bluuebunny/crossref_metadata_2025 huggingface dataset:
GIS-Based Spatial Analysis and Strategic Placement of Fine Dust Alert Systems for Vulnerable Populations in Gangseo District
Authors: Lee Jaewook , Jang Junyoung , Im Jaeuk , Lee Jae Hong
Published: November 2024

Air pollution, particularly fine particulate matter (PM), poses significant health risks to vulnerable populations such as children, older adults, and individuals with chronic illnesses. Understanding the spatial distribution of these populations and their access to air quality information is crucial for effective interventions. In urban areas like Gangseo District, the distribution of essential facilities and accessibility varies greatly. While studies have highlighted the health impacts of PM, research on optimizing air quality monitoring for at-risk groups remains limited. This study aims to identify optimal locations for air quality monitoring by analyzing the spatial distribution of vulnerable populations and facility accessibility. Using Geographic Information Systems (GIS) and isochrone maps, we identified areas with high concentrations of vulnerable groups and poor access to healthcare facilities. Our findings revealed significant disparities in access to air quality information, with some high-risk areas underserved by current monitoring systems. This study integrated demographic data and spatial analysis to propose strategic monitoring placements. The methodology can be applied to other urban settings and offers a framework for improving air quality management. This study underscores the importance of targeted air quality monitoring to protect vulnerable populations and suggests practical steps for policymakers to enhance public health.
PIC Simulation of Enhanced Electron Acceleration in a Double Nozzle Gas Target Using Spatial–Temporal Coupling with Axiparabola Optics
Authors: Girdauskas Valdas , Tomkus Vidmantas , Abedi-Varaki Mehdi , Račiukaitis Gediminas
Published: November 2024

In this paper, the results of a Particle-in-Cell (PIC) simulation of electrons accelerated using a 10 fs Top-hat (TH) beam with a limited pulse energy of 85 mJ, focused on a double nozzle gas target using an off-axis parabola (OAP), an axiparabola (AXP), and an axiparabola with additional spatial–temporal coupling (AXP+STC), are discussed. The energy of accelerated electrons was predominantly determined through self-focusing and the ionisation injection effects of the laser beam propagating in plasma. The maximal energy of electrons accelerated using an AXP+STC could be higher by 12% compared to the energy of electrons accelerated by the regular OAP.
AI-Driven Smart Transformation in Physical Education: Current Trends and Future Research Directions
Authors: Hu Zhengchun , Liu Zhaohe , Su Yushun
Published: November 2024

Although the rapid development of Artificial Intelligence (AI) in recent years has brought increasing academic attention to the intelligent transformation of physical education, the core knowledge structure of this field, such as its primary research topics, has yet to be systematically explored. The LDA (latent Dirichlet allocation) topic model can identify latent themes in large-scale textual data, helping researchers extract key research directions and development trends from extensive literature. This study is based on data from the Web of Science Core Collection and employs a systematic literature screening process, utilizing the LDA topic model for in-depth analysis of relevant literature to reveal the current status and trends of AI technology in physical education. The findings indicate that AI applications in this field primarily focus on three areas: “AI and data-driven optimization of physical education and training”, “computer vision and AI-based movement behavior recognition and training optimization”, and “AI and virtual technology-driven innovation and assessment in physical education”. An in-depth analysis of existing research shows that the intelligentization of physical education, particularly in school and athletic training contexts, not only promotes sustainable development in the field but also significantly enhances teaching quality and safety, allowing educators to utilize data more precisely to optimize teaching strategies. However, current research remains relatively broad and lacks more precise and robust data support. Therefore, this study critically examines the limitations of current research in the field and proposes key research directions for further advancing the intelligent transformation of physical education, providing a solid theoretical framework and guidance for future research.
Experimental Investigations on Repair and Permeability Reduction for Single Sandstone Fracture Using a Mixed CaCO3 and Fe(OH)3 Precipitate
Authors: Ju Jinfeng , Li Quansheng , Wang Chenyu , Fan Yanan
Published: November 2024

