir casting followed by T6 heat treatment. This study aimed to improve the mechanical properties of pure aluminum, which has insufficient strength for most engineering applications. The main objective of this study is to develop environmentally and economically efficient AMCs with improved properties, namely, the balance between strength and ductility, for further application in caliber rolling processes. Attention is also paid to minor reinforcements using a low concentration of microsilica (~0.36%wt), which minimizes the problems with the wettability of the reinforcing material particles. The composites reinforced with ultrafine microsilica exhibited enhanced mechanical performance, including a 59.7% increase in Vickers microhardness and a significant improvement in tensile strength, reaching 73 MPa. Additionally, T6 heat treatment synergistically improved ductility to 60.3% elongation while maintaining high strength, achieving a balanced performance suitable for forming processes. The study results confirm that using microsilica as a reinforcing material is an effective way to improve the performance of aluminum alloys, while minimizing costs and solving environmental problems.
Generation of Penetrometric Profile of the Soil Applying Machine Learning to Measure While Drilling Data from Deep Foundation Machinery
Authors: Martínez García Eduardo , Alberti Marcos García , Arcos Álvarez Antonio Alfonso
Published: January 2025

The study performed in this article aimed to reproduce the penetrometric profile of the soil from the perforation parameters of deep foundation and ground improvement. This could allow for more easily interpretable information on the soil strength during execution as well as validate the design hypotheses. To achieve this goal, a series of Machine Learning algorithms have been used and compared with traditionally applied analytical formulas. Dynamic time warping is used to measure the likeness of the results with the expected shape. The results show that the algorithms are capable of better fitting the penetrometric profiles of the soil. Tree ensemble methods stand out with the best results.
A Novel Hybrid Methodology Based on Transfer Learning, Machine Learning, and ReliefF for Chickpea Seed Variety Classification
Authors: Kılıç İbrahim , Yalçın Nesibe
Published: January 2025

Seed quality is a critical factor in crop production. Therefore, seed classification is required to obtain high-quality seeds and to enhance agricultural sustainability and productivity. This study focuses on the varietal classification of chickpeas, an important source of protein and fiber. Chickpea seed varieties can currently be identified by domain experts; their reliability and efficiency depend on the experience and skills of experts and are prone to human error. The design of classification models with high accuracy to assist in selection mechanisms is required for chickpea varieties. In this study, a novel hybrid methodology is proposed for the chickpea classification problem. This methodology combines three well-suited and robust components: feature extraction using three pre-trained models, feature selection with the ReliefF algorithm, and classification employing classical machine learning methods to enhance classification accuracy and efficiency. Various experiments have been conducted using the four hybrid models developed. Their performance has been compared in terms of accuracy, recall, F1-score, precision, and AUC. TL+SVM and TL+LDA outperformed the other models, with test accuracies of 94.4% and 94%, respectively. These results demonstrate the potential of a powerful model that will be beneficial as a component of computer vision systems in smart agriculture applications.
Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications
Authors: Terras Nuno , Pereira Filipe , Ramos Silva António , Santos Adriano A. , Lopes António Mendes , Silva António Ferreira da , Cartal Laurentiu Adrian , Apostolescu Tudor Catalin , Badea Florentina , Machado José
Published: January 2025

Collaborative robotics and computer vision systems are increasingly important in automating complex industrial tasks with greater safety and productivity. This work presents an integrated vision system powered by a trained neural network and coupled with a collaborative robot for real-time sorting and quality inspection in a food product conveyor process. Multiple object detection models were trained on custom datasets using advanced augmentation techniques to optimize performance. The proposed system achieved a detection and classification accuracy of 98%, successfully processing more than 600 items with high efficiency and low computational cost. Unlike conventional solutions that rely on ROS (Robot Operating System), this implementation used a Windows-based Python framework for greater accessibility and industrial compatibility. The results demonstrated the reliability and industrial applicability of the solution, offering a scalable and accurate methodology that can be adapted to various industrial applications.
Age Prediction from Korean Speech Data Using Neural Networks with Diverse Voice Features
Authors: Ku Hayeon , Lee Jiho , Lee Minseo , Kim Seulgi , Yoon Janghyeok
Published: January 2025

