bluuebunny/crossref_metadata_2025 huggingface dataset:
Tabular Data Models for Predicting Art Auction Results
Authors: Mauer Patryk , Paszkiel Szczepan
Published: November 2024

Predicting art auction results presents a unique challenge due to the complexity and variability of factors influencing artwork prices. This study explores a range of machine learning architectures designed to forecast auction outcomes using tabular data, including historical auction records, artwork characteristics, artist profiles, and market indicators. We evaluate traditional models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM, and SAINT. By comparing the performance of these models on a dataset comprising extensive auction results, we provide insights into their relative effectiveness across different scenarios. Additionally, we address the interpretability of models, which is crucial for understanding the influence of various features on predictions. The results suggest that while some models perform better than others, no single approach offers consistently high accuracy across all cases. This study provides guidance for auction houses, art investors, and market analysts in refining predictive approaches, identifying key challenges, and understanding where further improvements are needed for more accurate data-driven decisions in the art market.
Structure and Strength Optimization of the Bogdan ERCV27 Electric Garbage Truck Spatial Frame Under Static Loading
Authors: Holenko Kostyantyn , Dykha Oleksandr , Koda Eugeniusz , Kernytskyy Ivan , Horbay Orest , Royko Yuriy , Fornalchyk Yevhen , Berezovetska Oksana , Rys Vasyl , Humenuyk Ruslan , Berezovetskyi Serhii , Żółtowski Mariusz , Baryłka Adam , Markiewicz Anna , Wierzbicki Tomasz , Bayat Hydayatullah
Published: November 2024

Taking into account the requirements to reduce the release of harmful emissions into the environment, the EU’s environmental standards when transitioning to the Euro 7 standard in 2025 will actually lead vehicles having to operate without producing emissions in all driving situations. Carmakers believe that the new, much stricter regulations will mark the end of the internal combustion engine era. For example, in 2030, the manufacturer SEAT will cease its activities, leaving behind the Cupra brand, which will be exclusively electric in the future. This trend will apply not only to private vehicles (passenger cars), but also to utility vehicles, which is the subject of our research, namely the spatial tubular frame in the Bogdan ERCV27 garbage truck, presented in the form of a solid model. The peculiarity of the studied model is the installation of a battery block behind the driver’s cabin, causing an additional load to be placed on the spatial frame of the garbage truck, which in terms of its architecture is more like the body of a bus. During the conditions involving various modes of operation of a full-scale Bogdan ERCV27 garbage truck sample, questions about the strength and uniformity of its load-bearing spatial frame inevitably arise, which are decisive, even at the stage of designing and preparing the technical documentation. The main static load mode, which, despite its name, also covers dynamic conditions, was modeled using the appropriate coefficient kd = 2.0. The maximum stresses on the model during the “bending” mode were 381.13 MPa before structure optimization and 270.5 MPa as a result of the improvement measures. The spatial frame mass was reduced by 4.13%. During the “torsion” mode, the maximum deformation values were 12.1–14.5 mm, which guarantees the normal operation of the aggregates and units of the truck.
Immunostimulatory Effects of Red Beet Ferment Extract on Cyclophosphamide-Induced Immunosuppression in Wistar Rats
Authors: Hwang Hai-Min , Park Young-Mi , Lee Hak-Yong , Shin Dong-Yeop , Kim Jae-Gon , Bae Jun-Sang , Haam Dae-Sik , Yang Hye-Jeong , Kim Min-Jung , Kim Myung-Sunny , Lee Jeong-Jae , Lee In-Ah
Published: November 2024

Red beet extract contains a variety of bioactive compounds, including antioxidant and immune-stimulating properties. Red beets are rich in nutrients and have been reported to help mitigate cardiovascular disease, while also possessing potent antioxidant capacity. In this study, we sought to determine the immune-enhancing effects of red beet fermented extract. The immunosuppressive drug cyclophosphamide (Cy) and red beet fermented extract were orally administered to Wistar rats once daily for four weeks. Blood analysis was performed to measure the expression of immune cytokines. We also examined the immunostimulatory effects of red beet fermented extract by measuring the activity and viability of spleen cells and the expression of immune cytokines. The expression of immune cytokines in the blood and spleen cells was increased in the red beet fermented extract treatment group compared to that in the negative control group, which induced immunosuppression of cytokines. It also increased the activity of NK cells and improved the viability of spleen cells. In conclusion, red beet fermented extract increased the expression of immune cytokines and increased the activity of adipocytes and NK cells, confirming its immunostimulatory effect. Therefore, red beet fermented extract has the potential to be utilized as a nutraceutical ingredient to enhance immunity.
Layered Query Retrieval: An Adaptive Framework for Retrieval-Augmented Generation in Complex Question Answering for Large Language Models
Authors: Huang Jie , Wang Mo , Cui Yunpeng , Liu Juan , Chen Li , Wang Ting , Li Huan , Wu Jinming
Published: November 2024

