bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Simulation of Oil Pipeline Leakage Diffusion in Dashagou Yellow River Crossing Section
Authors: Liu Shaokang , Qiu Mingyang , Zhao Guizhang , Jia Menghan , An Jie , Guo Xi , Lin Dantong , Tian Yangsheng , Zhou Jiangtao
Published: January 2025

In this study, the ANSYS 2020R1 software simulation is employed to examine the diffusion process of oil leakage and the underground water solute transport law in the Dashagou Yellow River crossing section of the oil pipeline. The simulation results demonstrate that under identical leakage pressure conditions, diesel fuel leakage in powdery, sandy soil is diminished, the emergency window is extended, and the corresponding leakage risk is reduced. In addition, the leakage rate of crude oil is slower than that of diesel oil. After 850 days of downward migration of approximately 190 m, the pollutant reaches quasi-static equilibrium in the big sand ditch. The results of the surface water oil spill analysis demonstrated that the oil film on the river surface migrated for 100 min after the spill, with a thickness that remained between 0.02 and 0.05 mm and a concentration that approached equilibrium.
Depth-Oriented Gray Image for Unseen Pig Detection in Real Time
Authors: Seo Jongwoong , Son Seungwook , Yu Seunghyun , Baek Hwapyeong , Chung Yongwha
Published: January 2025

With the increasing demand for pork, improving pig health and welfare management productivity has become a priority. However, it is impractical for humans to manually monitor all pigsties in commercial-scale pig farms, highlighting the need for automated health monitoring systems. In such systems, object detection is essential. However, challenges such as insufficient training data, low computational performance, and generalization issues in diverse environments make achieving high accuracy in unseen environments difficult. Conventional RGB-based object detection models face performance limitations due to brightness similarity between objects and backgrounds, new facility installations, and varying lighting conditions. To address these challenges, this study proposes a DOG (Depth-Oriented Gray) image generation method using various foundation models (SAM, LaMa, Depth Anything). Without additional sensors or retraining, the proposed method utilizes depth information from the testing environment to distinguish between foreground and background, generating depth background images and establishing an approach to define the Region of Interest (RoI) and Region of Uninterest (RoU). By converting RGB input images into the HSV color space and combining HSV-Value, inverted HSV-Saturation, and the generated depth background images, DOG images are created to enhance foreground object features while effectively suppressing background information. Experimental results using low-cost CPU and GPU systems demonstrated that DOG images improved detection accuracy (AP50) by up to 6.4% compared to conventional gray images. Moreover, DOG image generation achieved real-time processing speeds, taking 3.6 ms on a CPU, approximately 53.8 times faster than the GPU-based depth image generation time of Depth Anything, which requires 193.7 ms.
The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
Authors: Huang Mingzheng , Chen Dejin , Feng Dewang
Published: January 2025

Precision agriculture technology based on computer vision is of great significance in fruit recognition and evaluation. In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the fruit area, and then the cropped image was input into the classification module for detailed classification. Finally, the classification results were optimized by the feature matching network. In the method, the detection model was based on YOLOv8, and the model was improved by introducing a TripletAttention structure and an Attention Mechanism-Based Feature Fusion (AFM) structure. The improved YOLOv8 model improves the P, R, mAP50, and MAP50-95 indicators by 2.4%, 2.1%, 1%, and 1.3%, respectively, compared with the baseline model on only one generalized “fruit” label dataset. The classification model Swin Transformer used in this study has a classification accuracy of 92.6% on a dataset of 27 fruit categories, and the feature matching network based on cosine similarity can calibrate the classification results with low confidence. The experimental results show that the proposed method can be applied to the maturity assessment of apples and tomatoes, as well as to the non-destructive testing of apples.
Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
Authors: Cabrera-Peña Jose M. , Leon Raquel , Ortega Samuel , Fabelo Himar , Quevedo Eduardo , Callico Gustavo M.
Published: January 2025

Illumination is a crucial factor in hyperspectral imaging systems. In this respect, this work is focused on analyzing the influence of the light power source in acquiring hyperspectral images. To this end, a custom regulated power supply was designed and developed. This power supply was then integrated into a hyperspectral acquisition system, and several light stability measurements were conducted. Finally, several parameters related to the stability of the light produced by those systems were extracted using image analysis techniques, and a statistical comparison among the different power supplies was performed. Two commercial power supplies were also analyzed under the same experimental conditions and compared with the proposed power supply. The hyperspectral measurements were conducted using light transmission and reflectance. The results indicate that the proposed power supply performs better than or at least as well as commercial power supplies in terms of light stability. Additionally, this study shows the impact of power supply design on the stability and quality of hyperspectral illumination, especially concerning the signal-to-noise ratio (SNR) across different spectral bands. It is shown that optimizing the design of the power supply could improve light stability in hyperspectral imaging applications.
Impact of Arch Dam Cracking on Monitoring Data
Authors: Conde André , Toledo Miguel Á. , Salete Eduardo
Published: January 2025