A person’s voice serves as an indicator of age, as it changes with anatomical and physiological influences throughout their life. Although age prediction is a subject of interest across various disciplines, age-prediction studies using Korean voices are limited. The few studies that have been conducted have limitations, such as the absence of specific age groups or detailed age categories. Therefore, this study proposes an optimal combination of speech features and deep-learning models to recognize detailed age groups using a large Korean-speech dataset. From the speech dataset, recorded by individuals ranging from their teens to their 50s, four speech features were extracted: the Mel spectrogram, log-Mel spectrogram, Mel-frequency cepstral coefficients (MFCCs), and ΔMFCCs. Using these speech features, four deep-learning models were trained: ResNet-50, 1D-CNN, 2D-CNN, and a vision transformer. A performance comparison of speech feature-extraction methods and models indicated that MFCCs + ΔMFCCs was the best for both sexes when trained on the 1D-CNN model; it achieved an accuracy of 88.16% for males and 81.95% for females. The results of this study are expected to contribute to the future development of Korean speaker-recognition systems.
Hot-Pressing of Ti-Al-N Multiphase Composite: Microstructure and Properties
Authors: Sitek Ryszard , Bochenek Kamil , Maj Piotr , Marczak Michał , Żaba Krzysztof , Kopec Mateusz , Kaczmarczyk Grzegorz Piotr , Kamiński Janusz
Published: January 2025

This study focuses on the development and characterization of a bulk Ti-Al-N multiphase composite enriched with BN addition and sintered through hot pressing. The research aimed to create a material with optimized mechanical and corrosion-resistant properties suitable for demanding industrial applications. The composite was synthesized using a powder metallurgy approach with a mixture of AlN, TiN, and BN powders, processed under a high temperature and pressure. Comprehensive analyses, including microstructural evaluation, hardness testing, X-ray tomography, and electrochemical corrosion assessments, were conducted. The results confirmed the formation of a multiphase microstructure consisting of TiN, Ti₂AlN and Ti₃AlN phases. The microstructure was uniform with minimal porosity, achieving a hardness within the range of 500–540 HV2. Electrochemical tests revealed the formation of a passive oxide layer that provided moderate corrosion resistance in chloride-rich environment. However, localized pitting corrosion was observed under extreme conditions. The study highlights the potential of a BN admixture to enhance mechanical and corrosion-resistant properties and suggests directions for further optimization in sintering processes and material formulations.
Some Critical Thinking on Electric Vehicle Battery Reliability: From Enhancement to Optimization
Authors: Lin Jing , Silfvenius Christofer
Published: January 2025

Electric vehicle (EV) batteries play a crucial role in sustainable transportation, with reliability being pivotal to their performance, longevity, and environmental impact. This study explores battery reliability from micro (individual user), meso (industry), and macro (societal) perspectives, emphasizing interconnected factors and challenges across the lifecycle. A novel lifecycle framework is proposed, introducing the concept of “Zero-Life” reliability to expand traditional evaluation methods. By integrating the reliability ecosystem with a dynamic system approach, this research offers comprehensive insights into the optimization of EV battery systems. Furthermore, an expansive Social–Industrial Large Knowledge Model (S-ILKM) is presented, bridging micro- and macro-level insights to enhance reliability across lifecycle stages. The findings provide a systematic pathway to advance EV battery reliability, aligning with global sustainability objectives and fostering innovation in sustainable mobility.
The Role of Circulating Biomarkers in Patients with Coronary Microvascular Disease
Authors: Quarta Rossella , Martino Giovanni , Romano Letizia Rosa , Lopes Giovanni , Greco Francesco Fabio , Spaccarotella Carmen Anna Maria , Indolfi Ciro , Curcio Antonio , Polimeni Alberto
Published: January 2025