Retrieval-augmented generation (RAG) addresses the problem of knowledge cutoff and overcomes the inherent limitations of pre-trained language models by retrieving relevant information in real time. However, challenges related to efficiency and accuracy persist in current RAG strategies. A key issue is how to select appropriate methods for user queries of varying complexity dynamically. This study introduces a novel adaptive retrieval-augmented generation framework termed Layered Query Retrieval (LQR). The LQR framework focuses on query complexity classification, retrieval strategies, and relevance analysis, utilizing a custom-built training dataset to train smaller models that aid the large language model (LLM) in efficiently retrieving relevant information. A central technique in LQR is a semantic rule-based approach to distinguish between different levels of multi-hop queries. The process begins by parsing the user’s query for keywords, followed by a keyword-based document retrieval. Subsequently, we employ a natural language inference (NLI) model to assess whether the retrieved document is relevant to the query. We validated our approach on multiple single-hop and multi-hop datasets, demonstrating significant improvements in both accuracy and efficiency compared to existing single-step, multi-step, and adaptive methods. Our method exhibits high accuracy and efficiency, particularly on the HotpotQA dataset, where it outperforms the Adaptive-RAG method by improving accuracy by 9.4% and the F1 score by 16.14%. The proposed approach carefully balances retrieval efficiency with the accuracy of the LLM’s responses.
Proactive Production Scheduling Approach for Off-Site Construction with Due Date Uncertainty
Authors: Kim Taehoon , Kim Yong-Woo
Published: November 2024

This study proposes a robust precast concrete (PC) production scheduling model for PC construction projects with schedule uncertainty by adopting a proactive scheduling approach. The proposed model consists of a PC production simulation module that simulates and evaluates the total tardiness of the schedule at a certain confidence level with contractor schedule uncertainty and a GA-based production schedule optimization module that finds an optimal schedule through iterative schedule generation and evaluation using a PC production simulation module. The experimental study shows that the proposed model can find the best schedule with 303.8 h of tardiness at a 100% confidence level, followed by NEHedd 324.7 h, basic GA 328.5 h, and EDD 335.8 h. The results of this study will help PC production schedulers to perform robustly despite contractors’ schedule changes and will thus contribute to the successful completion of PC construction projects.
Evaluating the Thermal Shock Resistance of SiC-C/CA Composites Through the Cohesive Finite Element Method and Machine Learning
Authors: Deng Qiping , Xiong Yu , Du Zirui , Cui Jinping , Peng Cheng , Luo Zhiyong , Xie Jinli , Qin Hailong , Sun Zhimin , Zeng Qingfeng , Guan Kang
Published: November 2024

Silicon carbide-coated carbon fiber-reinforced carbon aerogel (SiC-C/CA) composites are ideal for high-temperature applications due to their ability to endure rapid temperature changes without losing structural integrity. However, assessing and optimizing the Thermal Shock Resistance (TSR) of these composites is challenging due to the complexities in measuring thermal and mechanical responses accurately under rapid fluctuations. Herein, we introduce a novel approach combining the cohesive finite element method (CFEM) with machine learning (ML) to address these challenges. The CFEM simulates crack initiation and propagation and captures mechanical behavior under thermal stress, while ML predicts TSR using simulation datasets, reducing the need for empirical trial-and-error processes. Our method achieves a prediction error for coating residual stress within 15.70% to 24.11% before and after thermal shock tests. Additionally, the ML model, developed to predict the average stiffness degradation factor of the SiC coating after three thermal shock cycles, achieves a coefficient of determination (R2) of 0.9171. This combined approach significantly improves the accuracy and efficiency of TSR assessment and can be extended to other coating materials, accelerating the development of high-temperature-resistant materials with optimized TSR for industrial applications.
How Gait Nonlinearities in Individuals Without Known Pathology Describe Metabolic Cost During Walking Using Artificial Neural Network and Multiple Linear Regression
Authors: Mohammadzadeh Gonabadi Arash , Fallahtafti Farahnaz , Burnfield Judith M.
Published: November 2024

This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics—Lyapunov Exponents based on Rosenstein’s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf’s algorithm (LyEW)—were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg–Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R2 values, indicating stronger predictive accuracy. The results suggest that gait’s nonlinear characteristics significantly impact the metabolic cost, and ANNs are more effective for analyzing these dynamics than MLR models. The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization, particularly for hip exoskeletons. These findings support the development of personalized interventions that improve walking efficiency and reduce metabolic demands, offering insights into the design of advanced assistive technologies.
Spatio-Temporal Evolution and Interactive Relationship Between Digital Economy and Green Development in China
Authors: Chen Tingting , Lu Chunyan , Lai Yuting , Zhou Mengxing , Hu Qingping , Wang Tingyan , Bao Lingxin
Published: November 2024