It is well established that the proper functioning of dams plays an important role in society. Therefore, as a fundamental part of their safety, the identification of anomalies by monitoring their deformations must be given special consideration. The ability to detect cracks based on monitoring device records depends largely on the number of devices included in the dam’s original design, their arrangement, and their accuracy. This paper aims to help determine the appropriate spacing and accuracy of the devices for detecting a given crack. For this purpose, numerical simulations of cracks that match the most likely cracks in arch dams are performed based on a real dam, ensuring that such cracks result in an opening wide enough to be of concern. Afterward, the study analyzes the impact of the length and depth of the crack and the accuracy and position of the monitoring device on the ability of the system to detect the crack.
Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
Authors: Wan Wei , Bo Jingshan , Peng Da
Published: January 2025

This study collects all strong motion records from 134 seismic events in the Loess Plateau region of China between 2020 and 2023. The corresponding ground motion attenuation dataset is constructed through data processing and screening, including 1149 soil horizontal strong ground motion records. Based on this dataset, two ground motion attenuation models are developed. Specifically, through magnitude grouping and weighted processing, two-step fitting methods are used to establish prediction equations for the peak ground acceleration (PGA), effective peak acceleration (EPA), and peak ground velocity (PGV) at the horizontal soil layers. These equations can estimate ground motion parameters with magnitudes ranging from MS 3.0 to MS 6.5 and epicentral distances between 0 and 100 km. Furthermore, attenuation curves are plotted based on the prediction equations. A comparison between the actual observed values and the predicted curves and an evaluation of the fitting residuals assess our models’ fitting performance. The attenuation characteristics of horizontal ground motion parameters in the Loess Plateau region are systematically summarized.
Integrating Ground and UAV Mapping for GIS-Based Application of the Flash Flood Impact Severity Scale (FFISS) for the 2009 and 2020 Evia (Greece) Flash Floods
Authors: Spyrou Nafsika Ioanna , Diakakis Michalis , Mavroulis Spyridon , Deligiannakis Georgios , Andreadakis Emmanuel , Filis Christos , Kotsi Evelina , Antoniadis Zacharias , Melaki Maria , Gogou Marilia , Katsetsiadou Katerina-Navsika , Stanota Eirini-Spyridoula , Skourtsos Emmanuel , Vassilakis Emmanuel , Lekkas Efthymios
Published: January 2025

Flash floods have been the cause of some of the most devastating events worldwide. The wide diversity of the effects, as well as the variety in the severity of the impacts, lead to major obstacles in obtaining a realistic understanding of the damages caused by a flood event, thus hampering at the same time our ability to predict future impacts. In assessing flood impacts and their severity, most existing methods use a qualitative characterization (e.g., major, catastrophic, etc.) or view the impacts from a single viewpoint or discipline (e.g., economic losses). In this study, we apply the Flash Flood Impact Severity Scale (FFISS) to assess, map, and classify the impacts of two flash floods from the Lilas River in Greece in 2009 and 2020. This application aims to discuss the different severity levels in terms of how one flood can affect the impacts of the next event. The methodology encompasses comprehensive field research, including the collection of ground-based and aerial observations utilizing UAV technology to document the impacts. These observations are subsequently georeferenced, followed by application of the Flash Flood Impact Severity Scale (FFISS) and generation of detailed maps to assess and evaluate the severity of the impacts associated with the two flood events. The results show that despite the higher water stage of the second flood, the impacts in previously hit areas indicate lower severity values, attributed to the gradual adaptation of the community and its infrastructure, as well as significant local widening of the river channel. On the contrary, high severity remains an issue in newly flooded areas during the second event. Overall, the application of the FFISS can show the spatial patterns of severity impacts, providing insights into the nature of floods locally but also indicating a potential reduction in the overall risk in the post-flood period.
Bidirectional Semantic Communication Between Humans and Machines Based on Data, Information, Knowledge, Wisdom, and Purpose Artificial Consciousness
Authors: Mei Yingtian , Duan Yucong
Published: January 2025

Large language models (LLMs) and other artificial intelligence systems are trained using extensive DIKWP resources (data, information, knowledge, wisdom, purpose). These introduce uncertainties when applied to individual users in a collective semantic space. Traditional methods often lead to introducing new concepts rather than a proper understanding based on the semantic space. When dealing with complex problems or insufficient context, the limitations in conceptual cognition become even more evident. To address this, we take pediatric consultation as a scenario, using case simulations to specifically discuss unidirectional communication impairments between doctors and infant patients and the bidirectional communication biases between doctors and infant parents. We propose a human–machine interaction model based on DIKWP artificial consciousness. For the unidirectional communication impairment, we use the example of an infant’s perspective in recognizing and distinguishing objects, simulating the cognitive process of the brain from non-existence to existence, transitioning from cognitive space to semantic space, and generating corresponding semantics for DIKWP, abstracting concepts, and labels. For the bidirectional communication bias, we use the interaction between infant parents and doctors as an example, mapping the interaction process to the DIKWP transformation space and addressing the DIKWP 3-No problem (incompleteness, inconsistency, and imprecision) for both parties. We employ a purpose-driven DIKWP transformation model to solve part of the 3-No problem. Finally, we comprehensively validate the proposed method (DIKWP-AC). We first analyze, evaluate, and compare the DIKWP transformation calculations and processing capabilities, and then compare it with seven mainstream large models. The results show that DIKWP-AC performs well. Constructing a novel cognitive model reduces the information gap in human–machine interactions, promotes mutual understanding and communication, and provides a new pathway for achieving more efficient and accurate artificial consciousness interactions.
Targeting Ataxia Telangiectasia-Mutated and Rad3-Related for Anaplastic Thyroid Cancer
Authors: Lin Shu-Fu , Hsueh Chuen , Chen Wei-Yi , Chou Ting-Chao , Wong Richard J.
Published: January 2025

