bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Physical Activity and Screen Time on Motor Creativity in Kindergarteners
Authors: Ghanamah Rafat
Published: January 2025

Background/Objectives: Motor creativity and physical activity are essential to early childhood development, impacting physical, cognitive, and socio-emotional development. This study investigates the relationships among motor creativity, motor working memory (MSTM), screen time, and physical activity (PA) in kindergarten children, focusing on the mediating roles of cognitive functions and screen time. Methods: Data were collected from 124 Arab Israeli kindergarten children through assessments of Thinking Creatively in Action and Movement (TCAM) for motor creativity and the Hand Movement Test for MSTM. Parents reported the children’s screen time and days engaged in moderate-to-vigorous physical activity (MVPA). Results: The results show significant positive associations between motor creativity and both MSTM and PA, underscoring the role of cognitive processes in creative motor expression. Linear regression and mediation analyses showed that MSTM significantly mediates the relationship between PA and motor creativity. Conversely, screen time negatively correlates with PA and motor creativity, serving as a significant mediator that restricts opportunities for physical and creative activities. Conclusions: This study emphasizes the bidirectional relationship between PA and motor creativity, wherein diverse physical activities stimulate creativity, and creative movements encourage active participation. The combined mediating effects of MSTM and screen time highlight the complexity of these relationships, suggesting the need for integrated interventions. The findings inform early childhood education by advocating for strategies that promote physical activity, enhance cognitive functions, and limit excessive screen time, fostering holistic development in young children.
Fabrication of Microcrystalline Silicon Thin Film by Ionized Physical Vapor Deposition Process
Authors: Saikia Rimlee , Kakati Bharat , Hazarika Tonmoi , Sharma Shivam , Rajbongshi Tapan , Das Mausumi , Biswas Subir , Kundu Sarathi , Mahanta Manoj Kumar
Published: January 2025

The present manuscript describes the fabrication of microcrystalline silicon (µc-Si) thin films at room temperature using the ionized physical vapor deposition (iPVD) process. The iPVD chamber incorporates a planar DC magnetron and an additional RF coil to generate an intermediate dense plasma region between the target and the substrate. The intermediate dense plasma enhances the ionization of sputtered neutral Si atoms before deposition in the iPVD process. This process greatly impacts the structural, morphological, and optical characteristics of the Si thin films. X-ray diffraction (XRD) reveals that conventional PVD produces an amorphous Si thin film, while iPVD yields a µc-Si thin film with peaks at 28.5° and 47.3°, corresponding to the (111) and (220) planes of Si. Raman spectroscopy confirms the microcrystalline nature of the Si thin film, showing approximately 70% crystallinity in the iPVD process. FESEM images display a granular structure for PVD and a cauliflower-like structure for the iPVD process. AFM images indicate a significant reduction in surface roughness for iPVD films compared to the PVD process. UV-Visible absorption spectroscopy shows that the optical band gap (Eg) decreases from (1.7 ± 0.08) eV to (1.4 ± 0.05) eV while shifting from the PVD to iPVD process.
Correction: Kato-Noguchi, H.; Kato, M. Invasive Characteristics of Robinia pseudoacacia and Its Impacts on Species Diversity. Diversity 2024, 16, 773
Authors: Kato-Noguchi Hisashi , Kato Midori
Published: January 2025

In the originally published version of the paper [...]
Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI
Authors: Leone Augusto , Di Napoli Veronica , Fochi Nicola Pio , Di Perna Giuseppe , Spetzger Uwe , Filimonova Elena , Angileri Flavio , Carbone Francesco , Colamaria Antonio
Published: January 2025

Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including “MGMT”, “methylation”, “glioma”, “glioblastoma”, “machine learning”, “deep learning”, and “radiomics”, were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice.
Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?
Authors: Romeo Mario , Dallio Marcello , Napolitano Carmine , Basile Claudio , Di Nardo Fiammetta , Vaia Paolo , Iodice Patrizia , Federico Alessandro
Published: January 2025

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous “pandemic” spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this “cold war”, machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best “tailored” individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics–radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.
Comparative Analysis of Whitening Outcomes of Over-the-Counter Toothpastes: An In Vitro Study
Authors: Islam Md Sofiqul , Padmanabhan Vivek , Shanati Kamar Ali , Naser Ahmed Malalah , Hashim Nada Tawfig , Aryal A C Smriti
Published: January 2025