In China, groundwater loss caused by underground coal mining is becoming increasingly serious. The key to groundwater restoration is to repair mining-induced water-conducting fractures (WCFs) in the overlying strata. In this study, the adsorption–consolidation sealing characteristics of chemical precipitates were used to conduct permeability reduction (PR) experiments, including adding mixed CaCO3 and Fe(OH)3 to a sandstone specimen with a single fracture at room temperature. An aqueous solution of Na2CO3 was used as the simulated groundwater, and a solution of mixed CaCl2 and FeCl2 was used as the repair reagent to simulate the water seepage conditions of a fractured rock mass. The two aqueous solutions were simultaneously injected into a single-fractured rock specimen at a constant flow rate. The experimental results show that the Fe(OH)3 colloid encapsulated CaCO3 crystals in a mixed precipitate, reducing the overall structural stability of the mixed precipitate and restricting repair and PR efficiency. However, the Fe(OH)3 precipitate had better PR efficiency in the initial stage of the experiment. Therefore, a better scheme was put forward to repair the WCF, utilizing a mixed Fe(OH)3 and CaCO3 precipitate with a molar ratio close to 1:4 in the early stage and a single CaCO3 precipitate in the later stage.
The Role of Chemical Treatments on Curaua Fibers on Mechanical and Thermal Behavior of Biodegradable Composites
Authors: de Oliveira Janaíne M. , Sousa Vitor M. Z. , Teixeira Linconl A. , Leão Rosineide M. , Sales-Contini Rita C. M. , Steier Volker F. , da Luz Sandra M.
Published: November 2024

Biodegradable composites combining thermoplastic polymers and natural fibers could originate materials with synergetic mechanical and thermal properties, keeping their biodegradability. This paper describes biodegradable polymers’ mechanical and thermal properties, such as polylactic acid (PLA) and polyhydroxybutyrate (PHB) reinforced with curaua fibers. To improve the interface between matrix and reinforcement, the curaua fibers were treated by two routes: (1) treatment with hot water and subsequent mercerization with NaOH; (2) treatment with chlorite and subsequent mercerization with NaOH. The composites of PLA and PHB reinforced with natural or modified fibers (10 and 20 wt%) were obtained by extrusion and injection molding. The influence of fiber content and treatment on composite properties was studied by tensile and flexural tests, scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The results showed the removal of hemicellulose and lignin from the fibers, increasing their crystallinity and slightly decreasing their thermal stability after chemical treatments. Also, the DSC technique showed that the insertion of the curaua fibers increased the crystallinity index of all composites/PLA. The mercerized-curaua (20 wt%)/PLA composite showed the best result in the mechanical behavior, both in tensile and bending tests. The PHB composite, reinforced with curaua fibers and treated with hot water and mercerization (20 wt%), showed the best result regarding mechanic performance. To conclude, all composites improved mechanical properties compared to pure polymers.
Leveraging Multi-Agent Systems and Decentralised Autonomous Organisations for Tax Credit Tracking: A Case Study of the Superbonus 110% in Italy
Authors: De Gasperis Giovanni , Facchini Sante Dino , Letteri Ivan
Published: November 2024

This study aims to develop a Secured Fiscal Credits Model to address the challenges of managing Italy’s “Superbonus 110%” tax credit. Using a decentralised governance approach, our research objective is to provide a feasible system to track and control the entire tax credit process, from generation to redemption. The method integrates Artificial Intelligence and blockchain technology within a Decentralised Autonomous Organisation architecture, combined with a Multi-agent System to establish a tokenomics model. The system is structured to prevent accidental errors, such as double spending or overspending, and detect fraudulent behaviours, like false claims of completed work. Our main findings indicate that deploying two Decentralised Autonomous Organisations on the Algorand blockchain significantly enhances trust and security, supporting effective oversight of the Superbonus process and facilitating transparent value exchange among stakeholders. This decentralised governance model introduces substantial automation, reduces biases, and offers a viable solution to strengthen tax credit management. This work proposes an innovative, technology-driven framework that can be generalised to similar fiscal and governance contexts, enhancing transparency and control.
Establishing Lightweight and Robust Prediction Models for Solar Power Forecasting Using Numerical–Categorical Radial Basis Function Deep Neural Networks
Authors: Loh Chee-Hoe , Chen Yi-Chung , Su Chwen-Tzeng , Su Heng-Yi
Published: November 2024