A digital economy parallel with green development holds profound significance for achieving sustainability. The primary objective of this study was to explore the synergistic interaction effects between the digital economy and green development in China and forecast their future development. This study analyzed the spatio-temporal characteristics of provincial digital economy and green development in China by integrating a combined assignment method, an unconditional spatial kernel density estimation method, and a standard deviation ellipse model. The interplay between the digital economy and green development was examined using a panel vector autoregression model. Additionally, digital economy and green development levels were forecasted using univariate time series and radial basis function kernel ε-support vector regression models. The results indicate that both the digital economy and green development levels in China exhibited an upward trend from 2013 to 2021, with the digital economy increasing at a faster rate. However, both domains demonstrated regional disparities in their development processes. The mutual interaction between the digital economy and green development intensified as the lag period increased. The digital economy contributed 21% to green development, whereas green development contributed 18% to the digital economy. The initial effect of the digital economy on green development was negative, however, this impact gradually diminished over time. Additionally, the influence of green development on the digital economy was shown to follow a consistent trend of transitioning from negative to positive across the eastern, central, and western regions. Therefore, it can be seen that the digital economy exerts a sustainable impact on green development, albeit with a one-phase lag. This research provides a scientific basis for the deep integration of the digital economy and green development, thereby fostering sustainable socioeconomic growth.
Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
Authors: Shen Zhihua , Wang Caiju , Li Fei , Liang Jinshuo , Li Xiaomao , Qu Dong
Published: November 2024

Unsupervised low-light image enhancement methods have gained attention and shown improvement with low data dependence. However, the lack of a ground truth presents challenges, notably in pronounced noise and color bias. This paper proposes a Self-Guided Pixel-wise Calibration method to overcome associated issues by leveraging inherent features from the input as a self-guide. Specifically, a Pixel-wise Guided Filter is introduced to decrease noise, utilizing a low-light image for guidance and deep features as regularization maps. Additionally, a Color Correction Module is introduced to enhance saturation by adjusting the shadow threshold. Finally, a pixel-wise exposure control loss is formalized to optimize overall naturalness by adjusting brightness to a well-exposedness map from the low-light image. Extensive experiments demonstrate that our method outperforms many state-of-the-art methods, producing enhanced results with fewer distortions across various real-world image enhancement tasks.
Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
Authors: Zhang Hanwen , Jin Sun , Li Bing , Xu Bo , Xiao Yuanbin , Zhou Weixin
Published: November 2024

Gravel is the most frequently used material in infrastructure construction. However, the irregular shape of the gravel pile makes it challenging for the loader to predict a stable shoveling position, which can easily result in partial collapse or a complete landslide, thereby posing a serious threat to the equipment. In view of the imperfect method of determining the shoveling position of the pile by the current unmanned loader and the high hardware requirements for the deployment of the identification model, this paper first establishes a mathematical model of the loader, and preliminarily determines the influence of the concave and convex edges of the gravel pile on the shoveling position selection through discrete element joint simulation; secondly, the influence of the pile with different edge curvatures on the loader operation process is analyzed in the simulation software, and the radar map is used to further identify the superior position features; finally, Ghost Net is used as the backbone network, the RFB module is introduced into the Backbone, and the CBAM attention mechanism is integrated into the C3 module to identify the lightweight YOLOv5s shoveling position. Discrete element analysis and a lightweight network model were used in the above study to find the safest and most effective shoveling positions. During the test that mimicked how the loader would actually shovel, the number of parameters in the improved model was cut down to 32.5% of the original, the number of calculations was cut down to about 55.2% of the original, and the average accuracy of finding the shoveling position of the gravel pile reached 98%.
Processing Fresh-Cut Potatoes Using Non-Thermal Technologies and Edible Coatings
Authors: Drosou Christina , Sklirakis Ioannis , Polyzou Ekaterini , Yakoumis Iakovos , Boukouvalas Christos J. , Krokida Magdalini
Published: November 2024

The increasing consumer demand for minimally processed and ready-to-cook food products has elevated the significance of fresh-cut potatoes, which offer health benefits, high sensory properties, and convenience. However, extending the shelf life of fresh-cut potatoes while preserving their organoleptic qualities remains a significant challenge. This review examines the effectiveness of emerging non-thermal technologies, such as osmotic dehydration (OD), high-pressure processing (HPP), pulsed electric field (PEF), and ohmic heating (OH), in processing fresh-cut potatoes. Among these, HPP and PEF have shown particular promise in extending shelf life and preserving sensory attributes, while OD and OH present advantages in maintaining nutritional quality. However, challenges such as high energy consumption, equipment costs, and industrial scalability limit their broader application. The use of natural preservatives and edible coatings is also explored as a means to enhance product quality and address the demand for clean-label foods. Further research is needed to optimize these technologies for large-scale production, reduce energy usage, and explore combined approaches for improved shelf life extension. This comprehensive review provides a critical analysis of the operational parameters of these technologies and their impact on the quality and shelf life of fresh-cut potatoes, identifying current research gaps and proposing directions for future studies.
Image Biomarker Analysis of Ultrasonography Images of the Parotid Gland for Baseline Characteristic Establishment with Reduced Shape Effects
Authors: Kim Hak-Sun
Published: November 2024