Background/Objectives: Whiter teeth are widely accepted as the most beautiful. With the growing demand for whiter teeth, several manufacturers have launched different brands of whitening toothpaste claiming to be effective in removing tooth stains and whitening teeth. The objective of this in vitro study was to evaluate and compare the whitening effect of eight over-the-counter available toothpastes by measuring the changes in color using a digital colorimeter in a simulated staining–brushing cycle model. Methods: A total of 32 extracted bovine enamel were polished with 1000–2000 grit SIC paper and immersed in a tea staining solution for 30 min at 37 °C to create extrinsic staining. The specimens were randomly divided into eight groups (n = 4) and subjected to a staining–brushing cycle for 2 weeks. During this period, the specimens were stained for 5 min twice, brushed for 2 min twice per day, and immersed in artificial saliva for the remaining time. The colors of the enamel surfaces L*, a*, and b* were recorded, and the color difference (ΔE) was measured before the treatment and after 1 and 2 weeks of the staining–brushing cycle. Results: The repeated measures showed a significant reduction of (ΔE) after 1 week of the staining–brushing cycle in all eight experimental groups (p < 0.05). The (ΔE) was significantly reduced after the second week of the staining–brushing cycle in groups 1, 3, and 5 (p < 0.05). However, it was insignificant in groups 2, 6, 7, and 8 (p > 0.05). Among the tested materials, group 1 showed the highest and group 8 showed the lowest teeth-whitening effects. Conclusions: The over-the-counter toothpaste used in this study showed effective teeth-whitening. Charcoal-based toothpaste showed the highest efficiency in teeth whitening.
Super-Twisting Algorithm Backstepping Adaptive Terminal Sliding-Mode Tracking Control of Quadrotor Drones Subjected to Faults and Disturbances
Authors: Zhang Ye , Fu Yihao , Han Zhiguo , Wang Jingyu
Published: January 2025

The rapid advancement of quadrotor systems has introduced significant challenges across multiple disciplines. Among these, fault tolerance and trajectory tracking in complex environments have long been recognized as critical challenges in quadrotor control research. To address issues such as rotor performance degradation and external disturbances, a novel position-attitude control system was developed, aimed to achieve precise position and attitude tracking. Initially, a dynamic model of the quadrotor was formulated, serving as the foundation for the controller design. Super-twisting algorithm terminal sliding-mode control (STATSMC) was then employed within the position loop to suppress chattering by the super-twisting algorithm. Subsequently, a new super-twisting algorithm beckstepping adaptive terminal sliding-mode control (STABATSMC) was proposed to mitigate the controller output and merge enable adherence to the desired Euler angles in case of failure. This approach enables the quadrotor to accurately follow position commands and achieve the desired attitude angles. The introduction of terminal sliding-mode control enhances convergence speed and tracking precision, while the super-twisting algorithm mitigates chattering and smoothens the control output. Finally, a series of simulation experiments were conducted within the Simulink environment to validate the proposed control system. The experimental results are compared with the state-of-art terminal sliding-mode control method, demonstrating the superior performance and effectiveness of the proposed method.
Research on Automatic Tracking and Size Estimation Algorithm of “Low, Slow and Small” Targets Based on Gm-APD Single-Photon LIDAR
Authors: Guo Dongfang , Qu Yanchen , Zhou Xin , Sun Jianfeng , Yin Shengwen , Lu Jie , Liu Feng
Published: January 2025

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction.
Measurement and Analysis of the Rician K-Factor for Low-Altitude UAV Air-to-Ground Communications at 2.5 GHz
Authors: Aoki Kaisei , Honda Kazuhiro
Published: January 2025