Background: Anaplastic thyroid cancer (ATC) is one of the most aggressive human malignancies and has a poor prognosis. Ataxia telangiectasia mutated and Rad3 related (ATR) is a key regulator for the DNA damage response and a potential target to treat cancer. Methods: We assessed the efficacy of BAY 1895344, an ATR inhibitor, in three ATC cell lines. Results: BAY 1895344 caused dose–response cytotoxicity in three ATC cell lines. BAY 1895344 induced S-phase and G2-phase arrest, activated caspase-3 activity and induced apoptosis in ATC cells. BAY 1895344 meaningfully retarded the tumor growth of an ATC xenograft model. BAY 1895344 therapy, combined with dabrafenib and trametinib, had synergism in vitro and revealed robust tumor growth suppression in vivo in two xenograft models of ATC harboring mutant BRAFV600E. Furthermore, the combination of BAY 1895344 with lenvatinib was more effective than either agent alone in a xenograft model of ATC. Conclusions: These results reveal that BAY 1895344 has potential in treating ATC.
Diastereoselective Synthesis of 2-Amino-spiro[4.5]decane-6-ones Through Synergistic Photocatalysis and Organocatalysis for [3 + 2] Cycloaddition of Cyclopropylamines with Olefins
Authors: Hu Tianxiao , Lin Xufeng
Published: January 2025

This research employs 2-methylene-tetrahydronaphtalene-1-ones and N-cyclopropylanilines as starting materials, integrating photocatalysis and organic phosphoric acid catalysis to synthesize 2-amino-spiro[4.5]decane-6-ones via a [3 + 2] cycloaddition approach. This method boasts the advantage of mild reaction conditions that are photocatalyst-free and metal catalyst-free. It achieves 100% atom conversion of the substrates, aligning with the principles of green chemistry. Additionally, it attains a high diastereoselectivity result of up to 99:1, demonstrating good stereoselectivity. In the derivatives of 2-methylene-tetrahydronaphtalene-1-ones, substrates with alkane rings of different sizes or thiophene replacing the phenyl ring are also amenable to this method, enabling the synthesis of different [4.4], [4.5], and [4.6] spirocyclic compounds. In the derivatives of N-cyclopropylanilines, substrates with para-fluoro and meta-fluoro substitutions are also amenable to this method. Finally, a preliminary mechanistic investigation was conducted, proposing a plausible reaction mechanism pathway initiating from the intermediate N-cyclopropylanilines with chiral phosphoric acid.
Evaluation of Citizen Science Project on Birdwatching in Germany: Advantages and Limits
Authors: Ehrmann Andrea , Tillmanns Oliver
Published: January 2025

Since 2005, The Nature and Biodiversity Conservation Union (NABU, Germany) has called upon people in Germany to count birds in their gardens during a defined time (Friday to Sunday) in May. This “Stunde der Gartenvögel” (hour of the garden birds) aims at obtaining a yearly overview of the birds in villages and towns. While this birdwatching action gives a broad overview due to the large number of gardens and parks involved (around 40,000 in the year 2024), several drawbacks must be mentioned that may impede the comparability of the counted bird numbers from one year to another. Here we give an overview of the chronological development of the overall numbers of species and specifically of the most frequent garden birds. We discuss correlations with external factors, such as weather and numbers of participants, and compare the results with ornithological studies in the same period as well as with the results of the yearly Birdrace in Germany, where ornithologically interested people count birds in a defined area on the full first Saturday in May. The main drawback of the citizen science project was found to be misidentifications of birds, especially evident in the case of the Tree Sparrow, combined with birds not being recorded because they could not be identified. The main advantage, on the other hand, is the large amount of data from the “Stunde der Gartenvögel”, which is important for measuring especially the trends of well-known, frequent species much better than the Birdrace. Ideally, all data taken in different ways should be combined, and misinterpretations should be reduced by showing male and female birds of species with strong gender dimorphism.
Altitudinal Variation of Limb Size of a High-Altitude Frog
Authors: Liu Xueyan , Chen Hongzhou , Wu Yanfang , Xiong Huan , Liu Song , Ling Huafan , Chen Wei
Published: January 2025