Background: This study aimed to analyze image biomarkers of the parotid glands in ultrasonography images with reduced shape effects, providing a reference for the radiomic diagnosis of parotid gland lesions. Methods: Ultrasound (US) and sialography images of the parotid glands, acquired from September 2019 to March 2024, were reviewed along with their clinical information. Parotid glands diagnosed as within the normal range were included. Overall, 91 US images depicting the largest portion of the parotid glands were selected for radiomic feature extraction. Regions of interest were drawn twice on 50 images using different shapes to assess the intraclass correlation coefficient (ICC). Feature dimensions were statistically reduced by selecting features with an ICC > 0.8 and applying four statistical algorithms. The selected features were used to distinguish age and sex using the four classification models. Classification performance was evaluated using the area under the receiver operating characteristic curve (AUC), recall, and precision. Results: The combinations of the information gain ratio algorithm or stochastic gradient descent and the naïve Bayes model showed the highest AUC for both age and sex classification (AUC = 1.000). The features contributing to these classifications included the first-order and gray-level co-occurrence matrix (high-order) features, particularly discretized intensity skewness and kurtosis, intensity skewness, and GLCM angular second moment. These features also contributed to achieving one of the highest recall (0.889) and precision (0.926) values. Conclusions: The two features were the most significant factors in discriminating radiomic variations related to age and sex in US images with reduced shape effects. These radiomic findings should be assessed when diagnosing parotid gland pathology versus normal using US images and radiomics in a heterogeneous population.
Determining the Compaction Temperature of Warm-Mix Anti-Rutting Asphalt Mixture
Authors: Liu Zhaohui , Liu Zebin , Huang You , Cao Yue
Published: November 2024

In order to study the effect of a warm-mix agent on the compaction characteristics of an anti-rutting asphalt mixture, this study compared the compaction temperature of an anti-rutting asphalt mixture with different warm-mix-agent contents from two aspects: asphalt viscosity and asphalt mixture voids. Based on the rheological properties of asphalt, the optimal content of the anti-rutting agent was first determined as 6% by the weight of asphalt. Four warm-mix-agent contents of 0% (control group), 1%, 2%, and 3% were designed. The viscosity–temperature curve of the warm-mix anti-rutting modified asphalt was obtained by the Brookfield viscosity tests. After that, AC-20 standard Marshall specimens were prepared to conduct a series of consecutive temperature compaction tests. The voids were calculated based on the bulk density of the specimen measured by the saturated surface-dry method. Industrial Computerized Tomography (CT) was employed to further quantify the internal voids. Two voids–compaction temperature curves were constructed based on the saturated surface-dry and CT results, respectively. The comparative results show that significant differences exist between the compaction temperatures obtained from the three curves. The viscosity–temperature curve shows that when the warm-mix agent is increased from 0% to 3%, the compaction temperature only declines about 7.9%. However, the voids–compaction temperature curves from saturated surface-dry and CT, respectively, indicate a temperature decrease of 22.7% and 19.2%. This is because a warm-mix agent will interact with asphalt, resulting in a decrease in asphalt intermolecular adsorption, whereas an anti-rutting agent mixed with asphalt will increase the degree of cross-linking and aggregation between asphalt molecules. Both additions have a certain impact on the viscosity of the asphalt binder; thus the traditional method of using the asphalt viscosity–temperature curve to determine the compaction temperature of warm-mix anti-rutting asphalt mixture has become ineffective. It is suggested to use the equal voids method to determine the compaction temperature of warm-mix anti-rutting asphalt mixtures.
A Rectangular Spiral Inward–Outward Alternating-Flow Polymer Thermal Collector for a Solar Water Heating System—A Preliminary Investigation in the Climate of Seri Iskandar, Malaysia
Authors: Mohamad Taib Iskandar , Mohd Shaifudeen Mohammad Danish Shareeman
Published: November 2024