The research and development of unmanned aerial vehicles (UAVs) is progressing rapidly, and they are expected to be used in a wide range of applications. In this paper, we evaluated the propagation characteristics of air-to-ground (A2G) communications used by UAVs. Specifically, we investigated the Rician K-factor, which is one of the indicators representing the impact on communication quality. We carried out radio wave propagation measurements for A2G communications at low altitudes in propagation environments with simple (S environment) and complex (C environment) structures within the measurement area and then performed a detailed evaluation of the effect of the distance from buildings, UAV altitude, and antenna installation on the Rician K-factor and propagation characteristics. The measurement and analytical results reveal that the Rician K-factor in an S environment was observed to be high due to the strong dominance of the direct wave. On the other hand, the Rician K-factor in a C environment decreased because of complex multiple reflected and diffracted waves caused by surrounding buildings. In addition, dummy fading signals generated from the useful path calculated with the ray-tracing method using a simple 3D analytical model showed a high degree of agreement with the experimental results. These outcomes provide key parameters for the optimal design of UAV-based A2G communication systems, contributing to the practical application of UAV operations.
Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predictions
Authors: Wang Yuxuan , Zhang Zhouyuan , Pi Shu , Zhang Haishan , Pi Jiatian
Published: January 2025

Spatio-temporal prediction is crucial in intelligent transportation systems (ITS) to enhance operational efficiency and safety. Although Transformer-based models have significantly advanced spatio-temporal prediction performance, recent research underscores the importance of learning dynamic spatio-temporal dependencies for these tasks. This paper introduces the Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L), a framework specifically designed to address the complex demands of spatio-temporal prediction. The DG3L model includes a memory-based graph learning module capable of generating dynamic graphs to accurately reflect ongoing changes in spatio-temporal dependencies. By integrating the strengths of Transformer and Graph Convolutional Recurrent Unit (GCRU) technologies within its Dual-Gated Graph Convolutional Recurrent Unit architecture, DG3L provides a mechanism for fusing Transformer features with contextual features from recurrent units. In practical applications, DG3L acts as an advanced representation learning module, delivering highly accurate context features for complex downstream tasks in ITS.
Predicting Multi-Scale Positive and Negative Stock Market Bubbles in a Panel of G7 Countries: The Role of Oil Price Uncertainty
Authors: van Eyden Reneé , Gupta Rangan , Sheng Xin , Nielsen Joshua
Published: January 2025

While there is a large body of literature on oil uncertainty-equity prices and/or returns nexus, an associated important question of how oil market uncertainty affects stock market bubbles remains unanswered. In this paper, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium- and long-term stock markets of the G7 countries. While detecting major crashes and booms in the seven stock markets over the monthly period of February 1973 to May 2020, we also observe similar timing of strong (positive and negative) LPPLS-CIs across the G7, suggesting synchronized boom-bust cycles. Given this, we next apply dynamic heterogeneous coefficients panel databased regressions to analyze the predictive impact of a model-free robust metric of oil price uncertainty on the bubbles indicators. After controlling for the impacts of output growth, inflation, and monetary policy, we find that oil price uncertainty predicts a decrease in all the time scales and countries of the positive bubbles and increases strongly in the medium term for five countries (and weakly the short-term) negative LPPLS-CIs. The aggregate findings continue to hold with the inclusion of investor sentiment indicators. Our results have important implications for both investors and policymakers, as the higher (lower) oil price uncertainty can lead to a crash (recovery) in a bullish (bearish) market.
Combining Virtual and Hands-on Lab Work in a Blended Learning Approach on Molecular Biology Methods and Lab Safety for Lower Secondary Education Students
Authors: Haberbosch Maximilian , Deiters Marvin , Schaal Steffen
Published: January 2025

Molecular biology is becoming increasingly important in everyday life. Virtual and authentic hands-on out-of-school labs have proven effective in teaching it, especially for high-achieving older learners. We developed a blended learning approach integrating the advantages of both methods for lower secondary education students. In a quasi-experimental-control group study we accessed its impact on declarative knowledge in laboratory safety and molecular biology methods, along with perceived authenticity, in comparison to teaching through a virtual desktop simulation, a hands-on wet lab, or regular teaching with a worksheet. N = 229 students took part in the pre–post-follow-up data collection. The results showed a significant difference in laboratory safety knowledge between the blended learning and the other intervention groups. The wet lab group differed significantly in molecular biology knowledge and perceived authenticity from the other intervention groups. Learning success was positively correlated with the authenticity in the overall sample. The blended learning group’s reduced authenticity may contribute to lower learning success in molecular biology.
TheraSense: Deep Learning for Facial Emotion Analysis in Mental Health Teleconsultation
Authors: Hadjar Hayette , Vu Binh , Hemmje Matthias
Published: January 2025