As green energy technology develops, so too grows research interest in topics such as solar power forecasting. The output of solar power generation is uncontrollable, which makes accurate prediction of output an important task in the management of power grids. Despite a plethora of theoretical models, most frameworks encounter problems in practice because they assume that received data is error-free, which is unlikely, as this type of data is gathered by outdoor sensors. We thus designed a robust solar power forecasting model and methodology based on the concept of ensembling, with three key design elements. First, as models established using the ensembling concept typically have high computational costs, we pruned the deep learning model architecture to reduce the size of the model. Second, the mediation model often used for pruning is not suitable for solar power forecasting problems, so we designed a numerical–categorical radial basis function deep neural network (NC-RBF-DNN) to replace the mediation model. Third, existing pruning methods can only establish one model at a time, but the ensembling concept involves the establishment of multiple sub-models simultaneously. We therefore designed a factor combination search algorithm, which can identify the most suitable factor combinations for the sub-models of ensemble models using very few experiments, thereby ensuring that we can establish the target ensemble model with the smallest architecture and minimal error. Experiments using a dataset from real-world solar power plants verified that the proposed method could be used to build an ensemble model of the target within ten attempts. Furthermore, despite considerable error in the model inputs (two inputs contained 10% error), the predicted NRMSE of our model is still over 10 times better than the recent model.
Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
Authors: Chatzipanagi Anatoli , Kakoulaki Georgia , Szabó Sandor , Jäger-Waldau Arnulf
Published: November 2024

The global cumulative installed PV capacity is anticipated to surpass 2 TWp by the end of 2024, which is an increase in three orders of magnitude from the 1 GWp at the end of 2004. Depending on the energy transition scenarios, up to 8 kWp per capita have to be operational by 2050 globally. In Europe, this capacity could even be 50% higher, and this will have consequences on the decisions regarding where to install this capacity; thus, these decisions will be significantly impacted. This paper gives an overview of the technology options for installing PV systems in areas already used for other human activities as well as integrating them into ships and vehicles without the acquisition of extra land. The focus of this paper is on the situations within the European Union, such as land competition, and presents innovative solutions.
Features of the Influence of the Arrangement of Parts in the Chamber of Installation During Vacuum Arc Deposition on the Properties of Coatings
Authors: Sotova Catherine , Volosova Marina , Vereschaka Alexey , Bublikov Yuri , Milovich Filipp , Seleznev Anton , Shmakov Ilya , Grigoriev Sergey
Published: November 2024

The effect of the spatial arrangement in the physical vapor deposition (PVD) chamber on the composition and properties of coatings is considered using the example of the deposition of the (Ti,Al)N coating. The proposed method is one of the ways (along with varying the arc current of the cathodes and the bias voltage, as well as using alloy cathodes) to change the ratio of elements in the coating, and achieves this across a wide range of values. The three samples were located, respectively, opposite the evaporator with a titanium cathode, opposite the evaporator with an aluminum cathode and in an intermediate position between the two evaporators. The coating was deposited without rotating the turntable. The aluminum content in the coatings decreases from 94.2 at.% for the sample located directly opposite the evaporator with an Al cathode to 10.3 at.% for the sample located opposite the evaporator with a Ti cathode. In the coating deposited on the sample located opposite the aluminum cathode, the formation of a nitrided layer with a thickness of about 250 nm was observed in the substrate. The maximum hardness (32.3 ± 1.7 GPa) belongs to a coating on the sample occupying an intermediate position. The coating on the sample located opposite the aluminum cathode has a hardness of 16.7 ± 0.8 GPa. The coating hardness on the sample located opposite the titanium cathode is 28.5 ± 1.1 GPa. The best fracture strength in the scratch test was observed for the coating on the sample occupying an intermediate position. The nature of the coating fracture in the scratch test was studied. A sufficiently high-quality coating can be obtained without rotating the turntable, and the coating composition can be controlled by changing the position of the sample relative to the evaporators.
Digitalization and Dynamic Criticality Analysis for Railway Asset Management
Authors: Hernández Mauricio Rodríguez , Sánchez-Herguedas Antonio , González-Prida Vicente , Contreras Sebastián Soto , Crespo Márquez Adolfo
Published: November 2024

The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment.
Robots in Partially Known and Unknown Environments: A Simulated Annealing Approach for Re-Planning
Authors: Figueroa Andrea , Riff María-Cristina , Montero Elizabeth
Published: November 2024

The path planning problem using mobile robots, also known as robot motion planning, is a key problem in robotics. The goal is to find a collision-free path from a starting point to a target point in an environment with obstacles. These obstacles can be known, partially unknown, or completely unknown to the robot. The robot’s decisions are different according to its degree of knowledge of the environment. If the environment is fully known, using an offline approach is sufficient. Otherwise, online techniques are required to find a path. They must be fast, effective, and adaptive to tackle the complexity of changing environments. In this work, we propose a framework based on a simulated annealing approach to solve the path planning problem with different degrees of knowledge of the environment for the robot. We evaluate our approach with a set of large-scale instances with different features. The results show that our framework can quickly find quality solutions and is also able to manage environmental changes.
A Versatile 100 Hz Laser System with Few-Cycle and TeraWatt Pulses for Applications
Authors: Gaál Péter , Gilinger Tibor , Nagyillés Bálint , Nagymihály Roland , Seres Imre , Kovács Ádám , Füle Miklós , Karnok Maté , Balázs Péter , Novák Tibor , Kovács Attila P. , Osvay Károly
Published: November 2024