A flat-plate unglazed solar water heater (SWH) with a polymer thermal absorber was developed and experimented with. Polymer thermal absorbers could be a viable alternative to metal thermal absorbers for SWH systems. The performance of this polymer SWH system was measured based on inlet and outlet water temperature, water flow rate, ambient air temperature and solar irradiance. The polymer thermal absorbers were hollow Polyvinyl Chloride (PVC) tubes with a 20 mm external diameter and 3 mm thickness and were painted black to enhance radiation absorption. The pipes are arranged in a rectangular spiral inward–outward alternating-flow (RSioaf) pattern. The collector pipes were placed in a 1 m × 1 m enclosure with bottom insulation and a reflective surface for maximized radiation absorption. Water circulated through a closed loop with an uninsulated 16 L storage tank, driven by a pump and controlled by two valves to maintain a mass flow rate of 0.0031 to 0.0034 kg·s−1. The test was conducted under a partially clouded sky from 9 a.m. to 5 p.m., with solar irradiance between 105 and 1003 W·m−2 and an ambient air temperature of 27–36 °C. This SWH system produced outlet hot water at 65 °C by midday and maintained the storage temperature at 63 °C until the end of the test period. Photothermal energy conversion was recorded, showing a maximum value of 23%. Results indicate that a flat-plate solar water heater with a polymer thermal absorber in an RSioaf design can be an effective alternative to an SWH with a metal thermal absorber. Its performance can be improved with glazing and optimized tube sizing.
Machine Learning Models for Traffic Prediction on Arterial Roads Using Traffic Features and Weather Information
Authors: Abduljabbar Rusul , Dia Hussein , Liyanage Sohani
Published: November 2024

This study addresses the challenges of predicting traffic flow on arterial roads where dynamic behaviours such as passenger pick-ups, vehicle turns, and parking interruptions complicate forecasting. The research develops and evaluates unidirectional and bidirectional Long Short-Term Memory (LSTM) models using a dataset of 70,072 observations collected over 12 months from Hoddle Street in Melbourne, Australia. The models were trained to predict traffic speeds and volumes up to 60 min ahead. The results indicated that the BiLSTM model significantly outperformed unidirectional LSTM, achieving over 99% accuracy for speed predictions and over 96% for volume predictions. The research also tested the impacts of incorporating weather variables such as rainfall, temperature, humidity, and wind speed on model performance, which was found to provide small improvements. Traffic flow prediction accuracy increased from 97.5% to 97.6% for 30-min horizons, and from 96.9% to 97.6% for 60-min horizons. Although the inclusion of weather data only marginally enhanced prediction performance, its inclusion has practical implications for public awareness of travel impacts under severe weather. The findings in this study highlight the effectiveness of deep learning techniques for traffic forecasting on arterial roads, establishing a foundation for future research in this area.
Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data
Authors: Song Hanbin , Yeo Sanghyeop , Jin Youngwan , Park Incheol , Ju Hyeongjin , Nalcakan Yagiz , Kim Shiho
Published: November 2024

This paper presents a novel approach to material classification using short-wave infrared (SWIR) imaging, aimed at applications where differentiating visually similar objects based on material properties is essential, such as in autonomous driving. Traditional vision systems, relying on visible spectrum imaging, struggle to distinguish between objects with similar appearances but different material compositions. Our method leverages SWIR’s distinct reflectance characteristics, particularly for materials containing moisture, and demonstrates a significant improvement in accuracy. Specifically, SWIR data achieved near-perfect classification results with an accuracy of 99% for distinguishing real from artificial objects, compared to 77% with visible spectrum data. In object detection tasks, our SWIR-based model achieved a mean average precision (mAP) of 0.98 for human detection and up to 1.00 for other objects, demonstrating its robustness in reducing false detections. This study underscores SWIR’s potential to enhance object recognition and reduce ambiguity in complex environments, offering a valuable contribution to material-based object recognition in autonomous driving, manufacturing, and beyond.
Smart Cities and Resident Well-Being: Using the BTOPSIS Method to Assess Citizen Life Satisfaction in European Cities
Authors: Roszkowska Ewa , Wachowicz Tomasz
Published: November 2024

With rapid urbanization, maintaining a high quality of life (QoL) for city residents has become a critical challenge for policy-makers and urban planners. Smart cities, leveraging advanced technologies and data analytics, present a promising pathway to enhance urban services and promote sustainability. This paper introduces an innovative adaptation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, integrating a Belief Structure (BTOPSIS) to improve the evaluation and interpretation of survey data. Our approach effectively addresses the distribution of responses across categories and the uncertainty often present in such data, including missing or ambiguous answers. Additionally, we perform a sensitivity analysis to assess the stability of the BTOPSIS rankings under varying utility function parameters, further validating the robustness of our method. We apply this framework to the 2023 ‘Quality of Life in European Cities’ survey, analyzing diverse urban factors such as public transport, healthcare, cultural facilities, green spaces, education, air quality, noise levels, and cleanliness. Additionally, our study offers a comparative analysis of BTOPSIS against other multi-criteria methods used for evaluation data from this report, showcasing its strengths and limitations in addressing the dataset’s complexity. Our findings reveal significant variations in residents’ perceived QoL across European cities, both between cities and within countries. Zurich and Groningen rank highest in satisfaction, while Tirana, Skopje, and Palermo are ranked lowest. Notably, residents of cities with populations under 500,000 report higher satisfaction levels than those in larger cities, and satisfaction levels are generally higher in EU and EFTA cities compared to those in the Western Balkans, with the highest satisfaction observed in northern and western Member States. To aid urban planners and policy-makers, we propose a ranking tool using the BTOPSIS method, capturing nuanced resident perceptions of living conditions across cities. These insights provide valuable guidance for strategic urban development and advancing the smart city agenda across Europe.
Impact of WRF Model Parameterization Settings on the Quality of Short-Term Weather Forecasts over Poland
Authors: Kendzierski Sebastian
Published: November 2024