Geographical variation in morphological traits represents a significant ecological phenomenon. According to Allen’s rule, animals inhabiting cooler environments typically exhibit shorter extremities compared to those in warmer regions. While Allen’s rule has been extensively validated along latitudinal gradients, its applicability to altitudinal variation in morphological traits remains less well understood. In this study, we analyzed morphological data—including forelimb length and hindlimb length—from 492 individuals of Rana kukunoris from 26 populations to assess whether relative limb size in both sexes declines with altitude, aligning with Allen’s rule. However, this pattern exhibited geographical regional variation. In the northern and central regions, relative limb length consistently followed the predictions of Allen’s rule. In contrast, the southern region showed no significant altitudinal variation in limb proportions. These results highlight that the applicability of Allen’s rule may be modulated by regional environmental factors and altitude vertical differences, underscoring the need for a nuanced understanding of how local contexts influence morphological adaptations.
Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging
Authors: Kumar Rahul , Pan Cheng-Tang , Lin Yi-Min , Yow-Ling Shiue , Chung Ting-Sheng , Janesha Uyanahewa Gamage Shashini
Published: January 2025

Background: The global burden of respiratory diseases such as influenza, tuberculosis, and viral pneumonia necessitates rapid, accurate diagnostic tools to improve healthcare responses. Current methods, including RT-PCR and chest radiography, face limitations in accuracy, speed, accessibility, and cost-effectiveness, especially in resource-constrained settings, often delaying treatment and increasing transmission. Methods: This study introduces an Enhanced Multi-Model Deep Learning (EMDL) approach to address these challenges. EMDL integrates an ensemble of five pre-trained deep learning models (VGG-16, VGG-19, ResNet, AlexNet, and GoogleNet) with advanced image preprocessing (histogram equalization and contrast enhancement) and a novel multi-stage feature selection and optimization pipeline (PCA, SelectKBest, Binary Particle Swarm Optimization (BPSO), and Binary Grey Wolf Optimization (BGWO)). Results: Evaluated on two independent chest X-ray datasets, EMDL achieved high accuracy in the multiclass classification of influenza, pneumonia, and tuberculosis. The combined image enhancement and feature optimization strategies significantly improved diagnostic precision and model robustness. Conclusions: The EMDL framework provides a scalable and efficient solution for accurate and accessible pulmonary disease diagnosis, potentially improving treatment efficacy and patient outcomes, particularly in resource-limited settings.
Body Weight Estimation of Cattle in Standing and Lying Postures Using Point Clouds Derived from Unmanned Aerial Vehicle-Based LiDAR
Authors: Wang Yaowu , Mücher Sander , Wang Wensheng , Kooistra Lammert
Published: January 2025

This study aims to explore body weight estimation for cattle in both standing and lying postures, using 3D data. We apply a Unmanned Aerial Vehicle-based (UAV-based) LiDAR system to collect data during routine resting periods between feedings in the natural husbandry conditions of a commercial farm, which ensures minimal interruption to the animals. Ground truth data are obtained by weighing cattle as they voluntarily pass an environmentally embedded scale. We have developed separate models for standing and lying postures and trained them on features extracted from the segmented point clouds of cattle with unique identifiers (UIDs). The models for standing posture achieve high accuracy, with a best-performance model, Random Forest, obtaining an R2 of 0.94, an MAE of 4.72 kg, and an RMSE of 6.33 kg. Multiple linear regression models are trained to estimate body weight for the lying posture, using volume- and posture-wise characteristics. The model used 1 cm as the thickness of the slice-wise volume calculation, achieving an R2 of 0.71, an MAE of 7.71 kg, and an RMSE of 9.56 kg. These results highlight the potential of UAV-based LiDAR data for accurate and non-intrusive estimation of cattle body weight in lying and standing postures, which paves the way for improved management practices in precision livestock farming.
Τhe Political Economy of Green Transition: The Need for a Two-Pronged Approach to Address Climate Change and the Necessity of “Science Citizens”
Authors: Angelakis Antonios , Manioudis Manolis , Koskina Anthi
Published: January 2025

Given the need to strengthen responses to the growing challenges posed by climate change, the purpose of this paper is to explore innovative approaches and interdisciplinary perspectives for tackling these issues, focusing on the role of the institutional framework, emerging technologies, and the necessity to also encourage the involvement of small-scale actors (such as citizens). The main approaches of this paper involve, first, the technological developments spurred by the necessity to effectively address climate change problems, emphasizing macro-level dimensions in terms of the political economy of green transition and the technological components of climate solutions. Parallel to that, it provides results and presents key elements of the legal context that promoted the sustainable transition, such as the establishment of a science-based policymaking process, the development of scientific data and tools, and efforts to encourage the participation of all relevant actors in sustainable economic development. Against this background, this paper puts forward the idea that a combined approach is required to address climate change issues, integrating top-down, e.g., macro-policy approaches with bottom-up strategies (with the latter allowing for a more dynamic participation of citizens and individuals), in order to complement current institutional, legal, policy, and technological measures. The result of the analysis is that this paper provides evidence for the introduction of guidelines strengthening macro-economic approaches in addition to the concept of a “science citizen” as a major component of new problem-focused solutions. The principal results and findings offer interpretations and insights while encouraging further discussion on transitioning to a sustainable science society. In this context, the analysis results elucidate that there is evidence for an increased policy emphasis on technology development (economy-based approaches) rather than on technology diffusion and assessment, and/or the integration of key small-scale actors, such as citizens. Thus, this paper provides evidence for the need to incorporate “science citizens” as a key parameter into the technology and innovation chain (e.g., data provision) and the public policy domain. Overall, this paper outlines a holistic analysis of the international economic, technological, institutional, legal, and policy environment regarding innovation, sustainability, and the climate crisis.
Psychological Factors Impacting Academic Performance Among Business Studies’ Students
Authors: Rožman Maja , Vrečko Igor , Tominc Polona
Published: January 2025