We developed a versatile 100 Hz laser system based on negatively and positively chirped pulse amplification. The few-cycle output provides pulses with 7.1 fs and 0.25 mJ, while the power output supports 26 fs pulses with 50 mJ. The energy as well as the pulse duration stability of the system are below 1%, while the pointing stability is within 25% of the diffraction-limited spot size. We also show applications in high repetition rate target development and preparation for a laser-generated X-ray source for industrial CT imaging.
Inactivation of Anisakis simplex Allergens in Fish Viscera by Acid Autolysis
Authors: Bald Carlos , Lavilla María , Abaroa Carmen , Aboitiz Xabier , Díez Guzmán , Iñarra Bruno
Published: November 2024

The evisceration of infested species on board commercial fleets and the throwing of viscera into the sea, as is mostly the case for hake, is common practice for reducing the occurrence of Anisakis in fishery products. Moreover, the high levels of infestation and the lack of technically and economically feasible solutions hinder the possibility of recovering viscera with other fish processing byproducts as raw material for feed without the risk of transmission of allergens to humans, with the subsequent risk for public health. The aim of this work was to study the fate of Anisakis simplex allergens during 11 days of silage of infested hake (Merluccius merluccius) viscera, as a potential method for eliminating this risk. While the viscera were almost completely liquefied, an increase in the allergenicity of the soluble fraction was observed, which decreased only slightly after day 9. As we are aware of the resistance of parasite allergens to gastrointestinal enzymes, silage was also analyzed after 12 and 15 months. While the lower molecular weight fragments of Anisakis proteins are fully digested, some larger fragments with potential allergenicity resisted autolysis after long silage periods, but they were present in a very low concentration. The study concludes that there is the potential of silage as a method for recovering fish viscera infested with Anisakis.
A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning
Authors: Hao Jiaxing , Yang Sen , Gao Hongmin
Published: November 2024

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.
Influence of Electrohydrodynamics on the Drying Characteristics and Volatile Components of Ginger
Authors: Zhu Jie , Ding Changjiang , Che Chuanqiang , Liu Liqiang , Lian Junjun , Song Zhiqing , Qin Chunxu
Published: November 2024

This article studies the electrohydrodynamic drying of ginger. In this work, drying experiments were performed in an electrohydrodynamic drying (EHD) system at various AC voltages (0 kV (control), 15 kV, 20 kV, 25 kV, and 30 kV). The drying properties and volatile components of ginger were thoroughly examined and studied using IR spectroscopy and GC–MS. The findings revealed that electrohydrodynamics significantly increased the drying rate and reduced the drying time of ginger, with a sevenfold increase in drying rate and a one-third reduction in drying time at 30 kV. The peak of the infrared spectrum of ginger remains unchanged. We found 240 volatile chemicals under different drying voltages. The primary volatile chemicals were esters and aldehydes. The ginger products dried at 30 kV had the lowest aldehyde concentration. These findings give an experimental and theoretical foundation for applying electrohydrodynamics to the subject of ginger drying.
Development of a Numerical Tool for Laminate Composite Distortion Computation Through a Dual-Approach Strategy
Authors: Banu Cesar , Bugaru Mihai
Published: November 2024

Unintended shape distortions, such as spring-in, spring-back, and warping, commonly occur during the curing process of laminate composites. The source of dimensional changes at the macro-scale is residual stress. The main triggers for residual stresses at this scale are anisotropic behavior and the constraint effect of individual plies and tooling constraints. Thermoelastic residual stresses are quasi-reversible, whereas non-thermoelastic residual stresses are permanent, and the underlying mechanisms contributing to them are highly intricate. The challenges associated with simulating the curing of large-scale parts to obtain reliable engineering data are significantly influenced by factors such as thermal anisotropy, polymerization shrinkage, tool–part interaction, resin flow, and compaction. A comprehensive grasp of the involved phenomena can facilitate the creation and application of numerical tools that model the curing process, providing essential information on geometry distortion that is crucial for the overall manufacturing of structural components and assemblies. To ensure that a numerical prediction tool is dependable in terms of both accuracy and precision, it is essential to have significant experimental backing throughout the entire process, from selecting the appropriate mathematical models to calibrating the calculations of distortion values.
IWF-TextRank Keyword Extraction Algorithm Modelling
Authors: Zhang Liyan , Wang Wenhui , Ma Jian , Wen Yuan
Published: November 2024

Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model’s semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (p-value), recall (R-value), and F-value.
Transformer-Enhanced Retinal Vessel Segmentation for Diabetic Retinopathy Detection Using Attention Mechanisms and Multi-Scale Fusion
Authors: Kim Hyung-Joo , Eesaar Hassan , Chong Kil To
Published: November 2024

Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or ailments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening.
Infrared Imaging System with a Local Polarization Channel for Target Detection
Authors: Liu Xin , Jiang Zikang , Niu Chunhui , Li Xiaoying , Lv Yong , Chang Jun
Published: November 2024

Infrared imaging can detect the targets from their backgrounds during the day and night. As a supplementary tool, polarization imaging can visually characterize the polarized information of targets in complicated environments. In this study, an infrared imaging system with a local polarization channel is developed and simulated. The system can acquire local infrared and polarization information of the target without compromising the monitoring of the entire field of view. This system is composed of two infrared imaging channels with a common image plane: a peripheral channel for the entire field of view and a local imaging channel for the target of interest to achieve local magnification and polarization imaging. Herein, an imaging simulation of the infrared imaging system with a local polarization channel is performed, and the results show that the system performs well.
Prediction of Intraday Electricity Supply Curves
Authors: Vivó Guillermo , Alonso Andrés M.
Published: November 2024

The electricity market in Spain, as in many European countries, is organized into daily, intraday, and reserve markets. This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. To achieve this, we approximate these curves using a non-uniform grid of points and evaluate the quality of these approximations with a weighted distance, both based on empirical market data. We employ neural network models, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and a Transformer network alongside a naive model for benchmarking. The MLP and CNN models demonstrated significant improvements in predicting these supply curves for the six market sessions.
Hubert Goltzius’s Lebendige Bilder Gar Nach Aller Keysern, Emperor Maximilian II, and Renaissance Cycles of Fresco Portraits of Emperors in Palaces in Silesia
Authors: Kozieł Andrzej
Published: November 2024

At the beginning of the 21st century, there were sensational discoveries in two palaces located in Ciechanowice and Struga in Silesia (Poland). During their renovations, Renaissance fresco cycles of portraits of emperors from the Roman, medieval, and early modern times appeared under the layer of plaster in the representative dining rooms (27 in the palace in Struga and about 50 in the palace in Ciechanowice). They were painted in the 1580s (in Ciechanowice, the date is 1588) by the same unknown artist. This article is the first to attempt to establish the most important facts related to the creation of both fresco cycles. The frescoes were founded by representatives of influential Silesian Protestant nobility: Heinrich von Reichenbach (Ciechanowice) and Abraham von Czettritz und Neuhaus (Struga). Both nobles attended the funeral of Emperor Maximilian II, which took place in Prague in 1577. This is where they purchased a work by the Dutch printmaker, painter, and numismatist, Hubert Goltzius, Lebendige Bilder Gar Nach Aller Keysern […] (published in Antwerp in 1557), whose specially prepared copy had been solemnly presented to Emperor Maximilian II in 1562. The book contains 133 monochrome woodcut illustrations with portraits of emperors in circular frames by the Dutch artist Joss van Gietleughen, which—together with accompanying inscriptions—were used as models for fresco paintings in both Silesian palaces. The foundation of the cycles of portraits of the emperors of the Roman Empire and the Holy Roman Empire of the German Nation in the representative halls of the palaces in Ciechanowice and Struda, which ended with a joint representation of Emperor Maximilian II and the King of Spain, Philip II Habsburg, was a way to show the gratitude of both prominent and wealthy Protestant nobles towards Emperor Maximilian II. During his reign, imperial power was the source and guarantee of religious freedoms for Protestants in Silesia. It is significant that both cycles left out the person of emperor regnant, Rudolf II, who soon after assuming the imperial throne abandoned his father’s tolerant policy towards Protestants. Although the author of both series of frescoes was probably a local painter, they are a unique artistic realization not only in Silesia but also in the whole of Central Europe, and they can only be compared to the popular Renaissance portrait galleries of “famous men” (uomini famosi).
Trajectory Preview Tracking Control for Self-Balancing Intelligent Motorcycle Utilizing Front-Wheel Steering
Authors: Lai Fei , Hu Hewang , Huang Chaoqun
Published: November 2024