Background: This paper presents TheraSense, a system developed within the Supporting Mental Health in Young People: Integrated Methodology for cLinical dEcisions and evidence (Smile) and Sensor Enabled Affective Computing for Enhancing Medical Care (SenseCare) projects. TheraSense is designed to enhance teleconsultation services by leveraging deep learning for real-time emotion recognition through facial expressions. It integrates with the Knowledge Management-Ecosystem Portal (SenseCare KM-EP) platform to provide mental health practitioners with valuable emotional insights during remote consultations. Method: We describe the conceptual design of TheraSense, including its use case contexts, architectural structure, and user interface layout. The system’s interoperability is discussed in detail, highlighting its seamless integration within the teleconsultation workflow. The evaluation methods include both quantitative assessments of the video-based emotion recognition system’s performance and qualitative feedback through heuristic evaluation and survey analysis. Results: The performance evaluation shows that TheraSense effectively recognizes emotions in video streams, with positive user feedback on its usability and integration. The system’s real-time emotion detection capabilities provide valuable support for mental health practitioners during remote sessions. Conclusions: TheraSense demonstrates its potential as an innovative tool for enhancing teleconsultation services. By providing real-time emotional insights, it supports better-informed decision-making in mental health care, making it an effective addition to remote telehealth platforms.
SCAGAN: Wireless Capsule Endoscopy Lesion Image Generation Model Based on GAN
Authors: Xiao Zhiguo , Zhang Dong , Chen Xianqing , Li Dongni
Published: January 2025

The wireless capsule endoscope (WCE) has been utilized for human digestive tract examinations for over 20 years. Given the complex environment of the digestive tract and the challenge of detecting multi-category lesion images, enhancing model generalization ability is crucial. However, traditional data augmentation methods struggle to generate sufficiently diverse data. In this study, we propose a novel generative adversarial network, Special Common Attention Generative Adversarial Network (SCAGAN), to generate lesion images for capsule endoscopy. The SCAGAN model can adaptively integrate both the internal features and external global dependencies of the samples, enabling the generator to not only accurately capture the key structures and features of capsule endoscopic images, but also enhance the modeling of lesion complexity. Additionally, SCAGAN incorporates global context information to improve the overall consistency and detail of the generated images. To further enhance adaptability, self-modulation normalization is used, along with the Structural Similarity Index (SSIM) loss function to ensure structural authenticity. The Differentiable Data Augmentation (DiffAug) technique is employed to improve the model’s performance in small sample environments and balance the training process by adjusting learning rates to address issues of slow learning due to discriminator regularization. Experimental results show that SCAGAN significantly improves image quality and diversity, achieving state-of-the-art (SOTA) performance in the Frechet Inception Distance (FID) index. Moreover, when the generated lesion images were added to the dataset, the mean average precision (mAP) of the YOLOv9-based lesion detection model increased by 1.495%, demonstrating SCAGAN’s effectiveness in optimizing lesion detection. SCAGAN effectively addresses the challenges of lesion image generation for capsule endoscopy, improving both image quality and detection model performance. The proposed approach offers a promising solution for enhancing the training of lesion detection models in the context of capsule endoscopy.
Research on Ship-Type Recognition Based on Fusion of Ship Trajectory Image and AIS Time Series Data
Authors: Pu Zhengpeng , Hong Yuan , Hu Yuling , Jiang Jingang
Published: January 2025

Achieving accurate and efficient ship-type recognition is crucial for the development and management of modern maritime traffic systems. To overcome the limitations of existing methods that rely solely on AIS time series data or navigation trajectory images as single-modal approaches, this study introduces TrackAISNet, a multimodal ship classification model that seamlessly integrates ship trajectory images with AIS time series data for improved performance. The model employs a parallel structure, utilizing a lightweight neural network to extract features from trajectory images, and a specially designed TCN-GA (Temporal Convolutional Network with Global Attention) to capture the temporal dependencies and long-range relationships in the AIS time series data. The extracted image features and temporal features are then fused, and the combined features are fed into a classification network for final classification. We conducted experiments on a self-constructed dataset of variable-length AIS time series data comprising four types of ships. The results show that the proposed model achieved an accuracy of 81.38%, recall of 81.11%, precision of 80.95%, and an F1 score of 81.38%, outperforming the benchmark single-modal algorithms. Additionally, on a publicly available dataset containing three types of fishing vessel operations, the model demonstrated improvements in accuracy, recall, and F1 scores by 5.5%, 4.88%, and 5.88%, respectively.
Additively Manufactured Three-Dimensional Microstrip Balun for Wearable Applications
Authors: Rice Allyanna , Kiourti Asimina
Published: January 2025