Coronary microvascular disease (CMD) comprises a spectrum of conditions characterized by the functional and structural abnormalities of coronary microcirculation, affecting vessels typically smaller than 500 μm. Despite its clinical significance as a contributor to myocardial ischemia, CMD frequently remains underdiagnosed due to the limitations of current diagnostic approaches. Invasive testing, including coronary reactivity assessment, is considered the gold standard, but it is resource-intensive and not always accessible. Non-invasive methods, such as positron emission tomography (PET) and transthoracic Doppler echocardiography (TTDE), offer alternatives but are limited by varying accuracy and accessibility. Amid these diagnostic challenges, there is increasing interest in circulating biomarkers as adjuncts in CMD evaluation. Biomarkers associated with endothelial dysfunction, inflammation, and oxidative stress, detectable through routine blood tests, may assist in CMD diagnosis, risk stratification, and therapeutic monitoring. These biomarkers can offer insights into CMD pathogenesis and enable early, non-invasive screening to identify patients who may benefit from more invasive investigations. This narrative review examines studies assessing biomarkers in CMD patients with diagnoses confirmed through invasive techniques. Our objective is to focus on circulating biomarkers linked to the invasive evaluation of coronary microcirculation, aiming to advance the understanding of the underlying mechanisms of this prevalent condition and enhance diagnostic accuracy and the clinical management of affected patients.
“One Pot” Enzymatic Synthesis of Caffeic Acid Phenethyl Ester in Deep Eutectic Solvent
Authors: Tripon Maria Roberta , Tulcan Camelia , Marc Simona , Camen Dorin-Dumitru , Paul Cristina
Published: January 2025

Caffeic acid phenethyl ester (CAPE) represents a valuable ester of caffeic acid, which, over time, has demonstrated remarkable pharmacological properties. In general, the ester is obtained in organic solvents, especially by the esterification reaction of caffeic acid (CA) and 2-phenylethanol (PE). In this context, the purpose of this study was the use of the “one pot” system to synthesize CAPE through biocatalysis with various lipases in a choline-chloride-based DES system, employing the “2-in-1” concept, where one of the substrates functions as both reactant and solvent. The synthesis process of CAPE is contingent on the molar ratio between CA and PE; thus, this factor was the primary subject of investigation, with different molar ratios of CA and PE being studied. Furthermore, the impact of temperature, time, the nature of the biocatalyst, and the water loading of the DES system was also examined. This ‘green’ synthesis method, which has demonstrated encouraging reaction yields (%), could secure and maintain the therapeutic potential of CAPE, mainly due to the non-toxic character of the reaction medium.
Natural Compounds and Histone Deacetylase Inhibitors: A Combined Approach Against mCRPC Cells
Authors: Alimudin Janiah , Betts Zeynep , Ozkan Asuman Deveci
Published: January 2025

Background: Sodium butyrate (NaBu), a short-chain fatty acid, modulates global gene expression through histone deacetylase (HDAC) inhibition, suppressing proliferation and inducing apoptosis in various cancers. Rutin (RUT), a polyphenolic flavonoid found in many plants, exhibits notable anticancer properties. Combining chemotherapeutic agents with natural polyphenols represents a promising strategy for cancer therapy. This study aims to evaluate, for the first time, the potential effects of NaBu and RUT combination therapy on metastatic castration-resistant prostate cancer (mCRPC) cells. Methods: PC-3 cells were treated with varying concentrations of NaBu, RUT, and their combinations. Cell viability was assessed using the WST-1 assay. Based on combination index values, selected treatments were further analyzed for apoptosis (Annexin V assay), intracellular reactive oxygen species (ROS) production, mRNA expression levels, and changes in cell and nuclear morphology. Results: The combined treatment of NaBu and RUT significantly reduced cell viability compared to individual treatments. Enhanced apoptotic induction and elevated ROS levels were observed in combination-treated cells, alongside notable changes in cellular and nuclear morphology and mRNA expression levels. Conclusions: NaBu and RUT combination therapy exhibits a synergistic anticancer effect in mCRPC cells by inhibiting cell viability, inducing apoptosis, and increasing ROS production. These findings suggest a promising therapeutic approach that warrants further investigation to elucidate the underlying molecular mechanisms and assess its potential in preclinical and clinical settings.
Cryoballoon Pulmonary Vein Isolation in Obese Patients with Atrial Fibrillation Compared to Non-Obese Counterparts: A Meta-Analysis
Authors: Vrachatis Dimitrios A. , Papathanasiou Konstantinos A. , Kazantzis Dimitrios , Anagnostopoulos Ioannis , Kousta Maria , Giotaki Sotiria G. , Deftereos Gerasimos , Lambadiari Vaia , Giannopoulos George , Basdra Efthimia K. , Papaioannou Theodore G. , Siasos Gerasimos , Deftereos Spyridon
Published: January 2025