This research examines the impact of various parameterization settings within the Weather Research and Forecasting (WRF) model on the accuracy of short-term weather forecasts for Poland. The study focuses on the sensitivity of key meteorological variables—namely, air temperature, wind speed, relative humidity, and atmospheric pressure—to different combinations of physical parameterization schemes. Utilizing data from the Global Forecast System (GFS) spanning 2019 to 2022, a series of model simulations were conducted with support from the Poznań Supercomputing and Networking Center (PCSS). To assess the model’s performance across different weather stations, statistical metrics such as the mean absolute error (MAE) and root mean square error (RMSE) were employed. The findings indicate that the configuration labeled “p2” produced the most accurate forecasts for temperature, wind speed, and atmospheric pressure, achieving MAE values of 1.5 °C, 1.6 m/s, and 2 hPa, respectively. However, forecast inaccuracies were notably higher in mountainous regions, particularly regarding wind speed. These results underscore the importance of selecting appropriate parameterization settings tailored to regional characteristics, as different configurations can significantly impact the forecast accuracy, especially in complex terrains. This study contributes to the understanding of short-term weather forecasting models for Central Europe, offering potential pathways for improving localized forecast accuracy.
A Kuramoto Model for the Bound State Aharonov–Bohm Effect
Authors: Nasir Alviu Rey , Da Silva José Luís , Magallanes Jingle , Suryawan Herry Pribawanto , Nasir-Britos Roshin Marielle
Published: November 2024

The Aharonov–Bohm effect can be described as a phase difference in interfering charged particles that travel through two distinct pathways oppositely surrounding a perpendicularly-positioned solenoid. The magnetic field emanates from the solenoid but does not intersect the pathways. On the other hand, the Kuramoto model can be used to identify the synchronization conditions that lead to a particular phase difference by treating the phases as coupled oscillators. Starting with the overall wave function expression for the electron in an Aharonov–Bohm potential, we derive a version of the Kuramoto model describing the phase dynamics of the bound state of the quantum mechanical system. We show that the resulting synchronization condition of the model coincides with the allowable values of the flux parameter for our case to achieve an Aharonov–Bohm effect.
Some Innovative Results for Interpolative Reich–Rus–Ćirić-Type Contractions in Rectangular m-Metric Spaces
Authors: Zahid Muhammad , Raza Ali , Khan Safeer Hussain
Published: November 2024

In this paper, we study the existence of fixed points for interpolative Reich–Rus–Ćirić-type contractions in the setting of rectangular m-metric spaces. The use of the rectangular inequality, in place of the conventional triangle inequality, introduces a higher level of complexity in the computations, requiring more careful and refined analysis. We consider two distinct cases based on the sum of the interpolative exponents: one where the sum is less than 1, and another where the sum exceeds 1. The results we present generalize several existing theorems in the literature, and each is supplemented with illustrative examples to demonstrate their applicability. Moreover, we deduce corresponding results on m-metric spaces from these results, which are new themselves. They are also validated through examples.
A Few Kinds of Loop Algebras and Some Applications
Authors: Sun Yanmei , Zhang Weiwei , Xue Nina , Zhang Yufeng
Published: November 2024

In this paper, we search for some approaches for generating (1+1)-dimensional, (2+1)-dimensional and (3+1)-dimensional integrable equations by making use of various Lie algebras and the corresponding loop algebras under the frame of the Tu scheme. The well-known modified KdV equation, the heat conduction equation, the nonlinear Schrödinger equation, the (2+1)-dimensional cylindrical dissipative Zaboloskaya–Khokhlov equation and the (3+1)-dimensional heavenly equation are obtained, respectively. In addition, some new isospectral integrable hierarchies and their nonisospectral integrable hierarchies are singled out. All the Lie algebras and their loop algebras presented in the paper can be extensively applied to investigate other integrable hierarchies of evolution equations.
Differences in Physiological and Agronomic Traits and Evaluation of Adaptation of Seven Maize Varieties
Authors: Ding Shuqi , Zhang Dan , Hao Ying , Hu Mengting , Tian Huijuan , Yang Kaizhi , Zhao Guolong , Xu Ruohang , Du Wentao
Published: November 2024