We present a three-dimensional, additively manufactured microstrip balun design for the balanced feeding of wearable antennas. Extensive research has been performed regarding wearable antennas, but the balun design is often ignored. The balun may be omitted, a commercial off-the-shelf balun may be used, or a bulky microstrip balun may be implemented; however, these options are either incorrect or add a significant size to the antenna that is not acceptable for wearable applications. We propose a three-dimensional, conformal microstrip balun enabled by additive manufacturing (AM) technology, and demonstrate its performance using the wearable High-Contrast Low-Loss Antenna (HCLA) as an example. First, the electromagnetic properties of potential substrate materials are characterized from 0.5 to 3 GHz. Exponential tapered baluns are designed, simulated, and tested in a back-to-back configuration to verify the measured material properties for the substrates. Then, the baluns are integrated with the HCLA using a conformal configuration. The measurement results from 0.5 to 3 GHz on the phantoms agree with the simulation for both the reflection coefficient and transmission loss. Importantly, the proposed balun allows the antenna to be used in wearable applications, where balun size would have previously hindered its implementation. The flexibility of the proposed design also allows for the integration with other antennas aside from the HCLA.
Multi-Label Classification of Complaint Texts: Civil Aviation Service Quality Case Study
Authors: Cai Huali , Shao Xuanya , Zhou Pengpeng , Li Hongtao
Published: January 2025

Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management.
Using Numerical Analysis to Design and Optimize River Hydrokinetic Turbines’ Capacity Factor to Address Seasonal Velocity Variations
Authors: Shaabani Bahador , Chatoorgoon Vijay , Bibeau Eric Louis
Published: January 2025

Seasonal velocity variations can significantly impact the total energy delivered to microgrids produced by river hydrokinetic turbines. These turbines typically use a diffuser to increase the velocity at the rotor section, adding weight and raising deployment costs. There is a need for practical solutions to improve the capacity factor of such turbines. Our solution involves using multiple turbine rotors that can be interchanged to match seasonal velocity changes, eliminating shrouds to simplify design and reduce costs. This solution requires turbines that are designed to have an easily interchanged rotor, which requires us to limit the rotor to a two-blade design to also lower costs. This approach adjusts the turbine power curve with different two-blade rotor sizes, enhancing the yearly capacity factor. BladeGen ANSYS Workbench is used to design three two-blade rotors for free stream velocities of 1.6, 2.2, and 2.8 m/s. For each turbine rotor, 3D simulation is applied to reduce aerodynamic losses and target a coefficient of performance of about 45%. Mechanical stress analyses assess the displacement and stress of the used composite materials. Numerical results show good agreement with experimental data, with rotor efficiencies ranging from 43% to 45% at a tip speed ratio of 4 and power output between 5.4 and 5.6 kW. Results show that rotor interchangeability significantly enhances the turbine capacity factor, increasing it from 52% to 92% by adapting to river seasonal velocity changes.
Enhanced COVID-19 Optimization Algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid
Authors: ALBaaj Basim , Kaplan Orhan
Published: January 2025