Introduction: Obesity is an important risk factor for atrial fibrillation (AF) development. Data on cryoballoon ablation (CBA) outcomes in obese patients have so far been scarce. We reviewed the existing literature to compare the efficacy and safety of CBA in obese versus non-obese AF patients. Methods: A systematic literature search was conducted for studies comparing clinical outcomes (arrhythmia recurrence and/or procedural data and/or safety outcomes) between obese and non-obese patients undergoing CBA for AF. Statistical pooling was performed according to a random-effects model with generic inverse-variance weighting of relative risks (RRs) and standardised mean differences (SMDs) computing risk estimates with 95% confidence intervals (CIs). Results: Obese and non-obese patients had comparable arrhythmia recurrence rates (normal versus overweight, RR = 0.95, 95% CI: 0.82–1.11, p = 0.55, I2% = 91%; normal versus class I obesity, RR = 0.97, 95% CI: 0.82–1.13, p = 0.68, I2% = 87%; normal versus class II obesity, RR = 0.98, 95% CI: 0.91–1.07, p = 0.29, I2% = 65%). Procedure time was marginally increased in obese patients compared to non-obese counterparts (normal versus overweight, SMD = 0.05, 95% CI: −0.15–0.26, p = 0.62, I2% = 74%; normal versus class I obesity, SMD = 0.10, 95% CI: −0.00–0.19, p = 0.06, I2% = 2%; overweight versus class I obesity, SMD = 0.11, 95% CI: 0.01–0.21, p = 0.048, I2% = 25%). Regarding radiation exposure, fluoroscopy time was increased in patients with class I obesity compared to normal-weight or overweight patients and dose area product was also increased in obese patients compared to non-obese patients. Lastly, the risk of complications did not differ between obese and non-obese patients. Statistical heterogeneity and the small number of patients included are the main limitations of this study. Conclusion: CBA seems to be effective for obese patients suffering from AF, featuring also similar safety outcomes with non-obese individuals. Radiation exposure was increased in obese patients.
Advancing Atrial Fibrillation Research: The Role of Animal Models, Emerging Technologies and Translational Challenges
Authors: Ferreira Monica , Geraldes Vera , Felix Ana Clara , Oliveira Mario , Laranjo Sergio , Rocha Isabel
Published: January 2025

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, presenting a significant global healthcare challenge due to its rising incidence, association with increased morbidity and mortality, and economic burden. This arrhythmia is driven by a complex interplay of electrical, structural, and autonomic remodelling, compounded by genetic predisposition, systemic inflammation, and oxidative stress. Despite advances in understanding its pathophysiology, AF management remains suboptimal, with ongoing debates surrounding rhythm control, rate control, and anticoagulation strategies. Animal models have been instrumental in elucidating AF mechanisms, facilitating preclinical research, and advancing therapeutic development. This review critically evaluates the role of animal models in studying AF, emphasizing their utility in exploring electrical, structural, and autonomic remodelling. It highlights the strengths and limitations of various models, from rodents to large animals, in replicating human AF pathophysiology and advancing translational research. Emerging approaches, including optogenetics, advanced imaging, computational modelling, and tissue engineering, are reshaping AF research, bridging the gap between preclinical and clinical applications. We also briefly discuss ethical considerations, the translational challenges of animal studies and future directions, including integrative multi-species approaches, omics technologies and personalized computational models. By addressing these challenges and addressing emerging methodologies, this review underscores the importance of refining experimental models and integrating innovative technologies to improve AF management and outcomes.
Forgotten Industrial Heritage: The Cement Factory from La Granja d’Escarp
Authors: Ramírez-Casas Judit , Gómez-Val Ricardo , Buill Felipe , González-Sánchez Belén , Navarro Ezquerra Antonia
Published: January 2025

In the municipality of La Granja d’Escarp, for over thirty years, an important natural cement factory was in operation. In 1876, the Girona family, who were businessmen and bankers from Barcelona, opened the factory with modern industrial facilities. It included kilns, mills, and crushers, alongside warehouses, a small railway for internal transportation of the various materials used, and even a housing area for workers. The neighboring Ebro River allowed distribution by river transport at first. Later, with the use of railways, transport to consumption points was possible. This industrial complex became a center of significant importance in Catalonia in the production of cement, which was used for building hydraulic and civil works. During the first decade of the twentieth century, the factory stopped its activity and the facilities were abandoned. Nowadays, this industrial heritage site is in a state of neglect, without any kind of protection or maintenance. In turn, this has caused the collapse of some buildings in recent times and the loss of historical value of the architectural ensemble. We have carried out initial geomatic research, which has highlighted the constructive properties of the kilns. We have also tested five samples from different buildings using XRD and TGA/DSC, which showed the use of lime mortars in their construction. This is the first study to be carried out at this site, with the aim of showing the historical importance of the ensemble. The goal of the study was to highlight the value of this industrial heritage site and illustrate that it was once a pioneer in the production of natural cement and a driving force for Catalonia.
Increasing the Market Value of Buildings Through Energy Retrofitting: A Comparison of Actual Retrofit Costs and Perceived Values
Authors: Gholamzadehmir Maryam , Pandolfi Alessandra Maria , Del Pero Claudio , Leonforte Fabrizio , Sdino Leopoldo
Published: January 2025