Known for their compact size, mobility, and off-road capabilities, motorcycles are increasingly used for logistics, emergency rescue, and reconnaissance. However, due to their two-wheeled nature, motorcycles are susceptible to instability, heightening the risk of tipping during cornering. This study includes some research and exploration into the following aspects: (1) The design of a front-wheel steering self-balancing controller. It achieves self-balance during motion by adjusting the front-wheel steering angle through manipulation of handlebar torque. (2) Trajectory tracking control based on preview control theory. It establishes a proportional relationship between lateral deviation and lean angle, as determined by path preview. The desired lean angle then serves as input for the self-balancing controller. (3) A pre-braking controller for enhanced active safety. To prevent lateral slide on wet and slippery surfaces, the controller is designed considering the motorcycle’s maximum braking deceleration. These advancements were validated via a joint BikeSim and Matlab/Simulink simulation, which included scenarios such as double lane changes and 60 m-radius turns. The results demonstrate that the intelligent motorcycle equipped with the proposed control algorithm tracks trajectories and maintains stability effectively.
Assessment of Environmental Parameters in Natural Coastal Scenery and Compositional by Means of an Innovative Approach
Authors: Mastromatteo Nicole , Drudi Lia , Gallione Davide , Bellopede Rossana , Clerico Marina
Published: November 2024

Three measurement campaigns were conducted on the island of Culuccia (Sardinia, Italy) to evaluate particulate matter (PM) concentrations and the contribution of sea spray aerosol (SSA) across different seasons in a largely uncontaminated coastal environment. The goal is not only to analyze PM concentration in relation to meteorological parameters such as temperature, relative humidity (rH), and wind speed but also to provide a chemical analysis of SSA. The chemical composition of PM was determined using Raman spectroscopy and SEM-EDX, allowing for precise identification of individual particles. Results showed seasonal variations in PM composition, with sodium nitrate and sodium chloride prevalent in March and June and sulfates dominating in October. A correlation between the PM composition and meteorological parameters was observed according to the value of the deliquescence relative humidity (DRH), highlighting the reciprocal influence of rH and coarse and fine PM trends. This multi-technique approach offers valuable insights into the relative abundance of different PM compound classes based on the varying conditions for SSA formation. This enhances our understanding of the behavior of sea spray aerosol and other PM in natural coastal environments.
Airborne Microplastics: Challenges, Prospects, and Experimental Approaches
Authors: Rednikin Alexey R. , Frank Yulia A. , Rozhin Artem O. , Vorobiev Danil S. , Fakhrullin Rawil F.
Published: November 2024

Airborne microplastics are emerging pollutants originating from disposable tableware, packaging materials, textiles, and other consumer goods. Microplastics vary in shape and size and exposed to external factors break down into even smaller fractions. Airborne microplastics are abundant in both urban and natural environments, including water bodies and glaciers, as particles can travel long distances. The potential toxicity of airborne microplastics cannot be underestimated. Microparticles, especially those < 10 µm, entering the human body through inhalation or ingestion have been shown to cause serious adverse health effects, such as chronic inflammation, oxidation stress, physical damage to tissues, etc. Microplastics adsorb toxic chemicals and biopolymers, forming a polymer corona on their surface, affecting their overall toxicity. In addition, microplastics can also affect carbon dynamics in ecosystems and have a serious impact on biochemical cycles. The approaches to improve sampling techniques and develop standardized methods to assess airborne microplastics are still far from being perfect. The mechanisms of microplastic intracellular and tissue transport are still not clear, and the impact of airborne microplastics on human health is not understood well. Reduced consumption followed by collection, reuse, and recycling of microplastics can contribute to solving the microplastic problem. Combinations of different filtration techniques and membrane bioreactors can be used to optimize the removal of microplastic contaminants from wastewater. In this review we critically summarize the existing body of literature on airborne microplastics, including their distribution, identification, and safety assessment.
Variations over 20 Years in Vegetation Dynamics and Its Coupled Responses to Individual and Compound Meteorological Drivers in Sichuan Province, China
Authors: Deng Qian , Zhang Chenfeng , Dong Jiong , Li Yanchun , Li Yunyun , Huang Yi , Zhang Hongxue , Fan Jingjing
Published: November 2024