The optimal power flow (OPF) problem is a critical component in the design and operation of power transmission systems. Various optimization algorithms have been developed to address this issue. This paper expands the use of the coronavirus disease optimization algorithm (COVIDOA) to solve a multi-objective OPF problem (MO-OPF), incorporating renewable energy sources as distributed generation (DG) across multiple scenarios. The main objective is to minimize fuel costs, emissions, voltage deviations, and power losses. Due to its non-convex nature and computational complexity, OPF poses significant challenges. While COVIDOA has been utilized to solve engineering problems, it faces difficulties with non-linear and non-convex issues. This paper introduces an enhanced version, the enhanced COVID-19 optimization algorithm (ENHCOVIDOA), designed to improve the performance of the original method. The effectiveness of the proposed algorithm is validated through testing on IEEE 30-bus, 57-bus, and 118-bus systems, as well as a real-world 28-bus system representing Iraq’s standard Iraq super grid high voltage (SISGHV 28-bus). The two-point estimation method (TPEM) is also applied to manage uncertainties in renewable energy sources in some cases, leading to cost reductions and annual savings of ($70,909.344, $817,676.64, and $5,608,782.144) for the IEEE 30-bus, 57-bus, and reality 28-bus systems, respectively. Thirteen different cases were analyzed, and the results demonstrate that ENHCOVIDOA is notably more efficient and effective than other optimization algorithms in the literature.
Correction: Adibhesami et al. Optimization of Urban-Scale Sustainable Energy Strategies to Improve Citizens’ Health. Energies 2023, 16, 119
Authors:   Energies Editorial Office
Published: January 2025

Following publication [...]
Research on the Carbon Footprint Accounting Method of Transformer’s Whole Life Cycle Under the Background of Double Carbon
Authors: Li Wei , Bian Yifan , Zhang Yunyun , Zhou Erbiao , Xie Lirong
Published: January 2025

In response to the existing gaps in the carbon footprint assessment framework for core electrical equipment transformers, which impedes power companies from effectively supporting low-carbon procurement of materials and products, this study proposes a novel evaluation method for transformer carbon footprints. This method comprehensively considers all stages of the transformer lifecycle, including manufacturing, transportation, installation, operation, and decommissioning. A review of mainstream carbon footprint accounting schemes, both domestic and international, is first presented, summarizing established accounting methods and calculation processes. The paper then introduces a novel, integrated carbon footprint accounting approach for transformers, covering the entire ‘cradle-to-grave’ lifecycle, along with an associated calculation model. This framework analyzes the carbon footprint composition across production, assembly, transportation, usage, and recycling stages for four commonly used, high-efficiency transformers at State Grid Xinjiang Electric Power Company, the carbon footprint of an oil-immersed 100 kVA/10 kV transformer is 2.353 × 106 kg CO2e, approximately half that of a conventional 100 kVA/10 kV transformer. Finally, the study provides recommendations for carbon reduction pathways for transformers, considering both functional substitution and technological carbon reduction strategies.
Digital Twins and AI Decision Models: Advancing Cost Modelling in Off-Site Construction
Authors: Serugga Joas
Published: January 2025

The rising demand for housing continues to outpace traditional construction processes, highlighting the need for innovative, efficient, and sustainable delivery models. Off-site construction (OSC) has emerged as a promising alternative, offering faster project timelines and enhanced cost management. However, current research on cost models for OSC, particularly in automating material take-offs and optimising cost performance, remains limited. This study addresses this gap by proposing a new cost model integrating Digital Twin (DT) technology and AI-driven decision models for modular housing in the UK. The research explores the role of DTs in enhancing cost estimation and decision-making processes. By leveraging DTs and AI, the proposed model evaluates the impact of emergent technologies on cost performance, material efficiency, and sustainability across social, environmental, and economic dimensions. As proposed, this integrated approach enables a cost model tailored for OSC systems, providing a data-driven foundation for cost optimisation and material take-offs. The study’s findings highlight the potential of combining DTs and AI decision models to enhance cost modelling in modular construction, offering new capabilities to support sustainable and performance-driven housing delivery. The paper introduces a dynamic, data-driven cost model integrating real-time data acquisition through DTs and AI-powered predictive analytics. This dynamic approach enhances cost accuracy, reduces lifecycle cost variability, and supports adaptive decision-making throughout the OSC project lifecycle.
Molecular Characterization of Propolis-Resistant Saccharomyces cerevisiae Obtained by Evolutionary Engineering
Authors: Demir-Yılmaz Filiz , Arslan Mevlüt , Holyavkin Can , Topaloğlu Alican , Kısakesen Halil İbrahim , Sürmeli Yusuf , Çakar Zeynep Petek
Published: January 2025