This study explores the significant psychological factors that impact academic success among students. The sample included 287 undergraduate and postgraduate students of business studies. Structural Equation Modeling was used to analyze the data, investigating the impact of students’ motivation, mental and physical health, work-life balance, and anxiety on both perceived academic success as well as quantitative indicators of academic success. The findings reveal that students’ motivation is significant for enhancing academic success. Additionally, while mental and physical health are significant contributors to academic success, balancing academic work with personal life is essential for fostering a positive perception of academic success. Conversely, anxiety, particularly exam-related, negatively affects students’ perceived academic success, highlighting the need for comprehensive support systems. These findings offer crucial insights for educators and policymakers, paving the way for innovative strategies that enhance academic success and empower students to thrive holistically, both academically and personally. By addressing the full spectrum of challenges students face, we can foster a more resilient, motivated, and successful generation of students.
A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting
Authors: Zhao Yitao , Li Jiahao , Chen Chuanxu , Guan Quansheng
Published: January 2025

With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses challenges. For instance, some characteristics (e.g., high-frequency, noisy and non-stationary) exacerbate the data processing and model training procedures, and the heterogeneity in household consumption patterns causes difficulties for models with the generalization capability. Further, the real-time data processing requirement calls for both the high forecasting accuracy and improved computational efficiency. Thus, we propose a diffusion–attention-enhanced temporal (DATE-TM) model with multi-feature fusion to address the above issues. First, the DATE-TM model could integrate residents’ electricity consumption patterns with climatic factors. Then, it extracts the temporal feature using an encoder and meanwhile models the data uncertainty through a diffusion model. Finally, the decoder, enhanced with the attention mechanism, creates the precise prediction for the household load forecasting. Experimental results reveal that DATE-TM significantly surpasses classical neural networks such as BiLSTM and DeepAR, especially in handling the data uncertainty and long-term dependency.
Potential of PN Reduction in Passenger Cars with DPF and GPF
Authors: Stepien Zbigniew , Comte Pierre , Engelmann Danilo , Czerwinski Jan , Mayer Andreas , Rubino Lauretta
Published: January 2025

Particle number concentration (PN) in vehicle exhaust and ambient air describes the number of ultrafine particles (UFPs) below 500 nm, which are recognized as a toxic and carcinogenic component of pollution and are regulated in several countries. Metal nuclei, ash, and organic matter contribute significantly to the ultrafine particle size fraction and, thus, to the particle number concentration. Exhaust gas filtration is increasingly being used worldwide to significantly reduce this pollution, both on diesel particulate filter (DPF) and gasoline particulate filter (GPF) engines. In recent years, the EU has also funded research projects dealing with the possibilities of retrofitting gasoline vehicles with GPFs. This paper presents the results and compares the PN emissions of different vehicles. An original equipment manufacturer (OEM) diesel car with a DPF is considered as a benchmark. The PN emissions of this car are compared with a CNG car without filtration and with gasoline cars equipped with GPFs. It can be concluded that the currently used GPFs still have some potential to improve their filtration efficiency and that a modern CNG car would still have remarkable possibilities to reduce PN emissions with an improved quality GPF.
Advancements and Challenges in Direct Air Capture Technologies: Energy Intensity, Novel Methods, Economics, and Location Strategies
Authors: Kotowicz Janusz , Niesporek Kamil , Baszczeńska Oliwia
Published: January 2025

Direct air capture (DAC) technology is increasingly recognized as a key tool in the pursuit of climate neutrality, enabling the removal of carbon dioxide directly from the atmosphere. Despite its potential, DAC remains in the early stages of development, with most installations limited to pilot or demonstration units. The main barriers to its widespread implementation include high energy demands and significant capture costs. This literature review addresses the most critical research directions related to the development of this technology, focusing on its challenges and prospects for deployment. Particular attention is given to studies aimed at developing new, cost-effective, and efficient sorbents that could significantly reduce the energy intensity and costs of the process. Alternative technologies, such as electrochemical and membrane-based processes, show promise but require further research to overcome limitations, such as sensitivity to oxygen presence or insufficient membrane selectivity. The economic feasibility of DAC remains uncertain, with current estimates subject to significant uncertainty. Governmental and regulatory support will be crucial for the technology’s success. Furthermore, the location of DAC installations should consider factors such as energy availability, options for carbon dioxide storage or utilization, and climatic conditions, which significantly affect process efficiency. This review highlights the necessity for continued research to overcome existing barriers and fully harness the potential of DAC technology.
Towards the Necessary Decarbonization of Historic Buildings: A Review
Authors: Almeida Manuela , Ascione Fabrizio , Iaccheo Anna , Iovane Teresa , Mastellone Margherita
Published: January 2025