This study presents an innovative investigation into the spatiotemporal dynamics of vegetation growth and its response to both individual and composite climatic factors. The Normalized Difference Vegetation Index (NDVI), derived from SPOT satellite remote sensing data, was employed as a proxy for vegetation growth. Multiple analytical methods, including the coefficient of variation, Mann–Kendall trend analysis, and Hurst index, were applied to characterize the spatiotemporal patterns of the NDVI in Sichuan Province from 2000 to 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using monthly precipitation and temperature data from 45 meteorological stations to examine the influence of composite climatic factors on vegetation growth, while the time lag effects between the NDVI and various climatic variables were also explored. Our findings unveil three key insights: (1) Vegetation coverage in Sichuan Province exhibited an overall increasing trend, with the highest NDVI values in summer and the lowest in winter. Significant NDVI fluctuations were observed in spring in the western Sichuan plateau and in winter in northern, eastern, and southern Sichuan. (2) A significant upward trend in the NDVI was detected across Sichuan Province, except for Chengdu Plain, where a downward trend prevailed outside the summer season. (3) On shorter time scales, the NDVI was positively correlated with precipitation, temperature, and the SPEI, with a one-month lag. The response of the NDVI to sunlight duration showed a two-month lag, with the weakest correlation and a five-month lag in western Sichuan. This research advances our understanding of the complex interactions between vegetation dynamics and climatic factors in Sichuan Province and provides valuable insights for predicting future vegetation growth trends.
CHIMBO Air Quality Modeling System: Verification and Processes Analysis
Authors: Landi Tony Christian , Paglione Marco , Morichetti Mauro , Grasso Fabio Massimo , Roccato Fabrizio , Cesari Rita , Drofa Oxana
Published: November 2024

This study presents an evaluation of the CHIMBO modeling chain applied to the Italian domain, specifically focusing on the Po Valley subdomain over the one-year period of 2019. The comparison between simulated and observed data indicates that the performance of the CHIMBO model aligns well with existing literature on other state-of-the-art models. The results demonstrate that the CHIMBO chain is particularly effective for regional-scale quantitative assessments of pollutant distribution, comparable to that of CAMS ensemble models. The analysis of key chemical species in particulate matter reveals that the CHIMBO model accurately represents the average concentrations of organic and elemental carbon, as well as secondary inorganic compounds (sulfate, nitrate, and ammonium), particularly at background monitoring stations in the flat terrain of the Po Valley, with the exception of Aosta, a city located at about 500 m asl. However, seasonal discrepancies were identified, especially during winter months, when significant underestimations were observed for several species, including elemental and organic carbon, predominantly at background sites. These underestimations are likely attributed to various factors: (i) inadequate estimations of primary emissions, particularly from domestic heating; (ii) the limited effectiveness of secondary formation processes under winter conditions characterized by low photochemical activity and high humidity; and (iii) excessive dilution of pollutants during calm wind conditions due to overestimation of wind intensity. In conclusion, while the CHIMBO modeling chain serves as a robust tool for mesoscale atmospheric composition investigations, limitations persist related to emissions inventories and meteorological parameters, which remain critical drivers of atmospheric processes.
Water Use Attribution Analysis and Prediction Based on the VIKOR Method and Grey Neural Network Model: A Case Study of Zhangye City
Authors: Jia Lige , Zhang Bo
Published: November 2024

Water consumption forecasting is a critical aspect of the increasingly strained water resources and sustainable water management processes. It is essential to explore the current status of water use patterns and future development directions in Zhangye City. In this study, 17 factors affecting water consumption in Zhangye City were selected to analyze changes in water consumption and to predict values from 2003 to 2022, utilizing the entropy weight–VIKOR model and the grey neural network model. The results indicate that agricultural water consumption and annual rainfall are the factors with the largest weights among the social and natural attribute indicators, respectively, significantly influencing water consumption in Zhangye City. As the proportions of water consumption for forestry, animal husbandry, fishery, livestock, urban public use, and ecological environment increase, while agricultural water consumption continues to decline, the overall water consumption trend in Zhangye City from 2003 to 2022 shows a positive trajectory. Each water consumption factor is tending toward greater balance, and the relationship between water supply and distribution is improving. The multi-year average relative error of the water consumption predictions for Zhangye City from 2003 to 2022 using the grey neural network model was 4.28%. Furthermore, the relative error values for annual predictions ranged from 0.60% to 5.00%, achieving an accuracy rate of 80.00%. This indicates a strong predictive performance. Ultimately, the model was used to predict a water consumption of 20.18 × 108 m3 in Zhangye City in 2027. The model can serve as a theoretical reference for short-term water consumption forecasting and for establishing a basin water resource allocation system in Zhangye City.
A Review of Large Language Models in Healthcare: Taxonomy, Threats, Vulnerabilities, and Framework
Authors: Hamid Rida , Brohi Sarfraz
Published: November 2024