Propolis is a highly complex, resinous natural product collected by honeybees from tree leaves and buds and mixed with pollen and enzymes. Due to its antimicrobial properties, it has various medical and industrial applications. As a nonconventional strategy, the use of propolis was suggested to control contaminating yeast growth in ethanol fermentations, without significantly affecting the starter yeast of the fermentation, Saccharomyces cerevisiae. In this study, we have developed a highly propolis-resistant S. cerevisiae strain using evolutionary engineering. The evolved strain FD11 had a higher growth rate (µmax = 0.21 h−1) than the reference strain (µmax = 0.17 h−1) under propolis stress and showed cross-resistance against caffeine stress. Moreover, it had significantly lower reactive oxygen species levels and higher cell wall integrity than the reference strain. Comparative transcriptomic analysis results revealed that the genes involved in oxidoreductase activity, transmembrane transporter activity, unfolded protein binding and pleiotropic drug resistance were upregulated in FD11. Whole genome re-sequencing analysis revealed mutations in multiple genes including PDR1, encoding a transcription factor regulating pleiotropic drug response. The results imply the importance of pleiotropic drug response and cell wall integrity in propolis resistance and the potential of using propolis-resistant, robust yeast strains in industrial applications.
Mutation of Genes Associated with Body Color, Growth, Intermuscular Bone, and Sex Differentiation in Onychostoma macrolepis Using CRISPR/Cas9
Authors: Gao Tian , Wang Feilong , Wu Qihui , Gan Lingyao , Jin Canbiao , Ma Li , Wang Deshou , Sun Lina
Published: January 2025

Onychostoma macrolepis is not only a protected Cyprinid species in the wild but also an emerging commercial aquaculture fish in China. The objective of this research was to genetically modify the genes associated with commercial traits by CRISPR/Cas9 for the protection and utilization of the germplasm resources of O. macrolepis. To that end, one-cell stage embryos were obtained via hormone-induced ovulation and artificial insemination in O. macrolepis. Eight genes related to body color, growth, intermuscular bone, and sex differentiation were mutated in O. macrolepis using the CRISPR/Cas9 system by microinjection of gRNA/Cas9 mRNA. The optimal dose of gRNA/Cas9 mRNA was determined by injection of different concentrations of tyr (tyrosinase)-gRNA/Cas9 and examination of the mutation rate and hatching rate of embryos. Indels were detected by T7 endonuclease I digestion and Sanger sequencing. F0 mutants with high mutation rates were selected for phenotype analyses. Disruption of body color gene tyr, mpv17 (mitochondrial inner membrane protein MPV17), and csf1ra (colony-stimulating factor 1 receptor, a) resulted in obvious phenotype with decreased or even absence of melanophores, iridophores, and xanthophores, respectively. Mutation of mstnb (myostatin b) led to improved growth performance. Mutation of mc4r (melanocortin 4 receptor) led to no obvious phenotype. Mutation of runx2b (RUNX family transcription factor 2b) and bmp6 (bone morphogenetic protein 6) resulted in decreased or absence of intermuscular bones, as revealed by alizarin red S staining. Mutation of cyp19a1a (cytochrome P450, family 19, subfamily A, polypeptide 1a) resulted in ovarian degeneration as revealed by gonadal histological examination. Therefore, this study successfully obtained mutants with obvious phenotypes of genes associated with body color, growth, intermuscular bone, and sex differentiation by CRISPR/Cas9 in O. macrolepis.
Unlocking the Potential of Bacillus subtilis: A Comprehensive Study on Mycotoxin Decontamination, Mechanistic Insights, and Efficacy Assessment in a Liquid Food Model
Authors: Greco Donato , D’Ascanio Vito , Santovito Elisa , Abbasciano Mariagrazia , Quintieri Laura , Techer Clarisse , Avantaggiato Giuseppina
Published: January 2025