The critical and urgent issue of decarbonization by 2050 needs to include the existing historical built environment in the process of energy requalification. These buildings, subjected to heritage preservation, are extremely inadequate to the modern standards of energy efficiency and thermal comfort, and they exhibit the poorest energy performance. In this study, a review of the existing scientific literature on the matter of energy renovation processes applied to historic buildings is provided. The reviewed papers, selected from scientific databases, were initially categorized according to their reference scale—either individual buildings or urban contexts. Subsequently, the papers were grouped on the basis of the main energy efficiency levels they investigated. The goal is to offer a comprehensive overview of the materials, technologies and strategies currently in use, as well as future perspectives, to aid the ecological transition and foster sustainable development, all while preserving the artistic, cultural and architectural heritage of these buildings.
Stochastic Scenario Generation Methods for Uncertainty in Wind and Photovoltaic Power Outputs: A Comprehensive Review
Authors: Zheng Kun , Sun Zhiyuan , Song Yi , Zhang Chen , Zhang Chunyu , Chang Fuhao , Yang Dechang , Fu Xueqian
Published: January 2025

This paper reviews scenario generation techniques for modeling uncertainty in wind and photovoltaic (PV) power generation, a critical component as renewable energy integration into power systems grows. Scenario generation enables the simulation of variable power outputs under different weather conditions, serving as essential inputs for robust, stochastic, and distributionally robust optimization in system planning and operation. We categorize scenario generation methods into explicit and implicit approaches. Explicit methods rely on probabilistic assumptions and parameter estimation, which enable the interpretable yet parameterized modeling of power variability. Implicit methods, powered by deep learning models, offer data-driven scenario generation without predefined distributions, capturing complex temporal and spatial patterns in the renewable output. The review also addresses combined wind and PV power scenario generation, highlighting its importance for accurately reflecting correlated fluctuations in multi-site, interconnected systems. Finally, we address the limitations of scenario generation for wind and PV power integration planning and suggest future research directions.
Engineering a Protease K for Efficient Degradation of Wool Scale Layer Using a Substrate Pocket Modification
Authors: Zhao Lei , Ma Xiangyang , Ding Yunan , Zheng Kaixin , Wang Kefen , Lu Fuping , Liu Yihan
Published: January 2025

The outermost surface of wool is covered by a scale layer, posing challenges to some steps of fabric processing. This layer, primarily composed of keratin, resists degradation by conventional proteases due to its high disulfide bond content. Protease K, an extracellular serine endo-proteinase derived from Tritirachium album Limber (tPRK), is known for its ability to digest native keratin. However, its limited activity against keratin has restricted its application in wool scale layer treatment. In this study, the substrate-binding pocket of tPRK was engineered, yielding the mutant N162A, which demonstrated an 84% increase in catalytic activity toward keratin. Additionally, the catalytic efficiency (kcat/Km) of N162A on keratin improved by 44.52%. Structural analysis indicated that modifications in the substrate-binding pocket reduced steric hindrance during substrate entry while enhancing substrate binding. Additionally, 3.3 mg/mL of amino acids were released within 6 h, which were catalyzed by N162A, with a 61% increase compared to the native tPRK. Moreover, the N162A variant effectively reduced the scale layer thickness without compromising the tensile strength of the wool, maintaining its mechanical properties. The findings provide a sustainable strategy for the wool industry while broadening the scope of biotechnological applications in the textile sector.
Optimizing Controlled-Resonance Acoustic Metamaterials with Perforated Plexiglass Disks, Honeycomb Structures, and Embedded Metallic Masses
Authors: Ciaburro Giuseppe , Iannace Gino , Romero Virginia Puyana
Published: January 2025