To better understand the growth adaptability of various maize varieties to the climate of the Alar region in Southern Xinjiang Province, an experiment was conducted using seven distinct maize varieties as test materials. A one-way randomized block design was applied to both experimental groups. In 2021 and 2022, a total of 19 indicators were observed for comparative analysis, including antioxidant enzyme activities and agronomic traits. Principal component analysis and cluster analysis were used to evaluate the adaptability of the maize varieties. The findings revealed that: (1) All seven maize varieties exhibited robust growth, with notable differences in their respective trait profiles. Specifically, the yield traits of Jin’ai 588 and Denghai 3672 showed relatively consistent performance over the two-year period. (2) Five principal components (100-kernel weight, bald tip length, catalase (CAT), number of leaves, and angle of leaf pinch at the ear) were extracted from the 19 traits via principal component analysis, with a cumulative contribution rate of 84.689%. This represented the majority of the information regarding the seven maize varieties. After calculating the comprehensive index F value, the results indicated that Xinyu 66 and Denghai 3672 had high composite scores, suggesting high production potential and suitability for cultivation in this region. Conversely, Xinyu 24 showed the lowest composite score, indicating that it is not suitable for planting in this area. (3) Ultimately, the seven maize varieties were categorized into three groups through cluster analysis; this is the same as the result of principal component analysis. This classification provides a reference for the promotion and utilization of different varieties in the southern border region and aims to optimize the comprehensive trait selection of the varieties studied.
Phylogenetics of Lepidonotopodini (Macellicephalinae, Polynoidae, Annelida) and Comparative Mitogenomics of Shallow-Water vs. Deep-Sea Scaleworms (Aphroditiformia)
Authors: Hiley Avery S. , Mongiardino Koch Nicolás , Rouse Greg W.
Published: November 2024

Within Polynoidae, a diverse aphroditiform family, the subfamily Macellicephalinae comprises anchialine cave-dwelling and deep-sea scaleworms. In this study, Lepidonotopodinae is synonymized with Macellicephalinae, and the tribe Lepidonotopodini is applied to a well-supported clade inhabiting deep-sea chemosynthetic-based ecosystems. Newly sequenced “genome skimming” data for 30 deep-sea polynoids and the comparatively shallow living Eulagisca gigantea is used to bioinformatically assemble their mitogenomes. When analyzed with existing scaleworm mitogenomes, deep-sea scaleworms exhibit increased gene order rearrangement events compared to shallow-water relatives. Additionally, comparative analyses of shallow-water vs. deep-sea polynoid substitution rates in mitochondrial protein-coding genes show an overall relaxed purifying selection and a positive selection of several amino acid sites in deep-sea species, indicating that polynoid mitogenomes have undergone selective pressure to evolve metabolic adaptations suited to deep-sea environments. Furthermore, the inclusion of skimming data for already known Lepidonotopodini species allowed for an increased coverage of DNA data and a representation of the taxa necessary to create a more robust phylogeny using 18 genes, as opposed to the six genes previously used. The phylogenetic results support the erection of Cladopolynoe gen. nov., Mamiwata gen. nov., Photinopolynoe gen. nov., Stratigos gen. nov., and Themis gen. nov., and emended diagnoses for Branchinotogluma, Branchipolynoe, Lepidonotopodium, and Levensteiniella.
Genetic Assessment of a Captive Population of Eurasian Stone-Curlew (Burhinus oedicnemus), Source for the Reinforcement of Wild Populations
Authors: Lesobre Loïc , Ostolani Alessia , Abi Hussein Hiba , Giunchi Dimitri , Aourir Mohamed , Teyar Yassine , Baratti Mariella
Published: November 2024

Although ex situ conservation programs are increasingly valuable support tools for in situ conservation measures, success depends on these captive individuals to be genetically representative of the recipient population. The Eurasian stone-curlew (Burhinus oedicnemus) inhabit steppes that represent some of the most degraded and exploited habitats worldwide. A captive breeding program was implemented in Morocco as a pre-emptive effort for the conservation of the North African subspecies Burhinus oedicnemus saharae. However, the genetic origins of the founders of the captive flock were unknown. We applied a multi-locus approach to characterize the genetic ancestry of the current captive breeding flock by comparing it to wild populations from both Western and Eastern Morocco. Mitochondrial DNA and microsatellite markers were employed to assess levels of genetic diversity and relatedness within each sample, as well as potential genetic differentiation between wild and captive samples through PCA and admixture analyses. We recovered similar genetic diversity estimates, low levels of relatedness, and little differentiation between captive and wild samples. These results confirmed the Moroccan origin of the founders. We provide recommendations for the optimization of the Eurasian Stone Curlew conservation breeding program but also for future conservation breeding programs to ensure the effective conservation of genetic diversity and wild populations.
Transcutaneous Non-Invasive Vagus Nerve Stimulation: Changing the Paradigm for Stroke and Atrial Fibrillation Therapies?
Authors: Förster Carola Y.
Published: November 2024