Mycotoxin detoxification by microorganisms offers a specific, economical, and environmentally sustainable alternative to physical/chemical methods. Three strains of B. subtilis, isolated from poultry farm environments and recognized by EFSA as safe in animal nutrition for all animal species, consumers, and the environment, were screened for their ability to remove mycotoxins. All of them demonstrated mycotoxin-dependent removal efficacy, being very effective against ZEA and its analogues (α- and β-ZOL, α- and β-ZAL, and ZAL) achieving up to 100% removal within 24 h under aerobic, anaerobic, and restrictive growth conditions with toxins as the sole carbon source. ZEA removal remained effective across a wide range of pH values (5–8), temperatures (20–40 °C), and at high toxin concentrations (up to 10 µg/mL). Additionally, up to 87% ZEA removal was achieved after 48 h of incubation (30 °C) of the strains in a contaminated liquid food model containing 1 µg/mL of the toxin. Mechanistic studies suggest that ZEA detoxification involves metabolic processes rather than physical adsorption or entrapment into bacterial cells. Enzymatic activities within the bacterial cells or associated with their cell walls likely play a role in the metabolization of the toxin. Interestingly, it has been observed that growth conditions and culture media can influence the metabolization and/or conjugation of the toxin, which can result in the production of various metabolites. Further investigation is needed to identify these metabolites and assess their safety.
Development and Characterization of In Situ Gelling Nasal Cilostazol Spanlastics
Authors: Salamah Maryana , Budai-Szűcs Mária , Sipos Bence , Volk Balázs , Katona Gábor , Balogh György Tibor , Csóka Ildikó
Published: January 2025

Cilostazol (CIL), a BCS class II antiplatelet aggregation and vasodilator agent, is used for cerebrovascular diseases to minimize blood–brain barrier dysfunction, white matter-lesion formation, and motor deficits. The current work aimed to develop and optimize cilostazol-loaded spanlastics (CIL-SPA) for nose-to-brain delivery to overcome the low solubility and absorption, the first pass-metabolism, and the adverse effects. The optimal CIL-SPA formulation was loaded into Phytagel® (SPA-PG), Poloxamer-407 (SPA-P407), and chitosan (SPA-CS) gel bases and characterized in terms of colloidal properties, encapsulation efficiency (EE%), mucoadhesive properties, and biopharmaceutical aspects. The developed in situ gelling formulations showed a <300 nm average hydrodynamic diameter, <0.5 polydispersity index, and >|±30| mV zeta potential with a high EE% (>99%). All formulations met the droplet size-distribution criteria of nasal requirements (<200 µm), and all formulations showed adequate mucoadhesion properties. Both the BBB-PAMPA and horizontal permeability study through an artificial membrane revealed that all formulations had higher CIL flux and cumulative permeability at in vitro nose-to-brain conditions compared to the initial CIL. The in vitro drug-release study showed that all formulations released ca. 100% of CIL after 2 h. Therefore, the developed formulations could be promising for improving the low bioavailability of CIL through nose-to-brain delivery.
The Characterization of the Lower Town of the UNESCO Archaeological Site of Arslantepe (Malatya, Türkiye) Using the Geophysical E-PERTI Method (Extended Data-Adaptive Probability-Based Electrical Resistivity Tomography Inversion Method)
Authors: Balossi Restelli Francesca , Cozzolino Marilena , Manuelli Federico , Mauriello Paolo
Published: January 2025

The UNESCO site of Arslantepe is located in Eastern Anatolia in the Malatya Plain (Türkiye) about 10 km from the Euphrates River. Here for about a century archaeological excavations have been carried out, reconstructing a long sequence of human frequentation starting from 5000 years BC up to the Middle Ages. The settlement, one of the most important and largest in the region, has undergone numerous changes over time, resulting in a complex superposition of structures, palaces, temples, and burials concentrated on the hill. With the aim of extending the knowledge of the site, in 2022, geophysical surveys were carried out through the application of electrical resistivity tomography, covering a surface of approximately 4300 m2 in an unexplored area at the foot of the hill. In this paper, the Extended data-adaptive Probability-based Electrical Resistivity Tomography Inversion approach (E-PERTI), recently published as a development of the probability tomography imaging approach, has been applied to a large apparent resistivity field dataset, providing the best estimate of the most probable estimate of the resistivity distribution through an intrinsic linear regression model implementing standard least squares routines. The results seem to prove the effectiveness of the E-PERTI approach in noise dejection, enhancing associated resistivity highs that can be ascribable to the trace of a potential fortification. The obtained information represents new, unexpected data that open new frontiers of archaeological research, adding value to the knowledge of the site.