Acoustic metamaterials offer new opportunities for controlling sound waves through engineered material configurations at the sub-wavelength scale. In this research, we present the optimization of a resonance-controlled acoustic metamaterial based on a sandwich structure composed of perforated plexiglass disks, honeycomb structures, and added metal masses. The innovative approach consists of integrating perforated plexiglass disks interspersed with honeycomb structures, which act as multiple and complex Helmholtz resonators, and adding metal masses to introduce resonances at specific frequencies. The metamaterial’s acoustic properties were experimentally characterized using an impedance tube (Kundt tube), allowing the measurement of the Sound Absorption Coefficient (SAC) over an expansive frequency selection. The results demonstrate a substantial enhancement in sound absorption at the target frequencies, demonstrating the effectiveness of the introduced resonances. Numerical simulations using an Artificial Neural Network (ANN) model in MATLAB environment were used to analyze the distribution of resonances and optimize the structural configuration. To effectively evaluate the acoustic properties of the metamaterial, various configurations were analyzed using perforated plexiglass disks combined with different layers of honeycombs arranged in a sandwich structure with a thickness ranging from 41 to 45 mm. A comparison of these configurations revealed a notable increase in the Sound Absorption Coefficient (SAC) when employing three layers of perforated plexiglass disks and adding masses to the first disk (about 14%). This study highlights the potential of resonance-controlled metamaterials for advanced applications in noise control and acoustic engineering.
Simple and Robust Approach for Determination of Total Protein Content in Plant Samples
Authors: Vershinina Yulia S. , Mitin Ilya V. , Garmay Andrey V. , Sugakov Gleb K. , Veselova Irina A.
Published: January 2025

The determination of total protein in plant samples is a difficult task, as classical nitrogen-based methods are not selective for the nature of nitrogen, and the results of biochemical methods are influenced by both associated compounds and the complex composition of the protein matrix. Using electrophoretic separation of three commercial sunflower protein samples, it was determined that the studied proteins are a mixture of salt-soluble globulins and water-soluble albumins of different molecular weights. The total protein content of the studied samples was determined using five spectrophotometric methods: direct spectrophotometry, bicinchoninic acid assay, and Benedict’s, Bradford’s, and Lowry’s methods. After comparing the results obtained, it was concluded that, for the determination of protein in these plant materials, the use of the Dumas nitrogen-based method in tandem with Lowry’s spectrophotometric method is the most suitable.
Mechanical Properties and Microscopic Fractal Characteristics of Lime-Treated Sandy Soil
Authors: Huang Hu , Li Ruihang , Chen Feihao , Cao Kelei , Guo Lixia , Qiu Qingming
Published: January 2025

In order to reveal the intrinsic mechanism of the mechanical properties of lime-treated sandy soil from a microscopic perspective, triaxial tests were conducted to analyze the macroscopic mechanical characteristics of sandy soil with different lime contents (0%, 5%, 8%, and 12%). The changes in the microstructure of the lime-treated sandy soil were studied through scanning electron microscopy, energy-dispersive spectroscopy, and mercury intrusion tests, combined with fractal theory for quantitative characterization. The results indicate that the stress–strain curve of lime-treated sandy soil can be divided into four stages: linear elastic, non-linear, failure, and residual strength. With the increase in lime content, the peak stress and cohesion first increase and then decrease, while the internal friction angle first decreases and then increases, suggesting the presence of an optimal threshold for lime content between 5% and 12%. The failure mode transitions from diagonal shear failure to bulging failure, significantly enhancing stability; both the fitted Mohr–Coulomb and Drucker–Prager failure criteria effectively reflect the failure patterns of the specimens in principal stress space. The results based on the three fractal dimensions demonstrate that lime-treated sandy soil exhibits clear fractal characteristics, with the highest fractal dimension value at a lime content of 8%, corresponding to the highest overall strength. In addition, the fractal dimension shows a binomial relationship with pore characteristic parameters and shear strength parameters; it can effectively characterize the complexity of the microstructure and accurately predict changes in shear strength parameters.
A Novel Approach for Solving Fractional Differential Equations via a Multistage Telescoping Decomposition Method
Authors: Chita Fouzia , Ayadi Souad , Erden Ege Meltem , Ege Ozgur , Ramaswamy Rajagopalan
Published: January 2025

This study presents a novel algorithm for solving nonlinear fractional initial value problems, utilizing a multistage telescoping decomposition method (FTDM). By combining the MFTDM with the Elzaki transform, this method significantly improves computational efficiency and accuracy. Through a series of numerical experiments, the proposed approach is demonstrated to be highly practical, reliable and straightforward, offering a robust framework for solving nonlinear fractional differential equations effectively.
Performance Evaluation of Fluorescent Polymer Gel Microspheres as a Reservoir Conformance Control Agent
Authors: Shagymgereyeva Saya , Sarsenbekuly Bauyrzhan , Kang Wanli , Turtabayev Sarsenbek
Published: January 2025

This study introduces fluorescent polymer gel microspheres (FPMs) as a novel approach to enhance conformance control in oil reservoirs. Designed to address the challenges of high-permeability zones, FPMs were synthesized via inverse suspension polymerization, incorporating 2-acrylamido-2-methylpropane sulfonic acid (AMPS) to improve thermal stability and swelling and fluorescein to enable fluorescence. Characterization using FT-IR, SEM, fluorescence spectroscopy, and thermal analysis revealed that FPMs swell significantly in brine, with diameters increasing from 46 μm to 210 μm, and maintain thermal stability up to 110 °C. These advanced properties make FPMs highly effective in reducing permeability and facilitating real-time tracking, offering a promising solution for improved oil recovery and efficient reservoir management.
Identification of Alterations in the Expression of Genes Related to the Implant Failure in Spanish Patients with Down Syndrome and Periodontal Disease
Authors: Cortés-Eslava Daniela , Gómez-Díaz Raquel , Torres-Lagares Daniel , Machuca-Portillo Guillermo , Gutiérrez-Pérez José-Luis , Serrera-Figallo María-Ángeles , Baus-Domínguez María
Published: January 2025