A new therapeutic approach, known as neuromodulation therapy—which encompasses a variety of interventional techniques meant to alter the nervous system in order to achieve therapeutic effects—has emerged in recent years as a result of advancements in neuroscience. Currently used methods for neuromodulation include direct and indirect approaches, as well as invasive and non-invasive interventions. For instance, the two primary methods of stimulating the vagus nerve (VN) are invasive VN stimulation (iVNS) and transcutaneous VN stimulation (tVNS). Since the latter is non-invasive, basic, clinical, and translational studies have focused on transcutaneous auricular VN stimulation (taVNS), the primary tVNS therapy, because of its advantages over iVNS, including ease of use, greater accessibility, and a lower side effect profile. taVNS is currently used as a novel neuromodulatory application to treat cardiovascular, mental, and autoimmune diseases. Future applications of this non-invasive neuromodulation technology to conditions like atrial fibrillation (AF) or ischemic stroke are highly likely due to its advancement.
Sex-Related Differences in the Immune System Drive Differential Responses to Anti-PD-1 Immunotherapy
Authors: Cotra Sonja , Kohandel Mohammad , Przedborski Michelle
Published: November 2024

Immune checkpoint inhibitors, such as anti-PD-1 antibodies, represent a significant advancement in cancer immunotherapy, but their efficacy varies notably between individuals, influenced by complex biological systems. Recent evidence suggests that sex-related biological differences play a pivotal role in modulating these responses. This study uses a systems biology approach to examine how sex-specific differences in the immune system contribute to variability in the response to treatment. Our model extends previous frameworks by incorporating sex-specific parameters that reflect observed immunological distinctions. The results from the simulation studies align with our clinical observations, showing that on average, males exhibit a more robust response to anti-PD-1 treatment compared to females. Additionally, this study explores the potential of combination therapy with recombinant IL-12, revealing sex-specific differences in treatment efficacy. These findings underscore the need for personalized immunotherapy strategies that consider individual immunological profiles, including sex, to optimize treatment outcomes.
Exosomes in Central Nervous System Diseases: A Comprehensive Review of Emerging Research and Clinical Frontiers
Authors: Li Jingrun , Song Jiahao , Jia Lina , Wang Mengqi , Ji Xunming , Meng Ran , Zhou Da
Published: November 2024

Exosomes, nano-sized lipid bilayer vesicles, have garnered significant attention as mediators of cell communication, particularly within the central nervous system (CNS). Their unique properties, including high stability, low immunogenicity, and the ability to traverse the blood-brain barrier (BBB), position them as promising tools for understanding and addressing CNS diseases. This comprehensive review delves into the biogenesis, properties, composition, functions, and isolation of exosomes, with a particular focus on their roles in cerebrovascular diseases, neurodegenerative disorders, and CNS tumors. Exosomes are involved in key pathophysiological processes in the CNS, including angiogenesis, inflammation, apoptosis, and cellular microenvironment modification. They demonstrate promise in mitigating ischemic injury, regulating inflammatory responses, and providing neuroprotection across various CNS conditions. Furthermore, exosomes carry distinct biomolecules, offering a novel method for the early diagnosis and monitoring of CNS diseases. Despite their potential, challenges such as complex extraction processes, the heterogeneity of exosomal contents, and targeted delivery limitations hinder their clinical application. Nevertheless, exosomes hold significant promise for advancing our understanding of CNS diseases and developing novel therapeutic strategies. This manuscript significantly contributes to the field by highlighting exosomes’ potential in advancing our understanding of CNS diseases, underscoring their unique value in developing novel therapeutic strategies and mediating cellular communication.
Is Hemopexin a Nephrotoxin or a Marker of Kidney Injury in Renal Ischemia-Reperfusion?
Authors: Jeon You Hyun , Oh Eun-Joo , Oh Se-Hyun , Lim Jeong-Hoon , Jung Hee-Yeon , Choi Ji-Young , Cho Jang-Hee , Park Sun-Hee , Kim Yong-Lim , Kim Chan-Duck
Published: November 2024

Destabilization of heme proteins is recognized to play a role in acute kidney injury (AKI). Hemopexin (Hpx), known for its role in binding heme, mitigates free heme toxicity. Despite this, the potential adverse effects of Hpx deposition in kidney tissues and its impact on kidney function are not fully understood. Deferoxamine (DFO) chelates iron released from heme and mitigates associated kidney damage. Therefore, this study aimed to evaluate whether Hpx contributes to kidney injury in an ischemia-reperfusion injury (IRI) induced AKI model and to investigate if DFO could alleviate this damage. Mice were categorized into five groups: Sham-Vehicle, Sham-Hpx, IRI-Vehicle, IRI-Hpx, and IRI-Hpx-DFO. Decline in kidney function was observed exclusively in the IRI group, independent of Hpx injection. Serum Hpx levels remained comparable across all groups, and administration of Hpx did not alter serum Hpx levels or kidney function after 24 hours. Although increased Hpx deposition in kidneys was noted in both the IRI and Hpx groups, this accumulation did not correlate with impaired kidney function. Additionally, DFO did not exhibit a protective effect against kidney injury. In summary, Hpx does not directly induce kidney injury and cannot be considered a biomarker for kidney damage caused by IRI.