This study investigates how energy retrofitting measures contribute to increasing the market value of multi-family residential buildings within the European real estate market. It examines how energy efficiency improvements, driven by EU decarbonization strategies, enhance the actual and perceived value of these properties. The research employs a dual-methodology approach, integrating the Cost Approach to estimate the financial impact of retrofitting with the Contingent Valuation Method (CVM) to evaluate consumer willingness-to-pay (WTP) for energy-efficient properties. Two real case studies are considered to evaluate the methodology and how the monetary value of buildings is affected by their energy efficiency. The results revealed that buildings subjected to deep energy retrofitting are more attractive to potential buyers, who are willing to pay a premium of 13.5% over properties in pre-retrofit conditions. This underscores the tangible market value increment attributed to energy efficiency enhancements. This study bridges the gap between the quantifiable costs of energy retrofitting and the market valuation, offering a dual perspective by integrating both actual cost analysis and perceived market value. Moreover, this work highlights the correlation between energy retrofit investments and increased market value in the European real estate sector.
Digital Evolution in Nigerian Heavy-Engineering Projects: A Comprehensive Analysis of Technology Adoption for Competitive Edge
Authors: Aliu John , Oke Ayodeji Emmanuel , Jesudaju Oluwatayo Timothy , Akanni Prince O. , Ehbohimen Tolulope , Dosumu Oluwaseun Sunday
Published: January 2025

The fourth industrial revolution has introduced a range of digital technologies (DTs) that possess the potential to significantly enhance the operations and competitiveness of heavy-construction firms. Grounded in the Technology–Organization–Environment (TOE) Framework, the Resource-Based View (RBV) and the Diffusion of Innovation Theory (DOI), this study investigates the relationship between the adoption of digital technologies and the competitive edge (CE) of heavy-engineering firms. Specifically, this research seeks to assess how the adoption of DTs impacts four critical competitive-edge metrics: efficient resource management (CE1), real-time monitoring and control (CE2), data-driven decision-making (CE3) and improved collaboration and communication (CE4). A quantitative research approach was employed, using a structured questionnaire distributed to construction professionals in Lagos State, Nigeria. The principal results of the study revealed that firms adopting artificial intelligence (AI), cloud-based technology and the Internet of Things (IoT) exhibited significantly higher competitive-edge metrics compared to their counterparts. Notably, AI and cloud-based technology were found to have a particularly strong association with improved resource management, real-time monitoring, and decision-making processes. A major contribution of this research is the development of a DT-adoption model which can serve as a benchmarking tool for firms to assess their current adoption levels and identify areas for improvement. This model can also guide policymakers and regulators in developing strategies to encourage the integration of digital technologies within the heavy-construction industry. The originality of this study lies in its holistic approach, examining a broad spectrum of digital technologies and their collective impact on enhancing the competitive edge of construction firms.
The Moderating Effect of Education Level and Income on Job Performance of Supervising Engineers
Authors: Katebi Ali , Keshtkar-Ghalati Ahmadreza , Katebi Bahareh , Ardestani Zahra Alsadat , Bordbar Ali
Published: January 2025