Background: Individuals with Down syndrome exhibit a higher prevalence of periodontal disease, which can lead to implant loss. This study aims to identify genetic markers associated with implant loss in these patients, providing insight into potential predictive and therapeutic approaches. Methods: A systematic analysis was conducted, including both clinical and genetic data from Down syndrome patients with a history of dental implants. Genetic profiling was performed using Transcriptome Analysis Console (TAC version 4.0 Applied BiosystemsTM, Thermo Fisher Scientific, Waltham, MA, USA), focusing on genes previously implicated in periodontal disease and bone metabolism. Statistical analysis identified correlations between genetic variants and implant survival rates. Results: The analysis revealed statistically significant alterations in several genes related to inflammation and bone remodeling. Key findings included alterations in the expression of the genes MMP15, MMP17, S100B, GHR, DNAH6, and ZCCHC14 in patients with implant failure. These genetic markers were strongly correlated with compromised osseointegration and implant loss. These findings underline the role of genetic predisposition in the failure of dental implants among individuals with Down syndrome. Conclusions: Genetic markers, particularly those involved in inflammation and bone metabolism, play a critical role in implant loss among Down syndrome patients with periodontal disease. Recognizing these markers can aid in early diagnosis and personalized treatment strategies, potentially improving implant success rates.
River Ice Effects on Sediment Transport and Channel Morphology—Progress and Research Needs
Authors: Shen Hung Tao
Published: January 2025

Sediment transport in alluvial channels has a long history of intensive research. River ice could affect sediment transport and channel morphology through the impact of various dynamic and thermal ice processes. However, studies on sediment transport under the influence of ice have been minimal until recent years. This phenomenon was partially due to the complicated interactions between ice, flow, and sediment dynamics, which require a good understanding of the river ice process, in addition to the difficult field data collection conditions. This paper reviews the progress and needs of river ice-related research on sediment transport and channel morphology, including the influence of ice cover and surface ice runs on sediment transport, the effects of frazil ice, anchor ice, and bank stability with freeze-thaw effects.
Tetrahydrocurcumin Alleviates Metabolic Dysfunction-Associated Steatohepatitis in Mice by Regulating Serum Lipids, Bile Acids, and Gut Microbiota
Authors: Peng Shang , Meng Moran , Luo Ping , Liu Jiao , Wang Junjun , Chen Yong
Published: January 2025

The aim of this study was to investigate the protective effects and potential mechanisms of Tetrahydrocurcumin (THC) on methionine–choline-deficient diet (MCD)-induced MASH in C57BL/6 mice by using multi-omics techniques. The C57BL/6 mice were fed with the MCD for 8 weeks to establish a MASH model, while THC (100 mg·kg−1·d−1) and obeticholic acid (6.5 mg·kg−1·d−1) were administered via gavage to the THC group and the positive control group, respectively. The biochemical indexes of the serum and liver were detected using kits. Liver tissue sections were taken to observe the pathomorphological changes. Serum lipid and bile acid contents were measured via LC-MS, and the changes in ileal intestinal flora were detected by 16S rDNA high-throughput sequencing technology. The results revealed that THC significantly attenuated oxidative stress and lipid accumulation in NCTC-1469 cells and relieved hepatic injury and oxidative stress, reduced hepatic TG content, and improved hepatic steatosis in mice. THC alleviated 34 lipid abnormalities caused by the MCD; increased the abundance and diversity of intestinal flora, the ratio of Firmicutes to Bacteroidota, and the abundance of the probiotic (Verrucomicrobiota, Christensenellaceae, Akkermansiaceae, Lachnospiraceae, Desulfovibrionaceae); and reduced the abundance of obesity-associated pathogenic flora such as Firmicutes. Bile acid analysis showed that THC administration reduced the levels of serum toxic bile acid 7-KDCA and CA. In addition, RT-qPCR studies showed that THC down-regulated the transcript levels of the hepatic lipogenesis-related genes Srebp1c, Acc1, Scd1, and Fas, and up-regulated the transcript levels of the hepatic bile acid secretion-related genes Mrp2 and Bsep. The above results suggest that THC may alleviate MCD-induced MASH by downregulating liver Srebp1c, Acc1, Scd1, and Fas levels to inhibit lipid synthesis, upregulating Mrp2 and Bsep levels to regulate serum toxic BA levels, up-regulating the abundance of intestinal probiotic flora, and down-regulating the abundance of intestinal harmful bacterial flora. The multi-omics findings from the above study identified potential new mechanisms by which THC alleviates MASH, providing new reference targets for the development of anti-MASH drugs. These results also offer a basis for screening clinical diagnostic biomarkers for MASH and provide new directions for personalized diagnosis and treatment.