Due to the widespread acceptance of ChatGPT, implementing large language models (LLMs) in real-world applications has become an important research area. Such productisation of technologies allows the public to use AI without technical knowledge. LLMs can revolutionise and automate various healthcare processes, but security is critical. If implemented in critical sectors such as healthcare, adversaries can manipulate the vulnerabilities present in such systems to perform malicious activities such as data exfiltration and manipulation, and the results can be devastating. While LLM implementation in healthcare has been discussed in numerous studies, threats and vulnerabilities identification in LLMs and their safe implementation in healthcare remain largely unexplored. Based on a comprehensive review, this study provides new findings which do not exist in the current literature. This research has proposed a taxonomy to explore LLM applications in healthcare, a threat model considering the vulnerabilities of LLMs which may affect their implementation in healthcare, and a security framework for the implementation of LLMs in healthcare and has identified future avenues of research in LLMs, cybersecurity, and healthcare.
Introduction of AI Technology for Objective Physical Function Assessment
Authors: Kouno Nobuji , Takahashi Satoshi , Komatsu Masaaki , Sakaguchi Yusuke , Ishiguro Naoaki , Takeda Katsuji , Fujioka Kyoko , Matsuoka Ayumu , Fujimori Maiko , Hamamoto Ryuji
Published: November 2024

Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could predict physical function scores from various patient data sources and reviewed methods to measure objective physical function using this technology. This review included relevant articles published in English that were retrieved from PubMed. These studies utilized AI technology to predict physical function indices from patient data extracted from videos, sensors, or electronic health records, thereby eliminating manual measurements. Studies that used AI technology solely to automate traditional evaluations were excluded. These technologies are recommended for future clinical systems that perform repeated objective physical function assessments in all patients without requiring extra time, personnel, or resources. This enables the detection of minimal changes in a patient’s condition, enabling early intervention and enhanced outcomes.
Preclinical Experimental Study on New Cervical Implant Design to Improve Peri-Implant Tissue Healing
Authors: Gehrke Sergio Alexandre , Cortellari Guillermo Castro , Júnior Jaime Aramburú , Treichel Tiago Luis Eilers , Bianchini Marco Aurelio , Scarano Antonio , De Aza Piedad N.
Published: November 2024

Objectives: In this preclinical study, we used an experimental rabbit model to investigate the effects of a new implant design that involves specific changes to the cervical portion, using a conventional implant design in the control group. Materials and Methods: We used 10 rabbits and 40 dental implants with two different macrogeometries. Two groups were formed (n = 20 per group): the Collo group, wherein implants with the new cervical design were used, which present a concavity (reduction in diameter) in the first 3.5 mm, the portion without surface treatment; the Control group, wherein conical implants with the conventional design were used, with surface treatment throughout the body. All implants were 4 mm in diameter and 10 mm in length. The initial implant stability quotient (ISQ) was measured immediately after the implant insertion (T1) and sample removal (T2 and T3). The animals (n = five animals/time) were euthanized at 3 weeks (T1) and 4 weeks (T2). Histological sections were prepared and the bone–implant contact (BIC%) and tissue area fraction occupancy (TAFO%) percentages were analyzed in the predetermined cervical area; namely, the first 4 mm from the implant platform. Results: The ISQ values showed no statistical differences at T1 and T2 (p = 0.9458 and p = 0.1103, respectively) between the groups. However, at T3, higher values were found for the Collo group (p = 0.0475) than those found for the Control group. The Collo samples presented higher BIC% values than those of the Control group, with statistical differences of p = 0.0009 at 3 weeks and p = 0.0007 at 4 weeks. There were statistical differences in the TAFO% (new bone, medullary spaces, and the collagen matrix) between the groups at each evaluation time (p < 0.001). Conclusions: Considering the limitations of the present preclinical study, the results demonstrate that the new implant design (the Collo group) had higher implant stability (ISQ) values in the samples after 4 weeks of implantation. Furthermore, the histomorphometric BIC% and TAFO% analyses showed that the Collo group had higher values at both measurement times than the Control group did. These findings indicate that changes made to the cervical design of the Collo group implants may benefit the maintenance of peri-implant tissue health.