The proper implementation of the monitoring process in construction projects helps strengthen sustainable development indicators. The aim of this study is to investigate the moderating effects of education and income on the relationship between job performance and effective factors on supervising engineers. Data gathering was performed by questionnaire, and analysis was performed with the PLS-SEM approach. According to the results of the moderator analysis, demographic variables of education and income levels, on the relationship between feedback and competence, as well as the relationship between job identity and job performance, had a significant moderating role. In addition, the moderating effect of demographic variables on the relationship between autonomy and competence, as well as the relationship between affective commitment and job performance, was confirmed. The moderating effect of income demographic variables on the relationship between independence and affective commitment and the relationship between competence and job performance was meaningful. To achieve a better understanding of the effects of variables on job performance, the mediation role of competence and affective commitment was studied in the research model. By using the results of this research, officials and managers of construction organizations can adopt appropriate payment and training policies, to better management of the performance of the supervising engineers.
An Airflow Output Control to Maintain a Constant Body Heat Loss During Sleep on Temperature-Changing Nights: Implementation in a Ventilated Sleep Capsule
Authors: Sakoi Tomonori , Kuroda Masaki , Kurazumi Yoshihito , Takaoka Yoshihisa , Narita Kaori , Apriliyanthi Sri Rahma
Published: January 2025

Good sleep is essential for a healthy life. While airflow improves sleep in a hot environment, it may cause an excessive drop in body temperature because thermal-adaptive behavior is inactive during sleep. This study aims to propose an airflow control theory that prevents the excessive drop in body temperature while maintaining good sleep. The theory changes the heat transfer coefficient between the skin and the environment by the intensity of the fan operation to maintain a heat loss of 30 W/m2 from a body with a skin temperature of 34.5 °C in a temperature-changing environment. We fabricated a ventilated sleep capsule in which this theory was embedded. Thermal manikin experiments were conducted to obtain the relationship between sensible heat transfer coefficients and the fan operating signal to establish control equations. We conducted a case study to evaluate whether the theory provided better sleep than sleeping with a regular fan running freely in homes in Ueda City, Japan, in one summer. Although the data used in the analysis were from only three subjects, the statistical analysis showed that sleeping in the ventilated sleep capsule provided better sleep, with a mean of 6% better sleep efficiency.
Amplitude-Scaling Bias Analysis of Ground Motion Record Set in Strip Method for Structural Seismic Fragility Assessment
Authors: Song Zhuo , Li Xiaojun , Wang Yushi , Zhou Bochang
Published: January 2025

The multi-strip method is often used to establish a demand model for fragility analysis. Using the multi-strip method to scale the ground motion record may cause uncertainty and bias in structural response calculation and fragility assessment. It is necessary to analyze the effect of differences in the amplitude scaling range in different strips on structural seismic response calculation and seismic fragility assessment. In this paper, the multi-strip method was used to analyze the seismic demand bias based on four multi-story reinforced concrete frame structures subjected to eight ground motion record sets. The bias, variance, and coefficient of variation in different strips in each group of ground motion records were obtained. The effect of different strips on the demand bias was investigated by analysis of variance (ANOVA). Uncertainty quantification of structural demand and fragility curves was carried out using the bootstrap sampling method. The results for structures in different ground motion record sets verify that the differences between the demand bias for different strips by amplitude scaling are statistically insignificant for a 95% confidence level. These findings will contribute to the use of scaling methods for ground motion record sets in a probabilistic seismic demand assessment, allowing for a more reliable prediction of structural seismic fragility.
Modelling Work–Study Conflict Effects on Built Environment Students’ Well-Being, Health, and Academic Performance
Authors: Samaratunga Marini , Kamardeen Imriyas
Published: January 2025

Balancing academic responsibilities and work commitments poses significant challenges for students in built environment (BE) programs, often leading to compromised well-being and academic difficulties. This study examines the causal effects of balancing work–study responsibilities on students’ health, well-being, and academic outcomes, addressing critical gaps in the literature. A positivist research approach was employed, with data collected via an online survey of 253 undergraduate BE students from Australian universities. The survey assessed academic and work stressors, health, mental well-being, coping strategies, and academic outcomes. Statistical analyses, including one-sample t-tests, factor analysis, and path analysis, were used to analyse the data. The findings reveal that BE students experience severe stress and moderate levels of anxiety and depression, significantly impacting their academic performance. Common health issues include fatigue and sleep disturbances. Work–study conflict and performance anxiety were identified as the strongest predictors of negative health outcomes, with maladaptive coping strategies amplifying these effects. These results underscore the urgent need for institutional interventions, such as flexible learning options, enhanced mental health services, and industry partnerships to alleviate work–study conflicts. Encouraging adaptive coping mechanisms and providing targeted support can help mitigate adverse effects, promoting improved academic performance and well-being among BE students. These findings can be generalised to students in other academic disciplines and countries, as similar work–study challenges and their associated impacts are observed globally.